A a a a a a P

P A A A A A A P

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources, and Climate Change

Proceedings Vol. I Plenary and Invited Papers V ol. I Plenary and Invited Papers

The 16th AAAP Congress

Ministry of Agriculture Indonesian Society of Animal Sciences Gadjah Mada University Gold Sponsor:

Silver Sponsor:

Bronze Sponsor:

Supporting Sponsor: SUSTAINABLE LIVESTOCK PRODUCTION IN THE PRESPECTIVE OF FOOD SECURITY, POLICY, GENETIC RESOURCES, AND CLIMATE CHANGE

PROCEEDINGS VOL. I PLENARY AND INVITED PAPERS

Editors:

Subandriyo Kusmartono Krishna Agung Santosa Edi Kurnianto Agung Purnomoadi Akhmad Sodiq Komang G. Wiryawan Siti Darodjah Ismeth Inounu Darmono Atien Priyanti Peter Wynn Jian Lin Han Jih Tay-Hsu Zulkifli Idrus

The 16th AAAP Congress

Perpustakaan Nasional Republik Indonesia Cataloguing-in-Publication Data

The 16th Asian-Australasian Associations of Animal Production Socities Proceedings Vol. I Plenary and Invited Papers Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources, and Climate Change 10-14 November 2014, Yogyakarta, Indonesia / editors Subandriyo et al;

201 p: ill.; 21 x 29,7 cm

Organized by Indonesian Society of Animal Sciences

In Collaboration with Ministry of Agriculture

Faculty of Animal Sciences Universitas Gadjah Mada

ISBN 978-602-8475-86-0

1. Livestock 2. Food Security 3. Policy

4. Genetic Resources 5. Climate Change

I. Title II. Subandriyo

 Scope of AAAP: AAAP is established to devote for the efficient animal production in the Asian-Australasian region through national, regional, international cooperation and academic conferences.

 Brief History of AAAP: AAAP was founded in 1980 with 8 charter members representing 8 countries-those are Australia, Indonesia, Japan, Korea, Malaysia, New Zealand, Philippines and Thailand. Then, the society representing Taiwan joined AAAP in 1982 followed by Bangladesh in 1987, Papua New Guinea in 1990, India and Vietnam in 1992, Mongolia, Nepal and Pakistan in 1994, Iran in 2002, Sri Lanka and China in 2006 , thereafter currently 19 members.

 Major Activities of AAAP: Biennial AAAP Animal Science Congress, Publications of the Asian-Australasian Journal of Animal Sciences and proceedings of the AAAP congress and symposia and Acknowledgement awards for the contribution of AAAP scientists.

 Organization of AAAP: ∙ President: Recommended by the national society hosting the next biennial AAAP Animal Science Congress and approved by Council meeting and serve 2 years. ∙ Two Vice Presidents: One represents the present host society and the other represents next host society of the very next AAAP Animal Science Congress. ∙ Secretary General: All managerial works for AAAP with 6 years term by approval by the council ∙ Council Members: AAAP president, vice presidents, secretary general and each presidents or representative of each member society are members of the council. The council decides congress venue and many important agenda of AAAP

 Office of AAAP: Decided by the council to have the permanent office of AAAP in Korea. Currently # 909 Korea Sci &Tech Center Seoul 135-703, Korea

 Official Journal of AAAP: Asian-Australasian Journal of Animal Sciences (Asian-Aust. J. Anim. Sci. ISSN 1011-2367. http://www.ajas.info) is published monthly with its main office in Korea

 Current 19 Member Societies of AAAP: ASAP(Australia), BAHA(Bangladesh), CAASVM(China), IAAP(India), ISAS(Indonesia), IAAS(Iran), JSAS(Japan), KSAST(Korea), MSAP(Malaysia), MLSBA(Mongolia), NASA(Nepal), NZSAP(New Zealand), PAHA(Pakistan), PNGSA(Papua New Guinea), PSAS(Philippines), SLAAP(Sri Lanka), CSAS(Taiwan), AHAT(Thailand), A H AV (Vietnam).

 Previous Venues of AAAP Animal Science Congress and AAAP Presidents

I 1980 Malaysia S. Jalaludin II 1982 Philippines V. G. Arganosa III 1985 Korea In Kyu Han IV 1987 New Zealand A. R. Sykes V 1990 Taiwan T. P. Yeh VI 1992 Thailand C. Chantalakhana VII 1994 Indonesia E. Soetirto VIII 1996 Japan T. Morichi IX 2000 Australia J. Ternouth X 2002 India P. N. Bhat XI 2004 Malaysia Z. A. Jelan XII 2006 Korea I. K. Paik XIII 2008 Vietnam N.V. Thien XIV 2010 Taiwan L.C. Hsia XV 2012 Thailand C.Kittayachaweng XVI 2014 Indonesia Yudi.Guntara.Noor

AAAP is the equal opportunity organization Copyright® : AAAP

Remark from Chairman of the 16th AAAP Congress Dear all of the scientists, delegates, participants, ladies and gentlemen, As the host of the 16th AAAP Animal Science Congress, we do impress, thankful, and present a high appreciation for your participation in joining the 16th AAAP Conference in Yogyakarta, Indonesia. We can see the very great enthusiasm of all the scientists to solve livestock problems as well as to share valuable information and knowledge for human prosperity all over the world. A large numbers of representatives are participating in this conference, which indicates that the interest in the field of animal science is continuously increasing among member countries. We have invited some Plenary Speakers and Invited Papers who are qualified as scientists and bureaucrats in animal science field to share their valuable information and knowledge. Other participants can deliver their precious research through oral and poster presentations. This congress is also paralleled to symposium held by livestock organization and institution as well as some academic meetings. The theme of the 16th AAAP Congress is “Sustainable Livestock Production in the perspective of Food security, Policy, Genetic Resources and Climate Change”. We believe that animal production in Asia and Australasia has become important and strategic sector to provide high quality food, opening up job opportunities, as well as improving farmer’s welfare. Animal science socities, therefore, have to support this growing interest by providing more appropriate and relevant technologies to improve efficiency of resources utilization to produce more animal protein food by member countries. Long term sustainable livestock production will, therefore, be significantly influenced by the national food policy, climate change issues, as well as conserved environments and genetic resources. On behalf of 16th AAAP Committee and all associates, we wish all of the participants having a great achievement of success and fulfill the expectation as well as enjoying the interaction with all scientists participating the Congress. High appreciation we may acknowledge to all of sectors, especially for His Majesty of Royal Palace of Yogyakarta, Sri Sultan Hamengku Buwono X, and Rector of Universitas Gadjah Mada, who have concerned to facilitate the Congress site host. Special thank to the Steering Committee, Scientific Committee, Reviewers and Editorial Boards for their great contribution to make the Congress successfully organized. To you, your excellencies, invited guests and delegates, thank you for choosing to come to this conference and to Indonesia. We hope the arrangements we have put in place meet with your requirements. We wish you fruitful deliberations and an intellectually and socially rewarding stay in Yogyakarta.

We are looking forward to meeting you all in the future congress to continue.

Terimakasih (Thank you)

Budi Guntoro Chairman of the 16th AAAP Congress

16th AAAP PRESIDENT’S REPORT Selamat pagi! Dear Ladies and Gentleman Attendants of 16 AAAP congress: It is my great pleasure and honor to welcome all of you at The 16th AAAP Congress on November 10 – 14, 2014 at Grha Sabha Pramana, Universitas Gadjah Mada, Yogyakarta Indonesia. This Congress is jointly organized by The Indonesian Society of Animal Science (ISAS), Indonesian Agency for Agricultural Research and Development, Indonesian Directorate General of Livestock and Animal Health Services-Ministry of Agriculture and Faculty of Animal Science Universitas Gadjah Mada. Universitas Gadjah Mada Campus is located in Yogyakarta, one of the Special Region in Indonesia where culture and tradition live in harmony with the modern nuance and educational spirit makes it a beautiful venue of this Congress. The 16th AAAP Program consists of scientific and technical programs as well as social and cultural activities. The scientific and technical programs offer five plenary sessions, two satellite symposia, field trip, and many scientific sessions, both oral and poster presentations. During this event distinguished scientists from all over the world will present plenary papers ranging from livestock policy, food security, local genetic resources, climate change, animal welfare, international trade, as well as global research agenda. I believe that around 1,200 scientists as well as livestock producers, companies, graduate and postgraduate students from 40 countries are attending the Congress and more than 770 research papers will be presented. The Congress also provides not only opportunities to discuss and exchange information and experience with scientists from different regions of the world, but also a good environment to build up friendship between nations is our ultimate goals for the Congress outcome. Moreover, this congress also keeps its tradition to be a forum of communication among researchers, academician, industries and related stakeholders among Asian-Australasian countries. The social and cultural programs are specially desgined to be very important for the congress participants since the promotion of friendship and future scientific cooperation are also central to this AAAP Congress. The Opening Ceremony will offer you the Congress Program at a glance. In addition, participants will also join at a warm Welcome Dinner gathering at Keraton Yogyakarta. Sri Sultan Hamengku Buwono X, His Majesty of The Royal Palace of Yogyakarta will give you the most memorable moment during this event. Moreover, cultural night offers us an opportunity to introduce significant culture from participants’ countries and gives a spectacular performance to enjoy in order to strengthen our friendship and future cooperation. Field trip, on the other hand, provides a wonderful sightseeing to the most valuable ancient heritage around Yogyakarta, such as Borobudur and Prambanan Temples, and more other interesting places to visit. I do hope that you enjoy your stay in Yogyakarta and not miss all of these spectacular opportunities. Closing Ceremony will be held on November 14, 2014 immediately after the last session of presentation. During this great moment we will welcome the next host of the 17th AAAP Congress to deliver a brief message. The AAAP Congress Award will provide and announce some participant who receive appreciation for their valuable research.

With all of our hospitability, we will try our best to make your brief visit to Yogyakarta and our beautiful country Indonesia, become a wonderful experience and memorable moments. I wish you all a very pleasant and most enjoyable stay in Yogyakarta, Indonesia. Terima kasih (Thank you).

Sincerely Yours

Mr. Yudi Guntara Noor President The 16th AAAP Congress

iii

PREFACE

The proceedings of the 16th Animal Science Congress of the Asian-Australasian Association of Animal Production Societies (AAAP) held on 10-14 November 2014 at Grha Sabha Pramana, Universitas Gadjah Mada, Yogyakarta, Indonesia, consist of two volumes. Those are Volume I of Plenary and Invited Papers and Volume II of Abstracts Contributed Papers. This is the first volume of the proceedings that contains a total of 15 manuscripts, consist of 6 papers for plenary presentation and 9 invited papers. The scientific committee is very grateful and would like to thank all invited distinguished speakers and appreciate their effort to the valuable time in preparing the papers and participating to the congress. We strongly believe that their significant contribution will be most useful to all of our society to enhance the development of animal production in the future. Similarly, we would also like to thank supporting staffs at the secretariat office of the Faculty of Animal Sciences, Universitas Gadjah Mada as well as of the Indonesian Center for Animal Research and Development who have helped in the preparation of the proceedings.

Editorial Team

CONTENTS

Page REMARK FROM CHAIRMAN OF 16 AAAP NOC-TEP i

16 AAAP PRESIDENTS’S REPORT ii

PREFACE iv

PLENARY PAPERS National Action Plan for Conservation and Sustainable Utilization of Animal Genetic Resources in Indonesia Haryono, Bess Tiesnamurti and Anneke Anggraeni 3 Manipulating Local Genetic Resources to Maintain Animal Biodiversity–The Practical Point of View Liang Chou Hsia 17 The Development of the Global Livestock Sector and its Impacts on Food Production and Trade Nicostrato D. Perez and Mark W. Rosegrant 21 The Impact of Climate Change on Animal Genetic Resources David Steane 53 The Effects of Human-Ruminant Interactions on Animal Welfare and Productivity in the Tropics Paul H. Hemsworth and Rebecca E. Doyle 73 Human-Animal Interactions and Opportunity to Improve Poultry Welfare and Productivity Zulkifli Idrus 85

INVITED PAPERS Novel Methods for Evaluating Sustainable Animal Production Systems Using Systems Analysis and Life Cycle Assessment (LCA) H. Hirooka and K. Oishi 93 Increasing Ruminant Production Efficiency and Reducing Methane Production M. Wanapat, S. Kang and K. Phesatcha 107 Conserving Endangered Breed: Case Study of Gembrong Goats I Gede Suparta Budisatria, Jafendi Purba Hasoloan Sidadolog, Dyah Maharani and Sumadi 135 Improvement of Forages Quality by Molecular Breeding in Tropical Grasses: the Case of Brachiaria ruziziensis Genki Ishigaki and Ryo Akashi 141 Linking Gene Expression Patterns with the Productivity of Sheep Peter Wynn, David McGill and Sue Hatcher 145

Risk Management Analysis of the Traditional Farm Decision to Improve Income Mogens Lund 151 Effects of Feeding Factors on Dairy Performance and Milk Fatty Acid Composition in Cows and Goats Ferlay A., Bernard L., Toral P., Martin C. and Chilliard Y. 169 Transgenic and Proteomic Approaches for Improving Abiotic Stress Tolerance in Forage Plants Md. Atikur Rahman, Yong-Goo Kim, Na-Young Ahn, Ki-Won Lee, Sang-Hoon Lee and Byung-Hyun Lee 183 Alternative Local Feed Resources for Lactating Goats H. T. Wang, C. S. Chen, T. J. Chou, M. H. Chen, C. Lee, B. Y. Chen, S. W. Chen and J. T. Hsu 187

PLENARY PAPERS

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

National Action Plan for Conservation and Sustainable Utilization of Animal Genetic Resources in Indonesia Haryono1, Bess Tiesnamurti2 and Anneke Anggraeni3 1Indonesian Agency for Agricultural Research and Development, 2Indonesian Center for Animal Research and Development, 3Indonesian Research Institute for Animal Production Corresponding email: [email protected] ABSTRACT Indonesia has a very large number of indigenous and local animal genetic resources (AnGR) for food and agriculture. AnGR have strategic roles in National Agriculture Development as sources of animal protein and other important roles for the community. As many other countries in the world, currently Indonesia has faced many challenges in managing and utilization of AnGR owned of which under certain circumstances leading into a tendency to decline these AnGR. Indonesia has adopted and committed to the Interlaken Declaration (FAO, 2007) emphasizing the world’s agreement of the Global Plan of Action (GPA) for Animal Genetic Resources. Four main priority areas of GPA for AnGR consist of : 1. Characterization, inventory and monitoring AnGR; 2. Sustainable utilization; 3. Conservation; and 4. Policy, institutional and capacity building. It is necessary for Indonesia to set policies and programs of our national livestock subsector development into a draft of National Plan of Action (NPA) of AnGR. Relative weights of consideration should be given to put priorities of NPA providing the existences of indigenous and local AnGR, production systems, environmental managements, farmers’ welfare as well as integration of livestock subsector development into national agricultural sector development and also any possible external strategic changes that can affect national AnGR sustainable use. This paper will focuse discussion on two priority areas of NPA, namely Conservation and Sustainable use of national AnGR. Key Words: Animal genetic Resources (AnGR), Sustainable use, Conservation

INTRODUCTION Indonesia is a country that has a very large of animal genetic resources (AnGR) for food and agriculture. Variety of indigenous and local AnGR have adapted over long periods and become parts of local agro-ecosystems. AnGR have strategic roles in National Agriculture Development, with a main function as sources of animal protein and food production for the community. AnGR are also important for economic values, health, knowledge, education, social, custom, cultures, norms and ethics. In attempting to meet the needs of food of animal protein for a large population and continuously increase, similar to many other countries in the world, currently Indonesia has faced many challenges in managing AnGR owned. Utilization and management of AnGR in certain circumstances have brought into a tendency to decline and degrade of local and native AnGR. Genetic diversity of AnGR needs to be preserved so that agriculture development can address the needs and challenges of the future. Indonesia has adopted and committed to the Interlaken Declaration (FAO, 2007a). The declaration stresses on the need for across countries in the globe to pay attention on their AnGR to meet the world's food and agriculture. The declaration resulted in the world’s agreement of Global Plan of Action for Animal Genetic Resources (FAO, 2007a; FAO, 2009).

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

There are four main priority areas of the global action plan, namely: 1. Characterization, inventory and monitoring AnGR; 2. Sustainable utilization; 3. Conservation; and 4. Policy, institutional and capacity building. As a form of implementing the Interlaken Declaration, Indonesia should establish National Action Plan for National AnGR. This paper will focus discussion on two priority areas of the National Action Plan on indigenous and local AnGR, i.e. Conservation and Sustainable use. ROLES AND VALUES OF NATIONAL AnGR National AnGR become important parts of the National Agricultural Development in Indonesia. Contributions of livestock subsector to the national agricultural sector are shown, among others, for its contribution to gross domestic product (GDP), creating labor, production of animal protein and fulfilling animal protein consumption for the community. Gross Domestic Product Gross Domestic Income (GDI) from livestock subsector in 2009 was Rp 104,040 billion that contributed for around 12% to GDI of the agricultural sector (Animal Husbandry Statistic, 2010). GDI values from livestock subsector continued to increase, which in 2011 reached Rp 129 578 billion (Animal Husbandry Statistic, 2012). Livestock subsector has grown rapidly due to the existences of livestock industry especially for chicken (broiler) and beef cattle. The GDI values from the highest are contributed by broiler, beef cattle, pig, dairy cow and layer chicken; while the rests are derived from sheep, goat, local chicken, duck, rabbit and others. Providing Labor Recently, AnGR for food and agriculture are being sources of employment for approximately 12,969,210 house hold farmers (Figure 1), as well as for 629 incorporated livestock companies (Animal Husbandry Statistics, 2012).

Layer 97188 Beef cattle Buffalo Goat Sheep Dairy cattle 118752 Pig Horse Local chicken Layer Local chicken 215096 Broiler Duck 28200 Buffalo 450605 35300 Sheep 920168 485300 66300 Pig 1526745 1337900 46800 Goat 3465721 Beef cattle 4572766 224800 Broiler 20851901 2200 264800 0 10000000 20000000 62100

Figure 1. Number of animal house hold Figure 2. Meat production (ton) based on farmers based on species species Nowadays, number of workers in the form of household farmers in the livestock subsectos, however, has decreased. Whilst the number of corporated companies increased. Compared to the year 2003, for example, the number of household keepers in 2013 decreased as much as 5,626,614, while that of corporated companies increased by 154. Production of Meat, Egg and Milk National meat production in 2011 was approximately 2.5537 million tons. Meat production mostly came from broiler, beef cattle, local chicken and pork. Meat in a smaller amount was also produced by goat, sheep, buffalo, duck, pig, horse and others (Figure 2).

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The need of eggs of the community can be fully met from the country for the amount of consumption of 1.456.200 tons (Animal Husbandry Statistics, 2012). The egg production was mostly came from layer chicken (70.58%), while the remaining was filled by local duck (17,59%) and local chicken (11.83%). Fresh milk was produced by 974.700 tons mostly coming from Holstein Friesian (HF) dairy cows. Indeed, at a very limited volume that fresh milk comes from dairy goat. National fresh milk production can supply about 35% of the domestic consumption. In a large portion of milk has still been fulfilled by imported raw milk. Consumption of Meat, Egg and Milk Indonesia with a large number of its population for 245 million heads and with a high rate of population around 1.49% per yr, resulting the demand on animal protein providing meat, milk and eggs has continually increased. National consumption of meat, eggs and fresh milk per capita per year in the year 2011 was successively 5.11 kg, 6.62 kg and 0.16 lts. Compared to the previous year 2001, there were some increased consumptions on these respective proteins by 5.38%, 1.55% and 50%. Demands for animal protein in the future are expected continuously rising due to some reasons, such as population growth, nutrition awareness, economic improvement, improved welfare and increased growing generation. STATUS OF NATIONAL AnGR Population, Distribution and Trend Biodiversity of species and breed of National AnGR consists of:  Large animal : beef cattle, dairy cattle, buffaloes and horse.  Small animal : goat, sheep, and pig.  Poultry: local and native chicken, layer chicken, broiler and duck.  Prospective animal: rabbit, quail, pigeon and others. National AnGR spread in various regions in Indonesia where some of them with the densest population in Java Island. Based on the spread across 33 provinces in Indonesia (Animal Husbandry Statistic, 2012), data of AnGR population showed beef cattle, dairy cattle, layer chicken and pigeon have the highest population in East Java Province. Goat, native chicken, rabbit, quail and pigeon are with the densest population in Central Java. Sheep, broiler and duck are for the highest population in West Java. Moreover, buffalo, pig and horse are with the largest population distribution in Aceh, East Nusa Tenggara (NTT) and South Sulawesi Provinces. Over the last decade, 2002-2011, population growth rates of large ruminants of beef cattle and dairy cattle gradually increased, instead in some years (2003-2006) population of beef cattle declined. However, populations of both buffalo and horse continually decreased at a quite fairly sharp. Growth population in small ruminants of sheep and goat showed a positive trend, though in some previous years the trend was slightly decline. Rapid population growth was evidences for broiler chicken and layer chicken; while for ducks showed a gradually increased population growth. For prospective animals such as rabbit, pigeon and quail were expected of getting positive trends in their growth populations, though supporting statistical data are still limited.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

BIODIVERSITY OF NATIONAL AnGR Various AnGR in Indonesia can be classified as: 1). Native or indigenous that is as domesticated result of its ancestor or wild relative, 2). Local that is imported animal then cultivated purely or crossing and undergoing in a quite long adaptation processes (> 5 generations) in local environment, and 3). Introduction that is exotic animal not cultivated in a quite long in its environment (FAOb; FAO, 2012). Beef Cattle Beef cattle has a strategic role to produce red meat for national community. Domestic beef cattle consists of various breeds, covering indigenous breed, local breed and exotic breed. Bali cattle is a native breed of Indonesian beef cattle. Bali breed was formed as the result of domestication process of wild bison from Bibos genus since thousands of years in Bali Island (Namikawa et. al., 1980; Handiwirawan and Subandriyo, 2004; Talib et. al., 2005; Diwyanto, 2008). Bali cattle has a very good ability to grow and spread in a wide different area even outside Indonesia. Some local beef catle breeds are Ongole Grade (in Indonesia is Peranakan Ongole, PO), Aceh, Pesisir, Jabres, Madura, Hissar and Sumba Ongole (SO). SO breed is the result of long period natural selection, since 1913, of the Ongole breed cattle and purely cultivated in Sumba. While OG, Aceh, Pesisir and Madura are examples of local beef cattle breeds as the crossing results between native cattle (Java cattle) and Bos indicus (India) especially Ongole cattle but having varied-crosses blood composition (Hardjosubroto, 2004; Kusdiantoro et. al., 2009; Diwyanto, 2012). Around two previous decades the government imported in large amounts of various beef cattle breeds for both of Bos taurus and Bos indicus for productivity improvement. Importation was generally conducted for those breeds with a large body weight, such as Brahman, Brangus, Angus, Simmental, Limousin and Santa Gertrudis. Dairy Cattle Almost all dairy cattles in the country are a single Bos taurus dairy breed of Holstein-Friesian (HF). Intensive national dairy development was initially started at early 1992, at that time the Government had a strong will to develop national dairy cattle industry (Soehadji, 1996). Importation of HF females was considered for old pregnant heifers and they were introduced continuously in a large amount then cultivated mostly by small dairy farmers. Current HF dairy cattle population are mostly descended of imported HF and in a lower number of newly introduction (Anggraeni, 2011). HF grade, the crosses of OG cows to HF bulls can be found in a limited number in Pasuruan District, East Java. Buffalo There are two types of local buffalo, namely swamp buffalo or water buffalo and river buffalo. Swamp buffalo has main functions as beef buffalo and worker, instead of river buffalo as dairy type. Local swamp buffalo passed through long times of natural selection process under various agro-ecosystems, so it is estimated that local swamp buffaloes have evolved into a variety of specific water buffaloes. Based on specific location of where natural and artificial selection process taking places, some swamp buffalo breeds currently are identified such as Aceh, Java, Toraya, Binanga, Moa (Maluku Island), Kalang (South Kalimantan and East Kalimantan) and Pampangan (South Sumatera). Uniqueness are expressed in terms of morphology such as body weight, body size, skin color as well as physiology such as disease resistance, heat tolerance and soaking in water. River buffalo, instead, has a limited number and spread in North Sumatera.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Sheep Local sheep breeds with some of specific characters are identified such as Donggala, Garut, Kisar, Fat Tail, Java Thin Tail, Sumatera Thin Tail, Wonosobo, Batur, Sapudi, Palu and others. Garut sheep is one local sheep breed as the three breeds crossing result among Java Fat Tailed, Merino (Australia) and Kaapstad from Africa. Garut sheep has ability to fight very well, so well known as a local fighting sheep (Mulliadi, 2009). Goat Meat and dairy goat are two types of local goat. Kacang goat (nut goat) is a local small size goat and has a very good ability to adapt on harsh and dry environment leading this goat widely spreading in many areas. Other specifically local goat breeds are Lakor, Gembrong, Costa and Merica (Pepper in English). Peranakan Etawah or Etawah Grade (EG) goat is one of good local dairy breed, as the cross of Etawah dairy goat and kacang goat. Saanen and Nubian are two exotic dairy goat breeds cultivated either purely or crossing for producing milk. Chicken and Duck For local poultry, especially native and local chickens have at least 31 breeds widespread. Some of these breeds are Pelung, crowing Balengek, Gaga, Merawang, Kedu and Nunakan. Commercial chicken of both broiler and laying chicken are hugely imported as Grand Parent Stock (GPS) and Parent Stock (PS) to meet the large volumes of the community’ consumption on meat and eggs (Animal Husbandry Statistic, 2012). A number of local duck breeds are well known such as Alabio, Tegal, Kerinci, Bayang, Talang Benih, Pegagan, Rambon and Magelang. Prospective Animal A number of local breeds of rabbit, pigeon and quail are found. Importation for some breeds are conducted, but only in a very small number. UTILIZATION OF AnGR Development and use of AnGR in Indonesia are historically series of processes of natural selection, artificial selection and crosses starting possibly since a hunting period until the era of today's modern farm development. The presence of a variety of indigenous and local breeds of any species across regions indicating that AnGR utilization in Indonesia had began since nomadic periods. The arrival of Chinese, Indian, Arab, European and other nations for trading activities to Indonesia also significantly gave contribution in enriching the diversity of AnGR. They introduced any species and various breeds and a number of indigenous AnGR were crossed leading to quality improvement of the crossed animals accordance to their purposes and needs. Reviewing at pre-independence era, before 1945, genetic improvement and quality development in AnGR were strongly influenced by decision of the Dutch Government through various policies and programs of purification, crossing and distribution of AnGR. Conservation on several local breeds also got attention (Hardjosubroto, 2004; Handiwirawan and Subandriyo, 2004; Kusdiantoro, 2009). Utilization of national AnGR in this paper are going to focus on describing conservation and utilization in the context of breeding program perception in various national species and breeds.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Large Ruminant Beef Cattle Large ruminants especially beef cattle has got main priorities for development and utilization by the government and relevant actors. This is related to the strategic function of this animal to produce red meat for the community, as well as related efforts of the government to build food self-sufficiency for the country. Important roles of beef cattle are also well known as sources of income, saving, traditional food, social status, religion, culture, arts, labor and compost. Beef cattles in some agricultural farming areas are still be functioned as labor or workers to help farmers plow in paddy field, carrying agricultural products, attractive goods as well as rural community transportation. Cultivations of indigenous and local beef cattles until recent days are majority (90%) hold by small farmers that are commonly kept under low input production systems. Small farmers put animals all or part of the day-times in houses, of where these are common in Java Island. Forages are gathered by utilizing various sources of agricultural crops by products. For regions outside Java, raising beef cattle is mostly under semi-intensive or extensive systems. In eastern parts of Indonesia, beef cattle (and buffalo) are commonly raised in pastures or by grazing system. The Government has a major role in setting programs and policies for genetic improvement and utilization of native and local beef cattles. Early in the year 1960s, the Government implemented 'beef cattle relocation program' that regulated regional development to local and indigenous beef cattle breeds. Ongole Grade (OG) was developed in western parts of Indonesia, instead of Bali cattle in eastern parts (Soehadji, 1996; Diwyanto, 2008; Hardjosubroto, 2004). However, in later periods the two breeds distributed widely in various area in Indonesia (Talib et. al., 2005). Early in the 1980s, the Government improved productivity of local and indigenous breeds by crossing them to various exotic beef cattle breeds from Bos taurus and Bos indicus that were mostly of having a large body size. Crosses were facilitated by intensive artificial insemination (AI) mating supported by the program of producing huge number of frozen semen of exotic bulls by national and regional AI centers (Dir. GLSH, 2010) This policy led to any kinds of crosses of local and indigenous beef cattles in a wider area especially in semi-intensive and intensive systems. AI mating was done massively causing of uncontrolled crossing to indigenous and local breeds. These could potentially contaminate the purity of beef cattle AnGR not only in development area, but also in breeding area as well as in conservation area. On the other side, to meet large quantities of community’s meat demand, private sectors or investors prefer fattening activities. Young cattle are fattened for a while to improve their body conformation and carcass quality, then slaughtered (Diwyanto, 2008; Hadi and Ilham, 2002). Fattening carried out by feedlots at a large scale with the increased activity gave negative impacts on depletion of young males and even on productive females. To prevent more serious erosion on genetic quality of indigenous and local beef cattles, particularly from genetic contamination as the result of widespread crosses, then the government attempted to maintain the purity and at the same time simultaneously improve productivity beef cattle AnGR conducted through a series of breeding activities. For the indigenous breed of Bali cattle, as an example, the government set priorities of breeding management (Pane, 1990; Soehadji, 1996), as the following:

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

• Bali cattle was mated purily in central breeding area, including Bali Island and some of the eastern parts of Indonesia (West Nusa Tenggara, Timor Island and South Sulawesi). These area were then determined as national Bali cattle breeding centers. • Superior bulls were identified through performance test and progeny test at the Government Bali Cattle Breeding Station, in Pulukan Subdistrict, Bali Island. • Establishment of base populations as sources of superior genes of Bali cattle in those breeding centers. • Performing national AI mating to accelerate the flow of superior genes from the progeny tested Bali bulls. Relevant activities required to ensure an appropriate implementation of breeding program (FAO, 2007a,b) for that Bali cattle breed were conducted. These provided identification of individual animals (and anchestors), completion on performance records, designing systematic mating system by also considering minimizing the risk of inbreeding, genetic evaluation using appropriate analytical methods, such as by the BLUP analysis of Animal Model, as well as optimizing use of genetically superior bulls to improve productivity of Bali cows. However, implementation of breeding activity in some conditions are still limited. In addition, some of those breeding activities are more driven by government, research institutes, university, and under government subsidies, so that the program should be continued to be implemented by farmer and related stakeholders. Some local beef cattle breeds that have high cultural values to the local community, so activities to maintain purity as well as to improve genetic quality of animals, are frequently driven by farmer groups, farmers, association and local community. As an illustration, Madura cattle has a very high cultural value to the community and breeding program conducted by the public (Wijono and Setiadi, 2004). This community based breeding program runs with strong sense of responsibility and awareness together. Determination of selection criteria although is done in a simple but fairly consistent. Dairy Cattle Application of AI mating in HF cows in principle is to implement outbreeding system for the aims to maintain the purity of HF blood. Through this mating system is expected the descendants with purely HF blood can maintain a quite high milk production under small farmers management. AI mating has been applied to almost all HF cow population using frozen semen from a number of strains of imported HF bulls such as from Australia, New Zealand, Japan and US (Anggraeni, 2011). In order to get superior HF bulls that more corresponding to local conditions, progeny test on HF young males were initially conducted through Progeny test pilot programs. This project was conducted through Partnership Project between Indonesian Government (Singosari AI Center) with ATA-233 of Japan International Cooperation Agency (JICA) (BIB Singosari, 1996). Progeny test was completed in two periods, 1986-1994 and 1989-1995 years. Currently the Government has a target to be self-sufficient in the production of HF superior bulls, so national progeny test has been conducted. Evaluation of 1st milk production of progeny-tested males’ daughters was performed widespread, both under intensive management (government dairy breeding station and dairy companies) and semi-intensive ones under small farmers in West Java, Central Java and East Java Provinces (Warukka and Talib, 2006). HF tested bulls resulted from the national progeny test are expected to be more appropriate in transmitting milk ability under local managements and tropical conditions.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Productivity improvement especially for HF female population in breeding area are attempted to by setting open nucleus breeding system (ONBS). In this breeding system that breeding populations are recommended to be classified into three strata including elite or foundation, multiplication and commercial. However, genetic improvement through this ONBS still has faced many technical and non-technical constraints in the field. Series of breeding activities related to recording, identification of individual (and parents), mating system with appropriate AI-mated HF bulls, genetic evaluation of milk traits, accurate breeding value estimation methods (ERPA/EBV and BLUP), processes of selection and culling are all have been conducted (Diwyanto et. al., 1997; Anggraeni, 2011), however they are still in very limited application, at the levels of researches and breeding models. Buffalo Selection to improve productivity and particularly growth traits has not received appropriate attention, further high mutation of local buffaloes especially for bulls and young males particularly from breeding area are still high. In addressing the lack of bulls with a good quality for servicing females in the field, the Government has captured good young buffalo males from the fields, kept them intensively at buffalo breeding stations then brought into performance test (Dir. GLSH, 2009). Males at the highest ranks from performance test results are then send to AI station for producing their frozen semen for widely AI mating or directly spread to the fields. Small Ruminants To increase the growth rate and slaughter weight of local sheep breeds, farmers in some parts have taken crossbreeding to exotic breeds through natural mating and at a more limited degree with AI mating. Crossing local sheep breeds to exotic ones if being done poorly controlled can become a source of the threat to local sheep AnGR. Goat especially for meat type has got less attention in improving their genetic potential by farmers or relevant actors. Most of goats are kept by the community under traditional low- input systems. Productivity improvements in this species (both dairy and meat goats) by farmers have been done in a limited frequency through crossing to both local and exotic breeds. Poultry Utilization of duck as a source of egg production has been carried out by some farmers, farmer groups and duck breeders. Selection on local laying ducks has been done simply on some egg traits such as for egg production, size and color, shell color and others. Currently, some good egg-producing breeds are well known such as: Bali, Magelang, Tegal and Mojosari ducks. Selection to improve productivity particularly growth traits in meat local duck breeds has been done under such ways by simply conducting selection within breeds (to improve Parent Stocks) continued by crossing between two or more selected breeds or strains. Productivity improvements are targeted in final stock (FS) to accumulate heterosis effects. Indigenous and local chickens are generally maintained in a traditional way, farmers are seldom kept their chicken under intensive management. Nowadays with the presence of increasing demand on meat and eggs from local and native chicken eggs, however, leads farmers into more intensive systems (Sartika et. al., 2004). Breeding program through selection activities in some occasions has been done with some goals of improvement for eggs production and growth rates.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

BREEDING RESEARCHES ON AnGR Formation of breeds or strains with genetically superiority should be done to various species of indigenous and local AnGR for the aims to produce breeding stocks and good seeds required by farmers, communities and markets. Activities in breeding researches have been intensified by a number of local and national research Institutions, universities and private sectors through crossbreeding and selection program. Indonesian Research Institute for Animal Production (IRIAP), under Indonesian Center Animal Research and Development (ICARD), IAARD, the Ministry of Agriculture has been successfully to produce a number of superior breeds or strains using local AnGR for some species, consisting of sheeps, ducks, chickens and rabbits (Table 1). To increase productivity and remove thick hairs of local sheep, so a composite sheep breed was formed through cross-breeding program, followed by selection to make stable on genotype composition. Superior composite sheep was produced by combining genetic material between local Sumatera sheep with two exotic sheep breeds. Table 1. Superior Breed and Strain of AnGR from Breeding Researches at IRIAP Breed/Strain Breeding Program Superiority

Superior strain Selection on native chicken for 6 1st laying age 175 h, high egg of layer generations for 1st laying age and production 160-180 eggs/yr, hen day chicken of reducing hatching period. 45-50%, peak eggs 60%, hatching KUB-1 period <10% Superior breed Crosses between Peking duck (male) and 1st laying age 5,5-6 mo., egg of meat duck of white Mojosari (female) and selected for production 73-78% (6 mo.,), body PMp 5 generations. Blood composition of weight > 2.5 kg age of 2.5 mo.), white 50% Peking and 50% white Mojosari. feather and clean carcass. Superior breed Crosses (FS) between selected Mojosari Egg production 265 eggs/yr (70%), of layer duck of (male line) and selected Alabio (female peak eggs production 94%, 1st laying Hibrida Master line) with selection by 'independent age 4.5 mo., nesting period 10-12 culling levels' for 4 generations. mo., sexing DOD by feather color. Composite Crossing and selection with the genetic Good growth rate 101 g / d, litter size Breed of composition of 50% Sumatera, 25% hair 1.5 hd., ewe productivity 23.3 kg Sumatera St. Croix and 25% hair Barbados black /lambs/yr., no mating season, adaptive Sheep of belly. Selection for 5 generations on tropical and humid environments. Compass lamb growth and ewe productivity. Agrinak Composite Crossing and selection with the genetic High growth rate 169.1 g /d, litter size Breed of Garut composition of 50% Garut, 25% hair St. 2.1 hd., ewe productivity 47kg/lambs Sheep Croix and 25% Moulton Charollais. /yr, no mating season, adaptive Selection for 5 generations on lamb tropical and humid environments. growth and ewe productivity. Composite Crossing and selection between Flemish Mature weight > 1300 gr (at 10 wk Rabbit of Giant and Reza. Selection for 3 ages) and fur area > 1.6 ft2. FZ-3 generations on growth trait and priority. This composite sheep has a genetic composition of 50% local Sumatera sheep, 25% St. Croix hair sheep and 25% Black belly Barbados. The advantages are obtained such as in the forms of higher productivity (vs. sheep Sumatera) and excellent adaptability to semi-intensive maintenance. Based on the Decree of the Minister of Agriculture of the Indonesian Republic, this Composite Superior Sheep Breed has been released with the name of Compass Agrinak Sheep.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Previously a superior strain of native chicken breed as the selection results for egg production traits from various native chicken populations was released with the name of KUB-1 (Balitbang Superior Native Chicken Vers. 1). Other breeding researches have also resulted in superior breeds and strains for being producing breeding stocks and quality seeds to meet the needs of farmers, society and markets. SUPPORTING BIOTECHNOLOGY AI Technology Application of AI technology has been intensified in beef cattle and dairy cattle. While in other species, AI has not been yet applied commercially, but still more at research levels. Researches have been to improve technical successes and pregnancy rates for buffalo, sheep, goat, chicken, duck, geese, rabbit and others. AI in breeding researches are also used to facilitate matings between or between species or breeds of AnGR, such as between geese and duck to produce final stocks. For beef cattle and dairy cattle, AI technology has been used to support genetic improvement through the distribution of frozen semen from superior bulls. Frozen semen is produced by two National AI Centers and by a number of local AI Stations. Local AI Stations are purposed to ensure quality and availability of frozen semen from local cattle breeds as AnGR properties possessed by respective provinces (Diwyanto, 2002; Anggraeni, 2011). Molecular Technology Information on genetic diversity is essential in optimizing both conservation and utilization strategies on local and indigenous AnGR. By progressing molecular technology today, it has been possible to identify genetic diversity between and within species, breeds and populations (Nei and Kumar, 2000; Sartika et. al., 2004; Sumantri et. al., 2006; Sumantri et. al., 2010). Genetic diversity of a population describes that appearance of animal diversity as a reflection of its genetic. The tools usually used to detect genetic diversity of population provide the techniques such as mitochondrial DNA, microsatellite DNA and Restricted Fragment Length Polymorphism (RFLP). Data can be used to get information about genetic diversity, phylogenetic or genetic relationship for indigenous and local AnGR. Studies of single nucleotide polymorphisms (SNP) and their haplotypes have been done using some techniques such PCR RFLP, RT PCR for the aims to determine SNPs in major genes associated to disease resistances, productivity traits (growth, milk production and milk quality) as well as reproduction (Sumantri et. al., 2009; Anggraeni et. al., 2009; Sumantri et. al., 2011). The latest molecular technologies such as whole genome sequencing method and SNP K-50 beat Chip have been used to study genetic diversity and improved productivity of local and indigenous AnGR, especially in beef cattle and dairy cattles. However, there are constraints on phenotype data collection and breeding design required. NATIONAL ACTION PLAN FOR CONSERVATION AND SUSTAINABLE USE OF AnGR Indonesia has adopted the Interlaken Declaration as the result of mutual agreement at the Technical Meeting of the FAO (2007), so it needs to set policies and programs of national sustainable livestock subsector development on draft of National Action Plans of AnGR for National Food and Agriculture.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Relative weights should be given to the implementation of priorities (short and medium terms) would need to consider the existence of indigenous and local AnGR (species and breeds), production systems and environmental management capacity at this time with regard to AnGR essential needs for welfare of farmers and community in rural area, and also take into account the livestock sub-sector development program as part of national agricultural development both currently running and to be achieved (Dir. GLSH, 2009). Priority should also consider any strategic changes happened (be happen) relating to management and conservation of AnGR in a sustainable manner to meet the needs of food consumption and to establish a national food self-sufficiency as well as for the world’s needs for food. Here are two priority areas of the National Action Plan for AnGR that will focus on consideration in the implementations of Sustainable Management and Utilization and Conservation of National AnGR. Sustainable Utilization To meet national food consumption and needs and to reach national food self-sufficiency program, utilization on AnGR should be brought in such ways of to be more effective and efficient. The existence of appropriate technologies and management improvements provide greater opportunity to increase production and productivity of indigenous and local AnGR for food and as well as to increase farmers’ income and to prevent losses of AnGR. A number of National Action Plans should be considered to assure sustainable utilization of AnGR: 1. Setting alignment of breeding programs of both indigenous and local AnGR into national agricultural development programs and environments with giving attentions to the welfare of small farmers and relevant actors in rural areas. 2. Establishing appropriate strategies on breeding programs in improving roles of local and indigenous breeds for food sources, economy, social, cultural, environmental, as well as considering increased utilization of unwell known breeds, especially under a low input production system. 3. Encouraging establishment of programs in forming either new breeds or new strains to meet food needs and market demands. Relevant technical information especially for phenotypic characteristics and evaluation of breeds and their production system should be supplied by research institutions, universities and related institutions or organizations. 4. Strengthening organizational structures and clearly define actors of breeding programs and breeding activities for main breeds among species, so that the process of genetic improvement could be focused and effective to achieve desired goals. 5. Breeding activities to improve productivity and to meet market demands through selection should give enough attention on possible effects on decreasing genetic diversity of breeds of indigenous and local AnGR. 6. Strengthening recording system on data of individual animal, ancestor, productivity, considered traits and production systems on breeding activities in correspondent to improved appropriate breeding programs. 7. Studying or exploring major genes or MAS to understand the function of molecular genetics in influencing the natures of productivity, production efficiency, disease resistance and adaptation to the environment, of which being expected to accelerate improvement by conventional breeding. 8. Encouraging deposits or reserves of frozen semen and frozen embryos from breeding programs to ensure preservation of genetic diversity of local and indigenous AnGR. 9. Encouraging traditional farming systems to maintain sustainability of indigenous and

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

local AnGR, by giving technical facilitation on production management, extension services and animal health services, protection and use of land, as well as by considering local cultural values and practices. Conservation A number of National Action Plan in AnGR conservation should be considered for: 1. Evaluating relevant policies in determining priorities and objectives of conservations, in situ and ex situ, by considering main aspects of production traits, functional traits and capacity buildings. 2. Setting policies to establish appropriate institutional structures for conservation activities of breeds at extinction or risk status by considering combination of implementation of both in situ and ex situ conservation. 3. Promoting in situ conservation of breeds and populations under at risk conditions by requiring support from governments and related actors such as researchers, universities, associations and farmers. 4. Promoting appropriate utilization of genetic diversity of indigenous and local AnGR as a basic consideration to make appropriate conservation decisions and to ensure sustainable conservation. 5. Promoting economic values and uniqueness of indigenous and local breeds with broader genetic biodiversity to ensure in situ conservation can be managed independently by farmers and community. 6. Developing sustainable ex situ conservation methods that simultaneously maintaining genetic diversity of AnGR so that conservation activities be secured in supporting genetic diversity of breeds conserved. 7. Establishing ex situ conservation facilities at nation level especially of storage methods of cryopreservation techniques (semen, embryos and primordial germ cells). 8. Developing techniques and methods of collecting samples in the form of genetic material (DNA) in supporting collection activities of in situ to avoid possible loss of genetic diversity of AnGR. 9. Studying comprehensive of genetic diversity through molecular analysis methods being very useful in providing information in determining priorities of breeds considered into conservation programs. 10. Improving collaboration for conservation by harmonizing between in situ conservation and ex situ conservation for trans boundary breeds across national borders. REFERENCES Anggraeni, A. 2011. Perbaikan genetik sifat produksi susu dan kualitas susu sapi friesian holstein melalui seleksi. Wartazoa. Vol. 22, No. 1., Thn 2012. Buletin Ilmu Peternakan dan Kesehatan Hewan Indonesia. Puslitbang Peternakan. Bogor. Hal: 1-11. Anggraeni, A., C. Sumantri, A. Farajallah dan E. Andreas. 2009. Verifikasi kontrol gen kappa kasein pada protein susu sapi Friesian-Holastein di daerah produksi susu Jawa Barat. JITV.14 (2):131-141. Animal Husbandry Statistic (Statistic Peternakan). 2010, 2012. Ditjen Peternakan dan Kesehatan Hewan. Jakarta. Aryogi, Sumadi dan W. Hardjosubroto. 2005. Performans sapi silangan Peranakan Ongole di dataran rendah (Studi kasus di Kecamatan Kota Anyar Kabupaten Probolinggo Jawa Timur) (The performance of Ongole Grade Cross cattle in low land area (a Case Study at Kota Anyar Sub District, Probolinggo District East Java). Seminar Nasional Teknologi Peternakan dan Veteriner.

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Astuti, M. 2004. Potensi dan keragaman sumberdaya genetik sapi Peranakan Ongole (PO). Wartazoa 2004 Vol. 14 No. 3. Hal: 98-106. Direktorat Perbibitan. 2014. Kepmentan Rumpun/Galur Ternak. http://bibit.ditjennak.deptan. go.id /index.php/blog/read/rumpun-ternak/ kepmentan-rumpungalur-ternak Dir. General Livestock Services and Health (Ditjen PKH). 2009. Rencana Strategis Direktorat Jenderal Peternakan dan Kesehatan Hewan 2010-2014, Direktorat Jenderal Peternakan dan Kesehatan Hewan, Kementerian Pertanian. Jakarta. http://- www.pertanian.go.id/sakip/admin/file/RENSTRA_DITJEN_PKH_2010-2014.pdf Diwyanto, K. 2008. Pemanfaatan sumber daya lokal dan inovasi teknologi dalam mendukung pengembangan sapi potong di Indonesia. Pengembangan Inovasi Pertanian, 1(3) : 173-18. Diwyanto, K. 2002. Program Pemuliaan Sapi Potong (Suatu Pemikiran). Pros. Seminar Nasional “Kebijakan Breeding”. Bogor, 30 September–1 Oktober 2002. Puslitbang Peternakan, Bogor. Diwyanto, K., A. Anggraeni and A. Djajanegara. 1997. Practical experiences in milk recording in Indonesia. Proc. of Int. Workshop Animal Recording for Smallholders in Developing Countries. ICAR Technical Series No.1, FAO. Anand, India, 20 – 23 October 1997. pp. 89 – 102. FAO. 2007a. Global Plan of Action for Animal Genetic Resources and the Interlaken Declaration. Commission on Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations. Rome. http://www.fao.org/3/a- a1404e.pdf FAO. 2007b. The State of the World’s Animal Genetic Resources for Food and Agriculture. Commiss ion on Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations. Rome. http://www.fao.org/docrep/ 010/a1250e/a1250e00.htm FAO. 2009. Preparation of National Strategies and Action Plans for Animal Genetic Resources. Commission on Genetic Resources for Food and Agriculture. Food and Agriculture Organization of the United Nations. Rome. ftp://ftp.fao.org/docrep/fao/ meeting/017/ak523e.pdf FAO. 2012. Cryopreservation of Animal Genetic Resources. Guidelines. Food and Agriculture Food and Agriculture Organization (FAO) of The United Nations. Rome. Hadi, P.U., N. Ilham. 2002. Problem dan prospek pengembangan usaha pembibitan sapi potong di Indonesia. Jurnal Litbang Pertanian. 21(4): 148-157 Handiwirawan, E. dan Subandriyo. 2004. Potensi dan keragaman sumberdaya genetik sapi Bali. Wartazoa 14:3-9. Hardjosubroto, W. 2004. Alternatif Kebijakan Pengelolaan Berkelanjutan Sumberdaya Genetik Sapi Potong Lokal dalam Sistem Perbibitan Ternak Nasional. Wartazoa Vol. 14 No. 3. Hal: 93-97. Kusdiantoro, M., M. Olsson, H. T. A. van Tol, S. Mikko, B. H. Vlamings, G. Andersson, H. RodrIguez-MartInez, B. Purwantara, R. W. Paling, B. Colenbrander, & J. A. Lenstra. 2009. On the origin of Indonesian cattle. Plos one 4:e5490. Mulliadi, D. 1996. Sifat Fenotipik Domba Priangan di Kabupaten Pandeglang dan Garut. Desertasi Program Pascasarjana. Institut Pertanian Bogor. Namikawa, T., J. Otsuka, & H. Martojo. 1980. Coat colour variations of Indonesian cattle. The origin and phylogeny of Indonesian native livestock (Part III): Morphological and genetically investigations on the interrelationship between domestic animals and their wild forms in Indonesia. The Research Group of Overseas Scientifi Survey 31-34. p. 19-27.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Nei, M. & S. Kumar. 2000. Molecular Evolution And Phylogenetics. Oxford University press, New York, USA. Pane, I. 1990. Improving the genetic quality of Bali cattle (Upaya Peningkatan Mutu Genetic Sapi Bali di P3Bali). Proceedings of Bali Cattle Meeting, 20–23 September 1990, Denpasar, A42. Sartika, T., M. Minezawa, H. Hihara, L.H. Prasetyo and H. Takahashi. 2004. Genetic relationships among Japanese and Indonesian native breeds of chicken based on microsatellite DNA polymorphism. Proc. 29th International Conference on Animal Genetics, ISAG-2004, Tokyo. Sartika, T., S, Iskandar, L.H. Prasetyo, H. Takahashi dan M. Minezawa. 2004. Kekerabatan genetik ayam Kampung, Pelung, Sentul dan Kedu Hitam dengan menggunakan penanda DNA mikrosatelit. I. Grup pemetaan pada makro kromosom. JITV 9(2): 81 – 86. Soehadji. 1996. Ditjen Peternakan. Kebijakan Pemerintah dalam Pengembangan Agribisnis Persusuan Menghadapi Pasar Bebas. Laporan Direktorat Bina Usaha Tani dan Pengelolaan Koperasi, Direktorat Jenderal Peternakan, Jakarta.

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Manipulating Local Genetic Resources to Maintain Animal Biodiversity–The Practical Point of View Liang Chou Hsia National Pingtung University of Science and Technology, Taiwan, ROC ABSTRACT The purpose of the paper tried to use more practical way to maintain animal biodiversity by local genetic resources. Biodiversity is important but do not over emphasize on some points: 1. heat stress tolerance; 2. higher digestibility and efficiency; 3. meat quality; 4. long history influence; 5. high or low production. There are new ways to maintain animal biodiversity: 1. specific function of animals and/or animal products; 2. animals can be used for making special products; 3. provided for experiment; 4. beauty and performance competition; 5. breeding farms cooperated with leisure farms, zoo and aquarium; 6. ecology conservation; 7. culture and/or entertainment;8. to understand the specific advantage of any endangered species; 9. meal and breeding purpose. It is easy to understand biodiversity but it is not easy to do it. It is important to find ways to help them survive. Key Words: Biodiversity, Animal, Management INTRODUCTION The definition of biodiversity is “variation of life at all levels of biological organization (Gaston and Spicer, 2004). The purpose of the paper tried to use more practical way to maintain animal biodiversity by local genetic resources. Here the animals will be concentrated on the biodiversity of livestock, poultry, fish and the related animals. ADVANTAGE OF BIODIVERSITY Here we only want to show you some practical cases of advantages in our animal production. Native chicken production Native chicken production in Taiwan occupies about 40-50% of our meat type chicken production; that means we do not need to eat 100% broiler chicken meat, which is good on fry and roast, but not good on stew. The result is we can keep some native chicken. Grouper is getting less and small in wild. Now is one of the most popular food fish in Chinese. The fish is from artificial cultured source now (Yunget al., 2009). The fish is not easily extinct in near future at least. Some native pigs are not only good on marbling of meat but also very good on fat taste (Taoyuan). Jinhua pig is another example. The pigs have very thin skin, which makes it become the one of most famous ham in the world (TECCJP, 1995). Cattle and yak (Wiener et al., 2003) can survive in cold areas and produce milk, meat, and leather for local people. These are just few practical examples. BIODIVERSITY IS IMPORTANT BUT DO NOT OVER EMPHASIZE ON SOME POINTS Heat stress tolerance People always point out some native animals can tolerate heat stress. We have to be careful to make this conclusion. If an animal has low growth rate, low milk, low egg and low wool production, then it must have low energy metabolism (Kleiber, 1961). The lower the energy metabolism, the lower the heat production of an animal produces (Kleiber, 1961; Mount, 1968).The lower heat production, the higher heat tolerance; the lower feed intake, the higher heat tolerance of an animal can be. The principle is the same as before. The lower feed intake

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change then produces less heat, consequently more tolerance to heat stress. Some animals of course may tolerate heat stress than other animals, but we have to consider the above items carefully. Higher digestibility and efficiency It has to be very careful to evaluate digestibility of animal on feed. The higher feed intake usually has lower digestibility and feed efficiency of feed and roughage (Forbes, 1995). We have to evaluate the digestibility and efficiency based on same feed intake. Meat quality Do not make conclusion too quickly about meat quality. The basic growth of animals can be divided into 4 stages. Animals grow faster on organs, bone and muscle, but grow slower on fat during the first stage. Animals grow faster on muscle, as for bone and organs grow slowly, and fat start to grow a little fast during the second stage. During the third stage, muscle growth slowsdown and fat growth is quite fast. On the fourth stage, animal growth is mainly on intramuscular fat, and muscle growth almost stop. Human want the slow growth animals, like some kinds of cattle, pigs, chicken and etc. They can reach a little bit higher body weight so we let them have longer raising time. Consequently their muscle quality, aromatic fat and tenderness become very favor to human’s requirement. If we keep the fast growing animal longer also can reach this level. Of course genetic different is also important, e.g. Durocgene for intramuscular fat of pigs (Gerbens et al., 1998). Long history influence The Chinese pigs have been long enough time to influence on some British pig breeds (Layley and Malden, 1935). This may be another reason why the Chinese breeds cannot have big influence on western pigs when western people introduce Chinese pigs to western about25 to 30 years ago. It is important to use these local breeds by some other ways. If we lose confidence for these local breeds, then they will disappear in the near future. High or low production Holstein cattle produce high amount of milk per day (e.g. 30 kg), and Jersey produce 15 kg of milk per day. Holstein cannot tolerate heat stress and so reduce 20% of milk production. Consequence the total milk production for Holstein is still higher than milk production for Jersey. Many people argue that fat content of Jersey milk is higher. However the total milk fat production for Holstein is 24 × 0.4% = 0.096 kg/day/cow, and as for Jersey is 15× 0.48% = 0.072 kg/day/cow. Consequence we cannot only consider production, we have to find other channel to maintain animal biodiversity. However we also need to consider about cost for milk production of both cattle. HOW TO FIND NEW WAYS TO MAINTAIN ANIMAL BIODIVERSITY Specific function of animals and/or animal products The best sample is silky chicken. Lin and Chen (2004) found silky chicken has high iron, melatonin and anti-cancer substances. It also can be found some other interesting functional products in other animals, for example whether black skin animals also have the same to above products. Animals can be used for making special products The most famous example is using Jinhua pig to make ham due to its thin skin. Another good example is using Mozzarella water buffalo milk to make cheese and yogurt in Italy. Provided for experiment There are three kinds of mini pigs become laboratory animals successfully. They are Lanyu Small Ear Pigs, Vietnamese Potbelly Pigs and KuneKunePigs. That is the reason the animals have big chance to survive in the future. When we use the animal for laboratory purpose, of course we need to consider their welfare based on welfare code.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Beauty and performance competition The most successful example is dog and cat show. There are a lot of shows really help for the survival of rare breeds of farm animals, e.g. Royal Show, Royal Highland Show, French agriculture, individual breed show etc. These kinds of shows are not easy to survive. It needs a lot of cooperation by people and organizations, e.g. individual rare breed associations (e.g. Rare Breeds Survival Trust), government support on biosecurity and business opportunity etc. This kind of competition not only show animal to people but also can attract people to understand and even buy the animal. Breeding farms cooperated with leisure farms, zoo and aquarium Animal breeding farm cannot survive by its own. There are no customers to buy animals how can breeding farms survive. If breeding farm can provide leisure farms animals for meal or exhibition, then the both sides can be benefit by this cooperation. It is not common that zoo has large amount of domestic animal exhibition. The best way is through an agent to collect these animals from breeding farms then sell to zoo, or aquarium. Zoo or aquarium put various and only healthy animals for exhibition, consequently the requirement from breeding farms will increase. To produce colorful animal based on pure breed animal is another way to maintain animal biodiversity. This is quite common in chicken, fish, and occasionally in pigs. Ecology conservation Grassland in many areas of the world was damaged by grasshoppers. It is a good idea to bring native chicken to the area (Sun et al., 2012). It is not only to conserve native chicken but chicken also can eat grasshoppers. The third advantage is chicken can excrete manure on land and add nutrients on earth. Culture and/or entertainment Boar body size competition is one of Hakka culture in Chinese society. This competition use local breed consequence can help endangered domestic pigs and animals. Some people think this is against animal welfare. We can change some rules and let it follow animal welfare rules. The same thing happened in cock fighting (Chantalakhana, 2012). If we can change the rule then we can preserve a lot of chicken, e.g. we can make decisions when the fighting should be stop, we also can separate chicken grade according to the size, consequently, more endangered breeds can survive by this way. A lot of entertainment can use animal to do it, e.g. fish competition on open the gate in water, pig racing in short distance by using endangered pig breeds, chicken fly distance or height, etc. Let people ride camel is another entertainment. There are at least 1000 camels in Dunhuang tourist area. These camels belong to more than 800 families. The families can earn money, the tourists can have riding camel’s experience, and the camels can survive not being replaced by cars. The only requirement during entertainment is you have to use local breeds to present in the culture, entertainment, competition, and show competition programs. To understand the specific advantage of any endangered species It is very important to understand how to use a specific animal. If cannot provide for ordinary purpose, then go to next step, that is to consider how to use in laboratory, leisure farm, zoo, ecology conversation, entertainment, etc. if still cannot be used, then let them have chance go to show.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Meal and breeding purpose Muscovy should be extinct from the world because they cannot produce more eggs and grow fast. However they still exist in Taiwan. This is because Taiwanese believe that Muscovy (cairinamoschato) cocked with ginger is good for health in winter. It is also because the crossbred between Muscovy♂ and Tsaiya ♀ or/and Pekin duck can produce fast growth mule duck. CONCLUSION It is easy to understand biodiversity but it is not easy to do it. It is important to find ways to help them survive. REFERENCES Chantalakhana, C. 2012. Rural farmers and in situ conservation of animal genetic resources through culture and traditions. Proceedings of the 15th AAAP Animal Science Congress, Vol. 1 Plenary and Invited Papers. Thailand. Forbes, J. M. 1995. Voluntary Food Intake and Diet Selection in Farm Animals. CAB International, Oxon. Gaston, K. J. and J. I. Spicer. 2004. Biodiversity: an Introduction. 2nd ed. Blackwell Publishing, Oxford, UK. Gerbens, F., A. Jansen, A. J. M. Van Erp, F. Harders, T. H. E. Meuwissen, G. Rettenberger. 1998. The adipocyte fatty acid-binding protein locus: Characterisaton and association with intramuscular fat content in pigs. Mammalian Genome, 9:1022-1026. Kleiber, M. 1961. The Fire of Life. John Wiley and Sons, Inc., New York. Layley, G. W. and W. J. Malden. 1935. The Evolution of the Brutish pig. Past, Present and Future. John Bale, Sons Danielson. Ltd., London. Lin, L. C., and W. T. Chen. 2004. The study of antioxidant effects in melanins extracted from various tissues of animals. Asian-Australas. J. Anim. Sci. 18:277-281. Mount, L. E. 1968. The Climate Physiology of the Pigs. Williams and Wilkins Co., Baltimore. Sun, T., Z. Liu, L. Qin, and R. Long. 2012. Meat fatty acid and cholesterol level of free-range broilers fed on grasshoppers on alpine rangeland in the Tibetan plateau. J. Sci. Food Agric. 92(11):2239-2243. The Editorial Committee of the Chinese Jinhua Pig (TECCJP). 1995. Chinese JinhuaPig. Shanghai Scientific and Technical Publishers, Shanghai, China. Wiener, G., H. Jianlin, and L. Ruijun. 2003. The Yak. Regional Office for Asia and the Pacific Food and Agriculture Organization of the United Nations, Bangkok, Thailand. Yung, Y. T., W. Y. Chen, and Z. X. Chen. 2009. Industry overview and trends of Grouper. Agricultural Biotechnology Industry Quarterly. 19:26-29.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The Development of the Global Livestock Sector and its Impacts on Food Production and Trade Nicostrato D. Perez and Mark W. Rosegrant International Food Policy Research Institute 2033 K Street, NW, Washington, DC 20006, USA Corresponding email: [email protected] ABSTRACT The demand-driven Livestock Revolution observed in the 1980s is revisited and found to have continued to the present. To examine its longer-term sustainability given the world’s production capacity, advances in technology, changing regional production and delivery systems, and projections of population and income, IFPRI’s IMPACT model is used in this study, under baseline conditions and climate change. The global livestock sector is projected to have unbalanced growth, with demand outpacing production in developing countries, causing increases in imports and world prices of livestock commodities. Four development policies were simulated in this study to determine their impact on future production, trade, world prices and food security. They are based on intensive and expansive growth policies, applied either to all countries or focused only to developing countries – coded as Int-all, Int-dvg, Exp-all, and Exp-dvg policies. The different policies for the development of the global livestock sector do not only help sustain the Livestock Revolution, but in the process contribute to food security through increased availability and accessibility of livestock products for food consumption to more people. They promote growth in production by combinations of increasing productivity, stock increase, and closing the yield-gap between developed and developing countries; which in turn lowers prices and contributes to rural incomes. Among these policies, the expansive growth policy focused on developing countries (Exp- dvg) is projected to generate the greatest impact in production, trade, world prices, and food security. Through this policy, in developing countries production is increased by 22.9% (meat), 21.6% (milk), 23.0% (beef), 24.2% (pork); 21.4% (poultry), and 25.0% (sheep meat); developing countries become net exporter of meat (10 million mt), milk (60 million mt), beef (11 million mt), and poultry meat (2.5 million mt); world prices are reduced by 34% (beef), 28% (pork), 32% (poultry), 42% (sheep meat) and 21% (milk). INTRODUCTION A seminal paper on livestock development, more than a decade ago, coined the term Livestock Revolution1to describe the combination of events - population growth, urbanization, and income growth in developing countries, which started fueling a massive increase in demand for food from livestock and poultry since the 1980s. It was said to parallel the Green Revolution in the sense that the revolution aspect comes from the large scale participation of developing countries. But unlike the Green Revolution, which was supply-driven, the ensuing Livestock Revolution was demand-driven. Therefore, even back then, the so-called Livestock Revolution, that offered income opportunities for many rural poor, were also expected to stretch the capacity of existing production and distribution systems.

1 Delgado, C., M. Rosegrant, H. Steinfeld, S. Ehui and C. Courbois. 1999. “Livestock to 2020: The Next Food Revolution”, 2020 Vision for Food, Agriculture and the Environment Discussion Paper 28. IFPRI, May 1999.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

This paper revisits the Livestock Revolution, analyzes the recent consumption, production and trade data of meat and milk products, simulates the future global livestock market and presents alternative livestock development scenarios that can possibly ensure that the revolution started in the past decades continues to the first half of this century with minimum strain in the livestock production and delivery systems. THE LIVESTOCK REVOLUTION REVISITED The onset of Livestock Revolution in the 1980 sushered radical transformations in the consumption of meat and milk products in the developing world. While per capita consumptions of meat and milk products remained high in developed countries, their growth rates had declined (and in some cases turned negative), while those of the developing countries increased at much higher paces. Trends in livestock consumption Recent statistics show the continuing transformations in the livestock food demand from 1980 to 2010s. Figure 1 graphically presents the historical trends in the per capita consumption of meat and milk products. Table 1 presents them with more details2.

Meat Consumption Milk Consumption 100 120 83 98 80 78 92 92 80 73 100 86 80 60 60 43 40 31 33 25 40 27

18 23 kg/capita/year kg/capita/year 14 20 20 0 0 Developed Developing Developed Developing

1980 1990 2000 2010 1980 1990 2000 2010

Meat Consumption Milk Consumption 140 160 120 140 120 100 100 80 80 60 60

40 kg/capita/year 40 kg/capita/year 20 20 0 0

1980 1990 2000 2010 1980 1990 2000 2010

Figure 1. Historical trends in meat and milk consumption by region, 1980, 1990, 2000 and 2010

2 Table 1 gives more details, including total and per capita consumption of the different meat products, including beef, pork, sheep meat, and poultry meat – all by region and selected countries, and growth rate values.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Table 1. Historical trends in food consumption of meat and milk products

Total Consumption Per Capita Consumption Growth Rates of Cons/capita

1980 1990 2000 1980 Region / Products 1980 1990 2000 2010 1980 1990 2000 2010 - - - - 1990 2000 2010 2010 ------million mt ------million mt ------million mt ------Beef East Asia and Pacific 2.1 3.7 8.8 10.9 1.3 2.0 4.3 4.9 4.14 7.93 1.39 4.45 Eastern Europe and 9.1 11.8 5.5 5.9 21.3 25.2 11.9 12.6 1.71 -7.23 0.55 -1.74 Central Asia Latin America and the 8.0 9.8 12.9 14.8 22.0 22.0 24.4 24.9 0.01 1.04 0.18 0.41 Caribbean Middle East and North 1.1 1.5 1.9 2.8 5.9 6.0 6.2 7.4 0.15 0.25 1.89 0.76 Africa North America 11.9 12.0 13.4 13.1 46.6 42.7 42.6 37.8 -0.89 -0.02 -1.19 -0.70 Oceania 1.0 0.9 0.8 0.9 53.4 45.3 36.0 34.4 -1.63 -2.27 -0.46 -1.46 South Asia 2.3 3.1 3.3 3.8 2.6 2.7 2.4 2.4 0.69 -1.25 -0.14 -0.24 Sub-Saharan Africa 2.6 2.9 3.4 4.5 6.9 5.7 5.1 5.2 -1.84 -1.18 0.22 -0.94 Western Europe 8.6 8.6 7.6 8.0 23.4 22.7 19.2 19.0 -0.30 -1.65 -0.11 -0.69 Selected countries Brazil 2.7 4.1 6.1 7.6 22.5 27.5 34.9 38.7 2.03 2.41 1.04 1.82 China 0.4 1.2 5.1 6.8 0.4 1.0 3.9 4.9 9.60 14.58 2.31 8.71 India 1.6 2.1 1.9 1.8 2.3 2.4 1.9 1.5 0.43 -2.31 -2.34 -1.41 Indonesia 0.3 0.3 0.4 0.6 1.8 1.7 2.0 2.4 -0.57 1.64 1.84 0.96 United States 10.9 11.0 12.4 12.1 47.3 43.4 43.5 38.7 -0.86 0.02 -1.16 -0.67 Pork East Asia and Pacific 15.4 28.6 44.7 61.4 10.0 15.6 21.9 27.9 4.56 3.43 2.45 3.48 Eastern Europe and 10.4 12.1 7.7 9.2 24.4 25.9 16.5 19.4 0.62 -4.39 1.61 -0.76 Central Asia Latin America and the 3.2 2.9 5.3 6.5 8.7 6.6 10.0 10.9 -2.79 4.28 0.82 0.73 Caribbean Middle East and North 0.0 0.0 0.1 0.1 0.2 0.2 0.2 0.2 1.26 1.94 -1.07 0.70 Africa North America 8.4 8.0 9.4 9.8 33.1 28.3 29.7 28.3 -1.55 0.47 -0.46 -0.52 Oceania 0.3 0.4 0.4 0.6 14.1 17.4 18.7 22.3 2.14 0.74 1.75 1.54 South Asia 0.3 0.4 0.5 0.3 0.3 0.4 0.3 0.2 2.30 -0.68 -4.63 -1.04 Sub-Saharan Africa 0.3 0.6 0.8 1.2 0.8 1.1 1.2 1.4 3.00 0.60 2.09 1.89 Western Europe 13.7 15.2 16.8 16.9 37.4 40.4 42.2 40.5 0.76 0.44 -0.42 0.26 Selected countries Brazil 1.0 1.0 2.4 2.4 8.0 6.9 13.9 12.3 -1.47 7.25 -1.22 1.44 China 12.0 23.2 37.1 49.9 11.9 19.5 28.3 35.9 5.06 3.79 2.41 3.75 India 0.3 0.4 0.5 0.3 0.4 0.5 0.5 0.3 2.26 0.00 -4.98 -0.95 Indonesia 0.2 0.5 0.4 0.7 1.2 3.1 2.0 2.9 9.96 -4.29 3.79 2.98 United States 7.6 7.2 8.4 9.0 33.0 28.4 29.6 28.8 -1.49 0.41 -0.27 -0.45 Mutton and Goat Meat East Asia and Pacific 0.7 1.4 3.0 4.5 0.5 0.8 1.5 2.1 5.28 6.55 3.33 5.05 Eastern Europe and 1.5 1.7 1.0 1.2 3.5 3.7 2.2 2.5 0.64 -4.82 1.04 -1.08 Central Asia Latin America and the 0.3 0.4 0.4 0.4 0.9 0.9 0.8 0.7 -0.05 -0.97 -1.75 -0.93 Caribbean Middle East and North 1.0 1.2 1.5 1.5 5.1 4.8 4.8 4.0 -0.55 0.11 -1.98 -0.81 Africa North America 0.2 0.2 0.2 0.2 0.7 0.7 0.6 0.5 0.25 -2.24 -1.54 -1.18 Oceania 0.4 0.5 0.4 0.3 22.1 24.0 19.0 12.8 0.87 -2.32 -3.88 -1.80 South Asia 0.9 1.3 1.5 1.7 1.0 1.2 1.1 1.1 1.33 -0.86 -0.21 0.08 Sub-Saharan Africa 0.9 1.1 1.7 2.2 2.5 2.2 2.5 2.5 -1.12 1.27 0.10 0.08 Western Europe 1.2 1.4 1.4 1.1 3.3 3.8 3.5 2.6 1.27 -0.78 -3.05 -0.87

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Total Consumption Per Capita Consumption Growth Rates of Cons/capita

1980 1990 2000 1980 Region / Products 1980 1990 2000 2010 1980 1990 2000 2010 - - - - 1990 2000 2010 2010 ------million mt ------million mt ------million mt ------Selected countries Brazil 0.1 0.1 0.1 0.1 0.4 0.8 0.6 0.6 7.18 -2.84 0.00 1.36 China 0.5 1.1 2.7 4.1 0.5 0.9 2.1 2.9 6.05 8.84 3.28 6.03 India 0.5 0.6 0.7 0.9 0.7 0.7 0.7 0.7 0.00 0.00 0.00 0.00 Indonesia 0.1 0.1 0.1 0.1 0.4 0.5 0.4 0.5 2.26 -2.21 2.26 0.75 United States 0.2 0.2 0.2 0.1 0.7 0.7 0.5 0.4 0.00 -3.31 -2.21 -1.85 Poultry Meat East Asia and Pacific 4.0 7.8 19.4 27.8 2.6 4.3 9.5 12.6 4.94 8.41 2.84 5.37 Eastern Europe and 4.0 5.6 4.5 8.6 9.4 12.0 9.6 18.3 2.48 -2.23 6.69 2.25 Central Asia Latin America and the 3.0 4.9 11.8 18.1 8.3 11.0 22.4 30.3 2.90 7.31 3.09 4.42 Caribbean Middle East and North 1.3 2.3 4.1 7.4 7.0 9.1 13.2 19.5 2.68 3.79 3.96 3.47 Africa North America 6.6 10.8 14.6 17.2 26.0 38.3 46.3 49.5 3.96 1.91 0.69 2.18 Oceania 0.3 0.5 0.7 1.0 18.8 22.7 31.4 38.8 1.91 3.29 2.12 2.44 South Asia 0.3 0.7 1.5 3.3 0.3 0.6 1.1 2.1 7.40 5.73 6.99 6.70 Sub-Saharan Africa 0.7 1.3 1.9 3.6 2.0 2.5 2.8 4.2 2.54 1.14 4.03 2.56 Western Europe 5.0 6.3 8.0 9.0 13.6 16.8 20.1 21.5 2.12 1.80 0.67 1.53 Selected countries Brazil 1.2 2.1 5.2 7.4 10.1 14.1 29.6 37.7 3.39 7.70 2.45 4.49 China 1.7 3.9 13.3 17.9 1.7 3.2 10.2 12.9 6.53 12.29 2.38 6.99 India 0.1 0.4 0.9 2.2 0.2 0.5 0.9 1.9 9.60 6.05 7.76 7.79 Indonesia 0.2 0.5 0.8 1.6 1.2 2.9 4.0 6.5 9.22 3.27 4.97 5.79 United States 6.1 10.0 13.5 15.9 26.4 39.4 47.4 50.9 4.09 1.87 0.71 2.21

All Meat Products* East Asia and Pacific 22.3 41.5 75.9 104.6 14.4 22.7 37.2 47.5 4.61 5.08 2.47 4.05 Eastern Europe and 24.9 31.1 18.8 24.9 58.5 66.8 40.3 52.8 1.34 -4.94 2.74 -0.34 Central Asia Latin America and the 14.6 18.1 30.4 39.8 40.0 40.6 57.7 66.8 0.16 3.57 1.48 1.73 Caribbean Middle East and North 3.4 5.1 7.6 11.9 18.1 20.1 24.4 31.1 1.03 1.98 2.43 1.81 Africa North America 27.1 31.0 37.6 40.2 106.4 110.0 119.1 116.1 0.33 0.80 -0.25 0.29 Oceania 1.9 2.2 2.4 2.9 108.3 109.5 105.2 108.2 0.10 -0.40 0.29 0.00 South Asia 3.8 5.6 6.8 9.2 4.2 4.9 4.9 5.7 1.58 0.01 1.58 1.05 Sub-Saharan Africa 4.7 5.9 7.7 11.5 12.1 11.5 11.5 13.3 -0.50 0.02 1.43 0.31 Western Europe 28.6 31.6 33.8 34.9 77.8 83.7 85.0 83.6 0.73 0.16 -0.18 0.24 Selected countries Brazil 5.0 7.4 13.8 17.4 41.0 49.3 79.0 89.3 1.86 4.83 1.23 2.63 China 14.5 29.3 58.2 78.7 14.5 24.6 44.5 56.6 5.43 6.11 2.43 4.64 India 2.5 3.5 4.0 5.2 3.6 4.1 4.0 4.4 1.31 -0.25 0.96 0.67 Indonesia 0.7 1.5 1.7 3.0 4.6 8.2 8.4 12.3 5.95 0.24 3.89 3.33 United States 24.7 28.5 34.4 37.1 107.4 111.9 121.0 118.8 0.41 0.78 -0.18 0.34 Milk East Asia and Pacific 8.7 14.3 20.8 50.3 5.6 7.8 10.2 22.9 3.34 2.70 8.39 4.78 Eastern Europe and 43.0 43.7 53.1 54.4 100.9 93.8 114.0 115.2 -0.72 1.96 0.11 0.44 Central Asia Latin America and the 26.1 31.6 45.2 55.5 71.7 70.9 85.8 93.0 -0.12 1.93 0.81 0.87 Caribbean

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Total Consumption Per Capita Consumption Growth Rates of Cons/capita

1980 1990 2000 1980 Region / Products 1980 1990 2000 2010 1980 1990 2000 2010 - - - - 1990 2000 2010 2010 ------million mt ------million mt ------million mt ------Middle East and North 4.7 6.7 10.6 14.2 25.1 26.4 34.0 37.3 0.52 2.54 0.93 1.33 Africa North America 33.8 35.2 36.1 37.6 132.7 124.9 114.4 108.5 -0.61 -0.87 -0.53 -0.67 Oceania 2.2 2.8 2.3 2.4 124.9 137.2 97.6 90.9 0.95 -3.35 -0.71 -1.05 South Asia 25.1 43.7 57.9 83.3 27.8 38.5 41.9 51.8 3.30 0.85 2.16 2.10 Sub-Saharan Africa 10.2 12.8 16.7 27.3 26.6 25.1 25.1 31.5 -0.60 -0.01 2.31 0.56 Western Europe 36.7 33.1 30.9 30.8 100.0 87.8 77.9 73.7 -1.29 -1.19 -0.56 -1.01 Selected countries Brazil 8.7 12.1 18.3 26.5 71.4 80.9 104.9 135.9 1.26 2.63 2.62 2.17 China 2.1 5.3 10.1 39.6 2.1 4.5 7.7 28.5 7.92 5.52 13.98 9.08 India 18.9 34.2 41.6 60.1 27.1 39.4 39.9 49.8 3.81 0.13 2.24 2.05 Indonesia 0.3 0.5 0.8 1.3 2.3 3.0 4.0 5.2 2.69 2.92 2.66 2.76 United States 31.5 33.0 34.3 36.3 137.0 129.6 120.4 116.2 -0.55 -0.73 -0.35 -0.55 Developed Countries Beef 31.1 34.3 28.3 27.7 26.4 27.1 22.7 21.3 0.30 -1.77 -0.62 -0.70 Pork 34.7 38.1 37.4 40.3 29.4 30.2 30.0 31.0 0.27 -0.07 0.34 0.18 Mutton & Goat Meat 3.1 3.6 2.4 2.0 2.6 2.8 1.9 1.5 0.80 -3.81 -2.22 -1.77 Poultry meat 17.1 24.9 29.7 37.6 14.5 19.7 23.9 29.0 3.13 1.92 1.96 2.34 Meat 86.0 100.8 97.8 107.5 72.9 79.9 78.5 82.9 0.93 -0.18 0.55 0.43 Milk 115.2 116.2 114.5 112.0 97.7 92.1 91.9 86.3 -0.59 -0.02 -0.62 -0.41 Developing Countries Beef 15.6 20.1 29.5 37.1 4.8 4.9 6.0 6.6 0.38 2.01 0.91 1.10 Pork 17.4 30.1 48.2 65.8 5.3 7.4 9.9 11.7 3.40 2.88 1.73 2.67 Mutton & Goat Meat 4.1 5.8 8.8 11.1 1.3 1.4 1.8 2.0 1.34 2.34 0.93 1.54 Poultry meat 8.3 15.3 36.7 58.5 2.5 3.8 7.5 10.4 4.08 7.15 3.31 4.83 Meat 45.3 71.3 123.1 172.5 13.9 17.6 25.2 30.7 2.40 3.68 1.99 2.69 Milk 75.5 107.8 159.1 243.9 23.1 26.6 32.6 43.4 1.41 2.07 2.91 2.13 WORLD Beef 46.7 54.3 57.7 64.8 10.5 10.2 9.4 9.4 -0.27 -0.80 -0.06 -0.38 Pork 52.0 68.2 85.5 106.0 11.7 12.8 14.0 15.3 0.92 0.85 0.95 0.91 Mutton & Goat Meat 7.2 9.4 11.2 13.1 1.6 1.8 1.8 1.9 0.89 0.36 0.37 0.54 Poultry meat 25.4 40.2 66.4 96.1 5.7 7.6 10.8 13.9 2.85 3.68 2.51 3.01 Meat 131.2 172.1 220.8 280.0 29.5 32.4 36.0 40.5 0.93 1.09 1.17 1.06 Milk 190.7 224.0 273.5 355.8 42.9 42.1 44.6 51.5 -0.18 0.59 1.43 0.61 All meat products - sum of beef, pork, mutton/goat meat and poultry meat. Note that fish and fishery products were not included in this study. Source of basic data: FAOSTAT. August 2014. Global trends Average annual per capita consumption of all meat products3in developing countries increased from 14 kg in 1980 to 18 kg in 1990. This trend continued to 2000 (25 kg) and 2010 (31 kg) – equivalent to 2.69 percent annual rate of growth for the entire period. In contrast, in developed countries, the average annual consumption per capita grew only by 0.4 percent/year during the same period. The contrasting trends were more pronounced for milk where developed countries as a group have negative growth (-0.4 percent/year), compared to developing countries’ positive growth

3 All meat products includes beef, pork, poultry and sheep meat.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change of 2.1 percent/year from 1980 to 2010. Meat and milk consumption remained relatively high for developed countries (83 kg/capita for meat in 2010 and 86 kg/capita for milk compared to, respectively, 31 and 43 kg/capita for developing countries), but the trends support the continuing global transformation in the food demand for livestock products. By commodity, per capita consumptions of poultry meat and pork have the highest growth rates for developing countries, respectively 4.8 and 2.7 percent/year, from 1980-2010. While sheep meat and beef consumptions per capita, have declined in developed countries, respectively, by 1.8 and 0.7 percent/year. Regional trends The East Asia and the Pacific region (EAP) has the highest growth in the consumption of meat products from 1980 to 2010. The region’s per capita consumption has grown 4.1 percent annually (with the 14.4 kg/capita consumption in 1980 more than tripled to 47.5 kg in 2010).The Middle East and North Africa (MENA) was next with 1.8percent/year growth of per capita consumption of meat (from 18 kg in 1980 to 31 kg in 2010), followed by Latin America and the Caribbean (LAC) region with 1.7 percent. On the other hand, the developed regions of Northern America (NAM), Oceania (OCE), and Western Europe (WEU) while continuing with high levels of per capita meat consumption (ranging from78 to108 kg in 1980 and 84 to116 kg in 2010), nevertheless, have generally lower growth rates (NAM 0.3 percent/year; OCE nil; and WEU 0.2 percent). The contrast was, again, more pronounced for milk consumption per capita. The developing regions of EAP, South Asia (SAS), MENA, LAC, and even the Sub-Saharan Africa (SSA) have generally increasing growth rates compared to the developed regions’ declining and negative rates. In developing regions, per capita beef consumption were increasing in EAP (4.5 percent/ year), MENA (0.7 percent) and LAC (0.4 percent), but were declining in SSA (-0.9 percent/ year) and SAS (-0.2 percent). Pork consumption per capita has increasing trends for all developing regions of EAP (3.5 percent/year), SSA (1.9 percent), LAC (0.7 percent) and MENA (0.7 percent). Similar trends, though with relatively higher rates, were seen for poultry meat in SAS (6.7 percent/year), EAP (5.4 percent), LAC (4.4 percent), MENA (3.5) and SSA (2.6 percent). Selected countries Among the selected countries of Brazil, China, India, Indonesia and the United States – meat consumption per capita, grew fastest in China with 4.6 percent/year growth for the period 1980-2010, followed by Indonesia (3.3 percent) and Brazil (2.6 percent). The United States managed to still have positive growth (0.3 percent/year) although for the more recent period of 2000-2010, growth rate was negative at -0.2 percent. The level of meat consumption were lowest for India (3.6 to 4.4 kg/capita) and Indonesia (4.6 to 12.3 kg/capita) for the period 1980-2010.4 Milk consumption in India, however, was much higher at 27.1 to 49.8 kg/capita for the same period. Beef consumption increased in China at a very high annual rate of 8.7 percent (from a low base level of 0.4 kg in 1980), followed behind by Brazil at 1.8 percent (but from an already high base level of 22.5 kg in 1980). Similarly, consumption of pork, poultry and sheep meat all increased at high rates in China and Brazil in the last three decades.

4 Indonesia, though, consumes and gets more protein from fish, which was consumed at 24.5 kg/capita/year in 2010 – which could not be said about India with per capita fish consumption of only 5.5 kg in 2010.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

TRENDS IN PRODUCTION AND TRADE Production, share, distribution and productivity Production and trade data are presented in Figures 2, and in Tables 2 to 7. Rapid growth areas of livestock production per capita tended to be in the same regions where consumption has increased – indicating that domestic supplies have generally kept up with increasing demand induced by both income and population growth.

Meat Production Milk Production 100 350 87 303 81 82 298 300 269267 80 74 250 60 200

40 31 150 25

100 kg/capita/year kg/capita/year 67 14 18 51 20 35 40 50 0 0 Developed Developing Developed Developing

1980 1990 2000 2010 1980 1990 2000 2010

Meat Production Milk Production 250 1200 1000 200 800 150 600 100

400

kg/capita/year kg/capita/year 50 200

0 0

1980 1990 2000 2010 1980 1990 2000 2010

Figure 2. Historical trends in meat and milk production by region, 1980, 1990, 2000 and 2010 Per capita meat production in developing countries grew by 2.7percent/year – from 14 kg in 1980 to 31 kg in 2010. Milk production, too, grew by 2.2 percent/year – from 35 kg in 1980 to 67 kg in 2010. Similar trends are seen by region and by commodity. Meat production growth rates were highest in EAP (3.9 percent/year), MENA (1.9 percent) and LAC (1.9 percent). Milk per capita production rates were also highest in EAP, SAS, LAC and MENA. Developed countries on the other hand, while continuing to have higher production per capita (87 kg of meat and 267 kg of milk in 2010), have slower rate of 0.6 percent/year for meat products and negative rate (-0.4 percent) for milk products.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Table 3 presents the changing landscape in both meat and milk production between developed and developing countries. The share in meat production of developed countries in 1980 was 66 percent. It declined to 59 percent in 1990, 45 percent in 2000, and to 40 percent in 2010. This means that developing countries’ share increased from 34 percent in 1980 to 60 percent in 2010. WEU’s and NAM’s shares were the highest in 1980 at 22 and 20 percent respectively, both of which declined to 13 and 16 percent in 2010. EAP’s and LAC’s share, on the other hand, increased respectively from 17 and 12 percent in 1980 to 35 and 16 percent in 2010. The landscape was also changing in milk production, although at a slower pace. Developed countries’ share in milk production has declined from 76 percent in 1980 to 48 percent in 2010. WEU and Eastern Europe and Central Asia (EECA) regions together produced more than half (59 percent) of milk in 1980, but their shares have both since declined. SAS’s and LAC’s shares, on the other hand, have increased from just 17 percent in 1980 to 34 percent in 2010. In terms of the distribution of world’s stock of animals (Table 4), traditionally the majority stocks of cattle/buffalo, pigs, and sheep/goats, except for chicken/fowl were raised in developing countries. In 1980, they were distributed to developing countries as follows: cattle/buffaloes and sheep/goats both 69 percent; pigs 57 percent; and chicken/fowl – 49 percent. What is more noteworthy, though, is that their numbers and percentage distribution in developing countries continued to grow up to 88 percent for sheep/goats; 83 percent for cattle/buffaloes; 77 percent for chicken/fowl; and 70 for pigs in 2010, while their distribution consistently declined in developed countries. Livestock productivity, measured as the amount of meat (or milk) yield per head of slaughtered (or milked) animal, was higher in developed countries due to more advanced livestock production technologies. In 2010, compared with developing countries, beef yields in developed countries were 60 percent higher (283 kg against 177 kg); 22 percent higher for pork (88 kg to 72 kg) and 17 percent for poultry (1.7 kg to 1.5 kg). The difference was much bigger in milk productivity, 374 percent (6,027 against 1,271 liters per head). Productivity growth rates from 1980-2010 were also higher in developed countries (for beef 0.84 percent/year against 0.45 percent; for poultry 1.70 percent/year to 1.45 percent; and for milk 2.11 percent/year to 1.82 percent) except for pork yields which grew annually by 0.8 percent in developing countries compared to 0.4 percent/year in developed countries (Table 5). Cereals and Feed Production Another important consideration in livestock production is feed production. Feeds compete with food consumption for cereals and grains. Coarse grains like maize and sorghum are known to be consumed as both food and feeds. But other cereals and grains, too, like rice, wheat, millet, barley and rye have become major ingredients in livestock feeds. In developed countries, 58 percent of cereals and grains produced were utilized as feeds in 1980 (Table 6). This proportion, however, has been consistently declining and was only 43 percent in 2010. In terms of volume, maize, wheat and barley/rye (other grains) were the major feedstuffs – at 218, 73 and 69 million mt in 2010. While as percentages of production, sorghum (74 percent), millet (70 percent) and barley/rye (66 percent) have the highest proportions in 2010. In developing countries, the trends are increasing both in volume and proportion of cereals and grains utilized as livestock feeds. Feeds as proportion of cereals and grains production has grown from 22 percent in 1980 to 28 percent in 2010. The actual volume has increased from 140 to 370 million mt at an annual rate of 3.3 percent from 1980-2010. Maize also was

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia the feedstuff of choice but sorghum, rice and barley/rye too have been widely utilized as feeds. As livestock production continues to increase to meet the growing demand, these trends in feed utilization of cereals and grains and their competing use as food and energy can potentially drive cereal and grain prices higher and can affect the accessibility of food to the poor. Net Trade The international market of livestock products is relatively thin compared to that of cereals and grains. In 2010 only 3.1 million mt of meat products were globally traded. More milk products were traded at 11.3 million mt. In contrast, traded cereals and grains in 1980-2010 amounted from around 66 to 122 million mt (Table 7). Developing countries were historically net exporter of beef (0.5 to 1.2 million mt from 1980 to 2010) and net importer of pork (initially marginal net exporter from 1980 to 1990, to net importer in 2000-2010 with 0.6 to 0.7 million mt), sheep meat (0.2 to 0.3 million mt 1980- 2010), poultry (0.4 to 1.0 million mt), and of milk (6.6 to 11.4 million mt). WILL THE LIVESTOCK REVOLUTION CONTINUE? The demand-driven Livestock Revolution, started in the 1980swas shown to have continued to the 2010s. But are these growth rates for livestock and feeds production systems sustainable? Can they continue into the next decades – to 2030 and 2050? Sustainability of the Livestock Sector Sustainability concerns are being raised in the livestock sector due to the observed slowing down of growth rates of production relative to population in the last decade; the general negative impact of changing climate on agriculture; the negative impact of livestock production to the environment. Deceleration of production growth Table 3 has shown the increasing share of developing countries in the production of meat and milk products through the last three decades. Table 2 has also shown that meat and milk production in developing countries grew much faster than their population. For the period 1980-2010, the average annual population growth in developing countries was 1.9 percent, while meat production grew at an annual rate of 4.6 percent and milk at 4.1 percent – effectively registering per capita production growth rates of 2.7 percent/year for meat and 2.2 percent for milk products.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

4.19 1.85 0.58 0.52 0.38 0.39 0.32 0.98 0.90 2.33 9.16 0.39 0.32 0.39 3.35 1.53 0.79 1.00 0.13 0.02 1.03 1.57 0.79 2.39 3.69 1.01 ------2010 1980

------

1.59 0.13 0.92 0.54 1.11 0.76 0.92 0.14 0.66 2.16 2.10 0.09 0.61 1.13 2.42 0.29 1.25 1.40 0.74 2.16 4.58 1.49 0.50 0.94 2.41 4.71 ------2010 2000

- million mt 7.62 7.04 0.52 1.67 0.66 0.52 0.63 0.91 2.23 3.21 1.25 0.84 0.50 3.10 4.97 4.11 2.29 1.25 0.12 0.69 0.27 0.83 7.82 3.58 0.61

------2000 13.55 1990

------3.44 1.58 0.30 0.65 0.67 0.92 0.68 2.16 0.22 1.62 0.16 0.50 0.55 4.54 0.75 2.86 2.14 1.59 2.14 2.30 2.97 1.03 1.36 5.08 2.42 ------1990 12.20 Growth Rates (per prodn)capita Rates Growth 1980

4.2 5.1 2.9 5.1 4.7 2.1 2.0 0.2 0.2 1.3 0.3 11.8 28.8 38.4 18.2 46.7 38.6 27.0 15.6 11.0 35.0 14.3 47.9 16.4 35.7 2010

102.6

------3.6 4.8 2.6 5.1 3.8 2.1 1.8 9.7 0.2 0.3 1.1 0.4 11.6 26.2 43.0 19.5 37.7 43.2 21.2 16.1 32.5 17.8 45.6 14.9 28.1 2000 110.7

1.7 4.1 2.8 5.6 1.1 2.4 1.7 6.5 0.2 0.4 1.1 7.0 0.5 24.1 24.9 40.3 24.4 27.5 41.1 15.6 26.7 28.7 17.6 42.0 19.8 million mt mt million 1990

105.2

Per Capita Production Per Capita 1.2 4.3 2.6 6.9 0.3 2.4 1.8 8.7 0.2 0.3 0.8 8.0 0.4 20.6 24.2 43.1 23.8 23.4 43.4 10.0 24.8 33.6 14.2 37.9 12.0 1980 115.4 ------

5.43 1.51 2.25 2.92 0.65 0.96 2.26 1.74 0.47 3.95 1.43 2.01 0.62 4.59 1.20 2.46 3.41 1.16 1.38 0.88 4.36 1.22 4.02 4.81 0.80 - - - 2010 10.34 1980 ------

- 2.37 0.27 2.18 2.59 0.18 0.70 2.45 2.80 0.15 3.31 2.72 1.38 2.05 0.21 3.21 0.15 2.52 0.61 1.70 0.71 3.13 4.20 1.01 2.08 3.03 3.32 ------2010 2000

- million mt mt million 8.79 7.04 2.22 3.77 1.78 1.73 1.34 1.78 1.73 4.80 0.57 2.43 1.63 4.22 4.97 5.86 4.40 2.38 1.33 1.28 3.00 1.35 9.49 4.56 1.21

- - - 2000 14.63 1990

- Growth Rates (Total Prodn.) (Total Rates Growth 5.23 2.49 2.34 2.40 0.35 0.45 3.00 0.64 0.49 3.74 2.36 1.57 0.46 6.35 1.65 0.88 5.28 0.58 3.56 4.65 5.91 1.31 0.69 6.87 4.68 ------1990 14.11 1980

9.2 5.6 1.9 2.7 4.6 4.5 7.6 9.1 6.6 2.6 0.5 7.4 6.6 0.1 0.4 0.4 1.1 3.2 0.3 17.1 13.3 12.0 59.3 12.1 20.0 49.6 2010

------7.3 5.4 1.5 2.6 3.6 3.4 7.7 6.6 5.0 2.2 0.4 7.5 5.1 0.1 0.4 0.5 0.7 2.6 0.5 13.8 13.6 12.3 43.3 10.2 18.1 36.8 2000

3.1 1.0 2.2 3.2 2.8 9.2 4.1 1.3 2.1 0.3 2.9 0.0 8.1 0.4 0.4 0.5 1.1 0.4 11.2 11.1 11.4 10.5 28.6 12.5 15.8 23.6 million mt

1990 Total Production Total

1.9 8.8 8.8 0.8 2.1 2.4 2.7 8.8 2.9 0.3 1.7 0.3 3.2 0.0 8.6 0.3 0.3 0.3 1.0 0.3 11.0 10.0 15.5 10.6 13.9 12.1 ------1980

Saharan Africa Saharan Africa Saharan - - Brazil China India Indonesia StatesUnited Brazil China India

EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Africa Middle and East North North America Oceania SouthAsia Sub EuropeWestern EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Africa Middle and East North North America Oceania SouthAsia Sub EuropeWestern Selected countries Selected countries Table 2. Trends in production of meat and milk products. milk products. and meat of in production Table 2. Trends ProductsRegion / Beef Pork

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

- 2.94 0.01 5.32 0.85 1.07 0.35 2.59 1.51 0.12 0.09 0.71 0.99 6.39 0.36 0.81 3.09 5.21 1.95 4.87 3.89 2.63 2.44 6.66 1.68 1.62 5.51 6.97 7.90 ------2010 1980

- 3.88 0.77 3.30 1.01 0.76 2.24 3.37 2.93 0.16 0.42 2.96 0.13 3.55 0.93 2.34 4.13 2.90 8.19 4.26 2.68 0.80 2.07 6.89 2.77 0.26 5.02 2.55 7.87 ------2010 2000

- 4.25 0.99 6.96 4.45 2.24 0.38 4.82 0.72 0.83 0.97 1.00 2.54 8.49 0.61 2.92 5.36 8.06 4.17 7.38 3.91 3.13 3.52 5.71 0.17 2.60 8.05 6.77 ------2000 11.93 1990

- 9.68 1.78 5.72 1.00 0.19 0.85 0.49 0.86 1.38 1.12 1.89 5.83 7.20 0.75 3.12 0.32 4.75 2.20 3.02 5.11 3.99 1.75 7.40 2.12 2.00 3.50 6.63 9.07 - - - - 1990 Growth Rates (per prodn)capita Rates Growth 1980

2.9 2.0 2.5 0.7 3.6 0.3 1.1 2.5 2.2 0.6 2.9 0.7 0.5 0.2 2.0 3.1 1.9 32.6 39.4 11.9 17.2 36.1 15.2 60.0 39.8 23.1 57.3 12.5 2010

2.0 1.4 2.3 0.8 4.5 0.4 1.1 2.4 3.0 0.6 2.0 0.7 0.4 0.4 8.9 7.8 1.1 2.4 9.7 0.9 30.2 53.0 23.8 11.7 55.4 32.4 22.5 35.1 2000

3.0 0.7 3.6 1.0 4.4 0.6 1.2 2.2 3.4 0.7 0.9 0.7 0.5 0.6 4.1 8.0 0.6 2.4 3.1 0.5 27.4 57.0 12.0 11.7 40.7 23.0 17.4 16.2 1990

Per Capita Production Per Capita 1.2 0.4 3.2 1.0 4.0 0.6 1.0 2.4 2.8 0.4 0.4 0.7 0.4 0.6 2.6 9.7 8.7 4.8 0.3 1.9 1.7 0.2 32.7 62.1 27.5 19.3 14.3 11.5 1980

4.68 1.01 6.57 0.51 0.57 2.03 1.59 0.17 2.06 2.83 0.28 2.60 7.54 2.20 2.52 2.10 6.47 2.30 6.61 6.38 3.69 3.83 8.72 4.47 2.06 7.18 8.13 9.88 - - - - - 2010 1980

- 5.35 1.71 4.09 1.15 0.49 0.25 2.46 1.50 1.36 3.10 2.45 1.00 4.17 2.41 3.80 3.24 3.68 8.33 5.56 4.77 1.75 3.58 8.51 5.50 0.78 6.21 3.16 9.45 - - - - - 2010 2000

- 2.74 2.13 8.12 4.46 0.59 2.45 3.75 0.48 1.14 3.71 0.49 1.03 9.52 1.22 1.38 4.30 9.24 4.18 9.19 6.05 4.28 4.77 7.81 2.89 3.13 9.72 8.73 ------2000 12.99 1990

- Growth Rates (Total Prodn.) (Total Rates Growth 0.79 7.55 1.90 1.84 3.95 1.53 0.51 3.71 1.70 2.17 8.04 9.02 2.97 5.26 1.33 6.56 3.12 5.11 8.34 5.06 3.16 9.87 5.04 2.28 5.66 8.44 - 1990 11.96 11.47 1980

0.7 4.4 1.2 0.4 1.4 0.1 1.1 1.7 2.2 0.9 0.1 4.0 0.9 0.1 0.1 8.1 5.8 1.1 3.3 2.7 9.7 2.2 10.2 26.2 21.5 20.8 11.2 17.3 2010

0.4 8.6 2.9 1.1 0.4 1.4 0.1 1.2 1.5 1.6 1.2 0.1 2.7 0.7 0.1 0.1 3.7 3.7 0.7 1.5 1.6 8.9 6.1 0.9 18.2 12.5 17.5 12.7 2000

0.5 7.0 1.3 1.7 0.4 1.1 0.2 1.2 1.3 1.1 1.3 0.1 1.1 0.6 0.1 0.2 7.5 5.6 5.2 2.0 0.5 0.7 1.2 6.6 2.4 3.7 0.4 11.5 1990 Total Production Total

0.2 7.5 0.6 1.4 0.4 0.8 0.1 1.1 0.9 0.9 1.0 0.1 0.5 0.5 0.1 0.1 4.0 4.1 3.2 0.9 7.0 0.3 0.3 0.7 5.2 1.4 1.7 0.1 1980

Saharan Africa Saharan Africa Saharan - - Indonesia StatesUnited Brazil China India Indonesia StatesUnited Brazil China India East Asia and EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Africa Middle and East North North America Oceania SouthAsia Sub EuropeWestern EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Africa Middle and East North North America Oceania SouthAsia Sub EuropeWestern Selected countries Selected countries

ProductsRegion / Meat Mutton and Goat Poultry Meat 31

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

- 5.74 2.70 3.91 0.71 1.98 1.99 0.81 0.25 1.29 0.00 0.50 3.48 4.59 1.08 3.21 0.82 4.31 0.97 1.08 1.07 0.23 1.17 2.54 0.28 0.59 1.57 8.04 2.72 - - - - 2010 1980

- 5.21 0.84 2.50 2.23 2.38 1.27 0.18 0.87 1.97 0.96 0.10 3.20 2.47 1.91 3.85 0.20 7.17 0.86 1.23 1.32 0.34 0.26 2.50 2.09 0.69 3.00 2.83 - - - 2010 12.08 2000

- 3.23 3.16 4.71 5.48 3.22 2.51 1.76 0.54 0.31 0.17 0.39 5.55 5.78 0.15 0.01 1.67 2.09 4.04 1.99 2.11 0.05 3.90 2.33 0.75 0.68 1.56 4.81 2.15 - - - - - 2000 1990

- 8.86 4.14 4.53 1.31 0.36 2.20 0.51 0.41 1.58 0.77 1.01 1.73 5.56 1.19 5.86 0.59 3.74 0.36 0.01 0.21 0.30 0.08 2.79 0.47 0.39 0.17 7.34 3.18 ------1990 Growth Rates (per prodn)capita Rates Growth 1980

6.5 6.2 5.0 62.7 45.0 47.1 76.6 24.1 12.1 91.5 55.7 11.8 25.3 69.9 37.2 29.6 2010

133.7 196.0 120.9 134.2 240.0 132.3 276.2 972.4 102.2 315.2 158.1 101.1

3.9 5.1 4.1 8.1 9.4 57.7 35.2 37.8 60.6 21.3 11.0 90.6 88.3 43.6 12.7 61.3 79.9 30.3 76.4 2000 131.2 213.9 131.5 220.4 117.1 266.9 998.5 337.9 117.6

2.8 5.0 4.1 8.1 5.9 42.3 22.2 66.4 44.1 16.6 11.2 87.1 51.4 24.9 10.3 96.1 49.8 63.4 32.6 61.8 1990 110.2 202.7 111.4 333.0 265.6 681.4 361.6 100.7

Per Capita Production Per Capita 1.2 4.2 3.6 4.6 7.1 2.9 28.2 14.3 58.3 42.5 13.3 12.1 78.8 43.4 14.5 96.0 50.9 48.2 34.2 99.1 45.2 1980 104.8 211.1 105.0 321.2 257.7 686.9 376.1

7.53 3.75 5.15 0.37 3.67 4.43 1.85 1.11 3.24 2.75 0.94 5.12 5.72 2.93 4.96 1.85 5.56 0.63 2.75 3.48 1.26 2.54 4.52 3.03 0.16 3.18 9.21 4.61 - - - 2010 1980

- 6.71 1.78 3.29 2.37 3.66 3.33 1.13 0.59 3.52 3.65 0.61 4.36 3.09 3.40 5.33 1.14 7.99 0.99 2.50 3.38 1.29 1.21 4.06 4.81 0.18 4.16 4.34 - 2010 12.75 2000

- 4.86 4.32 5.85 5.49 4.97 4.63 2.90 1.76 2.31 2.54 0.91 7.18 6.78 1.99 1.59 2.81 3.20 4.05 3.71 4.21 1.16 5.15 4.36 1.94 0.17 3.13 5.81 4.03 - - - 2000 1990

- Growth Rates (Total Prodn.) (Total Rates Growth 5.19 6.33 2.22 2.40 5.34 1.54 0.98 3.91 2.06 1.29 3.85 7.35 3.41 8.05 1.61 5.54 1.26 2.04 2.85 1.34 1.31 5.15 2.37 0.12 2.26 9.17 5.45 - 1990 11.12 1980

1.6 9.2 5.2 6.0 2.8 19.6 99.1 22.3 45.7 46.3 10.0 10.5 38.3 23.6 77.5 41.9 55.7 78.9 26.7 95.7 26.0 32.2 30.9 41.1 2010 113.4 164.3 131.8 121.8

0.8 6.6 4.9 7.1 7.3 4.3 1.7 16.4 71.7 17.6 31.9 41.4 36.0 15.4 57.1 37.4 25.8 61.6 19.2 84.2 23.1 20.2 20.5 12.4 79.7 2000 102.7 110.4 134.2

0.5 4.2 4.2 5.6 5.7 7.7 3.5 1.4 7.0 10.8 40.6 31.0 19.6 31.1 32.9 29.6 28.4 18.8 42.8 12.7 75.0 14.0 72.0 16.6 15.1 53.7 1990 155.2 136.4 Total Production Total

0.2 6.5 2.5 3.8 3.8 4.6 5.3 2.5 0.7 9.6 2.9 22.0 24.9 15.5 26.7 28.9 14.6 24.2 11.0 35.0 65.7 12.3 43.6 13.2 12.1 31.6 1980 137.0 138.1

East and North Africa andEast North Brazil China India Saharan Africa Saharan Africa Saharan - - Indonesia StatesUnited Brazil China India Indonesia StatesUnited Selected countries

East Asia and EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Africa Middle and East North North America Oceania SouthAsia Sub EuropeWestern EastPacific and Asia andEastern Asia Central Europe andCaribbean Latin the America Middle North America Oceania SouthAsia Sub EuropeWestern Selected countries

Region / ProductsRegion / All Meat products* Milk 32

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

- 3.98 0.34 0.65 0.29 1.37 2.45 0.55 0.36 1.11 2.63 1.61 4.98 2.69 2.22 0.32 0.92 0.54 3.07 1.10 0.01 - - - - 2010 1980

- 3.78 0.48 0.69 0.55 2.02 1.96 0.63 0.07 1.11 1.75 0.99 3.48 2.09 2.92 0.02 1.02 0.31 2.56 1.23 1.00 - - - 2010 2000

- 1.17 0.15 1.61 0.03 2.86 2.44 0.07 1.19 1.79 2.69 2.24 7.05 3.49 2.41 0.77 0.75 0.34 3.81 1.09 0.74 ------2000 1990

- 7.07 0.40 0.35 0.35 0.81 2.96 0.95 0.17 0.44 3.45 1.59 4.44 2.51 1.32 0.20 0.98 0.97 2.84 0.98 0.23 - - 1990 Growth Rates (per prodn)capita Rates Growth 1980

5.5 1.9 6.9 1.9 9.6 1.9 21.6 32.4 31.3 87.2 11.6 10.4 30.9 67.4 15.5 14.3 41.4 2010

280.1 266.8 104.8

3.8 2.3 6.2 9.8 1.8 7.4 9.6 1.9 23.1 30.7 25.8 81.9 25.1 50.5 14.0 11.1 36.6 94.9 2000 267.1 268.6

3.4 3.1 5.2 7.5 1.4 3.8 1.8 7.7 27.2 30.8 20.2 81.3 17.8 39.8 10.4 13.0 32.9 1990 263.3 302.6 102.2

Per Capita Production Per Capita 1.7 2.9 4.9 5.3 1.2 2.4 1.6 5.8 26.3 29.7 15.1 74.0 13.9 34.9 10.6 11.8 29.8 1980 253.0 297.6 104.6

5.74 1.36 0.34 0.61 1.06 2.78 0.87 0.05 2.95 4.50 3.46 6.89 4.57 4.08 1.16 2.41 2.03 4.59 2.60 1.49 - - - 2010 1980

- 5.25 1.41 0.29 0.96 1.63 2.37 1.04 0.34 2.54 3.19 2.42 4.94 3.53 4.37 1.24 2.25 1.53 3.81 2.47 2.23 - - 2010 2000

- 2.76 1.27 1.74 0.17 2.99 2.30 0.06 1.32 3.69 4.61 4.15 9.04 5.42 4.32 0.64 2.18 1.77 5.29 2.53 0.67 - - - - - 2000 1990

- Growth Rates (Total Prodn.) (Total Rates Growth 9.29 1.41 1.03 1.03 1.49 3.66 1.63 0.85 2.64 5.71 3.81 6.72 4.75 3.54 1.60 2.80 2.79 4.69 2.80 1.57 1990 1980

1.3 2.5 87.5 28.0 42.1 40.6 38.6 65.3 10.9 58.7 66.6 13.3 99.2 2010 113.1 346.0 173.4 378.6 107.3 286.5 724.7

0.8 2.9 8.6 76.0 28.8 38.2 32.1 30.0 47.7 36.2 58.8 85.9 11.5 68.3 2000 102.0 334.5 122.5 246.8 224.5 581.3

0.6 3.9 5.7 9.6 67.0 34.3 38.9 25.6 20.9 30.4 15.2 72.3 55.2 69.2 40.8 1990 102.6 381.9 161.6 174.9 543.6 Total Production Total

0.2 3.4 3.9 7.9 7.3 58.2 31.0 35.1 17.8 87.3 16.1 17.4 45.4 47.1 52.5 25.8 1980 351.1 114.2 132.7 465.2

Indonesia StatesUnited

Beef Pork Goat Mutton Meat & Poultry meat Meat Milk Beef Pork Goat Mutton Meat & Poultry meat Meat Milk Beef Pork Goat Mutton Meat & Poultry meat Meat Milk

ProductsRegion / Developed Countries Developing Countries WORLD 33

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

However, for meat products, per capita production growth has been slower in recent decade (2.1 percent in 2000-2010 period), compared to the two previous decades (2.5 percent from 1980-1990 and 3.5 percent from 1990-2000). At regional level, this slowing down in growth has become a concern for EAP (2.5 percent in 2000-2010 as compared to 4.7 percent for 1990-2000) and for LAC (2.4 percent in 2000-2010 from 3.2 percent in 1990-2000 period) since these regions have the largest share of meat production in the developing world – respectively with 35 and 16 percent). Milk production has lesser sustainability concern, in general for developing countries, since per capita production continued at increasing rates to 2010. This was also true for EAP, SAS and SSA, but could still be a concern for LAC and MENA. Impact of Climate Change Several studies have simulated strong probability of negative impacts of climate change on agricultural productivity in the future, especially for crop commodities. Although for livestock the simulated impact is less at 0.4 percent production decline in 2030 and 0.9 percent in 2050 (seen later in Table 9 and 10 in IMPACT forecast section). The concern is more on the impact of climate change for cereals and grains, which could potentially reduce global production by as much as 5.2 percent in 2030 and 10.0 percent by 2050 – especially as livestock production shifts to the more intensive industrial production system from rangeland/pasture production. Environmental Sustainability - Impact on the Environment Environmental impacts from the wastes generated from both relatively low-intensity livestock systems in most developing countries to the high-intensity industrial livestock production include green house gas emission (methane and ammonia) and pollution of surface (e.g. eutrophication) and groundwater (e.g. nutrient loading). In addition, grazing systems in developing regions can cause soil compaction and erosion and decreased soil fertility. Increasing livestock production also means that additional new land areas may need to be put into feed-grain production (Delgado et al. 1999 and World Bank 2005). LIVESTOCK TO 2030 AND 2050 – BASELINE PROJECTIONS This section examines these sustainability issues given the world’s capacity to produce livestock and feeds, advances in technology, the changing regional livestock production and delivery systems, and projections of population and income – with the use of a global economic model that simulates both the supply and demand to 2030 and 2050. The Impact Model The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) is a global partial equilibrium agricultural-sector model developed by the International Food Policy Research Institute (IFPRI). It has been used to study the effects of future scenarios of investment in agricultural research, improvement in technology, food policies, population and income growth on long-term food supply and demand, world prices of food, and food security (Rosegrant et al. 2012). IMPACT has also been used in conjunction with alternative climate and crop models to simulate the impact and relative effectiveness of different climate change adaptation technologies and investment policies (Rosegrant et al. 2014; Nelson et al. 2010), and successfully implemented in studies related to water scarcity, water allocation and irrigation development.5

5 For more information visit IFPRI’s website (www.ifpri.org) or follow this link, http://www.ifpri.org/book- 751/ourwork/program/impact-model.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Impact’s Baseline Projections: Demand, Production, Prices and Trade The baseline scenario is the business-as-usual scenario, where the historical trends in socio- economic parameters, level of investment in research and technology development, population and GDP growth – are simulated to continue, without additional external intervention and exogenous shocks to 2030 (medium term) and 2050 (longer term). It has become standard practice in agricultural policy research, including crop models and economic simulations, to include at least one climate change scenario as an alternative future. This study uses a baseline with climate change6from which the different livestock development scenarios, also under the same climate change assumptions, are compared. Although as earlier mentioned, both no climate change and with climate change scenarios were simulated and results presented in Tables 9 and 10 – to show the differential effects of climate on livestock and cereals production. Demand Figures 3a and 3b (and Table 8) present the historical trend in consumption and baseline projections of livestock (meat and milk products) demand to 2050.

Baseline Demand for Meat Baseline Demand for Milk Products Products 500 1.000 400 800 300 600

200 400

million million mt million million mt 100 200

0 0

2040 1980 1990 2000 2010 2015 2020 2025 2030 2035 2045 2050

1980 1990 2000 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year Year Developed Developing Developed Developing

Baseline Demand for Meat Baseline Demand for Milk WEU Products Products WEU 500 SSA 1.000 450 900 SSA 400 SAS 800 SAS 350 700 OCE 300 600 OCE 250 500 NAM NAM

200 400 million million mt 150 MENA million mt 300 MENA 100 200 LAC LAC 50 100 0 EECA 0 EECA

EAP EAP Year Year

Figure 3a. Historical trend and baseline projections of demand for meat and milk products, by region, 1980-2010 and 2010-2050

6 IMPACT is linked to the different climate models recommended in the IPCC assessment reports. In this study we used Hadgfem2-es climate model, under RCP 8.5 and SSP2, and DSSAT crop model.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Developed Countries: Baseline Developing Countries: Baseline Demand for Meat Products Demand for Meat Products 350 400

250 300

150 200

million million mt 100 million million mt 50 0

-50

2030 1980 1990 2000 2010 2015 2020 2025 2035 2040 2045 2050

1980 1990 2000 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year Year Beef Pork Poultry Meat Mutton/Goat Meat Beef Pork Poultry Meat Mutton/Goat Meat

Figure 3b. Historical trend and baseline projections of demand for meat products, by commodity, by region, 1980-2010 and 2010-2050 Worldwide demand for meat products is projected to increase by as much as 162 million mt, from 268 million mt in 1980 to 430 million mt in 2050. This is equivalent to 8 kg increase in per capita consumption, from 39 kg/capita in 1980 to 47 kg/capita demand in 2050 – for anestimated annual growth rate of 0.5 percent. Demand for milk worldwide is likewise projected to grow an additional 320 million mt, from 583 million mt in 1980 to 904 million mt in 2050 – an increase of 14 kg in per capita consumption from 85 kg/capita value in 1980 and an annual growth rate of 0.4 percent. The demand side of the Livestock Revolution is therefore simulated to continue to 2050 since demands for livestock products are projected to grow at higher rates in developing countries than in their developed counterparts. Developing countries’ per capita demands for both meat and milk products are projected continue at higher rate of 0.7 percent/year compared to developed countries’ rates of 0.2 percent/year for meat and 0.1 percent for milk. Demands for specific meat commodities are also projected to grow at higher annual rates in developing countries, at respectively 1.4 percent, 1.1 percent, 1.1 percent, and -0.2 percent for sheep meat, poultry meat, beef and pork. Corresponding developed countries’ annual growth rates are 0.5 percent, 0.5 percent, 0.1 percent, and -0.1 percent. In terms of quantity, developing countries demand for meat is projected to increase to 310 million mt in 2050(by 140 million mt from 170 million mt in 1980) and to 607 million mt for milk (by 274 million mt for milk from 333 million mt in 1980) – equivalent to an additional 10 kg of meat and 19 kg more of milk per person, from their 1980 levels. Sub-Saharan Africa (SSA) and South Asia (SAS) are the two regions projected to be high- growth areas in terms of demand per capita. SSA is projected to have the highest growth at 2.1 percent/year for all meat products (2.1 for beef, 2.8 for pork, 2.0 for sheep meat, and 1.9 for poultry meat). SAS is next, with 2.1 percent/year for all meat (1.4 for beef, 0.4 for pork, 1.8 for sheep meat, and as high as 3.2 percent/year for poultry meat). Production and Trade Meat production per capita is also projected to increase in developing countries, but at a lower rate than per capita demand (Figures 4a and 4b, and Table 10). Growth rate of per capita meat production in developing countries is project at 0.6 percent/year from 2010 to 2050 – lower compared to per capita demand growth rate of 0.7 percent/year for the same period. This relatively lower production growth rate in developing countries is projected to result in increasing meat import, from 5.6 million mt in 2010 to 24.3 million mt in 2050 – or at a rate of 4.0 percent/year (Table 12).

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Baseline Meat Production Baseline Milk Production 500 1.200

400 1.000 800 300 600

200 million mt million million mt 400 100 200

0 0

2000 1980 1990 2010 2015 2020 2025 2030 2035 2040 2045 2050

1980 1990 2000 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year Year Developed Developing Developed Developing

Baseline Meat Production Baseline Milk Production 500 1.200 WEU WEU 450 1.000 400 SSA SSA 350 SAS 800 SAS 300 OCE OCE 250 600

200 NAM NAM million million mt million million mt 400 150 MENA MENA 100 200 LAC LAC 50 0 EECA 0 EECA EAP EAP Year Year

Figure 4a. Historical and baseline projections of meat and milk production, by region, 1980-2010 and 2010-2050 By region, EAP, SAS and SSA are projected to have higher rate of demand growth than production, but at lower rates for LAC and MENA. This projected pattern in developing countries of higher growth rates of meat demand relative to production is also true for most commodities – pork, poultry and sheep meat, except for beef where per capita production rate is a little higher at 1.12 percent/year compared to 1.10 percent for demand, resulting to marginal net export of1.3 million mt in 2050. Net imports of pork can reach 14.2 million mt; poultry meat 9.4 million mt; milk 9.4 million mt; and sheep meat 2.0 million mt.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Developed Countries: Baseline Developing Countries: Baseline Meat Production Meat Production 300 300 250 250 200 200 150 150 100

million mt 100 million million mt 50 50

0 0

2015 2025 1980 1990 2000 2010 2020 2025 2030 2035 2040 2045 2050 1980 1990 2000 2010 2015 2020 2030 2035 2040 2045 2050 Year Year Beef Pork Poultry Meat Mutton/Goat Meat Beef Pork Poultry Meat Mutton/Goat Meat

Figure 4b. Historical and baseline projections of meat production, by commodity, by region, 1980- 2010 and 2010-2050 World Prices Due to the lower projected growth in production vis-à-vis projected demand, world prices of meat products in 2050 are then expected to be higher than their 2010 values – by 54 percent for pork, 53 percent for poultry meat, 22 and 13 percent respectively for beef and sheep meat. Of note for maize, a major feed grain, prices are projected to be higher by 135 percent (Figure 5 and Table 11). This reflects the pressure exerted on prices by maize’s increasing demand as both food and feed, and as source of bio-fuels. This also serves as reminder that any livestock development policy should also include parallel feed policy to minimize feed supply impediments.

Baseline World Prices 6.000 5.000 4.000 Beef

3.000 Pork $/mt 2.000 Sheep/Goat Meat 1.000 Poultry Meat 0 Milk 2010 2015 2020 2025 2030 2035 2040 2045 2050 Year

Figure 5. Baseline projection of world prices of meat and milk products, 2015-2050 GLOBAL LIVESTOCK DEVELOPMENT: SOURCES OF GROWTH AND POLICY OPTIONS The previous sections have shown that historical trends and future (baseline) projections of livestock demand and consumption patterns in developing countries are catching up and rapidly converging with those of developed countries. These trends in demand are putting severe pressure on the livestock production systems in developing countries to do the same – increase to the level of demand.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The baseline, business-as-usual, scenario (Table 10), however, has also shown that although per capita production in developing countries would be increasing, it would be at lower rates than projected demand (Table 8) – causing increases in world prices of meat products (Table 11) and increases in importation by developing countries (Table 12). The following sections present the policy options and the sources of production growth that could be promoted to support the sustainability of the Livestock Revolution to 2050. Growth in livestock production has been through productivity improvements, increases in stocks and production units, and potentially from closing the yield-gap between developed and developing countries. Sources of production growth Table 5 has presented earlier the historical levels and rates of growth in livestock productivity by region, by selected countries, by developed and developing countries, and for the entire world. Through technology improvements, livestock productivity has increased for all livestock commodities in both developed and developing countries. For the period 1980 to 2010, livestock productivity in developed countries has increased by at least 12 percent (pork) to as much as 90 percent (milk) - equivalent to annual growth rate of 0.4and 2.1 percent respectively for pork and milk. In developing countries, productivity has increased the least for beef at 14 percent from 1980 level (equivalent to annual growth rate of 0.45 percent) and highest increase for milk at 69 percent compared to 1980 level (equivalent to 1.8 percent/year of growth). The productivity or yield gaps between developed and developing countries were generally increasing. In 2010 the yield gap was around 60 percent for beef, 22 percent for pork, 17 percent for poultry and was at a very high 374 percent for milk. In Table 4, it has also been shown that the stocks (number of heads) of animals has increased in developing countries, both in percentages and in absolute values. The number of chicken and fowl more than quadrupled (349 percent increase) in 2010 from 1980 level – equivalent to an annual growth rate of 5.1 percent. This was followed by sheep and goats which increased by 74 percent (annual rate of 1.85 percent), then by cattle/buffalo and pig population with 50 percent increases (or 1.36 and 1.35 percent annual growth rates). Development Scenarios Growth in livestock production through productivity improvements (intensive growth) and increases in stocks and production units (extensive growth) have played important roles in continuing the Livestock Revolution and ushering it to this century. The same can again be tapped to support the livestock supply transformation with increased investment in agricultural (livestock) research and development (R&D) and more aggressive agribusiness policy of promoting livestock production enterprises to meet the increasing demand. Also a concerted extension effort aimed at closing the widening livestock yield gap between developed and developing countries can be an additional and important source of livestock production growth in sustaining the revolution to 2050 and beyond. Although policies for technological improvements and stock increases can be applied globally – that is in both developed and developing countries, production gains from closing the yield gaps are better achieved with developing-countries focus. These are the combinations of development strategies that define the policy options for global livestock sector development, which are simulated in IMPACT to determine their implications to production, trade and food security.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Intensive Growth in all countries (Int-All) This livestock development policy scenario involves the technological improvement across all livestock producing countries, both developed and developing countries, through increased investment in agricultural R&D. It assumes an additional 10 percent increase in productivity for all livestock commodities (beef, pork, poultry meat, sheep meat, and milk) in 2050over the baseline under changing climate. Intensive Growth in developing countries only (Int-Dvg) This livestock development scenario focuses only on developing countries. This assumes a 20 percent increase in productivity for all livestock commodities in developing countries in 2050 over the baseline with climate change. This higher increase in productivity is possible with combination of technological improvement through agricultural R&D and through increased extension effort for the wider adoption, in developing countries, of existing modern production technologies which are already widely practiced in developed countries. This latter strategy takes advantage of and aims to narrow, if not close, the yield gap between developed and developing countries. Expansive Growth in all countries (Exp-All) This scenario is a combined intensive and extensive livestock development strategy. It combines the gains in yield improvement (intensive) through agricultural R&D and increases in stocks/production units (extensive) both through aggressive agribusiness policy of promoting livestock production enterprises and the economic drive from increasing demand and prices. This scenario assumes an addition 10 percent increase in productivity and another 10 percent increase in livestock population in 2050 over the baseline under climate change. Expansive Growth in developing countries only (Exp-Dvg) This scenario is a combined intensive and extensive livestock development strategy focused on developing countries only. It assumes a higher (20 percent) productivity improvements due to agricultural R&D and additional yield-gap extension strategy, and another higher (20 percent) stocks and livestock production units to localize production in the relatively increasing demand areas, in 2050 over the baseline, again under climate change. Baseline – business-as-usual The baseline is the business-as-usual scenario used in Section C earlier to project demand, production and trade to 2050. It was based on the assumption that historical trends in socio- economic parameters, level of investment in research and technology development, population and GDP growth – are to continue, without additional external intervention and exogenous shocks to 2030 (medium term) and 2050 (longer term). It also assumes changing climate. The baseline, as the term implies, is also the benchmark for comparing the different livestock development scenarios above – also called external interventions or exogenous shocks introduced in the model. IMPACT POLICY SIMULATION RESULTS Results of IMPACT simulations of are presented in Figures 6-10 as deviations from the baseline7values. Comparative projections of: meat and milk production are presented in Figures 6a and 6b; world prices in Figure 7 and 8; net trade – Figure 9; and food availability in Figure 10.

7 For reference, baseline results are presented in Tables 8, 10, 11 and 12. Table 8 for baseline demand projections; Table 10 – baseline production; Table 11 – baseline world prices; and Table 12 baseline trade projections.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Production All of the livestock development scenarios projected incremental increases in meat and milk production to 2050 (Figure 6a) compared to baseline. Highest increase in meat production is projected by Expanded Growth in developing countries only (Exp-dvg) scenario at 9.9 percent increase from baseline values, followed by Expanded Growth in all countries (Exp- all) scenario with 7.0 percent, then by Intensive Growth in developing countries only (Int- Dvg) scenario with 4.7 percent projected increase. Least increase is projected for Intensive Growth in all countries (Int-All) scenario with 3.4 percent positive deviation from baseline.

World: Meat Production World: Milk Production 12 10 8,9 10 9,9 8 8 Baseline Baseline 7,0 6 6,2 6 Int-Dvg Int-Dvg 4,7 4 4,2 4 Int-all Int-all

3,4 3,0 % from from % Baseline 2 Exp-dvg from % Baseline 2 Exp-dvg Exp-all Exp-all 0 0

Year Year

Developed Countries: Meat Production Developed Countries: Milk Production 10 10 5,8 5 5,6 2,8 5 Baseline 2,7 0 0 Baseline Int-Dvg -5 -5 Int-Dvg -8,0 Int-all -8,5 -10 -10 Int-all

% from from % Baseline Exp-dvg % from from % Baseline -15 Exp-dvg -15 -15,9 -16,7 Exp-all -20 Exp-all

-20

2015 2020 2025 2030 2035 2040 2045 2050

2030 2015 2020 2025 2035 2040 2045 2050 Year Year

Developing Countries: Meat Production Developing Countries: Milk Production 25 25 22,9 21,6 20 20 Baseline Baseline 15 15 Int-Dvg Int-Dvg 10 11,0 10,5 Int-all 10 Int-all 7,7

6,5 Exp-dvg % from from % Baseline 5 from % Baseline 3,7 Exp-dvg 5 3,2 0 Exp-all Exp-all 0

Year Year Figure 6a. Comparative projections of meat and milk production, by livestock development scenario, percent deviation from baseline, 2015-2050

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

The same trends are projected for milk production, only with lower percentages of increase. The Exp-dvg scenario is projected to increase milk production by 8.9 percent from baseline value in 2050; Exp-all scenario 6.2 percent; Int-Dvg scenario with 4.2 percent. The Int-All scenario is with the least positive deviation of 3.0 percent from baseline. The two expansive growth scenarios, with yield and animal population increases, are projected to give the highest incremental increase in both meat and milk production. Although the Exp-dvg scenario, which includes the explicit policy of closing the livestock yield gap and development focus on developing countries, gives the highest incremental boost in both meat and milk production – at 9.9 and 8.9 percent respectively. For developed countries, however, the results are mixed and show the developing-countries bias of the two livestock development scenarios – Exp-dvg and Int-dvg. The livestock development policies for all countries (Exp-all and Int-all) projected almost similar positive deviations from the baseline for meat and milk production for developed countries – 5.8 and 2.8 percent for meat and 5.6 and 2.7 percent for milk products – respectively for Exp-all and Int-all scenarios. The Exp-dvg and Int-dvg scenarios, on the other hand, projected negative deviations (decreases in production) for developed counties, respectively, -15.9 and -8.0 percent for meat and -16.7 and -8.5 percent for milk products. But unlike the developed countries, the developing countries, are projected to gain from increased meat and milk production from all the simulated livestock development policies. Meat production is projected to increase by as much as 11.0 to 22.9 percent from the two developing-countries focused policies - Int-dvg and Exp-dvg, and by 3.7 to 7.7 percent increase from all-countries intensive (Int-all) and expansive (Exp-all) livestock development policies. Similar production trends are projected for milk products - Exp-dvg21.6 percent; Int- dvg10.5 percent; Exp-all 6.5 percent; and Int-all 3.2 percent positive deviation from baseline values in 2050. By meat commodity, the same developing-countries specific policies (Exp-dvg and Int-dvg) have the highest projected production increases – 23.0 and 11.0 percent for beef; 24.2 and 11.6 for pork; 21.4 and 10.4 for poultry meat; and 25.0 and 11.9 percent for sheep meat (Figure 6b). Followed by Exp-all and Int-all policy scenarios, all with positive increases but lesser percentage changes from the baseline.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Developing Countries: Beef Production Developing Countries: Pork Production 25 30 23,0 Baseline 20 25 24,2 Baseline Int-Dvg 15 20 Int-Dvg Int-all 11,0 15 10 11,6 8,5 Int-all 10 Exp-dvg

% from from % Baseline 7,1

5 from % Baseline 4,1 Exp-dvg 5 Exp-all 3,4 0 Exp-all 0

Year Year

Developing Countries: Poultry Meat Developing Countries: Sheep/Goat Production Meat Production 25 30 21,4 25 20 25,0 Baseline 20 Baseline 15 Int-Dvg 15 Int-Dvg 10 10,4 Int-all 11,9 10 10,9 Int-all 6,5

Exp-dvg from % Baseline % from from % Baseline 5 Exp-dvg 3,2 5 5,3 Exp-all 0 0 Exp-all

Year Year Figure 6b. Comparative projections of meat production in developing countries, by commodity, by livestock development scenario, percent deviation from baseline, 2015-2050 The differential and negative impacts of Exp-dvg and Int-dvg livestock development scenarios on meat and milk production in developed countries shown above, reflect the economic dynamics of the livestock global market. With projected increases in meat and milk production in developing countries, import demand would decline – resulting to net reduction of developed countries’ export demand and the consequent decrease in production. And since the overall effects of all these policies are all net increases in world production of meat and milk products, world prices are also expected to decline. World prices Comparative projections of world prices of meat and milk products due to the different livestock development policies are presented in Figure 7. Since increased livestock production would increase the demand for cereals as feeds, projections of cereal prices, especially maize, due to the different livestock development policies were also included in this study. They are presented in Figure 8.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

World Prices of Beef World Prices of Pork 20 20

10 10 Baseline 0 Baseline 0 -10 Int-Dvg Int-Dvg -12,7 -10 -18,1 -12,4 Int-all -20 Int-all -14,2

-23,8 from % Baseline % from from % Baseline -20 Exp-dvg -30 Exp-dvg -23,4 -33,6 -27,5 Exp-all

-40 Exp-all -30

2025 2015 2020 2030 2035 2040 2045 2050

2015 2020 2025 2030 2035 2040 2045 2050 Year Year

World Prices of Poultry Meat World Prices of Sheep/Goat Meat 20 20 10 10 Baseline Baseline 0 0 Int-Dvg Int-Dvg -10 -10 -13,1 -15,3 Int-all Int-all -20 -20 -16,9 -23,8 Exp-dvg -30 -28,3 Exp-dvg

% from from % Baseline -24,6 % from from % Baseline -30 -40 Exp-all -31,9 Exp-all -42,4

-40 -50

2015 2020 2025 2030 2035 2040 2045 2050

2015 2020 2025 2030 2035 2040 2045 2050 Year Year

World Prices of Milk 20 15 10 5 Baseline 0 Int-Dvg -5 -8,4 -10 Int-all -11,0

% from from % Baseline -15 -16,2 Exp-dvg -20 -21,4

-25 Exp-all

2015 2020 2025 2030 2035 2040 2045 2050 Year Figure 7. Comparative projections of world prices of meat and milk products, by livestock development scenario, percent deviation from baseline, 2015-2050

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

World Prices of Cereals World Prices of Maize 6 10 5,4 5 8 8,0 Baseline 4 4,1 Baseline 6 5,1 Int-Dvg 3 Int-Dvg 4 3,7 2,4 2,4 Int-all 2 1,9 Int-all 2

% from from % Baseline 0,3 % from from % Baseline 1 0 Exp-dvg Exp-dvg 0 -2 Exp-all

Exp-all

2030 2015 2020 2025 2035 2040 2045 2050 Year Year Figure 8. Comparative projections of world prices of cereals and maize, by livestock development scenario - and with corresponding maize production improvements, percent deviation from baseline, 2015-2050 Livestock products It was shown earlier from Figure 6a that the two livestock expansive development policy scenarios (Exp-dvg and Exp-all) are the major potential contributors to the global livestock production growth. They are the same policies, therefore, that are expected to reduce the world prices of meat and milk products the most. These livestock development policies are projected to reduce world prices of beef by 33.6 and 23.8 percent; pork prices by 27.5 and 23.4 percent; poultry meat by 31.9 and 24.6 percent; and by as much as 42.4 and 28.3 percent of sheep meat prices, compared to the baseline prices in 2050. World prices of milk are also projected to decline due to these policies, respectively by 21.4 and 16.2 percent for Exp-dvg and Exp-all development policies. More modest projected price declines from 12.4 percent for pork up to 23.8 percent for sheep meat are attributable to the other two intensive policy scenarios – Int-all and Int-dvg. Cereals - Maize As demand for cereals as feeds sharply increases with increasing livestock production, world prices of cereals, especially of maize8, are also expected to rise. Figure 8 shows the comparative increase in the average world price of cereals (as a group) and of maize. With the different livestock development policy simulated, cereal prices can increase by up to 5.4 percent, and maize to as much as 8.0 percent higher than the baseline. This additional derived demand for cereals and grains as feeds due to the growth and development of livestock sector makes it imperative to have a corresponding feeds development policy to minimize any feed supply restrictions. Therefore, an additional livestock development scenario, with a parallel cereal growth policy (Exp-dvg+Maize) resulting to an additional 10 percent maize yield in 2050, is simulated on the side to show that such parallel feeds policy can stabilize the feeds (specifically maize) market – to minimally rise by 0.3 percent in 20150 compared to baseline (Figure 8 right panel). Trade The baseline scenario has projected developing countries to be net importer of all meat and milk products starting in 2015 to 2050 (though starting 2045 to 2050 they would be marginal net exporter of beef). The different global livestock development policies which projected 3.0 to 9.9 percent increase in production over the baseline have opportunities to reduce the net imports of developing countries, if not totally reverse the trend – from net importers to net exporters.

8 Maize is the single major crop commodity utilized as feedstuff.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

For total meat products, only the two developing-countries focused policies (Exp-dvg and Int- dvg) are projected to reduce the net imports of developing countries starting 2020 – with Exp- dvg policy scenario expected to reverse it starting in 2040 and continuing to 2050 with projected 10 million mt net export (Figure 9). The other two policies uniformly targeting both developed and developing countries, Exp-all and Int-all, are not expected to change the baseline trend of developing countries as net importers of meat products. Similar results are projected for milk trade, only this time the Exp-dvg policy starts the reversal in 2035, while the Int-dvg policy also reverses the trend starting in 2040. Exp-dvg has the greater magnitude of trade change – from 22 million mt imports in 2010 to 60 million mt exports in 2050. By specific meat commodity though, there are no single livestock development policy that can make developing countries to be net exporter of all the meat products. Under all these livestock development policies, the developing countries are projected to remain net importers of pork and sheep meat, but net exporter of beef starting 2040 to 2050. Only under Exp-dvg policy can developing countries be net exporter of poultry meat starting in 2045.

Developing Countries: Net Trade of Developing Countries: Net Trade of Meat Products Milk Products 20 100 10 Baseline 50 Baseline 0 Int-Dvg Int-Dvg

-10 0 million million mt -20 million mt Int-all Int-all -30 Exp-dvg -50 Exp-dvg Exp-all Exp-all Year Year

Developing Countries: Net Trade of Developing Countries: Net Trade of

15 Beef Pork 0 Baseline 10 Baseline Int-Dvg -5 5 Int-Dvg

Int-all -10 million million mt million million mt 0 Int-all Exp-dvg -15 -5 Exp-dvg Exp-all Exp-all Year Year

Developing Countries: Net Trade of Developing Countries: Net Trade of Poultry Meat Sheep/Goat Meat 5 0 Baseline 0 Baseline -1 -1 Int-Dvg -5 Int-Dvg -2 Int-all

Int-all million mt -10 million mt -2 Exp-dvg Exp-dvg -15 -3 Exp-all Exp-all Year Year

Figure 9. Comparative projections of net trade of meat and milk products, by livestock development scenario, 2010-2050

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

But again, the two policies targeting the developing-countries have the general effect of either reducing imports of all livestock products of developing countries, if not completely making them net exporters. CONSUMPTION AND FOOD SECURITY The impact of the development of global livestock sector goes beyond the transformation of the livestock production system to sustain the demand-driven Livestock Revolution and in meeting the projected meat and milk demand. It extends to global food security and the objective of making food available and accessible to more people, especially to the developing world. By increasing production through the different livestock development policies, especially those focused on developing-countries, protein-rich livestock products are made more available to developing countries (e.g., by proximity to production and by import-substitution) and more accessible and affordable to the poor (e.g., by reducing world and domestic prices and increasing agricultural incomes in developing countries). The impact on production, world prices and trade sections were shown earlier in Figures 6, 7, and 9. Figures 10a and 10b present the impact of the different livestock development policies on the availability of meat and milk products for food consumption, compared to baseline.

World Meat Consumption World Milk Consumption 11 8 10 9,9 9 6,4 8 Baseline 6 Baseline 7 7,0 6 4,7 Int-Dvg 4 Int-Dvg 5 4,6 4 3,1 3,4 Int-all Int-all 3 2 2,3

2 % from from % Baseline 1 Exp-dvg from % Baseline Exp-dvg 0 0 Exp-all Exp-all

Year Year

Developed Countries: Meat Developing Countries: Meat Consumption Consumption 10 15 9,2 Baseline 8 Baseline 6,7 10 10,2 6 Int-Dvg Int-Dvg 7,1 4 4,3 3,3 5 4,8 Int-all 2 Int-all 0,0 3,5

Exp-dvg % from from % Baseline % from from % Baseline 0 Exp-dvg 0 Exp-all

Exp-all

2015 2035 2020 2025 2030 2040 2045 2050

2050 2020 2025 2030 2035 2040 2045 2015 Year Year

Developed Countries: Milk Developing Countries: Milk Consumption Consumption 3 10 2,4 Baseline 8 8,4 Baseline 2 1,8 Int-Dvg 6 6,1 Int-Dvg 1,2 4 4,0 1 0,9 Int-all Int-all 2 3,0

Exp-dvg Exp-dvg % from from % Baseline 0 from % Baseline 0 Exp-all Exp-all

Year Year

Figure 10a. Comparative projections of availability of meat and milk products for consumption, by livestock development scenario, percent deviation from baseline, 2015-2050.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Percent deviations from baseline consumption All meat products are projected to be more available and accessible by at least 3.4 percent and up to 9.9 percent globally in 2050, compared to baseline. The values are lower for milk products at 2.3 to 6.4 percent. For both meat and milk products the Exp-dvg policy has the most impact (9.9 percent for meat and 6.4 percent for milk) followed by the Exp-all policy (7.0 percent for meat and 4.7 percent for milk). The two intensive development policies (Int- dvg and Int-all) have lower projected impact respectively with 4.6 percent for meat and 3.1 percent for milk; and 3.4 percent meat and 2.3 percent milk (Figure 10a). The same positive impacts and policy rankings are projected in developed and in developing countries, although the relative impact are greater in developing countries for both meat and milk products. For example, meat products are more accessible by 10.2 percent difference from baseline in developing countries under Exp-dvg policy as compared in developed countries with 9.2 percent difference under the same policy. For milk products the comparisons are 8.4 percent and 2.4 percent change from baseline respectively for developing and developed countries. Under Exp-all policy meat products are more accessible for consumption by 7.1 percent in developing countries, with only 6.7 percent change from baseline in developed countries. For milk products the values are 6.1percent in developing countries and 1.8 percent in developed countries compared with baseline under the same policy. Considering developing countries only, by specific meat products, again the same policy ranking by differential impacts holds – Exp-dvg first, followed by Exp-all, then by Int-dvg and Int-all. By percentage, the greatest impacts from all these policies are on the availability and accessibility of sheep meat, followed by beef, poultry meat and pork. Relative to baseline, the available supply of sheep meat for food consumption is projected to be 18.2 percent more under the Exp-dvg policy; 10.4 percent more for Exp-all; 8.6 percent for Int- dvg; and 5.1 percent for Int-all. Beef availability for consumption are projected to increase by 12.5 percent under Exp-dvg; by 8.1 percent under Exp-all; and by 5.9 percent under Int-dvg. Poultry meat consumption can be increased by 8.9 percent under Exp-dvg; by 6.6 percent under Exp-all; and by 4.2 percent under Int-dvg. Similarly, pork consumption can be increased by 6.6 percent with Exp-dvg; by 5.7 percent with Exp-all; and by 3.1 percent with Int-dvg policy. Effect on total and per capita consumption Looking at these numbers in quantity perspective, the projected 9.9 and 6.4 percent increase in available meat and milk for food under the Exp-dvg policy, which project the highest incremental increases in consumption, are equivalent to 42.6 and 57.9 million mt increase in total meat and milk consumption or an additional 4.6 kg of meat and 6.3 kg of milk consumption for every individual worldwide (Table 13)9. The share of developing countries are 31.6 million mt of meat and 51.0 million mt milk products – or additional 4.1 kg of meat and 6.3 kg of milk per capita. By specific meat product, additional 10.8 million mt of beef, 5.6 million mt of pork, 5.3 million mt of sheep meat, and 9.8 million mt of poultry meat are made available and accessible by Exp-dvg policy to developing countries, equivalent additional 0.7 to 1.4 kg on the table per person per year in 2050. Again, these contributions to food security through increased availability and accessibility of livestock products for food consumption to more people are due to the different development policies for the global livestock sector. They promote growth in production by combinations of increasing productivity, stock increase, and closing the yield-gap between developed and developing countries, which in turn lowers prices and contributes to rural incomes. The two

9 Table 13 was derived from applying the deviations from Figures 10a and 10b to Table 8

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia expansive development policies, projected the most impacts on production, trade, world prices, and food security – with Exp-dvg policy delivering the greatest combined impact, followed by Exp-all policy. SUMMARY Continuing Livestock Revolution The Livestock Revolution that described the massive increase in demand for food from livestock and poultry spurred by population growth, urbanization, and income growth in developing countries, was revisited in this paper. Review of recent statistics has shown that this demand-driven transformation in the livestock sector, first observed in the 1980s, has been sustained up to the first decades of this century. Livestock production has likewise grown rapidly in the same regions of increased consumption, indicating that livestock supply has generally kept up with the Livestock Revolution. The share of developed countries in meat production has also declined, from 66 percent in 1980 to 40 percent in 2010, ensuring the increase of developing countries’ share from 34 percent to60 percent. Baseline Demand and Supply Projections to 2050 IFPRI’s IMPACT model was used in this study to examine the sustainability of the livestock sector given the world’s capacity to produce livestock and feeds, advances in technology, the changing regional livestock production and delivery systems, and projections of population and income – to simulate both the supply and demand to 2030 and 2050. The baseline projections has shown that per capita production in developing countries would be outpaced by demand, causing increases in world prices of meat products and increases in importation by developing countries. Baseline meat production per capita in developing countries is projected to grow at a slower rate of 0.6 percent/year than demand’s 0.7 percent/year – which would result to increasing meat import of 24.3 million mt in 2050 – or at a rate of 4.0 percent/year. World prices of meat products in 2050 are therefore expected to be higher than their 2010 values – by 54 percent for pork, 53 percent for poultry meat, 22 and 13 percent respectively for beef and sheep meat. Development Policies – Implications to Production, Trade and Food Security Livestock development policy options include intensive and expansive growth policies applied to all countries (globally) or focused only to developing countries. Intensive Growth in all countries (Int-All) a. Intensive Growth in developing countries only (Int-Dvg b. Expansive Growth in all countries (Exp-All) c. Expansive Growth in developing countries only (Exp-Dvg Results show that all of the livestock development policy scenarios projected incremental increases in world meat and milk production to 2050 compared to baseline. However, the two expansive growth scenarios (Exp-dvg and Exp-all), with yield and animal population increases, are projected to give the highest incremental increase in both meat and milk production. Although between the two, the Exp-dvg scenario, which includes the explicit policy of closing the livestock yield gap and development focus on developing countries, gives the highest incremental boost in both meat and milk production – at 9.9 and 8.9 percent respectively. The developing countries, are projected to gain from increased meat and milk production from all the simulated livestock development policies. Highest increases are projected from the Exp-dvg policy, by as much as 22.9 percent in total meat production; 21.6 percent for

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change milk; 23.0 percent for beef; 24.2 percent for pork; 21.4 percent for poultry meat; and 25.0 percent for sheep meat – compared to 2050 baseline projections. The same policies (Exp-dvg and Exp-all) that increase production are expected to reduce the world prices of meat and milk products the most – and similarly the Exp-dvg policy has the greatest impact in reducing world prices of meat and milk products – beef by 33.6 percent; pork by 27.5 percent; poultry meat by 31.9 percent; sheep meat by 42.4 percent; and milk by 21.4 percent from baseline values in 2050. Only the two developing-countries focused policies (Exp-dvg and Int-dvg) are poised to ease out the import burdens of developing countries – with Exp-dvg policy scenario expected to reverse it with projected 10 million mt net export of meat products. For milk products, the reversal starts earlier in 2035, with Int-dvg policy following suit in 2040 – and Exp-dvg with the greater magnitude of trade change – from 22 million mt imports in 2010 to 60 million mt exports in 2050. No single policy, within these policy options, can make developing countries net exporter of all the meat products. Developing countries are projected to remain net importers of pork and sheep meat, but increasingly net importer of beef. Under Exp-dvg policy developing countries can be net exporter of poultry meat starting 2045. In general though, the two policies targeting the developing-countries have the effect of either reducing imports of all livestock products, if not completely making them net exporters. Under the different livestock development policies, meat products are projected to be more available and accessible by at least 3.4 percent up to 9.9 percent globally, and by2.3 to 6.4 percent for milk. In developing countries, under Exp-dvg policy meat products are more accessible by 10.2 percent; milk by 8.4 percent; beef by 12.5 percent; pork by 6.6 percent; poultry meat by 8.9 percent; and sheep meat by 18.2 percent in 2050 compared to the baseline. CONCLUSION – TECHNOLOGY, ENVIRONMENT AND FOOD SECURITY Technology and Environment How will the higher productivity in the high growth scenario be realized? The growing demand in the developing world and stagnant demand in industrialized countries represents a major opportunity for livestock producers in developing countries, where most demand is met by local production. There are sizable opportunities to increase productivity in developing countries. Within-breed selection has not been practiced all that widely, in part because of the lack of the fundamental systems needed, such as performance recording and genetic evaluation schemes (Thornton 2010). Investment in these breeding support systems must be increased significantly. Breed substitution or crossing can result in rapid improvements in productivity, but new breeds and crosses need to be appropriate for the environment and to fit within production systems that may be characterized by limited resources and other constraints. There is much more potential in the use of crosses of European breeds with local Zebus that are well- adapted to local conditions. Given the prevalence of mixed crop–livestock systems in many parts of the world, closer integration of crops and livestock in such systems can increase productivity and soil fertility while protecting the environment. Crosses of European breeds with local Zebus that are well-adapted to local conditions have major potential. To mitigate greenhouse gas emissions, improved feeding practices (such as increased amounts of concentrates or improved pasture quality) can reduce methane emissions per kilogram of feed intake or per kilogram of product (Thornton 2010).

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Food Security The different policies for the development of the global livestock sector, especially the expansive growth policy focused on developing countries (Exp-dvg), do not only help sustain the livestock Revolution, but in the process contribute to food security through increased availability and accessibility of livestock products for food consumption to more people. While health and nutrition issues have been be raised about high levels of meat consumption in developed countries, evidence from several studies has indicated a strong positive association between meat and dairy intake, micronutrient status, and many human functions, showing the benefits of increased meat consumption from low levels of consumption. It is important that populations with currently inadequate intakes—especially the most vulnerable groups such as women and children—attain access to more adequate quantities of these foods (Allen 2003). The rapid demand growth in developing countries projected here will have significant nutritional benefits for the population in Africa and much of Asia where per capita consumptions is currently low. REFERENCES Allen LH (2003) Interventions for micronutrient deficiency control in developing countries: past, present and future. Journal of Nutrition 133(11-2): 3875S-3878S. Delgado, C., M. Rosegrant, H. Steinfeld, S. Ehui and C. Courbois. (1999). “Livestock to 2020: The Next Food Revolution”, 2020 Vision for Food, Agriculture and the Environment Discussion Paper 28. IFPRI, May 1999. Nelson, G. C., M. W. Rosegrant, A. Palazzo, I. Gray, C. Ingersoll, R. Robertson, S. Tokgoz, T. Zhu, T. B. Sulser, C. Ringler, S. Msangi, and L. You. 2010. Food Security, Farming and Climate Change to 2050: Scenarios, Results, Policy Options. Washington, DC: International Food Policy Research Institute (IFPRI). Rosegrant, M. W., and IMPACT Development Team (2012). International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description. Washington, DC: International Food Policy Research Institute (IFPRI), Washington, D.C.. Rosegrant, M. W., J. Koo, N. Cenacchi, C. Ringler, R. Robertson, M. Fisher, C. Cox, K. Garrett, N.D. Perez, and P. Sabbagh. (2014). Food security in a world of natural resource scarcity: The role of agricultural technologies. IFPRI, Washington, D.C. Thornton PK (2010) Livestock production: Recent trends, future prospects. Philosophical Transactions of the Royal Society B 365: 2853-2867. World Bank (2005). Managing the Livestock Revolution: Policy and Technology to Address the Negative Impacts of a Fast-Growing Sector. Report No. 32725-GLB.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The Impact of Climate Change on Animal Genetic Resources David Steane Hon. Adviser to Department of Livestock Development, Thailand, Board of Directors of Rare Breeds International 99 Moo 7 Baan Rong Dua,Thakwang, Saraphi, Chiang Mai 50140, Thailand ABSTRACT Global temperature will rise by 2-3ºC by mid-century, short-term weather events will increase and the temperature increase could be 4.8ºC by 2100.The effects will be greatest in developing countries and climate change will be a major obstacle to poverty alleviation. Farm breeds developed over centuries in different environments and for different outputs. Demand pressure has intensified and industrialized production systems now compete directly for human foods. Small scale livestock farms use more non- human food resources. Climate change will cause heat stress and reduce feed intake. High output breeds are more susceptible to heat stress. The areas and incidence of pests and diseases will favour greater exposure of farm animals. Crop yields and quality will be adversely affected and will seriously affect animal diet composition. Changes in micro-organisms and crop pests and diseases will create additional problems. Political pressure against the use of human feeds and for animal welfare will increase. IPCC predictions are that high yielding breeds will suffer most from climate change and recommends greater use of local breeds. Disease tolerance and resistance and traits that alleviate GHG emissions will increase in importance. Technically, global legal commitments through the CBD and FAO ensure the maintenance of genetic diversity but are not monitored effectively. Farm animal databanks exist but data is limited and lacks ‘Production Environment Descriptors’. Few breeds are properly characterized genetically. These last two elements are crucial for the proper assessment of breed biodiversity and adapting to climate change. Strenuous R&D efforts are needed to achieve a situation enabling proper actions. The decisions by policy makers for future production systems alongside public attitudes will be crucial in determining the overall impact of climate change. Given present actions, the prognosis is not good either for animal genetic resources of for food security. Key Words: Climate change impacts, Animal genetic resources, Selection, Production environments

INTRODUCTION While the effects of Climate change are not precisely identified for each and every local area of the world, the general changes are now well recognized although the detail may continue to be debated. Temperatures will increase globally with reduced precipitation in existing arid areas together with an increase in short-term weather events throughout the world (IPCC 2007). The predicted increase in global temperature is ranges from 1.8 to 4ºC with the general consensus being 2ºC but recent comment suggests that this is only achievable if governments do better than the present attitudes and policies indicate. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2014) provides much more evidence and modeling results to support the need for immediate and large scale efforts to reduce the emissions of Greenhouse Gases (GHG). A more likely scenario is a 3ºC average rise with the predicted temperature rise by 2100 being up to 4.8ºC ( University of Cambridge 2014b). This

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change should be compared to the average rise in temperature of the global land surface of 0.89 since 1901 and a rise of only 4-5º C since the end of the Ice Age indicated in an Overview (Royal Soc and US Nat Acad of Sciences 2014) A UK government report (GOV UK 2014) provides The Human Dynamics of Climate Change map of potential impacts and shows that some areas will experience temperature rises of 6º C on the warmest days and that 70% of Asia will have an increased risk of flooding emphasising the IPCC report’s comments regarding increased flooding, heat related deaths together with food and water shortages. Pressure on the global food system is clearly mounting given the prediction of 9.3 billion mouths to feed by 2050 and about 11 billion by 2100 (UN DESA 2013). It is a salutary fact that the industrial world discards about 300 million tonnes of edible food which is more than the total food production of Africa. The fact that total food waste amounts to some 1.3 billion tonnes annually suggests that proper attention to this aspect could be a major factor in resolving the problem (FAO 2013b). Stuart (2009) points out that trees planted on the land used to grow ‘waste food’ would offset the GHG emissions from fossil fuel emissions. The Stern Review (Stern, 2006) clearly states that developing countries and, particularly, the poorest are likely to be the major sufferers with poverty increasing and climate change effects becoming major obstacles to poverty reduction. The review points out that heat waves like that of 2003 in Europe when 35,000 people died will become commonplace by the middle of this century. Harvey et al (2014) point out that across the tropics, smallholder farms already face many risks and that climate change is likely to disproportionately affect them and that few have adjusted strategies in response to climate change due to limited resources and capacity. The National Research Council (2014) report of a workshop questioning whether the world could feed 10 billion summarises the various theories regarding the potential to cope with pressures and notes that while feeding the world, quality of life is still a major concern. It also supports the view that there is little understanding of the impact of climate change on livestock and that food prices are likely to rise, increasing levels of inequality. The paper by Polansky (2014) in the report identifies the failure of recognizing the value of ecosystem services in policies will result is poor outcomes and, in the context of ecosystems, paraphrases a well known saying as “You get what you pay for and you don’t get what you don’t pay for!” The Summary for Policy Makers (IPCC 2014) comments that if the rise is 3º C there will be extensive biodiversity loss and that many species will be unable to track suitable climates under mid and high range rates of climate change. Given that there are several factors which do give cause for concern that something similar could occur. Farm Animal Genetic Resources (AnGR) are likely candidates for such a dire warning, While it is predicted that 70% of the world population will live in global cities by 2130 (World Health Organization 2014), globally there are about 500 million smallholders of which 87% are in Asia where 58% of the population still lives in rural areas and 81% rely on agriculture. A large proportion of livestock production still remains in the hands of small scale farmers – many of them among the world’s poor (FAO 2011a). Animal breeds have developed over centuries in different environments and for producing different outputs. Until the last century most of this development was done within specific regions/areas so that a breed was well suited to its local conditions both in terms of climate and of feed resources while producing the locally desired products and services. These breeds adapted over time to changing conditions and requirements. Technologies such as Artificial Insemination (AI), cryogenic storage of semen and embryos together with increased air travel presented opportunities for the wide dissemination of genetic material to areas totally unrelated to those in which that material was developed. In recent times, the rapid and continuing increase in demand for livestock products (the Livestock Revolution, Delgado et al, 1999) has created the need for increased production and for more intense production

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia systems together with a need for better policies to address these challenges (FAO 2010a) while taking into account that the major share of production is in the hands of small-scale farmers (FAO 2011a). The climate changes expected are likely to be more rapid than previous ones and because livestock production is now considerably more complex than a century ago, the impacts will be much more serious Environments used for animal production are, in many cases, more controlled than ever before, feeds are made available from a much wider area and genetic material is now easily and quickly transported around the world. While such rapid and wide changes allow greater use of technologies - they also can provide greater problems in terms of the reliance on other countries, on resources which are likely to be limited over time, and in enabling mismatches of breeds and environments. The predicted climate changes will add severely to the problems of food security. CLIMATE CHANGE EFFECTS The direct impacts Many of the following examples were cited in a Background Study paper (no 53) for the Commission on Genetic Resources for Food and Agriculture (CGRFA) (FAO 2011a). The most obvious is probably that of heat stress to animals. This is particularly important given the fact that most of the breeds used on a global scale were developed in temperate climates in benign conditions. It is well known that the Holstein Friesian reduces production when temperatures are consistently over 21ºC (Ghosh et al, 2006), that fertility and longevity also decline (St Pierre et al 2003; West 2003). The Large White pig is less tolerant to heat than the Creole (Gourdine et al 2006: Renaudeau et al 2007) and few breeds can repeat the ability of the Min pig to cope with a range from -35 to +35ºC. In general, Hair sheep are much better able to cope with heat with high humidity than wool sheep. Similarly the ‘Naked Neck’ and ‘Frizzle feathered’ chicken strains can cope with heat stress better than most other breeds (Horst 1988, Mathur and Horst 1990, Cahaner et al 1993). In general, heat stress not only gives rise to potential deaths but also reduces output significantly – whether this be as milk yield, growth rate or reproduction rate (see West 2003, Bloemhof et al 2013). There is evidence of feedlots in the USA experiencing substantial deaths in heat waves (Hatfield et al 2008) and the IPCC (2007) predicts that heat waves and similar sudden events are likely to increase in future. A modeling exercise by Mader et al (2009) predicts lower productivity in the USA Great Plains area for cattle, sheep and pigs due to climate change effects. In the UK, the Health Protection Agency (2012) predicts that human fatalities from heat stress will rise to 12,000 per year by 2080 compared to the present annual figure of 2,000. Given this sort of predicted increase, the likely loss of farm animals clearly presents a serious threat to the industry. Heat stress also gives rise to a reduction in appetite so that feed intake reduces and has a detrimental effect on output – whether milk, growth or reproduction while, at the same time, water requirements rise. The costs of production will obviously rise. All these factors take place at a time when other effects of climate change will impinge on feed quantity and quality and water availability (see later). The impacts should not be underestimated. Another obvious change will be that of pests and diseases. Climate change will provide different conditions which, in many cases, will provide more beneficial conditions for the survival of pests and diseases (by warmer and shorter winters for example) as well as new environments for the movement of such vectors and diseases. There will be new threats for breeds in their traditional locations as well as differences in the incidence and distribution of vectors (de La Roque et al 2008). The increase predicted in short term weather events will trigger some diseases more frequently (Martin et al 2008). This change in disease occurrence is already happening. – an examples are the spread of Bluetongue in Europe (University of

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

York 2009) in the movement of the Schmallenberg virus (SBV) which arrived in northern Europe in 2011, was identified on 29 farms in southern England in January 2012 later that year was known to be in northern England having infected ruminants on 276 farms in England and is also found in Northern Ireland. (Defra, 2012). The movement of ticks as climates change may well have serious consequences especially for outbreaks of African Swine Fever which is one of the most devastating diseases known at present. The effects of movements of disease vectors and pathogens will depend on the precise changes in environment in specific locations and, indeed, whether local populations have either tolerance or resistance to the disease being moved into that area. The impacts from such disease implications could be among the most serious threats to AnGR. Water availability is predicted to become a problem with each degree of warming expected to decrease renewable water resources by at least 20% for an additional 7% of the global population (University of Cambridge 2014b). Water consumption by animal production is already significant and likely to increase with heat stress therefore exacerbating the problem. Another direct effect is potentially the losses due to ‘sudden weather events’ such as floods and hurricanes which are predicted to increase (IPCC 2014). The effects would be most serious if the event occurs in an area of a geographically restricted breed but records to date of such disasters rarely include relevant breed information. A similar consequence (lack of breed information) is also true for major disease outbreaks in a localized area where disease control policies dictate slaughter – the classic examples being Foot and Mouth disease in the UK in 2001 in which rare breeds suffered considerable losses (Bowles et al 2003, Roper 2005) and the outbreak in Asia of Avian Influenza (2002-4) in which 250 million birds were slaughtered without any recording of the breeds involved (Morzaria S. pers. comm.). Indirect effects Climate change will also affect crops and the availability of animal feed both in terms of quantity and quality– particularly given the present pressure and competition for human food and animal feed. Roughly one third of crop land is used for animal feed and pasture accounts for 26% of all ice free land (FAO, 2006). Parry et al (2004) point out that major vulnerabilities will occur in the low latitude countries with Africa and parts of Asia having the greatest decreases. The CGIAR (Vermeulen 2014) in summarizing some of the Fifth Assessment Report predictions points out a global reduction of 4% for maize, 5% for wheat, while the synthesis by Cambridge University (University of Cambridge/BSR 2014) includes figures for reductions of 10% for wheat and soybean. The prediction is an overall reduction of 0.5% in world crops but that this could be worse and that the risk of crop failure on a year to year basis is likely to increase. All this will be happening as the demand for crops is predicted to increase by 14% per decade. Temperature increases will lengthen the growing season providing more yield but high temperatures will also increase lignification and therefore reduce digestibility. It is predicted that there will be a movement from C3 to C4 plants (Christensen et al, 2004, Morgan et al, 2007) which will mean large amounts of low quality dry matter and they also suggest that there may well be an increase in shrub land globally. There is little conclusive evidence about rangelands although the potential for carbon sequestration must be considered as an important aspect of future livestock production. This aspect is one of the thematic areas of the Global Agenda for Action in support of sustainable livestock development (see www.livestockdialogue.org). The general conclusion is that systems relying on feeds that compete directly with human food will either have to change feeds or to accept much higher feed costs both having impacts on AnGR.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The weather changes in some areas will mean that crop production becomes more risky (Jones and Thornton 2009) and that there may be a need to change crops which may affect what is available in certain areas as well as the movement of feeds throughout the world The availability of some feeds will change and this may well have very serious implications for certain types of livestock production either in terms of availability or cost. Indeed a report (CGIAR/CCAFS 2012) suggests that wheat faces a difficult future and suggests that cassava and bananas may become staple substitutes. The report also indicates that soybeans are sensitive to temperature change and that cow peas may be used instead. Crop breeders are working to develop crops that will tolerate different conditions likely to be met more frequently in the future and much depends on their success. Others are attempting to affect GHG emissions (Abberton et al 2007). FAO has, in cooperation with INRA, CIRAD and AFZ developed a key piece of information by producing an open access database on animal feed resources (Feedipedia 2012-2014) which should be a crucial tool in planning future feeds given the changes that will be occurring. Linked to the consideration of crops is that of soil micro-organisms, general soil characteristics, crops pests and diseases and the effect on weeds (FAO 2011b, 2011c). Alongside the general considerations, the predicted climate changes will affect the type and distribution of mycotoxins providing an additional problem regarding animal feed (Kovalsky, 2014). Other predictions concern the severity of snowstorms in places like Mongolia therefore increasing the risk of Dzuds - massive losses of livestock during winters due to low fodder availability (Batima, 2006) and also the increasing frequency of droughts in some areas again with potentially disastrous consequences on livestock numbers. The role of plant breeding is important in the context of coping with changes in climate and the provision of animal feeds. The need is already recognized with several authors discussing forage crop improvements particularly with regard to improving nutrition and/or reducing the environmental footprint (Mba et al, 2012; Kingston-Smith et al, 2012; Abberton et al, 2007). Crop breeding holds the key in the mitigation of the effects of climate change since crop genetics can yield faster and greater change than most animal schemes simply due to the short generation, easy control of the crop, high selection intensities and fast multiplication by cloning. Without major crop breeding successes in general, crops will move northwards and yields of maize, rice, soybean and wheat will reduce having a serious impact on livestock production and, therefore, AnGR. Mitigation of climate change effects Alongside these effects is the present emphasis on the mitigation of climate change which includes pressure to reduce livestock production given the contribution that livestock make to the emission problem (FAO, 2006). At the same time there is now greater consideration of the sustainability of livestock production systems as well as efforts to increase the efficiency of production. Efficiency can be defined in several different ways and the methods used for such calculations need careful consideration since the consequences of such calculations will be far reaching and long-term. An FAO study into Greenhouse gas emissions from the Dairy Sector by Life Cycle Assessment shows that grassland systems emit more than mixed farming systems and that tropical grassland systems create greater emissions than temperate grassland systems (FAO 2010b). FAO has developed a system known as the Global Livestock Environmental Assessment Model (GLEAM) and now a special unit known as the Livestock Environmental Assessment and Performance Partnership (LEAP) studying techniques and further developing methodology for assessing and understanding the GHG

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change emissions along supply chains and to identify and prioritize areas for mitigation (Gerber et al. 2013). However it is important to consider what feeds were involved given that some animals can better use forages that cannot be used directly by humans (see later). Some mitigation of heat stress can be achieved by reducing diet induced thermogenesis with low fibre-low protein or by increasing nutrient concentration to compensate for the lower intakes although, in the latter case, measures to protect animals from excessive heat load may be required. It may be necessary in some areas to substitute breeds or even species to cope with the changes. Species substitution has already occurred in parts of Africa where dromedaries have increased (Guoro et al 2008, MacOpiyo et al 2008) and, in the context of breed substitution, Blench (1999) reports an expansion of drought –tolerant Azawak cattle in West Africa while, in Nigeria, Fulbe herders changed from Bunaji cattle to Sokoto Gudali which cope better with browse. Changes in management systems will also play a part in the mitigation of carbon emissions but the effect on breeds used in such cases is not known and probably the breed use will not change. Brazil has now invested in poultry production units which have a neutral impact on carbon emissions (FAO 2011d). IMPLICATIONS AND IMPACTS Given the serious changes which will occur, the implications for and impacts on animal genetic resources and actions to cope with climate change progression require urgent consideration. It is likely that the productions systems relying on controlling the animals’ environment may be able to cope most easily with climate change by increasing controls. This mainly involves poultry and pig production in most countries and perhaps dairying in more difficult environments (eg tropics). This would suggest that those relatively few breeds used in intensive systems may benefit while the majority of breeds will suffer further reductions in numerical strength. However, due to the predicted feed changes and heat stress, the present selection for high performance will only serve to exacerbate the problems. It is important to note that Key Findings from the Fifth Assessment Report of IPCC point out that varieties bred for high yields are particularly at risk and that breeds in developing countries tend to be more tolerant to heat and to poor seasonal nutrition (University of Cambridge /BRC 2014). A study by Rivera-Ferre and López-i-Gelats (2012) suggests that Small-scale livestock farming (SSLF) is better at using non-human feeds including crop residues and marginal lands than Large scale livestock farming (LSLF), that biodiversity is the key to SSLF whereas LSLF uses very limited diversity and that the latter is much more vulnerable to climate change. The authors also suggest that socio-institutional innovation may contribute more to resilience than technical innovation. Gill and Smith (2008) suggest the use of ‘human edible return’ as an indicator. The precise consequences regarding any change to intensive environmentally controlled systems are likely to be dependent upon the relationship between feed and environment management costs. The environmental costs will depend on the type of system used both in terms of the capital investment (economic and carbon) as well as the operating costs. The fact that feeds may change means that the need is likely to favour breeds which can best utilize the new feeds – particularly feeds that do not compete directly as human food. In addition, handling of feed may become more costly for intensive systems. The likelihood is that more bi-products will need to be used and the modern popular breeds for intensive production systems, particularly monogastrics, have not been developed for such purposes. Indeed past experience in developing industries provides evidence to this effect. Certainly modern pig breeds when first used in hybrid production in China suffered serious infertility problems due to the lower protein level common in pig diets for local breeds (similar to earlier experience in the UK as described by Knap (2012).

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Indeed it is well recognized across most species and even in ruminants such as high yielding dairy cows that it is not usually possible to achieve full potential without the use of concentrate feeds based on cereals and protein feeds which can be used for human consumption. The changes of feed availability and cost (including the carbon costs of transport etc) will be crucial to decisions on which breed and which production systems will be preferred. Genetic aspects One key aspect of the potential impact on AnGR is whether or not breeds will be moved to areas more suited to them as climate change continues to affect their environment. While this may appear theoretically possible, the implications for land tenure and use and for crossing national boundaries are likely impose increasingly severe restrictions on the human movement. An option which perhaps is more possible would be the movement of breeds (but not their present owners) to the area best suited to the needs of that breed. This would imply continuous movement as climate change ‘progresses’ but how this would be achieved is unclear. Initially, crossbreeding programmes could be used and, as necessary, increasing the proportion of the ‘imported’ breed that is better suited to the changed environment. This would obviously have significant impacts on both pure breed populations. Given the general conclusion that mitigation of GHG emissions requires greater intensification of production systems by increased efficiency in all stages of production, it is only now that the world is beginning to realize the serious lack of well designed selection/breeding schemes for local breeds. Aid Agencies and governments have, for years, preferred to import high performance animals and then to adapt systems to suit and subsidise these rather than take the longer but more reliable option of within breed selection possibly with some introgression. Present day methods can utilize the ever increasing knowledge regarding genomic selection – this may provide a means of breaking some of the known detrimental correlations in the highly selected breeds. However to best utilize the potentials of breed movement and/or use of specific genes for newly desired traits (whether robustness traits to present high yielding breeds or production traits to robust breeds) the crucial factor is the knowledge of these genetic factors in local breeds. Unfortunately the body of evidence for genetic abilities of local breeds is extremely sparse. This is likely to result in the loss of breeds that are crucial to adaptation to climate change. The first question perhaps should address the rate of change possible for breeds and this will depend on just what specie is being discussed. The species that are used in the most controlled environments are those which could be changed most quickly since they have the shortest generation intervals. However without details of the genetic variability in the traits directly concerned with heat tolerance, methane emissions, feed utilization within breed, it is not possible to provide detailed probabilities of the likelihood of coping simply by normal breeding practices. There is an urgent need for more research into all aspects of farm animal reaction to stress and coping mechanisms if the impacts of climate change are to be minimized. Hegarty et al (2007) showed that cattle selected for lower residual feed intake have reduced daily methane production. Research in cattle in Australia indicates genetic differences between sires within an Angus population for methane emissions and there is clear evidence that selection for lower methane yield is possible (Herd et al 2012). Crocker and Robison (2002) demonstrated breed differences for nitrogen excretion in pigs. Knap (2012) reviews progress in pig breeding and points out the reduction in Nitrogen excretion relative to retention has been 25%-31% over 35 years. However the same author also discusses the fact that selection for efficiency can result in loss of other traits such as

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change robustness and that selection for this latter trait is difficult. Oltenacu and Broom (2010) report on the adverse responses due to selection on milk yield in dairy cows. Hansen (2013) reports on the adverse effects of increasing temperature on dairy cows - 20% yield reduction, lower fertility and heat expression and points out that modern day cows begin to lose the ability to regulate heat at air temperatures as low as 25-29ºC. The present indications are clearly that the high yielding dairy cow faces serious problems in the future. Knap and Neeteson (2005) point out that successful introgression of exotic genotypes into western animal breeding is very rare. It is likely that beef production will come under more intense pressure than other livestock given the present evidence regarding its relative efficiency. Skerrett (2014) comments on a report showing the considerable difference between beef production and other livestock products in the USA. Given this type of evidence, it would be of considerable benefit to organize more intense selection in local breeds for efficiency traits since most have not undergone much more than natural selection under local conditions and requirements. The major need will be to ensure effective recording schemes to enable the most potentially effective selection to occur. Such selection should use the ‘normal’ feeds for that breed and not compromise by using better rations based on non local feeds. Unfortunately most breeders involved with local breeds do not have the resources for such practices. Present developments in identifying marker genes and in genomics may offer more opportunity for genetic change in the future. Research into genetic selection of livestock breeds for factors involved with climate mitigation is on-going – and the Animal Selection, Genetics and Genomics Network ASGGN has been formed with a view to assisting researchers globally. (see www.asggn.org). The effects of climate change may well change the selection goals for high performing breeds so that introgression from local breeds could become a realistic option. There are in existence breeds with known attributes that could provide some solutions in the context of climate change. The Garole sheep can graze while knee-deep in water (Nimbkar, 2002) whereas Black Headed Persian sheep (Schoenman and Visser, 1995), the Black Bedouin goat (Shkolnik et al, 1980) and the Black Moroccan goat (Hossaini-Hilaii and Benhamlih, 1995) have been shown to be superior in their ability to cope with water shortage. Kuri cattle can withstand insect bites and remain close to Lake Chad when other breeds leave the area (Blench, 1999). Camels obviously have abilities which most other species lack. Such breed abilities, if identified together with the environmental details, may provide the ability to transfer genetic material suitable for the new conditions rather than to attempt (as is most practiced to date) to alter the environment to enable the exotic breed to survive and produce with its concomitant requirement for continued investment and operational costs. The research into the genetics of copper retention in sheep shows that there are between breed differences as well as within breed genetic variation. The North Ronaldsay sheep was developed grazing seaweed as a major part of its diet, and, as such, has developed sensitivity to be able to extract the required copper but when on normal grassland can be poisoned (Woolliams et al, 2008). Work reviewed by Weiner (1987) has shown the success of selection for copper retention and confirmed the effect of copper on the incidence of Swayback disease. This is one of many examples of disease tolerance and/or resistance. There have been many reviews on this subject in recent times (Axford et al, 2000; FAO, 2002; FAO, 2007a; FAO, 2009; FAO, 2010d). The 2010 review specifically focused on ruminants and indicates that DAD-IS records four goat and 13 sheep breeds as having tolerance or resistance to parasitic diseases and 86 cattle breeds similarly coping with trypanotolerance (N’dama) or ticks (Sahiwal, Nguni, Tuli for example). While these DAD-IS entries are not necessarily supported by scientific evidence, they cannot be dismissed out of hand. Baker et al (2004) point out differences in parasitic resistance under heavy and light

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia challenges. Given the likely changes in disease patterns and levels of challenge, this aspect of genetic variation will become more important in the coming years This is not only due to intensification of livestock production but also due to the progress made in the context of genomics and the association with specific diseases or conditions. Davies et al (2009) review evidence for genetic variation in resistance to infectious diseases across different species and develop a method of ranking each disease in terms of its overall impact, combining this with published evidence for genetic variation and current information on genomic tools. It is important that environmental information is made available for each situation so that, in future, this can be used to advantage in preparing for such diseases. New information required The obvious way to achieve the required knowledge would be to have proper information on the detailed environments in which specific breeds normally perform. In 2007, at the International Technical Conference held by FAO in Interlaken, The State of the World’s Animal Genetic Resources for Food and in the Agriculture (FAO 2007a) was presented- containing Country Reports from 169 countries. That meeting produced two major documents – The Interlaken Agreement and the Global Plan of Action (GPA) for Animal Genetic Resources (FAO 2007b). The documents were endorsed by the Commission on Genetic Resources for Food and Agriculture (CGRFA) which means that the GPA is binding on all FAO member countries. Indeed the first Strategic Priority covers the need for developing methods for characterizing animal genetic resources, monitoring trends and risks and establishing early-warning and response systems. This applies directly to the effects of climate change and is therefore a commitment already made by all member countries of FAO. As with all commitments made under the CBD which has been ratified by almost all countries (with the notable exception of the USA), it remains to be seen to what extent countries actually undertake their legal commitments. While there appears to be no formal monitoring of countries in this respect, a survey by FAO showed positive reactions with many of those surveyed having either commenced national planning or implementing national plans (FAO 2010c). Notably PR China has declared 138 breeds as protected and has 119 conservation farms/areas/genebanks. FAO is now engaged in preparing the Second State of the World Report on AnGR for presentation at the next meeting of the Commission on Genetic Resources for Food and Agriculture (CGRFA) in January 2015. FAO now has a website FAOLEX covering environmental law and treaties and linked to ECOLEX (combined FAO, IUCN and UNEP). FAO developed the first set of Breed Descriptors (FAO 1986a,b,c) but these failed probably because they were far too detailed for that time. Indeed, it was only in the early 90s that the first global breed survey was carried out with the first World Watch List of Domestic Animal Diversity being produced in 1993 (Loftus and Scherf, 1993). While later editions were more comprehensive (see Scherf 1995 and Scherf 2000), the actual data recorded in the Domestic Animal Diversity Information System (DAD-IS) remains very small compared to the need. Of the major farm species, DAD-IS contains information on 7202 local breeds and another 1060 transboundary breeds and holds information on 3482 avian breeds but has population data on only 48% (FAO, 2012a). The present system can record many items of performance and additional comment but relatively few contain such information. DAD-IS is recognized as the clearing house and early warning system for farm animal genetic resources by the Convention on Biological Diversity (CBD) and, therefore, its content is crucial to global action. In early 1998, after pressure from the National Coordinators of twelve Asian countries participating in the first FAO regional project on AnGR, a Workshop on what are now known

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change as “Production Environmental Descriptors” (PEDs) was held. A system was developed for recording and the elements that should be recorded. (FAO 1998a and b) but it was only in 2008 that a follow-up Workshop was held (FAO/WAAP 2008). The introduction of PEDs into the DAD-IS database has never taken place even though it was part of the agenda for the last Global National Coordinators Workshop (FAO, 2012b). While PEDs are crucial for any proper use of the breed data in DADIS, their absence suggests that FAO is less than fully committed to the maintenance of AnGR. Indeed, without PEDs, it can well be argued that the present data is virtually useless to anyone wishing to consider using a breed other than a local one. However, Hoffmann (2013) uses the limited data in DAD-IS to provide some useful indicators of breed performance coupled to environment which emphasizes the need for more complete global data. It is to be deplored that FAO has failed to expend sufficient energy and funding through its Regular Programme and/or donors to enable the database to be made of real value. The urgency of the need to know such information in the light of the forthcoming changes in climate cannot be exaggerated. A study of budget allocations shows that considerably more funding continues to be dedicated to Plant Genetic Resources (PGR) even though support for this has been operating for 30 years while AnGR was not formally included in the Commission until 1997. The budget allocations for the years 2012/3 from Regular Programme are US$ 6.88million for PGR and US $1.91 million for AnGR (FAO, 2013). It would appear that the Commission on Genetic Resources for Food and Agriculture (CGRFA), the Committee on Agriculture (COAG) and the ultimate governing body - the FAO Conference - are either unaware of the crucial nature of the problem or consider action can be delayed. Indeed there is no record that the CGRFA has ever supported PEDs which together with the apparent lethargy of the FAO’s governing bodies raises serious questions about FAO’s commitment and ability to carry out the requirements under CBD. A recent paper by Tixier-Bouchard (2014) discusses potential developments to resolve the present gaps in the FAO database. The author points out that the database completeness is below 50% , comments on PEDs and identifies three areas of further need - functional diversity, data sharing and access to AnGR. Basic conservation of breeds is an essential component of maintaining genetic diversity (Gibson et al, 2005; Woolliams et al, 2008) but the effects of climate change pose additional problems. In situ schemes will be open to the adverse effects of climate change such as new pest and disease challenges while cryogenic systems maintain present genes but do not allow the population to adapt to any of the changes taking place in the real environment. Given the new challenges, it is crucial that cryogenic storage is carried out more regularly within a breed with smaller samples per animal but, as a minimum, from all males used in each generation. This would enable sudden disasters to be counteracted by using the stored material from only one generation earlier. Without PED information, the ability to cope with climate change in the most efficient and sustainable manner is virtually impossible. Even with such data, there is another set of information which should be a major component in the options open to best tackle the future. While genomics will certainly provide some new opportunities it is unlikely to provide the major component of genetic change for some time to come. However genomics is likely to become increasing used especially in commercial populations and therefore add to the problems of using alternative gene sources (Hill and Zhang, 2008). However genome sequencing does offer some potential for providing opportunities for the investigation of genes and/or sequences which may be associated with traits or abilities of interest – especially regarding the ‘new’ environmental conditions which will be experienced in most areas of the world. ‘New’ in this context may simply mean different but similar to those experienced elsewhere. Given present computing power, it would not be unreasonable to

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia examine gene sequences alongside specific PEDs to see if there are sequences associated. Any such indications would enable more specific research which could then provide the opportunity either to exchange genetic material by introgression or to manipulate the relevant genomes. The amount of work on genomic options is clearly shown by the number of presentations at the recent World Congress on Genetics Applied to Animal Production (WCGALP 2014), through the International Society of Animal genetics (ISAG) - see issues of Animal Genetics, and at the recent ESF conference in Cardiff (ESF,2014). In a special issue of Livestock Science, (2014) entitled ‘Genomics Applied to Livestock Production’ papers concerning conservation (Toro et al 2014), breeding programmes for developing countries (Rothschild 2014) and animal diseases (Bishop and Woolliams 2014) are included. The use of GMOs to manipulate genomes in such cases would require the proper full assessment of the effects and, in this regard, the livestock sector should not follow the example of the plant sector where the use of GMOs has not been limited by the lack of full examination of the consequences of introducing a GMO. It is incumbent on the livestock industry to ensure that comprehensive evaluation is compulsory if such material is to be accepted publicly. Such developments would increase the desire to move genetic material across national boundaries with the consequent requirements regarding Access and Benefit Sharing and other legal matters (CBD 2011, Correa 2010, FAO 2005, Hiemstra et al, 2010). DISCUSSION It is well recognized that in order to maintain genetic diversity in farm animals it is not necessary to keep every breed. However in order to make good decisions, it is essential that all the necessary information is available. This is absolutely not the case and the evidence suggests that it will not be the case for many years – probably too late to make to best decisions since some breeds will have been lost in the meantime. It is important that more pressure is put on countries to comply with the various requirements under CBD. Some countries even fail to recognize breeds under the FAO accepted definition and, thereby, technically reduce their responsibilities. Given the paucity of data in the FAO database, greater resources are required to ensure that the required data is made available including that on PEDs. In addition, greater efforts need to be made to characterize breeds genetically since this is essential to the knowledge of diversity. Without urgent major investment by countries and by international agencies (including the UN), it is likely that genetic diversity will be lost and that the livestock industry will be less well placed to cope with the effects of climate change. All countries need to fully undertake their obligations if food security is not to be undermined and in jeopardy. The present emphasis on mitigation of GHG emissions is crucial to longterm food security but brings with it a dilemma. Unfortunately, when assessing livestock production, the general tendency is to use a single measure (feed efficiency, carbon efficiency, per unit of output, per unit of land etc) and, while this enables politicians and the general public to grasp the urgency of the need for action, it does little to benefit the global situation in terms of long- term sustainability. The general consensus appears to be that increasing the efficiency of existing intensive systems is the way forward but this ignores such evidence as provided by those supporting a move to more organic systems of production (e.g. Compassion in World Farming 2009). Similarly, the well recognized ability of organic matter to retain soil moisture is likely to be of major benefit as lower rainfall occurs in many areas (Pimental et al 2005). The same authors point out that organic farming can require 30% less fossil energy. Any neglect of the proper consideration of all aspects together will inevitably lead to more mistakes and additional problems for livestock production in the future. Economists for many years discussed ‘externalisation of costs’ when the real comment should have been about the

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change deception of hidden subsidies. Reliance on measures such as carbon per unit of food must consider all other aspects including the social implications of increased industrial farming. This is particularly important when the food supply for animal production is likely to reduce and to be of poorer quality. It must be recognized by the livestock industry that the use of human foods as animal feed is causing concern – an example is the Report by the Special Rapporteur to the UN General Assembly Human Rights Council 16th session in 2010 in which future use at present levels was challenged. Eisler et al 2014 comment on the fact that the 1 billion tonnes of wheat, barley, oats, rye, maize, sorghum and millet fed to livestock could feed some 3.5 billion humans but recognizes the benefits of eating modest amounts of meat. While the industry strives to improve its efficiency, to reduce its adverse impacts on the environment and on climate change, there is one other aspect which could enable the industry to reduce the concerns of the general public about both emissions and welfare aspects of livestock production. This would entail accepting the general principle made by Mc Michael et al (2007) which would involve the reduction of meat consumption in the ‘developed’ world thereby reducing obesity and increasing the general health of the public while reducing the costs involved in treating ill health. A more recent study (University of Cambridge 2014a) shows that closing yield gaps, eating healthier diets and reducing food waste could result in almost halving agricultural GHG emissions compared to those of 2009. Most discussions within the livestock industry appear to accept that the Livestock revolution should aim to provide the predicted demand for meat and dairy products whereas there are good reasons why this should not be the case. While the retail side is obviously interested in sales expansion, the whole industry must accept that consuming meat in large quantities can lead to serious health risks. A campaign to reduce consumption to more healthy levels would also enable consumers to reconsider the cost of such items and, with the right publicity, to make more choices regarding the way in which the product is produced. Given the mounting evidence of the greater GHG emissions from beef production than from other meats, milk and eggs (Skerrett, 2014) there could well be an effect on beef breeds. While intensification is likely to affect those breeds traditionally used for draft and now more used for beef, the affect of GHG emissions is unlikely to be considered in such cases since these breeds provide meat locally rather than to large conurbations and, therefore, are less at risk. The more commercial beef systems could well be under pressure but breed loss is unlikely. Possibly the major risk comes from the use and level of acceptance of synthetic meats and whether the cropping area required is feasible given the effects of climate change. There is already evidence that in the more affluent countries there is much more concern about welfare issues as well as about methods of production in general. In many countries, supermarkets are actively promoting organic and local produce including meat and dairy products. Achieving meaningful changes to public health and public conception of animal welfare could provide major benefits to local breeds supplying niche markets. Indeed it would appear that moving consumption patterns to improve the nutrition in developing countries while reducing the over consumption in others creates a win-win situation for all. It would also mean that breeds suited to the local conditions and providing the required products could benefit and this would ensure that farm animal genetic diversity could be more easily maintained providing long-term global benefit in these changing times. CONCLUDING REMARKS While it is obvious that climate change will have a significant impact on Animal Genetic Resources, it is not clear just what breeds will be impacted the most. The absence of adequate

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia research and information on specific environments and on genotypic characterisation is seriously damaging the chances of successfully coping with climate change. The latest IPCC Report, and the various commentaries summarizing it, are clear that the high yielding breeds will suffer most from climate change and that more reliance should be made on local breeds. If this is correct, then the effect on AnGR will be lower than many anticipate since the high performing breeds are at less risk from reducing numbers although still remaining at risk from small Effective Population Size. However the political influence of those involved in industrial farming is considerable and, with the generally agreed prediction that food prices will rise, it is likely that these systems will continue to be given preference. Nevertheless, the rise in public interest in animal welfare and organic production will create favourable conditions for the development of niche markets using local breeds and products. These developments alongside proper implementation of legal commitments under the Global Plan of Action, the Interlaken Declaration and the Convention on Biodiversity may maintain adequate biodiversity. Local breeds may well be less susceptible to climate change although this will depend on how fast and large these changes are over the generations ahead. In addition, if proper investment is made both in selection and in obtaining all the relevant information, and data on the genome of local breeds is achieved together with production environment descriptors, the genomic aspects of future improvements may well assist in maintaining the genetic diversity globally. However the countries have already made commitments to maintain their national farm animal genetic diversity – the real questions are how well countries honour these commitments and how best this diversity can serve the world and its future demands. Research is absolutely essential in the provision of information and options available together with their consequences. Policy makers have a crucial role to play in providing the answers but it is unclear how accurately briefed these decision makers are. It is clear that the challenges ahead resulting from climate change place animal genetic resources in a crucial role in facing future global food security.

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Nimbkar, C. 2002. Gains from Garole the wonder sheep of West Bengal. Partners in Research for Development. ACIAR. 15: 31–36 (accessed at http://www.une.edu.au/ers/ documents/Nimbkar2002-Partners-in-Res-15-31.pdf). Oltenacu P.A. and Broom D.M. (2010).The impact of genetic selection for increased milk yield on the welfare of dairy cows. Animal Welfare, 19: 39-49 Parry,M.L., Rosenweiz,C., Iglesias,A., Livermore, M. and Fischer,G. 2004. Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Global Environmental Change 14 (2004) 53-67. Pimental D., Hepperly P, Hansen J., Douds D. and Seidel R. (2005). Environmental, energetic and economic comparisons of organic and conventional farming systems. Bioscience 55, No. 7. Polansky, S. 2014 Biodiversity and Ecosystem Services in a World of 10 Billion People. National Research Council. Can Earth’s and Society’s Systems Meet the Needs of 10 Billion People? Summary of a Workshop, Washington, D.C. The National Academies Press, 2014 pp 37-Accessed at: mahb.stanford.edu/wp-content/uploads/2014/08/Can- Earths-and Societys-Systems-Meet-the-Needs-of-10-Billion-people.pdf Renaudeau, D., Huc, E. & Noblet, J. 2007. Acclimation to high ambient temperature in Large White and Caribbean Creole growing pigs. Journal of Animal Science, 85: 779–790. Rivera-Ferre M.G. and López-i-Gelats F. 2012. The Role of Small-Scale Farming in Climate Change and Food Security. Center for Agro-food Economy and Development CREDA-UPC-IRTA, Castelldefels (Barcelona), Spain Roper, M. 2005. Effects of disease on diversity. Paper presented at the international workshop Options and Strategies for the Conservation of Farm Animal Genetic Resources: Agropolis, Montpellier, 7–10 November 2005. Rome, CGIAR System- wide Genetic Resources Programme (SGRP)/Bioversity International. Rothschild M. and Plastow G.S., 2014. Applications of genomics to improve livestock in the developing world. Livestock Science, 166, pp76-83. DOI. http://dx.doi.org/10.1016/j.livsci.2014.03.020 Royal Society 2014. Climate Change, Evidence and Causes. An overview from the Royal Society and the National Academy of Sciences, USA. Accessed at http://royalsociety.org/~/media/Royal_Society_Content/policy/projects/climate- evidence-causes/climate-change-evidence-causes.pdf Scherf, B.D. 1995. WWL for domestic animal diversity. 2nd Edt. FAO Rome, September 1995. Scherf, B.D. 2000. WWL for domestic animal diversity. 3rd Edt. Rome, October 2000. Schoenman, S.H.and Vissier, J.A. 1995. Comparative water consumption and efficiency in 3 divergent sheep types. Journal of Agricultural Science,124: 139–143. Shkolnik, A, Maltz, E. & Gordin, S. 1980. Desert conditions and goat milk production. Journal of Dairy Science, 63: 1749–1754. Skerrett P.J. 2014. Raising beef creates more pollution than raising pork, poultry, dairy, or eggs. Harvard Health blog www.health.harvard.edu/blog/raising-beef-creates-pollution-raisng- pork-poultry-dairy-eggs-201407227289 Accessed via [email protected] Subject BBC News; Beef environment costs 10 times that of other livestock. 1st August 2014. Stern N. 2006. The economics of climate change. The Stern Review. Cambridge University Press, Cambridge. ISBN 9780521700801. St-Pierre N.R., Cobanov B. and Schnitkey G. 2003. Economic losses from heat stress by US livestock industries. Journal of Dairy Science, 86 (Suppl. E): E52-77. Stuart, T. 2009. Waste: Uncovering the global food scandal. Published by Penguin Books 2009. ISBN-10: 0141036346 ISBN-13: 978-0141036342

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Tixier-Bouchard M. 2014 Status and gaps in characterization of Animal Genetic Resources. Proc. 10th World Congress on Genetics Applied to Livestock Production, Vancouver, BC, Canada. Accessed at: www.wcgalp.com Toro M.A., Villanueva B. and Fernández J. 2014. Genomics applied to management strategies in conservation programmes. Livestock Science, 166, pp 48-53. DOI.http://dx.doi.org/10.1016.j.livsci.2014.04.020 United Nations Department of Economic and Social Affairs (UN DESA) 2013. World Population Data. Available at http://esa.un.org/wpp/unpp/panel_population.htm University of Cambridge 2014a. Changing diets is vital to reducing climate change. University of Cambridge. Accessed at: http://www.cam.ac.uk/research/news/changing-global-diets-is-vital-to-reducing- climate-change Full report in Nature Climate Change. Accessed at: http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate2353.htm University of Cambridge 2014b. IPCC BRIEFINGS Understanding Climate Science. Climate Change: Implications for Agriculture. Key findings from the Intergovernmental Pro on Climate Change Fifth Assessment Report. Accessed at www.cisl.cam.ac.uk/ipcc University of York, 2009. Heritage Sheep AGRI GEN RES action 040 co-funded by the European Commission under Council Regulation (EC) No 870/20041. York, UK, University of York (accessed at http://www.heritagesheep.eu/Heritagesheep.pdf). Vermeulen S.J. 2014. Climate Change, food security and small scale producers. CCAFS Info Note. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Copenhagen, Denmark. Accessed at: www.ccafs.cgiar.org West J.W. 2003. Effects of heat stress on production in dairy cattle. J. Dairy Science, 86: 2131-2144. Wiener, G. 1987. The Genetics of copper Metabolism in Animals and Man. In chpt.3 vol.1 Copper in Animals and Man. Eds. J McC Howell & J M Gawthorne. CRC Press, Florida. Woolliams, J.A., Matika, O. & Pattison, J. 2008. Conservation of animal genetic resources; approaches and technologies for in situ and ex situ conservation. Animal Genetic Resources Information, 42: 71–89 World Congress on Genetics Applied to Animal Production, 2014. Papers accessed at: www.wcgalp.com World Health Organization, 2014. Global Health Observatory(GHO): Urban population growth. Accessed at: www.who.int/gho/urban_health/situation_trends/urban_population_growth_ text/en/

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

The Effects of Human-Ruminant Interactions on Animal Welfare and Productivity in the Tropics Paul H. Hemsworth and Rebecca E. Doyle Animal Welfare Science Centre, Faculty of Veterinary and Agricultural Sciences, University of Melbourne Parkville, Victoria 3010, Australia ABSTRACT This paper will review the influence of human-animal relationships on animal welfare and productivity. The most studied aspect of this relationship has been the animal’s fear responses to humans. There are three main lines of evidence concerning the implications for the welfare and productivity of farm animals: handling studies in controlled experimental conditions, observations in commercial settings and intervention studies in commercial settings. Although handling at an early age may be highly influential, subsequent handling is also important and has the potential to modify early learning effects. Conditioning and habituation to humans, occurring both early and later in life, are probably the most influential factors affecting the behavioural responses of farm animals to humans. This review highlights the important role and responsibility of the human in the development of the human–animal relationship. The results of handling studies in the laboratory and intervention studies on farms investigating the relationship between stockperson attitudes, stockperson behaviour, animal behaviour and stress physiology provide evidence of causal relationshipsbetween these variables. Furthermore, this research provides a strong case for introducing stockperson training courses in the livestock industries which target stockperson attitudes and behaviour. This discussion demonstrates the important role and responsibility of the human in the development of human–animal relationships. Underestimating the role and impact of the stockperson will seriously risk the welfare and productivity of these livestock. Indeed, the stockperson may be the most influential factor affecting animal handling, welfare and productivity INTRODUCTION There arebasically three conceptual frameworks to assess animal welfare (Fraser 2003; Hemsworth and Coleman, 2011): how well the animal is performing from a biological functioning perspective; (2) the animal’s affective state, such as suffering, pain and other feelings or emotions; and (3) whether or not the animal is provided with a ‘natural’ environments that allows expression of ‘natural’ behaviour. The principle that management, including supervising and managing animals, affects animal welfare is widely recognised within all livestock industries, regardless of the species, the scale or the style of production system. Stockpeople require a range of well-developed husbandry skills and knowledge to effectively care for and manage farm animals. It is broadly considered that to maintain good welfare (and production) of animals within their care, a stockperson must have the following traits: a good general knowledge of the nutritional, climactic, social and health requirements of the animal in their care; practical experience; the ability to identify departures from good health, welfare and performance, and either correct the issue or seek the appropriate support; and the ability to work well independently and/or as a part of a team (Hemsworth and Coleman, 2009, 2011).Much is known about what measures constitute good health and production in ruminants and other production species. The complexities surrounding how a stockperson behaves, and how this affects animal welfare, both directly and indirectly, is not fully appreciated (Hemsworth and Coleman, 2009, 2011).

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

There are three main classes of factors that contribute to the performance of the stockperson: capacity, willingness and opportunity (Fig. 1; Hemsworth and Coleman, 2011). ‘Capacity’ includes variables such as the stockperson’s skills, health, ability and knowledge, ‘willingness’ includes the motivation, job satisfaction, attitude to the animals and work attitude of the stockperson and ‘opportunity’ includes working conditions, actions of co- workers and organisational policies and rules, where applicable. Appreciating the factors that affect work performance, as well as where deficiencies exist, at the level of the stockperson is the first stepto ensure that stockpeople have well-developed husbandry skills and knowledge, as well as access to the appropriate facilities and opportunity to use these skills and knowledge to effectively care for and manage farm animals.

Ability, knowledge, Capacity skills,personality, etc.

Performance

Willingness Opportunity

Motivation, job satisfaction, job status, Tools, equipment, materials & and supplies, self-image, attitudes working conditions, organisational policies, time, pay, etc.

Adapted from Blumberg and Pringle (1982)

Figure 1. The main factors that affect the work performance of the stockperson

Human-animal interactions are becoming a key feature of modern livestock production, and research has consistently shown that the quality of the human-animal relationship that is developed between stockpeople and their animals can have substantial effects on both the animals and the stockpeople. For example, there is good evidence based on handling studies and observations in the livestock industries that human-animal interactions can directly affect the welfare and productivity of farm animals through animal fear and stress associated with handling and husbandry procedures involving humans (Hemsworth and Coleman, 2011). Furthermore, by influencing the behavioural response of animals to humans, and in particular the ease with which animals can be observed, handled and managed, human-animal interactions may also have implications for a number of work-related characteristics of the stockperson, such as job satisfaction and work motivation. Thus human-animal relationships may indirectly affect the welfare and productivity of farm animals through changes in these other work-related characteristics. There is a large and growing body of evidence from both field and laboratory studies that the interactions between stockpeople and their livestock have a substantial effect on the

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia behaviour, welfare and productivity of farm animals (Hemsworth and Coleman, 2011). This research has focussed on the adverse effects of a negative human-animal relationship: negative or aversive handling by affecting fear responses to humans has been shown to stressful with adverse effects on animal welfare and productivity (Fig. 2).

Stockperson Animal

Attitudes Behaviour Fear Productivity & WelfareStress Stress

Job Satisfaction

Work Motivation Stockperson Work Performance Motivation Technical Skills to learn & Knowledge

Figure 2. Important work-related characteristics amongst the sequential relationship of attitude to welfare and productivity One important methodological feature associated with this concept of the human-animal relationship is the necessity of characterizing those interactions that have significance for the human and animal partners, so that the influence of the quantity and nature of these interactions on the human–animal relationship can be understood. As discussed later, evidence from handling studies and observations on human–animal interactions in the livestock industries indicate that it is the history of interactions between humans and animals that leads to the development of a stimulus-specific response of farm animals to humans: through conditioning, a farm animal may associate humans with rewarding and punishing events that occur at the time of human–animal interactions, and thus develop conditioned responses to humans. Similarly, the stockperson’s direct and indirect experiences with animals are influential determinants of the stockperson’s attitudes and behaviour towards farm animals. These relationships between stockpeople and animals and the effect of these human-animal relationships on animal welfare are evident in a variety of different species, and range from intensive to extensive production settings. This paper reviews these effects drawing on research conducted in a variety of different species, production settings, and geographical regions. As these same relationships are identifiable across a variety of animal industries, similarities as to how they may affect the welfare of ruminants in the tropics are evident. Human-animal relationships in agriculture The concept of the HAR and its assessment Human-animal relationships, which develop from the interactions between the partners, can be viewed to allow the partners to predict the actions and responses of their partners and therefore guide their own actions and responses (Estep and Hetts, 1992). Consequently, human-animal relationships can be studied by investigating each partner’s perception of the other, which should reflect their perception of the human-animal relationships. The most studied aspect of the human-animal relationship from the perspective of the farm animal has been the animal’s fear responses to humans. As reviewed by (Waiblingeret al., 2006; Hemsworth and Coleman, 2011), tests measuring the animal’s approach and avoidance

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change behaviour to humans in standard testing settings have been used to assess the animals’ fear response to humans, together with physiological responses associated with fear such as responses of the autonomic nervous system (e.g., secretions of catecholamines: adrenalin and noradrenalin) and the neuroendocrine system (e.g., secretions of corticosteroid hormones, cortisol or corticosterone). Similarly, the quality of the human-animal relationship from the human’s perspective can be studied by examining the behaviour and attitude of the human towards interacting with the animal. Effects of stockperson behaviour on animal welfare and productivity There are three main lines of evidence that demonstrate that negative or aversive handling by affecting fear responses to humans can affect the welfare and productivity of farm animals: handling studies under controlled conditions; observed relationships in the field; and intervention studies in the field targeting stockperson behaviour. This evidence has been reviewed by Hemsworth and Coleman (2011) but the main findings are reviewed briefly here. Handling studies, predominantly on dairy cattle, pigs and poultry, show that negative or aversive handling, imposed briefly but regularly, will increase fear of humans and reduce the growth, feed conversion efficiency, reproduction and health of farm animals (see Hemsworth and Boivin, 2011; Hemsworth and Coleman, 2011). A chronic stress response has been implicated in these effects on productivity since for example in many of the pig experiments (see Hemsworth and Coleman, 2011), handling treatments that resulted in high fear levels also produced a sustained elevation in the basal free cortisol concentrations. In addition to these effects of stress, high levels of fear resulted in reduced growth and reproductive performance in pigs. Handling studies on dairy cattle and poultry implicate the effects of fear-related stress on depressed milk yields in cows (Rushen et al., 1999; Breuer, 2000; Breuer et al., 2003) and egg production in poultry (Barnett et al., 1994). It should be also recognised that handling studies on a number of farm animals, including beef and dairy cattle and pigs, have shown that fearful animals are generally more difficult to handle (see Hemsworth and Coleman, 2011). In addition to ease of handling, fear of humans is also likely to have implications for the ease with which farm animals can be closely observed and promptly treated if health and welfare problems. Field studies examining inter-farm correlations indicate sequential relationships between stockperson attitudes, stockperson behaviour, animal fear of humans and animal productivity (see review by Hemsworth and Coleman, 2011). For example, Coleman et al. (1998) and Hemsworth et al. (1989) found that the use of a high proportion of negative tactile behaviours by stockpeople, such as slaps and hits, was correlated with increased avoidance by breeding sows of humans. In studies on dairy cows housed outdoors all year round on pasture, Breuer et al. (2000) and Hemsworth et al. (2000) found that the use of a high proportion of negative tactile interactions, such as slaps, pushes and hits, was associated with increased avoidance of humans. Similarly, in a study of dairy cows in indoor farms, Waiblingeret al. (2002, 2003) found that positive stockperson behaviours, such as talking, petting and touching, and negative behaviours, such as forceful slaps and hits and shouting, were respectively negatively and positively associated with avoidance of humans. Lensink et al. (2001) studied stockperson and calf behaviour at 50 veal calf units and found that the frequency of positive behaviour towards calves by the stockperson, such as touching, petting and allowing calves to suck the stockperson’s fingers, was negatively associated with avoidance of humans. In studies on commercial meat chickens, Hemsworth et al. (1994b) and Cransberg et al. (2000) found that the speed of movement by the stockperson was positively correlated with avoidance of humans. Edwards (2009) studied caged laying hens and found that the incidence of noise made by stockpeople, such as shouting and cleaning with an air hose or leaf blower,

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia was associated with greater avoidance of humans, while the times that stockpeople spent standing stationary and spent close to the birds’ cages were associated with less avoidance of humans. A number of these field studies on stockperson and animal behaviour have shown that the attitudes of stockpeople towards interacting with their animals are predictive of the behaviour of the stockpeople. In general, positive attitudes to the use of petting and the use of verbal and physical effort to handle animals were negatively correlated with the use of negative tactile interactions such as slaps, pushes and hits in the dairy (Breuer et al., 2000; Hemsworth et al., 2000; Waiblinger et al., 2002) and pig (Hemsworth et al., 1989; Coleman et al., 1998) industries. Lensink et al. (2000) also found that a positive attitude to the sensitivity of calves to human contact was predictive of the frequency of positive behaviour used by stockpeople towards veal calves, while Edwards (2009) found that negative attitudes to the sensitivity of hens to human contact as well as negative general beliefs about hens were associated with more noise, faster speed of movement and less time spent stationary near the hens. Intervention studies in the field also provide evidence of these sequential stockperson-animal relationships. Studies in the dairy and pork industries (Coleman et al., 2000; Hemsworth et al., 1994, 2002) have shown that cognitive-behavioural training of stockpeople, in which the key attitudes and behaviour of stockpeople are targeted, can be successfully used to reduce fear of humans and improve animal productivity. These intervention studies resulted in improvements in the attitudes and behaviour of stockpeople and, in turn, reductions in fear of humans and improvements in the milk yield of dairy cows and the reproductive performance of sows. Effects of human-animal relationships on animals post-farm gate Recent research has shown considerable variation between abattoirs and between animals within abattoir in the pre-slaughter behaviour and stress of sheep and cattle (Hemsworth et al., 2011). Furthermore pre-slaughter handling was significantly associated with behavioural and ohysiologicalstress responses of sheep and cattle (Hemsworth et al., 2011) and stockperson attitudes were significantly associated with pre-slaughter handling in pigs, as well as in sheep and cattle (Coleman et al., 2003, Coleman et al., 2012). Lensink et al. (2001a,b) found that the dairy calves that predominantly received positive handling during rearing required less effort to load for transport and had lower heart rates during loading than those that received either minimal human contact or predominantly negative handling, such as hitting and shouting, during rearing. This research on human- animal interactions together with research in experimental conditions and on commercial farms, demonstrate that handling of farm animals by affecting the animal’s fear of humans, can markedly affect fear and stress in farm animals at abattoirs. In addition to the implications on animal welfare, another incentive to minimise stress immediately prior to the slaughter of livestock is to improve meat quality. There is evidence that both acute and chronic stressors detrimentally affect meat quality. Warner et al. (2007) concluded that whilst the association between pre-slaughter stress and muscle glycogen depletion has been extensively studied in ruminants, the same cannot be said for the association between stress pre-slaughter and post mortem glycolytic rate. This is in contrast with the large body of recent research in pigs (e.g. Klont and Lambooy 1995; Warriss et al. 1995; Channon et al. 2000; Stoier et al. 2001). There is a paucity of similar data in ruminants, but it is highly likely that acute stress pre-slaughter may alter the rate of post mortem pH fall and thus change cooking and eating characteristics of the carcass. Warner et al. (2007) found that cattle subjected to acute pre-slaughter stress using electric goads produced meat which

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change the consumer rated as tougher with inferior quality. The inferior quality induced by the acute stress treatment was associated with reduced water-holding capacity but was independent of muscle pH and temperature. Thus while pre-slaughter stress, both acute and chronic, can affect animal welfare and meat quality, little is known of the effects of the human-animal relationship on fear and stress of farm animals at abattoirs and their implications for meat quality. Development of human-animal relationships Fear is a powerful emotional state that gives rise to defensive behaviour or escape. In concert with these behavioural effects, fear normally activates the autonomic nervous system and the neuroendocrine system, which both assist the animal to meet physical or emotional challenges through their effects on regulatory mechanisms such as energy availability and use, and cardiac, respiratory and cognitive functions (Hemsworth and Coleman, 2011). While acute stress responses are potentially beneficial, chronic stress comes at a physiological cost to the animal, such as a decreased metabolic efficiency, impaired immunity and reduced reproductive performance (Sapolski, 1992; Moberg, 2000; Kaltas and Chrousos, 2007). Fear is also generally considered an undesirable emotional state of suffering in both humans and animals (Jones and Waddington, 1992), and one of the key recommendations proposed to the UK Parliament by the Brambell Committee (1965) was that intensively housed livestock should be free from fear. As indicated in handling and field studies cited earlier, fear responses of farm animals to humans are affected by the history of the animals’ interactions with humans, particularly the nature and quantity of human contact. Habituation will occur over time as the animal’s fear of humans is gradually reduced by repeated exposure to humans in a neutral context; that is, the presence of the human has neither rewarding nor punishing elements. Over time, young farm animals that have had limited experience with humans may habituate to the presence of humans and so may perceive them as part of the environment and without any particular significance. Furthermore, conditioned approach–avoidance responses develop as a consequence of associations between the stockperson and aversive and rewarding elements of the handling bouts. Although there is some controversy over the mechanism by which avoidance behaviour becomes conditioned by punishment (Walker, 1987), it is well established that animals learn to avoid conditioned stimuli that are paired with aversive events. Thus, through conditioning, the behavioural responses of animals to humans may be regulated by the nature of the experiences occurring around the time of their interactions with humans (Hemsworth and Coleman, 2011). Other factors, such as age, social environment and genetics can also modulate an animal’s responses to humans (Hemsworth and Boivin, 2011; Hemsworth and Coleman, 2011). Opportunities to improve human-animal relationships The results of the intervention studies cited above, taken in conjunction with previous research on the relationship between stockperson attitudes, stockperson behaviour, animal fear and animal productivity, and research on handling farm animals, provide evidence of causal relationships between these stockperson and animal variables. Furthermore, this research provides a strong case for introducing stockperson training courses in the livestock industries that target the attitudes and behaviour of the stockperson (Hemsworth and Coleman, 2011).

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Interactions between stockperson attitudes and behaviour and other job-related characteristics Stockpeople clearly require a basic knowledge of both the requirements and behaviour of farm animals, and also must possess a range of well-developed husbandry and management skills to care for and manage their animals effectively. Therefore, while cognitive- behavioural training addressing the key attitudes and behaviour of stockpeople that affect animal fear is important in improving animal welfare and productivity, it is obvious that knowledge and skills training are also fundamental to improving the welfare of commercial livestock. In addition to the direct effects of the stockperson's behaviour on animal welfare and productivity, stockperson attitudes and behaviour may also have indirect effects on animal welfare and productivity by affecting other important job-related characteristics, such as job satisfaction, work motivation and motivation to learn. In many industries outside agriculture, the effects of motivating factors on job satisfaction and, thus in turn, work motivation are well recognized. Hemsworth and Coleman (2010, b) have proposed that the attitude of the stockperson towards the animal may affect job-related characteristics, such as job satisfaction, work motivation, motivation to learn new skills and knowledge about the animal, which in turn may affect work performance of the stockperson. In fact, Coleman et al. (1998) in a study of pig stockpeople found that the willingness of stockpeople to attend training sessions in their own time was correlated with attitudes towards characteristics of pigs and towards most aspects of working with pigs. Job enjoyment and opinions about working conditions showed similar relationships with attitudes. Thus, the stockperson’s attitudes may indeed be related to aspects of work apart from handling of animals and consequently improvements in stockperson attitudes towards animals may influence other important job- related characteristics such as job satisfaction, work motivation and motivation to learn. Future research and training Due to the significant influence that stockperson behaviour has on the welfare of animals, it may be improved in order safeguard the welfare of animals in stressful situations. Most husbandry practices, including those common to ruminant production systems, involve two components, pain, as well as fear and stress arising from handling. Indeed, Marchant-Forde et al. (2009) have shown with piglet castration and tail docking that handling is a potent stressor of at least a similar magnitude to that of the pain component. Surprisingly, there have been few attempts to examine strategies to reduce animal stress associated with routine husbandry practices through one of the most obvious avenues: the development of a positive relationship with humans. Rewarding experiences, such as provision of a preferred feed or even positive handling, around the time of a stressful procedure may ameliorate the aversiveness of the procedure and reduce the chances that animals associate the punishment of the procedure with humans. For example, studies with pigs have shown that they will associate the rewarding elements of feeding with humans if handlers are present at feeding (Hemsworth et al., 1996d). Recently Muns Vila et al. (2012) examined the effects of positive human contact on day-old piglets around the time of suckling on their behavioural response to subsequent tail docking. The ‘positively-conditioned’ piglets displayed a behavioural response to tail docking and capture that was less intense and of shorter duration than those that were not handled at suckling. Hutson (1985) found that although the effectiveness of food rewards diminished as the severity of the handling treatment increased, rewarding sheep with barley food rewards improved subsequent ease of handling in the location in which the aversive treatment was previously imposed. Surprisingly, daily injections were not highly aversive to pigs (Hemsworth et al., 1996c) and the authors suggested that there may have been some

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change rewarding elements for the pigs in these handling bouts, such the presence of the handler and the opportunity to closely approach and interact with the handler before and after injection. Previous positive handling has been shown to improve ease of handling and reduce heart rates during loading of calves for transport (Lensinket al., 2001a, b), reduce heart rate and salivary cortisol concentrations in lambs following tail docking (Tosi and Hemsworth, 2002) and reduce heart rates, kicking and restless behaviour in dairy cows during rectal palpation (Waiblingeret al., 2004). These data indicate that previous positive human contact may ameliorate stress associated with routine husbandry practices. Clearly further research is required to understand the effects of a positive relationship with humans on how animals respond to stressful situations in the presence of humans because of the implications of these positive effects on animal welfare and ease of inspection and handling. CONCLUSIONS This review demonstrates the important role and responsibility of the stockperson in the development of human–animal relationships in the livestock industries and thus underlines the need to understand not only these relationships but also the opportunities to improve them in order to safeguard animal welfare and productivity. The attitudes of stockpeople are amenable to change, so stockperson training can improve human-animal relationships in the livestock industries. Technical skills and knowledge are important attributes of the work performance of stockpeople and clearly training targeting these attributes is important in improving animal welfare and productivity via the technical skills and knowledge competencies of stockpeople. Indeed, both technical and cognitive-behaviour training are necessary to not only reduce the stress associated with handling and husbandry procedures involving humans, but also to improve the motivation of stockpeople to learn new technical skills and knowledge and to apply these competencies to the management of the animals under their care. The relationships between stockperson attitudes and behaviour on work performance, both direct via handling of animals and indirectly via important job-related characteristics such as job satisfaction, work motivation and motivation to learn, together with the obvious importance of technical knowledge, highlight the need to include training targeting the attitudes and behaviours of stockpeople towards farm animals in conjunction with the technical skills and knowledge of stockpeople. Implications for ruminants in the tropics Research to date has focussed on strategies to reduce fear of humans and stress in farm animals by improving the human-animal relationship, and much of this research has been conducted in both small and large scale, and both outdoor and indoor industries (e.g. dairy, pig and poultry farms). All of findings are common across species and production systems, demonstrating the robustness of the human-animal relationships and their implications for animal welfare and productivity in large and smaller production systems and in other farmed species. Many of the findings from human-animal relationships featuring dairy cattle in particular are likely to be similar to beef cattle in feedlots and small pasture-based systems. Moreover, it may well be that human-animal relationships in extensive systems, may be more extreme than those presented, due to the low frequency of interactions. Similarly, relationships between handlers and tethered animals may be more extreme and more influential, as the animal has less opportunity to avoid the presence of the stockperson. Pedersen et al. (1998) showed the importance of stockperson behaviour on the welfare of tethered sows. As in other production systems in other geographical regions, there is a continuing need to train stockpeople to effectively care for and handle their ruminants in the tropics. Underestimating the role and impact of the stockperson will seriously risk the welfare and productivity of these livestock. Indeed, the stockperson may be the most influential factor affecting animal handling, welfare and productivity.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

REFERENCES Barnett, J.L., Hemsworth, P.H., Hennessy, D.P., McCallum, T.M. and Newman, E.A. (1994).The effects of modifying the amount of human contact on the behavioural, physiological and production responses of laying hens. Applied Animal Behaviour Science 41, 87-100. Breuer, K. (2000). Fear and productivity in dairy cattle.Ph.D. thesis, Monash University, Australia. Breuer, K., Hemsworth, P.H., Barnett, J.L., Matthews, L.R. and Coleman, G.J. (2000).Behavioural response to humans and the productivity of commercial dairy cows. Applied Animal Behaviour Science 66, 273–288. Breuer, K., Hemsworth, P.H. and Coleman, G.J. (2003).The effect of positive or negative handling on the behavioural responses of nonlactating heifers. Applied Animal Behaviour Science 84, 3-22. Channon, H.A., Payne, A. and Warner, R.D. (2000). Halothane genotype, preslaughter handling and stunning method all influence pork quality. Meat Science 56, 291–299. Coleman, G.C., Hemsworth, P.H., Hay, M., Cox, M. (1998). Predicting stockperson behaviour towards pigs from attitudinal and job-related variables and empathy. Applied Animal Behaviour Science58, 63-75. Cransberg, P.H., Hemsworth, P.H. and Coleman, G.J. (2000).Human factors affecting the behaviour and productivity of commercial broiler chickens. British Poultry Science 41, 272–279. Edwards, L.E. (2009). The human–animal relationship in the laying hen.PhD thesis, University of Melbourne, Victoria, Australia. Estep, D.Q. and Hetts, S. (1992). Interactions, relationships, and bonds: The conceptual basis for scientist-animal relations, In: “The Inevitable Bond - Examining Scientist-Animal Interactions”, edited by H. Davis and A.D. Balfour, Cambridge University Press, Cambridge, UK, pp. 6-26. Hemsworth, P.H. and Boivin, X. (2011).Human contact. In “Animal Welfare”, edited by M. C. Appleby, J. A. Mench, I. A. S. Olsson and B. O. Hughes. CAB International, Oxon UK, pp. 246-262. Hemsworth, P.H. and Coleman, G.J. (2009).Animal welfare and management. In “Food Safety Assurance and Veterinary Public Health. Volume 5, Welfare of Production Animals: Assessment and Management Risks”, edited by F.J.M. Smulders and B. Algers, Wageningen Academic Publishers, The Netherlands, pp. 133-147. Hemsworth, P.H., and Coleman, G.J. (2011).“Human-Livestock Interactions: The Stockperson and the Productivity and Welfare of Farmed Animals”, 2nd Edition CAB International, Oxon UK. Hemsworth, P.H., Barnett, J.L., Coleman, G.J. and Hansen, C. (1989).A study of the relationships between the attitudinal and behavioural profiles of stockpersons and the level of fear of humans and reproductive performance of commercial pigs. Applied Animal Behaviour Science 23, 301–314. Hemsworth, P.H., Coleman, G.J. and Barnett, J.L. (1994a). Improving the attitude and behaviour of stockpersons towards pigs and the consequences on the behaviour and reproductive performance of commercial pigs. Applied Animal Behaviour Science 39, 349-362. Hemsworth, P.H., Coleman, G.J., Barnett, J.L. and Jones, R.B. (1994b). Fear of humans and the productivity of commercial broiler chickens. Applied Animal Behaviour Science 41, 101–114.

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Hemsworth, P.H., Barnett, J.L. and Campbell, R.G. (1996a). A study of the relative aversiveness of a new daily injection procedure for pigs. Applied Animal Behaviour Science 49, 389-401. Hemsworth, P.H., Verge, J. and Coleman, G.J. (1996b). Conditioned approach avoidance responses to humans: The ability of pigs to associate feeding and aversive social experiences in the presence of humans with humans. Applied Animal Behaviour Science 50, 71-82. Hemsworth, P.H. Coleman, G.J., Barnett, J.L and Borg, S. (2000). Relationships between human–animal interactions and productivity of commercial dairy cows. Journal of Animal Science 78, 2821–2831. Hemsworth, P.H., Coleman, G.J., Barnett, J.L., Borg, S. and Dowling, S. (2002). The effects of cognitive behavioural intervention on the attitude and behavior of stockpersons and the behavior and productivity of commercial dairy cows. Journal of Animal Science 80, 68–78. Hutson, G.D. (1985). The influence of barley food rewards on sheep movement through a handling system.Applied Animal Behaviour Science 14, 263-273. Kaltas, G.A. and Chrousos, G.P. (2007) Theneuroendorinology of stress. In: “Handbook of Psychophysiology“, edited by J.T. Cacioppo, L.G. Tassinary and G.G. Berntson. Cambridge University Press, Cambridge, UK, pp. 303-318. Klont, R.E. and Lambooy, E. (1995). Effects of preslaughter muscle exercise on muscle metabolism and meat quality studied in anesthetized pigs of different halothane genotypes. Journal of Animal Science73, 108–117. Lensink, J., Boissy, A. and Veissier, I. (2000).The relationship between farmers’ attitude and behaviour towards calves, and productivity of veal units. Annales de Zootechnie49, 313–327. Lensink, B.J., Fernandez, X., Cozzi, G., Florand, L and Veissier, I. (2001a). The influence of farmers’ behaviour on calves’ reactions to transport and quality of veal meat. Journal of Animal Science 79, 642-652. Lensink, B.J., Raussi, S., Boivin, X., Pyykkonen, M and Veissier, I. (2001b). Reactions of calves to handling depend on housing condition and previous experience with humans. Applied Animal Behaviour Science 70, 187-199. Lensink, B.J., Veissier, I. and Florland, L. (2001c). The farmers’ influence on calves’ behaviour, health and production of a veal unit. Animal Science 72, 105–116. Marchant-Forde, J.N., Lay, Jr. D.C., McMunn, K.A., Cheng, H.W., Pajor, E.A. and Marchant-Forde, R. M. (2009). Postnatal piglet husbandry practices and well-being: The effects of alternative techniques delivered separately. Journal of Animal Science 87, 1479-1492. Moberg, G.P. (2000). Biological response to stress: implications for animal welfare. In: “Biology of Animal Stress“, edited by J.A. Mench and G. Moberg. CAB International, Wallingford, Oxfordshire, UK, pp. 1-21. Muns Vila R., Farish M., Rault J-L.and Hemsworth, P.H. (2012). A positive mindset in the face of stress. In “Proceedings of 46th Congress of the International Society of Applied Ethology”, edited by S. Waiblinger, C. Winckler and Gutmann, A.K. 31 July-4 August 2012, Vienna, Austria, pg. 26 (abstract). Pedersen, V., Barnett, J.L., Hemsworth, P.H., Newman, E.A. and Schirmer B. (1998).The effects of handling on behavioural and physiological responses to housing in tether- stalls in pregnant pigs.Animal Welfare 7, 137-150. Rushen, J., de Passille, A.M.B. and Munksgaard L. (1999). Fear of people by cows and effects on milk yield, behaviour and heart rate at milking. Journal of Dairy Science 82, 720-727.

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Sapolsky, R.M. (1992) Neuroendocrinology of the stress-response. In: “Behavioral Endocrinology”, edited by J.B. Becker, S.M. Breedlove and D. Crews. The MIT Press, Cambridge, Massachusetts, USA, pp. 287–324. Stoier, S., Aaslyng, M.D., Olsen,E.V. and Henckel, P. (2001). The effect of stress during lairage and stunning on muscle metabolism and drip loss in Danish pork. Meat Science 59, 127–131. Tosi, M.V. and Hemsworth, P.H. (2002).Stockperson-husbandry interactions and animal welfare in the extensive livestock industries. In:“Proceedings of the 36th Congress of the International Society for Applied Ethology”. Utrecht, The Netherlands, pg. 129 (abstract). Waiblinger, S., Menke, C., Korff, J. and Bucher, A. (2004). Previous handling and gentle interactions affect behaviour and heart rate of dairy cows during a veterinary procedure. Applied Animal Behaviour Science 85, 31–42. Waiblinger, S., Boivin, X., Pedersen, V., Tosi, M-V., Janczak, A.M., Visser, E.K. and Jones, R.B. (2006).Assessing the human–animal relationship in farmed species: A critical review. Applied Animal Behaviour Science 101, 185–242. Warner, R.D., Ferguson, D.M., Cottrell, J.J. and Knee, B.W. (2007). Acute stress induced by the preslaughter use of electric prodders causes tougher beef meat. Australian Journal of Experimental Agriculture 47, 782–788. Warriss, P.D., Brown, S.N., Nute, G.R., Knowles, T.G., Edwards, J.E., Perry, A.M. and Johnson, S.P. (1995).Potential interactions between the effects of preslaughter stress and postmortem electrical stimulation of the carcasses on meat quality in pigs. Meat Science. 41, 55–68.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Human-Animal Interactions and Opportunity to Improve Poultry Welfare and Productivity Zulkifli Idrus Institute of Tropical Agriculture, and Department of Animal Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia Corresponding email: [email protected] ABSTRACT Humans and poultry are in regular and at times close contact in modern intensive farming systems. The quality of human-animal interactions can have a profound impact on the productivity and welfare of poultry. Interactions by humans may be neutral, positive or negative in nature. Regular pleasant contact with humans may result in desirable alterations in the physiology, behaviour, health and productivity of poultry. On the contrary, birds that were subjected to aversive human contact were highly fearful of humans and their growth and reproductive performance could be compromised. Pleasant human contact may alter ability to tolerate various stressors through enhanced heat shock protein (hsp) 70 expression. The induction of hsp is often associated with increased tolerance to environmental stressors and disease resistance in animals. The attitude, behaviour, technical skills, knowledge, job motivation, commitment and job satisfaction of stockpeople are prerequisites for high quality stockmanship which will affect the birds’ fear of human and eventually their productivity and welfare. Key Words: Human-animal interactions, Poultry, Welfare, Productivity INTRODUCTION Over the past 10,000 years, humans have learned to control their access to food and other necessities of life by changing the behaviours and natures of wild animals. All of the animals that we use today started out as wild animals but were changed over the centuries and millennia into tamer, more docile animals. The domestic fowl (Gallus gallusdomesticus) are the descendants of the red jungle fowl (Gallus gallus), a bird that still runs wild in the forests of South-east Asia. Domestication of chickens may have occurred in China as early as 6000 B.C. (West and Zhou, 1989) in geographically widespread sites such as Cishan (Heibei province, ca 5300 BC), Beixin (Shandong province, ca 5000 BC), and Xian (Shaanxi province, ca 4300 BC). Domestication is a continuing genetic process aimed at modifying the animal’s behaviour, anatomy and physiology to suit mankind’s specific needs (Siegel, 1993). Hence, the domestic fowl should be adapted to man and captive environment. However, previous studies (Jones, 1996; 1997) on fear and distress suggest that chickens still perceive contact with humans as an alarming predatory encounter. High levels of fear and distress could be damaging to both productivity and welfare of poultry. The quality of human-animal interactions will determine whether the influence on an animal’s physiology and behaviour is desirable or otherwise. According to Estep and Hetts (1992), human-animal interactions can be defined as the degree of relatedness or distance between animal and human beings. Work under both laboratory and industrial settings showed a clear relationship between human-animal interaction, animal well-being and productivity (Hemsworth and Barnett, 2000). Regular positive contact with humans is desirable in avian species (Jones, 1996) while poultry that were handled aversively were highly fearful of humans, distressed and consequently their welfare and productivity will be compromised (Hemsworth and Gonyou, 1997). The impact of human-animal interactions on the productivity and welfare of poultry are described in the following sections.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

IMPROVING POULTRY WELFARE THROUGH ENHANCED HUMAN-ANIMAL INTERACTIONS The ‘welfare’ of an animal refers to its quality of life, and this involves many different elements such as health, happiness and longevity, to which different people attaché different degrees of importance (Fraser, 1995). Duncan (2002) indicated that the public at large associated poor welfare with animals that are ill, injured, in pain, hungry, thirsty, neglected, frightened, frustrated, deprived, or bored. Like “sin” and “love”, ‘animal welfare’ means different things to different people. According to Broom (1986), the welfare of an individual is its state as regards its attempt to cope with the environment. Failure or difficulty to cope will compromise the welfare of an animal. When the biological cost of coping with environmental challenges diverts resources away from other biological functions, such as immune response, reproduction or growth, the animal experiences stress (Moberg, 2000). Hence, stress refers only to failure to cope. Stress and fear are not synonymous but the latter may contribute to overall stress, particularly if the frightening stimulation is intense, prolonged or inescapable (Jones, 1987; Craig and Adams, 1984). Gray (1987) defined fear as a form of emotional reaction to a stimulus that the animal works to terminate, escape from, or avoid. Jones (1996) regarded fear as an emotional (psychophysiological) response to perceived danger. High levels of fear not only represent a state of suffering but they are also a powerful and potentially damaging stressor. Two of the commonest and potentially frightening events encountered by poultry are sudden changes in their social or physical environment and their exposure to people (Jones, 1996). Chickens probably perceive a new unfamiliar environment with a degree of uncertainty that acts as a psychological stimulus. Novel environment is a potent fear- and stress-elicitor in all farm animals. Zulkifli et al. (1993), and Zulkifli and Siti Nor Azah (2004)] noted elevation of heterophil / lymphocyte ratios (HLR) 24 h following transfer of chicks to new home cages. Translocation of chicks from the hatcher to brooding cages or pens may result in behavioural inhibition and panic (Jones, 1996). There is considerable report to suggest that regular positive human contact is a powerful and reliable method to dampen stress and fear reactions in poultry (Hemsworth and Coleman, 1998). Al-Aqil et al. (2013) subjected broiler chicks to a pleasant physical contact daily for 30 seconds from 1 to 28 days of age. The authors found that those birds had lower HLR, plasma levels of corticosterone (CORT), and shorter tonic immobility (TI) duration than their neglected counterparts following road transportation. Jones (1996) suggested that the benefit of regular handling was specifically reducing birds’ fear of humans rather than through any effect on their underlying fearfulness. However, during transit birds may be exposed to an array of stressful and fearful stimuli including thermal extremes, acceleration, vibration, motion, impacts, feed and water deprivation, social disruption and noise (Nicol and Scott, 1990). Similarly, Lyons (1988) reported that early human contact not only influenced behavioural responses to humans but also to novel stimuli. Hence, the findings of Lyons (1988), and Al-Aqil et al. (2013) showed that regular pleasant human contact may attenuate nonspecific underlying fearfulness in animals. There is evidence that poultry are also sensitive to visual contact with humans (Jones, 1993; Barnett et al., 1994; Zulkifli et al., 2002a) reported that visual contact procedure involving the experimenter standing in the centre of a pen (with no attempted physical contact with birds) for 10 min twice daily from 0 to 3 weeks reduced fear and stress reactions to handling and crating. Jones (1993) demonstrated that visual contacts without tactile interaction was more effective in reducing fear of humans than picking up and stroking the birds. Visual contact is obviously more feasible and practical than physical contact in commercial poultry flocks.

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Zulkifli et al. (2002a) compared the effect of regular visual contact from 0 to 3 weeks, 0 to 6 weeks and 3 to 6 weeks in chickens subjected to crating at 42 days of age. Birds subjected to visual contact from 3 to 6 weeks showed longer TI durations and higher HLR in response to crating than those interacted at other ages. Based on these studies, it appears that early age human contact may have long-term effects. On the other hand, Jones and Waddington (1993) reported that fear of humans in 20-day-old chicks was equally reduced irrespective they were handled from 0 to 9, 10 to 18, or 0 to 18 days of age. It is not clear whether the quality of human contact experienced by chickens at an early age can be modified by subsequent pleasant or unpleasant interaction with human beings. This is critical under commercial setting because there will be variation both between and within stockpersons in their behaviour toward farm animals. Al-Aqil et al. (2013) subjected chicks to either a combination of pleasant-unpleasant or unpleasant-pleasant physical contacts from 1 to 14 days and 15 to 28 days of age, respectively. Based on HLR and CORT reactions to road transportation, the authors concluded that the benefits of early age positive human contact can be modified by subsequent unpleasant experience with humans. The authors also indicated that birds which had experienced pleasant human contact early in life may perceive the presence of humans as a signal for continuous positive interaction. Hence, subsequent exposure to unpleasant human contact may result in disappointment with consequent elicitation of the physiological stress response. HUMAN-ANIMAL INTERACTIONS AND PERFORMANCE A negative relationship between underlying fearfulness and productivity in poultry has been well documented (Hemsworth and Gonyou, 1997; Hemsworth and Coleman, 1998).Egg production of laying hens (Barnett et al., 1992) and feed efficiency of broilers (Hemsworth et al., 1994) in commercial farms were reported to be inversely related to the level of fear of humans. Working with broilers in 24 commercial farms, Cransberg et al. (2000) concluded a relationship between birds’ fear level and farm productivity. Hens that showed increased fear of humans had lower laying rate and body weight (Bessei et al., 1984).Subjecting hens to frightening procedures such as handling, crating and translocation have been associated with abnormalities of the egg shell (Mils et al., 1991). Shabalina (1984) reported that broiler breeder flocks with calm cockerels showed better fertility and hatchability of eggs as compared to those of fearful ones. Because positive interaction can reduce underlying fearfulness, such practice may enhance productivity of farm animals. Gross and Siegel (1982) postulated that positive human contact may reduce the resources otherwise required by animals to respond to their human associates and that resources can be utilised for productivity. In poultry, some authors (Gross and Siegel, 1983; Jones and Hughes, 1981; Collin and Siegel, 1987) reported a significant improvement in weight gain and feed efficiency in positively handled chickens. The enhanced disease resistance and immune response in those studies could be associated with the stress modulating effect of human contact. However, others demonstrated that positive tactile interaction either had negligible (Reichmann et al., 1978) or negative effect (Freeman and Manning, 1979) on growth performance. Nature of the physical contact, breed and age differences may have accounted for the discrepancies. Zulkifli et al. (2002a) reported that regular visual contact, irrespective of age, had no effect on weight gain, feed intake, FCR and mortality rates of broiler chickens. Zulkifli and Siti Nor Azah (2004) compared the effects of physical and visual contacts and showed only the former was beneficial in enhancing growth performance. Physical contact which involved picking up and stroking the birds appeared to be more “interactive” than visual contact in broiler chickens. On the contrary, in laying hens, however, Barnett et al. (1994) showed that regular visual contact which reduced the subsequent avoidance behaviour of laying hens improved egg production.

Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

HOW DO POSITIVE HUMAN-ANIMAL INTERACTIONS IMPROVE POULTRY PRODUCTIVITY AND WELFARE? There is the question of how positive human-animal interaction can improve productivity and modify physiological stress response of farm animals. At any particular time, resources available to an individual are finite. Hence, competition for resources between body functions such as growth, reproduction and health will always occur (Siegel and Gross, 2000). The resources required to respond to prolonged and severe stress may be significant. Gross (1983) suggested that habituation to humans reduces the resources otherwise needed by the bird to respond to subsequent human contact, and these resources could be used either for coping ‘with other environmental stressors of for productivity. When living organisms are exposed to thermal stresses, the synthesis of most proteins is retarded but a group of highly conserved proteins known as heat shock proteins (hsp) are rapidly synthesized (Zulkifli et al., 2002b). In a heat shocked cell, hsp may bind to heat sensitive proteins and protect them from degradation, or may prevent damaged proteins from immediately precipitating and permanently affecting cell viability. It has been documented that stressors other than thermal stressors, for example feed restriction, confinement in crates, transportation and social isolation (Zulkifli et al., 2002b; 2009; Al-Aqil and Zulkifli, 2009; Soleimani et al., 2011) may also elicit hsp 70 response in poultry. The induction of hsp is often associated with increased tolerance to environmental stressors and disease resistance (Zulkifli et al., 2002; Liew et al., 2003) .Al-Aqil et al. (2013) subjected broiler chickens to either pleasant or unpleasant human handling from 1 to 28 days of age. Following 3 hours of road transportation, the birds had lower HLR, shorter TI duration and greater hsp 70 expression than those that were ignored or handled unpleasantly. Thus, it can be concluded that pleasant human contact may alter ability to tolerate road transportation stress through enhanced hsp 70 expression. CONCLUSION Evidence presented earlier indicates the opportunity to improve welfare and productivity of poultry through positive human-animal interactions. Thus, the quality of stockmanship may have profound influence on the productivity and welfare of poultry. The attitude, behaviour, technical skills, knowledge, job motivation, commitment and job satisfaction of stockpeople are prerequisites for high job performance. It is also very important for the poultry industry recognizes and appreciates the stockpeoples’ role in determining poultry performance and welfare. Better financial rewards and clear career pathway for stockpeople would contribute to better motivation and job performance. REFERENCES Al-Aqil A, Zulkifli I. 2009. Changes in heat shock protein 70 expression and blood parameters in transported broiler chickens as affected by housing and early age feed restriction. Poult Sci, 88:1358-1364. Al-Aqil A, Zulkifli I, Hair Bejo M, Sazili AQ, Rajion MA, Somchit MN. 2013.: Changes in heat shock protein 70, blood parameters and fear-related behavior in broiler chickens as affected by pleasant and unpleasant human contact. Poult Sci, 93:33-40. Barnett JL, Hemsworth PH, Hennesy DP, McCallum TH, Newman EA.1994. The effects of modifying the amount of human contact on behavioural, physiological and production responses of laying hens. Appl Anim Behav Sci, 41:87-100. Barnett JL, Hemsworth PH, Newman EA.1992.Fear of humans and its relationships with productivity in laying hens at commercial farms. Br PoultSci, 33:699-710. Bessei W. 1984. Genetische Beziehungenzwischen Leistung, Befiederung und Scheubei Legehennen. Arch für Geflügelk, 48:231-239.

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Broom DM. 1986. Indicators of poor welfare.Br Vet J, 142:524. Collins JW, Siegel PB.1987. Human handling, flock size and response to an E. coli challenge in young chickens. Appl Anim Behav Sci, 19:183-188. Craig JV, Adams AW. 1984. Behaviour and well-being of hens (Gallus domesticus) in alternative housing environments. Wld’s Poult Sci J, 40:221-240. Cransberg PH, Hemsworth PH, Coleman GL. 2000. Human factors affecting the behaviour and productivity of commercial broiler chickens.Br Poult Sci, 41:272-279. Estep DQ, Hetts S. 1992. Interactions, relationships and bonds: the conceptual basis for scientist-animal relations. In The Inevitable Bond-Examining Scientist-Animal Interactions.Edited by Davis H, Balfour AD. Cambridge: CAB International; 6-26. Fraser D. 1995. Science, values and animal welfare.Exploring the ‘inextricable connection’.Anim. Welfare, 4:103-117. Freeman BM, Manning ACC. 1979. Stressor effects of handling on the immature fowl. Res. Vet. Sci, 26: 223-226. Gray JA. 1987. The Psychology of Fear and Stress. Cambridge, England: Cambridge University Press. Gross WB and Siegel PB. 1982. Socialization as a factor in resistance to disease, feed efficiency, and response to antigen in chickens. Am J Vet Res, 43:2010-2012. Gross WB, Siegel PB. 1982. Influence of sequences of environmental factors on the response of chickens to fasting and to Staphylococcus aureus infection. Am J Vet Res, 43:137- 139. Gross WB, Siegel PB. 1983. Socialization, the sequencing of environmental factors, and their effects on weight gain and disease resistance of chickens. Poult Sci, 62:592-598. Gross WB. 1983. Chicken-environment interactions. In Ethics and Animals. Edited by Miller HB and Williams WH: Cliffton, New Jersey: Human Press; 329-337. Hemsworth PH, Barnett JL. 2000. Human-Animal Interactions and Animal Stress. In The Biology of Animal Stress.Edited by Moberg GP, Mench JA. Wallingford: CAB International, 309-315. Hemsworth PH, Coleman GJ, Barnett JL, Jones RB. 1994. Behavioural responses to humans and the productivity of commercial broiler chickens. Appl Anim Behav Sci 41: 101-114. Hemsworth PH, Coleman GJ. 1998Human-Livestock Interactions: The Stockperson and the Productivity and Welfare of Intensively Farmed Animals. Wallingford: CAB International; 205-218. Hemsworth PH, Gonyou HW: Human contact. 1997. In Animal Welfare. Edited by Appleby MC, Hughes BO. Wallingford: CAB International; 205-218. Jones RB, Hughes BO. 1981. Effects of regular handling on growth in male and female chicks of broiler and layer strains. Br Poult Sci, 22:461-465. Jones RB, Waddington D. 1993. Attenuation of the domestic chick’s fear of human beings via regular handling: in search of a sensitive period. Appl Anim Behav Sci, 36:1021- 1033. Jones RB, Waddington D: Modification of fear in domestic chicks, Gallus gallusdomesticusvia regular handling and early environmental enrichment. 1992. Anim Behav, 43:1021-1033. Jones RB. 1987. The assessment of fear in the domestic fowl. In Cognitive Aspects of Social Behaviour in the Domestic Fowl. Edited by Zayan R, Duncan IJH: Amsterdam: Elsevier; 40-81. Jones RB. 1993. Reduction of domestic chick’s fear of humans by regular handling and related treatments. Anim Behav, 46:991-998. Jones RB. 1995. Ontogeny of the response to humans in handled and non-handled female domestic chicks. Appl Anim Behav Sci, 42:261-269.

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Jones RB. 1997. Fear and distress. In Animal Welfare.Edited by Appleby MC, Hughes BO. Wallingford: CAB International;75-88. Jones RB.1996. Fear and adaptability in poultry: insights, implications and imperatives. Wld’s Poult Sci J, 52:131-174. Liew PK, Zulkifli I, Hair-Bejo M, Omar AR, Israf DA. 2003. Effects of early age feed restriction and thermal conditioning on heat shock protein 70 expression, resistance to infectious bursal disease and growth in male broiler chickens subjected to chronic heat stress. Poult Sci, 1879-1885. Lyons DM, Price EO, Moberg GP. 1988. Individual differences in temperament of domestic dairy goats: Constancy and change. Anim Behav, 36:1323-1333. Mills AD, Nys Y, Gautron J, Zawadski J. 1991. Whitening of brown shelled eggs: individual variation and relationship with age, fearfulness, oviposition interval and stress. Br Poult Sci, 32:117-129. Moberg GP: Biological response to stress. 2000. Implications for animal welfare. In Biology of Animal Stress: Basic Principles and Implications for Animal Welfare. Edited by MobergGP, Mench JA: Wallingford: CAB International; 1-21. Nicol CJ, Scott GB. 1990. Pre-slaughter handling and transport of broiler chickens. Appl Anim Behav Sci, 58:57-73. Reichmann KG, Barram KM, Brock IJ, Standfast NF. 1978. Effects of regular handling and blood sampling by wing vein puncture on the performance of broilers and pullets. Br Poult Sci, 19:97-99. Shabalina AT. 1984. Dominance rank, fear scores and reproduction in cockerels. Br Poult Sci, 25:297-301. Siegel PB, Gross WB. 2000. General principles of stress and well-being. In Livestock Handling and Transport.Edited by Grandin T. Wallingford: CAB International; 27-42. Siegel PB1993. Behavior-genetic analyses and poultry husbandry.Poult Sci, 72:1-6. Soleimani AF, Zulkifli I, Omar AR, Raha AR 2011.Neonatal feed restriction modulates circulating levels of corticosterone, and expression of glucorticoid receptor and heat shock protein 70 in aged Japanese quail exposed to acute heat stress. Poult Sci, 90:1427-1434. West, B., Ben-Xiong Zhou.1989.Did chickens go north? New evidence for domestication.Wld's Poult Sci J, 45:205-218. Zulkifli I, Al-Aqil A, Omar AR, Sazili AQ, Rajion MA. 2009. Crating and heat stress influences blood parameters and heat shock protein 70 expression in broiler chickens showing short or long tonic immobility reactions. Poult Sci, 88:471-476. Zulkifli I, Che Norma MT, Chong CH, Loh TC. 2001. The effects of crating and road transportation on stress and fear responses of broiler chickens treated with ascorbic acid. Arch für Geflügelk, 65:33-37. Zulkifli I, Dunnington EA, Gross WB, Larsen AS, Martin A, Siegel PB. 1993. Responses of dwarf and normal chickens to feed restriction, Eimeriatenella infection and sheep red blood cell antigen. Poult Sci, 72:1630-1640. Zulkifli I, Gilbert, J, Liew PK, Ginsos J. 2002a. The effects of regular visual contact on tonic immobility, heterophil/lymphocyte ratio, antibody and growth responses in broiler chickens. Appl Anim Behav Sci, 79:103-112. Zulkifli I, Che Norma MT, Israf DA, Omar AR, 2002b. The effects of early-age food restriction on heat shock protein 70 response in heat-stressed female broiler chickens. Br Poult Sci,, 43:141-145. Zulkifli I, SitiNorAzah, A. 2004. Fear and stress reactions, and the performance of commercial broiler chickens subjected to regular pleasant and unpleasant contacts with human beings. Appl Anim Behav Sci, 88: 77-87.

INVITED PAPERS

Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Novel Methods for Evaluating Sustainable Animal Production Systems Using Systems Analysis and Life Cycle Assessment (LCA) H. Hirooka and K. Oishi Graduate School of Agriculture, Kyoto University, Kyoto 606-8502, Japan Corresponding email: [email protected] ABSTRACT Projections of rapid population growth in the next 50 years and the associated increases in global demand for animal products impose difficult challenges for the development of sustainable animal production systems. Such sustainable systems should balance environmental impact, economic viability, and social acceptability. In animal science, biological and economic efficiencies in animal production have been discussed often for more than 50 years. In the past two decades, however, more attention has been paid to the environmental impacts of animal production. To achieve sustainable animal production systems, novel indicators and procedures that can simultaneously consider biological, economic, and environmental variables would be needed. In this paper, first, existing biological, economic, and environmental indicators were reviewed. Then, novel methods for evaluating the sustainability of animal production systems were demonstrated through three case studies at individual, farm, and national levels. The results suggested that sustainability can be achieved with the use of steers with higher daily gain in feedlot systems and later cow culling in cow-calf systems for Japanese Black beef production, and that the use of rice as feed may provide an opportunity to increase total profit and decrease the environmental impacts of animal and rice production. Key Words: Animal production, Economic indicator, Environmental indicator, Life cycle assessment, Sustainability INTRODUCTION The sustainability of animal production systems has been the subject of debate within both public and scientific discussions on the future of agriculture, because trends in global population and the associated demand for animal products and resource availability (land use) support the need for improved sustainability of animal production systems. Though animal production systems have been enormously successful in supplying high-quality foods (milk and meat), the question is often raised as to whether most animal production systems are intrinsically unsustainable. In fact, ruminant animals produce about 80 million tons of methane annually, accounting for about 30% of anthropogenic methane emission, and large amounts of nitrogen and phosphorus are excreted in feces and urine from animals, resulting in environmental pollution. The sustainability concept is holistic but may be partitioned into three components: economic viability, environmental impact, and social responsibility. As pointed by Capper (2013), all three components must be balanced; that is, if one component is missing, then long-term sustainability cannot be realized. These days, global animal production is characterized by a dichotomy between developing and developed countries. In developing countries, animal production and demand have rapidly increased, whereas in developed countries production and consumption levels of animal products are stable or growing slowly. Nevertheless, since the environmental impact of animal production is arguably the greatest concern regarding sustainability, the development and realization of sustainable animal production systems are crucial for both developing and developed countries.

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So far, several biological indicators (various feed efficiencies) and economic indicators (profit and economic efficiency) have been used to evaluate production performance (productivity) for an individual animal or an animal production system (Dickerson, 1970; Harris, 1970). These indicators have been predicted using simulation models based on systems analysis (Hirooka 2010). In the past 20 years, life cycle assessment (LCA) has been used to study environmental indicators (global warming, acidification, and eutrophication) and thus to evaluate the environmental impact of animal production through the life cycle (Ogino et al. 2004, 2007; Casey and Holden 2006;de Vries and de Boer 2010). However, only a limited number of studies have simultaneously evaluated both production performance and the environmental impact of animal production. It is therefore necessary to develop new procedures to simultaneously evaluate animal production from biological, economic, and environmental points of view. The objectives of this study were to show existing biological, economic, and environmental indicators from the literature and to propose novel methods of combining more than two components for evaluating sustainability by using three case studies at the individual, farm, and national levels. HIERARCHY IN ANIMAL PRODUCTION SYSTEMS There are many different levels of systems for animal production systems analysis, and there are interactions among components (subsystems) within a given level. Among those levels, an individual animal is generally considered the basic unit in modeling animal production systems. Sometimes, a herd is considered the production unit, and consists of a breeding female (e.g., a cow) and her progeny (e.g., her calves) and is regarded as the smallest unit for biological and economic analysis of animal production systems. The farm is the agricultural operational level at which a farmer makes decisions. The farm corresponds to the herd when only animal production is managed in a system. In many cases, however, crop production systems are integrated with animal production (i.e., mixed farming systems) at the farm level. Political decisions resulting in government and economic interventions are commonly made at the regional or national level. While systems below the individual level (organ, cell, and gene) are important, only systems at the individual level or higher were considered in this study. PRODUCTION INDICATORS Biological and economic indicators Feed efficiency is a measure of efficiency of production, and the term ‘efficiency’ implies a ratio of outputs to inputs. Daily weight gain (DG) and daily dry matter intake (DM) are typically used to measure feed efficiency (DG/DM; or the inverse, feed conversion rate: DM/DG). Outputs can also be expressed as carcass, lean, and protein weights, and inputs can be expressed as energy and protein intakes. Residual feed intake has been increasing in popularity recently as a measure of feed efficiency, and the indicator is traditionally calculated as the difference between actual feed intake and intake predicted on the basis of mean requirements for metabolic body weight (W0.75) and the production level (Koch et al. 1963; Berry and Crowley, 2013). On the other hand, two economic indicators have been used for the economic evaluation of animal production systems: profit, expressed by returns minus costs, and economic efficiency, expressed by returns on costs (returns/costs). Returns include prices of slaughter animals as well as culled breeding females for meat production and prices of milk for milk production. Costs include feed, labor, veterinarians, medicine, and so on. Further, Dickerson (1970) proposed a measure of life cycle efficiency of animal production systems at the herd level. In recent years, Oishi et al. (2011a, 2013) used annualized net revenue, which is based on a

94 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia discounted cash flow calculation, for the economic comparison of lifetime production in cow- calf production systems. Nitrogen use efficiency and cycling index excretion There is a growing awareness worldwide of the need to protect the global environment. Although nitrogen (N) is a major nutrient for both animals and crops, it contributes to environmental pollution as ammonia in the air or as nitrate in soil and ground water. Powell et al. (2010) proposed three potential indicators for evaluating nitrogen utilization: feed N use efficiency, defined as the ratio of N in animal products to N intake;manure/fertilizer N use efficiency, defined as the ratio of N uptake by crop/pasture to N applied as manure and fertilizer; and whole farm N use efficiency, defined as the sum of N exported off-farm to the sum of N imported tothe farm. The first indicator is used at an individual level and the others at a farm level. At a farm level, however, farm gate balance, which is calculated as the difference between imported N and exported N (N inputs – N outputs at the farm gate), has more often been used (Schroder et al. 2003). In this indicator, a positive result is called a nitrogen surplus and is adopted as a tool for assessing environmental regulations and policies. Although nitrogen use efficiency and farm gate balance are simple indicators for assessing N utility in whole farms, they cannot explain all of N cycling within a farm. Rufino et al. (2009) and Tabata et al. (2009) introduced an indicator (cycling index) based on network analysis developed in ecology (Finn 1976) to assess the N cycling within the farm. Tabata et al. (2009) mentioned that the cycling index may provide new information on the effect of internal N cycling within the farms. Environmental impact of emissions from animal production systems As a result of the Kyoto Protocol, the environmental impacts of emissions from animal production systems have received increasing attention, and animal scientists have sought ways to reduce such emissions. Among the issues surrounding emissions from animal production, the most important is considered the reduction of enteric methane (CH4) emission from ruminants without altering animal production, since enteric CH4 is the largest contributor greenhouse gas (GHG) emitted on a farm scale in ruminant production systems. Hence, a lot of studies have tried to find effective CH4 mitigation strategies: by altering diet composition or using additives such as plant extracts (Martin et al. 2010). Moreover, nitrous oxide (N2O) emission from animal wastes has been important because N2O is a major factor in global warming. Other emissions from animal wastes, such as those of nitrogen oxide (NOx) and ammonia (NH3), are also considered environmental impact indicators because they contribute to acidification and eutrophication. On the other hand, implementing a mitigation strategy aimed at one part of a production system could lead to an increase in environmental loads from other parts, and therefore could not always guarantee a reduction in the total environmental load throughout the production cycle (Beauchemin et al. 2010). Therefore, in order to assess the emissions from an entire animal production system, the LCA method, which accounts for all changes in environmental emissions arising from a prospective mitigation practice in an entire farming system, has become an internationally accepted method (Guinée et al. 2002). In animal science, LCA is usually performed as a “cradle-to-farm-gate” system, and the activities in animal production systems taken into account are: feed production, feed transport, animal management, the biological activities of animals, and waste management. The amounts of emission gases are estimated in each category of activity and summed up to be used as environmental indicators for whole animal production systems. The following substances that are generally evaluated as emissions from animal production systems: carbon dioxide (CO2), CH4, N2O, NH3, NOx, and sulfur dioxide (SO2). They are aggregated by characterization factors (e.g., IPCC 2007; Heijungs et al. 1992) to evaluate the potential impacts for three potential impact categories:

95 Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change global warming potential (CO2-equivalent), acidification potential (SO2-equivalent), and eutrophication potential. These three potentials are now widely used as environmental indicators. In some cases, the values to one environmental indicator are also normalized and aggregated in order to evaluate the balance between the environmental output and the economic output of production systems (e.g.,overall environmental index by Oishi et al. (2013)). Moreover, there are wide uses of monetary-based weighting for the results of LCA, such as ExternE (European Commission 2005), EPS (Steen 1999), Ecotax (Finnveden et al. 2006), and LIME (Itsubo et al. 2004) in various regions. The normalization values and monetary-based weighting values could also be used as environmental indicators, although the use of these methods remains controversial. Table 1. Groups of production indicators Group Explanation Examples of indicators1) Biological Calculated based on input (e.g., feed efficiency, residual feed DM intake, energy intake) and intake output (e.g., DG, milk yield) Economic Calculated based on economic Economic efficiency, gross returns and costs margin, annualized net revenue, profit Environmental: Nitrogen Calculated based on nitrogen Nitrogen surplus, nitrogen use use flows at individual and farm efficiency, cycling index levels Environmental:Emissions Calculated based on amounts of Potentials for global warming, emission gases using LCA acidification, andeutrophication;Overall environmental index Aggregated Aggregating economic and Ecotax, LIME environmental indicators using economic weighting factors 1)See text

CASE STUDIES FOR SUSTAINABILITY ASSESSMENT IN COMBINATION WITH ECONOMIC AND ENVIRONMENTAL EVALUATIONS Here, we show our three case studies in which sustainability was assessed in combination with economic and environmental evaluations for animal production systems on an individual scale, a farm scale (Oishi et al. 2013), and a national scale (Nishida et al. 2014), respectively. On an individual scale The case study was conducted using systems analysis to show the effects of altered daily gain (DG) and finishing weights in Japanese Black feedlot production. The increased daily gain reflects genetic improvement of growth performance of animals, and the change in finishing weight reflects a management option by farmers. Feed energy efficiency and economic efficiency were used as biological and economic indicators, respectively, and feed nitrogen use efficiency and body weight gain produced by 1kg of enteric methane emission were used as environmental indicators. The growth curve was set by assuming typical Japanese Black steers under Japanese feedlot situations, and feed metabolizable energy intake was predicted on a daily basis according to Japanese feeding standards (NARO 2009). The result showed that the use of animals with higher growth genetic potential (higher DG) enhanced all indicators, indicating that this option would be sustainable (Table 2). In contrast, lighter finishing weight led to increases in feed energy efficiency in both environmental indicators, but to a decrease in economic efficiency. The decreased economic efficiency was attributable

96 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia to the fact that carcass price (a return) largely depended on marbling score, which in Japan is a function of the age of feedlot animals in Japan; animals slaughtered at lighter weight and earlier age have a low degree of marbling. In this situation, definite trade-offs were found between economic efficiency and the other indicators of beef cattle feedlot production in Japan. Hence, optimization may be needed to assess the potential for synergies and the alleviation of trade-offs. Table 2. The effects of changes in daily gain and finishing weight on production indicators Finishing Average daily gain of 0.70 kg/d Average daily gain of 0.80 kg/d weight (kg) BioI1 EcoI2 EnvI(N)3 EnvI(CH )4 BioI1 EcoI2 EnvI(N)3 EnvI(CH )4 4 4 600 0.0366 0.903 0.0973 3.774 0.0395 0.903 0.1074 4.185

625 0.0360 0.929 0.0933 3.755 0.0388 0.940 0.1031 4.163

650 0.0353 0.950 0.0891 3.732 0.0382 0.970 0.0985 4.142

675 0.0346 0.967 0.0846 3.710 0.0374 0.996 0.0935 4.122

700 0.0338 0.981 0.0794 3.687 0.0366 1.016 0.0877 4.096

725 0.0329 0.994 0.0726 3.667 0.0357 1.033 0.0803 4.075

1) BioI: feed efficiency (kg of body weight gain/Mcal of metabolizable energy intake) 2) EcoI: Economic efficiency (yen of returns/yen of costs) 3) Envi(N): nitrogen use efficiency (kg of Ngain/kg of Nintake) 4) EnvI(CH4): body weight gain produced by enteric methane emission (kg/kg) At a farm scale Improvements in the efficiency of a production system can have favorable effects on the reduction of overall emissions from the system (Wall et al. 2010). In cow-calf production systems, the effect of the parity of cows in culling strategy can be an important factor when economic optimization of a production system is targeted at a herd level. Some studies reported on the effect of dairy cows’ longevity on the reduction of greenhouse gas emissions (e.g., Bell et al. 2011). However, the effects of culling strategy of cows on environmental loads in beef cow-calf production systems have received little attention. Therefore, in this study the effects of changes in culling parity of cows on economic and environmental outputs in Japanese beef cow-calf production systems were analyzed at a farm level using a model simulation. Moreover, the effects of changes in diet composition on the outputs were also analyzed. Table 3. Three feed formulation methods by linear programming in this study Item Explanation Objective function Conventional Ordinal least-cost method Feed cost only (Method 1) with constraints for nutritional requirements such as DM, TDN, and CP Least-excretion Modified method to reduce Feed cost plus penalty cost for nitrogen and (Method 2) both feed cost and nitrogen phosphorus contents in each feed, using the and phosphorus excretions levies for nitrogen and phosphorus excretions (Oishi et al. 2011b) by the Mineral Accounting System in the Netherlands (Hanegraaf and den Boer 2003) Least-emission Modified method to reduce Feed cost plus penalty cost for emissions at (Method 3) both feed cost and emission feed production and transport stages in each gases at the feed production feed, using the weight constants for and transport stages (also emissions derived from Ecotax weighting see Table 4) factors (Finnveden et al. 2006)

97 Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Table 4. Amount of emissions1) for each feed ingredient (g/kg as-fed basis) at feed production and transport stages estimated in this study

2) 3) 3) Ingredient CO2 SO2 NOx CH4 N2O NH3 (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) Corn 388.3141 0.1514 1.1978 0.0001 0.3144 2.0169 Soybean meal 522.8198 0.2088 2.0993 0.0008 0.1548 0.4196 Wheat bran 473.5041 0.1663 1.7211 0.0005 0.3188 1.6040 Alfalfa hay cube 209.2550 0.0904 0.4616 0.0000 0.0578 0.1261 Hay 241.5274 0.0916 0.8079 0.0000 0.1430 0.8928 Rice straw 135.2593 0.2111 0.3410 0.08144) 0.0840 0.7587 1) Emissions relevant to energy use at feed production and transport stages were estimated from the inventories used by Ogino et al. (2007), although the domestic land transport distance was slightly modified. 2) Only rice straw was assumed to be domestically produced; others were imported. 3) NH3 and a portion of N2O are emitted from soil (crop fields and paddy fields) at the feed production stage, as estimated by the amount of nitrogen input from the chemical fertilizer applied. NH3 from soil was estimated from the inventory used by Ogino et al. (2007), but N2O from crop fields and paddy fields was estimated by the Ministry of the Environment, Japan (2011). 4) A large amount of CH4 is emitted from flooded paddy fields, as estimated by the Ministry of the Environment, Japan (2011). In this study, annualized net revenue was evaluated as an economic indicator, and the aggregated overall environmental index derived from LCA, which was calculated using the method of Hermann et al. (2007), was evaluated as an environmental indicator. The model also included three feed formulation methods, allowing us to analyze the effects of reductions in environmental loads caused by changes in diet composition (Table 3). The results indicated that later culling was economically and environmentally optimal under the current production system (Fig. 1). The use of modified feed formulation methods could also reduce environmental loads at a higher rate than would result from economic benefits (Fig. 2).

Figure 1. Effects of the change in culling parity of cows on annualized net revenue and the overall environmental index

98 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia

Figure 2. Reduction rate of the annualized net revenue and the overall environmental index. The reduction rate of the annualized net revenue indicates an economically negative effect, and that of the overall environmental index indicates an environmentally positive effect At a national scale In recent years in Japan, the production of whole crop rice silage (WCS) and feed rice has been promoted to improve feed self-sufficiency ratios and to utilize excess paddy fields (Senda and Ogino 2012). WCS is generally used as roughage for cattle, and feed rice is used as concentrates for cattle, pigs, and chickens, as alternatives for commercial imported feeds. However, the availability of WCS and feed rice in association with the balance of supply and demandon a national scale is unclear. Therefore, optimal allocation of animal and crop production on a national scale was evaluated in this study by linear programming, from both economic and environmental points of view. Crops of paddy fields including grass in paddy fields, WCS, feed rice and food rice, feed crops and purchased roughage and concentrate were set as categories for crop production systems, and Holstein, Japanese Black and their crossbred (F1) cattle, pig, layer and broiler were set as categories for animal production systems in this study (Fig. 3).

Figure 3. Animal and crop production categories for evaluating optimal allocation of production on a national scale in this study

99 Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Table 5 shows the coefficient matrix for linear programming. Twenty-one production systems (10 animal production systems, 7 crop production systems, and 4 purchased feed production systems) were treated for the optimization. These figures were derived from several statistical sources in Japan. The objective function of the model maximized total benefits from animal and crop production systems under constrained conditions. Total TDN and ME requirements for animal production systems were set to be equal to the sum of feed supply, and environmental impacts (i.e., nitrogen excretion, GHG emission, and energy use) and labor time were set to be below the values calculated from the current production level in Japan. The ratio of the use of WCS and feed rice was set to be above the level of the current situation in order to maintain the feed self-sufficiency ratio. Other constraints, such as the number of animals for each animal production category and the total area of arable land, were also set to be balanced in order not to differ substantially from the real production situation. The simulated optimization results suggested that the total benefit may be maximized when the numbers of Japanese Black, layer and broiler increase and the numbers of other animals decrease, and when forage rice for WCS is cultivated instead of food rice (Table 6). Moreover, when optimization is achieved, 10% of the increase in economic benefit and reductions in environmental impacts on the order of several percentage points can be expected without decreasing labor time (Table 7).

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101 Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change

Table 6. Comparison of the number of animals (heads) and the area of arable land (ha) between the current situation1) and the simulated result 2) 3) Holstein Holstein JB F1 Pig Layer Broiler (cow) (Steers) Current 1,467,000 538,500 1,490,600 631,606 8,220,430 181,664,000 102,987,000 Simulated 1,299,393 430,099 1,788,720 439,915 6,576,344 217,996,000 123,584,000 Change(%) -11.4 -20.0 +20.0 -30.0 -20.0 +20.0 +20.0 Feed crop Grass in WCS Feed rice Feed rice Feed rice Food rice paddy (Cattle) (Pigs) (Poultry) fields Current 864,910 0 23,123 5,091 14,254 13,576 1,624,000 Simulated 864,910 0 250,772 4,836 13,540 12,895 1,398,000 Change(%) 0 0 +1,084.5 -5.0 -5.0 -5.0 -14.0 1)The values for the current situation were derived from the actual numbers of animals and areas of arable lands in the statistical data in Japan 2)Japanese Black cattle 3)Crossbreeds of Japanese Black sires and Holstein dams Table 7. Environmental impacts, labor time, and benefit under the current situation1) and under the simulated result Nitrogen surplus GHG emission Labor time Energy use Benefit

(1000 t) (1000 t) (million hours) (1000 GJ) (100 billion yen) Current 361 47,642 942 199,990 15.97 Simulated 341 45,479 942 181,972 17.57 Change(%) -5.4 -4.5 0 -9.0 +10.1 1)The values for the current situation were derived from the actual numbers of animals and areas of arable land in the statistical data in Japan. IMPLICATIONS Improved technology and the resulting productivity gains have benefitted producers (farmers) and consumers of animal products since the 20th century. Nevertheless, animal industry faces a considerable challenge to reduce environmental impacts while improving or at least maintaining productivity, because the global population will continue to increase and the demand for animal products will increase in the coming 50 years. Therefore, the animal products industry should aim to develop sustainable systems and make efforts to find an optimum solution for solving this complex and difficult puzzle. In this study, three case studies at three different levels (individual animal, farm, and nation) were demonstrated in order to illustrate the use of methods to evaluate the sustainability of animal production systems, indicating that some options (e.g., improved dairy gain in feedlot production, later culling in cow-calf production, and the introduction of rice as feed to animals on a national scale) may lead to not only increased productivity but also a reduction in environmental impact. These findings may encourage us to improve economic viability, environmental stewardship, and social responsibility, resulting in the establishment of sustainable animal production. For this purpose, the novel methods demonstrated here, using systems analysis and LCA, would be useful and valuable tools to find clues as the first step toward the goal of sustainability. REFERENCES Beauchemin, K.A., H.H. Janzen, S. M. Little, T. A. McAllister and S. M. McGinn. 2010. Life cycle assessment of greenhouse gas emissions from beef production in western Canada: A case study. Agric. Syst. 103: 371-379.

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Bell, M.J., E. Wall, G. Russel, G. Simm and A. W. Stott. 2011. The effect of improving cow productivity, fertility, and longevity on the global warming potential of dairy systems. J. Dairy Sci. 94: 3662-3678. Berry, D. P., and J. J. Crowley. 2013. Genetics of feed efficiency in dairy and beef cattle. J. Anim. Sci. 91:1594-1613. Capper, J. L. 2013. Should we reject animal source foods to save planet? A review of the sustainability of global livestock production. S. Afr. J. Anim. Sci. 43:233-246. Casey, J.W., and N. M. Holden. 2006. Greenhouse gas emissions from conventional, agri- environmental scheme, and organic Irish suckler-beef units. J. Environ. Qual. 35: 231- 239. Center of Environmental Science (CML). 2010. CML-IA Characterization Factors (Excel spreadsheet). Institute of Environmental Sciences, Leiden University, Leiden, The Netherlands. http://cml.leiden.edu/software/data-cmlia.html De Vries, M., and I. J. M. De Boer. 2010. Comparing environmental impacts for livestock products: A review of life cycle assessments. Livest. Sci. 128: 1-11. Dickerson, G. 1970. Efficiency of animal production - molding the biological components. J. Anim. Sci. 30:849-859. Finn, J. T. 1976. Measures of ecosystem structure and function derived from analysis of flow. J. Theor. Biol. 56:363-380. Finnveden, G., P. Eldh and J. Johansson. 2006. Weighting in LCA based on Ecotaxes – Development of a mid-point method and experiences from case studies. Int. J. LCA 11: 81-88. Guinée, J.B., M. Gorrée, R. Heijungs, G. Huppes, R. Kleijn, A. de Koning, L. van Oers, A. Wegener Sleeswijk, S. Suh, H. A. Udo de Haes, H. de Bruijn, R. van Duin, M. A. J. Huijbregts, E. Lindeijer, A. A. H. Roorda, B. L. van der Venand B. P. Weidema (Eds.). 2002. Handbook on Life Cycle Assessment: Operational Guide to the ISO Standards. Institute for Environmental Science, Leiden, The Netherlands. Hanegraaf, M.C., and D. J. den Boer. 2003. Perspectives and limitations of the Dutch mineral accounting system (MINAS). Eur. J. Agron. 20: 25-31. Harris, D.L. 1970. Breeding for efficiency in livestock production: defining the economic objectives. J. Anim. Sci. 30:860-865. Heijungs, R., J. Guinée, G. Huppes, R. M. Lankreijer, H. A. Udo de Haes, A. Wegener Sleeswijk, A. A. M. Ansems, P. G. Eggels, R. van Duin, H. P. de Goede. 1992. Environmental Life Cycle Assessment of Products: Background and Guide. Center of Environmental Science (CML), Leiden University, Leiden, The Netherlands. Hermann, B.G., C. Kroeze and W. Jawjit. 2007. Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators. J. Cleaner Prod. 15: 1787-1796. Hirooka, H. 2010. Systems approaches to beef cattle production systems using modeling and simulation. Anim. Sci. J. 81:411-424. Intergovernmental Panel on Climate Change (IPCC). 2007. P. Forster, V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D. W. Fahey, J. Haywood, J. Lean, D. C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulzand R. van Dorland. Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basic. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Quin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignorand H. L. Miller. (Eds.) Intergovernmental Panel on Climate Change, Cambridge, U.K. and New York.

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Itsubo. N., M. Sakagami, T. Washida, K. Kokubu and A. Inaba. 2004. Weighting across safeguard subjects for LCIA through the application of conjoint analysis. International Journal of Life Cycle Assessment 9: 196–205. Koch, R. M., L. A. Swiger, D. Chambers and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486-494. Martin, C., D. P. Morgavi and M. Doreau. 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4(3): 351-365. Meadows, C., P. J. Rajala-Schultzand G. S. Frazer. 2005. A spreadsheet-based model demonstrating the nonuniform economic effects of varying reproductive performance in Ohio dairy herds. J. Dairy Sci. 88: 1244-1254. Ministry of Agriculture, Forestry and Fisheries of Japan (MAFF). 2012. Statistics of Agriculture, Forestry and Fisheries. Statistics Department of the Ministry of Agriculture, Forestry and Fisheries of Japan, Tokyo. (In Japanese) Ministry of the Environment, Japan. 2011. National Greenhouse Gas Inventory Report of Japan. Greenhouse Gas Inventory Office of Japan (GIO), Center for Global Environmental Research (CGER), and National Institute for Environmental Studies (NIES) (Eds.), CGER, NIES, Tsukuba, Japan. National Agriculture and Food Research Organization (NARO). 2009. Japanese Feeding Standard for Beef Cattle. National Agriculture and Food Research Organization, Central Association of Livestock Industry, Tokyo. (In Japanese) Nishida, T., K. Oishi, Y. Choumei, H. Kumagai and H. Hirooka. 2014. Environmental and economic evaluation for integration systems between crop and animal production using linear programming -Utilization of paddy rice fields at national level-. Nihon Chikusan Gakkaiho 84: 475-486 (In Japanese with English Abstr.). Ogino, A., K. Kaku, T. Osada and K. Shimada. 2004. Environmental impacts of the Japanese beef-fattening system with different feeding lengths as evaluated by a life-cycle assessment method. J. Anim. Sci. 82: 2115-2122. Ogino, A., H. Orito, K. Shimada and H. Hirooka. 2007. Evaluating environmental impacts of the Japanese beef cow-calf system by the life cycle assessment method. Anim. Sci. J. 78: 424-432. Oishi, K., T. Ibi, A. K. Kahiand H. Hirooka. 2011a. Optimal culling strategy in relation to biological and economic efficiency and annualized net revenue in the Japanese Black cow-calf production system. J. Agric. Sci. 149: 783-799. Oishi, K., H. Kumagai and H. Hirooka. 2011b. Application of the modified feed formulation to optimize economic and environmental criteria in beef cattle fattening systems with food by-products. Anim. Feed Sci. Technol. 165: 38-50. Oishi, K., Y. Kato, A. Ogino and H. Hirooka. 2013. Economic and environmental impacts of changes in culling parity of cows and diet composition in Japanese beef cow-calf production systems. Agric. Syst. 115: 95-103. Powell, J. M., C. J. P. Gourley, C. A. Rotz and D. M. Weaver. 2010. Nitrogen use efficiency: A potential performance indicator and policy tool for dairy farms. Environ. Sci. Pol. 13: 217-228. Rufino, M. C., H. Hengsdijk and A. Verhagen. 2009. Analysing integration and diversity in agro-ecosystems by using indicators of network analysis. Nurt. Cycl. Agroecosyst. 84:229-247. Schroder, J. J., H. F. M. Aart, H. F. M. ten Berge, H. van Keulen and J. J. Neeteson. 2003. An evaluation of whole-farm nitrogen balances and related indices for efficient nitrogen use. Europ. J. Agronomy 20:33-44.

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Senda, M., and A. Ogino. 2012. Environmental analysis of a beef cow-calf production using paddy feed evaluated by life cycle assessment. Proceedings of Annual Conference of the Agricultural Economics Society of Japan 267-274. (In Japanese) Shibata, M., and F. Terada. 2010. Factors affecting methane production and mitigation in ruminants. Anim. Sci. J. 81: 2-10. Steen, B. 1999. CPM Report 1999. A Systematic Approach to Environmental Priority Strategies in Product Development (EPS), Version 2000. Center for Environmental Assessment of Products and Material Systems. Chalmers University of Technology, Göteborg, Sweden. Tabata, Y., K. Oishi, H. Kumagai and H. Hirooka. 2009. Application of cycling index and input-output environs on nutrient cycling in mixed rice-beef production systems. Anim. Sci. J. 80:352-359. Wall, E., G. Simmand D. Moran. 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4:3: 366-376.

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Increasing Ruminant Production Efficiency and Reducing Methane Production

M. Wanapat1, S. Kang1, 2 and K. Phesatcha1 1 Tropical Feed Resources Research and Development Center (TROFREC), Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Khon Kaen 40002, Thailand 2 Faculty of Animal Science, Royal University of Agriculture, Phnom Penh, Cambodia Corresponding email: [email protected] ABSTRACT Ruminants are importantly contributable to the well-being and their livelihood of the global population. Ruminant production systems can vary from subsistence to intensive type of farming depending on locality, resource availability, infra-structure accessibility, food demand and the market potentials. The growing demand for sustainable animal production is compelling to researchers to explore the potential approaches to reduce emissions of greenhouse gases from livestock that are mainly produced by enteric fermentation. Strategies, such as use of plant secondary metabolites and dietary manipulations have emerged to reduce the methane emission, but still require extensive research before they can be recommended and employed in the livestock industry sector. Research have revealed the impacts of using plant secondary compounds on rumen ecology and mitigating of methane production, as well as implications on ruminant production. This paper attempts to review current research findings and potential approaches using dietary means in improving rumen fermentation efficiency and the consequences on rumen methane production. Key Words: Ruminants, Methane, Plant secondary compounds, Feeding system INTRODUCTION Animal agriculture has been an important component in the integrated farming systems in the crop-livestock farming systems in developing countries. It serves in a paramount diversified role in producing animal protein food, draft power, farm manure as well as ensuring social status-quo and enriching livelihood (Wanapat et al., 2010). As the world population is expected to increase from 6 billion to about 8.3 billion in the year 2030 with the average growth of 1.1 per annum, it is essential and vital to be prepared to produce sufficient food for the increased population especially in the developing countries especially using locally available resources. It has been reported that consumption of animal food was 10 kg/per annum in 1960s and increased to 26 kg/per annum in the year 2000 and is expected to be 37 kg/per annum in the year 2030, respectively (FAO, 2008; 2009). Livestock production, in particularly buffalo, cattle and small ruminants, are an integral part of the food production systems, making important contributions to the quality and diversity of human food supply as well as providing other valuable services such as work and nutrient recycling. Large increases in per capita and total demand for meat, milk and eggs are forecast for most developing countries for the next few decades. In developed countries, per capita intakes are forecast to change slightly, but the increases in developing countries, with larger populations and more rapid population growth rates, will generate a very large increase in global demand. Most importantly, the human-inedible materials such as roughages, tree fodders, crop residues and by products into human food by ruminant animals will continue as a very important function of animal agriculture. However, since much of the projected increase is expected to come from pork, poultry and aquaculture production, i.e. from species consuming diets high in forage carbohydrate, meeting future demand will depend substantially on achievable increases in cereal yields (Delgado et al., 1999). Therefore, there are opportunities and

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change challenges for researchers to increase in animal productivity through the application of appropriate technologies, particularly in production systems, nutrition and feeding. Global warming is a hot issue which affects environment and livestock production. Total emissions of greenhouse gases (GHGs) from agriculture, including livestock, are estimated to be between 25–32%, depending on the source (USEPA, 2006; IPCC, 2007) and on the proportion of land conversion that is ascribed to livestock activities. Moreover, Goodland and Anhang (2009) reported that livestock production and its by-products are responsible for at least 51 percent of global warming gases or account for at least 32.6 billion tons of carbon dioxide per year. While, CO2 is the largest green house gases at 55-60% and methane are the second green house gases at 15-20%. Therefore, livestock is the one sector of methane producer from the rumen. It has been estimated that global anthropogenic greenhouse gas (GHG) emissions from the livestock sector approximate to between 4.1 and 7.1 billion tonnes of CO2 equivalents per year, equating to 15-24% of total global anthropogenic GHG emissions (Steinfeld et al., 2006). Tropical plants normally contain high or medium contents of secondary compounds. Among these compounds are the crude saponins (CS) and condensed tannins (CT), which have been shown to exert a specific effect against rumen protozoa while the rest of the rumen biomass remains unaltered (Wang et al., 2000). Numerous studies have been conducted to determine the effects of feeding ruminants with saponin rich plants, such as Enterelobium cyclocarpum, Spinadus saponaria, Sapindus rarak, Sesbania sesban, Quillaja saponaria and Acacia auriculoformis and Yucca schidigera (Wang et al., 2000). Results have indicated that the saponins have strong antiprotozoal activity and could serve as an effective defaunating agent for ruminants. The detergent action of saponins is believed to be responsible for killing the rumen protozoa (Makkar et al., 1998). DEVELOPMENT OF PELLETED FEEDS Local feed resources, seasonal availability and feeding systems in ruminants, have been reported in details by Wanapat (2009); Wanapat et al. (2013). Furthermore, food – feed – system (FFS) has been developed to by uses on farms (Wanapat, 2009). Pelleted feeds have been successfully utilized in the feeding of fish and animals including monogastric and ruminant animals, fish, shrimp and the like. The advantages of pelleted feeds include: (1) pelleted feeds prevent selective feeding on those ingredients in the formulation which are more palatable and thus more desirable to the animal; (2) pelleting of the feed ration prevents segregation of the various size and density constituents that are inherent in animal feeds; (3) pelleting animal feed results in higher bulk density, which is advantageous for both shipping and handling, resulting in maximum load efficiency and reduced storage requirements; and (4) pelleting also increases nutritional utilization of the feed components, thus increasing conversion rate of the feed formulation. The pelleting also improves acceptability, density and keeping quality of feedstuffs (Hale and Theurer, 1972). Generally pelleted feeds are produced in an extrusion type thermoplastic molding operation in which finely divided particles of a feed ration are formed into compact, easily handled pellets. Binder additives may be utilized to improve the strength, durability and stability of the pellets, and to reduce fines produced during the pelleting process. Preferably, nutritive binder additives are utilized which in addition to providing these improvements also provide essential recognized nutrients such as magnesium, calcium, potassium and/or sulfur to the feed. Recently, scientists have been interested in pelleting local feed resources and agricultural crop-residues such as mangosteen (Garcinia mangostana) peel, mulberry (Morus alba), Leucaena (Leucaena leucocephala), sweet potato (Ipomoea batatas) vine, etc to improve the nutritive value and its utilization. Pellet products such as Mago-pel (mangosteen peel pellet), Maga-lic (mangosteen peel with garlic powder pellet), Maga-ulic (mangosteen peel pellet

108 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia with urea and garlic powder), LLP (leucaena leaf pellet), MUP (mulberry leaf pellets), SWEPP (sweet potato vine pellet with 10 % urea) were prepared following steps shows in Table 1 and Figure 1.

Figure 1. Processing chart for pelleting the products (Mago-pel, Maga-lic, Maga-ulic, LLP, MUP and SWEPP) Huyen et al. (2012) and Tan et al. (2012) reported that the mulberry leaf pellet (MUP) supplementation improved nutrient digestibility and rumen fermentation. MUP could be used as a protein source to improve rumen efficiency and production especially supplementation at 600 g/day for beef cattle when fed on low-quality roughages such as rice straw. Norrapok et al. (2012) showed that when combined use of concentrates containing 16% CP with supplementation of Mago-pel at 300 g/hd/d resulted in changes in rumen fermentation and microbial population and improvement in milk production in lactating dairy crossbreds. Manasri et al. (2012) reported that supplementation of mangosteen peel with garlic powder pellet (Maga-lic) at 200 g/h/d improved ruminal fermentation, especially increasing C3 proportion and reducing CH4 gas production in beef cattle steers. Furthermore, Trinh et al. (2012) compared between non supplement and pellet supplement group (mango-pel, Maga- lic and mango-ulic at 200 g/h/d) in beef cattle. It was found that total DMI and digestibility of DM and CP were not significantly affected by pellet supplementation when compared with control group (p<0.05). In addition, C2, C2:C3 ratio, protozoa population and CH4 production were reduced, whereas, C3 production and bacterial population were increased in the pellet supplemented group and highest in maga-ulic supplemented treatment. Moreover,

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change maga-ulic supplemented treatment was the highest in microbial protein synthesis when compared with other treatments. Hung et al. (2013) reported that Leucaena leaf pellet (LLP) supplementation significantly increased rice straw intake and total intake. There was an increase in the population of fungal zoospores, amylolytic bacteria, proteolytic bacteria and cellulolytic bacteria with the increasing level of LLP supplementation while the population of rumen protozoa decreased. The population of total bacteria and the three predominant cellulolytic bacteria increased when the level of LLP supplementation increased; meanwhile, the population of methanogenic bacteria decreased. Supplementation of LLP resulted in the improvement of N balance and microbial nitrogen supply. Recently, Kampanat and Wanapat (2013) revealed that sweet potato vine pellet (SWEPP) was a good source of protein supplement could improve apparent digestibility, rumen fermentation, and milk yield in lactating dairy cows. Table 1. Feed ingredients and chemical composition of Mago-pel, Maga-lic, Maga-ulic, LLP, MUP and SWEPP Iterms Mago-pel Mago-lic Mago-ulic LLP MUP SWEPP Ingredients ------% of dry matter------Mangosteen peel powder 98.5 93.5 91.5 - - - Garlic powder - 5 5 - - - Leucaena leaf meal - - - 81 - - Mulberry meal - - - - 82 - Sweet potato vine - - - - - 81.5 cassava starch 0.5 0.5 0.5 0.5 0.5 0.5 urea - - 0.2 10 10 10 Molasses 1 1 1 5 4.5 5 Sulfur - - - 1 1 1 Mineral mixture - - - 1 1 1 Salt - - - 1 1 1 Chemical composition Dry matter 93.3 93.1 92.7 92.9 92.3 95.6 ------% of dry matter------Organic matter 96.5 96.4 96.5 91.3 88.2 81.4 Crude protein 21.2 21.5 22.1 42.2 48.7 40.5 Neutral detergent fiber 57.3 57.2 57 44 20.4 33.1 Acid detergent fiber 48.6 48.2 48.3 20 14.5 27.8 Mago-pel= mangosteen peel pellet, Maga-lic = mangosteen peel with garlic powder pellet, Maga-ulic = mangosteen peelpellet with urea and garlic powder, LLP = Leucaena leaf pellet, MUP = mulberry leaf pellets, SWEPP = sweet potato vine pellet with 10 % urea.

YEAST FERMENTED CASSAVA CHIP PROTEIN (YEFECAP) Cassava chip or other forms of cassava root can be successfully fermented with yeast (Saccharomyces cereviceae) to obtain the final product with high crude protein and a relatively high profile of amino acids (Boonnop et al., 2009; Polyorach et al., 2012). Amino acid profile of the YEFECAP is presented in Figure 2, with high level of lysine, glutamic acid, leucine and phenylalanine. Supplementation of YEFECAP in replacement for soybean meal in concentrate for lactating dairy cows resulted in good performance of milk yield (15.7 kg/day) (Wanapat et al., 2011a). Dietary yeast can be used as a ruminant feed especially Saccharomyces cerevisiae because the yeast cell contained useful nutrients for ruminant feed

110 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia especially with high lysine composition (7.6±0.7 g/16gN) (Nelson et al., 1959; Polyorach et al., 2012, 2013). Moreover, yeast addition in ruminant diet can only not improve rumen environment but also enhance microbial activities especially cellulolytic activities in such a way that they increased their total number, fiber digestion, reduced lactate accumulation and concentration of oxygen in rumen fluid and improved utilization of starch (Robinson, 1997; Lila et al., 2004). Moreover, S. cerevisiae also could stimulate DM intake and productivity in growing and lactating cattle (Robinson and Garrett, 1999) improved microbial protein synthesis and milk production of dairy cows (Hristov et al., 2010; Strohlein, 2003). However, Desnoyers et al. (2009) reported that highly variable effects of live S. cerevisiae cultures could be associated with the respective ratio of forage and concentrate used. Cassava chip is an energy source with low crude protein, when fermented with yeast could increase crude protein from 1-3%CP to 30.4%CP (Boonnop et al., 2009). Recently, Polyorach et al. (2012, 2013) reported that yeast fermented cassava chip protein (YEFECAP) could be prepared to increase crude protein level up to 47%. The YEFECAP preparation process was done according to the method of Polyorach et al. (2013) as shown in Table 2 and Figure 2. Figure 3 shows the data on essential amino acid profile containing in YEFECAP. Table 2. Chemical composition of yeast fermented cassava chip protein (YEFECAP). Chemical composition YEFECAP Dry matter 90.6 ------% of dry matter------Organic matter 97.2 Crude protein 47.5 Ether extract 7.9 Neutral detergent fiber 6.1 Acid detergent fiber 4.3 Source: Polyorach et al. (2012)

Figure 2. Amino acid profile of YEFECAP products Source: Polyorach et al. (2012)

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Figure 3. Process chart for yeast fermented cassava chip products (YEFECAP) preparation Source: Polyorach et al. (2012) The beneficial use of YEFECAP has been evaluated by Polyorach et al. (2010), Boonnop et al. (2010) and Wanapat et al. (2011a,b). Boonnop et al. (2010) studied replacement of soybean meal by yeast fermented-cassava chip protein (YEFECAP) on rumen ecology and nutrient digestibility in dairy crossbred steers. It was found that YEFECAP could completely replace soybean meal and was beneficial to cattle in terms of efficiency of rumen fermentation, microbial protein synthesis, nitrogen retention and nutrient digestibilities. Khampa et al. (2010) reported that supplementation of yeast fermented cassava chip could replace 75% of concentrate in which improved ruminal fermentation efficiency, average daily gain and reduced cost of production in daily heifers. Supplementation of yeast fermented cassava chip (YFCC) could improve population of bacteria and fungal zoospore, but decreased population of Holotrich and Entodiniomorph protozoa in the rumen of dairy steers (Khampa et al., 2009). Polyorach et al. (2010) and Wanapat et al. (2011a) revealed that using YEFECAP replacement of soybean meal at 0, 33, 67 and 100%CP could be enhance milk yield, milk fat and milk protein when increasing YEFECAP level and was highest at 100% of

112 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia replacement. Moreover, Wanapat et al. (2011b) compared from 4 sources of protein in concentrate diets, soybean meal (SBM), cassava hay (CH), Leucaena leucocephala (LL) and YEFECAP in lactating dairy cows and found that CP digestibility was highest (P<0.05) in CH and YEFECAP supplemented groups. Propionic acid were found highest in cows receiving CH and YEFECAP (P<0.05), while ruminal fungi, proteolytic and cellulolytic bacteria were highest in YEFECAP supplementation, Milk fat and milk protein were significantly increased (P<0.05) in cows fed with CH and YEFECAP. Based on the studies, YEFECAP can practically prepared and used as an alternative protein source in ruminant feeding (Table 3). Wanapat et al. (2011b) reported on the study of using YEFECAP (in replacing soybean meal (SBM) in concentrate mixtures early lactating cows. It was found that YEFECAP can fully replace SBM in concentrate mixtures for milking dairy cows on enhancing rumen fermentation, dry matter intake, nutrient digestibility, milk yield and compositions. Table 3. Effect of YEFECAP as a protein source in concentrate mixtures on milk production, milk composition and economic return Treatments Contrasts Items SEM T1 T2 T3 T4 L Q C Production Milk yield, kg/d 13.5 14.0 14.5 15.0 0.27 ** ns ns 3.5% FCM1, kg/d 13.7 14.7 15.9 17.1 0.49 ** ns ns Milk composition, % Protein 4.0 4.1 4.5 4.7 0.17 ** ns ns Fat 3.2 3.3 3.4 3.5 0.06 ** ns ns Lactose 4.5 4.6 4.6 4.7 0.07 ns ns ns Solids-not-fat 8.2 8.4 8.4 8.5 0.29 ns ns ns Total solids 12.3 12.7 12.8 13.0 0.78 ns ns ns Milk urea N, mg/dl 14.8 12.5 12.3 12.0 0.58 * ns ns Economic return, $US/hd/d Feed cost 2.5 2.6 2.6 2.7 0.14 ns ns ns Milk sale 9.5 9.8 10.2 10.5 0.19 ** ns ns Profit 7.0 7.2 7.6 7.8 0.16 ** ns ns Source: Wanapat et al. (2011b) METHANE PRODUCTION FROM RUMINANTS Agricultural emissions of methane in the EU have recently been estimated at 10.2 million tonnes per year and represent the greatest source. Of these, approximately two-thirds come from enteric fermentation and one-third from livestock manure. Fermentation of feeds in the rumen is the largest source of methane from enteric fermentation (Moss et al., 2000) . Methane is produced as a result of anaerobic fermentation in the rumen and the hindgut. Microbial enzymatic activity in the rumen (and salivary enzymes), hydrolyses much of the dietary organic matter to amino acids and simple sugars. These products are then anaerobically fermented to volatile fatty acids (VFA), hydrogen and CO2. Some of the CO2 is then reduced through combination with hydrogen to produce methane. The rumen is a highly diverse ecosystem comprising different microbial groups including methanogens that consume a considerable part of the ruminant’s nutrient energy in methane production. The consequences of methanogenesis in the rumen may result in the low productivity and possibly will have a negative impact on the sustainability of the ruminant’s production (Kumar et al., 2014).

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The methane pathway involves several unique coenzymes, some of which carry the one carbon unit during its 7 step reduction to methane (Figure 4).

1) Methanofuran (MF) picks up the CO2, H2 and carries it for the first reduction step which gives formyl methanofuran; FMF. 2) Remove formyl group from FMF which gives Tetrahydromethanopterin and formyl tetrahydromethanopterin; FTHMPT. 3-5) The FTHMPT reduction step requires H2 and F420 to remove one carbon from THMPT to methenyltetrahydromethanopterin; MTHMP, methyllenyltetrahydromethanopterin; MLTHMPT and methyltetrahydromethanopterin; MET-MTHMP, respectively. 6) Remove methyl group from MET-THMPT for CoM to methyl CoM. 7) The methyl CoM reductase step requires the cofactor F430 for produce CH4, CoM and ATP.

Figure 4. Methane pathway in rumen. (MF = methanofuran, THMPT = tetrahydromethanopterin, F420 = factor 420, CoM = coenzyme M) Source: Feery (1995) Alternatively, hydrogen can by used in the formation of some VFA. The stoichometry of the formation of the main VFA is shown in the following equations: 2H producing reactions: Glucose 2 pyruvate + 4H (Embden-Meyerhof-Parnas pathway) Pyruvate + H2O acetate (C2) + CO2 + 2H

2H using reactions: Pyruvate + 4H propionate (C3) + H2O 2 C2 + 4H butyrate (C4) + 2H2O Acetate and butyrate promote methane production while propionate formation can be considered as a competitive pathway for hydrogen use in the rumen. Such theoretical calculations have been confirmed in vitro where the end products can be easily quantified.

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Methane production was measured when the molar proportions of individual VFAs, was altered by adding monensin to the diet of animal donors (Figure 4) (Moss et al., 2000).

Figure 5. Relationship between methane and (C2 + C4)/C3 ratio Source: Moss et al. (2000) Methane was not correlated to C2 production (r2 = 0.029) but, there was a good negative correlation between methane and C3 (r2 = 0.774). The correlation between methane and C2/C3 ratio (r2 = 0.772) was slightly lower. The ratio (C2 + C4)/C3, which accounts for acetate and butyrate both of which are involved in H2 production, and propionate which is involved in H2 utilisation, improved the relationship slightly (r2 = 0.778). This result is consistent with the idea that propionate production and methanogenesis are competing, and are alternative pathways for regenerating oxidized co-factors in the rumen. However, this result alone gave no information on the regulating mechanisms involved. Van Kessel and Russell (1969) observed in vitro, using rumen fluid sampled from animals fed on roughage- based diets, that ruminal methanogens lose the ability to use H2 at low pH, giving rise to free H2 in the gas phase when the pH was less than 5.5. Thus on roughage diets a low pH leads to a decrease in methanogenesis independent from propionate formation. On the contrary, starch-fermenting bacteria can compete against methanogens for hydrogen use by producing large amounts of propionate ( Russell, 1998) . However, H2 accumulated and propionate decreased dramatically while acetate increased when the pH reached non-physiological values below 5.3. This means that the microbial ecosystem involved in propionate formation differs with the dietary conditions. The cellulolytic bacteria Fibrobacter succinogenes is the major propionate producers through the succinate pathway in roughage diets, while lactate is the main intermediate in the conversion of starch to propionate. Unlike cellulolytic bacteria

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change and methanogens, lactic bacteria are known to be tolerant to low pH making them able to use H2 and be competitive with methanogens even in unfavourable pH conditions. METHANE PRODUCING BACTERIA Methane is produced by strict anaerobes belonging to the sub-group of the Archae domain (Woese et al., 1990) There is a large phylogenetic diversity of methanogens in natural media. Also, the different genera and species of methanogens have various shapes and physiological characteristics: cocci, rods, spirilla, thermophylic and mesophylic species, motile and nonmotile cells. Rumen methanogens grow only in environments with a redox potential below –300 mV ( Stewart and Bryant, 1 9 8 8 ) . More than sixty species were isolated from various anaerobic habitats like sanitary landfills, peat bogs, waterlogged soils, salt lakes, thermal environments, and intestinal tracts of animals. Only five of these species belonging to Methanobrevibacter and Methanosarcina genera, were isolated from rumen digesta ( Table 4). Table 4. Methane producing bacteria in the rumen Bacteria Substrate Product Methanobacterium ruminantum CO2, H2, HCOOH CH4, CO2, H2O Methanobacterium fomicicum HCOOH CH4, CO2, H2O Methanobrevivactor ruminantum HCOOH CH4, CO2, H2O Methanomicrobium mobile HCOOH CH4, CO2, H2O Methanosarcina barkerii Methanol, Methylamine, Acetate CH4, CO2, NH4 Source: Yokoyama and Johnson (1988) Protozoa have affect to methanogenesis in the rumen ( Newbold et al., 1995a) . Because methanogens are closely associated with ciliate protozoa. Protozoa will produce H2 as one of the main metabolic end products (Ushida and Jouany, 1996) similarly with Newbold et al. (1995a) estimated that methanogens associated with ciliate protozoa were responsible for between 9 and 25% of the methanogenesis in rumen fluid. Therefore, reduction in protozoa population indirectly may affect methanogens and can be reduce methane production. MEASUREMENT OF METHANE EMISSION FROM RUMINANTS

There are many methods available which would be suitable for measuring CH4 produced from the various stages of animal production. However, several factors need to be considered in order to select the most appropriate technique like the cost, level of accuracy required and the scale and design of the experiments to be undertaken (Johnson et al., 2000). Common abbreviations used in CH4 measurement equipments: ECD-Electron capture detector; FID- Flame ionization detector; FTIR-Fourier transform infrared (spectroscopy); GC-Gas chromatography/Gas chromatograph; TCD-Thermal conductivity detector; TDL-Tuneable diode laser; TGA-Trace gas analyzer; SF6- Sulphur hexafluoride. There are many options available by which methane emissions from ruminants may be measured. Sampling of individual or group gaseous emissions may be accomplished using enclosure techniques or tracer methods. Selection of a technique depends on the accuracy as each one has its advantages and disadvantages. Screening of mitigation strategies may be best evaluated using individual animal before large scale tests on herds of animals are conducted (Johnson et al., 2000; Storm et al., 2012). Respiration calorimeter The classical standard for ruminant CH4 measurement by nutritionists is the respiration chamber, or calorimeter. Respiration calorimetry techniques such as whole animal chambers, head boxes, or ventilated hoods and face masks have been used effectively to collect most of the available information concerning CH4 emissions in livestock. The predominant use of

116 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia calorimeters has been to measure gaseous exchange as part of energy balance measurements, CH4 loss being a necessary part of this procedure. There are various designs of calorimeters (Blaxter, 1962), but the most common one being the open circuit calorimeter. The principle behind open-circuit indirect-respiration techniques is that outside air is circulated around the animal’s head, mouth and nose and well mixed inside air is collected (Mclean and Tobin, 1987). The animal is placed in open circuit respiration chamber for a period of several days, the inputs (feed, oxygen, CO2) and outputs (excretion, oxygen, CO2 and CH4) were measured from the chamber. The chamber should be well sealed and capable of a slight negative pressure. This ensures that all leaks will be inward and not result in a net loss of CH4. Air conditioning, dehumidification, feeders, waterers and a method by which faeces and urine could be removed are necessary in order to create a comfortable environment within the chamber. Animal movement and normal behaviour should be provided for as much as possible however, some degree of restraint is necessary within the chamber. Experimental factors that should be considered are a) restrictions to the animal’s intake to ensure that the experiment can be reproduced, b) stresses on the animal from being in confinement c) lack of environmental stresses on the animal (e.g. lack of heat stress) d) lack of exercise) experimental duration.

Figure 6. Respiration calorimeter Source: Bhatta et al. (2007) Advantage The ability to make accurate measurements of emissions including CH4 from ruminal and hindgut fermentations Disadvantages While this technique is satisfactory for measuring CH4 emission from dried diets, there are difficulties in deriving values that are applicable to the grazing ruminant. i) The restriction of the animal movement ii) The expenses associated with the construction and maintenance of the chambers Ventilated hood A ventilated hood could also be used to quantify CH4 emissions using the same principles. This technique involves the use of an airtight box (as shown in the picture) that surrounds the animal’s head. A sleeve or drape could be placed around the neck of the animal to minimize

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change air leakage. The box must be big enough to allow the animal to move its head in an unrestricted manner and allows access to feed and water.

Figure 7. Ventilated hood Source: Bhatta et al. (2007)

Advantages The primary advantage of this technique is the relatively lower cost of the ventilated hood system as compared to a whole animal chamber. Disadvantages i) As with the chamber, use of a hood also requires a restrained and trained animal ii) The inability to measure all the hindgut CH4 Ventilated flow-through method with a face mask In the facemask technique, the mask for the collection of expired air has to be tightly sealed around the face of the animal, which may be stressful for the animal and also the Douglas- bag for the collection of gas is not easy to handle. Kawashima et al. (2002) developed a ventilated flow through method with a face mask. The system consists of four major components 1) main air flow system component 2) air- sampling component 3) gas analysis component 4) data record and calculation system. Terada (1999) examined 3 factors viz. individual animal, daily difference and diurnal changes to influence the accuracy of the CH4 measurement. The variance related to diurnal change was the largest among the three factors. It was suggested, based on these analyses, that a respiration trial should be conducted for 2-3 days, 4-6 times a day with 4 experimental animals.

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Figure 8. Ventilated flow-through method with a face mask Source: Bhatta et al. (2007) Calculation The principle is based on the Brouwer’s equation for measurement of heat production. HP = 16.18 × V O2 + 5.16 × V CO2 - 5.90 × N-2.42 ×V CH4 Where, HP: heat production (kJ); V O2: oxygen consumption (liter at STP); V CO2: carbon dioxide production (liter at STP); N: urinary nitrogen excretion (g); V CH4: methane production (liter at STP). Tracer gas techniques Methane emission from ruminants can also be estimated by using the ERUCT (Emissions from Ruminants Using a Calibrated Tracer) technique. The tracer can either be isotopic or non-isotopic. Isotopic tracer techniques generally require simple experimental designs and relatively straight forward calculations, at least for the lower number pools (Johnson and Johnson 1995). Isotopic methods involve the use of (3H-) methane or (14C-) methane and ruminally cannulated animals (Murray et al., 1976). Using the continuous infusion technique, infusion lines deliver the labeled gas to the ventral part of rumen and sampling of gas takes place in the dorsal rumen. After determining the specific activity of the radio-labeled methane gas, total methane production can be calculated. It is also possible to measure CH4 production from a single dose of injection of tracer (France et al., 1993). France et al. (1993) described models for up to three and higher CH4 pools. Disadvantages Difficulty in the preparation of the infusion solution is the major limitation when isotopic tracers are used because of low solubility of CH4 gas. Non-isotopic tracer techniques are also available for measurement of CH4 emissions. Johnson et al. (2000) described a technique using sulphur hexafluoride (SF6), an inert gas tracer.

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Figure 9. Sulphur hexafluoride (SF6) tracer gas techniques Source: Bhatta et al. (2007) Emissions from groups of animals in a room or groups on pasture are possible through the release of a tracer into the room or pasture area. For individual animal measurement, a calibrated source of SF6 is placed in the rumen per os prior to an experiment. The source of SF6 is a permeation tube, and the rate of release of SF6 is controlled by a permeable TeflonTM membrane held in place by a porous stainless steel frit and a locking nut. The release rate of the gas permeation tube is calibrated at 39°C by regular weighing for a period, prior to its insertion into the rumen. The tubes for sheep, typically 35 mm length by 11 mm in external diameter, are made from brass rods and weigh about 32 g. Each test animal is fitted with a halter, which supports an inlet tube that is placed so that its opening is close to the nose. As the vacuum in the sampling canister/yoke slowly dissipates a steady sample of the air around the mouth and nose of the animal is taken. By varying the length and diameter of the capillary tube the duration of sampling may be regulated. The yoke is easily isolated for daily changing by means of a shut off valve and quick connect fittings. Yoke volumes are typically 1.7 and 2.5 litres for sheep and cattle respectively, and the capillary system is designed to deliver half this volume during the collection period of 24 h. An identical apparatus needs to be placed each day to collect an integrated background air sample. After collection of a sample the yoke is pressurized with nitrogen, and CH4 and SF6 concentrations are determined by gas chromatography. Methane emission rate is calculated as follows;

QCH4 = QSF6 ×[CH4]/ [SF6]

Where QCH4 is the emission rate of methane in g/day; QSF6 is the known release rate (g/day) of SF6 from the permeation tube; [CH4] and [SF6] are the measured concentrations in the canister.

Johnson et al. (1994a) compared 55 measurements using the SF6 technique with 25 chamber measurements of cattle, and showed that while the SF6 estimates were 0.93 of those in the chamber, the difference was not significant. Pinares-Patino (2000) found in an experiment with 10 sheep fed chaffed Lucerne hay that CH4 production estimated from SF6 was 0.95 chamber emission. Boadi et al. (2002) compared estimates of CH4 production using the SF6 tracer technique (137.4 L/d) with an open circuit hood calorimeter (130.0 L/d) using yearling beef heifers and found no significant difference (p = 0.24). However, Pinares-Patino (2000) in two experiments with sheep obtained SF6 results that were extremely variable compared to chamber. The estimate of CH4 production made with the respiration chamber (7.7±0.67 L/h) was twice that (p<0.005) estimated using SF6, either in pens (4.1 ±0.35 L/h) or in the

120 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia chamber (4.0±0.46 L/h). SF6 has also been used successfully as tracer to estimate the total CH4 emission from all the cattle in a barn (Kaharabata and Schuepp, 2000). Table 5. Advantages and disadvantages of tracer technique Class Tracer Advantages Disadvantages 14 3 Isotope CH4, CH 4 Very low detection limits Radioactive- not radioisotopes Very expensive useful in food chain OK for food chain 2 CH 4 stable isotope Cheap, safe, OK for food Hydrogen transfer to chain other molecules Noble gases Argon Low detection limit and High background moderate cost concentration Xenon Low background High cost Other Gases Ethane Availability High detection limit Propane Availability High detection limit Source: Hegarty et al. (2004) Advantages i) This technique eliminates the necessity to restrain or enclose the animal, thus allowing the animal to move about and graze. ii) It is also not necessary to sample directly from the animal’s rumen or throat because the use of the tracer accounts changes in dilution associated with head or air movement. Disadvantages i) SF6 is a greenhouse gas itself, with a GWP 23,900 times that of CO2 and an atmospheric lifetime of 3,200 years (Machmüller and Hegarty, 2005). ii) The residue of SF6 in meat and milk from farm animals is another issue. iii) It is necessary to train the animal to wear a halter and collection yoke/canister. iv) This tracer technique does not measure all of the hindgut CH4. Any CH4 from the hindgut that is absorbed into the blood stream will be expired and collected but any CH4 that escapes absorption and is released from the rectum is not collected.

Ethane (C2H6) has also been used as a marker to estimate CH4 emission (Moate et al., 1997) using essentially the same principle as SF6. The major difference in the use of the two tracers is that ethane has to be bubbled from an external source into the rumen, and so is not suitable for use in grazing experiments. Hegarty et al. (2004) listed possibility of other markers for measuring enteric CH4 emissions and approaches being considered. Machmüller and Hegarty (2005) identified ethane and stable isotopes of methane as promising alternative tracer gases for the ERUCT technique. Methane collection via rumen method Poungchompu et al. (2009) collected CH4 used via rumen method by a close-fitting plastic tube was inserted in the rumen fistula, where the arm end was covered with two layers of cheesecloth and positioned above the ingesta. The other end of the tube was fitted with two layers of a plastic bag, ensuring no air coming inside before collecting gas (modified method of Johnson et al. 1972). Samples of gas were obtained via rubber tubing to a plastic bag by three times of rumen movement. The gas in the trapped plastic bag was transferred to a 30 ml glass bottle, where gas replaced water. The bottle was closed with a sealing rubber and with a paraffin film as shown in figure 10. Advantages i) Material can easy find and cheaper than other method ii) This method not complicated, easy to used and understood Disadvantages i) Can measure only proportion of CH4, can not measure total CH4 emission/head/day ii) Must be used rumen fistulated animal

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Figure 10. Methane collection via rumen method Source: Poungchompu et al. (2009) In vitro techniques Although in vivo studies using actual animals are ultimately needed, however an in vitro simulation of the rumen can often be effective and efficient because of its relative readiness and cheapness of operation. It can also be used to define the effects of a specific factor which might be concealed in an in vivo study behind the complexity of a lot of related factors in the rumen environment. Rumen simulation technique (RUSITEC): In the RUSITEC, solid feeds are confined in nylon bags that are normally replaced by new bags once a day. The amount of ration is small (10-25 g DM/day/L of vessel), and the set points of the liquid dilution rate are also small (2-5%/h) as compared with the actual in vivo values. The gas produced from each fermenter is collected in polythene/rubber bags and the volume of gas is recorded using a dry gas meter. From the gas samples, the concentration of CH4 is measured in gas chromatograph (x). The volume of CH4 gas produced is calculated from the total volume of gas produced after 24 h in the fermenter (y). The CH4 production should be converted to STP value (1 atm, 0°C) for comparison with CH4 measured by other techniques. Methane in ml (at STP) = (Methane ml) ×[273/ (273+39)] x (atmospheric pressure at the experiment) (standard atmospheric pressure) Methane is expressed as ml/g DM incubated in the fermenter or ml/g DDM (if DM digestibility is estimated) or as % of GE. Bhatta et al. (2006) observed that RUSITEC (p<0.001) underestimated CH4 production as compared to that estimated by SF6 technique. Eun et al. (2004) reported that CH4 estimated from head space gas in dual-flow fermenters was significantly lower than CH4 estimated from the stoichiometric calculation at a dilution rate of 3.2 %/h from diets ranging in concentrate and roughage ratio (Table 6).

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Table 6. Comparison of CH4 production from stoichiometric equation and head space gas HF MF LF CH4 estimated* (mmol/d) 13.1 13.1 13.4 CH4 measured** (mmol/d) 9.6 6.1 4.2 HF = High forage (70% roughage: 30% concentrate); MF = Medium forage (50% roughage: 50% concentrate). LF = Low forage (30% roughage: 70% concentrate); Dilution rate: 3.2%/h.* (Acetate, mmol/d) + (2×butyrate, mmol/d)-(CO2, mmol/d); ** Methane measured from head space gas. Source: Eun et al. (2004) Advantages i) Constructional simplicity and operational easiness. ii) The easy operation of the device can increase the number of fermenters that can be used at a time. Disadvantages i) A higher dilution rate of more than 4.0 %/h would make the concentration of the fermentation products lower than the values normally observed in the actual rumen (Czerkawski and Breckenridge, 1977). ii) The difficulty in obtaining a uniform sample because of the stratification of the fermenter contents into compartments that are typically depicted in three parts: free liquid, a solid associated part which can be washed out and a solid associated part which cannot be washed out. The effluent sample from the RUSITEC, however only represents the liquid part of the contents, and this liquid has a lower microbial density than the solid associated compartments. iii) Protozoa numbers in the effluent gradually decreases as the incubation proceeds and settles at around 3,000/ml after the 8th day for 3.0 %/h dilution rate (Kajikawa et al., 2003). Hence, CH4 output will be significantly low, if the gas sampling is commenced after the 8th day (Bhatta et al., 2006). Moreover, Van Nevel et al. ( 1 9 7 0 ) estimated methane by take a sample of 1 ml from the gas phase of each syringe and injected into gas chromatograph for analysis of methane. Recently, many reports dealing with measuring and estimating of rumen methane have been published including those of Storm et al. (2012); Ramin and Huhtanen (2013). Molecular techniques for rumen microorganisms Methanogens, members of the domain Archaea, are obligate anaerobes and can be unmistakably differentiated from other organisms since they all produce methane as a major catabolic product (Bergey, 1994). Interest in rumen methanogens has resulted from the role of methane as a contributor to global warming and from the fact that that cattle typically lose 2–15% of their ingested energy as eructated methane (Giger-Reverdin and Sauvant 2000). Moreover, Sharp et al. (1998), suggested that most methanogens associated with ruminal ciliate protozoa belonged to the Methanobacteriaceae. Tothova et al. (2008) also confirmed methanogenic archaea associated with anaerobic protozoan Entodinium caudatum in longterm (2 years) in vitro culture and reported that a methanogen having very low similarity with the sequences in the database was dominant endosymbiont specific to the rumen ciliate Entodinium caudatum. This association between methanogens and protozoa indicates that methanogenesis in the rumen is influenced by protozoa. Hegarty (1999) concluded that this symbiotic association results in 40% of methanogenesis in rumen fluid. To study and monitor natural anaerobic ecosystems, as well as anaerobic engineering processes, the accurate and quick method for methanogens quantification should be used. Such methods as direct count Galyen (1989) based on the use of a hemocytometer (Boeco, Hamburg, Germany). The use of traditional microbiological techniques in determining population structures and characteristics is limited as it has been shown that many organisms are not readily cultured on

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change selective media and can only determine the microbial diversity grossly due to some microbes that can not be cultivated with the current technique (Deng et al., 2007). Molecular biological method for determination of methanogens by real-time polymerase chain reaction (PCR), using a broad-range (universal) probe and primers set, also can be used for quantification of methanogens (Nadkarni et al. 2002; Sawayama et al. 2004; Kongmun et al. 2010) and a recent study quantified the protozoal biomass in the rumen. Pei et al. (2008) showed that Methanobrevibacter sp., Methanomicrobium mobile, Methanobrevibacter ruminatum, Methanosphaera stadtmanae, Methanobacterium alcaliphilum, Methanobacterium aarhusense and Aciduliprofundum booni were the most related methanogens present in the solid or liquid fraction of rumen. Wright et al. (2004) analysed 733 clones from the rumen of sheep and demonstrated that most of the clones were closely related to Methanobrevibacter. And using the polymerase chain reaction (PCR)-based denaturing gradient gel electrophoresis (DGGE) to identify the Archaea species. Tothova et al. (2008) used the culture-independent DGGE fingerprinting method for determining the diversity of methanogenic archaea associated with anaerobic protozoan Entodinium caudatum in longterm (2 years) in vitro culture and reported that a methanogen having very low similarity with the sequences in the database was dominant endosymbiont specific to the rumen ciliate Entodinium caudatum. Wanapat et al. ( 2 0 0 9 ) shown, the methanogenic diversity were similar among difference treatments which presented 7 bans of DNA by using PCR-DGGE technique.

Figure 11. Photographed gel after DGGE electrophoresis of 16sDNA fragments from four treatments of rumen fluid. Lane: T1 = cassava chip + 15 g/kg urea, T2 = cassava chip + 30 g/kg urea, T3 = corn cobs + 15 g/kg urea and T4 = corn cobs + 30 g/kg urea Source: Wanapat et al. (2009) USE OF PLANT SECONDARY COMPOUNDS IN MITIGATING RUMEN METHANE REDUCTION Plant secondary compounds (condensed tannins and saponins) are more important as ruminant feed additives, particularly on CH4 mitigation strategy because of their natural origin in opposition to chemicals additives. Tannins containing plants, the antimethanogenic activity has been attributed mainly to condensed tannins. There are two modes of action of tannins on methanogenesis: a direct effect on ruminal methanogens and an indirect effect on hydrogen production due to lower feed degradation. Also, there is evidence that some condensed tannins (CT) can reduce CH4 emissions as well as reducing bloat and increasing amino acid absorption in small intestine. Methane emissions are also commonly lower with

124 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia higher proportions of forage legumes in the diet, partly due to lower fibre contact, faster rate of passage and in some case the presence of condensed tannins (Beauchemin et al., 2008). Legumes containing condensed tannin (e.g., Lotuses) are able to lower methane (g kg-1 DM intake) by 12-15% (Beauchemin et al., 2008; Rowlinson et al., 2008). Also, some authors reported that condensed tannins to reduce CH4 production by 13 to 16% (DMI basis) (Grainger et al., 2009; Woodward et al., 2004), mainly through a direct toxic effect on methanogens. More recently Woodward et al. (2004) carried out a similar trial with cows fed Lotus corniculatus, on methane was 24.2, 24.7, 19.9 and 22.9 g kg-1 DMI for the respective treatments. The CT in lotus reduced methane kg-1 DMI by 13% and the cows fed lotus produced 32% less methane kg-1 milk solids (fat+protein) compared to those fed good quality ryegrass. McAllister and Newbold (2008) reported that extracts from plants such as rhubarb and garlic could decrease CH4 emissions. However, there is only limited information on the effect of different saponins on rumen bacteria. In addition Sirohi et al. (2009) have recently shown that plant secondary metabolites (PSM) at lower concentrations could be used to manipulate rumen fermentation favorably. At appropriate dose, saponins or saponins containing plants have been shown to suppress protozoal population, increase bacteria and fungi population, propionate production, partitioning factor, yield and efficiency of microbial protein synthesis and decrease methanogenesis, hence improve performance in ruminants. Tannins especially condensed tannins (CT) also decrease methane production and increase efficiency of microbial protein synthesis as reported by Plant extracts rich in flavonoids increase degradation of cell wall constituents, yield and efficiency of microbial protein synthesis. Saponins are natural detergents found in many plants. There have been increased interests in saponin-containing plants as possible means of suppressing or eliminating protozoa in the rumen. A decrease in protozoa numbers has been reported in the rumen of sheep infused with saponins or fed on saponin-containing plants. Decreased numbers of ruminal ciliate protozoa may enhance the flow of microbial protein from the rumen, increase efficiency of feed utilization and decrease methanogenesis. Saponins are also known to influence both ruminal bacterial species composition and number through specific inhibition, or selective enhancement, of growth of individual species. Saponins have been shown to possess strong defaunating properties both in vitro and in vivo which could reduce CH4 emissions (Rowlinson et al., 2008). Beauchemin et al. (2008) recently reviewed literature related to their effect on CH4 and concluded that there is evidence for a reduction in CH4 from at least some sources of saponins, but that not all are effective (Rowlinson et al., 2008). While extracts of CT and saponins may be commercially available, their cost is currently prohibitive for routine use in ruminant production systems. However, still required on the optimum sources, level of CT astringency (chemical composition), plus the feeding methods and dose rates required to reduce CH4 and stimulate production. Effects of MP supplementation on ruminal microorganism population are shown in Table 7. MP supplementation had remarkably reduce rumen protozoa production, while predominant cellulolytic bacteria were increased (P<0.05). In addition, methanogens tended to be decreased. However, it was found that mangosteen peel powder significantly increased (P<0.05) cellulolytic bacteria population (Kongmun et al., 2009). The condensed tannins and saponins present in the MP could influence such changes in the rumen.

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Table 7. Effects of mangosteen peel powder supplement on population of rumen microbes Substrates Level, Protozoa Methanogens RF RA FS Species References g/h/d (+/−) (+/−) (+/−) (+/−) (+/−) MP 100 −* nd nd nd nd Beef cattle Ngamsaeng et al. (2006) MP 100 − − +* +* +* Native Kongmun et al. beef cattle (2009) MP 300 −* nd + + + Dairy Norrapoke et al. cows (2012) MP= Mangosteen peel powder, plus symbol, minus symbol increase or decrease from control group, nd =not determined, RF= Ruminococcus flavefaciens, RA=Ruminococcus albus, FS=Fibrobactor succinogenes *P<0.05, significantly different from control group. There are five possible mechanisms by which lipid supplementation reduces CH4: reducing fibre digestion (mainly in long chain fatty acids); lowering DMI (if total dietary fat exceeds 6-7%); suppression of methanogens (mainly in medium chain fatty acids); suppression of rumen protozoa and to a limited extent through biohydrogenation (McGinn et al., 2004; Beauchemin et al., 2008; Johnson and Johnson, 1995). Oils offer a practical approach to reducing methane in situations where animals can be given daily feed supplements, but excess oil is detrimental to fibre digestion and productions. Oils may act as hydrogen sinks but medium chain length oils appear to act directly on methanogens and reduce numbers of ciliate protozoa (Machmuller et al., 2000). However, Kongmun et al. (2010) reported that supplementation of coconut with garlic powder could improve in vitro ruminal fluid fermentation in terms of volatile fatty acid profile, reduced methane losses and reduced protozoal population. In contrast, Johnson et al. (2002) and (2008) found no response to diets containing 2.3, 4.0 and 5.6% fat (cottonseed and canola) fed over an entire lactation. Beauchemin et al. (2008) recently reviewed the effect of level of dietary lipid on CH4 emissions over 17 studies and reported that with beef cattle, dairy cows and lambs, there was a proportional reduction of 0.056 in CH4 (g kg-1 DM intake) for each 10 g kg-1 DM addition of supplemental fat. While this is encouraging, many factors need to be considered such as the type of oil, the form of the oil (whole crushed oilseeds vs. pure oils), handling issues (e.g., coconut oil has a melting point of 25°C) and the cost of oils which has increased dramatically in recent years due to increased demand for food and industrial use. In addition, there are few reports of the effect of oil supplementation on CH4 emissions of dairy cows, where the impact on milk fatty acid composition and overall milk fat content would need to be carefully studied. Strategies based on processed linseed turned out to be very promising in both respects recently. Most importantly, a comprehensive whole system analysis needs to be carried out to assess the overall impact on global GHG emissions (Rowlinson et al., 2008). Manh et al. (2012) and Khodyhotha et al. (2012) who reported that supplementation of Eucalyptus leaf meal at 100 g/day for ruminants could be on alternative feed enhancer which reduces rumen methane gas production in cattle, while nutrient digestibilities were unchanged. On the other hand, Pilajun and Wanapat (2011) reported that increasing coconut oil and mangosteen peel pellet levels decreased proportion of methane reduction, but suitable level should not exceed than 6% for coconut oil and 4% DM for MPP supplementations, respectively. Recently, the comprehensive research based on individual components of essential oils, physiological status of animals, nutrient composition of diets and their effects on rumen microbial ecosystem and metabolism of essential oils will be required to obtain consistent beneficial effects (Patra, 2011). Moreover, Wanapat et al. (2011) comprehensively reported based on both in vitro and in vivo trials, concerning rumen microorganisms, methane production and the impacts on rumen mitigation of methane using plants secondary

126 Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia compounds and oils, showing great potential for improving rumen ecology in ruminant productivity (Table 8). Table 8. Effects of plant secondary compounds and plant oil on digestibility and methane gas production in various studies Methane, Substrates Level Animal References % Garlic powder 16 mg (-) 22.0* Buffalo Kongmun et al. (2010) (rumen fluid) Coconut oil 16 mg (+) 6.4* Buffalo Kongmun et al. (2010) (rumen fluid) Soapberry fruit 4% 10.0 Poungchompu et al. (2009) and mangosteen peel pellet Mangosteen peel 100g/h/d (-) 10.5 beef Kongmun et al. (2009) powder cattle Tea saponins 0.01 1.4 Wongnen and Wachirapakorn 0.02 9.7 (2011) 0.03 10.0 0.04mg/mg 2.6 diet Coconut oil 7% (+) 39.5* beef Kongmun et al. (2009) cattle Coconut oil 7% (-) 10.2* Buffalo Kongmun et al. (2010) Coconut oil and 50:50 ratio at 10 Buffalo Pilajun et al. (2010) Sunflower oil 5% in concentrate Coconut oil 8:4 (mg) (-) 18.9* Buffalo Kongmun et al. (2010) Garlic powder Coconut oil + 7% + 100g (-) 9.1* Buffalo Kongmun et al. (2010) Garlic powder Eucalyptus oil 0.33-2 ml L-1 30.3- Sheep Sallam et al. (2009) 78.6% Eucalyptus oil 0.33-1.66 ml 4.47- Buffalo Kumar et al. (2009) L-1 61.0% Eucalyptus meal 100 g/d reduce Cow Manh et al. (2012), leaf Khodyhotha et al. (2012) *Values are significantly different (p<0.05) from control group; +,- the values were increased or decreased from control group CONCLUSIONS Ruminant production are vitally important to the well being of the stakeholders especially the farmers engaging in the tropical and sub-tropical farming systems. Increasing production efficiency while maintaining the environmental scenario by feed resources development and their potential use are of high priority. Many good methods for measuring and estimating methane emissions both directly and indirectly from ruminants have been investigated. Interestingly mitigating rumen methane from enteric fermentation are closely related to any changes in the production efficiency. As reported, feeding of feeds containing plant secondary compounds can potentially mitigate rumen methane, thus decreasing global

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change warming. However, research on using these plant secondary compounds, their effects on rumen microorganisms, the practical implementations as well as, the measurement methods still warrant further undertakings. Practically, local feed resources and the food–feed–system (FFS) have been highly recommended for on-farm implementation ACKNOWLEDGEMENTS The authors wish to sincerely express the most thanks to TROFREC, Department of Animal Science, Faculty of Agriculture, Khon Kaen University, Thailand and the 16th AAAP congress Organizing Committee for their kind invitation and financial support for participating in the conference. Assistances in preparing the paper from the graduate students are highly appreciated. REFERENCES Beauchemin, K. A. and S. M. McGinn. 2006. Methane emission from beef cattle: effects of fumaric acid, essential oil and canola oil. J. Anim. Sci. 84: 1489–1496. Beauchemin, K. A., M. Kreuzer, F. O'Mara and T. A. McAllister. 2008. Nutritional management for enteric methane abatement: A review. Aust. J. Exp. Agric. 48: 21-27. Bergey, D. H. 1994. The methanogens. In: Bergey's manual of determinative bacteriology (ed. J.G. Holt) 9 ed. Bhatta, R., K. Tajima, N. Takusari, K. Higuchi, O. Enishi and M. Kurihara. 2006. Comparison of sulfur hexafluoride tracer technique, rumen simulation technique and in vitro gas production techniques for methane production from ruminant feeds. International Congress Series (Elsevier) 1293C: 58-61. Bhatta, R., O. Enishi, N. Takusari, K. Higuchi, I. Nonaka and M. Kurihara. 2007. Diet effects on methane production by goats and a comparison between measurement methodologies. J. Agric. Sci. Cambridge. Blaxter, K. L. 1962. The Energy Metabolism of Ruminants. London, Hutchinson. Boadi, D. A., K. M. Wittenberg and A. D. Kennedy. 2002. Validation of the sulphur hexafluoride (SF6) tracer gas technique for measurement of methane and carbon dioxide production by cattle. Can. J. Anim. Sci. 82: 125-131. Boonnop, K., M. Wanapat, N. Nontaso and S. Wanapat. 2009. Enriching nutritive value of cassava root by yeast fermentation. Sci. Agric. (Piracicaba, Braz.) 66: 616-620. Chanthakhoun, V., M. Wanapat, C. Wachirapakorn and S. Wanapat. 2011. Effect of legume (Phaseolus calcaratus) hay supplementation on rumen microorganisms, fermentation and nutrient digestibility in swamp buffalo. Livestock Sci. 140: 17-23. Czerkawski, J. W. and G. Breckenridge. 1977. Design and development of a long-term rumen simulation technique (Rusitec). Br. J. Nutr. 38: 371-383. Delgado, C. L., M. Rosegrant, H. Steinfeld, S. Ehui and C. Courbois. 1999. Livestock to 2020: The Next Food Revolution. Food Agriculture, and Environment Discussion Paper 28. International Food Policy Research Institute, Washington D.C. Deng, W., D. Xi, H. Mao and M. Wanapat. 2007. The use of molecular techniques based on ribosomal RNA and DNA for rumen microbial ecosystem studies: a review. Mol. Biol. Rep. 35: 265–274. Desnoyers, M., S. Giger-Reverdin, G. Bertin. C. Duvaux-Ponter, and D. Sauvant. 2009. Metha-analysis of the influence of Saccharomyces cerevisiae supplementation on ruminal paramitters and milk production of ruminants. J. Dairy Sci. 92: 1620-1632. Eun, J. S., V. Fellner and M. L. Gumpertz. 2004. Methane production by mixed ruminal cultures incubated in dual-flow fermenters. J. Dairy Sci. 87: 112-121. F.A.O. 2008. Food and Agriculture Organization. Rome Italy. STAT database. Available online: www.fao.org.

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Wanapat, M., R. Pilajun and P. Kongmun. 2009. Ruminal ecology of swamp buffalo as influenced by dietary sources. Anim. Feed Sci. and Technol. 151: 205-214. Wanapat, M., S. Polyorach, V. Chanthakhoun and N. Sornsongnern. 2011a. Yeast-fermented cassava chip protein (YEFECAP) concentrate for lactating dairy cows fed on urea–lime treated rice straw. Livest. Sci. 139: 258-263. Wang, Y., T. A. McAllister, L. J. Yanke and P. R. Cheek. 2000. Effect of steroidal saponin from Yucca schidigera extract on ruminant microbes. J. Appl. Microbiol. 88: 887-896. Woese, C. R., O. Kandler and J. L. Wheelis. 1990. Towards a natural system of organisms: proposal for the domains Archaea, Bacteria and Eucarya, Proc. Nat. Acad. Sci. 87: 4576-4579. Woodward, S. L., G. C. Waghorn and P. Laboyrie. 2004. Condensed tannins in birdsfoot trefoil (Lotus corniculatus) reduced methane emissions from dairy cows. Proc. N. Z. Soc. Anim. Prod. 64: 160-164. Wright, A. G., A. J. Williams, B. Winder, C. T. Christophersen, S. L. Rodgers and K. D. Smith. 2004. Molecular diversity of rumen methanogens from sheep in western Australia. Appl. Environ. Microbiol. 70: 1263-1270. Yokoyama, M. T. and R. A. Johnson. 1988. Microbiology of the rumen and intestine. In: The Ruminant Animal: Digestive Physiology and Nytrition. Church, D. C. ( Ed.) , Prentice Hall. New Jersey, USA.

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Conserving Endangered Breed: Case Study of Gembrong Goats I Gede Suparta Budisatria, Jafendi Purba Hasoloan Sidadolog, Dyah Maharani and Sumadi Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta BACKGROUND Animal germ plasm is a unique genetic property owned by one variety of animal which is formed through domestication processes of a certain species, and the respective inheritable genetic properties can be used or formulated to form new superior varieties. Variability of animal germ plasm is the individual genetic variability within a flock, and variability between flocks is the picture of the domesticated species and its wild relatives. The animal genetic resources available throughout the world are in a dramatic state of decline (Henson, 1992). Over the course of the last 100 years we have been faced with a rapid and continuous decline of wild mammalian species and an indiscriminate dilution of farm animal breeds (Santos et al., 2010). One in every four mammalian species is under threat around the world (IUCN, 2010) and every week, an average of two domestic breeds is being lost (FAO, 2009). The development of artificial insemination and other techniques that facilitate easy transfer of breeding material from one geographical region to another, have resulted in widespread cross breeding and the replacement of local stocks through prolonged dilution. In many cases this has been carried out without initial characterization or evaluation of indigenous breeds and with no effort to conserve local strains. It has resulted in the disappearance of a substantial number of local populations, with the consequent loss of their inherent genetic adaptation to their local environments. This increasing loss of identifiable diversity in animal genetic resources has been recognized for many years. Particular concern has been growing with respect to the speed at which uncharacterized breeds are disappearing in some rapidly developing regions of the world where climatic, parasitic or disease pressures could have produced important genetically adapted breeds (Hodges, 1990). Indonesia has various animals owning characteristic genetic properties and germ plasm, and they are considered as valuable asset for the development of animal kind. Variability of animal germ plasm in Indonesia is worthy to be explored, developed, and promoted for international research collaboration. Various types of livestock animals each at specific location, recognized as well as not being recognized can be found in every province without any confirmation concerning their population numbers and potencies. The flocks have comparable superiority compared to the imported animals, for instance in the case of adaption capability to tropical climate with good reproductive system due to long natural selection. Among various local domestic animals to breed, goat is found widely being kept by many people. In fact goat is an important asset owned by small farmers in Indonesia, but ironically its existence is very often being forgotten and do not get enough attention. Goat is well accepted by many people, but goat farming including its rearing and breeding management is still done as small scale farming in a traditional way. Goat has an important role in farming system in Indonesia, which is shown by its population numbers as many as 14.874 million involving about 0.6 million family of livestock farmers. The respective promising data on goat unfortunately is not supported by its productivity, contribution on meat supply is only as much as 2.9%, and population numbers from 2003 to 2007 had increased only 2 to 3% annually. Indonesia owns several goat germ plasm some of which are known as Kacang goat, Etawah Crossbred goat, Bligon goat, Kejobong goat, Gembrong goat, Kosta goat, and Marica goat. Each has its own different characteristics related to its natural spreading areas, most of

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change those breeds have limited spreading areas which mean they are not widely distributed. Amongst those breed, the most endangered population is Gembrong goats (Budisatria, 2009). The superiority of local breeds as national germ plasm has not been recognized yet, while its protection or conservation as well as its utilization were still on papers only. Consequently erosion and pollution on germ plasm were going on without any protection at all, it became a worry to many people that their existence would not be there anymore (Astuti et al., 2007). In most cases breeds that are not very profitable under current production and market conditions are left out and run the risk of extinction and once lost, genetic material is irreplaceable. It is therefore, protecting, conserving, as well as developing and thinking of utilization of local animals germ plasm need to be supported by certain guidance that can protect the genetic potency of local bred and its family, developed domesticated bred as well as those being kept by subsystem way. The breeds thus conserved will provide valuable resources for the future of agriculture, especially in the developing world. The need for parallel conservation of animal genetic resources, as raw material for future animal breeding programs, is also recognized and is becoming an important issue in international, regional and national agricultural planning. Conservation is of particular concern in regions of rapid agricultural change, where indigenous stocks and farming methods are being replaced. Areas where climatic extremes or particular parasitic conditions have resulted in genetically modified and unique local stocks which are able to survive under extreme conditions are also a high priority. CHARACTERISTICS OF GEMBRONG GOAT The origin of Gembrong goat is still uncertain until now due to the lack of available information and researches been conducted, it is not easy to find references concerning these goat. It is suspected that Gembrong goats are derived from cross mating between Kashmir and Turkish goats, based on similarity being found. The two breeds entered Bali island during empireship era, as a present for a Balinese nobleman. Furthermore Astuti et al. (2007) added that besides entering as a present, Kashmir goats were brought into Bali by Indian traders. Cross mating between the two introduced breeds produce Gembrong goats which were developed became an unique breed, and were initially found many at coastal area of Karangasem regency. Fishermen very often use the goats’ hairs to be tied on fish hook for when they go fishing. The population is getting less and less, data from Livestock Bureau of Bali Province (2006) (Dinas Peternakan Propinsi Bali 2006) indicated that the population numbers was only 43 heads reported in Karangasem and Jembrana regencies. The recent research found that the population of Gembrong goats at Karangasem district were only 26 heads, consisted of 10 head male and 16 head female (Sidadolog et al., 2012). The reducing population numbers might be caused by low reproductive ability and also the farmers’ believe that to much mating will lead to falling hairs on male goats, so that farmers try to avoid or minimize males to mate. This fact is supported by expensive price of hairs of Gembrong goats, which reach as much as Rp 400,000 (USD 40) per kg. Other opinion concerning the reason of declining population numbers is the difficulty of feeding caused by the thick hairs on its’ head covering eyes and mouth. The difficulty on eating have caused the goats cannot receive enough good nutritious feeds, which leads to disturbance on the immune system and goats become easily sick and dead. The true facts concerning all of these opinions have not been confirmed because of the limitation on research being done.

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Figure 1. Male Gembrong goat Figure 2. Female Gembrong goat

Gembrong goats characteristically have thick long white shiny hairs. The thick long hairs which is only found on males can be as long as 30 cm (normally is between 15 to 25 cm long) as presented in Figure 1, hairs on females do not grow very well only 2 to 3 cm long (Figure 2). The body of the males is almost all covered by hairs, especially on its head (almost covering its ears, eyes, and mouth), as well as its legs. The long white hairs is cut in average two times a year. The white color is another characteristic of Gembrong goats. Dominantly white was 61.5%, a portion of light brown 23.o8% and brown 15.38%. General pattern of body color was a single color of about 69.23%, and the rest were combination of two colors 15.38%, three colors 15.38% (Batubara, 2007). Beard is also obviously observed on females, faceline is a bit concave, both males and females have small horns. Ears are straight upright, although hanging ears are also noticed, and tails are horizontally flat. THE REASON FOR CONSERVATION Food and Agriculture Organization of United Nation mentioned that there are three reasons why conservation must be done (Henson, 1992). It is includes economic potential, scientific use and cultural interest. In terms of economic potential, benefit of conservation related to the production of meat, milk, fiber, skin or draught power. Endangered populations with economic potential may have regional adaptation developed for the country of origin, or adaptations which may be beneficial in other areas of the world where similar or complementary conditions exist. Conservation for the possible scientific use may include the use of conservation stocks as control populations, in order to monitor and identify advances and changes in the genetic makeup and production characteristics of selected stocks. They may include basic biological research into physiology, diet, reproduction or climatic tolerance at the physiological and genetic level. Genetically distinct breeds are needed for research into disease resistance and susceptibility which could help in the development of better medication or management of disease. Many populations have played an important role in specific periods of national or regional history. There are also breeds which have been associated with social and cultural development, and may be conserved for their aesthetic value. Cultural and historical values of most communities are reflected by the type of breeds they keep, therefore, conserving them is necessary to maintain their identity. Conserving of farm animal genetic resources could be useful for opportunities to meet future demands, regenerating population after disease outbreaks, rescuing rare or endangered species or breeds, providing a source of genetic material for research purposes, supplying germ-plasm for the development of new breeds, and maintaining indigenous livestock gene pool diversity. In the case of Gembrong goats, conservation is required due to the fact that the population of Gembrong goats are decreased annually. Furthermore, by keeping Gembrong goats, the farmers obtained additional income from selling those goats. They also have socio-economic and cultural values such as the expensive prices of their hair, the hair is usually used by the

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change fishermen to be tied on fish hook when they go fishing and the famous dance in Bali called Barong dance used the Gembrong hairs as ornament. CONSERVATION OF GEMBRONG GOATS IN INDONESIA Henson (1992) stated that there are three methods for the conservation of animal genetic resources. The first involves the conservation of animal genetic material in the form of living ova, embryos or semen stored cryogenically in liquid nitrogen (-196 degrees centigrade). The second is the preservation of genetic information as DNA, stored in frozen samples of blood or other animal tissue or as DNA segments. The third is the conservation of live populations. The condition of Gembrong goats is very discouraging, based on criteria of save number population of genetic sources declared by FAO, population numbers of Gembrong goats is considered in endangered state or almost extinct. Therefore, protecting, conserving, as well as developing and thinking of utilization of Gembrong goats need to be supported by certain guidance that can protect their genetic potency. Efforts to keep the existence of Gembrong goats have been done by in-situ; ex-situ; cryogenic and crossbreeding program. A certain foundation have been involved, however, it only for two years, the termination was caused by financing limitation. At present the respective foundation is working together in collaboration with the Livestock Research and Technology Bureau (Balai Penelitian Teknologi Peternakan/BPTP) try to do the conservation program. The program is done in situ or inside its original habitat in Karangasem residency. In-situ conservation is the maintenance of live populations of animals in their adaptive environment or as close to it as is practically possible. For domestic species the conservation of live animals is normally taken to be synonymous with in situ conservation (Henson, 1992), while Braverman (2014) stated in-situ conservation’’ defined as the conservation of ecosystems and natural habitats and the maintenance and recovery of viable populations of species in their natural surroundings. In-situ conservation focuses on the maintenance of species diversity within their natural habitats. Weiner (1989) stated that in- situ conservation has a number of advantages, and may be the only option available in some instances. In-situ conservation also very flexible in its application and allows for the development and utilization of breeds. However, because of limited facilities and budget constraints, in-situ conservation may be restricted to a small population (Furukawa et al., __). Furthermore, the most important problem in considering in-situ conservation is how to keep genetic variability within the population while maintaining genetic peculiarities without reducing allelic or genotype frequencies. Ex-situ conservation is defined in the same text as the conservation of components of biological diversity outside their natural habitats (Braverman, 2014). It is used to refer to the collection and freezing in liquid nitrogen of animal genetic resources in the form of living semen, ova or embryos, it may also be the preservation of DNA segments in frozen blood or other tissues (Henson, 1992). The ex-situ conservation of Gembrong goats have also been done in the experimental farm of BPTP Bali, village of Sawe, Jembrana residency. The collaboration is expanding by cross mating Gembrong goats with Etawah goats to produce Gettah (Gembrong-Etawah) goats. Apart from in-situ/ex-situ, the conservation of endangered breed can also be carried out through assisted reproductive technologies (ART), like semen and embryo cryopreservation, artificial insemination and embryo transfer are routinely used in farm animal husbandry (Hansen and Block, 2004). Such biotechnologies allow more offspring to be obtained from selected parents to ensure genetic diversity and may reduce the interval between generations (Andrabi and Maxwell, 2012). Recently, the same approach is in use for conservation of

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia wildlife and endangered mammalian species (Pukazhenthi and Wildt, 2004). According to Amstislavsky et al. (2006), the package of reproductive technologies aimed at ex-situ conservation should include embryo and sperm cryo-banking. In practice, current reproductive biotechnologies are species-specific or inefficient for many endangered animals because of insufficient knowledge on basic reproduction like estrous cycle, seasonality, structural anatomy, gamete physiology and site for semen deposition or embryo transfer of non-domestic species (Andrabi and Maxwell, 2012). In regard with the ex-situ conservation of Gembrong goats, assisted reproductive technologies have been applied by Faculty of Animal Science, Universitas Gadjah Mada in collaboration with the private company named PT. HRL and the Centre of Artificial Insemination Bureau (Balai Besar Inseminasi Buatan) Singosari, East Java. In this project, the semen of male Gembrong goats were collected regularly twice a week and preserved as frozen semen. Those semen will be used to inseminated the female Gembrong goats owned by the private company. The next step that will be done to conserve Gembrong goats is grading-up. Grading-up is the sequential use of pure-bred animals with grade animals over a series of generations to provide a "nearly purebred" result. Grading-up has been widely used in a number of livestock species, especially with recently imported breeds. It allows for rapid numerical expansion of the breed, and also provides a demand for purebred males for crossbreeding. Grading-up has a lot of positives, a few negatives, and several facets that make it an interesting biological phenomenon. As a backdrop to this issue, it is important to reflect on the character and utility of breeds. To carried out the grading-up of Gembrong goats, artificial insemination will be applied. The purebred Gembrong frozen semen that already preserved in the previous conservation will be inseminated to female local goats, preferably Kacang goats with hair colors. Any resulting male kids will be discarded from the breeding program, but the female are retained. These females are genetically 50% Gembrong and 50% of the Kacang goats; they are known as Grade One or G1 Gembrong. These are in turn mated to a purebred Gembrong buck and the female kids are retained as Grade Two Gembrong which are statistically 75% Gembrong and 25% Kacang goats. By repeating the above process two more times an animal is obtained which at Grade Four is almost 94% Gembrong. At this level does can be admitted to the “purebreds”. Buck, however have to be taken to Grade Five (nearly 97%) before being considered purebred. CONCLUSIONS Conserving genetic of domestic animals such as Gembrong goats urgently required, primarily because their population decreased annually and once lost, genetic material is irreplaceable. The conservation should includes animal genetic material in the form of living ova, embryos or frozen semen, preservation of genetic information as DNA, and conservation of live populations. Stakeholders in Indonesia who is working with animal genetic resources need to be mobilized to conserve Gembrong goats in support of other organizations (e.g. FAO) and private companies. REFERENCES Andrabi, S.M.H., and W.M.C. Maxwell, 2012. A review on reproductive biotechnologies for conservation of endangered mammalian species. Anim. Reprod. Sci. 99(3–4): 223-243. Amstislavsky, S., H. Lindebergb, J. Aalto, M. Ja¨rvinen, M. Valtonen, E. Kizilova, G. Zudova, Y. Ternovskaya, 2006. Embryo cryopreservation and transfer in Mustelidae:

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Approaches to ex situ conservation of the endangered European mink. International Journal of Refrigeration 29:396–402. Astuti, M., A. Agus, I.G.S. Budisatria, L.M. Yusiati, M.U.M. Anggraini, 2007. Peta Potensi Plasma Nutfah Ternak Nasional. 1st ed., Ardana Media, Yogyakrta. Batubara, A., 2007. Tujuh Plasma Nutfah Kambing Lokal Indonesia. Majalah Sinar Tani, 25 April – 01 Mei. Braverman, I., 2014. Conservation without nature: the trouble with in situ versus ex situ conservation. Geoforum 51:47–57. Budisatria, I.G.S., 2009. The productivity of goat germ plasm in Indonesia. In : K.A. Santosa and I.G.S. Budisatria (Eds.). Germ Plasm of Goat in Indonesia. 1st ed., CV. Bawah Sadar, Yogyakarta. Dinas Peternakan Propinsi Bali (Livestock Bureau of Bali Province), 2006. Informasi Data Peternakan Propinsi Bali Tahun 2006. Denpasar. Henson, E.L., 1992. In situ conservation of livestock and poultry. FAO Animal Production and Health Paper 99, Food and Agriculture Organization of the United Nations, Rome. FAO, 2009. Domestic animal genetic diversity. Available at :http:// www.fao.org/ biodiversity/geneticresources/bio-domesticanimals/en/. Accession date: Furukawa, T., H. Takeda, M. Satoh, K. Ishii and C. Hicks, ___. In situ Conservation Methodology for Farm Animals. Available at: http://www.angrin.tlri.gov.tw/ english/apec/ in_situ.htm. Accession date: September 10, 2014. Hansen, P., and J. Block, 2004. Towards an embryocentric world: the current and potential uses of embryo technologies in dairy production, Reprod. Fertil. Dev. 16:1-14. Hodges, J., 1990. Conservation of Animal Genetic resources in developing countries. Genetic Conservation of Domestic Livestock. CAB International, Wallingford, Oxon, UK. pp 128-145. Pukazhenthi, B., and D.E. Wildt. 2004. Which reproductive technologies are most relevant to studying, managing and conserving wildlife?, Reprod Fertil Dev 16 (2) (2004) 33-46. Santos, R.R., C. Amorim, S. Cecconi, M. Fassbender, M. Imhof, Lornage, M. Paris, V. Schoenfeldt, B. Martinez-Madrid, 2010. Cryopreservation of ovarian tissue: An emerging technology for female germline preservation of endangered species and breeds. Anim. Reprod. Sci. 122:151-163. Sidadolog, J.H.P., Sumadi, L.M. Yusiati, I.G.S. Budisatria, S. Bintara, D. Maharani, 2012. Studi Karakteristik Biologis Kambing Gembrong Sebagai Plasma Nutfah Indonesia. Laporan Penelitian. Fakultas Peternakan, Universitas Gadjah Mada, Yogyakarta. Weiner, G. 1989. Animal Genetic Resources - A global programme for sustainable development. FAO Animal Production and Health Paper, 80. Rome.

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Improvement of Forages Quality by Molecular Breeding in Tropical Grasses: the case of Brachiaria ruziziensis Genki Ishigaki and Ryo Akashi Faculty of Agriculture, University of Miyazaki, Japan ABSTRACT We introduced the research strategy for improving the quality of ruzi grass (Brachiaria ruziziensis) by molecular breeding. We have established an effective method for plant regeneration system, and produced tetraploid plants by in vitro colchicine-treatment with tissue culture. In addition, transformation system mediated particle bombardment has been established. These new materials and techniques can contribute to develop the molecular breeding of Brachiaria genus. Key Words: Brachiaria, Tissue culture, Tetraploid ruzigrass, Ttransformation system INTRODUCTION Brachiaria is one of important tropical forage grasses, which has been cultivated in tropical and subtropical region as pasture mainly. At present, some species such as B. brizantha, B. decumbens, B. humidicola and B. ruziziensis have been important commercially because these species have many positive attributes, e.g., tolerance to acid soil, high productivity and high quality forage. Thus, these species are also important as breeding material in Brachiaria. For the improvement of forage quality, besides productivity and digestibility, persistence, adaptation to multiple abiotic stresses (drought, cool, cold, high-temperature, high-salinity e.t.c.)is also important. However, it is very difficult to develop a Brachiaria breeding program through a traditional crossing approach because almost all species are predominantly facultative apomictic tetraploid.Here we report several approaches which resolve these breeding barrier and expand the genetic variation among Brachiaria genus. TISSUE CULTURE SYSTEM Figure 1 shows Brachiaria breeding scheme using bioengineering. Firstly, a plant regeneration system via multiple shoots formation, or somatic embryogenesis from seed- derived shoot apical meristems of ruzi grass was established (Ishigaki et al. 2009a). We used seed-derived shoot apical meristems not immature inflorescences as explant because immature inflorescence sources are restricted to a very short season. Multiple shoot clumps were generated in MS medium containing low-concentration2,4-D and high-concentration BAP. On the other hand, Embryogeniccalli of ruzi grass was formed on MS medium containing high-concentration2,4-D and low-concentration BAP. The plant regeneration via embryogeniccalli formation can be used as an alternative regenerable target tissue for genetic transformation using particle bombardment of ruzi grass.

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STEP1 Tissue culture system of ruzigrass

Mature Seeds

Multiple-shoot clumps Embryogenic calli

Regenerated plants

STEP2 STEP3 Induction of sexual tetraploid ruzigrass Establishment of transformation system Multiple-shoot clumps / Seedlings Embryogenic calli (Diploid)

Colchicine treatment Particle bombardment

Tetraploid plants GM plants

Figure 1. Brachiaria breeding scheme INDUCTION OF TETRAPLOID SEXUAL PLANTS Development of sexual tetraploid plants would be useful in the cross breeding. Earlier research reported that a sexual tetraploid ruzi grass was useful for the Brachiaria breeding program as a source of sexuality; however, this germ plasm has not been widely circulated or used in conventional breeding programs. Therefore, we have produced tetraploid ruzi grass using in vitro multiple-shoot clumps or seedlings to develop germ plasm suitable for cross breeding among the Brachiaria genus (Ishigaki et al., 2009b).These tetraploid ruzi grass growth was vigorous, and these plants had wider leaf blades than those of diploid ruzi grass. These characteristics may contribute to high dry-matter yield and will likely also expand the breeding potential of the Brachiaria genus. In order to put the raised new cultivars on a commercial base in future, it is essential to keep high seed productivity and secure the circulation of the new cultivarspromptly. At present, we performed recurrent selection of the population which has high seed yield components using these tetraploid ruzi grass strains named Miyaokikoku-ichigou, and investigated the variation of the seed yield components. TRANSFORMATION SYSTEM Plant transformation can contribute to Brachiaria breeding because new characteristics can be introduced into the species using genetic engineering techniques. We have produced transgenic plants by particle bombardment–mediated transformation with embryogeniccalli of ruzi grass. The particle inflow gun apparatus was constructed as previously described by Akashi et al. (2002). In addition, we also investigated somaclonal variations in callus- regenerants of diploid ruzi grass and autotetraploid ruzi grass produced by colchicine- treatment, and evaluated their morphological traits especially their fertility, to facilitate the development of a methodology for the production of stable transgenic lines.FCM-based analysis of the ploidy levels of callus-regenerants of diploid ruzigrass revealed that calli genomes spontaneously reduplicated at high frequencies, resulting in polyploidregenerants (tetraploid and octoploidregenerants). The viability of pollen as well as the fertility rates were significantly decreased in tetraploid and octoploidregenerants. On the other hand, callus- regenerants of autotetraploid ruzi grass keep the ploidy as tetraploid. Therefore, we have applied embryogeniccalli of autotetraploid ruzi grass for the following transformation. In addition, a combination between transformation system and crossbreeding with a

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia tetraploidruzigrass and tetraploid apomictic species will plays a role in immobilizing the transgene and increasing gene pool of Brachiaria (Figure 2). At present we have successfully produced transgenic plants carrying the DREB1A gene from Arabidopsis thaliana to increase drought and salinity tolerance of ruzigrass. These transgenic plants exhibited fertile and showed high potential tolerance under drought and high salinity conditions.

Figure 2. Brachiaria breeding strategy using bioengineering Brachiaria GERM PLASM RESOURCES The Brachiaria genus has about 100 species recognized in the world. Recently, we analyzed the cytology and determined the DNA content of 28 Brachiaria accessions consist of 11 species. FCM-based analysis revealed that the degree of variation of DNA content differed among each Brachiaria species. Especially, B. brizantha accessions showed a wider-variation of DNA content than that of other Brachiaria species, indicating that the species have high potential to expand the genetic variation via crossbreeding with tetraploid sexual ruzi grass.

REFERENCES Akashi R, C. Yuge, T Gondo, O Kawamura, and F. Hoffmann (2002). Bialaphos-resistant cells of dallis grass (Paspalum dilatatum Poir.) through particle bombardment with a simple self-built inflow gun. Grassl Sci 47: 588–593. Ishigaki, G., T. Gondo, K. Suenaga, and R. Akashi (2009a) Multiple shoot formation, somatic embryogenesis and plant regeneration from seed-derived shoot apical meristems in ruzigrass (Brachiaria ruziziensis). Grassl Sci 55: 46–51.

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Ishigaki, G., T. Gondo, K. Suenaga, and R. Akashi (2009b) Induction of tetraploid ruzi grass (Brachiaria ruziziensis) plants by colchicine treatment of in vitro multiple-shoot clumps and seedlings. Grassl Sci 55: 164–170. Ishigaki, G., T. Gondo, K. Suenaga, and R. Akashi (2011). Fertile transgenic Brachiaria ruziziensis (ruzi grass) plants by particle bombardment of tetraploidized callus. J. Plant Physiol 169: 546–549. Ishigaki, G., T. Gondo, MM Rahman, N Umami and R. Akashi (2013).Spontaneous appearance of polyploids in plants regenerated from embryogeniccalli derived from seedling-meristems of ruigrass (Brachiaria ruziziensis Germain et Everard). Grassl Sci 60: 24–30.

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Linking Gene Expression Patterns with the Productivity of Sheep Peter Wynn1, David McGill1 and Sue Hatcher2 1 Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga 2650 Australia; 2 NSW Department of Primary Industries, Orange Agricultural Institute, Orange NSW, 2800 Australia Corresponding email: [email protected] ABSTRACT Australia’s sheep population is dominated by the Merino breed which has evolved from judicious crossing of breeds introduced from across Europe, South Africa and the Indian sub- continent. Their genetic diversity has ensured that the adoption of genetic selection strategies based on the traditional approach of selecting superior phenotypes of mature animals has achieved significant improvements in the productivity in both wool production and quality and body weight gain. The development of technology to sequence whole genomes and then map the genetic variation resulting from small differences in gene structure between specific allelic pairs, or single nucleotide polymorphisms (SNPs) has provided a more powerful mechanism for improving genetic gain. Importantly this allows for much more accurate estimates of breeding values for young animals, thereby accelerating the rate of genetic gain within the industry. This will be most important for the traits that are difficult to measure including carcase traits, resistance to parasites and reproductive capacity. Some of the current tools refined for the Australian Merino industry are included in this paper. Key Words: Sheep, Genomics, Productivity INTRODUCTION During the process of domestication of animals over the past 12,000 years mankind has been selecting for their ability to produce more food and fibre (Ueberberg et al. 2009). The origin of domesticated sheep dates back to the period of 11,000 to 9,000 BC in Mesopotamia where the primitive Mouflon sheep were selected for their ability to produce meat, milk and skins. The coat of these animals was short, hairy and brown in colour (Ryder 1965). They weighed between 35kg (females) and 50kg (males) which is consistent with some of the staturally small breeds found in commercial use today (Annemie et al. 2009). Since then animals have been selectively bred to obtain phenotypes that were superior to that of the previous generation (Singh et al. 2010). Currently the requirements for sheep in different populations around the world vary according to need. Sheepmeat provides a major source of animal protein in many countries irrespective of their economic status. The fibre from sheep has been highly sought after to process to provide warm clothing particularly in cold climates. Australia has been well endowed with the development of the Merino breed, which historically developed from the crossing of the various breeds introduced by English settlers from 1788 onwards. These sheep came from Spain, France, Germany, South Africa and even India with subsequent infusions of animals from the USA. The Indian wire-haired sheep was a particularly interesting and perceptive introduction since it introduced the ability to breed under harsh condition even though their fleece served no value to wool producers. The direction of cross breeding was determined largely by the survivability of animals in the harsh environmental conditions of Australia. The interaction of these breeds has resulted in 3 major phenotypes in Australia, the larger framed broader woolled South Australian Merino, the medium woolled Peppin Merino and then the finer woolled smaller Saxon Merino. Thus genetic diversity is a hallmark of the Merino breed.

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The Merino has been supplemented in Australia with many other breeds more specialised for meat production including the major British breeds such as the Border Leicester, the Dorset Horn, Suffolk, Southdown and the Wiltshire while the South African Dorper and Meat Merino also have been successfully introduced. Other specialist sheep have been bred for course carpet wool including the Drysdale, Tukidale, Carpetmaster and Elliottdale, all of which have been bred from the Romney or Perendale breeds. More recently other exotic breeds such as the fat tailed Awassi and Damara have been introduced, the former as a means to satisfy the end consumer requirements of the middle eastern live sheep trade and the latter to provide an easy care low maintenance option for producers in the rangeland production zones. The development of breeds adapted to tropical environments is well illustrated by the thin and fat tailed sheep of West and east Java in Indonesia (Stelwagen et al. 2009). There are many similar breeds found throughout tropical climes which have not been the subject of intensive breed improvement strategies. So how can we maximise the productivity of these animal resources to ensure that we are able to convert rate limiting feed resources into animal protein and fibre for apparel? The basis of genetics. The genotype of an animal is comprised of the 22,000 genes which are arranged in 26 pairs of autosomes and 2 sex chromosomes: these genes comprise the genome of the animal (Wheeler et al. 2007b). These in turn consist of nucleic acids or bases arranged in specific sequences which are translated to form amino acids which in turn make up the functional proteins of the body. Either individual or groups of genes specify the processes that control the productivity of the animal including muscle and milk synthesis or wool growth. Genetic variation is based on small differences in gene structure occurring between allelic pairs. Thus parents will contribute different alleles to their offspring thereby changing the expression of the characteristics controlled by the specific gene (Wheeler et al. 2007a). The subtle differences in base sequence involving a single base substitution, termed single nucleotide polymorphisms (SNPs), provide the genetic variation which form the basis of current methods used for genomic selection. The expression of some parameters is controlled by a single polymorphism. These genes may control, for example, fecundity as with the Merino Booroola gene (Smolenski et al. 2007), muscle hypertrophy through the callipyge gene (Qian et al. 2011) or the expression of horns in the Merino through 3 alleles (Catala-Clariana et al. 2011). However most sheep production traits are controlled by a multiplicity of genes which interact to result in the observed phenotype. The traditional approach. The basis for genetic selection is dependent on measuring the phenotype of an animal for a specific trait and relating it to the performance of other animals while incorporating information on the animal’s relatives. Thus an estimated breeding value is obtained from the performance measures of the individual, its offspring and relatives (Wheeler et al. 2007a). Genetic progress can be accelerated by increasing the accuracy and intensity of selection and also by decreasing the generation interval using the latest in reproductive technologies. The assumption is always there that there is sufficient variation in the trait between animals to choose from. Genomic selection. This process simply makes use of our ability to utilise the huge array of polymorphisms found across the genome which are now available through the process of high throughput genotyping. To date the use of autosomal microsatellites or short tandem base sequence repeats has provided some information on genetic diversity and Y

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia chromosomal markers have been useful for measuring the introgression of Bos Taurus genes in the zebu genome (Rousseau-Ralliard et al. 2010). However the most dramatic advances have been through the analysis of 50,000 polymorphisms or SNPs from 2,800 sheep spread over 74 breeds. These studies have shown that the ovine genome has retained a significant pool of genetic diversity which can be used to advance the selection of production traits (Ricci et al. 2010). Most of the polymorphic sites that control economically important production traits are unknown. Thus the use of genetic markers such as microsatellites or short tandem repeats among many others has been of limited value. Genomic selection was not possible until the dense coverage of these SNPs across the genome was identified. This process then relies on the linkage disequilibrium between markers and causal polymorphisms which result in associations between markers and the production traits that these causal polymorphisms affect (Brawand et al. 2008). Essentially this involves some sophisticated statistics that does not require knowledge of the genes or sites responsible for variation in the trait. Accuracy of prediction of breeding values from SNP genotypes can be as high as 0.7. (Rijnkels et al. 2010). Genomic information does not improve the accuracy of breeding values for traits with already have moderate to high accuracy estimated using the traditional approach, such as body and fleece weight and then muscle and yield assessed by ultrasound scanning or wool quality characteristics such fibre diameter and staple strength. However the information is a valuable addition to the prediction of hard to measure traits with low accuracy such as resistance to parasitism, mature animal fleece weight, reproductive efficiency and carcass and eating quality traits (Devinoy and Rijnkels 2010). Further improvements in breeding values will depend on the availability of reference or validation flocks displaying a wide range of phenotypes provided by the industry to improve genomic predictions. The Sheep Co-operative Research Centre resource flock for example, included 100 sires covering meat terminal sires, as well as maternal and Merino sires used with 4,000 Merino or Border-Leicester Merino ewes (Lemay et al. 2009). Determination of dam pedigree for Australian Merinos Genetic evaluation of animals is dependent on the acquisition of accurate pedigree information. Records of pedigree are largely absent in Australia’s Merino breeding flocks (Richards and Atkins 2007). The traditional method of insemination, lambing in sire groups and recording the dam of each lamb while minimising mis-mothering can take up to 6 weeks and is laborious. This may cost up to $A10 per lamb when labour is accounted for (Russell et al. 2006). The development of “Pedigree Matchmaker” provides an alternative technology which uses a walk-over weighing system that records the movement of lambs and their mothers as they pass a portable radio frequency identification panel when seeking food or water or changing paddocks. The continued recording of the same lamb with a specific ewe ensures that mismothering is avoided thereby increasing the accuracy of prediction of the pedigree of any lamb up to 85-96%. The technology provides information on twinning of lambs as well as a possible estimate of maternal bonding shown by the dam (Richards and Atkins 2007). In addition to providing confirmation of the pedigree of the dam of any lamb, the process is more cost effective through savings in labour. A SNP based parentage test for Australian Merino sires. The improvement in accuracy of estimated breeding values through the use of SNP technology has been described earlier in this paper. A 3 panel test using 190 SNP’s has now been refined through the International

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Sheep Genomic Consortium which provides accurate results universally for all breeds (Bell et al. 2013). This tests costs $A17 in Australia. To obtain reliable information on pedigree, all rams in a flock need to be blood sampled for testing and the information combined with the ewe pedigree information provided by the “Pedigree Matchmaker” technology. If this has not been used then all ewes and lambs also require testing. However there is more than a single SNP genotyping chip available to growers and so duplication of testing can be avoided. The value of genomics in improving genetic gain from using EBV’s. Traditional means of selection translate to relatively poor breeding values for young animals as the animal has to mature before the measurement of wool growth, wool quality, reproductive efficiency and adult bodyweight can be assessed. However the incorporation of genomic information ensures that breeding values are improved for these young animals, thereby accelerating the rate of genetic advancement in a breed. This is demonstrated in Table 1, which shows the value of the addition of genomic information to improving the Australian sheep breeding values (ASBVs) for a range of fleece characteristics and adult body weight. Table 1. Effects of the inclusion of genomic selection information into an ASBV assessed at 6 months of age relative to the same effect on ASBVs assessed at 18 months of age Ram at first use 6 months 18 months Trait ASBV + Genomics ASBV + Genomics Yearling clean fleece weight 0.43 0.63 0.67 0.75 Yearling mean fibre diameter 0.54 0.71 0.80 0.84 Yearling staple strength 0.29 0.48 0.46 0.56 Adult body weight 0.51 0.69 0.59 0.72 Merino Production Index 0.28 0.40 0.38 0.45 Source: Sheep CRC genomics tests for Merinos – taking genetic gain to the next level www.sheepcrc.org.au The Merino Production Index (http://www.sheepgenetics.org.au/Getting-started/ASBVs-and- Indexes/MERINOSELECT-Indexes) incorporating both bodyweight and fibre characteristics are clearly improved by the inclusion of the genomic data. Expression of the genome and the impact of the environment. It is now recognised that many environmental factors in addition to nutrition influence gene expression patterns and therefore development from placentation onwards. These so-called epigenetic mechanisms challenge the very basis of Darwinian evolutionary theory that the variability in populations occurs exclusively through random mutations. The mechanism through which epigenesis occurs is by altering gene methylation patterns (Khosla et al. 2001) and by changes in post- translational modification patterns of nuclear proteins or histones to alter chromatin structures which then influence genome stability (Ricci et al. 2010) The most compelling aspect of epigenesis is that these environmental mutations then form part of the germline to pass to subsequent generations (Surani 2001). These new concepts present a major challenge to the geneticists of the future as we attempt to quantify the contribution of these subtle changes to the genome to animal productivity. CONCLUSION The use of genomics has and will continue to extend the accuracy of breeding values for a range of traits of importance to sheep breeders as well as enable ‘hard-to-measure’ traits such as carcase and meat quality, parasite resistance and reproduction to be routinely included in genetic evaluation programs. While this may bring some added complexity to the assessment, measurement and selection of animals in breeding programs, the increase in accuracy of selection together with the ability to select superior animals at much younger ages will

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia accelerate the rate of genetic improvement into the future. However, further work is required to ensure that the extended range of traits with varying genetic relationships between them, both favourable and unfavourable, are combined into selection strategies that maximise rates of genetic gain, while improving management efficiency. REFERENCES Annemie VD, Debby VD, Valentijn V, Bart DS, Walter L, Liliane S, Peter Paul DD. 2009. Central administration of obestatin fails to show inhibitory effects on food and water intake in mice. Regulatory Peptides 156: 77-82. Bell AM, Henshall JM, Gill S, Gore KP, Kijas JW. 2013. Success rates of commercial SNP based parentage assignment in sheep. Paper presented at Proceedings of the Association for the Advancement of Animal Breeding and Genetics. Brawand D, Wahli W, Kaessman H. 2008. Loss of egg yolk genes in mammals and the origin of lactation and placentation. PLoS Biology 6: e63. Catala-Clariana S, Benavente F, Gimenez E, Barbosa J, Sanz-Nebot V. 2011. Identification of bioactive peptides in hypoallergenic infant milk formulas by capillary electrophoresis-mass spectrometry. Analytica Chimica Acta 683: 119-125. Devinoy E, Rijnkels M. 2010. Epigenetics in mammary gland biology and cancer. Journal of Mammary Gland Biology & Neoplasia 15: 1-4. Khosla S, Dean W, Brown D, Reik W, Feil R. 2001. Culture of preimplantation mouse enbryos affects development and the expression of implanted genes. Biology of Reproduction 64: 918-926. Lemay DG, Rijnkels M, German JB. 2009. Lessons from the bovine genome: implications for human nutrition and research. Journal of Nutrition 139: 1271-1272. Qian B, Xing M, Cui L, Deng Y, Xu Y, Huang M, Zhang S. 2011. Antioxidant, antihypertensive, and immunomodulatory activities of peptide fractions from fermented skim milk with Lactobacillus delbrueckii ssp. bulgaricus LB340. Journal of Dairy Research 78: 72-79. Ricci I, Artacho R, Olalla M. 2010. Milk protein peptides with angiotensin I-converting enzyme inhibitory (ACEI) activity. Critical Reviews in Food Science & Nutrition 50: 390-402. Richards JS, Atkins KD. 2007. Determining pedigree by association in Merino flocks. Proceedings of the Association for the Advancement of Breeding and Genetics 17: 403-406. Rijnkels M, Kabotyanski E, Montazer-Torbati MB, Beauvais CH, Vassetzky Y, Rosen JM, Devinoy E. 2010. The epigenetic landscape of mammary gland development and functional differentiation. Journal of Mammary Gland Biology & Neoplasia 15: 85- 100. Rousseau-Ralliard D, et al. 2010. Inhibitory effect of alphaS1- and alphaS2-casein hydrolysates on angiotensin I-converting enzyme in human endothelial cells in vitro, rat aortic tissue ex vivo, and renovascular hypertensive rats in vivo. Journal of Dairy Science 93: 2906-2921. Russell AJ, Mortimer SI, Murray W, Atkins KD, Taylor P. 2006. The costs and benefits of improving selection accuracy in Merino studs. Pages 19-26 in Pope CE, ed. Trangie QPLU$ Merinos, Open Day 2006. Trangie NSW: NSW Department of Primary Industries. Singh K, Erdman RA, Swanson KM, Molenaar AJ, Maqbool NJ, Wheeler TT, Arias JA, Quinn-Walsh EC, Stelwagen K. 2010. Epigenetic regulation of milk production in dairy cows. Journal of Mammary Gland Biology & Neoplasia 15: 101-112.

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Smolenski G, Haines S, Kwan FYS, Farr V, Davis SR, Stelwagen K, Wheeler TT. 2007. Characterisation of host defence proteins in milk using a proteomic approach. Journal of Proteome Research 6: 207-215. Stelwagen K, Carpenter E, Haigh B, Hodgkinson A, Wheeler TT. 2009. Immune components of bovine colostrum and milk. Journal of Animal Science 87: 3-9. Surani MA. 2001. Reprogramming of genetic function through epigenetic inheritance. Nature 414: 122-128. Ueberberg B, Unger N, Saeger w, Mann K, Petersenn S. 2009. Expressionof ghrelin and its receptor in human tissues. Hormone and Metabolic Research 41: 814-821. Wheeler TT, Hodgkinson AJ, Prosser CG, Davis SR. 2007a. Immune components of colostrum and milk--a historical perspective. Journal of Mammary Gland Biology & Neoplasia 12: 237-247. Wheeler TT, Hood KA, Maqbool NJ, McEwan JC, Bingle CD, Zhao S. 2007b. Expansion of the Bactericidal/Permeability Increasing-like (BPI-like) protein locus in cattle. BMC Genomics 8: 75.

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Risk Management Analysis of the Traditional Farm Decision to Improve Income Mogens Lund Norwegian Agricultural Economics Research Institute Storgata 2-4-6, Postboks 8024 DEP, 0030 Oslo (Norway) Tel.: +47 22367270; Mobile: +4794690386 Corresponding email: [email protected] ABSTRACT The recent worldwide economic crisis has reminded us about the continuous need for better methods for risk assessment and management in agriculture in both developing and developed countries. Extensive analyses of risk management in agriculture performed by OECD suggest that attention must be given to the interaction and trade-offs among all risks, strategies and policies, and thus avoid a narrow focus on a single risk or risk management tool. However, a recent Danish study has revealed that there are significant differences in the perceptions of agricultural risk management among farmers and agro and food industries, financial institutions and other stakeholders in the agricultural and food cluster. Therefore, there is an urgent need for the formation of new partnerships between farmers, the public sector, the civil society and the private sector to mitigate agricultural risks and capturing potential opportunities to enhance farm incomes more efficiently and effectively especially among smallholders in developing countries. INTRODUCTION The recent worldwide economic crisis has shown us that even well-known methods for risk assessment and management in agriculture and other sectors of the society do not hinder high volatility and great losses to occur. The dramatic increases in food prices from 2007 through mid-2008 have been influenced by (1) income and population growth, (2)rising energy prices, (3) subsidized biofuel production,(5) declining productivity and output growth due to among other natural resource constraints, (6) underinvestment in rural infrastructure, and (7) weather disruptions (von Braun, 2008). For example, the prices of wheat and rice doubled within one year; and corn prices more than tripled (Songwe, 2011). Food price volatility has not only reduced poor people’s spending on essential goods and services, it has also exacerbated nutritional deficiencies in children as people shift to cheaper, lower-quality, and less micronutrient-dense foods in an effort to cope with price increases. Although the causes of the financial crisis are fundamentally different from those of the food crisis, the consequences of the food and financial crises are intertwined. For example, as the financial crisis hit, more institutional investors and speculators diversified into food commodities and commodity future markets, hence enhancing the volatility of food prices. Thus, the recent economic crisis actually consists of several underlying crises which are interconnected in complex ways (von Braun, 2008). As one result of the recession, farmers’ access to the capital market has been blocked or severely restricted in many parts of the world. Banks have becoming less interested in assuming part of the risks associated with productive agricultural activities and even less interested when their potential clients are smallholder farmers. It has then become much harder for farmers to make new investments as banks cut lending. Meanwhile, the generalized poverty in the countryside of many developing countries also blocks the use of informal credit. Rural households are still largely dependent on informal sources for their financial needs. Thus, informal lenders provide the bulk of the loans to rural households in many countries (HLPE, 2013).

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The worldwide economic crisis also has had a negative impact on farm incomes. This has been the case in both developed and developing countries although the effects have been graver in the developing countries. Furthermore, reduced opportunities for non-farm employment have in many cases limited the possibilities of those farmers to compensate for reduced farm incomes. Smallholder farmers are often among the most vulnerable in the society as many of them are poor and are exposed to a wide array of risks. Often they have low income which implies that they are less able to save and accumulate assets (Cervantes- Godoy et al., 2013). This paper discusses the implications of the world economic crisis for the need for development of new conceptual approaches to income risk management in agriculture. Risk management is defined here broadly as “the process by which decisions about risks are evaluated, made and executed. ”The definition differs from some usages where the term is more narrowly defined (Hardaker et al., 2004). In risk analysis related to public policies, it is common to distinguish among risk assessment (i.e. systematic processing of available information to identify and evaluate the frequency and magnitude of specific stochastic events), risk management (i.e. the range of measures adopted by individuals and organizations that contribute to reducing, controlling and regulating risks) and risk communication (i.e. sharing and exchange of information about risks among decision makers and other stakeholders) (International Risk Governance Council, 2008). The paper is organized as follows. Section 2 examines the risk management system in agriculture by presenting some survey results from a Danish study of farmers’ and other supply chain stakeholders’ risk perceptions. Section 3 reviews some modern approaches to agricultural risk management while Section 4 discusses how these approaches may be further elaborated to better secure and raise farm incomes in an increasingly dynamic, complex and uncertain world. Finally, Section 5 closes the paper with some concluding remarks and challenges for the future. IMPORTANT RISK FACTORS AND RISK MANAGEMENT STRATEGIES Typologies of risks and options for managing risk are often made in order to better understand risks and to design appropriate policy responses. Risks can be classified by the level of which they occur (i.e. micro, meso and macro) and by the type of the event (e.g. natural, economic, political). Another common distinction is between idiosyncratic and systemic (or covariant) risks. Idiosyncratic risks are shocks that affect specific individuals, firms or households whereas systemic risks strike groups of individuals, firms, households or communities. Such shocks are common to all participants in the affected group. The extent to which a risk is covariant or idiosyncratic depends considerably on the underlying course. Job loss may be an individual risk or the result of a macroeconomic crisis; and the risk of being ill may be idiosyncratic or a result of an epidemic. Huirne et al. (2000) and Hardaker et al. (2004) differentiate business risks from financial risks. Business risks include production, market, institutional and personal risks while financial risks encompass risks associated with different methods of financing the farm operations. Moschini and Henessy (2001) categorize agricultural uncertainty1 into four sources: (1) production uncertainty (e.g. weather conditions, amount and quality of output), (2) price uncertainty (e.g. input and output prices), (3) technological uncertainty (e.g. R&D, evolution of production technologies) and (4) policy uncertainty (e.g. tax policy, government interventions). Musser and Patrick (2001) add a fifth risk source which is human risks concerning the labor and health of the farming family and the employees. Boehlje et al. (2011) separate traditional business risks from strategic uncertainty in agriculture, e.g. risks

1 Unless stated otherwise, no distinction between risk and uncertainty is made in this paper.

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia resulting from changes in government policies, mergers and acquisitions and food safety crises. Available management tools and strategies dealing with the identified risks may also be grouped in different ways. One common classification made by Holzmann and Jorgensen (2001) is to distinguish among prevention, mitigation, and coping strategies. Prevention strategies aim at reducing the probability of adverse events occurring in the first place (e.g. choice of technologies, prevention of plant pests and animal diseases).Mitigation strategies reduce the potential impact of an adverse event (i.e. diversification in production and assets, off-farm work).Coping strategies, on the other hand, reduce the negative consequences of risks once a risk hazard has occurred (e.g. selling financial assets, insurance, use of savings or borrowing from the bank). While prevention and mitigation strategies focus on income smoothing, coping strategies mainly focus on consumption smoothing. Another distinction is between formal and informal risk management strategies (Cervantes- Godoy et al. 2013). The formal strategies are those that are based on market activities (e.g. private insurance schemes, credit markets, futures and options) and publicly provided schemes (e.g. tax income smoothing, counter-cyclical programs and disaster relief). Informal strategies are often characterized by local arrangements between individuals and households, or communities or villages (e.g. crop sharing, social reciprocity and informal risk pooling) (Cervantes-Godoy et al., 2013). The use of informal risk management strategies is much more common in developing countries than in developed countries due to the greater vulnerability of many farmers and less developed economic and legal institutions in these countries. A number of surveys in developed countries (e.g. Patrick and Musser, 1997; Meuwissen et al., 2001; Flaten et al., 2005; Lien et al., 2006; Patrick et al., 2007)have alsolooked closely at farmers’ perceived risks and the strategies they adopted to deal with these risks. In developing countries, empirical studies on smallholder farmers’ risk perceptions and management strategies have received much lesser attention in agricultural research. One of the few studies reported in the literature is a study by Gebreegziabher and Tadesse (2014) where they identify that the major sources of risk in smallholder dairy farms are of technological, price, market, production, financial, human and institutional nature. The major risk management strategies practiced include reducing cattle diseases, income diversification, financial management and expanding market networks. A recent Danish survey has assessed the most important risk factors and risk management strategies in the agricultural and food sector from a stakeholder perspective. The assumption is that not only farmers themselves but all businesses in the agriculture and food cluster are affected by the risk that the sector is subject to - directly or indirectly. It has thus been the object of the Danish survey to include all important agents in the horizontal and vertical food chain. Data on primary plant, cattle and pig farms and other holdings such as fur animals are included in the questionnaire survey. In addition, industry associations, financial institutions, agro- and food companies and advisory firms are all believed to constitute other key stakeholders in the value chain. The financial institutions include banks and mortgage institutions. Advisory firms include local advisory centers in Danish agriculture and the Knowledge Center for Agriculture in Aarhus, while the agro-and food industry include firms supplying production inputs, dairy and meat processing companies, and related food industry. Industry associations consist of the Danish Food & Agriculture Council and the Danish Food Section in Danish Industry and similar commercial and professional organizations. All these stakeholders including farmers areasked about their perceptions of risk management in agriculture.Statements related to agricultural risks are rated on a Likert Scale of -2 to 2, with -

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2 as very unimportant, -1 as unimportant, 0 as neutral, 1 as important and 2 as very important. There were 256 respondents interviewed of which 82 are from agro and food business and other stakeholders and 174 from farmer-respondents. PURPOSES OF AGRICULTURAL RISK MANAGEMENT There is apparent difference between what farmers and what their collaborative partners in the value chain generally perceive as the essential main purpose of agricultural risk management (Table 2.1). From the farmers’ perspective, the main purpose of risk management is to safeguard the family against the negative impacts of uncertainty (with 1.62 score) followed by ensuring the farm income (with 1.42 score). From the perspective of all the other stakeholders, with exception of the industry associations, the main objective of agricultural risk management is to ensure the income (i.e. liquidity and financing) of the farm holding. Table 1. What are the main objectives of agricultural risk management? Utilize Secure the Reduce price Ensure future Ensure farm strategic family volatility production income1 opportunities Farmers 1.62 0.97 0.84 1.42 0.91 Industry associations 1.17 0.83 1.00 1.00 0.50 Financial institutions 0.60 1.20 0.60 1.20 0.20 Agro- & Food industry 0.81 1.20 1.21 1.42 1.23 Advisory firms 1.17 1.11 1.11 1.61 0.78 Average 1.01 1.08 1.07 1.39 0.95 1Farm income was measured as cash flow available for financial payments including financing of new investments. Several interesting differences can also be identified. The industry associations perceive that ensuring the family as best as possible in an uncertain environment is the main purpose of risk management (with a score of 1.17). This is not surprising given that the raison d’être of these organizations typically are to protect farm families’ commercial and political interests. However, financial institutions believe that hedging of the price risks is as equally important as ensuring the farm income. For the agro-and food industry as well as for the advisory firms, ensuring the farm income is also the top priority among the goals of risk management. Furthermore, results indicate that farmers apparently are generally risk-averse, i.e. risk management is to a larger extent interpreted as a tool to protect against downside risks than as a tool to identify and exploit new potential opportunities. This is because utilization of new strategic opportunities is considered as one of the least important purposes of risk management among all farmers.Smallholder farmers are often assumed to be risk-averse (Bacic et al., 2006). MAJOR RISKS IN AGRICULTURE Farmers and the other stakeholders in the agro-food value chain are alsoasked which risk factors are considered to be the most important in agriculture (Table 2.2). Here they evaluate six selected risk factors: (1) Financial risks (e.g. interest rate fluctuations, currency fluctuations, credit risk and liquidity risk); (2) Human risks primarily covering accidents caused by illness and/or death; (3) Price risks which include both price fluctuations in sales of products and purchases of production factors; (4) Production risks which include both weather and yield risks as well as plant and animal diseases and animal welfare; (5) Institutional risks consist primarily of the risks of new stricter laws and regulations; and (6) Technological risks include the risk of technological obsolescence, collapse and possible liability.

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Table 2. Which risks are most important in agriculture? Financial Human Price Production Institutional Technological risks Risks risks risks Risks risks Farmers 1.14 1.26 1.11 0.74 0.99 0.32 Industry associations 1.33 0.33 1.00 0.67 -0.50 0.00 Financial institutions 1.20 0.60 1.00 0.40 -0.40 -0.40 Agro- & Food industry 1.42 0.81 1.32 1.19 1.02 0.58 Advisory Firms 1.26 0.74 1.16 0.53 0.78 0.37 Average 1.30 0.74 1.19 0.79 0.74 0.42 Human, financial, and price risks are the top three important sources of risks for farmers. Danish farmers also consider institutional risks more important than production risks. The ranking of different risk sources among farmers seems, however, to be context dependent. For example, according to a Dutch study of livestock farmers’ perception of risk and risk management the highest scores were related the meat price, epidemic animal diseases and milk price (Meuwissen et al., 2001); and in a Norwegian study of risk perceptions among organic and conventional dairy producers published in 2005, uncertainty about the continuation of general government support payments stands out as the most important source of risk for both groups(Flaten et al., 2005). Note, however, that the ranking of agricultural risk factors among the other stakeholders differssignificantly from that of the farmers’ ranking. All the other participants in the value chain have prioritized financial risks as the main source of riskand price risks come second. As opposed to farmers’ responses, human risks have only been ranked as the third or fourth.Indeed, a significant difference seems to exist between what farmers and other stakeholders in the agro-food value chain perceive as the most important source of agricultural risks. MAIN TOOLS IN AGRICULTURAL RISK MANAGEMENT The survey also aims to find out what risk management tools are most important in agricultural risk management - both from the perspective of the farmers and from all the other stakeholders. Respondents evaluate the importance of eight different risk management tools (Table 2.3). Table 3. What tools are the most important in agricultural risk management? Financial Insurance Interest Production Risk di- Production Economic Saving contracts coverage contracts versifying advisory advisory Farmers -0.09 0.32 0.68 1.00 0.87 0.46 0.72 1.36 Industry 0.00 1.00 0.67 0.71 1.17 0.67 1.50 1.00 associations Financial 1.00 -0.40 1.20 1.00 -0.20 0.80 1.00 1.60 institutions Agro- & Food 0.27 0.65 0.84 0.81 0.78 0.60 0.80 1.54 industry Advisory firms -0.16 0.32 0.58 1.00 0.68 0.89 1.32 1.37 Average 0.23 0.59 0.80 0.91 0.67 0.77 1.05 1.32 There seems to be a considerable agreement on which risk management tool is the most important between the farmers and theother stakeholders. Apart from the industry associations, who pointedout that to economic consulting as the most important, all other groups of respondents, as expected, rankedthe farmer's own savings as the most important risk management tool.Most farmers are aware of the importance of having some equity. The result is also in fairly good accordance with results from studies in other countries e.g. (Flaten et al.,2005; Huirne et al., 2003).

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Production contracts are perceived as the second most important risk management tool by the farmers in the Danish survey. However, there were disagreements about what is the second most important risk management tool after the farmer's savings among the other stakeholders. Both the industry associations and the advisory firms think that it is the economic consulting which farmers only perceived as the fourth most important risk management tool. MAIN COOPERATIVE PARTNERS IN AGRICULTURAL RISK MANAGEMENT The last part of the survey is concerned with who is the farmer's most important cooperative partners in risk management (Table 2.4). Farmers considered their family to be the most important collaborative partner in the process of risk management. There is no clear pattern revealed however fromthe responses of the other stakeholders. Three out of four stakeholder groups (i.e. industry associations, financial institutions and the agro & food industry) perceive that financial institutions are the most important collaborative partners. Farmers, on the other hand, think that their advisors are more important collaborative partners than the financial institutions.Interestingly, financial institutions and advisory bodies both perceive themselvesas the best partners. The financial institutions have given themselves an average score of 1.20, which is the highest of all their scores. Similarly, economic and production advisors have received an average score of 1.26 and 1.16, respectively, from the voting by these advisors. Thus, there is a remarkable tendency to perceive oneselfas the best partner in the farmer’s risk management process. Table 4. Who are the farmer’s most important collaborative partners in risk management? Employees Colleagues Family Production Economic Financial Food Advisors advisers institutes companies Farmers 0.33 0.40 1.03 0.73 0.96 0.63 0.56 Industry 0.50 0.50 -0.29 0.33 1.00 1.00 0.33 associations Financial 0.40 0.40 0.40 0.80 0.80 1.20 0.40 institutions Agro- & Food 1.18 0.46 0.00 0.85 1.08 1.26 1.04 industry Advisory firms 0.53 0.37 0.89 1.16 1.26 0.63 0.42 Average 0.76 0.45 0.40 0.92 1.08 0.93 0.67 MODERN APPROACHES TO AGRICULTURAL RISK MANAGEMENT Recently the Organization for Economic Cooperation and Development (OECD) has developed a conceptual framework for the analysis of risk management in agriculture (OECD, 2009and 2011). The framework suggests two main aspects to be considered. The first is that policy design must give attention to the interaction and trade-offs among all risks, strategies and policies, and thus avoids a narrow focus on a single risk or risk management tool as there is evidence of significant interactions between risks and responses. Second, there is a need for a policy approach with differentiated responses to different types of risks (OECD, 2009). The first aspect requires adaptation of a holistic approach as opposed to a linear approach. Risk management should be analyzed as an integrated system in which there are interactions between many elements. According to the conceptual framework developed by OECD, the integrated risk management system has to be organized around three sets of elements: (1) the sources of risk, (2) farmers’ risk management strategies and (3) government policies (OECD, 2009). As the linkages among these sets are not linear, an analysis starting with identifying the risks, then finding the optimal management tools to deal with these risks and finally choosing the appropriate governmental policies is not appropriate. Rather the linkages move in many different directions and there are interrelationships and feedback mechanisms among

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia these sets of elements. The use and benefits of specific risk management tools is more or less determined by the whole system including the risks embedded in the system itself, the other instruments already adopted to manage risks and the existing policies deployed by the government. One example that illustrates these links is shown in box 1. It is not possible to analyze risks, farmers’ strategies to cope with risks, and government policies in isolation because of these linkages and dynamic interactions. The request for a more holistic approach is not only recognized by OECD, but also by the World Economic Forum (2014), which since 2006 has provided unique analyses of the interrelationships and interconnectedness among a large number of economic, environmental, geopolitical, societal and technological global risks. One example of such systemic risks in agriculture is the water-food-energy nexus (World Economic Forum, 2011). Food security requires water and energy; water security requires energy and energy security requires water. Economic growth and population growth are common drivers for all three risks. Environmental pressures also drive these insecurity risks as too much rainfall may lead to floods and too little may lead to severe drought, which in both cases may have devastating effects on food availability. Another example analyzed is the inextricable links between food consumption, malnutrition and devastating health outcomes. The risks of spending a high proportion of the income on food due to adverse shocks among the poor, amplify the risk of malnutrition, especially among children and other vulnerable groups, which risk causing physiological and mental damage, increasing vulnerability and poverty and diminishing productivity (World Economic Forum, 2010). As shown in Section 2, there are many risks and sources of risks in agriculture that may be classified in different ways. Most sources of agricultural risks affecting farmers in both developed and developing countries are not significantly different as they come basically from shocks in production (e.g. weather, pest, etc.), in prices (i.e. markets) and institutional and political settings, none of which are particularly exclusive to any particular country (Cervantes-Godoy et al., 2013). Box 1. Examples of interrelated risks from Latin America

A bitter, but widespread, feature of many smallholder landscapes, especially in Latin and central America, is the deprived smallholding family suffering from malnutrition and that is, at the same time, surrounded by fields lying barren. This is described, in everyday language, as tierra sin brazos (land that is not worked by a human labour force) and brazosa sin tierra (labour force without land). Land and labour is separated here, thus simultaneously provoking low levels of production and hunger. This absurd situation is mostly due to the lack of credit. Credit is no longer provided because previous debts have not been paid (probably owing to natural disasters, bad harvests, illness, low market prices, etc.). And even when enough credit can be obtained, there might be no access to promising markets (owing to transaction costs being too high). Or credit is only available, for example, for export crops, while many smallholder farmers are interested in, for example, fruit trees, goats, dairy cows, etc. (which function at the same time as a mechanism for capital formation,, and provide food for the family and surplus for the market). Many reasons might converge here, but the dramatic result is time and again the same: stagnation, deprivation and underutilization of resources. This situation evidently translates into several risks. The family becomes too poor to risk the few resources that remain in any further investment in agriculture. But equally for other market partners, the smallholder families suffering in this situation become insecure or risky partners to deal with.

Source: HLPE (2013: 36)

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However, the effects of risk and responses to risk are fundamentally different in developing countries. Smallholders in developing countries are often less able to cope with shocks compared to those in OECD countries. These smallholders are more likely to use coping strategies that could have negative welfare implications in the long term. For instance, households cope with shocks (e.g. floods or drought) by depleting valuable assets to allow the family to maintain a certain level of consumption in the aftermath of a shock. This in turn may cause the house hold to fall into a poverty trap created by low level of assets. Poverty traps can be explained by risk and available risk management strategies as they could lead smallholders to select lower-risk/lower-return activities and undermine smallholders’ investment to escape poverty. For example, households reduce essential consumption which can have long-term negative consequences on the household’s health and capacity. Whether a shock is sudden or prolonged, most households, both the wealthiest and the poorest, experience a loss of assets and a reduction of disposable household income. In the case of smallholders in developing countries, the main implication might be that they face devastating consequences in case of additional shocks (Cervantes-Godoy et al., 2013). The relationship and interconnectedness between risks also tends to be even stronger for poor people than for more rich people. For example, poor people’s health risks are strongly connected to the availability of food, which is affected by almost all the risks the poor facessuch natural disasters, harvest failures and food price fluctuations (Anderson, 2003). The second aspect of the conceptual framework made by OECD implies that a range of risk management instruments should be available to the individual farmer so he can choose the instruments that best fit his needs (OECD, 2009). In addition, policies should be developed and targeted to specific objectives where specific market failures and equity concerns in risk management are identified. The solutions implemented should also be efficient and minimally distorting. Furthermore, the risk management system should facilitate the production and sharing of information as trade-offs are likely to emerge between objectives, instruments and sources of risks. In OECD countries, household consumption decisions in farms tend to be independent from agricultural production decisions due to well-developed credit markets and social security systems. This is, however, not usually the case in developing countries where household’s production decisions typically are dependent of its consumption decisions. Farmers have to invest in seeds, fertilizers and labor for current production; however, for poor smallholders, limited income and assets constrain both direct investments and access to credit. Natural and production hazards may also lead to increased indebtedness. Family labor is often diverted towards more remunerative off-farm activities. In commercial farms, the family budget tends to become distinct from the farm/enterprise budget, whereas in smallholder farms the family side and the productive and economic sides are closely interrelated: domestic or family risks such as illness, or life-related events such as marriage, may lead to a reduction in productive assets in order to fulfill such needs (HLPE, 2013). DEVELOPMENT OF NEW RISK MANAGEMENT STRATEGIES TO SECURE AND RAISE FARM INCOME Agriculture is entering a new era with many new opportunities but also with many risks. In the next 40 years the world population is expected to reach over 9 billion people, one-third more than today. The food consumption is expected to grow even faster as income levels are rising in emerging countries like China, Brazil, India and other countries where millions of new middle class consumers living in big cities are going to demand more meat and dairy which require more resources to produce. It is projected that the annual meat production will

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia need to rise by 75%, i.e. by 470 million tons – by 2050 (World Economic Forum, 2011). Such agricultural growth is putting increasingly stress on the earth’s resources. Agriculture consumes 70% of total global water demand and accounts for up to 30% of all greenhouse gas emissions. Poor agricultural practices during the last decades have contributed to depletion of soil fertility, species diversity and water availability and quality. Meanwhile, broader environmental changes, notably climate change, may affect agriculture in ways that are not yet fully understood (World Economic Forum, 2011). Greater pressure on scarcer resources associated with the greater demand for food and bioenergy may lead to higher price and supply volatility as well as aggravate the risks of further environmental degradation, loss of biodiversity, poverty and riots. Addressing these challenges effectively requires a substantially new approach to income risk management in which the actors in the agro-food system collaborate to develop new innovative solutions through multi-stakeholder partnerships (World Economic Forum, 2010, 2012 & 2013). These partnerships should emphasize the need to take a long-term perspective to improving the resilience of whole food systems and thus go beyond point interventions in the value chain; and they should represent a shift from focusing exclusively on uncertainty avoidance to focus on market opportunities where stakeholders receive the incentives to innovate, resilience to endure risks and capital to invest in growth. Therefore, risk management has to be integrated into collaborative decision-making which shift the ways that agricultural systems operate (Lund, 1993). A multi-stakeholder approach to risk management is an attempt to make this discipline more relevant, operational, and beneficial to especially smallholder farmers in developing and developed countries. Such an approach is, however, at odds with mainstream risk analysis and management, which is essentially concerned with how decisions under uncertainty are made by individuals. The study of risk management in agriculture undertaken by OECD is also grounded on this conventional paradigm: “Risk management is primarily an individual process that should take place at the farm household level” (OECD, 2000: 18) and “[i]t is the farmer’s responsibility as manager of his own farming business to take the appropriate decisions to manage the risks associated with his economic activity: farming” (OECD, 2009: 21). Such perceptions totally neglect that many smallholder farmers, and especially those in developing countries, are facing many constraints in the optimization of their risk strategies based on their resources, analytical capacity, and available information on their risk environment. Furthermore, the individualistic view of risk management, as is typically the case, also seems to overlook that the institutional and political structures in developing countries are often less developed leading to greater risks of market imperfections and market failures in key areas such as credit and insurance markets (Cervantes-Godoy et al., 2013). The consequence is that the farmers’ access to risk management tools and strategies are lower than in developed countries. Farmersrely instead on informal risk coping mechanisms and local community strategies. A wide range of informal risk-sharing arrangements has evolved in developing countries, and it is unlikely that all are as efficient as they are desired by all concerned. These include share tenancy contracts, traditional money lending, and risk sharing within extended family and other community networks. A major limitation to these arrangements is that the participants come from the same region, or even perhaps the same village, often face the same risks. The arrangements, therefore, do not pool risks as efficiently as they would if they spanned regions or broader sections of a national economy, as do nation-wide crop-insurance of credit schemes (Anderson, 2003).

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The assumption that decision making is or should be an individual process is no longer acceptable nor depicts how the economic and social world is functioning in the 21st century. The choices people make, their perceptions and their incentives are affected by and do affect others. The medium whereby this happens is generally considered to be social networks. Indeed, network effects of traders in the financial markets, where the propensity to follow the herd leading to booms and crashes, is an important part of any explanation of the recent worldwide economic crisis (Ormerod, 2012). A network perspective of the world gives rise to the concept of collective action. Only very few examples of multi-stakeholder approaches to agricultural risk management are yet available in the sense they have really benefited and empowered smallholder farmers. One example of a stakeholder approach to income risk management is a Danish action research project published in Lund et al. (2007). The risk model is constructed as a context dependent process, which includes four main phases: (1) Recognition and demarcation; (2) Identification and prioritizing; (3) Search and evaluation of alternatives; and (4) Implementation and monitoring. The model is aimed at agricultural advisors, who wish to facilitate and disseminate risk management to farmers. It is developed and tested by an action research approach in an attempt to make risk management more applicable on family farms. It is questionable, however, whether such action research qualifies as a participatory stakeholder approach in the sense that all participants affected are included. Farmers were not directly participating in the research effort although they were supposed to be end-users. As emphasized by Guntoro and Lund (2013) participatory research is a combined research, education and action method in which people are actively involved in conducting a systematic assessment of social problems and identifying means for solving them. Participatory research should contribute to the engagement and empowerment of smallholder farmers, and other disadvantaged groups including women, especially in developing countries, to improve their income opportunities and livelihood by increasing productivity, market access and reducing market volatility. Thus, participatory research is not value-free or ideologically neutral. Meanwhile, a strong commitment to risk communications as a means to increase the involvement of non-experts into decision making under uncertainty have been developed during the last decades, but this have seldom let into shared responsibilities for research activities and decision making. A basic assumption underlying risk communication is that authorities regulating for example health and safety issues need to understand how people perceive and respond to different types of risks (Slovic, 1987). Historically, such studies of risk perceptions within agricultural economics have traditionally been based on the psychometric paradigm (e.g. Patrick and Musser, 1997; Meuwissen et al., 2001; Flaten et al., 2005; Lien et al., 2006; Patrick et al., 2007). In these models, risk is assumed to be well-defined, independent, quantifiable and comparable (van Winsen et al., 2013). This approach is built on psychology and decision theory. Perceived risk is typically measured by listing risky events or activities and scoring them by the use quantitative or mixed (quantitative and qualitative) methods. However, in multi-stakeholder cooperation risk perceptions cannot be assumed to be well- defined and independent. Furthermore, as noted in World Economic Forum (2014) there is not always a perfect overlap between the interests and incentives of the stakeholders involved in risk management. Although they may share some interests, such as the security of people and property, the private sector is also interested in the continuity of business operations and sustaining competitive advantages. To be successful, all stakeholders in a risk management partnership should be able to identify benefits from partnering and obtaining expected returns

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia from investments. Thus, different interests and incentives have to be aligned and mutual benefits must be obtained among all participating stakeholders. One of the important steps a firm can take to improve its own risk management is to work with others in its supply chain to ensure that they are doing the same (World Economic Forum 2014). If we look at income risk management from the responsesprovided by the farmers in the Danish survey presented in Section 2 we may get a picture as shown in Figure 4.1. This picture rests on the fact that most farmers in the survey consider ensuring the family as the main purpose of agricultural risk management (Table 2.1), human risks as the most significant risk factor (Table 2.2), and consider savings as the main risk management tool (Table 2.3). It is reasonable to assume that what the farmer gives highest priority is also what he will give his greatest attention. Therefore, the relationship in Figure 4.1 provides a realistic perspective of many farmers' way of thinking about risk management.

Human risks

Savings

Ensure the family

Figure 1. The farmer's perspective on risk management Let us then look at agricultural income risk management from the perspective of the financial institutions (Figure 4.2). Financial institutions prioritize financial risks as the most significant risk (Table 2.2), ensuring the farm income as the main purpose (Table 2.1) and savings as the main risk management tool (Table 2.3). It can therefore be assumed that many banks and other credit institutions focus on this context when assessing farmers' creditworthiness and determining how much money they will lend to agriculture.

Financial risks

Savings

Ensure the income

Figure 2. The financial institution's perspective on risk management But what if we aggregate the two perspectives into one? The result is shown in Figure 4.3. The crucial point of such an integrative perspective is that the farmer - together with his bank for example - can become aware of more risk factors and more relationships in the risk management process. It is not only necessary to make savings to ensure the farm income (for

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change e.g. financing investments), but also in order to ensure the future of household family in case of deaths or serious illness in the family. More importantly, by choosing a holistic perspective, more relevant risk factors may be uncovered and evaluated as a whole. For example, whether there are any causal relationships between the human and financial risks (which are shown as a dashed arrow in Figure 4.3). Such causality may exist if the farmer suddenly dies and there is nobody to take over. In such a situation there is a real risk of a fast decline in production efficiency and then a lack of income for payments.

Human risks Financial risks

Savings

Ensure the Ensure the family income

Figure 3. A holistic perspective on risk management Multi-stakeholder approach to income risk management can indeed be challenging as it requires stakeholders to look beyond the usual business practices and be open for new ways of collaboration. While it is unclear who should initiate the process, it may be beneficial to create a joint platform which can provide a neutral ground for developing a shared agenda and common goals for actions. The creation of such a platform might also require the involvement of neutral facilitators in order to create the right framework for collaboration and common understanding.It is also important to engage diverse viewpoints, including dissenting and critical voices, to ensure the integrity of the partnerships (World Economic Forum, 2014). Evidence from environmental decision making indicates that stakeholder participation makes better decisions, contributing new information and ideas and utilizing technical resources in the decision processes (Beierle, 2002). Barker et al. (2010) explore how a participatory approach may contribute to food chain risk analysis by coupling information sets obtained by different stakeholders. These information sets become part of an extended domain by including additional variables – as shown in Figure 4.3. The concentration of pesticides in apples is used as an illustrative example. A graphical representation is obtained by supplementing the experts’ belief of this value expressed as a conditional probability distribution with corresponding probability distributions for participants from particular stakeholder groups and eventually for aggregation of additional influences such as trust. It is, however, doubtfulhow participatory the approaches discussed in Barker et al. (2010) really are. According to them the participatory information does not affect or alter the experts’ information set but can only extent the information domain; and the dependency is always going in one direction as the participatory information may dependent on the experts’ knowledge domain but the opposite cannot happen.

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CONCLUDING REMARKS Main conclusions are that proper income risk management in agriculture cannot ignore the human and soft elements and that an exclusive focus on technical and computational matters is insufficient. Greater attention to risk perceptions, the interests and incentives of different stakeholders, and the alignment of goals, expectations and efforts are thus required. The context of risk also seems to be crucial. Furthermore, it appears that dependencies and interactions among risk factors are becoming of more importance and that the risky problems we have to handle increasingly involve technical, social and environmental components. Finally, there seems to be a growing recognition of needs for a more holistic or systemic approach to problem solving as emphasized by OECD and a need to adopt a more strategic stakeholder perspective in agricultural risk management as argued by the World Economic Forum and many others. One future challenge is to change the mindset among stakeholders about the perception of uncertainty. Current agricultural risk management is mainly focused on the potential negative consequences of risks for farmers and the capacity to mitigate these risks. There is, however, an urgent need to also consider the upside effects of uncertainty in agricultural income risk management. Practical decision makers often perceive change and uncertainty as a threat, and there is a natural tendency to resist threatening environments. However, uncertainty can also be seen to provide opportunity for inventions and prosperity changes, and utilization of these innovations may enable managers not just to adjust, but also profit from them. Innovation is and will remain essential in the food and agribusiness sectors in responding to the critical concerns of society such as climate change and global warming, food/energy scarcity, environmental challenges and resource use/sustainability (Boehlje et al., 2005 &2011). Innovation implies experimentation with new forms of physical and social technology which are core drivers of increases in productivity and efficiency, economic growth and the generation of wealth. One caveat is that majority of the past research on innovation in agriculture, especially in the developing countries, have exclusively focused on technology adaption. As emphasized by Teece and Chesbrough, among others, technology by itself has no objective value (Chesbrough, 2010; Teece, 2010). The economic value of a technology remains latent until it is in some way commercialized. In recent years innovation platforms have been established in both developed and developing countries as a mechanism to implement effective multi-stakeholder solutions and address recurrent production and markets risks in complex farming systems (Tenywa et al., 2011). Another major challenge is to develop relevant tools for assessment and decision making under uncertainty. Recently, score carding and heat mapping have been suggested as tools for quantification and communication of the impact of new strategic uncertainty (Detre et al., 2006). The basic idea of score carding is to assess the potential and exposure of identified risks in terms of probability of likelihoods and the magnitude of consequences associated with these potentials and exposures, respectively, for example by using an ordinal measurement scale. A heat map is an aid to visualize and communicate the aggregated impacts of uncertainties. A main limitation of scorecard risk assessment is that each category of uncertainties is evaluated independently of each other and thus neglects the systemic characteristics of many if not most agricultural risks. A promising alternative is to adopt participatory modelling approaches using Bayesian Networks (BN). This approach aims to (1) represent and integrate knowledge from diverse disciplines, (2) support the inclusion of stakeholder knowledge and perspectives, and (3) take into account the uncertainty of knowledge (Düspohl et al., 2012). A BN consists of three elements namely, (1) chance variables referred as notes and visualized as ovals; (2) causal links between these variables visualized as direct arcs between the nodes; and (3) a set of conditional probabilities

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change measuring the strength of the causal relationships between the nodes (NadkarniandShenoy, 2001; Fenton and Neil, 2013). In fact, Figures 4.1 - 4.3 are examples of qualitative structures of BNs. Ribay is one recent example of a BN model for stochastic simulation of farm income at the firm level (Rasmussen et al., 2013). Although BNs are already applied in many disciplines, it is believed that this approach has not yet achieved the mainstream penetration in multi-stakeholder risk management within agriculture which it deserves.

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Ormerod, P. (2012): Networks and the Need for a New Approach to Policymaking. In T. Dolphin & D. Nash (ed.) (2012): Complex New World – Translating New Economic Thinking Into Public Policy. Institute for Public Policy Research, London Patrick, G.F. and W.N. Musser (1997): Sources of and responses to risk: factor analysis of large-scale cornbelt farmers. In Huirne, R.B.M; J.B. Hardaker & A.A. Dijkhuisen (eds.): Risk Management Strategies in Agriculture, vol. 7, Wageningen Agricultural University, Wageningen Patrick, G.F.; A.J. Peiter; T.O. Knight; K.H. Coble and A.E. Baquet (2007): Hog producers’ risk management attitudes and desire for additional risk management education. Journal of Agricultural and Applied Economics, vol. 39: 671 – 688 Rasmussen, S.; A.L. Madsen and M. Lund (2013): Bayesian network as a modelling tool for risk management in agriculture. IFRO working paper 2013/12, Department of Food and Resource Economics, University of Copenhagen, Copenhagen Slovic, P. (1987): Perception of Risk. Science 236(17 April): 280 - 285 Songwe, V. (2011): Food, Financial Crises, and Complex Derivatives: A Tale of High Stakes Innovation and Diversification. Economic Premise No. 69, the World Bank, Washington D.C. Teece, D. J. (2010): Business Models, Business Strategy and Innovation. Long Range Planning, vol. 43: 172 – 194 Tenywa, M.M; Rao, K.P.C.; Tukahirwa, J.B.; R. Buruchara; A.A. Adekunle; J. Mugabe; C. Wanjiku; S. Mutabazi; B. Fungo; N.I.M. Kashaija; P. Pali; S. Mapatano; C. Ngaboyisonga; A. Farrow; J. Njukiand A. Abenakyo (2011): Agricultural Innovation Platform as a Tool for Development Oriented Research: Lessons and Challenges in the Formation and Operationalization. Learning Publics Journal of Agricultural and Environmental Studies, vol. 2(1): 117 - 146 Van Winsen, F.; Y. de Mey; L. Lauwers, S.V. Passel, M. Vancauterenand E. Wauters (2013): Cognitive mapping: A method to elucidate and present farmers’ risk perception. Agricultural Systems, vol. 122: 42 – 52 Von Braun, J. (2008): Food and Financial Crises – Implications for Agriculture and the Poor. Food Policy Report, International Food Policy Research Institute, Washington D.C. World Economic Forum (2011): Global Risks 2014. Sixth edition, Geneva World Economic Forum (2014): Global Risks 2014. Ninth edition, Geneva World Economic Forum (2010): Realizing a New Vision for Agriculture: A roadmap for stakeholders. A report prepared by the World Economic Forum in collaboration with McKinsey & Company, Geneva World Economic Forum (2012): Putting the New Vision for Agriculture into Action: A Transformation Is happening. A report prepared by the World Economic Forum in collaboration with McKinsey & Company, Geneva World Economic Forum (2013): Achieving the New Vision for Agriculture: New Models for Actions. A report prepared by the World economic Forum in collaboration with McKinsey & Company, Geneva BIBLIOGRAPHY Mogens Lund is an agricultural and food economist, who received his Ph.D. degree in agricultural economics from the Royal Veterinary and Agricultural University of Denmark (KVL) in 1987. Since January 2014 Mogens Lund has been Research Director at Norwegian Agricultural Economics Research Institute (NILF). From 1986 until the end of 2013 he was employed at KVL and University of Copenhagen (KU). He has been Head of the Production and Technology Division at the Institute of Food and Resource Economics (KU) from 2004 until

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2012 and during the period 2003 – 2013 he was the chief editor of the scientific journal “Food Economics”. His research experiences include extensive work with risk and efficiency analysis, strategic management, cost-benefit evaluations as well as chain-wide and cross-border implementation of HACCP. The research results have been published in numerous articles and books.

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Effects of Feeding Factors on Dairy Performance and Milk Fatty Acid Composition in Cows and Goats Ferlay A123, Bernard L123, Toral P4, Martin C123, and Chilliard Y123 1Inra, UMR1213 Herbivores, F-63122 Saint-Genès-Champanelle, France, Pays; 2 Clermont Université, VetAgro Sup, UMR1213 Herbivores, BP 10448, F-63000 Clermont-Ferrand, France, 3Université de Lyon, VetAgro Sup, UMR1213 Herbivores, F-69280 Marcy l’Etoile, France ;4Instituto de Ganadería de Montaña (CSIC-ULE), FincaMarzanas s/n, 24346 Grulleros, León, Spain Corresponding email: [email protected] ABSTRACT This paper, after summarizing the digestive and metabolic origins of milk fatty acids (FA), presents the main nutritional factors regulating their concentration in cows and goats. Compared to diets based on conserved grass plus concentrate, grazed grass, decreased the milk concentration of saturated FA, in favor of trans isomers of 18:1 and 18:2, cis9trans11 of conjugated linoleic acid (CLA) and 18:3n-3. Oilseed supplementation increased the milk transFA concentration. Linseed, when compared to rapeseed, increased milk 18:3n-3 concentration. The milk FA responses to oilseed supplementation were time dependent. In cows,milk FA concentration responses to supplementation were transient, whereas they were more persistent in goats. Feeding low-fibre/high-starch diets and/or lipid supplements rich in polyunsaturated FA induced milk fat depression (MFD) incows, whereas these diets increased milkfat secretion ingoats. The milk fat synthesis during diet-induced MFD was associated with increase in the milk concentration of specific transFA, including trans10cis12-CLA. Differences in milk fat yield responses to oilseed supplementation between cows and goats seem to be related toboth differences in ruminal lipid metabolism and formationof specific biohydrogenation intermediates and the relativesensitivity of mammary lipogenicgene expression between these two species.Indeed, goats are less sensitive to shift between trans11-18:1 to trans10-18:1 in the rumen, explaining the higher milk concentration of trans11-18:1 and cis9trans11-CLA.The addition of tannins or essential oils in diet had little effect on milk FA composition.Milk FA concentrations have been suggested as a tool of predicting enteric methane (CH4) in ruminants because of the common biochemical pathways among CH4 and precursors of de novo synthesized FA (acetate and butyrate) in the rumen. Published relationships between CH4emissions and milk FA concentrations were established in cows receiving lipid supplements, indicating that milk FA profile may be a potential indicator of CH4production. INTRODUCTION Cow and goat milk lipids are essentially triacylglycerols (97-98 %, Jensen, 2002). Cow and goat milk fat typically contains a high proportion of saturated fatty acids (FA) [71% of total FA (50.2-82.1%)], and monounsaturated FA (MUFA, 20-26 %), and small amounts of polyunsaturated FA (PUFA, 3.3-5.4 %). The trans FA represent approximately 4-7.4% (Andueza et al., 2013; Chilliard et al., 2006; Ferlay et al., 2008; Raynal-Ljutovac et al., 2008; Shingfield et al., 2008). Apeculiarity of goat milk is its richness in FA with 8 and especially 10 carbon atoms.However, in both species these average groups can be largely altered through various physiological, geneticand feeding factors. It has been recognised for many years that diet plays a role as a risk factor for chronic disease in humans. Prospective cohort studies can identify associations between dietary fat type (like saturated FA (SFA)) and cardiovascular disease (CVD) (Givens, 2010) by increasing the concentrations of serum low-density lipoprotein cholesterol (LDL-C, Givens, 2010), a

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change predictor of CVD risk. In many European countries, milk and dairy products supply in average ca. 40% of all SFA intake. As a result there has been a great deal of interest in manipulating the FA profile of milk fat to respond to consumer’ concerns. The main MUFA in milk is oleic acid, followed by trans 18:1. Replacement of dietary SFA by oleic acid has been estimated to reduce CVD (Lopez-Huertas, 2010). In contrast, trans FA if consumed in excess have been associated with a substantially increased risk of coronary heart disease (Shingfieldet al., 2008; Givens, 2010). Nevertheless, recent data seem to indicate that industrial and ruminant transFA have different effects on CVD risk factors (Bassett et al., 2010). Moreover, several intervention studies that have specifically fed milk and other dairy fats have not given rise to significantly increased LDL-C (Givens, 2012). Linoleic (18:2n-6) and α-linolenic (18:3n-3) acids are the main PUFA in milk fat. These FA cannot be synthesized by the body and must be obtained from the diet. These FA can be metabolized to form arachidonic(20:4n-6) and eicosapentaenoic (EPA, 20:5n-3) acids, which are precursors for the synthesis of prostaglandins and leukotrienes (Palmquist, 2009). The n-3 FA, and more particularly EPA and docosahexaenoic acid (22:6n-3), could reduce the risk of CVD (Mills et al., 2011). Milk fat is also the main dietary source of conjugated linoleic acids (CLA). Although there are several isomers, health benefit effects have been mainly attributed to cis9trans11-CLA, which has capacities to prevent cancer, hypertension, atherosclerosis and diabetes in animal models (Mills et al., 2011). The ruminant milk FA composition is linked to intrinsic (stage of lactation, pregnancy, breed or genotype) or extrinsic factors (nutrition, season, temperature) (Chilliard and Ferlay, 2004). Major changes in milk FA composition are induced by nutrition manipulation, such as feeding pasture, conserved forages, starchy concentrates, or diets supplemented with oilseeds or additives (tannins and essential oils). Manipulation of milk fat content and its FA composition could become an important target for the dairy industry, which could develop a quality payment for milk, and thus stimulate farmers to adapt their feeding systems correspondingly. This paper reviews recent data on the role of major feeding factors (pasture, interaction between nature of forage, starchy concentrates and oilseeds, and additives) on milk FA composition in cows and goats, this latter species presenting certain peculiarities compared to the cows, with special attention to oleic acid, 18:3n-3, cis9trans11-CLA, SFA and transFA. Finally, we examine the possibility of certain milk FA to predict methane (CH4) emissions from cows and goats. Lipid metabolism: digestion and mammary lipogenesis Dietary lipids are galactolipids, phospholipids and triglycerides. PUFA more commonly present in the ruminant diets are linoleic acid (18:2n-6) in cereals, maize silage and oilseeds (soya, sunflower seed), and linolenic acid (18:3n-3) in grass and linseed. Lipolysis is the first step of rumen metabolism of dietary lipids (Doreau et al., 2011). Then the free FA are extensively biohydrogenated or incorporated directly into bacterial lipids in the rumen. Among the first intermediates of linoleic acid metabolism in the rumen is found rumenic acid (cis9trans11-CLA) due to isomerization of linoleic acid. Then the rumenic acid is hydrogenated into trans11-18:1 and eventually into stearic acid (18:0). Linolenic acid generates a larger number of intermediates, including trans11-18:1, with a very small production of cis9trans11-CLA (Lee and Jenkins, 2011, Shingfieldet al., 2010). The cis 9- 18:1 and trans11-18:1 areisomerised in several cisand trans isomers in the rumen (Doreau et al., 2011).

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All these intermediates of ruminal biohydrogenation are then absorbed in the gut, essentially transported in the plasma as triglyceride-rich lipoproteins, and either directly secreted into milk, or transformed by body tissues, especially by the mammary gland. In addition, these intermediates act as regulators of mammary lipogenesis, which may result in changes in the amount of secreted milk fat and also in milk FA composition (Shingfield et al., 2010).The plasma NEFA are mainly due to the FA release by adipose tissue stored as triglycerides (mainly 16:0, 18:0 and cis9-18:1) when the energy balance is negative, which occurs mainly in early lactation. In milk FA have a dual origin: they are (1) imported from the plasma, either released by the enzyme lipoprotein lipase (LPL) from triglycerides circulating in chylomicra or very low density lipoprotein or derived from the plasma NEFA that circulate bound to albumin, for the long-chain FA (16 and over) (on average 60% of the FA secreted in milk), and (2) de novo synthesized in the mammary gland from circulating acetate and beta-hydroxybutyrate (produced by ruminal fermentation of carbohydrates and by rumen epithelium from absorbed butyrate, respectively) via two key enzymes: acetyl-CoA carboxylase (ACC) and fatty acid synthetase (FAS), thus resulting in short- and medium-chain FA (4:0 to 16:0) (on average 40%). Whatever their origin, these FA may be further desaturated by the enzyme stearoyl-CoA desaturase (SCD), which introduces a cis double bond between carbon atoms 9 and 10, converting in particular stearic acid into oleic acid (responsible for 60%) and trans11-18:1 into cis9trans 11-CLA (responsible for 70 to 95%, Bernard et al., 2008, Glasser et al., 2008; Mosley et al., 2006; Shingfield et al., 2010).Thus, the milk FA composition results from rumen metabolism and from these mammary metabolic pathways (uptake of long chain FA, de novo synthesis, desaturation, Chilliard et al., 2007). Effects of nutritional factors on milk fatty acids Pasture feeding Fresh grass contains 1 to 3 % of FA, of which 50 to 75% is 18:3n-3. In cows, a number of studies have compared milk FA composition from grazing cows to milk from cows fed hay or silage-based diets. Grazed grass generally increases levels of milk oleic acid (+8.0 g/100 g of total FA), PUFA, especially 18:3n-3 (+1.0) and cis9trans11-CLA (+0.6), and decreases saturated medium-chain FA (Chilliard et al., 2007). Some additional factors are reported to explain the important variability of milk FA composition observed for grazing animals, such as the phenological stage of the grass, its botanical composition, and interaction with the grazing management. Young grass has higher content of lipids and 18:3n-3 than mature grass and grazing young grass induced higher levels of this FA (+ 0.3 g/100 g) and cis9trans11- CLA (+ 0.9 g/100 g) in milk (Ferlay et al., 2006). In cows, differences in milk FA composition according to the altitude (lowland vs. highland vs. Alpine) have been also reported (Collomb et al., 2002, Leiber et al., 2004). Generally, milk from cows grazing mountain pastures had higher concentration of 18:3n-3 and cis9- 18:1, and lower SFA concentration. Mountain pastures have been characterized by a higher diversity in the botanical composition than in the lowlands (Falchero et al., 2010). Thus, the presence of secondary ingredients as terpenoids or polyphenols could inhibit the biohydrogenation of PUFA, and could explain in part the high content of 18:3n-3 (Chilliard et al., 2007). Another hypothesis to explain the high content of oleic acid is that the decrease in temperature or the prolonged walking of the cows on mountain pastures could induce an increased lipomobilization (Leiber et al., 2004).

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Still in cows, we have evidenced other factors such as level of botanical diversity and grazing management. Two different grazing managements on upland grasslands have been compared: a highly diversified pasture with a low stocking density and continuous grazing vs. a weakly diversified pasture with a higher stocking density and rotational grazing (Coppa et al., 2011). Some differences on milk FA profile have been observed. The milk total trans18:1 content was higher for cows grazing under continuous than those grazing under rotational mode. With the continuous grazing, the milk PUFA content decreased during the season whereas it remained constant with the rotational grazing. These differences could be explained by a combined effect of the phenological stage of the grass and selection of grass by the cows. In goats, grazing mountain spring pasture, compared to winter diet (alfalfa hay, straw and concentrate), increased milk 18:3n-3 concentration (+0.5 g /100 g of total FA), without changing cis9trans11-CLA (Chilliard et al., 2007). In contrast, grazing pasture rich in leguminosae, compared to alfalfahay diet, increased milk MUFA (including trans11- and cis9-18:1) and PUFA concentration (notably total CLA without change in 18:3n-3 concentration), and did not alter milk SFA content (D’Urso et al., 2008). The differences in goat milk FA composition due to grazing are consistent with the results in dairy cows. Oilseed feeding This section is focused on the recent studies on the effects of diets supplemented with linseed and rapeseed on the milk FA composition in cows and goats. Linseed and rapeseed contain a high oil level (40%) with 55% of 18:3n-3 and 60% of cis9-18:1, respectively (Glasser et al., 2008; Petit 2010). Dairy performance In cows, during short-term studies, feeding up to 15% linseed in diet dry matter (DM) had no effect generally on DM intake. In early lactation, discrepancies among experiments on the effect of whole or processed linseed supplementation on milk yield could result from differences in diet composition and length of experiment. The whole linseed supplementation did not modify milk yield and milk fat content and yield in mid or late lactation (Petit, 2010). Nevertheless, heat treatment of linseeds resulted in variable effects on milk fat concentration, with a possible decrease. One explanation could be the possible increased rate of oil release from extruded seeds into the rumen compared to whole seeds, which could result in an increased production of trans FA in rumen and then a decrease in milk fat content (Chilliard et al., 2009). A decrease in milk fat yield with linseed oil feeding is often reported (Glasser et al., 2008) whereas it was unaffected with rapeseed. In goats, supplementation of hay-based diets with 5.5% linseed oil had no effect on DMI and milk yield, whereas it increased milk fat content and yield (Bernard et al., 2009). In other respects, when goats were fed 5.5% of oil from whole rapeseed DMI did not change whereas a decrease in milk yield and an increase in milk fat content were observed (Ollier et al., 2009). Milk fatty acid composition In cows, in order to evaluate the general responses of milk FA composition to oilseed feeding a meta-analysis approach has been used (Glasser et al., 2008). Published experiments with linseed and rapeseed lipid supplements were selected, allowing to study the relationships between milk FA variations and supplemental lipids (0.65 and 0.59 kg/d for linseed and rapeseed, respectively). Oilseed supplementation induced a decrease in milk short- and medium-chain FA percentages and simultaneously an increase in the percentages of total FA with 18 carbons. The meta-analysis evidences that the form of presentation of oilseed plays a role. More precisely, percentages of 6:0 to 14:0 were linearly decreased with increasing either linseed as seeds and more largely as oils, or rapeseed (similarly with either seeds or oils). The

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia decrease in 16:0 percentage was quadratic, in the order linseed seeds ≥ rapeseeds (all forms) ≥ linseed oils. The percentage of the total C18 increased quadratically with supplemental lipids (from 35.4 for unsupplemented diets to more than 50%). Percentages of cis and trans- 18:1 increased linearly with increasing lipids. These increases were higher with oil than with seeds. Concerning the trans-18:1, the increase ranked according to linseed oil ≥ rapeseed oil≥ linseed as seed whereas rapeseed as seed did not significantly modify its concentration. With grass-based diets, the cis9trans11-CLA percentage was more increased by extruded linseed than extruded rapeseed or cold-pressed fat-rich rapeseed meal, and was decreased by whole rapeseed (Lerchet al., 2012c). The linseed increased 18:3n-3 percentage (on average 1.0% of total FA) whereas rapeseed seed increased it marginally (Glasser et al., 2008). Detailed studies have been published for increasing doses of extruded linseed in the diet (Brunschwig et al., 2010, Hurtaud et al., 2010, Ferlay et al., 2013). The major changes concern trans 18:1 and total CLA. These FA concentrations increased linearly with increasing amounts of linseed whereas 18:3n-3 concentration increased slightly, confirming that this FA was highly biohydrogenated in the rumen. In contrast, the milk SFA decreased linearly with increasing amounts of linseed. For the same level of cis9trans11-CLA in the milk fat, linseed supplementation increased the milk concentration of trans FA (not containingtrans11-18:1 and cis9trans11-CLA) more than grazed grass (Ferlay et al., 2008, 2013). In goats, data on the effect of oil seeds on milk FA composition data are more scarce compared to cows. However, from the few available studies allowing indirect comparison among these species, conclusions have been drawn: plant oils in the diet decreased the concentration of milk fatty acids (10:0-16:0) synthesized de novo (Bernard et al., 2005, 2009; Chilliard et al., 2007) in goats but in lower extent than in cows whereas the milk long-chain FA (≥C18) were always increased in goats whatever the basal diet (Bernard et al., 2006) which is not the case in cows (Roy et al., 2006).Plant oils in the diet of goats in most cases enhanced milk cis9-18:1 content (Bernard et al., 2006) with the responses being lower than observed in cows (Chilliard et al., 2007, 2014). Otherwise, in most cases larger increases in trans-11-18:1 and cis9trans11-CLA in goat milk fat in responses to plant oils were observed in the goat compared with the cow (Shingfield et al., 2010). Temporal changes in milk FA composition The milk FA response to oilseed supplementations is time dependent, probably reflecting adaptations of number or activity of ruminal bacteria involved in biohydrogenation, or metabolic adaptations. Indeed, in cows the maximal milk cis9trans11-CLA and trans11-18:1 responses to supplementation were transient, with a maximum observed 4-6 days after the start of supplementation with diets rich in maize silage or starchy concentrates supplemented with sunflower oil (SO), although the response was stable for at least 3 weeks when the diet was rich in hay and supplemented with linseed oil (Roy et al., 2006). The decreases in milk cis9trans11-CLA and trans11-18:1 with diets rich in maize silage and concentrate supplemented with SO were associated with concomitant increases in milk fat trans10-18:1 content and a decrease in milk fat content, whereas concentrations of trans10-18:1 in milk on the hay diet supplemented with linseed oil remained low throughout the experiment. Temporal effects were also observed for isomers of CLA. The milk fat trans11cis13-CLA, trans11trans13-CLA and trans12trans14-CLA contents were enhanced on the hay diet, while the diets rich in concentrate or maize silage increased trans8cis10-CLA, trans10cis12-CLA and trans9cis11-CLA concentrations (Roy et al., 2006). Recently, we conducted a study to evaluate the effects of long-term supplementation (2.5 to 3 % of oil in DM) with rapeseed (whole or extruded seeds, or cold-pressed fat-rich meal) or

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change extruded linseed on dairy cow performance over 2 consecutive lactations (including 2 indoor and outdoor periods). During indoor periods, cows were fed a diet based on grass silage and hay, and cows grazed during outdoor periods. During the first year of experimentation, oilseed supplementation had no effect on the milk and fat yields compared to the control diet. Whole rapeseed increased the milk fat content during the outdoor period (+5.3 g/kg). Thus, long-term effects of supplementation with oilseeds were similar to those observed during short-term (1 to 3 months) studies (Lerchet al., 2012a). Linseed and rapeseed supplementation decreased significantly milk SFA concentration, and increased concentrations of cis9-18:1 and trans isomers of 18:1, except for whole rapeseed. Only linseed increased markedly milk 18:3n-3 concentration during the entire lactation (Lerchet al., 2012b). However, increases in milk fat cis9trans11-CLA content to plant oils have been shown to persist over a 10-week period in goats fed diets based on hay, concentrates or maize silage (Chilliard et al., 2007), whereas responses were transient and decreased over time in cows fed high-concentrate or maize silage-based diets(Roy et al., 2006). Interaction between nature of forage and oilseed supplementation In cows, the responses of milk FA concentrations to oilseed feeding are also depended on the nature of the forage. Glasser et al. (2008) reported higher increase in milk cis 18:1 concentration with alfalfa-based diets supplemented with linseed, followed by maize silage, grass hay and finally grass silage. Linseed supplementation of pasture diets did not change milk cis9-18:1 content whereas it was enhanced by linseed supplementation of other diets (Brunschwig et al., 2010). Moreover, the decrease in 4:0 to 14:0 concentration was higher with maize silage-based diets than with grazed grass or grass silage (Glasser et al., 2008). Concerning the response to rapeseed supplements, the decrease in 10:0 to 14:0 was maximal with grass silage and maize silage and then ranked according to alfalfa ≥ grass hay ≥ pasture. The increase in total 18 FA was maximal with grass silage and maize silage (Glasser et al., 2008). As in cows, the changes in goat milk fatty acid composition in response to plant oil (linseed or sunflower-seed oils) are dependent on forage type (hay or maize silage) and composition, thus evidencing an interaction among these factors (Bernard et al., 2009). However in goat, the responses to oilseed supplementation of cis9-18:1 and 18:3n-3 concentrations are lower and higher than in cow, respectively, (Chilliard et al., 2007). Moreover, the cis9-18:1/18:0 ratio is decreased by oilseed supplementation more markedly in goat than in cow, suggesting that mammary SCD could be more sensitive to oilseed supplement (Chilliard et al., 2007, 2014). High starch content and oilseed supplementation In cows, milk fat secretion can be dramatically reduced by high-concentrate/low-fiber diets supplemented with PUFA from oilseeds or by fish oil and marine algae (Bauman and Griinari, 2003). This phenomenon is called milk fat depression syndrome (MFD). The biohydrogenation theory is the more commonly accepted and indicates that MFD is due to some intermediates of PUFA biohydrogenation, having an inhibitory effect on de novo FA synthesis (Shingfield et al., 2010). The trans10cis12-CLA is the most studied for its inhibitory effect on mammary lipogenesis, but some other CLA isomers have been identified (trans9cis11), and several other trans isomers of 18:1, notably trans10-18:1(Roy et al., 2006; Chilliard et al., 2007; Lerch et al., 2012b). Other 18:2 or 18:3 produced in the rumen are also candidates (Shingfield et al., 2010). This phenomenon is commonlyaccompanied by a dramatic downregulation of mammary lipogenic gene expression which was not always observed for SCD (Bernard et al., 2013, Shingfield et al., 2010).

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In goats, in contrast to the bovine, the MFDis not common. Indeed, milk fat content and yield are not decreased, but are almost always increased by oilseed supplementation, even with low-fiber or maize silage-based diets (Chilliard et al.,2003, 2007). Regarding at the milk FA, oilseed supplementation decreased milk 12:0 to 16:0 concentration and increased total 18- carbon atom FA concentration, without marked increase in milk trans10-18:1 or trans10cis12-CLA (Bernard et al., 2009, Shingfield et al., 2010). These changes in goat milk FA composition are not accompanied by significant changes in mammary lipogenic gene expression or activity of the corresponding enzymes, but in some cases, oilseed supplementation decreased mammary SCD mRNA and/or activity (Bernard et al., 2013, Shingfield et al., 2010). Additives Tannins Tannins are phenolic plant secondary compounds that have been demonstrated to favorably alter ruminal biohydrogenation in vitro by inhibiting the reduction of trans11-18:1 to 18:0 (Khiaosa-ard et al., 2009; Vasta et al., 2009), but this has not always been validated in vivo. Thus, while the addition of an extract of condensed tannins from quebracho to sheep diet induced an accumulation of trans11-18:1 in rumen fluid, probably through changes in biohydrogenating bacteria (Vasta et al., 2010), increases in ewe milk concentration of this 18:1 isomer, and cis9trans11-CLA were only transient when quebracho tannins were added to a diet rich in 18:2n-6, and concentrations of other isomers of 18:1 (such as trans-10 18:1) and 18:2 were increased in the long term (Toral et al., 2013). In cows, the responses to the addition of quebracho condensed tannin extract into the diet have been found to be inconsistent, with either no effects (Benchaar and Chouinard, 2009), or only slight increases in milk total trans 18:1 concentration (Dschaak et al., 2011), which could be probably explained by the levels of inclusion in the diet (up to 3% of diet DM) compared with those used in in vitro studies (up to 18% of the DM incubated; Khiaosa-ard et al., 2009; Vasta et al., 2009). In goats, to our knowledge, the report by Ghaffari et al. (2013) provides the only available information on the effect of tannins on milk FA composition. In this latter case, the addition of a tannin-rich pistachio by-product increased milk total trans 18:1 concentration, but no information is available on individual 18:1 isomers. Essential oils The use of antibiotics in animal feeds is facing reduced social acceptance because of the appearance of residues and resistant strains of bacteria. Their use has been banned in the European Union since January 2006 (Directive 1831/2003/CEE, European Commission, 2003). For this reason, scientists have become interested in evaluating other alternatives to modulate rumen fermentation, including the use of plant extracts (Calsamiglia et al., 2007). Essential oils are mixtures of secondary metabolites obtained from the plant volatile fraction by steam distillation. They often exhibit antimicrobial activity against a wide range of bacteria (Calsamiglia et al., 2007).There are a diversity of essential oils in composition, nature, and activities. The most important active compounds included terpenoids, (monoterpenoids and sesquiterpenoids) and phenylpropanoids (Calsamiglia et al., 2007). It has been supposed that essential oils could limit the ruminal biohydrogenation by inhibiting the bacteria implicated in this pathway, and then could modify the milk FA composition with increasing the concentration of PUFA. Benchaar et al. (2006, 2007) reported the absence of effect of the addition of a mixture of thymol, eugenol, vanillin, guaiacol, and limoneneat different doses on dairy performance, fat content and milk FA composition. Indeed, the addition of eugenolalone in diet has no effect on these parameters (Benchaar et al.,2012).

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In goats, diallyl disulfide and propyl propane thiosulfinate (two organosulfur compounds found in garlic essential oil) have been shown to inhibit the saturation of trans 18:1 to 18:0 in vitro (Ramos-Morales et al., 2013), even though in vivo studies examining the effects of diet supplementation with garlic oil have shown inconsistent results on milk FA composition in this species (Kholif et al., 2012; Zhu et al., 2013). On the other hand, feeding distilled rosemary leaves or thyme leaves increased the concentration of PUFA in goat milk, but no information was available of individual biohydrogenation-derived FA, in particular trans 18:1 isomers (Boutoial et al., 2013a,b). Feeding distilled thyme leaves only increased milk concentration of PUFA (Boutoial et al., 2013b). Because the concentration of active components in essential oils can vary widely depending on the cultivar, growing conditions, or processing methods for oil extraction, the efficiency of essential oils must be verified through further in vivo studies.

Prediction of CH4 emissions by milk fatty acids

Enteric CH4 produced by ruminants represents a loss of productive energy for the animal and is also the most important greenhouse gas (GHG) at the farming scale (Gerber et al., 2013). So, decreasing CH4 emissions from ruminants without altering animal production is desirable both as a strategy to improve feed conversion efficiency and to reduce global GHG emissions (Martin et al., 2010). A need exists for simple and non invasive techniques to quantify CH4 emissions on a large scale and under field conditions. Current measurement techniques (whole-animal chambers, tracer gas based methodology) require complex instrumentation and, thus, are low throughput and mainly limited to experimental settings. In this way, it seems to be interesting to analyze relationships between CH4 emissions and peripheral indicators such as milk FA.

On the one hand, a stoichiometric relationship between CH4 and ruminal acetate, propionate, and butyrate was proposed (Demeyer and Van Nevel, 1975). On the other hand, these short chain FA formed in the rumen are precursors for the de novo synthesis of milk FA in the mammary gland. Because the precursors for the synthesis of CH4 and de novo synthesis of milk FA arise in the rumen, Chilliard et al. (2009)proposed relationships between CH4emissions and milk FAfor dairy cows receiving a maize silage-based diet supplemented or not with linseed under different forms (whole crude seed, extruded seed and oil). Two high predictive equations containing milk FA concentrations (cis9-14:1, 16:0, trans16+cis14-18:1, and 18:2n-6) and forage intake were established. Mohammed et al. (2011) studied these relationships on dairy cows receiving diets supplemented with calcium salts of palm oil, sunflowerseed, linseed and rapeseed. The predictive equation having the highest correlation coefficient contained milk FA concentrations (cis9-17:1, iso-16:0) combined with DM intake, and rumen protozoa concentration. Dijkstra et al. (2011) developed predictive equations using diets with calcium fumarate, diallyldisulfide, caprylic acid, capric acid, lauric acid, myristic acid, extruded linseed, linseed oil and yucca powder. The predictive equation having the correlation coefficient equal to 0.73 contained certain milk FA (anteiso-17:0, trans10+11-, cis11- and cis13-18:1). In goats, only 2 publications studied the effect of addition of cumin seed extract (Heidarian Miri et al., 2013) or chemical compound (bromochloromethane) in the diet (Abecia et al., 2012) on CH4emissions.Although these authors did not analyze the relationships between CH4 emissions and milk FA concentrations, reductions in CH4emissions with bromochloromethane were accompanied by several changes in milk FA composition, namely increases in saturated FA (8:0 and 10:0), and decreases in unsaturated FA (e.g., cis9-16:1, cis13-18:1, trans5- to trans9-18:1, trans 18:2, and total CLA; Abecia et al., 2012). On the

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Proceedings of the 16th AAAP Animal Science Congress Vol. I 10-14 November 2014, Gadjah Mada University, Yogyakarta, Indonesia contrary, cumin seed extract reduced the concentration of saturated FA in milk (e.g., 14:0, 16:0, and 18:0) and increased that of unsatured FA (e.g., 18:2n-6, and 18:3n-3; Heidarian Miri et al., 2013), which suggest potential differences in the mechanisms underlying the effects of both antimethanogenic products.

Although milk FA profile may be a potential indicator of CH4 production, published relationships in vivo are limited to diets varying mainly in type and availability of dietary FA. A wider variety of diets is required to explore the more general potential of milk FA profile as an indicator of CH4 emissions in dairy ruminants. Concluding remarks Ruminant nutrition is the main driver of the milk FA composition. Pasture feeding, compared to diets based on conserved grass, decreased the milk concentration of SFA, and increased those of cis9-18:1, cis9trans11-CLA and 18:3n-3 in both two species. In cows, linseed or rapeseed supplementation has similar effects than pasture feeding although enhancing more the milk concentration of some trans isomers of 18:1 and 18:2. Linseed increased markedly 18:3n-3, whereas rapeseed increased it slightly. Goats, when compared to cows, seem less sensitive to diets rich in starch content and supplemented with dietary PUFA, especially concerning the shift of ruminal biohydrogenation from trans11 to trans10. Indeed, responses of milk FA composition to oilseed supplementation have been characterized by higher content of PUFA, notably 18:3n-3 and cis9trans11-CLA, in goats. The addition of tannins or essential oils has little effect on milk FA composition at the doses studied so far. Milk FA can be used as good predictors of CH4 emissions in cows, at least in some feeding conditions. Further studies are needed in goats in order to confirm this aspect. However, the effect of oilseed supplementation on sensory quality of dairy products and economic results in dairy production systems needs to be betterevaluated in the future. REFERENCES Abecia, L., Toral, P.G., Martín-García, A.I., Martínez, G., Tomkins, N.W., Molina-Alcaide, E., and C. J. Newbold (2012). Effect of bromochloromethane on methane emission, rumen fermentation pattern, milk yield, and fatty acid profile in lactating dairy goats. J. Dairy Sci. 95, 2027-2036. Andueza,D., Rouel, J., Chilliard,Y., Leroux,C., and A. Ferlay (2013). Prediction of the goat milk fatty acids by near infrared reflectance spectroscopy. Eur. J. Lipid Sci. Technol. 115, 612-620. Bassett, C.M.C., Edel,A.L., Patenaude,A.F., McCullough,R.S., Blackwood,D.P., Chouinard,P.Y., PacquinP., Lamarche B., and G.N. Pierce (2010). Dietary vaccenic acid has antiatherogenic effects in LDR-/- mice. J. Nutr. 140, 18-24. Bauman, D.E., and J.M.Griinari (2003). Nutritional regulation of milk fat synthesis. Annu. Rev. Nutr., 23, 203-227. Benchaar, C., and P.Y.Chouinard (2009). Shortcommunication: assessment of the potential of cinnamaldehyde, condensed tannins, andsaponins to modify milk fatty acid composition of dairy cows. J. Dairy Sci. 92, 3392-3396. Benchaar, C., Petit, H.V., Berthiaume, R., Ouellet, D.R., Chiquette, J., and P.Y. Chouinard (2007). Effects of essential oils on digestion, ruminal fermentation, rumen microbial populations, milk production, and milk compositionin dairy cows fed alfalfa silage or corn silage. J. Dairy Sci. 90, 886-897. Benchaar, C., Petit, H.V., Berthiaume, R., Whyte, T.D., and P.Y.Chouinard (2006). Effects of addition of essential oils and monensin premix on digestion, ruminal fermentation, milk production,and milk composition in dairy cows.J. Dairy Sci. 89, 4352-4364.

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Benchaar,C.,Lettat,A., Hassanat,F., Yang,W.Z., Forster,R.J., Petit,H.V., and P.Y. Chouinard (2012).Eugenol for dairy cows fed low or high concentrate diets: Effects on digestion, ruminal fermentation characteristics, rumen microbialpopulations and milk fatty acid profile. Anim. Feed Sci. Technol.178, 139-150. Bernard, L., Leroux C., Bonnet, M., Rouel, J., Martin, P. and Y. Chilliard (2005). Expression and nutritional regulation of lipogenic genes in mammary gland and adipose tissues of lactating goats.J. Dairy Res. 72, 250-255. Bernard, L., Leroux, C. and Y.Chilliard (2006). Characterisation and nutritional regulation of the main lipogenic genes in the ruminant lactating mammary gland. In Ruminant physiology, pp. 295-326. Bernard, L., Leroux, C. and Y. Chilliard (2008). Expression and nutritional regulation of lipogenic genes in the ruminant lactating mammary gland. Bioactive Components of Milk, Adv. Exp. Med. Biol. 606, 67-108. Bernard, L., Leroux, C. and Y. Chilliard (2013). Expression and nutritional regulation of Stearoyl-CoA desaturase genes in the ruminant mammary gland: relationship with milk fatty acid composition. In Book 'Stearoyl-CoA Desaturase Genes in Lipid Metabolism', (J.M. Ntambi, ed.), Springer Science+Business Media New York. http://www.springer.com/978-1-4614-7968-0, pp. 161-194. Bernard, L., Shingfield, K.J., Rouel, J., Ferlay, A. and Y. Chilliard (2009). Effect of plant oils in the diet on performance and milk fatty acid composition in goats fed diets based on grass hay or maize silage. Br. J.Nutr. 101, 213-224. Boutoial, K., Ferrandini, E., Rovira, S., García, V., and M.B. López (2013a). Effect of feeding goats with rosemary (Rosmarinusofficinalis spp.) by-product on milk and cheese properties.Small Rumin. Res. 112, 147-153. Boutoial, K., García, V., Rovira, S., Ferrandini, E., Abdelkhalek, O., and M.B. López (2013b). Effect of feeding goats with distilled and non-distilled thyme leaves (Thymus zygissubp. gracilis) on milk and cheese properties. J. Dairy Res. 80, 448-56. Brunschwig, P., HurtaudC., ChilliardY., and F. Glasser (2010). L’apport de lin dans la ration des vaches laitières : Effets sur la production, la composition du lait et des produits laitiers, les émissions de méthane et les performances de reproduction. INRA Prod. Anim. 23, 307-318. Calsamiglia, S, Busquet, M, Cardozo, PW, Castillejos, L, and A.Ferret (2007). Invited review: Essential oils as modifiers of rumen microbial fermentation. J Dairy Sci. 90, 2580-95. Chilliard, Y., and A. Ferlay (2004). Dietary lipids and forages interactions on cow and goat milk fatty acid composition and sensory properties. Reprod. Nutr. Develop. 45, 467- 492. Chilliard, Y., F. Glasser, A. Ferlay, L. Bernard, J. Rouel, and M. Doreau (2007). Diet, rumen biohydrogenation and nutritional quality of cow and goat milk fat: a review. Eur. J. Lipid Sci. Technol. 109, 828-855. Chilliard, Y., Ferlay, A., Rouel, J., and G. Lamberet (2003).A review of nutritional and physiological factors affecting goat milk lipid synthesis and lipolysis. J Dairy Sci. 86, 1751-1770. Chilliard, Y., MartinC., RouelJ., and M. Doreau (2009). Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed or linseed oil, and their relationship with methane output. J. Dairy Sci. 92, 5199-5211. Chilliard, Y., Rouel, J., and C.Leroux (2006). Goat’s alpha-s1 casein genotype influences its milk fatty acid composition and delta-9 desaturation ratios. Animal Feed Sci. Technol. 131, 474-487.

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Chilliard, Y., Toral, P.G., Shingfield, K.J., Rouel, J, Leroux, C. and L. Bernard (2014). Effects of diet and physiological factors on milk fat synthesis, milk fat composition and lipolysis in the goat: A short review. Small Ruminant Research, in press. Collomb, M., BütikoferU., SieberR., JeangrosB., and O. Bosset (2002). Composition of fatty acids in cow’s milk fat produced in the lowlands, moutains and highlands of Switzerland using high-resolution gas chromatography. Int. Dairy J. 12, 649-659. Coppa, M., FerlayA., MonsallierF., Verdier-MetzI., PradelP., DidienneR., Farruggia,A., Montel, M.C., and B.Martin (2011). Milk fatty acid composition and cheese texture and appearance from cows fed hay or different grazing systems on upland pastures. J. Dairy Sci. 94, 1132–1145. Demeyer, D., and C.J.Van Nevel (1975). Methanogenesis, an integrated part of carbohydrate fermentation, and its control. In: McDonald, I.W., Warner, A.C.I. (Eds.), Digestion and Metabolism in the Ruminant. The University of New England Publishing Unit, Armidale, NSW, pp. 363-383. Dijkstra, J., Van Zijderveld, S., Apajalahti, J., Bannink, A., Gerrits, W., Newbold, J., Perdok, H., and H.Berends(2011). Relationships between methane production and milk fatty acid profiles in dairy cattle. Animal Feed Sci. Technol. 166, 590-595. Doreau, M., Bauchart, D., and Y. Chilliard (2011). Enhancing fatty acid composition of milk andmeat through animal feeding. Anim. Prod. Sci.,51, 19-29. Dschaak, C. M., Williams, C. M., Holt, M. S.,Eun, J.-S.,Young, A.J., and B.R. Min (2011).Effects of supplementing condensed tannin extract on intake, digestion, ruminal fermentation, and milk production of lactating dairy cows. J. Dairy Sci.94, 2508-2519. D'Urso, S, Cutrignelli, MI, Calabrò, S, Bovera, F, Tudisco, R, Piccolo, V, and F. Infascelli (2008). Influence of pasture on fatty acid profile of goat milk. J. Anim. Physiol. Anim. Nutr. 92, 405-410. Falchero, L., Lombardi,G., Gorlier,A., Lonati, M., Odoardi,M., and A. Cavallero (2010). Variation in fatty acid composition of milk and cheese from cows grazed on two alpine pastures. Dairy Sci. Technol. 90, 657-672. Ferlay, A., Martin,B., Pradel,Ph., Coulon,J.B., and Y. Chilliard (2006). Influence of grass- based diets on milk fatty acid composition and milk lipolytic system in Tarentaise and Montbéliarde cow breeds. J. Dairy Sci. 89, 4026-4041. Ferlay, A., Agabriel,C., Sibra,C., Journal,C., MartinB., and Y. Chilliard (2008). Tanker milk variability of fatty acids according to farm feeding and husbandry practices in a French semi-mountain area. Dairy Sci. Technol. 88, 193-215. Ferlay, A., Doreau, M., Martin, C., and Y.Chilliard (2013). Effects of incremental amounts of extruded linseed on milk fatty acid composition in dairy cows receiving hay or corn silage. J. Dairy Sci.96, 6577-6595. Gerber, P.J.,Steinfeld, H., Henderson,B., Mottet,A., Opio,C., Dijkman,J., Falcucci,A., and G. Tempio (2013). Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations, Rome, Italy. Ghaffari, M.H., TahmasbiA.M., KhorvashM., NaserianA.A., and A.R. Vakili (2014). Effects of pistachio by-products in replacement of alfalfa hay on ruminal fermentation, blood metabolites, and milk fatty acid composition in Saanen dairy goats fed a diet containing fish oil. J. Appl. Anim. Res. 42, 186-193. Givens, D.I. (2010). Milk and meat in our diet: good or bad for health? Animal 4, 1941-1952. Givens, D.I. (2012). Milk in the diet: good or bad for vascular disease? ProcNutr Soc. 71, 98- 104. Glasser, F., FerlayA., and Y. Chilliard (2008). Oilseed lipid supplements and fatty acid composition of cow milk: a meta-analysis. J. Dairy Sci. 91:4687-4703.

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HeidarianMiri, V., Tyagia, A. K., Ebrahimi, S. H., and M. Mohini (2013).Effect of cumin (Cuminumcyminum) seed extract on milk fatty acid profile and methane emission in lactating goat. Small Ruminant Res. 113, 66-72. Hurtaud, C., Faucon, F., Couvreur, S., and J.L. Peyraud (2010). Linear relationship between increasing amounts of extruded linseed in dairy cow diet and milk fatty acid composition and butter properties. J. Dairy Sci. 93, 1429-1443. Jensen, R.G. (2002). The composition of bovine milk lipids: January 1995 to December 2000. J Dairy Sci. 85, 395-350. Khiaosa-ard, R., Bryner, S.F., Scheeder, M.R.L., Wettstein, H.R., Leiber, F., Kreuzer, M., and C. R. Soliva (2009).Evidence for the inhibition of the terminal step of ruminal alpha-linolenic acid biohydrogenation by condensed tannins. J. Dairy Sci. 92, 177-188. Kholif, S.M., Morsy, T.A., Abdo, M.M., Matloup, O.H., and A.A. Abu El-Ella (2012). Effect of supplementing lactating goats rations with garlic, cinnamon or ginger oils on milk yield, milk composition and milk fatty acids profile. J. LifeSci. 4, 27-34. Lee, Y.J., and T.C. Jenkins (2011). Biohydrogenation of linolenic acid to stearic acid by the rumen microbial population yields multiple intermediate conjugated diene isomers. J. Nutr. 141, 1445-1450. Leiber, F., Scheeder,M.R.L., Wettstein,H.R., and M. Kreuzer (2004). Milk fatty acid profile of cows under the influence of alpine hypoxia and high mountainous forage quality. J. Anim. Feed Sci. 13, 693-696. Lerch, S., Ferlay, A., Pomiès, D., Martin, B., Pires, J.A.A., and Y.Chilliard (2012a). Rapeseed or linseed supplementation of grass-based diets: effects on dairy performance of Holstein cows over two consecutive lactations. J. Dairy Sci 95, 1956-1970. Lerch, S.,Ferlay, A., Shingfield, K.J., Martin, B., Pomiès, D., and Y.Chilliard (2012b). Rapeseed or linseed supplements in grass-based diets: effects on milk fatty acid composition of Holstein cows over two consecutive lactations.J. Dairy Sci. 95, 5221- 5241. Lerch, S.,Shingfield, K. J., Ferlay, A., Vanhatalo, A., and Y.Chilliard (2012c). Rapeseed or linseed supplementation of grass-based diets: effects on the distribution of conjugated linoleic (CLA) and conjugated linolenic (CLnA) acid isomers in milk fat from Holstein cows over two consecutive lactations J. Dairy Sci.95, 7269-7287. Lopez-Huertas, E. (2010). Health effects of oleic acid and long chain omega-3 fatty acids (EPA and DHA) enriched milks. A review of intervention studies. Pharmacol. Res. 61, 200-207. Martin, C., Morgavi, D., and M. Doreau (2010). Methane mitigation in ruminants: from microbe to the farm scale. Animal 4, 351-365. Mills, S., Ross,R.P., Hill,C., Fitzgerald,G.F., and C. Stanton (2011). Milk intelligence: mining milk for bioactive substances associated with human health. Int. Dairy Sci. 377- 401. Mohammed, R., McGinn, S., and K.Beauchemin (2011). Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds. J. Dairy Sci.94, 6057-6068. Mosley, E.E., Shafii, B., Moate, P.J., and M.A.McGuire (2006). Cis-9, trans-11 conjugated linoleic acid is synthesized directly from vaccenic acid in lactating dairy cattle. J. Nutr. 136, 570-575. Ollier, S., Leroux, C., de la Foye, A., Bernard, L., Rouel, J. and Y. Chilliard (2009). Whole intact rapeseeds or sunflower oil in high-forage or high-concentrate diets affects milk yield, milk composition, and mammary gene expression profile in goats. J. Dairy Sci.92, 5544-5560.

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Palmquist, D.L. (2009). Omega-3 fatty acids in metabolism, health, and nutrition and for modified animal product foods. The Professional Animal Scientist, 25, 207-249. Petit, H.V. (2010). Review: feed intake, milk production and milk composition of dairy cows fed flaxseed. Can. J. Anim. Sci. 90, 115-127. Ramos-Morales, E., Martínez-Fernández, G., Abecia, L., Martin-García, A.I., Molina- Alcaide, E., and D.R. Yáñez-Ruiz (2013). Garlic derived compounds modify ruminal fatty acid biohydrogenation and induce shifts in the Butyrivibrio community in continuous-culture fermenters. Anim. Feed Sci. Technol. 184, 38-48. Raynal-Ljutovac, K., Lagriffoul, G., Paccard, P., Guillet, I. and Y.Chilliard (2008). Composition of goat and sheep milk products: an update. Small Rum. Res., 79, 57-72. Roy, A., Ferlay,A., Shingfield,K.J., and Y. Chilliard (2006). Examination of the persistency of milk fatty acid composition responses to plant oils in cows fed different basal diets, with particular emphasis on trans-C18:1 fatty acids and isomers of conjugated linoleic acid. Animal Sci. 82, 479-492. Shingfield, K.J., Bernard,L., Leroux,C., and Y. Chilliard (2010). Role of trans fatty acids in the nutritional regulation of mammary lipogenesis in ruminants. Animal 4, 1140-1166. Shingfield, K.J., ChilliardY., ToivonenV., KaireniusP., and D.I. Givens (2008).Trans fatty acids and bioactive lipids in ruminant milk. In: Bioactive components of milk (Ed. Z. Bösze, Springer, USA) Advances in Experimental Medicine and Biology. 606, 3-65. Toral, P.G., Hervás, G., Belenguer,A., Bichi, E., and P. Frutos (2013). Effect of the inclusion of quebracho tannins in a diet rich in linoleicacid on milk fatty acid composition in dairy ewes. J. Dairy Sci. 96, 431-439. Vasta, V., Makkar, H.P.S., Mele, M., and A.Priolo (2009). Ruminalbiohydrogenation as affected by tannins in vitro. Br. J. Nutr. 102, 82-92. Vasta, V., Yanez-Ruiz, D.R., Mele, M., Serra, A., Lucinao, G., Lanza, M., Biondi, L., and A.Priolo (2010). Bacterial and protozoal communities and fatty acid profile in the rumen of sheep fed a diet containing added tannins. Appl. Environ. Microbiol. 76, 2549-2555. Zhu, Z., Hang, S., Zhu, H., Zhong, S., Mao, S., and W. Zhu (2013). Effects of garlic oil on milk fatty acid profile and lipogenesis-related gene expression in mammary gland of dairy goats. J. Sci. Food Agric. 93, 560-567.

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Transgenic and Proteomic Approaches for Improving Abiotic Stress Tolerance in Forage Plants Md. Atikur Rahman1, Yong-Goo Kim1, Na-Young Ahn1, Ki-Won Lee2, Sang-Hoon Lee2 and Byung-Hyun Lee1 1Division of Applied Life Science (BK21Plus), IALS, PMBBRC, Gyeongsang National University, Jinju 660-701, Korea, 2Grassland and Forage Division, National Inststitute of Animal Science, Cheonan 330-801, Korea Corresponding email: [email protected] ABSTRACT Abiotic stresses including drought, salt, heavy metals, extreme temperatures are main constraints to decrease forage productivity. Therefore, it is need to develop new cultivars with improved productivity and enhanced tolerance against abiotic stresses. We applied transgenic and proteomic approaches to improve stress tolerance in forage plants. In this study, we developed transgenic tall fescue plants by introducing several stress tolerance genes under control of CaMV 35S or stress-inducible promoter. In our result, transgenic tall fescue showed enhanced abiotic stress tolerance compared to wild-type plants. Plant genetic engineering is benefited from the knowledge of proteomics by which found some potential candidate genes. We studied alfalfa, miscanthus, and rice proteomes, identified several abiotic stress responsive candidates and introduced into forage plants to improve multiple stress tolerance. The potential contribution of proteomics towards biotechnology may help to improve forage plant’s performance against various environmental stresses. Key Words: Abiotic stress, Forage, Proteomics, Transgenic crop INTRODUCTION Forages are the backbone of sustainable agriculture and contribute extensively to world economy. Grassland covers 26% of the world’s total land area that is estimated to be twice of cropland acreage (Jauhar, 1993). Grasslands are reliable source of forage as primary feed for ruminant animals (Barnes and Baylor, 1995). However, environmental stresses greatly limit yield and quality of forage crops. It has been estimated that more than 70% of yield is reduced by several abiotic stresses (Acquaah, 2007). Among a number of abiotic stresses, drought and high temperatures are most severe and frequent (Lobell and Field, 2007). Therefore, development of new cultivars with improved stress tolerance is prime important for sustainable forage production. To date, forage cultivars with high productivity are still being developed using traditional breeding methods. Conventional breeding of forage plants has been based on the use of natural genetic variation as found within ecotypes or generated through sexual hybridization (Spangenberg et al., 2001). Molecular breeding is a relatively new term that describes the use of modern technology such as genomic and transgenic tools in conjunction with conventional breeding. Compare to model plants with other major crop plants, progress of molecular studies in stress tolerance of forage plants are very slow (Zhang et al., 2006). Previous studies report that transgenic approach was applied in other forage crop with stress tolerance (Luo et al., 2004). However, transgenesis as well as proteomics are important area of forage plant improvement. Proteomics is a powerful molecular tool for the systemic analysis of proteins expressed by the genome (Porubleva et al., 2001). Plant genetic engineering leads the benefits from proteomics by the identification of new candidates that confers stress tolerance. Therefore, transgenic and proteomic approaches may effective for developing in new forage crop cultivars with enhanced tolerance. In this study, we focused on the application of transgenic and proteomic approaches for improving molecular traits of forage plants.

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MATERIALS AND METHODS In order to develop stress tolerant forage plants, we applied transgenic approaches by using our previously described techniques (Kim et al., 2010). Several stress tolerance genes against abiotic stresses including drought, salt, high temperature, metal toxicity, and ABA responsive gene were isolated and introduced into expression vectors under the control of stress- inducible or CaMV35S promoter. Forage plants were infected with Agrobacterium containing expression vectors, and transformed plants were selected. Transgenic plants were confirmed by Southern blot or genomic PCR. Expression of each transgene was confirmed by RT-PCR or northern blot analysis. Finally, transgenic plants were grown in green house. In addition, we applied proteomics techniques (Alam et al., 2012) in alfalfa and miscanthus under abiotic stresses (drought, salt, metal toxicity) to find out possible target of proteins/genes in terms of stress tolerance, identified candidates were engineered in plant. In our target, forage plants were grown in growth chamber maintained at 25°C under white florescent light (80 µmol m- 1s-1) with 16 h photoperiod. Plants were exposed to several abiotic stresses, samples were collected and proteins were extracted from leaves or root tissues. Reproducibly differentially expressed protein spots were visualized by Coomassie Brilliant Blue (CBB) staining. Then spots were excised from the gel, digested by trypsin and subjected to the matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis or LC MS/MS analyzer. The obtained peptide picks of each spot were matched in NCBI database using the profound program (http://prowl.rockefeller.edu/prowl-cgi/profound.exe). Finally, it was confirmed and selected suitable/desire candidates. RESULTS AND DISCUSSION In our transgenesis study, we developed transgenic forage plants overexpressing several stress tolerance genes under the control of constitutive (CaMV 35S) or stress-inducible promoter (SWPA2). Transgenic tall fescue plants showed increased stress tolerance compared to wild- type plants under several abiotic stresses. Transgenic plants overexpressing low molecular weight heat shock proteins also showed enhanced tolerance against heat and heavy metal stresses. These results suggest that increased antioxidant and chaperone activity could increase tolerance against environmental stresses. Proteomics is advantageous over genomics for the most forage and bioenergy crops. Plant genetic engineering also hopes to benefit from proteomics by the identification of new genes/proteins that confer stress tolerance. We studied alfalfa and rice proteome in response to salt, drought, heat and heavy metal stresses to find out some promising candidates .In our current study, we screened number of potential genes including mitochondrial HSP and GST omega.

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Figure 1. Improvement of tall fescue plants by transgenesis (Kim et al., 2014). Expression of transgene in transgenic tall fescue plants (A). Transgenic plant shows increased tolerance compared to wild-type plant under drought stress (B) Among the potential genes found by proteomics, we overexpressed small heat shock protein genes in tall fescue. Transgenic tall fescue plants showed greater photosynthetic capability and overall stress tolerance under heat stress compared to wild-type plants. In addition, we constructed a reference map for the leaf proteome of Miscanthus sinensis. This information may provide the basis for future studies to elucidate stress response mechanisms.

Figure. 2. Screening of possible candidates in forage plants by using proteomic approach (Kim et al., 2014). Abiotic stress responsive candidates in alfalfa plants found on 2-DE gel (A). Functional categorization of stress-induced candidates (B) ACKNOWLEDGEMENTS This work was supported in part by a grant from the Next-Generation BioGreen21 Program (No. PJ008139), Rural Development Administration, Republic of Korea. M.A. Rahman, Y.-G. Kim and N.-Y. Ahn were supported by a scholarship from the BK21Plus Program, the Ministry of Education Korea. REFERENCES Acquaah, G. 2007. Principles of plant genetics and breeding. Blackwell, Oxford, UK Alam, I., S. A. Sharmin, K. -H. Kim, Y. -G. Kim, J. J. Lee and B. -H. Lee. 2012. An improved plant leaf protein extraction method for high resolution two-dimensional polyacrylamide gel electrophoresis and comparative proteomics. Biotech. Histochem. 88 : 61-75.

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Barnes, R. F. and J. E. Baylor. 1995. Forages in a changing world. In: Forages, An introduction to grassland Agriculture, 5th edn (Ed. R.F. Barnes, D.A. Miller and C.J. Nelson). Iowa State University Press, Ames, Iowa. pp 3-13. Jauhar, P. P. 1993. Cytogenetics of the Festuca-Lolium complex: relevance to breeding, Springer, Berlin, pp.1-8. Kim, Y. G., M. A. Rahman, N. -Y. Ahn, K. -W. Lee, S. -H. Lee and B. -H. Lee. 2014. Improvement of forage and energy plants by transgenesis and proteomics. In: Proceedings of the 5th China-Japan-Korea grassland science symposium. Changchun, China. Kim, K. -H., I. Alam, K. -W. Lee, S. A. Sharmin, S. -S. Kwak, S. Y. Lee, and B. -H. Lee. 2010. Enhanced tolerance of transgenic tall fescue plants overexpressing 2-Cys peroxiredoxin against methyl viologen and heat stresses. Biotechnol. Lett. 32: 571- 576. Lobell, D. B and C. B. Field. 2007. Global scale climate-crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2: 14002-14008. Luo, H., Q. Hu, K. Nelson, C. Longo, A. P. Kausch, J. M. Chandlee, J. K. Wipff and C. R. Fricker. 2004. Agrobacterium tumefaciens-mediated creeping bentgrass (Agrostis stolonifera L.) transformation using phosphinothricin selection results in a high frequency of single-copy transgene integration. Plant Cell Rep. 22: 645-652. Porubleva, L., K. V. Velden, S. Kothari, D. J. Oliver and P. R. Chitnis. 2001. The proteome of maize leaves: Use of gene sequences and expressed sequence tag data for identification of proteins with peptide mass fingerprints. Electrophoresis 22: 1724- 1738. Spangenberg, G., R. Kalla, A. Lidgett, T. Sawbridge, E. K. Ong and U. John. 2001. Breeding forage plants in the genome era. In: Molecular Breeding of Forage Crops (Ed. G. Spangenberg et al.). Kluwer Academic Publishers, pp. 1-39. Zhang, Y., M. A. R. Mian and J. H. Bouton. 2006. Recent molecular and genomic studies on stress tolerance of forage and turf grasses. Crop Sci. 46:497-511.

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Alternative Local Feed Resources for Lactating Goats H. T. Wang1, C. S. Chen2, T. J. Chou3, M. H. Chen3, C. Lee3, B. Y. Chen3, S. W. Chen3 and J. T. Hsu3 1Chinese Culture University, Taiwan, 2Livestock Research Institute, Taiwan, 3National Taiwan University, Taipei, Taiwan Corresponding email: [email protected] ABSTRACT The main goal of our research is to cut down dependence on imported feed resources in feeding lactating dairy goats. First experiment used 8 Alpine goats in mid-lactation in a cross- over design with two treatments: (1) alfalfa hay+bermudagrass hay (3:1) as imported forage group; (2) peanut stover+corn silage (3:1) as local forage group at a fixed forage/concentrate ratio (40/60) and equal crude protein (CP) content (20%). Second experiment used same set up to compare (1) alfalfa hay+bermudagrass hay; and (2) pangola-siratro mixed hay+corn silage+alfalfa hay. Third experiment used 6 Alpine goats in mid-lactation in two replicated 3 x3 Latin square design experiment including 3 treatment diets: (1) control (alfalfa hay, bermudagrass); (2) rice straw (alfalfa hay, rice straw); and (3) sun hemp (sunn hemp, bermudagrass) at a fixed forage/concentrate ratio (40/60) and equal CP content (22%). In groups 2 and 3, 80% of rice straw and 60% of sunn hemp were treated with alkaline hydrogen peroxide (AHP) to improve quality to be close to bermudagrass and alfalfa hay, respectively. Experiment 1 showed no significant difference in dry matter intake (DMI) (1.52 vs. 1.45 kg/day), 4% fat corrected milk (FCM) yield (1.82 vs. 1.92 kg/day), feed efficiency (1.4 ± 0.3 vs. 1.3 ± 0.2), but higher milk protein and fat contents in local forage group. Feed cost of local forage group was 22% lower. In experiment 2, DMI was significantly higher in imported forage group, no difference in 4% FCM yield, but significantly higher milk fat and solid nonfat contents, and higher feed efficiency in local forage group. Feed cost of local forage group was 25% lower. In experiment 3, there was no significant difference in DMI, 4%FCM, milk composition and feed efficiency among all 3 groups, indicating AHP treatment did improved feeding quality of rice straw and sunn hemp. Key Words: Peanut stover, Corn silage, Pangola-siratro mixed hay, Rice straw, Sunn hemp, Lactating goat INTRODUCTION Our previous studies showed that off-grade vegetable soybean pod from export food sector in Taiwan can be included in corn silage up to 50% without any undesirable effect on lactating goats. Also, perennial peanut hay and peanut stover can replace alfalfa hay without negative effect (Chen et al., 2010). Partial incorporation of by-products in lactating goat feeding can be easily adopted, but the feed cost reduction is limited. In order to largely reduce feed cost, local forage and by-product feed resources should be utilized as much as we can. For poor quality feed sources such as rice straw, alkaline treatment can yield promising improvement in digestibility (Chaudhry, 2000). MATERIALS AND METHODS Animals and treatments In experiment 1, eight lactating Alpine dairy goats (55.6 ± 7.6 kg of BW) past peak lactation were used in a cross-over experiment design and allocated to two treatments: (1) alfalfa hay+bermudagrass hay (3:1) as imported forage group, or (2) peanut stover+corn silage (3:1) as local forage source. Rations were formulated of equal forage/concentrate ratio (40:60) and equal crude protein (CP) content (20%) in rations. In experiment 2, a similar set up as the

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Sustainable Livestock Production in the Perspective of Food Security, Policy, Genetic Resources and Climate Change first experiment was employed to compare (1) alfalfa hay+bermudagrass hay as imported forage source; (2) pangola-siratro mixed hay+corn silage+alfalfa hay as local forage source at a forage/concentrate ratio of 40/60 and equal CP (18.5%) and ME (2.47 Mcal/kg) contents in rations. Experiment 3 used 6 multiparous Alpine goats in mid-lactation in two replicated 3 x3 Latin square design experiment including 3 treatment diets: (1) control (alfalfa hay, bermudagrass); (2) rice straw (alfalfa hay, rice straw); and (3) sun hemp (sunn hemp, bermudagrass) at a fixed forage/concentrate ratio of 40/60 and equal CP content (22%) in rations. In vitro fermentation tests were used to evaluate the in vitro DM digestibility (IVDMD), in vitro NDF digestibility (IVNDFD) and total gas production of rice straw and sunn hemp with or without alkaline hydrogen peroxide (AHP) treatment (5% NaOH and 2.5% H2O2). Based on the in vitro results, 80% of rice straw and 60% of sunn hemp were AHP treated to improve their quality to be close to bermudagrass and alfalfa hay, respectively. Sample collection and analyses Each experiment period lasted for 21 days with the last 4 days as collection period for refusal and milk sample collection. Milk composition was analyzed by CombiFoss 5500 (Foss Electric, Denmark). Fecal, feed and refusal samples were collected and analyzed nutrient contents to calculate the nutrient digestibility of experimental rations. Statistical analyses Data were analyzed with a general linear model (SAS, 1988). Means of treatment groups were compared by least square mean with significant level set at p<0.05. RESULTS AND DISCUSSION The first experiment showed no difference in DMI and 4% FCM yield, but the local forage group had significantly higher milk protein and fat contents (Table 1). No treatment difference was observed in somatic cell count (SCC). It is common to see large variation of SCC in goat milk and SCC of the present study were within the variation ranges in literatures (Ying et al., 2002; Zweifel et al., 2005). In literature, alternative feed resources has shown various effect on milk fat and protein contents of small ruminants, but not on SCC (Vasta et al., 2008). Soybean pulp and brewers grain were included as concentrate ingredients for the local forage group and resulted in higher NDF and fat contents, but lower non-fiber carbohydrate (NFC) content. The local forage group showed significantly lower organic matter (OM), CP and NFC digestibility, but higher TDN (83.5 ± 3.6 vs. 84.8 ± 5.3%). The feed cost of the local forage group was 22% lower than the imported forage group. Overall, the local forage group has strong economic incentive without drawback of lactating goat performance. In the second experiment, DMI was significantly higher in imported forage group, but no difference in milk yield and 4% FCM yield (Table 2). The local forage group had significantly higher milk fat and solid nonfat contents, and higher feed efficiency. The feed cost of the local forage group was 25% lower. In the third experiment, AHP treatment decreased NDF and ADL contents of rice straw 16% and 37%, and 5% and 19% for sunn hemp. IVDMD of rice straw and sunn hemp were improved 63 and 15% by AHP treatment. INDFD of rice straw and sunn hemp were improved 63 and 24% by AHP treatment. To cut down the pretreatment cost of rice straw and sunn hemp, only 80% of rice straw and 60% of sunn hemp were subjected to AHP treatment before incorporated into the rations. IVDMD of control, rice straw and sunn hemp total mixed rations were also tested and shown to be similar (72.3, 74.4, 73.8%). Lactating goats feeding showed no significant difference in DMI, 4%FCM, milk composition and feed efficiency among all 3 groups. These results indicated that AHP treatment can improve the feeding value of rice straw and sunn hemp to be close to bermudagrass and alfalfa hay, respectively.

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In conclusion, local by-products and alternative forages can be assembled to cut down the usage of imported forage and grains to the minimum and lower feed cost at least 20% without inferior effect to lactating goat performance.

Table 1. Intake and lactation performance of goats fed alfalfa hay-bermudagrass (AB) and peanut stover-corn silage (PC) rations Items AB PC Dry matter intake (g/head/day) 1522 ± 261 1449 ± 260 Milk yield (g/head/day) 2079 ± 463 * 1865 ± 394 4% fat corrected milk yield (g/head/day) 1817 ± 426 1916 ± 491 Milk composition

Protein (%) 2.99 ± 0.25 * 3.14 ± 0.18 Fat (%) 3.87 ± 0.67* 4.36 ± 0.46 Solid nonfat (%) 8.16 ± 0.37 8.24 ± 0.31 Somatic cell count (103/mL) 654 ± 580 918 ± 632 Milk yield/Dry matter intake 1.4 ± 0.3 1.3 ± 0.2 *P < 0.05

Table 2. Intake and lactation performance of goats fed alfalfa hay-bermudagrass (AB) and pangola- siratro mixed hay+corn silage+alfalfa hay (PSC) rations Items AB PSC Dry matter intake (g/head/day) 1790 ± 414* 1044 ± 385 Milk yield (g/head/day) 1194 ± 781 1248 ± 510 4% fat corrected milk yield (g/head/day) 1076 ± 703 1287 ± 540 Milk composition

Protein (%) 3.12 ± 0.29 3.19 ± 0.22 Fat (%) 3.41 ± 0.47 * 4.20 ± 0.37 Solid nonfat (%) 8.33 ± 0.31 * 8.51 ± 0.28 Milk yield/Dry matter intake 0.7 ± 0.4 * 1.2 ± 0.2 *P < 0.05

Table 3. Intake and lactation performance of goats fed alfalfa hay-bermudagrass (AB), rice straw (RS), and sunn hemp (SH) rations Items AB RS SH MSE Dry matter intake (g/head/day) 2042 2136 1877 89 Milk yield (g/head/day) 2518 2789 2297 142 4% fat corrected milk yield (g/head/day) 2166 2393 2049 101 Milk composition Protein (%) 3.2 3.2 3.2 0.02 Fat (%) 3.5 3.4 3.6 0.04 Solid nonfat (%) 8.2 8.2 8.2 <0.01 Milk yield/Dry matter intake 1.2 1.2 1.2 0.02

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CONCLUSION In conclusion, local by-products and alternative forages can be assembled to cut down the usage of imported forage and grains to the minimum and lower feed cost at least 20% without inferior effect to lactating goat performance. REFERENCES Chaudhry, A. 2000. Rumen degradation in sacco in sheep of wheat straw treated with calcium oxide, sodium hydroxide and sodium hydroxide plus hydrogen peroxide. Animal Feed Science and Technology 83: 313-323. Chen, C. S., H. T. Wang, C. H. Wang, T. C. Hu, W. C. Hsu, and J. T. Hsu. 2010. Application of green manure and by-product in lactating goat feeding. Asian-Australasian Association of Animal Production Societies 14th Animal Science Congress Proceedings Vol. 1 pp.450-453. SAS Institute, Inc., 1988. SAS/STAT User’s Guide. Releasse 6.03 Ed. SAS Institute Inc., Cary, NC. Vasta, V., A. Nudda, A. Cannas, M. Lanza, and A. Priolo. 2008. Alternative feed resources and their effects on quality of meat and milk from small ruminants. Anim. Feed Sci. Technol. 147:223-246. Ying, C., H. T. Wang, and J. T. Hsu. 2002. Relationship of somatic cell count, physical, chemical and enzymatic properties to the bacterial standard plate count in dairy goat milk. Livestock Production Sci. 74:63-77. Zweifel, C., J. E. Muehlherr, M. Ring, and R. Stephan. 2005. Influence of different factors in milk production on standard plate count of raw small ruminant’s bulk-tank milk in Switzerland. Small Ruminant Res. 58:63-70.

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ACKNOWLEDGMENT

The 16th AAAP Congress Organizing Committee would like to extend the appreciation and gratitude to all supporting organizations, sponsors, partners and volunteers who give great supports and contributions to our conference success. They are:

1. PT Citra Agro Buana Semesta 2. PT Agro Investama 3. PT Berkah Citra Agro 4. South East Asia Livestock Service (SEALS) 5. Asosiasi Produsen Daging dan Feedlot Indonesia (APFINDO) 6. Australian Center for International Agriculture Research (ACIAR) 7. PT Agrisatwa Jaya Kencana 8. PT Agro Giri Perkasa 9. Elanco Animal Health 10. PT Great Giant Livestock Co 11. PT Bina Mentari Tunggal 12. PT Fortuna Megah Perkasa 13. PT Lembu Jantan Perkasa 14. PT Eldira Fauna Asahan 15. PT Widodo Makmur Perkasa 16. PT Kadila Lestari Jaya 17. PT Tanjung Unggul Mandiri 18. Meat and Livestock Australia 19. PT Santosa Agrindo 20. PT Elders Indonesia 21. PT Napindo Ashatama (Indolivestock) 22. Bank Danamon 23. Zoetis 24. Trouw Nutrition