Evaluation of Pig and Cattle Performance Under Small Scale Farmers' Management Conditions

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2017 Evaluation of pig and cattle performance under small scale farmers’ management conditions Muhammed Walugembe Iowa State University

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This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Evaluation of pig and cattle performance under small scale farmers’ management conditions

Muhammed Walugembe

A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY

Major: Animal Science

Program of Study Committee: Max. F. Rothschild, Co-major Professor Kenneth J. Stalder, Co-major Professor Philip M. Dixon John W. Mabry Curtis R. Youngs

Iowa State University Ames, Iowa 2017

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... vii ABSTRACT ...... ix CHAPTER 1 . GENERAL INTRODUCTION ...... 1 Dissertation organization...... 3 CHAPTER 2 . REVIEW OF LITERATURE ...... 4 Introduction ...... 4 Livestock production systems ...... 5 Gender and its role in livestock production ...... 7 Swine growth traits...... 10 Berkshire breed ...... 12 Livestock body weight estimation without scales ...... 13 Summary ...... 17 Literature cited ...... 18 CHAPTER 3 . PREDICTION OF LIVE BODY WEIGHT USING VARIOUS BODY MEASUREMENTS IN UGANDAN VILLAGE PIGS ...... 28 Abstract ...... 28 Materials and methods ...... 31 Study location ...... 31 Weighing and measuring pigs ...... 32 Categorization of pigs ...... 33 Data analyses ...... 33 Results ...... 34 Discussion ...... 35 Acknowledgements ...... 37 Literature cited ...... 39 iii

CHAPTER 4 . EVALUATION OF GROWTH, DEPOSITION OF BACKFAT, AND LOIN MUSCLE FOR PUREBRED BERKSHIRE PIGS HOUSED IN BEDDED HOOP BUILDINGS ...... 44 Abstract ...... 44 Introduction ...... 45 Materials and methods ...... 46 Animals and experimental design ...... 46 Dietary treatments ...... 47 Data collection ...... 47 Data analyses ...... 48 Results ...... 48 Discussion ...... 50 Acknowledgements ...... 52 Literature cited ...... 53 CHAPTER 5 . GENDER INTRA-HOUSEHOLD CONTRIBUTIONS TO LOW-INPUT DAIRY PRODUCTION IN SENEGAL ...... 60 Abstract ...... 61 Introduction ...... 62 Materials and methods ...... 63 Overview ...... 63 Project site description ...... 63 Data collection...... 64 Data analyses ...... 65 Results ...... 66 Household livelihood sources and demographics ...... 66 Intra-household responsibilities on dairy - male headed households and from male respondents ...... 68 Intra-household responsibilities on dairy - male headed households and from female respondents ...... 70 iv

Intra-household responsibilities on dairy - female headed households and from female respondents ...... 70 Gendered perceptions on dairy cattle breeds ...... 70 Gendered perceptions on constraints to dairy...... 72 Gendered training needs on dairy ...... 72 Discussion ...... 72 Acknowledgments ...... 76 Literature cited ...... 77 CHAPTER 6. GENERAL CONCLUSIONS ...... 87

LIST OF FIGURES Figure 3.1: A graphical depiction showing the location where various body dimensional measurements were obtained that were utilized to predict live body weight in pigs from Uganda...... 43

Figure 4.1: Average daily weight gain across the four trials comparing Berkshire barrows and gilts...... 57

Figure 4.2: Cumulative backfat accretion across the four trials comparing Berkshire barrows and gilts...... 57

Figure 4.3: Cumulative loin muscle area across the four trials comparing Berkshire barrows and gilts...... 58

Figure 4.4: Average daily feed intake across the four trials comparing Berkshire barrows and gilts...... 58

Figure 4.5: Gain-to-feed ratio across the four trials comparing Berkshire barrows and gilts ...... 59

Figure 5.1: Labor provision on dairy activities in the lower and higher market...... 83

Figure 5.2: Decision making on dairy activities in the lower and higher market ...... 84

Figure 5.3: Payment of costs on dairy activities in lower and higher market ...... 85

Figure 5.4: Control of benefits on dairy in lower and higher market oriented households, according to male respondents ...... 86

LIST OF TABLES Table 3.1: Model effects, prediction values and their significance for live body weight using analysis of variance from a study of Ugandan market pigs ...... 41

Table 3.2: Change in R2 with inclusion of each additional body measurement using stepwise regression when predicting live body weight by three weight classes of pigs raised by smallholders in Uganda...... 42

Table 4.1: Experimental design for the study evaluating the body weight growth, backfat, and loin muscle accretion in purebred Berkshire pigs ...... 55

Table 4.2: Analysis of variance for a study evaluating body weight, backfat and loin muscle accretion across four trials (two winters and two summers) and sex in purebred Berkshire pigs1 ...... 55

Table 4.3: Least squares means (± SE) for a study evaluating body weight, backfat and loin muscle accretion across the four trials (two winters and two summers) and sex in purebred Berkshire pigs ...... 56

Table 5.1: Control of dairy enterprise benefits between adult males and adult females ...... 81

Table 5.2: Perceived main constraints to dairy production ...... 81

Table 5.3: Household training needs on dairy ...... 82 vii

ACKNOWLEDGEMENTS First, I would like to thank God for the gift of life, spiritual recruitment, and guidance.

Special thanks to my major professor Dr. Max F. Rothschild for the opportunity he gave me to continue my education at Iowa State University (ISU). Your support, kindness, patience, and guidance during graduate school made my stay at ISU much easier. I was honored to work with you, and I was glad to be one of your graduate students. Your mentoring philosophy made me a better person, and I can’t thank you enough. Dr. Rothschild is one of the most hardworking professionals I know, and he takes the initiative to instill that work ethic in the people around him.

I would like to thank my co-major professor, Dr. Kenneth J. Stalder for all the guidance and support during the entire study period. Both Dr. Rothschild and Dr. Stalder have gone above and beyond to help me broaden my knowledge in the field of Animal Science.

Great thanks to Dr. Philip J. Dixon for his support and willingness to serve on both my

MS and PhD committees. He is a great instructor, and his STAT 401 class was one of the most interesting classes during my time at Iowa State. I will forever be grateful for his efforts and sacrifice of time, particularly when I was preparing for my preliminary exam. I received a great deal of help and guidance from him.

I would also like to thank both Drs. John W. Mabry and Curtis R. Youngs for serving on my POS committee. Despite his retirement, Dr. Mabry sacrificed some time and continued to serve on my committee. It was an honor to have people like you serve on my committee.

Over the past six years in graduate school, I have received tremendous help and developed friendships within the Department of Animal Science. Thanks to everyone who helped me during my time at ISU, especially members of Dr. Rothschild’s and Dr. Stalder’s labs. viii

I am very grateful to my family for all the unconditional support and love I have received. Mom, Hanifah, Joweria, and Juma you have always provided any form of support I needed to keep me going.

Finally, I thank my beloved wife, Shifa Walugembe for the unconditional love and support that I have always received from her. I tremendously love and cherish Shifa.

ABSTRACT Livestock farming is an important enterprise in small-scale farming households. It is a source of assets to the households and provides protein-rich animal products as well as income.

Small-scale farmers mainly practice a mixed farming system with limited investment and in regions where animal products are sold to niche markets. The enterprise may involve contributions from both genders across different age groups. Three studies were conducted to evaluate pigs and dairy cattle performance under small scale farmers’ management conditions with the aim of empowering small-scale producers, particularly rural women, with knowledge to predict pig weights to improve bargaining power while marketing pigs. Additionally, we evaluated the role of gender in dairy cattle production in a developing country setting.

In the first chapter (chapter 3), a study was conducted in the rural Kamuli district,

Uganda (East Africa) to develop body weight prediction equations based on five body measurements: body length, heart girth, height, body width, and flank-to flank. Body length and heart girth were the most important predictors (R2=0.88) of pig live body weights across all body weight values. Four body measurements (body length, heart girth, height, and body width) were strongly predictive of live body weight for pigs ≥ 40 kg. Chapter 4 describes a study that was conducted to evaluate body weight, backfat thickness and loin muscle of purebred Berkshire pigs raised in mini-hoop barns in Castana, Iowa in two winter and two summer trials. Body growth rate (weight gain) and ultrasonic backfat deposition were significantly greater in trial 1 (first summer) compared to other trials. Overall, barrows grew heavier and deposited more backfat than gilts (P<0.05). Barrows averaged 31 mm of backfat at 125 kg whereas gilts had an average of 23 mm of backfat at market weight of 121 kg. Chapter 5 discusses two surveys that were conducted at two sites in Senegal (West Africa) to determine intra-household gender roles in x

small holder dairy cattle herds. Adult males (> 15 years) were more often responsible for the costs and decision-making for most dairy-related activities. Adult males, hired males (> 15 years), and any household members (except women) were the main labor source for dairy activities. Adult females controlled most of the benefits from milk sales whilst adult males controlled the benefits from live animal sales. However, the proportion of males who control the benefits from milk sales increased as the market orientation shifted from lower to higher.

In conclusion, small scale producers/farmers are faced with a variety of challenges such as poor animal nutrition, extreme environmental conditions and inferior breeds that, together with limited production knowledge, may lead to below optimum production. Technology and best practices can improve production, marketing and income. 1

CHAPTER 1 . GENERAL INTRODUCTION Livestock production is an important household enterprise that may be operated on small or large scale. Large scale livestock production systems such as ranches, co-operative farming, and large-scale commercial farming involve use of large resources to raise livestock and, depending on the species, can be operated on large land acreages especially in the developed countries.

Small-scale farming systems, for example agro-pastoralism, pastoralism, and mixed smallholder farming, involve use of limited resources and are characteristic of those in the developing world

(ILRI, 1995). Small scale livestock farmers invest varying inputs in livestock production and in animal management practices (nutrition, breeding, etc.) that may greatly impact animal production. Small scale farmers are mainly mixed farmers, where livestock is part of an integrated farming system, and they have a diverse activity portfolio that may include livestock, crop production and other non-agricultural related activities. Livestock, particularly in the developing countries serve multiple purposes that include: food provision, income, manure, traction, financial assets, social capital, and crop waste recycling (FAO, 2011). Small scale livestock farmers, based on their level of investment, must rely on their own knowledge, and they are highly involved in the planning and decision-making processes regarding the livestock enterprises. Because most livestock farms, particularly in the developing countries, are operated on a small scale, approaches to improve small scale livestock production may be more impactful in livelihood improvement and quality of life for poor rural households (VSF Europa, 2012).

This thesis consists of three research projects under small-scale management setting, with two of these research projects conducted in developing countries (Uganda and Senegal) and one research project conducted in a developed country (United States). The aim of the dissertation

was to provide a platform for evaluating of pig and dairy cattle performance under small scale farmers’ management conditions. The hypotheses were:

1) There is variation in performance between barrows and gilts when fed ad-lib on a

common diet. Growth curves may be similar at the early growth phases, but differences

in growth rates would occur in the later growth stages as barrows deposit more backfat

and less loin muscle as harvest weight increases when compared to gilts. It is assumed

that because pigs (barrow and gilts) in the developing countries might not have access to

ad lib feeding and are kept under poor management conditions, they will follow a

different growth pattern at the different growth phases compared to pigs raised in the

developed world. Methods to predict such weights, therefore, must be recalculated for

pigs in the developing world.

2) The responsibilities associated with dairy production are split between men and women

in small scale households with women providing the most labor while men are likely

making payments for production costs and controlling the highest proportion of benefits

from the livestock enterprises.

The objectives of this dissertation were: 1) to evaluate pig and dairy cattle performance under small scale farmers’ management conditions of low inputs, 2) develop a tool to empower women farmers to predict market weights that would enable them have a better bargaining power when marketing their pigs, and understand the role gender in dairy cattle production in a developing country setting.

Dissertation organization This dissertation consists of six chapters. Chapter 1 is the general introduction. Chapter 2 addresses a detailed literature review of different livestock management and farming approaches of smallholder farmers in developing and developed countries, livestock production systems, livestock body weight prediction using body measurements, gender and its role in livestock production, (particularly at household level), and growth traits in pigs. Chapter 3 consists of a manuscript describing live pig body weight prediction using various linear body measurements

(height, width, heart girth, length, and flank to flank) across 11 villages in the Kamuli district of

Uganda (East Africa) to empower women with knowledge of pig body weight estimates that will improve their bargaining power when marketing their pigs. Chapter 4 of this dissertation is a manuscript characterizing growth curves from purebred Berkshire pigs. The Berkshire breed was popular in the early 1900s, but its popularity faded after the 1950s when emphasis was shifted greatly toward pork leanness. Although the Berkshire breed has a high carcass fat percentage, its high meat quality attributes enables it to be marketed in niche markets (Honeyman, 2006). This chapter details the evaluation of growth, deposition of backfat, and loin muscle accretion for purebred Berkshire pigs housed in bedded hoop buildings in Castana, Iowa. Chapter 5 reports the gender intra-household contributions to low-input dairy production in Senegal (West Africa).

It examines the roles played by different household members in regard to payment of costs and control of benefits in relation to dairy. Chapter 6 reviews the findings in the three different research studies and draws overall conclusions and implications.

CHAPTER 2 . REVIEW OF LITERATURE

Introduction Livestock production is an important contribution to farmer households and to countries’ economies in general. Livestock production provides food (milk, eggs and meat), enhances crop production through provision of manure, generates income, provides annual employment, enables farmers to spread risks, (particularly for farmers with a diversified farming approach), and serves as source of capital to households. Livestock are key to improving household livelihoods, and they present three major pathways out of poverty: 1) improving pastoral and smallholder productivity, 2) enabling the poor to secure assets, and 3) increasing market participation for the poor (ILRI, 2007). In rural areas, small-scale livestock enterprise development is key in eradicating extreme poverty (FAO, 2010). Livestock production may be operated on both a small or large scale, based on farmer income levels. Large scale production is often practiced in developed countries and mostly involves a single livestock species, whilst small scale production is common in developing countries and mostly involves keeping different livestock species together with crops. Production goals in the developed world are quite different from those of developing countries, and farmers might have access to different inputs for production. For example, in developed countries, the pig industry moved away from extremely heavy (lard) type pigs to enable more productive and reproductively efficient pigs, though this approach may have sacrificed meat quality (Cromwell, 1999). Swine production in the developed world, pork producers are primarily paid based on the carcass fat to lean ratio, with limited attention to pork quality. The consistent production of leaner pork has resulted in poor quality pork that is tougher and less juicy. Thus, this has created opportunities for small scale producers in the developed countries such as those producing the less efficient but high quality meat-

producing Berkshire pigs. Some consumers are willing to pay premiums for the juicier, more flavorful and tender pork (Salvage, 2005). However, the developing countries, livestock production is mainly subsistent and requires input from both men and women as each gender may be responsible for a specific livestock species (Njuki and Mburu, 2013). Development programs that target improved livestock production directly or indirectly improves the quality of life for rural households.

Livestock production systems Livestock production systems for domestic livestock such as cattle and swine vary greatly in approach, size and goals for both developed and developing countries. Livestock production is categorized into the following six systems: Solely livestock, landless livestock production, grassland based, mixed farming, rain mixed farming, and irrigated mixed farming (Sere and

Steinfeld, 1996; Steinfeld et al., 2006). The solely livestock system can be described as one where more than 90% of the dry matter fed to animals is obtained from annual forages, pastures, rangelands, or purchased forages, with non-livestock activities accounting for 10% or less of the production system. The landless livestock production system is a subset of the solely livestock system, with the farm having little land and 10% or less of the dry matter fed to animals produced at the farm. The grassland based system is a subset of the solely livestock system and is practiced in the different climatic zones/regions including: temperate/tropical highlands, humid/sub-humid tropics and sub-tropics, and arid/semi-arid tropics and sub-tropics. Ten percent of the dry matter fed to the animals in this system is farm produced, and the annual mean stocking rate is less than ten livestock units per hectare of agricultural land. For the mixed farming systems, 10% of the dry matter fed to livestock is from crop by-products or 10 % of the total production value is obtained from non-livestock farming enterprises. The rainfed and

irrigated mixed farming systems are subsets of mixed farming systems where the non-livestock farm production contributes 90% from the rainfed land use whilst in the irrigated farming, there is interspersion with livestock and a significant proportion of cropping utilizes irrigation.

In developing countries, like those in Africa, livestock production systems are mainly categorized into small scale and large scale farming systems (ILRI, 1995). In cattle production for instance, small scale farming includes pastoralism, agro-pastoralism, and mixed smallholder farming, while large scale farming includes ranching, cooperative farming, and large scale commercial farming. Large scale farming systems account for a very small portion of farming in developing countries such as those in the sub-Saharan Africa where most the farming occurs under traditional small scale farming systems (ILRI, 1995; VSF Europa, 2012).

Small scale farming, known as family farming, is the type of farming where farmers operate on a small scale and manage only a few hectares of land. The farms are very diverse ranging from farms that practice agroforestry to highly mechanized farms where a single farmer cultivates several hectares of land. The farm sizes greatly vary based on the country’s development level. Farmers from developing countries (such as those in Africa and Asia) operate on a few hectares of land compared to farmers from developed countries that have highly mechanized farms with a variety of tools used to boost production. However, small-scale farmers in both the developing and developed countries are characterized by family farm ownership, and farm owners are the main workers and the main people making decisions (VSF Europa, 2012).

There are various categories of small scale farms depending on the context, but with common characteristics ranging from relationship of individuals involved in farm ownership, management and strong relationship in knowledge transmission regarding livestock management to proceeding generations. Livestock in developing countries, (e.g those in rural Africa) are a key

livelihood resource and may be part of an integrated farming system, managed in a way that fits the farmer’s needs, available labor, and enterprise demands. Livestock may directly or indirectly benefit households through provision of food (milk and meat), manure, income, animal traction and in some cases, are used as financial assets or social capital (FAO, 2011; FAO, 2012a).

Another small-scale livestock farming component is pastoralism, especially in developing tropical countries where animals, (e.g cattle), are moved to different places in search of forages and water. This is common with indigenous cattle breeds under extensive grazing systems with large grazing areas dominated by natural vegetation (VSF Europa, 2012).

Pigs are raised under three different management systems in developing countries: 1) free-range ‘scavenging’ system, 2) semi-intensive system, and 3) small-scale intensive pig keeping (Muys and Westenbrink, 2004). The free-range ‘scavenging’ pig keeping system is characterized by pigs moving around their surroundings and finding a large portion of the food required themselves, but supplemented with agricultural wastes or kitchen refuse. In the semi- intensive pig keeping system, pigs are housed in a limited space and provided with feed, (usually agricultural wastes and kitchen wastes). This confinement style of production opens possibilities for improved feeding and disease control that ultimately results in healthier and faster growing pigs. The small-scale intensive pig system is characterized by intensive pig keeping where pigs are raised in complete confinement and are provided with feed, water, medicines and other concentrates (Muys and Westenbrink, 2004). Exotic breeds are often used, and large numbers of pigs may be raised in intensive systems.

Gender and its role in livestock production Gender is not only associated with a biological difference between males and females, but also with their physical attributes, contributions and opportunities in society where males

typically are regarded as the most powerful and providers of households and women are more submissive nurturers. Gender defines specific roles, status, and expectations of men and women, boys and girls among different households, communities, and cultures (CARE International

Gender Network, 2012). Gender influences the nature or type of work/tasks that men or women perform, and those roles may vary per country, group or generation. Those defined roles may thus confer specific opportunities, challenges, and status for individuals (Risman, 2004; CARE

International Gender Network, 2012; Blackstone, 2013).

Just like other activities in the household, livestock production activities for men and women and boys and girls differ greatly and may affect all aspects of livestock production. The gender roles, ranging from livestock ownership to making decisions regarding livestock production, are slightly different in both developed and developing countries. In developed countries, there is mostly resource co-ownership, and either men or women can decide their level of involvement in the livestock production sector. A previous study conducted on Wisconsin dairy farms indicated co-ownership of dairy farms between men and women, but women were more likely to handle farm bookkeeping, bill payment, management of calf or heifer bans and making of important decisions, and their farm decision-making because greatest when they reached middle age. However, some roles, such as managing calf or heifer barns might shift to the men when the dairy farm grows larger (Vogt et al., 2001). In developing countries, the gender differences in livestock production activities mainly arise from customary or traditional roles that view certain activities as more suitable for males or females. For instance, milking animals has traditionally been viewed as a woman’s activity, whilst sale and slaughter of dairy animals has been done mainly by the male counterparts (Beck, 2001). The children’s roles and responsibilities in livestock production are based on their age group (FAO, 2012b). Women are

the main contributors to livestock management compared to men. Women contribute the most to livestock management activities by performing feeding, watering, health care, fodder collection, value addition and marketing, milking and house-level processing (Patel et al., 2016). Despite their high involvement in livestock management and (production in general), women in rural areas are less empowered than men and are faced with more challenges because of the cultural or traditional structures that prevent them from accessing required tools. Thus, women often can’t reach their full potential in most agricultural enterprises (FAO, 2011). Women tend to have limited access to production resources such as land, extension services, and management information, and they are less likely to make to livestock production decisions compared to men

(FAO, 2011). Women are faced with limited credit access and favorable-market which ultimately makes it more difficult for them to access profitable niche markets. Men, unlike women, often have more access to the resources/services and inputs needed for livestock production (IFAD,

2009; Shicai and Jie, 2009; FAO, 2011). Women are not the main decision makers for the livestock enterprises within the households due to unequal power relations (IFAD, 2009; FAO,

2011). Women often do not own the land and livestock, particularly large animals like cattle

(Galab and Rao, 2003). Regarding livestock ownership, women are more likely to own small animals such as sheep, goats and poultry, while men are likely to own the larger animals like buffalo and cattle (Bravo-Baumann, 2000; Heffernan et al., 2003; Grace, 2007; Yisehak, 2008).

A study conducted in Kenya, Tanzania and Mozambique concerning gender and ownership of livestock assets indicated that majority of women owned goats and poultry but only a few owned cattle (Njuki and Mburu, 2013). Small livestock are very important to women in developing countries as they are easy to access, own and manage and control sale of their products. Women are responsible for milking of ewes, providing water and fodder/feed, processing and selling of

milk products, and taking care for newborn kids/lamb and sick animals (Lo, 2007). For larger animals like cattle, women might not own the animals but they may have some control of products such as milk (Dieye et al., 2005).

Although women may not own cattle or make decisions regarding cattle enterprises, they perform some activities in the management of cattle, especially within small farms. These activities include: milking of animals, selling of milk, as well as feeding and watering (Vogt et al., 2001; Parisse, 2012). However, when the enterprise generates more income or becomes more profitable, there might be a shift in the sale of milk from women to men due to a change in the division of labor (Parisse, 2012). The labor division by gender in agriculture based on task is not static, but rather may change in response to growth or new economic opportunities (Doss, 2001).

Swine growth traits Swine growth traits are economically important, and they are measured often by weight gain during a test period or age at which pigs are expected to attain a specific weight. However, differences in growth rates or other traits such as loin muscle areas and backfat thickness of individual pigs exist due to genetic differences associated with breed (Chen et al., 2002), as well as animal management and nutrition (Kumaresan et al., 2007; Wiseman et al., 2007a, b). The differences in trait composition eventually affects the carcass lean percentage, feed efficiency and ultimately the profitability of different sexes and pig genetic populations (Schinckel et al.,

2008a,). A previous study (Kumaresan et al., 2007) about the performance of Hampshire, Large

White, Yorkshire, and nondescript local pigs reared under a low input production system indicated significant differences in performance that were attributed to genetics of the pigs, poor management and nutrition. Pigs were fed locally available feed stuffs (such as bamboo shoots,

pumpkins, banana (pseudostem, leaves and green banana fingers), and ankasa (Spilanthus Sp)) that were very low in energy, protein, essential amino acids and vitamins (Kumaresan et al.,

2007). It is important to note that the local nondescript breeds had a very low performance compared to the Hampshire, Large White and Yorkshire for entire test period, contrary to reports by Nguyen et al., (1997) who indicated that local pigs were more prolific than the “exotic” breeds when fed low nutrient density diets in the poor farmer households of Vietnam.

Sex is one of the important factors that greatly affects pigs’ growth traits and feed efficiency. Barrows and gilts have different backfat and muscle deposition rates and feed efficiency during the entire growth phase. Barrows grow faster, have a higher average daily feed intake (ADFI), and tend to have a lower feed conversion, gain to feed ratio (G: F ratio).

Ultrasonic measurements taken at the end of the studies indicated that gilts had less backfat, larger longissimus muscle area, and greater carcass lean weight (Wagner et al., 1999; Latorre et al., 2008; Shull, 2013). Backfat depth at the 10th rib was predicted to increase linearly with increase in the live weight, while longissimus muscle area accretion increased quadratically. The rate of increase in backfat accretion is normally greater than that of longissimus muscle area accretion as harvest weight becomes greater because both barrows and gilts deposit more backfat than lean beyond the harvest weight of about 100 kg. Several studies have reported an increase in backfat depth ranging between 0.16 and 0.25 mm per 1 kg increase in harvest weights beyond

100 kg (Cisneros et al., 1996; Latorre et al., 2004; Latorre et al., 2008; Shull, 2013), while longissimus muscle area increased linearly by 0.18 cm2 per 1 kg increase in the harvest weight

(Cisneros et al., 1996). Regression analyses showed significant linear, quadratic, and cubic terms for average daily gain (ADG) against live weight, with ADG peaking at different live weights for barrows and gilts. Both ADG and average daily feed intake (ADFI) increased with increases in

barrow and gilt live weights, but ADFI decreased towards the end of the growth phase or after attaining live weights between 115 to 121 kg (Schinckel et al., 2006; Shull, 2013). These studies did not address clearly why feed intake decreases towards the end, though there are other factors such as changes in environmental temperatures that may potentially impact feed intake and could potentially affect swine performance. Because of the changes in ADFI during the growth phase, feed efficiency (G: F) may be positively or negatively affected. Reports have shown quadratic decreases in instantaneous G: F with increases in live weight for both barrows and gilts, with the greatest reduction occurring after weaning at approximately 25 kg, and gilts having greater feed efficiency than barrows between 50 to 150 kg. (Augspurger et al., 2002; Latorre et al., 2004).

However, performance differences due to sex may not be observed, irrespective of the breed when both males and females are fed diets low in nutrients, particularly in the developing countries where pigs are only fed low nutrient readily-available feed stuffs (Kumaresan et al.,

2007).

Other factors such as birth weight have significant effects on both pre- and post-weaning growth of pigs, irrespective of breed. Piglets with heavier weights at birth were heavier at weaning (Wolter et al., 2002; Quiniou et al., 2002; Beauleiu et al., 2010), and had a higher performance from weaning to harvest (Gondret et al., 2005; Beauleiu et al., 2010) than lighter pigs.

Berkshire breed The Berkshire pig breed originated from England in 1875 and was the first registered swine pure breed in the United States (ABA, 2005). This breed was popular in the early 1900s, but faded out of favor after the 1950s when a great emphasis was shifted toward pork leanness

due consumer demands. Although Berkshire pigs had a low percentage of fat-free lean, the breed still maintained its excellent meat quality attributes including: redder color, greater ultimate pH, higher moisture content, and more tenderness (NPPC, 1995; Goodwin, 2004). Due to these meat quality attributes, the demand for Berkshire pork in the United States and overseas rebounded tremendously. The Japanese pork consumers have long recognized the superiority of Berkshire pork, known as “kurobota” and are willing to pay up to 50% premium for Berkshire meat

(Honeyman et al., 2006). Because of inadequate domestic and global supply of Berkshire pork, producers can ask for higher prices and this ultimately created a niche market. The breed association, the American Berkshire Association (ABA) registers about 5000 litters annually, but that number of pigs is not enough to satisfy the existing domestic market.

The Berkshire breed’s numerous advantages are basically associated with its meat quality attributes. However, Berkshire are not commonly used by economically oriented commercial swine producers because they have lower growth rates, smaller loin muscle area, poorer feed conversion, and fatter carcasses if compared to other pig breeds. They also have undesirable maternal traits. Previous research that compared pigs sired by eight breeds, (Berkshire, Chester

White, Duroc, Hampshire, Landrace, Poland China, Spot, Yorkshire) for loin muscle areas

(LMA) and 10th rib of backfat (BF10) indicated that Berkshire breed had the least LMA and the highest backfat thickness (Goodwin, 2004).

Livestock body weight estimation without scales Body weight is one of the most important traits for a genetic selection program aimed at improving animal performance, and it is commonly used as a trait to determine sale value. The standard method of weighing animals is by use of properly calibrated scales, but this is too

expensive for most rural small-scale farmers, especially those in developing countries (Mahieu et al., 2011). Livestock body weight has direct and indirect relationship with body linear measurements such as heart girth, paunch girth, height at back, height at fore legs, flank to flank and body length (Banik et al., 2012). These relationships between body weight and linear measurements could be used to estimate body weights when scales are not available and if the measurements are easily obtained (Alade et al., 2008). Although this weight prediction approach could be used in both developed and developing countries (Groesbeck et al, 2004; Mutua et al.,

2011), it would be more useful in developing countries where most farmers operate on a small scale, do not own scales, and market pigs primarily based on live weight. Using linear measurements to predict animal live weights empowers farmers with knowledge about the animal’s market weight and value. Such knowledge improves their bargaining power and could also be used as a tool in livestock management and breeding (Slippers et al., 2000).

Because most farmers in developing countries have no access to scales or can’t afford them, they may rely solely on visual animal weight estimates which are inaccurate. Often there are errors associated with visually estimates of animal body weights because this approach uses the same estimation technique for more than one breed of animal or species (Otoikhian et al.,

2008). The most commonly used linear body measurements include: heart girth, height at withers, chest depth, fore cannon bone, rump height, paunch girth, tail length, ear length, ear width, distance between the eyes, height at back, height at fore legs, flank to flank and body length (Abegaz and Awgichew, 2009; Banik et al., 2012). The heart girth and cannon bone length are less affected by the animal’s posture.

Live weight prediction using linear body measurements has been used in most livestock species, including large domestic animals (Mayaka et al., 1995; Enevoldsen and Kristensen,

1997; Lukuyu et al., 2016), as well as goats and sheep (Thiruvenkadan, 2005; Yakubu et al.,

2005). The prediction model is determined by regressing animal body weight on the linear body measurements, and the best fitted regression model is determined by evaluating and comparing

2 residual mean square (MSE), coefficient of multiple determination (R ), and the error standard deviation (SDE) for the different generated regression models (Snedecor and Cochran, 1989).

Because body weight is related to age, breed and sex of the animal, data should be grouped/categorized by these factors as grouping greatly improves the estimation model reliability for live weight (Enevoldsen and Kristensen, 1997; Nesamvuni et al., 2000; Ozkaya and Bozkurt 2009; Yan et al., 2009; Yakubu, 2010). Numerous studies have reported using linear measurements to predict live weights in various age groups and breeds of cattle (Nesamvuni et al., 2000; Goe et al., 2001; Essien and Adesope 2003. Those studies indicated that linear body measurements were useful live body weight predictors for the local cattle breeds, with similar findings observed when predicting live weights of exotic dairy cows (Yan et al., 2009) and beef cattle breeds (Ozkaya and Bozkurt, 2009). However, high standard deviations ranging between

45 to 74.4 kg were attributed to variability in age, breed composition, and possibly to nutritional management across the different farms. Mwacharo et al. (2006) characterized two zebu breeds using linear measurements and reported significant effects of sex, age group, and breed group on all measurements. Variability in nutritional management was associated with variability in animal body condition which resulted in differences in animal weights (Kahi et al., 2000; Juma et al., 2006, Yan et al., 2009; Lukuyu et al., 2016). The variability in live weight could be influenced by pregnancy or gut content (Essien and Adesope, 2003). It is advisable to eliminate animals that are obviously pregnant and to weigh animals very early in the morning hours before they are fed to minimize this source of variation.

For small ruminants, body measurements have been used to predict live body weight in goats (Slippers et al., 2000; Singh and Mishra, 2004; Khan et al., 2006; Rahman, 2007) and sheep (Kunene et al., 2009; Ravimurugan et al., 2013). Heart girth was the most predictive parameter in live weight estimation for sheep and goats, but different models may need to be developed to predict body weight due to differences among breeds and environmental conditions.

There are limited studies about the live weight prediction using linear body measurements for pigs, and no studies have simultaneously used more than two linear measurements for prediction. Recent studies indicated that heart girth is the most important predictor of pig body weight followed by body length (Murillo and Valdez, 2004; Mutua et al.,

2011). Research conducted to develop prediction equations for Kenya and Philippine native pigs using external body measurements indicated that heart girth explained 91% and 88% of the total body weight variation, respectively (Mutua et al., 2011). These results were consistent with findings from a study conducted at Kansas State University where a body weight prediction equation was determined using heart girth for finishing pigs (Groesbeck et al., 2004). Heart girth was strongly correlated (R2 = .98) with body weight, and a 95% confidence interval showed the projected weight to be approximately ± 5 kg of the pig actual weight. However, care should be taken when evaluating the appropriateness of the model under alternative conditions as pig management may affect the accuracy of prediction models. Prediction models are only applicable to pigs under similar management conditions or standards. Marvelous et al. (2014) reported a low positive correlation between actual and predicted weights from pigs reared under communal conditions and those reared under intensive management.

Summary

This literature review examined performance of pig and cattle under small-scale management conditions and the role of gender in dairy/livestock production. Smallholder farmers, particularly women, are important for household livelihood improvements, and the technological tool use, such as using linear body measurements (body length, height, width, heart girth, and flank to flank) to help predict livestock weights can empower women with knowledge to improve their bargaining power when selling their pigs. This in turn increases income levels that are and improves household livelihoods. The fact that gender plays crucial roles in livestock production, especially in the rural areas of developing countries, is important for development agencies to consider when directing production resources. Resources must be allocated in a way that targets both household genders, with greater emphasis on women especially in the developing world where they are still constrained by certain traditional/cultural norms.

Finally, there are several challenges faced by small scale farmers and these range from inadequate animal nutrition, breeding, and management to extreme weather conditions that cause poor livestock production. However, with appropriate animal production practices and technologies, those challenges could be ameliorated.

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CHAPTER 3 . PREDICTION OF LIVE BODY WEIGHT USING VARIOUS BODY MEASUREMENTS IN UGANDAN VILLAGE PIGS

Paper has been published in Livestock Research for Rural Livelihood: 2014. 26(5): http://www.lrrd.org/lrrd26/5/walu26096.htm.

M. Walugembe1, G. Nadiope3, J. Stock1, K.J. Stalder1, D. Pezo2, M.F. Rothschild1

1Department of Animal Science, Iowa State University, Ames, IA 50011, USA 2International Livestock Research Institute, c/o Bioversity International, PO Box 24384, Kampala, Uganda 3Volunteer Efforts for Development Concerns, c/o Center for Sustainable Rural Livelihood, Kamuli, Uganda

Abstract A study to develop body weight prediction equations based on various body measurements was conducted in rural Kamuli district, Uganda. Body weight (kg) and body measurement data (cm) were collected from 411 pigs between 15 and 127 kg from both local and exotic (mainly crossbreds) pigs. Five body measurements; body length, heart girth, height, body width and flank-to-flank were taken from each pig. Prediction models were developed by regressing weight on pig body measurements. The models were developed for pigs categorized as < 40kg, ≥ 40 kg and an overall single prediction model. Mean weights of < 40 kg and ≥ 40 kg were 27 ± 6.5 kg and 63 ± 19.6 kg, respectively. Body length and heart girth were used to predict

(R2 = 0.89) weight for the < 40 kg pigs with the prediction equation; Weight = -41.814 + 0.2955

(body length) + 0.6543 (heart girth). Four body measurements; body length, heart girth, height and body width were strongly predictive (R2 = 0.92) of live body weight for the ≥ 40 kg pigs with the prediction equation; Weight = -108.1977 + 0.2281 (body length) + 1.0941 (heart girth)

+ 0.2665 (height) + 0.9215 (body width). The flank-to-flank measurement was not significant (P

> 0.05) and quadratic terms also did not improve accuracy and were not included in any prediction models. These results suggest that live weight could be accurately estimated using two or more pig body measurements. It was concluded that this weight estimation tool would empower Ugandan small scale pig farmers by providing them with an accurate estimate for the animal’s live weight and giving them better bargaining power when selling their pigs.

Keywords: pigs, weight prediction model, body length, girth, height and body width

Introduction

Most Ugandan smallholder farmers do not have access to a scale to weigh their livestock and this creates both management and sales problems. Without scales, farmers may have to travel long distances to weigh their livestock, but most commonly traders buy livestock based on visual weight estimates, and almost always underestimate it, which adversely affects farmers because they receive less money for their animals than they are worth.

Live weight estimations predicted using body measurements have been used in different animal species (Enevoldsen et al., 1997; Thiruvenkadan 2005). In the case of pigs, backyard farmers in the Philippines used body measurements such as body length and heart girth to predict weight, because they could not afford to purchase weighing scales (Murillo and Valdez 2004).

Previous work by McGlone et al. (2004) individually weighed and obtained several body dimensions including sow body length, height, body width (lateral body length, left to right from the mid-line) and depth were also determined. Prediction equations were used to estimate live body weight and size relations in these different pigs. This information is vital to provide accurate live body weight estimates for the pigs when marketed. Other research has reported a strong correlation between live body weight and heart girth in finishing pigs (Groesbeck et al.,

2004). In a recent study conducted in Western Kenya, Mutua et al. (2011) reported accuracy of weight prediction using body length and heart girth for pigs from different age categories.

The Uganda commercial swine industry, like that in Kenya, is mostly made up of small scale farmers, many of them poor women and pig production is critical to provide food and help raise money for their families (Tatwangire 2013). The producers/small scale pig farmers do not have means for transporting pigs to an organized market so they must rely on traders to come to their farms and buy the pigs. Often they are given lower prices because the pigs’ live body

weights are poorly estimated at the time the sales transaction is occurring. Since the breed types may be quite different in Uganda compared to other places, data must be collected to develop prediction equations for local and improved pigs (cross between the local and exotic breeds).

Limited peer review work has addressed the weights and dimensions for market pigs and sows in

Uganda and at present, no reliable data exits that could be used to develop a body weight prediction tool for pigs raised by smallholders in Uganda. Additionally, using body weight prediction information from other locations might not be accurate. It is critical that the Ugandan local pig producers have accurate live body weight information at the time any sales transaction occurs to establish some “true” total body weight estimate and hence establish a fair market value for each animal. Such information will be vital to the Ugandan smallholder and medium size pig farmers who cannot afford weighing scales and need fair prices for their pigs. The objective of this study was to develop prediction models for determining pig weight using various body measurements.

Materials and methods All animal procedures were approved by the Institutional Animal Care and Use

Committee (IACUC Log#8-13-7614-S) of Iowa State University before the start of the research project.

Study location The study was conducted in Eastern Uganda, Kamuli district, in Kakindu, Kabalira,

Buburiki, Bugulumira A and B, Buweryo, Bukaaya, Buyengere, Bususwa, Bunabiryo, and

Kabaganda villages. The project focused on small holder pig farms (less than 10 pigs) and medium scale commercial farmers (less than 50 pigs). The district veterinary officer and

community based mobilizers working with a local non-governmental organization (Volunteer

Efforts for Development Concerns, VEDCO) guided the researchers in locating the pig farmers, and in ensuring a good relationship among researchers, pig farmers and the community at large.

Weighing and measuring pigs Farms were visited, pigs weighed, and live body measurements were taken at each location using an electronic digital scale and a wire cage to hold pigs steady for measurement purposes. A picture of each pig was taken to phenotypically characterize the pigs by breed and to maintain for future reference. A total of 411 pigs that ranged between 15 and 127 kg were evaluated and the corresponding measurements, in centimeters (cm) were then taken twice and included heart girth, height, body width, body length and flank-to-flank. Data were not collected from pregnant females and very aggressive pigs that could not be reasonably restrained in the cage.

For every farm visited, the farmer or a member of the household was asked to estimate the weight and age of the pig(s) measured. Prior to each pig’s entry on the scale, researchers zeroed-out the scale to be sure an accurate weight for each animal was obtained. A small amount of commercial feed was placed at one end of the cage floor; the animal was directed towards the cage for weighing from the opposite side of the cage. The cage was designed such that it would accommodate both small and larger pigs. A cloth measuring tape was used to determine the heart girth, height, body width and body length in centimeters. Heart girth was measured just behind the front legs. A string was wrapped around the heart girth of the pigs and the intersection of the string was then used to transfer the heart girth circumference to a cloth tape for the measurement.

A modified caliper made with Poly Vinyl Chloride (PVC) pipe was used to determine height directly behind the front legs of the pig. The caliper was adjusted so that the upper PVC pipe

touched the back of the pig. The height and the corresponding body width were noted and recorded. Body length was measured with a cloth tape from the mid-point of the ears (crown) to the tail head. Flank-to-flank was measured with a cloth tape being placed over the top of the tip, from the bottom of the flank on one side of the pit to the bottom of the flank on the other side of the pig (Figure 1). The pig’s sex was noted and recorded after weight and pig body measurements.

Categorization of pigs Pigs were categorized into two groups based on body weight: young or below market age

(< 40 kg) and market weight pigs (≥ 40 kg). In Uganda, pigs are sold for slaughtering beginning at an average weight of 40 kg.

Data analyses Prediction equations to estimate pig weight using heart girth, height, body width, and body length measurements were developed using GLM procedures (SAS 9.3 2012). Analyses were performed by stepwise regression using PROC REG to determine the change in R square when additional body measurements were included in the model. Data from two pigs from the heaviest weight class (167 and 180 kg) were not included in the analyses as they were identified as statistical outliers based on standard deviations. Both pig breed and sex were included in the model to determine whether they significantly influenced body weight in this study. Three prediction equations were developed during the analyses. Equation 1 was computed for the young pigs below market weight, equation 2 was computed for the market weight pigs, and equation 3, the single prediction model was developed to assess the overall effect of body length, heart girth, height and body width on the pig’s body weight. A body measurement parameter was

considered significant and included in the prediction model at P < 0.05. Quadratic terms were also tested and only included if they significantly improved prediction.

Results The model data sets included 411 pig observations, of which 202 pigs weighed less than

40 kg and the remaining 209 pigs’ body weight was greater or equal to 40 kg. The mean body weights of pigs < 40 kg and ≥ 40 kg were 27 kg (SD = 6.5; 95% confidence interval, 26.3 - 28.1) and 63 kg (SD = 19.6; 95% Confidence interval, 60.3 - 65.6), respectively Pigs were classified into one of five breeds that included; Large White, Landrace, Camborough, Local Black and a crossbred among Local Black and the other breeds. Crossbred pigs were the most prevalent in the study. When examining the data by breed classification, there were 150 crossbred pigs, 86

Large White and 79 Camborough, 62 Local Black and 34 Landrace. Breed and sex were not significant sources of variation when predicting pig weights in any of the pig categories (P >

0.05, Table 1). The heart girth was the most important pig weight predictor with R2 > 0.84 in the three pig categories. Limited additional contribution to the overall R2 in the three pig categories was observed when other body dimensional measurements were included in the model (Table 2).

Body length and heart girth measurements explained a substantial portion of the variation in pig body weight (R2 = 0.89; P < 0.05) for the pigs that were in the < 40 kg weight class from the present study. The prediction equation, equation 1 for this data is as follows:

The model that included all significant effects was observed for the ≥ 40 kg pigs when weight was regressed on all the four parameters of body length, heart girth, height and body width (R2 = 0.92; P < 0.05). The prediction model (equation2) developed was:

Only three parameters; body length, heart girth and body width were identified as significant sources of variation when trying to predict live body weight (P < 0.05) in a single prediction model from the current data. The most predictive (R2= 0.95) model using the three parameters and weight was:

Flank-to-flank was not included in any of the three prediction models (P > 0.05).

Quadratic terms were not significant and were not included in any of the models (data not shown).

Discussion The average pig market weight is relatively low in the villages in Kamuli district. This agrees with previous reports in Africa (Mutua et al., 2011) where pigs were sold at <10 months of age due to lack of available food that typically occurs on a seasonal basis. In addition, farmers tend to sell their pigs to provide food and help raise money for their families (Tatwangire 2013).

Some farmers do not have shelter for their pigs, but instead tether pigs on pastures to graze or provide them with leftover food and do not follow regular vaccination schedules or disease/parasite control measures (Mutua et al., 2011). This ultimately negatively impacts the pigs’ market weight. Sex did not significantly affect prediction of pig weights in the present study. This is consistent with recently published findings in rural western Kenya for young, market-age and breeding pigs (Mutua et al., 2011). Other well-designed experiments have

reported that sex was a significant source of variation when predicting the pigs’ average daily gain (Latorre et al., 2004). The possible explanation for the differing results when compared to the current experiment is that pigs managed by small scale poor subsistence farmers cannot express their genetic potential for growth due to lack of proper feeding regimes, in which kitchen leftovers, forages and crop residues are the main components of the diet as was the case in the present study (Tatwangire 2013). Breed did not significantly influence the pig weight in the current experiment which is in line with previous research (McLaren et al., 1987). Although breed may not affect the pig weight, it does affect the carcass and meat quality measures

(Edwards et al., 2003). The lack of breed significance could be a result of the fact that the genetic potential from the improved group of animals is not that high and that the feeding regimes do not allow improved animals to express their potential. Most farmers mate their gilts or sows with any readily available boars from the neighborhood.

All three prediction models from the present study indicate that heart girth and body length are the most important prediction measurements when estimating the pigs’ live body weight. Heart girth could as well provide an accurate live body weight prediction when used alone (Groesbeck et al., 2004). The importance of both heart girth and body length for weight prediction agrees with previous reports conducted in Western Kenya where live body weight was predicted using body length and heart girth for young, market-age and breeding age pigs (Mutua et al., 2011). Farmers in rural western Kenya and those in rural Kamuli district likely use similar management practices. However, the results for the young pigs in the study conducted in the

Philippines are dissimilar to the findings from the present study and may not be applicable to

Kamuli (Murillo and Valdez, 2004). This is attributed to the fact that all the pigs involved in the

Murillo and Valdez, (2004) study were from improved breeds (three way crosses from purebred

Landrace, Large White and Duroc), that were raised under confinement conditions and had ad libitum access to a diet that more closely matched its nutritional needs (NRC, 2012) when compared to the present study. But these findings could be extrapolated to Uganda pigs in better managed farms.

The three prediction models could provide better weight estimation tools, but problems may occur when taking pig measurements. Pigs tend to move around and often moved their heads which may have caused variations in measurements (Groesbeck et al., 2004). Therefore, it is recommended to take at least two body measurements, as was done in the current study, and use the computed average for prediction. Pigs should be properly restrained prior to measurement and muddy areas must be avoided so the pigs do not become coated with mud which would bias live body weight measures. A cage was most effective in the current study in restraining pigs and in obtaining all measures utilized in this study.

This research is intended to enable farmers to compute pig weights using simple devices like calculators and mobile phones. The goal is to eventually develop a cell phone application that will allow farmers to predict the weights from simple body measurements of body length, heart girth, body width and height.

Acknowledgements This research was conducted as part of the Smallholder Pig Value Chains Development in Uganda Project (IFAD-EU) led by the International Livestock Research Institute (ILRI), and the first author appreciates the support by the Iowa State University Ensminger Endowment. The

authors would also like to acknowledge the Volunteer Efforts for Development Concerns staff for their assistance, before and during the research.

Financial assistance

Funding for this work for student support was provided by Iowa State University, State of Iowa and Hatch funds and for work in Uganda by the research project “Catalyzing the emerging smallholder pig value chains in Uganda to increase rural incomes and assets” with grant Number

COFIN-ECG-63-ILRI provided by the International Fund for Agricultural Development (IFAD) and the European Union (EU).

No benefits in any form from any commercial party related directly or indirectly to the subject of this manuscript were derived from this activity.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Literature cited Edwards, D.B., Bates, R.O., Osburn, W.N., 2003. Evaluation of Duroc- Pietrain-sired pigs for

carcass and meat quality measures. Journal of Animal Science, 81, 1895-1899.

Enevoldsen, C., Kristensen, T., 1997. Estimation of body weight from body size measurements

and body condition scores in dairy cows. Journal of Dairy Science. 80, 1988-1995.

Gichohi, C.M., Mitaru, B.N., Munyua, S.J., Wahome, R.G., 1988. Pig production and

consumption in relation to oil-seed cake production and utilization in Kenya. Working

paper, # 7b, Egerton University.

Groesbeck, C.N., Goodband, R.D., DeRouchey, J.M., Tokach, M.D., Dritz2, S.S., Nelssen, J.L.,

Lawrence, K.R., Young, M.G., 2004. Using heart girth to determine weight in finishing

pigs. Kansas State University. Available online:

http://www.thepigsite.com/articles/1106/use-heart-girth-to-estimate-the-weight-of-

finishing-pigs. Accessed February 21st, 2014.

Latorre, M.A., Lázaro, R., Valencia, D.G., Medel, P., Mateos, G.G., 2004 The effects of gender

and slaughter weight on the growth performance, carcass traits, and meat quality

characteristics of heavy pigs. Journal of Animal Science, 82, 526-533.

McGlone, J.J., Vines, B., Rudine, A.C., DuBois, P., 2004. The physical size of gestating sows.

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McLaren, D.G., Buchman, D.S., Johnson, R.K., 1987. Growth performance for four breeds of

swine: Crossbred females and purebred and crossbred boars. Journal of Animal Science,

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Murillo, M.C.K.M., Valdez, C.A., 2004. Body weight measurement in triple weight estimation

cross pigs (Large white-landrace-duroc) using external body measurements. Philippine

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Mutua, F.K., Dewey, C.E., Arimi, S.M, Schelling, E., Ogara, O.W., 2011. Prediction of live

weight using body length and girth measurements for pigs in rural Western Kenya.

Journal of Swine Heath Production, 19(1), 26-33.

Mutua, F.K., Dewey, C.E., Arimi, S.M., Ogara, W.O., Githigia, S.M., Levy, M., Schelling, E.,

2011. Indigenous pig management practices in rural villages of Western Kenya.

Livestock Research for rural development, 23(7).

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Washington, DC: The National Academies Press, 2012.

SAS Institute. 2012. SAS User’s Guide: Statistics. Version 9.3 edition. SAS Institute. Inc., Cary,

N.C., USA.

Tatwangire, A., 2013. Uganda smallholder pigs value chain development: Past trends, current

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Table 3.1: Model effects, prediction values and their significance for live body weight using analysis of variance from a study of Ugandan market pigs

Weight2 (kg) Parameter body body Height1 width1 Height girth1 Flank1 length1 Breed Sex < 40 Mean Square 0.0273 1.7380 390.8551 1.0765 295.1983 10.1527 1.9590 Estimate - 0.006 0.1026 0.6543 - 0.0265 0.2955 P value 0.9401 0.5487 < 0.0001 0.6368 < 0.0001 0.0813 0.5243

≥ 40 Mean Square 170.5180 331.5851 2814.4543 68.5582 748.7685 39.6506 0.1616 Estimate 0.2665 0.9215 1.0941 0.1666 0.2281 P value 0.0233 0.0023 < 0.0001 0.1487 <0.0001 0.3055 0.9440

All weights Mean Square 6.4504 570.2342 3684.2063 0.2082 1167.877 52.4624 6.5199 Estimation 0.0437 1.0158 1.0238 - 0.0068 0.2514 P value 0.6318 < 0.0001 <0.0001 0.9314 <0.0001 0.1148 0.6300 N= 202 for pigs < 40 kg and N= 209 kg for pigs ≥ 40 kg 1Body measurements 2Pig categories

Table 3.2: Change in R Square with inclusion of each additional body measurement using stepwise regression when predicting live body weight by three weight classes of pigs raised by smallholders in Uganda

Weights1 (kg) Parameter2 R Square < 40 Heart girth 0.8424 Heart girth, Body length 0.8873 Heart girth, Body length, Body width 0.8878 Heart girth, Body length, Body width, Height 0.8880 Heart girth, Body length, Body width, Height, Flank 0.8880

≥ 40 Heart girth 0.8968 Heart girth, Body length 0.9103 Heart girth, Body length, Body width 0.9140 Heart girth, Body length, Body width, Height 0.9163 Heart girth, Body length, Body width, Height, Flank 0.9170

All Heart girth 0.9403 Heart girth, Body length 0.9454 Heart girth, Body length, Body width 0.9478 Heart girth, Body length, Body width, Height 0.9479 Heart girth, Body length, Body width, Height, Flank 0.9479 N=202 pigs for <40kg, N=209pgs for ≥ 40kg 1Pig categories 2 Body measurements

Figure 3.1: A graphical depiction showing the location where various body dimensional measurements were obtained that were utilized to predict live body weight in pigs from Uganda.

CHAPTER 4 . EVALUATION OF GROWTH, DEPOSITION OF BACKFAT, AND LOIN MUSCLE FOR PUREBRED BERKSHIRE PIGS HOUSED IN BEDDED HOOP BUILDINGS

Paper has been published in Journal of Animal Science. 2016. 94(2): 800 – 804

M. Walugembe, P. M. Swantek, M. S. Honeyman, J. W. Mabry, K. J. Stalder, M. F. Rothschild

Department of Animal Science, Iowa State University, Ames, IA, 50011, USA

Abstract This study was conducted to evaluate the accretion of body weight, backfat and loin muscle from purebred Berkshire pigs raised in bedded hoop barns in Iowa. The growth of a total of 144 purebred Berkshire pigs (18 barrows and 18 gilts per trial) was evaluated from 4 trials (2 winter and 2 summer trials). Pigs were fed ad libitum utilizing a five-phase standard corn- soybean meal, feeding program that met or exceeded National Research Council (2012) nutrient requirements. Pigs were housed in bedded hoop barns (unheated) to approximate common niche market requirements. At 21-day intervals, pigs were serially weighed and ultrasonic backfat depth and loin muscle area (LMA) measurements were taken. Live body weight measurements began at the trial initiation at approximately 18 to 32 kg, but ultrasonic scans for 10th backfat depth and LMA began at between 36 and 45 kg until market weight of about 122 ± 2.5 kg. The rate (µ) of live body growth (weight) and ultrasonic backfat depth were influenced (P<0.01) by trial and sex, with no significant interactions between trial and sex. Both live body weight and backfat deposition were significantly greater in trial 1 than all other trials (2, 3 and 4). Rate of accretion and maximum growth of loin muscle area depth were not affected (P>0.05) by trial and sex. Overall, barrows averaged 31 mm of backfat at 125 kg whereas gilts had an average of about 23 mm at 121 kg of market weight. Results suggest that due to the sex differences in

growth and backfat deposition between Berkshire barrows and gilts it may be important to formulate their diets differently in commercial pork production systems.

Key Words: purebred Berkshire pigs, backfat, loin muscle area, body weight

Introduction Niche market pork production has increased across Iowa and the United States due to the increased demand for high meat quality and locally sourced products. However, limited production standards exist for niche market pork producers to benchmark their pig performance.

There has been a resurgence of increased production of Berkshire pigs that has mainly been driven by the demand in Japan and increased domestic demand in the United States

(McLaughlin, 2004). Demand for pork from Berkshire pigs has also been driven by consumers who are interested in the environmental impact, animal welfare, and farm size where the pigs that produce pork they consume are raised. The breed registration increased 4-fold in a period of

10 years and customers are willing to pay a 50% premium for the pork from the Berkshire pigs

(Goodwin, 2004; Honeyman et al., 2006)

The Berkshire breed is well known for its excellent meat quality attributes such as more pork tenderness, more desirable flavor, darker color, greater ultimate pH, and more moisture content after cooking (NPPC, 1995; ABA, 2005; Suzuki et al., 2003; Goodwin, 2004). Berkshire pigs have less loin muscle area, are less feed efficient and have more backfat when compared to other pig breeds (Hasty et al., 2002; Goodwin, 2004), but there is limited information on rate of backfat and loin muscle area deposition in purebred Berkshire barrows and gilts. Also, many niche pork markets have growing condition requirements including the use of bedding

(Honeyman et al., 2006).

The objective of this study was to evaluate the effects of sex and trial on the accretion rates for body weight, backfat thickness and loin muscle area of purebred Berkshire pigs. The information gained will aid producers of purebred Berkshire pigs to provide niche markets with a quality pork product in a timely manner.

Materials and methods

Animals and experimental design This study was approved by the Institutional Animal Care and Use Committee of Iowa

State University (10-11-7246-S). The study was conducted at the Iowa State University Western

Research Farm, Castana, IA (latitude 42.0639, longitude -95.8372) in bedded hoop barns

(unheated) to approximate common niche market feeding condition requirements. A mini-hoop barn had dimensions 6.1 x 9.1 m and in each hoop, were two pens of dimensions 2.7 x 5.8 m.

There was an extra area with no pigs in front of each mini-hoop barn of 2.4 x 5.5 m. The pen flooring was concrete with corn stover bedding which was added at the discretion of the farm manager to keep the pens dry. There were 6 feeder spaces per pen. Thus, space allocation and feeder space allocation exceeded the standard 1.1 m2 per pig and 5 pigs per feeder space in a bedded hoop barn. Because the pens were small and the fact that this was not a stocking density study, more space per pig was allowed. Four distinct trials, two winter trials (Trial 1 and 3) and two summer trials (Trial 2 and 4) were conducted to include the environmental extremes of Iowa

‘s climate using a total of 144 pigs. In each trial, 36 purebred Berkshire pigs (18 gilts; 18 barrows) were purchased from the same genetic source, a long-time Berkshire breeder in Iowa

(Steve Kerns, Clearfield, IA) and housed in bedded mini-hoop barns.

The targeted grow-out weight range was between 22 to 121 kg of live weight. Pigs were allocated to pens by size and weight (light, medium, and heavy classes) to six pigs per pen; two pens per hoop, across three hoops (Table 1). Barrows and gilts from the same weight class were housed in one of the three mini-hoop barns, which were each divided into two pens of six pigs

(12 pigs per hoop).

Dietary treatments The diets were a corn-soybean meal mix in complete meal form formulated to meet or exceed all nutrient requirements for swine per National Research Council (2012) nutrient recommendations. The diets were formulated using calculated analysis of the feed ingredients and laboratory analyses were not conducted. Pigs were placed on trial at approximately 18 to 32 kg of weight and were fed ad libitum utilizing a five-phase feeding -program of corn-soybean meal based diets that met or exceeded amino acid requirements. Diet changes occurred when the average pen weights reached 40, 61, 82, and 102 kg of body weight, respectively

Data collection Pigs were serially weighed initially and at 21-day intervals and ultrasonically evaluated for loin muscle area (LMA, cm2) and 10th-rib backfat with a minimum of four scans per pen beginning between 36 and 45 kg. The ultrasound images were collected using the Aloka 500V

SSD ultrasound machine of 3.5 MHz, 12.5 cm linear-array transducer (Carometric Medical

System Inc., Wallingford, CT), and all the scans were taken by NSIF certified personnel. Pigs were scanned more often as the pens neared the target market average pig weight of 122 ± 2.5 kg.

Data analyses Data were analyzed using pen means in the R software package, grofit (Kahm et al.,

2010). The growth curves were fitted with gcFitSpline and models mu (maximum slope µ) and

(maximum growth) were generated. Analysis of variance (ANOVA) was performed for each trait to estimate the effects of sex and trial (two in each season) on the rate of growth µ and A.

The lambda model (lag-phase λ) was not considered because pigs were put on trial post-weaning

(average weight = 22 kg). The initial models were fit to the data as spline models and included trial, weight class (low, medium, heavy), sex, and interaction of sex and trial. Because there were no statistical differences in µ and A with weight class categories that variable was removed from the model for all final analyses. Model coefficients were generated and trial one (first winter trial) was set as the reference trial and all other trials (trials 2, 3 and 4) were then compared to trial 1. The random effect of hoop was also investigated and there was no effect due to hoop. To understand the pattern of growth, polynomials up to the second order were fitted with pen averages to daily average weight gain, backfat depth, and LMA. The linear pattern was also fitted but did not appropriately cover the data and had low R square values compared to the polynomials. Pen averages were also used in the regression analyses of average daily feed intake

(ADFI) and feed efficiency (G: F) curves.

Results The results from the ANOVA for fixed effects of trial, sex and the respective interactions are shown in Table 2 and the Least Squares Means are shown in Table 3. There were no significant interactions between trial and sex for body weight, backfat, and loin muscle area, but

a near significant interaction (P=0.06) of maximum growth (A) was observed with backfat. The values of µ (rate of growth) were different (P<0.01) among trials and between sexes for body weight, but the difference in A values were only observed between sexes. The body weight µ values were highest in trial one and lowest in trial three. Averaging µ values for barrows and gilts for trial one were significantly greater than µ values for trials 2, 3 and 4, with values of

0.02, 0.08 and 0.05 kg/day, respectively. Barrows had a greater mean µ for body weight in each trial and across all trials compared to the gilts (Table 3; 0.87±0.04 vs 0.79±0.03 kg/day).

Significant weight differences were observed between barrows and gilts for A values (126 vs 121 kg) with barrows having a significantly greater average daily weight gain (Figure 1). Both barrows and gilts followed a similar growth pattern.

There were significant differences in µ values among trials (P=0.04) and between sexes

(P<0.01) for backfat deposition, with the greatest rate of backfat deposition during trial one

(winter). The rest of the trials (2, 3, and 4) had almost the same rate of backfat deposition. Trial one had an estimated µ for backfat of 0.05, 0.05 and 0.09 mm/day greater than trials 2, 3 and 4, respectively. Unlike body weight, significant differences (P<0.01) were observed among trials and between sexes in A values for backfat (Table 2). Trial one A values were greater than trial 2,

3 and 4 by 8.6, 4.8 and 8.3 mm, respectively. The µ of backfat deposition was greater for barrows compared to gilts (0.22 vs 0.14 mm/day) and as expected, the barrows had a greater backfat thickness at market weight (31.5 vs 22.6 mm). Barrows and gilts showed similar backfat deposition patterns, with barrows having a greater rate of deposition than gilts (Figure 2). Loin muscle area was not significantly different (Table 2; P>0.05) among trials or between sexes.

However, gilts were more muscular (P=0.06) than barrows at market weight (Table 3) and their

LMA growth exhibits a quadratic pattern (Figure3).

Discussion In the commercial swine industry, it is important to have a clear understanding of tissue deposition rates to properly feed animals and determine the proper end point to market animals.

Barrows grew faster than gilts in each of the four trials. This agrees with previous reports

(NPPC, 1995) that were conducted to provide extensive gender comparisons of growth performance, carcass composition and meat quality traits. Regarding differences in body weight due to trial effects, it is surprising these differences are not consistent in our study. The first and third trials were conducted in winter while the second and fourth trials were conducted in summer. Across the four trials, the highest performance (body weight) was observed in the first winter trial while the lowest weight was observed in a winter (third trial). It would be informative to understand the causes for trial differences in growth because the feeding and management practices were constant throughout the hoops in both the winter and summer. For future trials, it could be useful to have individual feed intake data to assess whether trial differences found could be associated with different feeding patterns. The trials’ variation particularly in temperature could affect feed consumption that may cause differences in pigs’ backfat and loin muscle area deposition and this would ultimately affect market performance and carcass quality. Based on the historical weather data, trial one, the first winter trial (winter 2011,

-2.8-11.1oC) of our study had greater average daily temperatures ranges than trial 3 winter period

(-6.1-3.9oC) while trial 2, the first summer trial (summer 2012) of our study had greater average daily temperatures ranges (18.3-26.7oC) than the trial 4 summer trial (14.4-22.8oC) which was cooler than the 30-year average (U.S. climate data, 2015). The higher winter temperatures for

trial one could explain the greater rate of growth and maximal growth for trial one compared to trial 3.

Across the four trials, barrows had greater backfat compared to gilts (Table 3). There were differences among the four trials averaged over the sexes, with greater backfat in trial one compared to the other three trials, an indication to show that pigs’ backfat deposition is affected by trial (either winter or summer). The barrows’ increased backfat deposition could partially be explained by their greater growth rate that results in greater average daily weight gain compared to the gilts.

As expected, the Berkshire pigs in the current study were not as lean as published information on commercial pig lines. This agrees with previous research which compared rates of lean deposition among eight pig breeds including; Duroc, Chester White, Hampshire, and

Berkshire breeds (Martin et al., 2008). In those previous trials, Berkshire pigs deposited loin muscle at the slowest rate of the eight breeds that were on trial and the Hampshire pigs deposited loin muscle the fastest (Moeller et al., 1998; Martin, 2008). In the current study, a comparison was made between purebred Berkshire gilts and barrows and it was noted that gilts were leaner than barrows. A possible explanation is that within a genetic line, gilts are virtually always leaner at the same weight when compared to barrows (Figure 3), which makes the gilts more feed efficient (Figure 4 and Figure 5). A similar observation was noted in earlier studies (Zobrisky et al., 1961; Bruner et al., 1968). The data in the current study shows consistent differences for growth performance as well as backfat and loin muscle deposition. The results from this study depict differences in sex and trial for growth performance, backfat and loin muscle area deposition rates for Berkshire pigs, which may ultimately affect the production practices. These differences make it critical to properly feed barrows when compared to gilts in niche or

commercial production systems and to monitor pigs’ weight and composition to market them at the correct time.

Acknowledgements Support was provided by the Ensminger Endowed Chair in Agriculture, Center for

Sustainable Rural Livelihoods, State of Iowa, Iowa Pork Industry Center and Hatch funding, and the Iowa Pork Producers Association with assistance from the National Pork Board (NPB Project

#12-116). The authors acknowledge Wayne Roush, Don Hadden, Harry Riesberg, and Jacob

Clemon, for their assistance in feeding, weighing and the care and well-being of the pigs during these trials; and Dallas MacDermot (MacScan) for ultrasonic scanning of the pigs at the Iowa

State University Western Research Farm, Castana, IA.

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influenced by breed, season and sex. J. Animal Sci. 20:923.

TABLES

Table 4.1: Experimental design for the study evaluating the body weight growth, backfat, and loin muscle accretion in purebred Berkshire pigs

Pen Hoop Weight Sex # Pigs A 1 Light Barrow 6 B Light Gilts 6 C 2 Medium Barrow 6 D Medium Gilts 6 E 3 Heavy Barrow 6 F Heavy Gilts 6

Table 4.2: Analysis of variance for a study evaluating body weight, backfat and loin muscle accretion across four trials (two winters and two summers) and sex in purebred Berkshire pigs1

……………………………. P value……………………………. Loin muscle Body weight Backfat area µ A µ A µ A Trial 2 <0.01 0.10 0.04 <0.01 0.06 0.78 Sex3 <0.01 <0.01 <0.01 <0.01 0.64 0.06 Trial x Sex 0.45 0.75 0.18 0.06 0.66 0.43 µ = maximum slope (Rate of accretion) A = maximum growth 1Cells with P≤0.05 indicates statistical significance 2 Trials; winter (1 and 3) and summer (2 and 4) 3Two sexes; Barrows (B) and Gilts (G)

Table 4.3: Least square means (± SE) for a study evaluating body weight, backfat and loin muscle accretion across the four trials (two winters and two summers) and sex in purebred Berkshire pigs

Traits Trial 1 Sex 2 Body weight Backfat Loin Muscle area µ A µ A µ A (kg/day) (kg) (mm/day) (mm) (cm2/day) (cm2) 1 B 0.91±0.011a 128±2.74a 0.27±0.01a 36.9±0.03a 0.284±0.040 39.3±0.12 G 0.82±0.020b 122±3.11b 0.16±0.02b 25.2±2.09b 0.304±0.022 42.2±1.42

2 B 0.87±0.019 a 124±1.28 a 0.22±0.05 a 28.4±0.44 a 0.295±0.046 41.8±1.11 G 0.78±0.015b 120±4.11b 0.12±0.02b 20.0±2.32b 0.290±0.036 42.5±0.45

3 B 0.84±0.033 a 125±0.65 a 0.22±0.04 a 32.1±2.80 a 0.289±0.380 41.5±3.11 G 0.78±0.042b 121±3.72b 0.13±0.03b 21.6±2.95b 0.273±0.012 43.5±2.19

4 B 0.87±0.035 a 126±0.97 a 0.19±0.03 a 28.7±2.51 a 0.346±0.031 41.9±2.87 G 0.77±0.014b 123±0.23b 0.15±0.03b 23.7±1.84b 0.321±0.015 42.2±1.42 a,b Values in the same column (across trials) not sharing a common superscript differ significantly at P≤0.05. 1Trials; winter (1 and 3) and summer (2 and 4) 2Two sexes; Barrows (B) and Gilts (G), n(B) = 72, n(G) = 72 µ = maximum slope (Rate of accretion) A = maximum growth

FIGURES

Figure 4.1: Average daily weight gain across the four trials comparing Berkshire barrows and gilts. ADG: Average daily weight gain; Red circles: Gilts cumulative ADG; Blue circles: Barrows cumulative ADG.

Figure 4.2: Cumulative backfat accretion across the four trials comparing Berkshire barrows and gilts. Red circles: Gilts cumulative backfat depth; Blue circles: Barrows cumulative backfat depth

Figure 4.3: Cumulative loin muscle area across the four trials comparing Berkshire barrows and gilts. Red circles: Gilts cumulative backfat depth; Blue circles: Barrows cumulative backfat depth.

Figure 4.4: Average daily feed intake across the four trials comparing Berkshire barrows and gilts. ADFI: Average daily feed intake; Red circles: Gilts ADFI; Blue circles: Barrows ADFI.

Figure 4.5: Gain-to-feed ratio across the four trials comparing Berkshire barrows and gilts: G:F – Gain to feed ratio; Red circles: Gilts G: F; Blue circles: Barrows G:F.

CHAPTER 5 . GENDER INTRA-HOUSEHOLD CONTRIBUTIONS TO LOW-INPUT DAIRY PRODUCTION IN SENEGAL

Paper prepared for submission to the Journal of Animal Science

M. Walugembe1, K. Danielsen2, S. Tebug2, M. Tapio3, A. Missohou2, J. Juga4, M. F.

Rothschild1, K. Marshall2

1Department of Animal Science, Iowa State University, Ames, IA 50011, USA

2International Livestock Research Institute, Nairobi, P.O. Box 30709, Kenya

3Natural Resources Institute, Forssa Area, Finland

4Department of Agricultural Sciences, University of Helsinki, Yliopistonkatu 4, 00100 Helsinki, Finland

Abstract Two surveys were conducted at two sites in Senegal to determine intra-household gender roles in small holder Senegal dairy cattle herds. These two surveys, baseline and longitudinal, were conducted, and the main survey respondents were heads of household. Households were grouped into two levels of market orientation (higher or lower) using longitudinal household milk production data. Baseline survey results revealed that adult males (> 15 years of age) were usually responsible for the costs and decision making of most of the dairy related activities, though less so when production was quite low. Adult males, hired males (> 15 years of age), and any other household members except females were the main labor source for the dairy activities.

When comparing those who received income from milk sales, milk sales income from lower market oriented households went to females for a larger proportion of households, than occurred in higher market orientated households where a larger percentage went to males. There were no differences in cattle breed perceptions between men and women, and the two genders had similar responses in regards to the constraints facing Senegal dairy enterprises.

Introduction The keeping of dairy animals is an important livelihood activity of the rural poor farmers in

Senegal, and dairy production also contributes to food and nutritional security. The Senegal dairy system, like most other dairy enterprises/systems in Sub-Saharan Africa, consists of mainly crop- livestock farmers, agro-pastoralists, pastoralists and a few semi-intensive or intensive farms that are close to urban areas (Ndambi et al., 2007; Garrity et al., 2012). Barring the more intensive farms the dairy industry is characterized by low inputs and relies heavily on indigenous local breeds such as the West African zebu and their crosses with exotic breeds (Okeyo et al., 2015).

The indigenous local breeds are well-adapted to the local environmental conditions, but are poor milk producers compared to commercial exotic breeds that are intensively managed (Renaudeau et al., 2012; Okeyo et al., 2015). Although the Senegal dairy system is not well developed (with

Senegal being a net importer of dairy products), reports have indicated potential for growth with improvement in animal productivity, milk hygiene and cold chain, and market access, amongst others (; Dieye, 2005; Parisse, 2012). Such growth would result in benefit to the varied dairy value chain actors, including poor livestock keepers and animal source food consumers.

There is growing recognition of the need to integrate gender in agricultural research and development initiatives (Okali, 2011). Gender relations define specific roles, status, and expectations within different households, communities, and cultures. In all societies, gender influences the nature or type of work or tasks that an individual performs. This gender division of labor may thus confer specific opportunities, challenges, and status for individuals (Risman,

2004; CARE International Gender Network, 2012; Blackstone, 2013). Rural women for example often experience serious challenges compared to men due to traditional perceptions and structures that may prevent them from accessing and controlling the necessary inputs and

resources to fully reach their potential in agricultural production. Although women are greatly involved in contributions to livestock management and overall production, they are generally less involved in decision making and less likely to make major decisions such as sale of high value animals compared to men (FAO, 2011a; Njuki and Sanginga, 2013). In this regard, it is important to understand the gender division of labor, access to and control over resources and benefits, as well as decision-making processes between women and men in dairy producing households to tailor programs accordingly (FAO, IFAD, World Bank, 2007).

The primary objective of the study was to determine the intra-household roles and responsibilities of the different household members (men and women or boys and girls) in the

Senegal dairy system. A second objective was to determine intra-household perceptions on the different Senegal cattle breeds and their constraints to dairy production.

Materials and methods

Overview This study was conducted as part of the Senegal Dairy Genetics project that aimed to identify and promote the most appropriate breed or crossbred type of dairy cattle for low-input systems in Senegal (Marshall et al., 2016). The Senegal Dairy Genetics project is part of the

Food Africa programme aimed at improving food security in West and East Africa (see https://portal.mtt.fi/portal/page/portal/mtt_en/projects/foodafrica).

Project site description Two study sites, the Thies and Diourbel regions of Senegal, were selected because of their cattle breed-type diversity (a requirement to meet the objectives of the overall project). The two sites are characterized by a semi-arid type climate, average annual rainfall of 662.9 mm, and short wet season in the months of July to October (Fall et al., 2006). The sites consist of mainly

agro-pastoralist farmers who graze their cattle on natural pastures in the wet season and supplement with crop residues and concentrates in the dry season. Cattle are generally kept for both meat and milk, amongst other objectives.

Data collection Two surveys, termed here the baseline and longitudinal, were conducted on 220 dairy cattle keeping households, owning more than 3,200 cattle over a 13-month period. The baseline survey was conducted between May to July 2013, while the longitudinal survey was conducted from July 2013 to April 2015, with households visited 13 times over this period. The baseline survey comprised survey components both to (a) the household head - which was predominately male (93% of households) and (b) to adult women in the household, (the spouse of the household head for male headed households or the household head for female headed households). The longitudinal survey respondents comprised household heads, other adult household members and herders, dependent on the question and availability of persons at the time of the survey visit.

Both baseline and longitudinal surveys collected a range of information pertaining to the household dairy enterprise, including animal performance and economics. Data used in this article comprised those from: (1) the longitudinal survey for animal milk yields and volume of milk sold by the household (used to group households as higher or lower market-oriented, as described below); (2) the baseline survey in relation to household livelihood portfolio and other household demographics as indicated by the male or female household head; (3) the baseline survey on intra-household contributions to dairy - namely who pays costs, controls benefits, provides labour or makes decisions- as indicated by male household heads and an adult female in the household, or female household heads, and (4) the baseline survey in relation to

perceptions on various issues - namely cattle breed preference, constraints to dairy, and training needs on dairy - as again indicated by male household heads and an adult female in the household, or female household heads

Approval for the survey was given by the institutional research ethics committee of the

International Livestock Research Institute and appropriate authorities in Senegal, and household participation in the survey was completely voluntary.

Data analyses Households were classified as higher or lower market orientation based on both the average milk sold per day (whether fresh or processed) and percent of milk produced that was sold, averaged over the 13 rounds of the longitudinal survey. Households with milk sales >2.5 liters per day and with sale of >50% of the milk produced were classified as higher market oriented, while those with low milk sales (≤ 2.5 liters of milk sold per day and/or ≤ 50% of milk produced sold) were classified as lower market oriented. The overall number of households classified as into the higher and lower categories was 104 and 116 households, respectively.

Livelihood portfolio and household demographics were summarized for all households, as well as for households falling into the two levels of market orientation, with simple summary statistics using the baseline survey.

For intra-household responsibilities on dairy, the percentage of household members (as women, men, girls and boys, where girls and boys were considered as being less than 15 years of age) responsible for the various aspects considered (who pays costs, receives benefits, provides labour or makes decisions) were calculated separately for the higher and lower market orientated households (using the number of respondents within each group as the denominator) using the baseline surveys. For selected cases, Chi-square tests or Fisher exact test in cases of cell

responses having less than 5 observations were performed to test significance between the higher and lower market orientated households, with a significance level of 0.05 used. As there were few (16/220) female headed households, these results are presented in detail for male headed households only (n=204; 95 and 109 for higher and lower levels of market orientation, respectively) considering both the responses from both the male household heads as well as the adult female household members. Results relating to the female headed households are also given briefly, but should be considered with care given the limited numbers.

Perception data (dairy breed preference, constraints to dairy and training needs on dairy) was compared between the male and female respondents using simple descriptive statistics, considering all households in the baseline adult female survey and male household heads in the baseline head of household survey. For breed preference analyses, preference levels of low, medium, and high breed preferences were assigned scores of 1, 2, and 3, respectively and these scores were averaged for each breed-type.

Results

Household livelihood sources and demographics The main livelihood source activities of the households were indicated as dairy cattle production, livestock related business, sheep and goat keeping, own business, and food crop production. Lower market oriented households indicated food crop production, dairy cattle production, own business and livestock related business as the main sources of their livelihood, whilst higher market oriented households carried out similar activities as the main sources of livelihoods, with limited focus on food crop production compared to lower market oriented households.

Household ethnicity was Wolof (58% of households), Fula (26%), Serere (12%), and

Toucouleur (4%) for the entire household survey. The main ethnicities in the lower market orientated households were the Wolof (45%) and Fula (31%), with the rest of the households belonging to the Serere (20%) and Toucouleur (4%) ethnic groups, whilst for higher market orientation it was Wolof (73%), Fula (19%) and Toucouleur ethnicity (5%) with the least number of households belonged to the Serere (3%) ethnic group.

Households kept herd sizes ranging between 2 – 18 animals, with larger herds kept by the higher market oriented households. The lower market oriented households had herd sizes of 2-16 animals with only16% of the households having herds of more than 10 animals. About 43% of the higher market oriented households kept herds of more than 10 animals and herd sizes ranged between 4-18 animals. The breeds of cattle kept by the households included indigenous breeds

(primarily Zebu Gobra and Zebu Maure), cross-breeds (between the indigenous breeds and the exotic Zebu breed Guzerat, and crosses between the indigenous breeds and exotic Bos Taurus breeds - primarily Holstein Friesian and Montbeliarde), and in a few cases, pure-bred exotic Bos

Taurus breeds. Households kept one to several different breed-types.

In relation to formal education of the household head (nearly all male), the most common was Coranic school (52%) or no formal education (13%). There were a few household heads that attended primary school (11%), secondary school (8%) or University (9%). A larger proportion of households in the lower market oriented households attended coranic school (55%) and the rest acquired formal education (14%), had no formal education but literate (3%), attended

4primary (11%), secondary (8%), University (10), or college (1%). Most the household heads in the higher market oriented group attended coranic school (48%), and a few attended primary school (11%), secondary (9%), and University (10%). Some households indicated that they did

not attend any formal education (13%), whilst others did not attend formal education, but were literate (7%). The 16 female household heads either attended coranic school (64%) or were illiterate (36%).

Intra-household responsibilities on dairy - male headed households and from male respondents According to the male heads of households and for lower-market orientated households, the main providers of labor on animal feeding, watering, and milking were hired males (26-53% of respondents, depending on the labour task), adult males (23-29%) or any household members (9-

36%). Most the labor to animal breeding and health was provided by hired males (57 – 66%) and adult males (20-32%). Adult household males (41-73%), together with hired males (16-47%) were main labor providers of purchase and sale of animals, whilst adult females (76%) provided the most labor on milk processing and sale of milk (Figure 1A). For higher market oriented households, labor on most of the dairy activities; animals feeding, watering, health, breeding, purchase, sale of animals, and milking, was provided by hired males (23-67%) and adult males

(21-46%). Like lower market oriented households, the provision of labor on milk processing and milk sales was a major responsibility of adult females (67%) (Figure 1B It is important to note that the higher-market orientated households had higher use of herders for all labor activities, significant (at P<0.05) in the cases of purchase and sale of live animals.

The main decision makers on animals feeding, watering, health, breeding, purchase, group memberships to groups on dairy (such as farmer co-operatives), hire of labor, sale of animals, and milking were adult males (70 to 100% of respondents, depending on decision), whilst the main decision makers on milk processing and sale of milk were adult females (56 to

69%), for both the higher and lower market oriented households (Figure 2). The proportion of adult females that were decision makers on milk processing was higher in the lower market oriented households compared to the higher market orientated households (at 64% versus 57%, respectively; though not significant at P>0.05), as was that for milk sale (at 63% versus 68%, respectively; again, not significant).

Payment of costs on animal feeding, watering, health, breeding, purchase, group membership, hire of labor, and milking was generally a responsibility of male adult household members in both levels of market orientation (85-100% of respondents, dependent on the cost and level of market orientation). For milk processing, the payment of costs was by both adult males and adult females, at 53 % and 43% respectively, for the lower market orientated households (the remainder comprising ‘others’) and 76% and 24% respectively for the higher market oriented households, with this difference statistically significant (P<0.05)

For the control of benefits from the sale of milk, adult females were the main controllers of benefits in the lower market oriented households (72% of households), whilst for the higher market oriented households it was more evenly split between adult females (47%) and adult male

(53%). This difference between the lower and higher market orientated households was significantly different (P<0.01). Control of benefits/income from the sale of live animals was mainly by adult household males for the two levels of market orientation (at 99% and 89% for the higher and lower levels of market orientation, respectively), with significantly higher male control in the higher market oriented households (Table 1: P<0.05; Figure 4)

Intra-household responsibilities on dairy - male headed households and from female respondents Results from female respondents on the various responsibilities on dairy in male headed households generally followed the same trend as the male respondents (given above). For example, for the provision of labour hired males and adult males provide the most labor on most activities on dairy production (equivalent to Figure 1), and control of income from sale of live animals and milk where adult males are the main controllers of benefits from sale of live animals, whilst women partially control benefits from the sale of milk (equivalent to Table 1).

This indicated that there were no large perception differences between male and female household members on the intra-household contributions to dairy production in Senegal.

Intra-household responsibilities on dairy - female headed households and from female respondents Intra-household responsibilities for dairy activities for female headed households were difficult to analyze because of the low number of female headed households (n=16). However, results showed that: hired males are the labor providers on most activities on dairy bar milk processing and sale; adult household males are primarily responsible for decision making and payment of costs, again bar in relation to milk processing and milk sale; the female household head / adult female members were responsible for labour, decision making and costs associated with milk processing and sale. Like the male headed households, respondents indicated that adult males control benefits from sale of live animals and there is a split in control of milk sales between adult males and adult females.

Gendered perceptions on dairy cattle breeds Both male and female respondents who were familiar with more than one breed-type of cattle were asked to indicate their preference level for local, exotic and cross-bred of local and

exotic breed-types (see above for more details on these breed-types). For both male and female respondents, the most preferred breed-types were cross-bred (with a mean score of 2.70 for males and 2.73 for females), followed by local (2.26 and 2.24) and exotic breeds (1.88 and 2.19).

Respondents were asked to name favorable aspects of each breed type. For the cross- breeds, the most commonly named answers were high milk yield (46% and 48% of male and females who named a favorable aspect, respectively), fast growth (15% and 16%), and animal weight and conformation (12% and 8%). For the exotic breeds, responses were similar and named high milk yields (42% and 25% of male and female, respectively), fast growth (17% and

10%) and animals’ weight and conformation (5% and 2%). For the local breeds, responses were the breed is well adapted (26% and 22% of male and female respondents, respectively), ease of management (14% and 14%) and milk quality (9% and 8%).

Similarly, respondents were asked to name unfavorable aspects of each breed-type. For cross-breeds, respondents named high feed intake (30% and 37% of male and females who named this an unfavorable aspect, respectively), poor adaptation to the climatic conditions (18% and 13%, male and female, respectively), and poor disease tolerance (13% and 7%, male and female, respectively). For exotic breeds, the most common unfavorable aspects were similar to those of cross-breeds with respondents naming, high feed intake (31% and 22%, male and female, respectively), poor adaptation to climatic conditions (22% and 24%, male and female, respectively), and poor disease tolerance (12% and 13%, male and female , respectively). For the local breeds, low milk yields (49% and 60%, male and female, respectively) was the unfavorable aspect.

Gendered perceptions on constraints to dairy The respondents were asked to name up to three main constraints to their household dairy enterprises (Table 2). The more commonly named constraints, for both male and female respondents, were lack of animal feed (45% of male respondents and 61% of female) and high feed cost (20% and 22%). Other constraints mentioned by a smaller number (<10%) of both male or female respondents were lack of land for cattle farming, animal theft, animal health problem, and lack of access to credit. Additionally, a few women (<5%) mentioned low milk and product prices and problems with milk marketing, whilst a few men (<5%) mentioned labor availability and cost as well as artificial insemination service access and costs.

Gendered training needs on dairy The main training needs on dairy differed for the male and female respondents (Table 3).

For women, the most commonly named training needs were milking and milk hygiene (26% of female respondents), animal health (24%), and milk processing (22%). For men, these were animal feeding (50% of male respondents) and animal health (18%). This is in-line with the decision making and labour roles that men and women make in relation to dairy.

Discussion This study clearly demonstrates that, for household dairy enterprises in the selected study sites in Senegal, intra-household responsibilities on dairy differ by gender of the household member. For most households, adult household males were mostly involved with aspects relating to herd management (such as feeding, watering, labour, animal purchase and sale etc.) including labour provision, decision making, payment of costs, and control of income from

animal sales. It is of note that for labor there was also a high involvement of hired male herders

(particularly for the higher market oriented households), and in the case of feeding and watering, any household member. In contrast, for milk processing and milk sale, adult household females were the main labour providers, and either adult household females or males were the main decision makers, payers of associated costs (which would be minimal), or controllers of income from milk sale. Notably, the control of benefit from milk sale showed as shift from women to men as household market orientation increased.

Several other studies have also shown intra-household responsibilities on livestock keeping enterprises to differentiate by gender and it is important to note that livestock ownership cannot be isolated from decision making (Njuki and Mburu, 2013; Gaile et al., 2015). In Kenya for example, a very low proportion of women could sell their dairy cattle compared to their male counterparts. Intra-household responsibilities on livestock also depend on societal norms and can vary across different socio-economic or other groups, for example a study in Arumeru district,

Tanzania on dairy showed that women were the most labor providers to dairy management activities including; milking, cleaning, feeding, and watering (Kimaro et al., 2013). This is consistent with previous findings of a study conducted in Tanzania, Nicaragua, and Ethiopia on exploring gender perceptions of resource ownership and their implications for food security among rural livestock owners. Here ownership of livestock was associated with the division of labour and decision making, and it was more likely that men or women provided labor to livestock activities where they have greater control of the assets or resources (Gaile et al., 2015).

The change in control of benefits with level of market orientation agrees with other studies that have shown a shift in benefits from women to men as the household livestock enterprise becomes more profitable (Shapiro et al., 1998; Tangka et al., 1999; Parisse, 2012). Men received

greater increments in their income levels than women with the introduction of crossbred cows that produced higher milk yields. Other reports have also shown that high value assets such as cattle are controlled by men while women control benefits/revenue from milk, particularly when yields are low and small livestock (Meinzen-Dick et al., 2011; Galiѐ et al., 2015).

In this study, women and men tended to make decisions in areas where they provided labour, paid costs, or controlled benefits (such as animal husbandry and sale for men; and milk processing and sale for women). Thus the benefits women can derive from the household dairy enterprise are largely dependent on men’s decisions in relation to the herd, such as what breed to keep and how much to invest in feed (both primary drivers of milk yield). Men’s decision on labour, for example to hire a herder or not, would affect have a major impact on the household members (including the men themselves) in relation to labour burden of the dairy enterprise.

This is consistent with previous reports that show that women often have less access to opportunities and assets, and are not generally involved in decision making compared to men and may thus be more vulnerable to shocks or poverty (Meinzen-Dick et al., 2011; FAO, 2011; Njuki and Sanginga, 2013).

Both male and female respondents indicated high preference for cross-breed and exotic breeds due to high milk yields, but with some differences in the perceived constraints. This is because most animals kept by these households were local breed, they kept fewer animals (<10), animals were poorly managed and households indicated that they lacked feed for their animals.

Local breeds would be the ideal breeds for many African farmers’ production environments as these breeds are tolerant/resistant to tropical animal diseases, such as trypanosomiasis and tick infestation, but are low milk producers (Renaudeau et al., 2012; Okeyo et al., 2015). To increase milk production, a cross of local and exotic breeds would be preferred as commercial exotic

breeds have been extensively selected for specific productivity traits such as meat and milk production (Okeyo et al., 2015). Cross-breeds, particularly of indigenous Zebu by Bos Taurus

(Montbeliarde and Holstein-Friesian) have been reported to produce over 1700 litres/per cow/year under better management in Senegal (Marshall et al., 2016). Providing adequate training about the proper management of cross-breeds or exotic breeds to household members majorly involved with dairy production could improve milk yields and ultimately milk sales for women. The improvement in technology (breeds) could also increase the labor burden of women due to increased animal production but might also increase their independence and control over their output/resources (Cheryl, 2001). However, the improved production could make the enterprise more profitable and men might take over the control and decision making regarding male sales and processing, resulting in women dependence.

In conclusion, our surveys demonstrated that various household members have different contributions to household dairy enterprises in Senegal and that adult males and female have different responsibilities in dairy production based. These results could be used as entry points towards strategies for livestock improvement projects through training programs that may target different household members based on the household gender division of labor, access to and control over resources and benefits as well as decision making. Further research is required to understand fully the different trade-offs between men and women across a range of dairy activities that involves different levels of dairy production.

Acknowledgments This project was funded by the Finnish Ministry of Foreign Affairs via the Food Africa program, the Livestock and Fish CGIAR Research Program, and the Ensminger and State of

Iowa funds.

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Table 5.1: Control of dairy enterprise benefits between adult males and adult females

Market orientation N Milk sales

Adult male (%) Adult female (%)

Higher 80 53 47

Lower 82 28 72

Chi Square P value, df=1 0.0015

Live animal sales

Adult male (%) Adult females (%)

Higher 75 99 1

Lower 93 89 11

Fishers exact test P value, df=1 0.014

N = Number of household responses; df = Degrees of freedom

Table 5.2: Perceived main constraints to dairy production1

Constraint % male respondents % female respondents Lack of feed 44.6 61.1 High feed cost 19.9 22.2 Lack of land for cattle farm 7.2 - Animal theft 7.7 - Animal health problems 6.2 6.5 Others 14.22 10.23

1Listed are the constraints named by at least 5% of either the male or female respondents 2Non constraint, lack of labor, high labor costs, lack of AI access, high AI costs, and no credit access, 3Low milk and product prices, No credit access, problems with milk marketing, animal theft, Lack of farming land

Table 5.3: Household training needs on dairy1

Training needs % male respondents % female respondents Animal health 50.0 24.2 Animal feeding 18.0 14.7 Keeping of cross and exotic breeds 9.0 - Milk and milk hygiene 8.5 26.3 Milk processing - 21.6 Others 14.52 13.23

1Listed are the constraints named by at least 5% of either the male or female respondents 2No training needed, Reproduction, marketing of milk and milk products, and making silage 3No training, keeping cross and exotic breeds, Animal reproduction, and marketing of milk and milk products

Figure 5.1: Labor provision on dairy activities in the lower (A) and higher (B) market. 84

Figure 5.2: Decision making on dairy activities in the lower (A) and higher (B) market

Figure 5.3: Payment of costs on dairy activities in lower (A) and higher (B) market

Figure 5.4: Control of benefits on diary in lower (a) and higher (b) market oriented households, according to male respondents

CHAPTER 6. GENERAL CONCLUSIONS

Small scale livestock production is a critical part of smallholders’ livelihoods and the global economy. Most of the farming population operates family farms, and these farms practice small scale farming. Small scale farming varies across countries, with the majority of the active populations in Asia, Africa and Latin America working on farms. Livestock production is part of an integrated system and is managed based on farm family needs, labor availability, and other enterprise demands. However, research examining the role of gender in small scale livestock production, particularly dairy, is somewhat limited. One of the main objectives of this dissertation was to understand the role of gender in dairy cattle production in a developing country setting, in this case Senegal the where main milk producers are small-scale producers.

Overall, adult males (household heads) were the main decision makers and paid costs in both higher and lower market oriented households for most of the dairy activities such as: animal feeding, watering, health, breeding, purchase and sale of animals; adult females were the main decision makers for milking and milk processing. The main providers of labor were household adult males, hired adult males, and sometimes any household members, for both levels of market orientation, with exception of labor on milk sales and milk processing where adult female household members were the main providers. Regarding control, men controlled benefits from the high income generating activities, such as sale of animals, while women benefited from the sale of milk. These results indicate that while men provide most of the labor and pay most of the costs on dairy, women benefit from the sale of milk. Improvements in dairy production in the developing world would be one of the avenues to improve household livelihoods as both genders are beneficiaries of dairy products. Because rural women are responsible with the wellbeing of

households through provision of necessities like food, it is important that development agencies tailor development programs around activities, such as dairy production that women are most involved to enable women increase their income levels. However, adult males should be fully involved in such programs as they are mostly the household heads and responsible with making important decisions like sale of the live animals. Programs or technological interventions that aim to address challenges associated with dairy production like lack of feed, high feed costs and low performing breeds could help boost dairy production and ultimately improve household livelihoods. There was generally low milk production in both higher and lower market oriented households. Approaches such as improvement of veterinary and A.I services might me key in improvement of cattle milk productions.

The Senegal dairy study addressed key areas where gender plays an important role in dairy production and indicated the importance of considering gender issues in relation to the development of dairy in Senegal. However, these results require further exploration in relation to trade-offs between men and women across the range of enterprises in which household is involved. It is known however, that if women are more involved then income will more likely return to the family.

The use of technological approaches to improve other livestock enterprises, such as pig production could be another avenue to empower households. In the third chapter, we addressed the prediction of live body weight using various body measurements of body length, heart girth, height, width and flank to flank in Uganda village pigs. The objective of that study was to empower women farmers to predict pig market weights to enable them have a better bargaining power and make more income while marketing their pigs. It is surprising that there is a lot of literature about weight prediction using several linear body measurements in other livestock

species like cattle, sheep and goats but not pigs. The existing literature on pigs uses a few body measurements. Our study indicated that four body measurements; body length, heart girth, height, and width were strong predictors of pig live body weight, and that live weight could accurately be estimated using two or more body measurements. Farmers could decide to use two measurements of heart girth and length in circumstances where they are not able to use all the four measurements, especially for slightly heavier (≥ 40 kg) market pigs. This tool may be an appropriate approach in equipping rural women with knowledge about the weight of their pigs prior to marketing and improve their incomes.

It is also important for small scale farmers to understand the differences in performance of barrows and gilts across different seasons (winter and summer). Berkshire, a typical breed for the niche market especially in the developed countries was evaluated to understand the effect of sex and trial (season) on the accretion rates for body weight, backfat thickness, and loin muscle area. Our findings indicate that sex and trial contributed to the differences in the rate of live body growth and ultrasonic backfat, with significantly higher values in the first winter compared to the two summers and last winter season. Barrows had higher rate of growth and thus reached harvest/market weight sooner than the gilts. There were no significant differences in loin muscle area between the two sexes. This is surprising as gilts are known to deposit greater loin muscle area than barrows in commercial settings. Further studies involving use of diets containing different protein and energy levels could be performed to have a better understanding of loin muscle area deposition rates between the two sexes. Gilts might require higher concentration of dietary amino acids to maximize the rate of loin accretion.

In conclusion, small scale farmers, based on country/regions invest varying levels of inputs and may be involved in multiple farming enterprises including both crop and livestock

production, as well as non-agricultural activities. Because of the limited investments and several challenges faced by small scale producers such as poor nutrition, inferior breeds, and extreme environmental conditions, livestock production is mostly much lower than in commercial setting.

The fact that women’s income is more likely to return to family, it is important to improve milk production and other livestock enterprises, such as swine production where women are highly involved by using available/feasible technological tools and improvements in livestock production inputs such as nutrition, breeding, animal health, etc. Together these inputs if used well might boost rural women income levels that are ultimately used for household livelihood improvement.