Impact of Pollinator Habitats and Managed Bee Pollination on Yields of Pollinator Dependent Crops, Evidence from North West Ethiopia

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Soil Science Thesis and Dissertations

2019-01-02 IMPACT OF POLLINATOR HABITATS AND MANAGED BEE POLLINATION ON YIELDS OF POLLINATOR DEPENDENT CROPS, EVIDENCE FROM NORTH WEST ETHIOPIA:

SISAY GETAHUN http://hdl.handle.net/123456789/9257 Downloaded from DSpace Repository, DSpace Institution's institutional repository

BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCE

GRADUATE PROGRAM

IMPACT OF POLLINATOR HABITATS AND MANAGED BEE POLLINATION ON YIELDS OF POLLINATOR DEPENDENT CROPS, EVIDENCE FROM NORTH WEST ETHIOPIA:

M.SC. THESIS

BY SISAY GETAHUN

October, 2018 Bahir Dar, Ethiopia

BAHIR DAR UNIVERSITY

COLLEGE OF AGRICULTURE AND ENVIRONMENTAL SCIENCE

GRADUATE PROGRAM

IMPACT OF POLLINATOR HABITATS AND MANAGED BEE POLLINATION ON YIELDS OF POLLINATOR DEPENDENT CROPS, EVIDENCE FROM NORTH WEST ETHIOPIA:

M.SC. THESIS

BY SISAY GETAHUN SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE (M.SC.) IN AGRICULTURAL ECONOMICS

October, 2018 Bahir Dar, Ethiopia

Bahir Dar University

College of Agriculture and Environmental Sciences

APPROVAL SHEET

As member of the Board of Examiners of the Master of Sciences (M.Sc.) thesis open defense examination, we have read and evaluated this thesis prepared by Miss. Sisay Getahun entitled Impact of Pollinator Habitats and Managed Bee Pollination on Yields of Pollinator Dependent Crops in: North Western Ethiopia. We hereby certify that, the thesis is accepted for fulfilling the requirements for the award of the degree of Master of Sciences (M.Sc.) in Agricultural Economics.

Board of Examiners ______Name of External Examiner Signature Date

______Name of Internal Examiner Signature Date

______Name of Chairman Signature Date

DECLARATION

This is to certify that thesis entitled IMPACT OF POLLINATOR HABITATS AND MANAGED BEE POLLINATION ON YIELDS OF POLLINATOR DEPENDENT CROPS IN NORTH WESTERN ETHIOPIA submitted in partial fulfillment of the requirements for the award of the degree of Master of Science in Agricultural Economics to the Graduate Program of College of Agriculture and Environmental Sciences, Bahir Dar University by Miss. Sisay Getahun (ID. No. 0906147) is a genuine work carried out by her under our guidance. The matter embodied in this project work has not been submitted earlier for award of any degree or diploma to the best of our knowledge and belief.

Name of the Student Sisay Getahun Signature & date ______

Name of the Supervisors Zewdu Birhanie (PhD) Signature & date ______Major advisor

Menale Kassie (PhD) Signature & date ______Co- advisor

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DEDICATED

This study document is dedicated to my parents Kes Getahun G/medhin and W/r Yeshareg Mulugeta for treating me with care, unlimited assistance and for their encouragement in my academic carrier and life.

ACKNOWLEDGEMENTS

First of all, I would like to express my honest thanks to the Almighty God and his mother for their love, benevolence, forgiveness and generosity.

I am sincerely and heartily grateful to my supervisors Dr. Zewdu Birhanie and Dr. Menale Kassie for the support and scientific guidance they showed me throughout my thesis writing from proposal development up to the completion of the thesis. As well as I don’t jump my heartily gratitude for Bedaso Taye (M.Sc.) for his voluntary, unlimited and intellectual support to being the survey data well And Dr. Workneh Ayalew for his continual instruction and technical comments.

I would like to thanks my sponsor Woldia University for the financial support of both education and research study. My deepest gratitude goes to Bahir Dar University College of Agriculture and Environmental Sciences department of Agricultural Economics staff for their friendly interaction, intellectual and technical comments, guidance and unreserved support from proposal development up to the completion of the thesis. I would like to thank all YESH project team including enumerators for their cooperation to collect available data from Woreda and kebele, and all respondents from each kebele. They are highly appreciated for their willingness and patience in providing answers to all of the questions in the questionnaire.

My deepest thanks to my father, mother, brothers and sisters for their commitment and sacrifices bring me to this stage. All my families are always with me whenever I need them; their encouragement keeps me going and their love empowers me.

I would also like to extend my gratitude to my postgraduate regular agricultural economics students the energetic, lovely and un boredom support in the two consecutive years.

Finally, it is my great pleasure to thank all of my best friends to support ideally and for their patience during my study.

TABLE OF CONTENTS

Contents page

APPROVAL SHEET ...... ii

DECLARATION ...... iii

DEDICATED ...... iv

ACKNOWLEDGEMENTS ...... v

TABLE OF CONTENTS ...... vi

LIST OF TABLES ...... ix

LIST OF FIGURES ...... x

LIST OF APPENDIX ...... xi

LIST OF ABBREVIATIONS AND ACRONYMS ...... xii

ABSTRACT ...... xiii

1. INTRODUCTION ...... 1

1.1.Background of the Study ...... 1

1.2. Statement of the problem ...... 3

1.3 Objectives of the Study ...... 4

1.3.1. General objective ...... 4

1.3.2. Specific objectives ...... 4

1.4. Research questions ...... 5

1.5. Significance of the Study ...... 5

1.6. Scope and Limitation of the Study ...... 5

1.7. Organization of the thesis ...... 6

2. LITERATURE REVIEW...... 7

2.1. Theoretical Framework ...... 7

2.1.1. Definitions and concepts ...... 7 vi

TABLE OF CONTENTS (continued)

2.2. Resource base and status of Beekeeping in Ethiopia ...... 8

2.3. Role of Beekeeping in Agriculture ...... 9

2.4. Role of managed Bees Pollination in crop productivity ...... 9

2.5. Relative advantages of beekeeping ...... 10

2.6. Environmental role of Beekeeping ...... 11

2.7. Pollinating Insects in Agricultural Crops ...... 11

2.8. Protecting Pollinators and Their Habitat ...... 12

2.9. Economic and ecological Consequences of Pollinators declines ...... 14

2.10. Foraging Efficiency of pollinators and Their Distance ...... 15

2.11. Some other factors affecting pollinating crop Production ...... 18

2.11.1. Education and crop production ...... 18

2.11.2. Sex difference and crop production ...... 18

2.11.3. Age, family size, landholding size and crop production ...... 19

2.11.4. Possession of livestock and crop production ...... 19

2.11.5. Extension services ...... 20

3. RESEARCH METHODOLOGY ...... 21

3.1. Description of the study areas ...... 21

3.2 Methods of Data Collection and Sources ...... 24

3.3. Sample Size and Sampling Technique ...... 25

3.4. Methods of Data Analysis ...... 26

3.4.1 Descriptive statistics ...... 26

3.4.2. Econometric Analyses ...... 27

4. RESULTS AND DISCUSSION ...... 33

4.1. Descriptive statistics ...... 33

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TABLE OF CONTENTS (continued)

4.2. Farm input utilization and access to services ...... 36

4.3. Livelihood source, pollinated dependent crops and knowledge on pollination ...... 38

4.3.1. Livelihood sources...... 38

4.3.2. Pollinating dependent crops ...... 39

4.3.3. Farmers knowledge on pollination ...... 40

4.4. Cost structure, production and utilization of pollinated crops ...... 41

4.4.1. Cost structure ...... 41

4.4.2. Crop production and utilization ...... 42

4.5. Econometrics Model Results ...... 43

4.5.1. Determinants of pollinating crops productivity ...... 43

5. SUMMERY, CONCLUSION AND RECOMMENDATION ...... 48

5.1. Summery and Conclusion ...... 48

5.2. Recommendations ...... 50

6. REFFERENCE ...... 52

7. APPENDIX ...... 63

BIOGRAPHICAL SKETCH ...... 84

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LIST OF TABLES

Table Page

Table 1 Some crop varieties and their pollination agents ...... 14 Table 2 Number of sampled households from each kebele ...... 26 Table 3 Description of variables and hypothesized relationships ...... 28 Table 4 Descriptive statistics of interest variables ...... 33 Table 5 Descriptive statistics for household education and sex ...... 34 Table 6 Descriptive statistics of continuous variables ...... 35 Table 7 Farm input, agronomic practice and access to services ...... 36 Table 8 Farmer’s knowledge on insect pollination and bee management ...... 41 Table 9 production cost of pollination dependent crops ...... 42 Table 10 Average annual pollinated crop production and Utilization ...... 42 Table 11 linear regression results of impact of determinants of pollinating crops yield ...... 47

LIST OF FIGURES

Figure Page Figure 1 Conceptual framework of Insect pollination ...... 17 Figure 2 Contribution of insect pollination to agricultural productivity ...... 17 Figure 3 Map of study area ...... 23 Figure 4 Reason for not taking credit from formal institution ...... 38 Figure 5 Major livelihood sources of the household ...... 39 Figure 6 Annual pollinating dependent crops grown in the study area...... 40 Figure 7 Perennial pollinating crops in the study area ...... 40

LIST OF APPENDIX

Appendix Table Page

Appendix table 1 Test of multicollinearity ...... 63 Appendix table 2 Test of Heteroscedasticity ...... 63 Appendix table 3 Test of Endogeneity ...... 64 Appendix table 4 Correlation coefficient of pollinating crops variables ...... 65 Appendix table 5 Conversion factors used to compute tropical livestock units ...... 66 Appendix table 6 Perennial ownership of farmer respondents ...... 66 Appendix table 7 Asset ownership of farm respondent households ...... 67 Appendix table 8 Research questionnaire ...... 68 Appendix table 9 Questions for the focus group discussions (FGD) ...... 82 Appendix table 10 Interview questions for key informant...... 83

LIST OF ABBREVIATIONS AND ACRONYMS

CAPI Computer Assisted Personal Interviewers CSPro Census and Survey Processing Software Das development agents FAO Food and agricultural organization of united nation FGD Focus group discussion GDP Gross Domestic Growth GPS Global positioning system HH House hold icipe International centre of insect physiology and ecology KM Kilometer MFI Micro fiancé institution SPSS Statistical Package for Social Scientists TLU Tropical Livestock Unit YESH Young Entrepreneurs in Silk and Honey

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ABSTRACT

This study analyzes the impact of pollinator habitats and managed bee pollination on yields of pollinator dependent crops in North West of Amhara region, Ethiopia. The data sources for this study include farm household surveys, focus group discussions and key informant interviews. The data was collected from 248 randomly selected pollinating crop producers selected from Ankesha, Dandila, Machakel and North Mecha woredas. Multiple linear regression model was used to analyze the impact of pollinator habitats and managed bees on pollinating crops yield. The results revealed that there is significant and negative correlation between value of pollinating crop and plot distance to forest, proxy to pollinator habitats. Value of pollinating crops reduce as the crop plots are far away from forest resources; this is probably because insect pollinators, especially widely pollinators, may not be effective if the distance of crop plots from forest is long. On the other hand, there is a significant and positive relationship between value of pollinating crops and number of managed bee colonies in a village. Other covariates that significantly influence value of pollinating crops include: distance of crop plots to grassland, household head sex, household head education status, household size, access to extension service, access to crop production training, crop rotation and location dummies. This study recommend on the importance of designing strategies to improve productivity of pollinating crops through enhancing pollinators, managed bees and farmers access to insect pollination service and beekeeping information at grassroots level.

Keywords: pollinator dependent crops; managed bee; pollinator habitats; value of crop

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1. INTRODUCTION

1.1. Background of the Study

Agriculture is the backbone of Ethiopian economy, which accounts about 41.4% for Gross domestic product (GDP) of the nation and 83.9% for export earnings and hold about 80% of the country’s labor force employment opportunity (Matous et al., 2013).

But, due to poor performance, crop failures due to inadequate pollination, lack of adequate number of pollinators as a result of decline in pollinator populations and diversity due to loss of habitat, land use changes, and unselective use of agricultural chemicals and pesticides the sector has not been able to feed the nation (Potts et al., 2010). The solution for these serious problems must be focused on high potential areas of agricultural sector and making them more productive.

Therefore, agricultural sector needs improved agricultural technology which can enhance the productivity of the sector and reduce poverty (Solomon Asfaw et al., 2010). So, pollinator habitats conservation and beekeeping are most important agricultural subsectors; those enable to play a significant role in the food security of the country through pollination services of major cultivated crops (Melaku Girma et al., 2008).

Pollinator insects provide an ecosystem service which enables plant reproduction, and food production called pollination (Kremen and Chaplin-Kramer, 2007). Many farmers in Ethiopia spend in fertilizer, pest control and composting, crop rotation and on other managing activities (Abebaw Belay, 2016; Asferachew Abate and Tadesse Amera, 2008). However, they never consider necessities of pollination for their crop productivity comparable to any other inputs.

These also encourage a reduction of both the number and Species of natural insect pollinators of crops and wild plants (Vanbergen, 2013). Globally, crop production benefits 35% from pollinator insects and 90% of all wild plants (Klein et al., 2007).

The contribution of insect pollination to economies is also highly important with the value of € 153 billion annually representing 9.5% of the value of world’s agricultural production (Gallai et al., 2009).

More recently, given that pollinator-dependent crops rely on insect pollination to varying degrees, it was estimated that 5–8 percent of current global crop production, with an annual market value of $235 billion–$577 billion worldwide, is directly attributable to insect pollination (Potts et al., 2016).

The ecological function of managed honey bees has even a higher economic importance than the direct beekeeping products. Bees can benefit 250-300 folds through pollinating particularly pulse seeds and vegetables in raising the production higher than the direct products honey and wax. Bees are important because of their ability to pollinate (Qaiser et al., 2013). If insect pollinate, Protein content in bean increased from about 17-23% to 19- 25% and the sunflower oil increased by 35-45%. One in every three bites of food we eat is a result of pollination of plants in which bees play a very important role.

From the six known types of pollination agents (insects, birds, wind, gravity, water and mammals) insects are by far the most important in pollination. Insect pollinated crops produce a large global share of micronutrients for human consumption (Chaplin-Kramer et al., 2014). And also pollinators contribute greatly to biodiversity conservation by pollinating the majority of Earth’s wild plant species (Ollerton et al., 2011). Recently, Garibaldi et al. (2013) found positive associations of fruit set with wild-insect visits to ﬂowers in 41 crop systems worldwide.

Pollinator habitats has been demonstrated to offer a wide range of benefits to farmers including the positive effect on their livelihoods through increasing crop yield and increased food security (Akinnifesi et al., 2010; Garrity et al., 2010). It is used as native for insect pollinators which provide a valuable pollination service. Carvalheiro et al. (2010) have shown the importance of natural or semi natural habitats in sustaining pollinator populations or pollination services close to fruit crops. Arthur et al. (2010); Watson et al. (2011), suggest that natural or semi natural habitats are important sources of pollinators, probably because they provide “partial habitats” such as complementary mating, foraging, and nesting sites for pollinators(Chaplin-Kramer et al., 2014; Smith et al., 2015).

Despite pollinators’ uses, nowadays, the natural habitat of the pollinators is disturbed for many reasons and the vegetation cover is declining worldwide (Klein et al., 2007).

The loss of habitats among pollinators has raised questions about whether the pollination services they provide are at risk (Garibaldi et al., 2011). Natural and human activities are causes for decline of pollinators in terms of abundance and species richness (Gill et al,. 2016). As a result of these problems, pollination becomes inadequate.

Therefore, diversified fields and the presence of high-quality habitats, would expect wild pollinators to be abundant and pollen limitation to be low in these sites (Garibaldi, 2013; Kennedy, 2013). Higher species richness of pollinators and visitation rates to crops, which leads to correspondingly higher fruit set and stability in fruit set, are often associated with proximity to undisturbed or semi-natural florally diverse vegetation (Garibaldi et al., 2011; Ricketts et al., 2008).

Therefore, it is vital to explore, if the local community can support the expansion of pollinator-dependent crops, as well as pollinators with conserving their habitats especially in developing countries like Ethiopia. This research is intended to investigate the impact of pollinator habitat and managed bee pollination on yields of pollination dependent crops.

1.2. Statement of the problem

Global agriculture has become increasingly pollinator-dependent with an unequal increase in the area cultivated with pollinator-dependent crops and this is obvious in the developing than in the developed world (Aizen et al., 2009).

Managed bee pollination of flowering plants is a process of major importance in terrestrial environments, and it provides vital ecosystem services for human well-being such as crop production (Garibaldi et al., 2011; Garibaldi et al., 2014).

Managed bees and flowering plants have mutual relationships. Nectar and pollen are food rewards for pollinators (Arenas and Farina, 2012; Siham Bezzi et al., 2010). Managed bees increase the yields, such as the number of pods, seeds per pod, seed weights per plant, and seed germination, fruit and seed sets (Rianti et a1., 2010). Seed set is mainly determined by the amount of pollen grains deposited on the stigma of flowers during the receptive period (Pechan, 2017). Wild pollinators are also efficient, like managed bee, in transferring pollen for some crops under certain conditions (Howlett, 2012) and/or carry pollen further distances (Rader et al., 2011). It has been suggested that this long-distance pollen transfer could have important genetic consequences for wild plants (Jha and Dick, 2010). 3

But now a day, population of pollinators is decline by different drivers like loss of habitat that supports host plants (Scheper, 2014) and nesting sites (Winfree et al., 2010). As a result, Pollen limitation in pollinator-dependent crops has been found to hinder the expected increase in yield and to decrease temporal stability of global production (Garibaldi et al, 2011).

The proximity and area of natural habitats are often associated with higher crop flower visitation and pollinators diversity (Kennedy et al., 2013; Winfree et al., 2011). Habitat requirements is a pillar for pollinators (Jonsson, 2015), little is known about how habitat availability affects crop pollination services from pollinators (Orford et al., 2015).

Generally, habitats, pollinators and flowering plants are interdependent each other. There is no previous research which shows the impact of pollinator habitats and managed bee pollination on yield of pollinator dependent crops through distance in Ethiopia. Thus, in this research, I want to study impact of pollinator habitats since many wild pollinators live in and managed bee pollination on crop productivity’s of pollinator dependent crops.

1.3 Objectives of the Study

1.3.1. General objective

The general objective of the study was evaluating the impact of pollinator habitats and managed bee pollination on Yields of pollinator dependent crops: Evidence from North West Ethiopia.

1.3.2. Specific objectives

 To identify the farming system of pollinator dependent crops in the study area;  To explore significance of pollinator habitats on yields of pollinator dependent crops in the study area;  To explore significance of managed bee on yields of pollinator dependent crops in the study area and  To identify the major factors that affect pollinating crop productivity in the study area.

1.4. Research questions

In order to analyze the impact of pollinator habitats and managed bee pollination on yields of pollinator dependent crops, the study attempted to answer the following basic research questions.

1. Do pollinator habitats have a significant effects on yields of pollination dependents crops in the study area? 2. Does managed bees pollination boost yields of pollinator dependent crops in the study area? 3. What are the major factors that affect the productivity of pollinating crops in the study area?

1.5. Significance of the Study

Now a days, rural farmers in developing countries faces different and difficult challenges even they knows in some extent the advantages of managed bee and pollinators habitats . The study will benefit to give due attention for pollination in order to achieve the new millennium development goals of poverty reduction through increasing productivities in Ethiopia.

Moreover, it is believed to be an indicator to YESH project, who intervene in west and east Gojam as well as Awi zone sites, to redesign its intervention so as to achieve its primary goal. In addition, the finding could provide a set of lessons for policy makers, other development agencies for future project design in similar agricultural interventions. The study will also use as a reference for further research conducting on similar topics and related issues.

1.6. Scope and Limitation of the Study

Analyzing all issues and all cereal and pulse crops in all zones of Ethiopia will make the study fruitful since managed bee and pollinator habitats has different advantages on pulse and cereals crops also are spread all over the country. But, in order to manage, the study was restricted in west, eastern Gojam and Awi zones from these three zones, the study covered only four districts to analyze impact of pollinator habitats and managed bee pollination on yields of pollination dependent crops.

1.7. Organization of the thesis

This thesis has five chapters. The second chapter reviews the theoretical and empirical literatures. The third chapter consists of methodology which includes the description of the study area, sample size and sampling technique, method of data collection and sources of data, data analyses and interpretation. The fourth chapter presents the empirical result and discussion of both descriptive and econometric results. The last chapter closes the thesis by concluding the main finding and suggesting some recommendations.

2. LITERATURE REVIEW

This part of the thesis discloses literatures reviewed in relation with pollination and beekeeping technology and productivity of crops, definitions, and measurement. After that, it address the major theoretical perspectives on managed bee, giving due emphasis on linkages and role of managed bee and pollinator habitats pollination technology to crop productivity. Finally, synthesis of empirical studies undertaken on the issue was also highlighted in this chapter.

2.1. Theoretical Framework

2.1.1. Definitions and concepts

Beekeeping is an applied science of raising honeybees for man’s economic benefits. According to Qaiser et al., 2013), Beekeeping or apiculture is the maintenance of honey bee colonies, commonly in hives, by humans. As well as According to King et al., (2009) Beekeeping is a sustainable form of agriculture that is beneficial to the environment and provides economic reasons for the preservation of native habitats and potentially increases yield from food and forage crops.

Gardebroek and Jony Girma (2015), stated that Beekeeping offers multiple potential benefits to the rural poor such as increased household income streams, (,it has nutritional and medicinal products) for sale or home use. As well as it can improve pollination services essential for increased crop yields (Klein et al., 2007).

Farm forestry: Farm forestry is the growing of trees which yield small timber, firewood, green manure, oil seeds, fruits and other non-timber forest products such as honey and beeswax, so that the farmer either becomes self-sufficient or obtains supplementary income by selling any excess quantity of forest products after reserving certain quantities for his own use. It has been developed as a strategy to alleviate the widespread loss of trees and forest cover in the developing world.

Farm forestry aims to encourage individual farmers to grow trees on their own land to meet their household needs through the direct production of fuel wood, fodder, poles.

Forest is one of the main sources of pollination services to the crops, but due to continued disturbance and intensive farming, pollination service provision is threatened (Raghubanshi and Tripathi, 2009).

Agroforestry is defined as “a collective name for land use systems and practices where woody perennials are deliberately integrated with crops and/or animals on the same land management unit (Mosquera-Losada et al., 2009)

Vegetation is the total plant life or cover in an area; also used as a general term for plant life; the assemblage of plant species in a given area (Rome, 2015).

Pollination; is a process by which the transfer of pollen grain from male part to female part or the transfer of pollen grain from anther to the stigma of the same flower. Pollination is one of the economically significant values of beekeeping (Klatt et al., 2014). It is common, especially for the poorest people of the world that live in or close to forests, to utilize many forest products. In agriculture, pollination is an important input of crop production, comparable to any other input such as fertilizer, labor or pesticides (Winfree et al., 2011).

Pollen: the male germ of flowering plants. Pollen is a fine powder is the sperm that flowers use to reproduce.it is produced by flowering plants as a part of their reproduction process (Twell, 2011).

2.2. Resource base and status of Beekeeping in Ethiopia

In Ethiopia traditional Beekeeping practice is one of the oldest agricultural activities, and about 99 % of beekeeping that farmers practice in Ethiopia is traditional (Tadele Alemu et al., 2014). In Africa, Ethiopia stands in the first place in terms of the number of beehives occupied by honeybee colonies. The annual production of honey in Ethiopia amounts to 30,000 tones, equivalent to about one–third of the total honey production in Africa (Legesse Gemechis, 2014). With this amount, Ethiopia is the largest honey producing country in Africa, and world–wide, it stands in the tenth place. Ethiopia is also one of the 5th biggest beeswax exporters to the world market (Bogdanov, 2009).

Farmers cultivate agricultural and horticultural crops; grow suitable forest trees which flower over long periods in a year to reduce migration of honeybees (Hanula et al., 2011).

This indicates the interdependence of beekeeping, conservation of biodiversity and agricultural production through pollination of wild and cultivated plants. But, deforestation, lack of knowledge, shortage of trained manpower, shortage of improved beekeeping equipment, overzealous use of herbicides and pesticides, inadequate research works and poor extension services are the major constraints on the beekeeping sector in Ethiopia (Yirga Gidey and Kibrom Ftwi, 2010.)

2.3. Role of Beekeeping in Agriculture

Beekeeping practice is deeply rooted within the Ethiopian farming community. Managed bees also play a crucial role as important and necessary pollinators of many plants and have demonstrated potential for increasing food crops, horticultural crops, and seed production (klein et al., 2007). In terms of nutritional and economic benefit to people, the role of bees in crop pollination is even more important than their role as producers of honey and other products (Partap, 2011).

2.4. Role of managed Bees Pollination in crop productivity

Bees are successful and effective pollinating agent for a large range of crops. It is usual for beekeepers to receive no payment for pollination services provided to growers of commercial crops. Alfalfa seed is a bee-pollinated crop with an annual value of $109 million (Abrol, 2012). This estimate was based on the value of the supply shock across all pollination–benefited crops arising from the hypothetical exclusion of all pollinators (of which bees are the most significant). Much of this valuation would be attributable to the activities of ‘feral’ honeybees and to incidental pollination.

Honey bees are the most important contributor of modern agriculture through pollination services that they provide. Meixner (2010) stated that 52 of the 115 leading global food commodities are depend on honey bee pollination for either fruit or seed set. This publication is probably the most widely referenced source of pollination recommendations in the world at present.

According to Hurst (2016), bees contribute to rural development which leads to secure and sustainable livelihoods. Bee pollination is the most effective and the cheapest method of increasing yield. Like soil, water and nutrients, pollination is also a limiting factor in crop productivity. Pollination plays a crucial role for the increasing agricultural productivity.

Farmers use better plant growing techniques, like better quality seed and planting material, and provide all agronomic inputs, including–good irrigation, use of organic and inorganic fertilizers and biocides. But if there is no pollination, no fruit or seed made. Owing to environmental degradation as well as poor pollination, the quantity and quality of many life-saving crops are declining significantly (Partap, 2004). Managed Bee services pollination increased agricultural productivity pollination, soil conservation natural pollination environment and biodiversity conservation increased income and food security improved livelihood (Tscharntke et al., 2012).

At the present time the problem of crop pollination needs due attention at this early stage, since the decline of the pollinators goes on in step with the prevailing rapid habitat and vegetation destruction. Pollination is one of the economically significant values of beekeeping. It is common, especially for the poorest people of the world that live in or close to forests, to utilize many forest products when conservation limits people’s possibilities to use these products, the legitimacy of conservation is questioned, and the issue of alternative sources of livelihood is raised (FAO, 2003).

2.5. Relative advantages of beekeeping

Beekeeping is a sustainable form of agriculture which is useful for providing preservation of native habitats and increases yield of food and forage crops (King et al., 2009). Beekeeping has various relative advantages and some of them are as follows.

A) Bees are adapted to wide range of environment. They can survive at altitude below 400 mas where a cattle production may be severely constrained due to tsetse or other reasons (Lelenguyah, 2013).

B) Small holders and landless peasants can practice beekeeping. The hive occupies very little space and bees can collect nectar and pollen from anywhere they can get (Teklu Gebretsadik, 2016).

C) Beekeeping can be run integrally with other agricultural activities. Man cannot harvest and utilize nectar and pollen in the absence of bees.

D) Globally, the honeybee provides pollination service. This is a vital activity in the crops and fruits production process. So, beekeeping plays significant role to the agricultural economy at large (potts et al., 2010). 10

2.6. Environmental role of Beekeeping

Bees provide numerous benefits to the natural environment and have a critical role in its sustainability. Their role is not readily recognized, even though bees are needed for the pollination of many cultivated crops and for maintaining biodiversity of non-cultivated areas (Bradbear, 2009). If we are cutting down forest to grow oranges, you still need to leave other trees for the bees that will pollinate the oranges.

There is a mutualistic relationship between bees and plants, in which flowering plants provide food for bees; and bees provide pollination services for the plants. Bees account for 75 % of the total pollination requirements (Kremen, 2004).

Honeybees play an unrecognized role in maintaining the vegetation cover and biodiversity by pollinating plants (Hurst, 2016).

Debissa Lemessa (2006), showed that the reason behind this is ,bees are probably the best natural pollinating agents for a wide variety of native trees, shrubs and grasses, and their role in preserving and maintaining biodiversity cannot be over-emphasized Beekeepers are quite aware of preserving biodiversity and bee fodder plants in a sustainable way.

The contribution of beekeeping to the conservation of biodiversity and agriculture, through pollination of wild and cultivated plants, is very high. Honeybees also enhance gene flow for the natural breeding of plant populations. Thus, a better understanding of tree pollen production would contribute to the generation of valuable information about bee-tree dynamics and forest farming options in beekeeping (Quezada, 2018).

2.7. Pollinating Insects in Agricultural Crops

Wild and managed flower-visiting insects pollinate crops. Addition of honey bee hives can drastically increase the number of pollinators in a flowering crop field, and they are therefore often used to increase crop pollination. Wild insects provide an added pollination benefit to crop pollination, irrespective of honey bee densities (Garibaldi et al., 2013; Mallinger & Gratton, 2015). Depending on the crop flower traits, the community of flower-visiting insects and the effectiveness of different pollinators vary.

Wild pollinators other than honey bees recently have been recognized for their role in increasing and stabilizing crop-pollination services (Garibaldi et al., 2011; Garibaldi et al., 2013). Wild pollinators are known to improve seed set, quality, shelf life, and commercial value of a variety of crops (Hoehn et al., 2008; Winfree et al., 2011).

Insect pollination of agricultural crops is provided by both managed and feral pollinators. Wild and managed pollinators have complementary relationship and contribute to efficient pollination service worldwide. Wild and managed flower-visiting insects pollinate crops. Managed bees are effective pollinators of many crops (klein et al., 2007).

About 85%of all flowering plants are pollinated by animals (Ollerton et al., 2011). And 35% of global crop production depends on pollinators (Klien et al., 2007). Pollinators visit flowers for many reasons, including feeding, pollen collection, and warmth. The different pollinating insects play important role in enhancing agricultural productivity (Fleming et al., 2009).

Klein et al. (2007) indicated that over 75% of the major world crops and 80% of all flowering plant species rely on insect pollinators. Animal-pollinated crops which make up most of the world’s food supply, 15% are pollinated by domestic bees, while at least 80% are pollinated by wild bee species and other wildlife (Kluser et al., 2010). Approximately 73% of the world’s cultivated crops, such as cashews, squash, mangoes, cocoa, cranberries and blueberries, are pollinated by some variety of bees, 19% by flies, 6.5% by bats, 5% by wasps, 5% by beetles, 4% by birds, and 4% by butterflies and moths (Mattu, 2014).

2.8. Protecting Pollinators and Their Habitat

According to Rands and Whitney (2011), most of the time pollinator habitats are floral source accessibility to numerous pollinators. Pollinators interchange between habitats in the land for many assets (Mandelik et al., 2012).

But as central-placed foragers, they have limited foraging ranges and nesting habitat must be located within range of crops that require pollination (Ricketts et al., 2008).

Pollinator habitats should also include a diversity of appropriate nesting areas, especially because nesting requirements vary greatly based on wild bee natural histories.

Maintaining diversity of season-long floral resources in these habitats is essential to support the diversity of bees (Williams et al., 2015).

Additionally, sustainable agro ecosystems are generally supported by a diverse pollinator community, thus species-specific resources must suit the requirements of multiple species found in that ecosystem (Winfree et al., 2011).

Natural habitat areas near or within farms provide two essential things to support pollinators: sources of food (nectar, pollen, and host plants) for pollinators and their larvae, and equally important, secure nesting sites (carvalheiro et al., 2010). Farmers and gardeners can encourage wild insects and other pollinators by maintaining, and creating spaces of natural habitat (Senapathi et al., 2017).

Pollinators, through facilitating plant reproduction, thus play a crucial role in the maintenance of ecosystem services. Pollination requires pollinating agents which themselves require resources for nesting, feeding and reproduction in the form of vegetation, prey, and certain habitat conditions, as well as the application of pollinator- friendly land-use management practices to ensuring their survival (Abrol, 2012).

Table 1 Some crop varieties and their pollination agents

Common Scientific name Pollinators Product of Insect name pollination dependency (%) Tomato Solsnumlycoprsicum Bumblebee, solitary bee Fruit 10-50% Sunflower Helianthusannuus Honeybee, bumblebee, solitary Fruit ≥90℅ Soybean Glycine Max honeybee, bumblebee, solitary bee Seed 40-90℅ Sesame Sesamumindicum Solitary bee, wasps, flies Fruit 40-90℅ Papaya Carica papa Honeybee, trips, moths, butterflies Fruit ≤10℅ Onion Alliumcepa honeybee solitary bee Seed 90–100% Mango Mangiferaindica Honeybees stingle bees Fruit 80–100% flies, Antes, wasps Cotton Daucuscerota Bumble bees,solitary bees Seed ≤10℅ ,honeybee Orange Citrus spp. honeybee ( Apis mellifera), Fruit 10-30% Rock bee, Golden wasp, Oriental wasp , Red pumpkin beetle, Housefly Cucumber Cucumis sativus honeybee ( Apis mellifera), Fruit 50-90% ,Lady beetle, Red pumpkin beetle Coffee Coffea Arabica honeybee ( Apis mellifera) Seed 20-40% Apple Malus domestica honeybee ( Apis mellifera), Fruit 80-100% Source; Klein et al. (2007); Freitas (2005)

2.9. Economic and ecological Consequences of Pollinators declines

Global agriculture has become increasingly pollinator-dependent with a disproportionate increase in the area cultivated with pollinator-dependent crops which is more obvious in the developing than in the developed world (Aizen et al., 2009). The decline in pollinators’ leads to pollen limitation in pollinator-dependent crops has been found to hinder the expected increase in yield and to decrease temporal stability of global production (Garibaldi et al., 2011).

Many human societies that directly rely on pollination services for local food supply and income generation live in geographical regions, where the knowledge about pollinators and the pollination a system is limited (Archer et al., 2014).

Therefore, it is vital to explore if the local pollinator community can support the expansion of pollinator-dependent crops, especially in developing countries.

The decline in both managed and wild insect pollinator’s population is often linked to one or a combination of the following stressors: land-use change and intensification, pesticide application, climate change, the spread of pests and pathogens and introduced alien species (Potts et al., 2010; Vanbergen et al., 2013). Pollinators higher species richness and visitation rates to crops, which leads to correspondingly higher fruit set and stability in fruit set, are often associated with proximity to uninterrupted or semi-natural florally diverse vegetation (Garibaldi et al., 2011; Ricketts et al., 2008).

In countries where large portion of the economy is agriculture, internationally, declines in pollinator populations and species diversity more broadly have raised concerns regarding potential risks to global food security and economic development (Kluser and Peduzzi, 2007). Pollinator declines present additional risks to ecosystem stability and loss of biodiversity, not only of the pollinator species themselves but also the plants they pollinate from an ecological perspective (Biesmeijer et al., 2006).

The potential adverse effects of pollinator declines include direct economic losses incurred by reduced crop yields as well as broader impacts on agricultural activity as a consequence of lower productivity in the ecosystems which sustain it. Flowering plants require pollination to produce seed or fruit. Some plants are depending on wind-pollination and others are self-pollinated, but many plant species require animal-mediated cross- pollination. At the global level, 75 percent of primary crop species and 35 percent of crop production rely on some level of animal pollination (Klein et al., 2007). According to the estimation of Gallai et al. (2009) the value of this pollination service is to be $153-200 billion.

2.10. Foraging Efficiency of pollinators and Their Distance

From the Crop pollinator insects, Honeybees are capable of foraging at an enough honey and pollen stores to sustain it. Substantial distance from bee’s hives but their efficiency is indirectly proportional to the distance covered.

Generally foraging range is 2.5 km for Apis mellifera, 1.5 km for a cerana, and 3 km for a dorsata and 1 km for a flora. Apismellifera have been practical to forage up to 11.3 km but foragers were focused within 0.6 km of their hives. 15

The productivity of the crops are more when the colonies are kept up to a distance of 0.5 km and decrease to almost half at a distance of 1.0 km and these impacts are even greater in unfortunate period. The number of seeking bees on a crop reduces with increase in the distance from the hive. In general, placing the hive within 0.5 km radius increases the crop pollination (Abrol, 2012).

Engagement of nest mates to the close sites is also greater as this information is more easily connected. Colonies placed near crops gather more pollen and nectar, use less time collecting load of pollen and nectar, the number of flights increases for both types with proximity to the floral source (Carvalheiro et al., 2010).

Figure 1 Conceptual framework of Insect pollination Processed

Processed Fruits and berries

Processed Insect Vegetables pollination Oil seeds

Livestock

Seeds Forages

Livestock Processed

Seeds Livestock

Source: Kevan et al., (2002)

Figure 2 Contribution of insect pollination to agricultural productivity Honey bee

Solitary bee Managed pollination Bumble bee Insect Environmental conservation pollination Flies Soil and conservation maintenance of Wild Wasps & fertility pollinatio biodiversity improvement Birds n butter fly

Mammals’ ants Increase agricultural Source: (Abrol, 2012) productivity & food security

2.11. Some other factors affecting pollinating crop Production

2.11.1. Education and crop production

The education level of household heads is one of the determinant factors of crop production. Research findings have indicated the importance of education in agricultural production.

To achieve agricultural development, the investment in production techniques and technology should be supported by a comparable investment in human, because information and knowledge are prerequisites for household to adopt technology, access input, change ways of doing things and market their produce (Chowa et al., 2013). Formal education develops farmers’ engagement in environmental programs and devices for the sustainability of agriculture (Burton, 2014). Education is also believed to stimulate economic growth by enhancing the productive capability of households.

2.11.2. Sex difference and crop production

In enhancing crop production full participation of men and women is very significant. Women tend to be the major group of actors in the farm labor force involved in production, harvesting and processing activities (Jafry and Sulaiman, 2013). It is also known that the majority of food is produced by women farmers and they are responsible for satisfying the basic needs of the family (Moussa et al., 2011).

Studies have also showed that women farmers are more environmentally mindful compared to men farmers (Burton, 2014). But they challenged by the absence of capital, information and access to markets which prevents them from producing enough to fulfil the basic necessities (Jafry & Sulaiman, 2013).

Traditionally, there were issues that slowed down women’s participation and influence in the agricultural sector. In the ownership of land, women becomes voiceless that land rights belonged only to men ( Wa Githinji et al., 2014).Therefore, there is a need to clarify which obstacles are unique to them since their contribution is vital in the agricultural sector (de Brauw et al., 2008).

Researchers found mixed results regarding to productivity differences between male and female headed households. For example, (de Brauw et al. 2013) in China presented that female headed household achieved the same crop yield as their male counter parts. Similarly, (Catherine et al. 2013) recognized that, if other influencing factors were constant, there was no productivity difference between plots controlled by female and male farmers but there is difference in access to extension services, access to inputs and the quality of the plot that created differences in productivity.

According to (de Brauw et al. 2008) there is no sound reason for women to be less productive than men, if they get equal access to the application of inputs, information and technologies.

2.11.3. Age, family size, landholding size and crop production

Crop production is influenced by other household characteristics such as the farm household age, family size and landholding size. The age of the household head is a proxy variable for the farming experience of farm operators. Since experienced farmers are expected to enhance the productivity of their holdings. Yet, it is not without limit as older farmers lack the required physical strength on the farm and lowers the probability of technology adoption (Burton, 2014; Moussa et al., 2011).

In Ethiopia Land is the most critical natural resource, where the agricultural sector is the engine of the national economy (Aklilu Amsalu et al., 2007). Farm household with larger landholding sizes would have a better farm income if sufficient family labor was available. This leads to an increased demand for children who can work on the land (Hedican, 2006; Kim and Park, 2009). It is difficult to expand the landholding size without corresponding it with an increase in the size of the household.

Even if households with larger families faces challenge to feed family members and have negative effect on the nutritional status of the family (Olayemi, 2012).

2.11.4. Possession of livestock and crop production

For history from livestock, for thousands of years, oxen have been documented as the first draft animals to help human beings, to cultivate land and pull heavy loads (Bryant, 2010).

The ownership of oxen controls the farming ability of farm households because if farmers do not have oxen they would be obliged to rent out their land to other farmers (Ali and Khan, 2013). This diminishes crop production and income of the household as the yield is shared with oxen owners. Oxen owner becomes benefited by cultivating and sowing their land at the right time. This has a positive impact on the productivity of land. Furthermore, oxen might also be rented out on a daily payment basis to till the land for other households.

2.11.5. Extension services

The main job of extension agents is to support and encourage farmers to enhance crop productivity (Adesoji, 2009). They are responsible for translating the findings of the research institutes to the farmers and sending the agricultural challenges of farmers back to the research institutes (Ajani and Onwubuya, 2013). Farmers also have the opportunity to influence the research agenda for the research institutes to focus on relevant outputs (Kibwika et al., 2009). Hence, the extension agents attempt to improve the livelihood of farmers by transferring research based knowledge to the agricultural sector (Rivera, 2011).Therefore; extension services has a significant impact on the crop production of the farm households.

3. RESEARCH METHODOLOGY

This study focuses on the analysis of yield impacts of managed bee pollination and pollinator habitats in YESH project site of Ethiopia. Thus, this part of the paper is about the methodological parts of the study that would be employed to achieve relevant objectives of the study. More specifically, it provides the area where the study was conduct, a clear picture of data type and sources, method of data collection, sampling techniques, sample size determination, econometric method of data analysis.

3.1. Description of the study areas

The study was conducted in East Gojjam, West Gojjam and Awi Administrative Zones of Amhara National Regional State. From the three administrative zones, 4 woredas have been selected. From West Gojjam North mecha, from East Gojjam Machakel and from Awi zone Dangila and Ankesha woreda were selected.

North Mecha is one of the fifteen woredas in west Gojam which is 30 km away from south-west of Bahir Dar town. It is bordered on the south by Sekela, on the southwest by Agew Awi Zone, on the west by the Lesser Abay River which separates it from South Achefer and North Achefer, on the northeast by Bahir Dar Zuria, and on the east by Yilmana Densa. Towns in Mecha include Merawi and Wetet Abay. Merawi is the administrative town of the woreda. Based on the information of the wereda agricultural office this woreda has a total population of 282,902 of whom 138,643 are men and 144,259 women as well as 28,019 are urban inhabitants and 254883 are rural inhabitants. 6970 hectare of the woreda is covered by forest and there are 14,492 bee colonies in the woreda. The majority of the inhabitants practiced Ethiopian Orthodox Christianity, with 98.91% % reporting that as their religion. Mixed farming system is practiced in all parts of the woreda and by each of the households in the community. In the crop sub-sector, the main crops grown include maize, teff, finger millet, wheat, chickpea, beans, Niger seed and cabbage. In the livestock subsector, cattle’s are dominant and large numbers of poultry, sheep and goats are also kept. Oxen, cows, heifers, bulls, calves, chickens, goats and sheep are found in numbers in most households. Therefore, Livelihood depends on a large extent on agricultural production and trading.

Dangila is one of the seven Woreda in Awi zone with an area 772.3 square kilometer. Its border linked in East with Mecha Woreda (West Gojjam), in West direction with Jawi Woreda, in south with Fageta Lekoma (Adis kidam) Woreda and to the Northeast direction with Achefer wereda (West Gojjam). The capital city of Dangila woreda is Dangila town and located 38 kms from Awi zone town Enjebara, 78kms from Amhara region city Bahir Dar and 475kms from Addis Ababa, the capital city of Ethiopia. The Woreda has 27 rural kebele2 administrative and six-urban kebele administrative. The Woreda has largely Orthodox Tewahedo Christian believers’ residential area and small numbers of Muslim followers. The farming system in the Dangila Woreda is characterized by mixed farming. The agro-climatic condition of the Woreda favorable for teff, maize, millet, potato, linseed, Niger seed (Nug), chick pea, pea, bean, wheat and barley are the dominant crops frequently grown in the Woreda. Livestock play a significant role in the mixed farming system of the area. Their main contribution is in providing draft power, guarantee, cash generation, food (example milk), and as a wealth status (symbol). The information of woredas agricultural office shows that there are 158,668 total population lives in this woreda. Out of these, about 80,235 are male and 78,453are female as well as 27,001 are urban inhabitants and 131,667 are rural inhabitants. 3252 hectare of the woreda is covered by forest and there are 8610 bee colonies in the woreda.

Ankesha Guagusa is one of the woredas in Awi zone, Amhara Region of Ethiopia. Ankesha Guagusa is bordered on the south by the West Gojjam Zone, on the west by Guangua, on the north by Banja Shekudad, and on the east by Guagusa Shekudad. Towns in Ankasha Guagusa include Agew, Gimjabet and Azena. Based on the Woreda agricultural office, this woreda has a total population of 104,250 of whom 50,252 are males and 53,998 females. Out of the total population 18,899 are urban inhabitants and 85,351 are rural inhabitants. The majority of the inhabitants practiced Ethiopian Orthodox Christianity, with 97.54% reporting that as their religion, and 2.34% of the population said they were Muslim. 2652 hectare of the woreda is covered by forest and there are 6227 bee colonies in the woreda. The farming system in the Ankesha woreda is characterized by mixed farming. The agro-climatic condition of the woreda favorable for teff, maize, millet, potato, Niger seed, chick pea, pea, bean, wheat, faba bean and barley are the dominant crops frequently grown in the woreda.

Machakel is one of the woreda in the Amhara Region of Ethiopia. Part of the East Gojjam Zone, Machakel is bordered on the south by Debre Elias, on the northwest by the West Gojjam Zone, on the east by Sinan, and on the southeast by Guzamn. Towns in Machakel include Amanuel. it is 330 km from addis abeba. The farming system in the machakel woreda is characterized by mixed farming. The agro-climatic condition of the district favorable for wheat, maize, barley teff, potato, chick pea, pea, bean, faba bean and are the dominant crops frequently grown in the district. Based on the information of the woreda agricultural office, there are 146,580 people lives in this woreda of whom 73229 are males and 73351are females. Additionally, 16,762 are urban inhabitants and 129818 are rural inhabitants. The majority of the inhabitants practiced Ethiopian Orthodox Christianity, with 98.87% reporting that as their religion, while 1.1% of the population said they were Muslim. 5383 hectare of the woreda is covered by forest and there are 107, 67 bee colonies in the woreda.

Figure 3 Map of study area; own source 2018

3.2 Methods of Data Collection and Sources

In this study, both qualitative and quantitative data were collected from primary and secondary sources. The primary sources of data are sample farmers selected randomly (woredas) and government officials in the study woredas and kebeles. We used structured questionnaire to collect data from selected households. Similarly, semi structured woreda and kebele level questionnaire was used to collect secondary data from government offices in respective woredas and kebeles. Additional secondary data from official records of kebeles (PAs) and woredas Livestock and Fishery Resource Development office were collected. Qualitative data were collected through focus group discussions with selected households by their knowledge of the village in two kebeles of each woreda.

Standard structured questionnaire was used to collect data on farmer socioeconomic characteristics, assets, farm practices, beekeeping practices, knowledge on pollination. The questionnaire was translated into the local language (Amharic and Agew). In addition, the questionnaire captures GPS coordinates of each plot cultivated by the household to calculate distance of the plots from pollinator habitats such as forest, water sources, grazing land and shrubs to estimate the impact of pollinators on crop productivity. The questionnaire was pretested two times before its use in current survey.

The data was collected using Computer Assisted Personal Interviewers (CAPI) using CSPro application. The household interview was conducted by trained and experienced enumerators working with icipe (YESH project).

The household and plot level data was collected through house to house visit to the sample households by trained enumerators and supervisors. Interview with key informants including development agents (DAs) based in each kebele and woreda Agricultural development experts was also conducted face to face. The interviews were carried out after the preliminary results of the structured questionnaire.

This arrangement helped the researchers to include more questions that needed further explanation. Check list was prepared to collect qualitative data from experienced households through focus group discussion (FGD). It was done in two sample kebeles from each woreda; there were about twelve participants for the discussion.

The households selected for the focus group discussion were not included in the household survey. They were selected with the help of development agents in each kebele. The survey provided plot-level data on inputs and outputs for the crops.

3.3. Sample Size and Sampling Technique

Multi-stage random sampling technique was employed to select sample respondents. In the first stage four woredas with high number of beekeeping were selected among the woredas targeted by the YESH Project from each of the intervention Zones. The woredas were identified for their beekeeping potential by the international centre of insect physiology and ecology (icipe) and its partners for apiculture intervention. Among the potential kebeles 3 kebeles were selected randomly for the survey. Finally, after the survey kebeles were identified, entire list of farmers in each kebeles is prepared. Based on this sample frame 248 households were selected for interview using random sample generator in MS-Excel1. And the following formula was used when the population is greater than 10,000 (Cochran, 1977).

푝푞 푧2 n = 푒2

Where n= the desired sample size

Z= the square of the confidence interval in standard error units (1.89)

P= estimated proportion of success (0.5)

Q= (1-p) or the proportion of failures (0.5) & e= the desired level of precision (0.06)

By using the above formula, 240 households were determined and by assuming non- response rate 5 %, a total of 248 households were selected for the survey.

1 In MS excel random sample generator is a 3 step procedure; (1) create a variable ‘random number’ using the formula =rand (). This generates a random number between 0 and 1 for each population. (2). Cut and paste the values as values under this column to avoid change during each refreshing (3). Sort the data by ascending order using ‘random number’. The top n are your random sample where n is your sample size. 25

Table 2 Number of sampled households from each kebele No Wereda Kebele Total household Number of sample households 1 Dangila Wumberi 715 12 Ligaba 611 10 Afesa 582 9 2 Machakel Kuashiba 844 14 Amanuel zuria 826 14 Girakedamini 772 12 3 North mecha Dile betgile 1485 24 Midir genet 1019 17 Gora gotte 1135 19 4 Ankesha Buyia 5364 88 Manja tenkos 702 11 Baynagushi 1092 18 Total sample households 15147 248 Note: *List of household heads obtained from Agricultural office of the woreda (2018) * Sample size is determined based on the proportion of population size

3.4. Methods of Data Analysis

Descriptive, inferential statistics and econometric methods were used to analyze the data collected from sample households.

3.4.1 Descriptive statistics

The descriptive statistics was used to discuss the socioeconomic characteristics of the sample households. The descriptive analysis was performed using frequencies, means, percentage, standard deviation standard errors as well as maximum and minimum values. And inferential statistics like t-test and correlation tests was also used to test association of some variables.

3.4.2. Econometric Analyses

Econometrics proposed to examine and explain factors that determine the productivity of pollinator dependent crops. The data collected from sample households were entered and cleaned using SPSS 20 and econometrics analysis carried out using STATA 13. The following multiple regression model is specified to assess the relationship between productivity and explanatory variables:

푌 = 훽0 + 훽1푋1 + 훽2푋2 + 훽3푋3 + 훽4푋4 + 훽5푋5 + 훽6푋6 − − − 훽푖푋푖 + ᴜ

푛 푌 = 훽0 + ∑ 훽푖푋푖 + ᴜ 푖=1

Where: β0= an intercept/ constant term

푌 = is the dependent variable which is value of pollinating dependent crops productivity

푋푖= vector of explanatory variables ’discussed below

훽푖= coefficients of the ith independent variables;

U= the error term/ disturbance term

There are a set of variables hypothesized that have an impact on the plot level value of Crop productivity, all the remaining variables are assumed included in the disturbance term. The main hypothesized variables that are included in the model are described as follows.

Dependent Variable

The dependent variable is the value of pollinating crop yield which is defined as the level of output multiplied by market price of the respective crops.

Independent variables

The explanatory variables used in the regression analysis based on previous studies and their descriptions are presented in Table 3.

Table 3 Description of variables and hypothesized relationships

Variables Description of variables Expected sign DFOREST Distance of crop plots to forest (km) - DGRASLAN Distance of crop plots to grassland (km) - DWATER Distance of plots to water + MABCO Number of Managed bee colony in village (number) + AGE Age of the household(years) + EDU Household head education + status(1=literate;0=illiterate) HH SIZE Household size (labours in numbers) + FRMSIZE Farm Size (hectare) + LIVESTO Total number of livestock(TLU) + EXTAD Access to Extension service(1=got + access;0=otherwise) CRDACC Credit utilization to crop production(1=utilize + - credit;0=otherwise) TRANG Access to crop production training (1=got + traning;0=otherwise) INCOM Annual income(ETB) + CROPRO rotation use crops(1=yes;0=otherwise) +

FERTZR Inorganic Fertilizer used(1=yes;0= otherwise) +

Distance of crop plots to forest (DFOREST): This variable is measured as distance (in km) of each crop plot to forest. It is hypothesized that the shorter the distance in km , the better the farm and value of crop yield. Insect pollinators are important for global food production (Chaplin-Kramer et al., 2014). Studies have shown the importance of natural or semi natural habitats in sustaining pollinator populations or pollination services close to fruit crops (Carvalheiro et al., 2010). Other studies have demonstrated a negative impact of the distance from forests on pollination services and value of pollinating crops (Arthur et al., 2010; Watson et al., 2011). Therefore, it is hypothesized that crop plots close to such habitats are expected to be more productive.

Distance of plots to grassland (GRASLAN): It is a continuous variable measured as distance in km which characterized as lands dominated by a range of grass species rather than large shrubs or trees. Grasslands contain higher abundances of potential larval host- plants for many of insect’s pollinators and thus are more likely to contain viable populations of these species.

A lower density of pollinators at larger distances from semi-natural grasslands may lead to fewer visits per flower of pollinating crops and reduced seed set (Knight et al., 2005).therefore, in the study this variable is estimated to have negative impact on value of pollinating crops yield.

Distance of plots to water (DWATER): This variable is a continuous variable measured as distance in KM from water which pollinators like butterflies need for many purposes like drinking and reproduction and shelter. Like all animals managed and wild pollinators need water to survive. When designing farms with wild pollinator conservation, the presence of water near the nesting habitat can significantly increase survivorship and pollination activity. This leads to an increase in value of pollinating crops. Therefore, this variable estimated that positive impact on value of pollinating crops yield.

Managed bee colony (MABCO): it is a continuous explanatory variable which measured in number that kept honey bee colonies, commonly in hives. Beekeepers collect honey, beeswax, propolis, pollen, and royal jelly from hives; bees are also kept to pollinate crops and to produce bees for sale to other beekeepers. Bees play an important role in pollinating pollinator dependent plants, and are the major type of pollinator in many ecosystems that contain flowering plants. It is estimated that one third of the human food supply depends on pollination by insects, birds and bats, most of which is accomplished by bees (Rader et al., 2016). A large number of honey bee colonies improved farmer’s profit. Studies have shown that correlation between value of yield and honey bee population is significant (Ghosh and Jung, 2016).Therefore, managed bee colony is hypothesized positively or negatively related to value of pollinating crops yield depending on the total number of the colony.

Household head age (AGE): Age is the continuous explanatory variable measured in number of years. This variable is a substitution for experience of farm households. It is associated with the learning process of households in handling their overall agricultural practices. If the age of the household head is increases, there is an increase in the overall value of agricultural production in line with (Aubert et al., 2006; Li and Sicular (2013). Therefore, it is hypothesized to influence value of crops yield positively as long as farmers are in the active age range.

Household head education status (EDU): It is a dummy variable which takes 1 if the head is literate and 0 illiterate. Educated households are estimated to have better experience to information that improves agricultural productivity. They are also expected to be innovators in accepting new ways of doing things (Nonvide, 2017). Therefore, this variable expected to have a positive impact on value pollinating crop yield.

Household size (HH SIZE): household size is a continuous variable which indicates that the total number of family members with in a household. Family members of the household are a potential source of labor in the agricultural sector for different activity like weeding, plowing, cultivating and harvesting. Households with many family members will have the chance to diversify their agricultural activities and rent the land of others. As a result, this research is hypothesized that the larger the number of members of the family who are engaged in agricultural activities, is the greater the value of pollinating crop yield from agriculture.

Farm size (FRM SIZE): It is the continuous variable measured in hectare, which measures the wealth status of the household. Land is the most critical natural resource for developing countries like Ethiopia where the agricultural sector is the train of the national economy (Aklilu Amsalu et al., 2007). Farm operators with larger landholding sizes would have a better productivity of pollinating crop. Therefore, it is hypothesized that farmers with grater landholding have greater probability to gain more value of pollinating crops yield.

Livestock ownership (LIVESTO): Usually livestock is considered as an asset that might be used either in the production process or be exchanged for cash for the purchase of inputs (fertilizer, herbicide, etc.) when a need arises. Livestock also provides manure which can enhance soil fertility and traction power for cultivation of crops. Oxen are the main assets in the rural areas of the country.

A household needs at least two oxen to plough a plot. For smooth controlling and timely cultivation of land, a household needs a pair of oxen. Crop production is directly influenced by the ownership of oxen. Therefore, it is hypothesized that ownership of livestock will have positive and significant impacts on value of pollinating crops yield (Chilot Yirga et al., 2015).

Access to Extension service (EXTAD): it is a continuous variable, which shows the frequency of household heads visited by extension workers per year. Extension service enables farmers to identify problems related to farming activity, crop and overall farming activities. Agricultural extension services are the major sources of information for improved productivity. Hence, access to this service is important to crop production. Therefore, it is hypothesized that households who had access to extension service have higher tendency to improve value of yield system and increase value than households who have no access to extension service.

Utilization of credit (CRDACC): it is a dummy variable taking a value of 1 if the farmer had utilized credit and 0 otherwise. It is measured in terms of money borrowed by the household head. Credit helps farm operators to improve the productivity of their land. It gives them access to farm inputs to benefit more from their land. Agricultural credit is described as banking finance for primary production, processing and trade of agricultural products, and the production and distribution of inputs (Aggelopoulos et al., 2011). In the absence of credit arrangements, farmers are forced to use a large proportion of their income to purchase inputs such as fertilizers and pesticides (Pfeiffer et al., 2009). Adeoti (2012), confirm that household with credit utilization are more probable to adopt new technology and improve their value of crop productivity than their counterpart. Therefore, it is expected that credit utilization affect value of crop productivity positively.

Access to training (TRANG): It is a dummy variable and taking a value of 1 for those household heads who have gotten training on crop production activities and 0 otherwise. It is the process of getting information from governmental and non-governmental organization. If the household head has got training, therefore it is hypothesized that the household can improve the value of productivity of his/hers crop (Pierce et al., 2010).

Annual household income (INCOM): It is a continuous explanatory variable measured by the amount of Ethiopian currency which is birr got annually other than pollinating crop. Income of the household is the major determinant for income diversification and food security status. According to Mohammed Adem et al. (2018), if the household head got more income from different sources, that he/she would be improving the crop productivity by assigning the income accordingly his/her needs. Therefore income is hypothesized that has a positive impact on value of pollinating crop productivity.

Crop rotation used (CROPRO): This is measured as dummy variable equals to 1 if the farmer practice crop rotation and 0 otherwise. Crop rotation is an agronomic practice growing different crops on the same plot overtime to maintain soil fertility and reduce infestation of crop pests and diseases (Knox et al., 2011).Therefore; farmers practicing crop rotation are expected to get better value of crop yield.

Fertilizer used (FERTZR): increasing crop production to feed the growing world population requires using new technology and intensifying production and management. Thus, according to (Vanlauwe et al. 2014) the increment of crop productivity is due to the use of inorganic fertilizer because, if soil fertility is not improved, the use of other technologies such as high-yielding varieties will not have a significant impact. Thus proper crop nutrition plays a vital role in maintaining the world’s food supply. Therefore, it is hypothesized that the farmers who use inorganic fertilizer can improve value of pollinating crop productivity than farmers who do not used fertilizer.

4. RESULTS AND DISCUSSION

In this section descriptive statistics and econometric results are discussed.

4.1. Descriptive statistics

In this section, the study presents plot level variables, continuous and categorical variables and their impact on value of crop production of the households.

Table 4 Descriptive statistics of plot level variables

Value of pollinated crop yield Variables Description of variables Mean (km) Correlation value

DFOREST Distance of crop plots to forest 0.50 -0.10

DGRASLAN Distance of crop plots to grassland 0.5 0.12**

DWATER Distance of plots to water 5 0.09 MABCO Number of Managed bee colony in 496 0.13** village Sources: own survey result, 2018

The average walking distance of crop plots to forest was 0.5kilometres. The average walking distance of plots to forest correlates negatively with value of pollinating crop productivity (Table 4).

The average walking distance of crop plots to grassland was 0.5km. The average walking distance of crop plots to grassland correlates positively with value of pollinating crop productivity. The average walking distance of crop plots to water was 5 kilometer. The average walking distance of crop plots to water correlates with value of crop productivity positively (Table 4).

The average number of managed bee colony in a village was 496. Managed bee colony correlates positively with value of pollinating crop productivity showing importance of pollination service (Table 4).

Table 5 Descriptive statistics for household education and sex

Value of pollinated crop yield Variable Category Percent Mean Stand.err T value SEX Male 98.79 360.38 42 0.242

Female 1.21 268 156.27 EDU Literate 41.94 599.88 86.32 5.17***

Illiterate 58.06 185.49 27.43 ***, ** and* Significant at P<0.01, 0.05 & p< 0.1, respectively.

Based on Table 5, about 98.79 % of sample households are male headed households. The educational status of the sample households which include religious, adult and formal education shows that majority of the respondents in the study area were illiterate (58.1 %). Educated farmers are able to synthesize information and easily access to information that leads to adopt yield augmenting technologies. The result of t- test revealed that there is statistically significant mean difference between the educational status of the household heads and their value of crop productivity at less than 1% significant level.

The sample household’s age distribution is an important determinant of crop productivity. Age can serve as proxy to experience. Also aging means loss of energy and short time horizon which has implication on adoption of technologies that enhance crop productivity. The average age of the household is 47 years with the maximum and minimum age of 74 and 23 years respectively. As the correlation coefficient (r2) indicates that there is negative relationship between age of the household and value of crop productivity.

Based on Table 6 shows the average household size for the surveyed households was 5. In the presence of limited market for labour household with more family have advantage to perform better in agricultural production. The correlation coefficient value shows that there is a positive relationship between household size and value of crop productivity.

Table 6 Descriptive statistics of continuous variables

Value of pollinated crop yield Variables Description of variables Mean Correlation value AGE Household head age 47 -0.10 LIVESTO Livestock ownership 8 0.11* FRMSIZE Farm size 1.63 -0.02 INCOM Annual income 16,329 0.0 4 HH SIZE Household size 5 0.09

***, ** and* Significant at P<0.01, 0.05 & p<0.1, respectively.

Land is one of the most important factors of crop production. In the study area, the average landholding size is 1.63 hectare. The correlation coefficient value shows that there is a negative relationship between farm size and value of crop productivity (Table 6).

Livestock ownership serves as source of income, food, manure and draft or traction power for cultivation of land in the study area. Livestock holding is assessing by calculating Tropical livestock unit TLU (see Appendix 5). Households with larger livestock holding have better access to draft power than those with less. Sample households own on average 8 TLU. The minimum and maximum tropical livestock unit was 0.05 and 22.86 TLU respectively. There is a significant and positive correlation between livestock ownership and value of crop productivity.

The average annual income of the household was 16,329 birr from different sources. The sample households got on average 2837 birr from non-farm activity, 4535 birr from tree crops, 2153 birr from honey production, and 5300 birr from non-pollinated crop production and 1504 birr from livestock production. The minimum and maximum annual household income was 140 and 171,350 birr respectively. Annual household income correlates positively with value of crop productivity.

4.2. Farm input utilization and access to services

Farm input utilization and access to different services have important contribution in increasing value of crop production and productivity. The most important services and farm inputs that expected to promote value of pollinating crops productivity in the study area include access to credit, access to extension service, fertilizer, crop rotation and training.

Table 7 Farm input, agronomic practice and access to services

Value of pollinating crop yield Variables Category Percent Mean Stand.err T value

Access to extension service Have access 78.63 326.39 44.01 1.52

Haven’t access 21.37 480.23 106.81

Used crop rotation Used 81.45 382.33 47.99 1.16

Not used 18.55 258.02 74.86

Access to training Have access 26.61 627.71 126.22 4.01*** Haven’t access 73.39 261.92 30.60

Utilization of inorganic Yes 23.39 421.16 124.15 0.82 fertilizer No 76.61 340.38 38.94

Utilization of credit Received 38.71 351.80 57.26 0.14

Not received 61.29 363.99 57.45

***, ** and* Significant at P<0.01, 0.05 & p< 0.1, respectively.

In agriculture, extension service is important for flow of information and transfer of knowledge and scientific findings to farmers. As shown in Table 7, about 78.63 % of pollinating crop producers had access to extension advice. The t-test result shows that there is no statistically significant mean difference between had accesses to extension service of respondents and value of pollinating crops productivity.

Crop rotation is used to protect crops from pests, improving the soil structure and soil nutrients. About 81.45 % of pollinating crop producing households had used crop rotation (Table 7). The t-test result shows that there is no statistically significant mean yield difference between plots with and without crop rotation and value of pollinating crop productivity.

Training is the process of getting information from governmental and non-governmental organization about farming activity, finance and credit management, health and gender based information. If the farm household had access to training then the household can improve the value of pollinating crop productivity (Pierce et al., 2010). Most of the households (73.39 %) were not take any kinds of training. The t- test indicates that being training users affect values of pollinating crop productivity significantly at 1 % probability level (Table 7).

One of the most important agricultural practices that used by pollinating crop growers in the study area is fertilizer application. Being non-user of inorganic fertilizer is one of the factors which results in the reduction of agricultural productivity. Out of the total households only 76.61 % are used inorganic fertilizer.

Utilization of credit is a method of improving production and productivity by purchased inputs such as improved seed and fertilizer. Farmers with access to credit can minimize their financial constraints and buy inputs more readily than those with no access to credit. About 38.71 % of farm households had access credit from banks, microfinance institution (MFI), farmer group/cooperatives, relatives and agricultural input dealers. While, the remaining 61.29 % of farm households had no access to input credit due to the following reasons (see Figure 4).

28.29 30 23.68 21.71 25 17.11 20

15 10 6.579 2.638 5

Percent 0

Reasons

Figure 4 Reason for not taking credit from formal institution Source: Own survey, 2018

4.3. Livelihood source, pollinated dependent crops and knowledge on pollination

4.3.1. Livelihood sources

The dominant livelihood sources of the farm households in the study area is farming followed by livestock production. In the study area, out of the total households, 97.6 % are engaged in crop farming, 33.5 % in livestock production and 15.3 % on farm labour activity. Farm households also involved in other livelihood generating opportunities as indicated in Figure5. Based on this Mixed farming is characterizes the farming system of the study area. Since both crop and livestock production activities were undertaken as major occupation in the study area. The dominant farming system in Ethiopia is mixed farming system where intensive multiple crop production is integrated with livestock production. The production system, particularly in crop production, is labor- intensive: that all members of the household participate throughout the year, thus fulfilling both the social and economic status and food needs of the members of the peasant family (Valbuena et al., 2012).

4.84 15.32 2.82 8.47 farming livestock production 8.47 non farm trader weaving

97.58 farm assistant farm labourer 33.47 mining

Figure 5 Major livelihood sources of the household Source: survey result, 2018

4.3.2. Pollinating dependent crops

According to the committee of pollinators in North America (2007), pollinating plants play crucial roles in many natural and agricultural ecosystems, proving food, fiber and shelter for wildlife and human alike. Consumption of fruits and vegetables are associated with decreased risk of chronic diseases (Blank et al., 2008). A reduction in the availability of pollinators and pollinator dependent crops may have consequences that are difficult to value. There are different types of pollinator dependent crops grow in the study area (see Figure 6 & 7). These crops propagate once per year on Meher or Belg season. Out of the total farm households 90 % are sow these crops in Meher. And about 97 % farm households practice sole cropping per plot. In terms of planting about 59 % farm households used broadcasting method of sowing. While, 41 % of farm households practiced row planting. Farm households plow their lands one to eight to suppress weeds of these pollinating crops. As frequency of plowing increase the probability of growing weeds becomes low.

2.8 4 13

1.6

33.9 25

2.4 13.7

faba bean haricot bean lin seed potato vech tomato rape seed chick pea pea lupin niger seed onion pepper

Figure 6 Annual pollinating dependent crops grown in the study area Source: Survey result, 2018

90 85.89 80 70 60 56.05 50

40 30 18.15 20.56 20 11.69 10.08 10.08 2.43 percent 10 0

Prennial crops

Figure 7 Perennial pollinating crops in the study area Source: Survey result, 2018

4.3.3. Farmers knowledge on pollination

In the study area, most of the farm households are aware that bees and other insects prefer flowering crops (Table 8). About 49 % of the respondents have knowledge on that flowering crops benefit from insect visits and also about 43 % of the household wants to increase pollinators in their farm land. About 48 % and 79 % of the respondents are agreed on the importance of bee`s pollination for crop productivity and environment conservation respectively.

On the management of bees, about 25 % of the respondents agreed beekeeping is men’s activity but 75 % of the respondents reported that bee management is undertaking by both men and women.

Table 8 Farmer’s knowledge on insect pollination and bee management

Activity Percent Do bees prefer flowering plants?(1=yes; 0=otherwise) 71 Do you know other insects visit crops during flowering? 74 (1=yes; 0=otherwise) Do you think flowering crops benefit from bees/ other 49 insects? (1=yes; 0=otherwise) Do you like to increase pollinators in your farm? (1=yes; 43 0=otherwise) Does bee`s pollination is important for crop production? 48 (1=yes; 0=otherwise) Does bee`s pollination is useful for environment conservation? 79 (1=yes; 0=otherwise) Do bee management is men activity? (1=men; 0=otherwise) 25 Total observation 248 Source: survey result, 2018

4.4. Cost structure, production and utilization of pollinated crops

4.4.1. Cost structure

The average cost of land rent per hectare was 169 birr per hectare per season with a standard deviation of 619. The minimum and maximum values of land rent in the study area were 0 and 6440 birr respectively. The average payments of the farm households for pesticide and seed are 246 and 68 birr per litter and kilogram respectively. The average payments of labors for weeding, harvesting and threshing are 343, 378 and 400 birr. The average cost of production per hectare was 1601 birr for pollinating crops in the study area (Table 9).

Table 9 production cost of pollination dependent crops

Cost Mean Stand.dev Min Max Cost of land rent birr/ha/season 169 619 0 6440 Cost of pesticide birr/ lit 246 231 30 670 Cost of seed birr/kg 68 120 0 350 Cost of labour birr/day for 343 222 80 550 weeding Harvesting cost 376 211 80 720 Threshing cost 400 - 400 400 Total cost 1601 Source: survey result, 2018

4.4.2. Crop production and utilization

The average annual total pollinated dependent crop production was 274 kilogram per hectare with the standard deviation of 177.12 (Table 10). From the total pollinated crop production on average 30.94 kilogram used for consumption, 120.70 kilogram for sell, 33.16 kilogram retained for seed, 82.10 kilogram for stored and 9.70 kilogram for given out. The minimum and the maximum annual pollinating crop production is 10 and 900 kilogram respectively.

Table 10 Average annual pollinated crop production and Utilization

Production Mean Stand.dv Quantity harvested kg/ha 274.15 177.12 Quantity consumed /kg 30.94 19.15 Quantity sold /kg 120.70 155.87 Quantity retained for seed/ kg 33.16 22.15 Quantity stored /kg 82.10 137.37 Quantity given out /kg 9.70 23.88 Source: Survey result, 2018

4.5. Econometrics Model Results

In this section, the main questions of the study were answered. The impact of pollinator habitats and managed bee pollination on yields of pollinating crops and other factors that can affect the productivity of pollinating crops in the study area are presented and discussed.

4.5.1. Determinants of pollinating crops productivity

In order to identify the impact of pollinator habitats and managed bee pollination as well as others variables on yields of pollinating crops, multiple linear regression model was used. Variables that were hypothesized are; managed bee colony, distance of crop plots to forest, distance of crop plots to grassland, distance of crop plots to water, access to extension service, access to credit service, access to training, livestock holding, sex of the household, educational level of household, total household size, size of land, usage of inorganic fertilizer, crop rotation, income of the household, and age of household. Before run the model some assumption tests were done. There is no multicollinearity tasted with the help of variance inflation factor (VIF) (Appendix 1). The linearity assumption is tested using a two way scatter plots indicated that there is no problem of linearity. The problem of heteroscedasticity or non-equality of the error variance is tested using the Breusch- Pagan / Cook-Weisberg test (Anselin L and Florax R, 2012). There was heteroscedasticity problem in the model since Prob > chi2 = 0.0000 (Appendix 2). Heteroscedasticity problem was solved using the robust command and robust standard errors. The endogeneity test is checked using Hausman endogeneity test (Semadeni et al., 2014). Endogeneity Test displayed that there is no endogeneity problem in the model (F= 1,232, (p= 0.2911) (Appendix 3). And correlation coefficient of independent and dependent variables also made in the study (Appendix 4).

Distance of crop plots to forest (DFOREST): this variable shows that the distance of each crop plots to forest. As shown in 13, distance of plots to forest had significant and negative impact on the value of productivity of pollinator dependent crops at 10 % significance level. The hypothesis was the shorter the distance, the better estimated value of crop production. The negative sign suggests that the value of productivity of pollinating crops declines as the distance from forest to farm increases.

The result indicates that increasing the average distance of the plots from forest leads to a decrease in the estimated value of crop production. As distance increases by 1 kilometer holding others constant the value of productivity of pollinating dependent crops will decrease by 274.914birr.

This is the reason that the pollination service decreases as distance from the forest increases, as a result of the inability of pollinator insects travel long as well as the population becomes decline. Isolation from the forest habitat often has a negative effect on bee and bee diversity which leads the decline on yields. Since the forest can play a major role as a reservoir of bees that pollinate crops (Kraemer and Favi, 2005; Klein, 2009). And also Ricketts et al. (2008) showed that pollinator visitation rate drops to 50% of the maximum at allocation 668 m away from forest. Similarly, Carvalheiro et al. (2010) stated that increasing isolation from forest led to approximately 40% strong declines in productions of mango.

Distance of crop plots to grassland (DGRASLAND): Semi‐natural grassland habitats are characterized by high levels of fine‐scale species richness and have a high conservation value Partel et al., 2007). Proximity to natural and semi-natural grasslands used to increase abundance of pollinators which used to increase value of pollinating crops productivity (Le Feon, 2010; Krewenka et al., 2011). According to Holzschuh et al. (2013) proximity to natural or semi-natural grassland in terms of flower abundance, pollen abundance, nectar diversity and nesting sites, have a positive impact on the pollinators, and especially the bees community. The study revealed that distance of plots to grassland had significant and positive impact on the value of pollinator dependent crops productivity at 5 % probability level. As distance becomes shorten, abundance of pollinators increase which leads to increase value of pollinating crops by 411.777 birr. This result was Similar with Williams et al. (2010) who confirm proximity to florally diverse natural or semi- natural habitats has a positive impact on abundance of pollinators, visitation rate and fruit set of pollinator-dependent crops. And Fruit set of coffee increases with increase the pollination effectiveness through species interactions near to habitats (Albrecht et al. 2012; Brittain et al. 2013).

Number of Managed bee colony (MABCO): This variable shows that the total number of managed bee colony the in a village. Managed Bees as a taxonomic group, are considered to be the most effective pollinators of most crops (Potts et al., 2010). 44

Managed honey bees were able to achieve comparable fruit set levels to apple orchards (Mallinger and Gratton, 2015; Martins et al., 2015). Garibaldi et al. (2013), reported that even under field conditions with sufficient natural pollinators, the addition of honey bees increased the production of pollinating crops.

This study revealed that number of managed bee colony had significant and positive impact on the value of pollinator dependent crops productivity at 10 % significant level. As number of bee colony increase by one, value of pollinating crops productivity increases by 0.30 birr. Similarly, Nicodemo et al. (2009) confirm that the higher the number of visits of honey bees, up to 16 honey bees per flower, the greater was the fruit set, fruit size, and seed set of pumpkin. And also the result is line with Freitas et al. (2016) who stated Cranberry yield was, on average, positively associated with the number of honey bee colony/ha.

Access to extension (EXTAD): As expected, access to extension service had positive impact on productivity. This recommends that pollinating crop farmers got higher value of pollinating crop productivity as more access to extension service. If farmers had access to extension service, the value of pollinating crop productivity increase by 241.945 birr. This variable is significant at 5 % level of significance. This result in line with the study conducted by (Adewuyi, 2002; Awotide, 2004), that extension services enhanced farmers’ productivity in the humid forest and dry savannah agro-ecological zones of Nigeria.

Education status of the household (EDU): Educational level was positively and highly significant at 1 % significance level. This indicates that as the number of years spent in formal education increases, it makes the pollinating crop producers more productive. On average, a household with literate heads tends to increase value of pollinating crop productivity increases by 340.883 birr. This suggests that higher literacy level affect value of pollinating crop productivity positively in the study area. This in line to the findings of Idjesa, (2007) which found that education was a key to enhanced value of crop productivity among households in humid forest, dry savannah and moist savannah agro- ecological zones of Nigeria.

Household size (HHSIZE): According to the result of the regression model household size had positive impact and statistically significant at 10 % significance level.

As labour household size increases by one person, value of pollinating crop productivity increase by 38.222 birr. This result in line with Million Tadesse and Belay Kassa (2004), who stated that the size of economically active family members within a given farming household affects the crop production activities positively. As well as Olumba (2014), found that most rural farmers look at large household size as a good and economical way of maximizing farm returns by using family labour.

Crop rotation (CROPRO): This variable had positive impact on value of pollinating crops productivity and significance at 10 % level of significance. The crop rotation variable got a coefficient of 165.087 which suggests that if the farm household was rotate crops on a plot of land, then value of pollinating crop productivity increase with163.86 birr. This result in line with Hag Hamad Abdelaziz et al. (2010) who support that crop rotation had a positive impact on groundnuts production.

Training access of the household (TRANG): Farmers can gain new knowledge through participation in training to increase their production performance through the use of improved agricultural technology. The regression result indicates that access to training was positive and statistically significant at 5 % level of significance. This implies that if farm households got training then the value of pollinating crops productivity increased by 263.378 birr than households who did not have. This result in line with, Pierce et al., (2010) initiate that participation in training gathering had positive and significant relationship with adoption of technology which is vital for crop production.

Total livestock unit (TLU-sum): This variable had positive impact on value of pollinating crops productivity and significance at 10 % level of significance. The total livestock unit variable got a coefficient of 13.375 which suggests that if the farm household had large number of livestock, then value of pollinating crop productivity increase with 13.375 birr. This result in line with (Gryseels, 1988; Omiti, 1995) that there are positive correlations between livestock and crop production.

Table 11 linear regression results of impact of determinants of value of pollinating crops yield

Variables Coef. Robust T P>t Std.Err. DFOREST -274.914* 149.335 -1.84 0.067 DGRASLAND 411.777** 174.329 2.36 0.019 DWATER 16.289 10.383 1.57 0.118 MABCO 0.305* 0.180 1.69 0.093 AGE -3.064 3.579 -0.86 0.393 EDU 340.883*** 67.752 5.03 0.000 HHSIZE 38.222* 22.338 1.71 0.088 TFL -86.865 66.718 -1.30 0.194 TLU_sum 13.375* 8.024 1.67 0.097 EXTAD 241.945** 116.601 2.07 0.039 CRDACC 36.494 73.637 0.50 0.621 TRANG 263.378** 108.669 2.42 0.016 lnINCOME -15.603 36.628 -0.43 0.671 CROPRO 165.087* 98.566 1.67 0.095 FRTZR 128.535 123.228 1.04 0.298 LOCATION North mecha -184.161 168.368 -1.09 0.275 Dangila -164.402 101.031 -1.63 0.105 Ankesha -60.342 113.454 -0.53 0.595 -cons 49.061 416.673 0.12 0.906 ***, **,* Significant at P<0.01, 0.05&P<0.1 respectively source: own survey, 2018

Number of obs =248 R-squared = 0.2470

F (18, 229) = 2.66 Root MSE = 589.54

Prob> F = 0.0000

5. SUMMERY, CONCLUSION AND RECOMMENDATION

5.1. Summery and Conclusion

In Ethiopia, agriculture is the basis of livelihood for a large proportion of society. But the production has been decline from time to time due to the traditional way of plowing, in appropriate usage of inputs, lack of access of services like credit, extension and skill training. Most of the time farmers are invest more for inputs like fertilizer and seed. But they forget the pollination requirements for their crops to be more productive. The purpose of this study was to evaluate impact of pollinator habitats and managed bee pollination on productivity of pollinating crops in study area. Both descriptive analysis and econometrics estimate results have been used to answer the stated key research questions.

Conserving pollinator-supporting habitat within farmlands can clearly bring benefits to both agriculture and pollinators. As distance of crop plots to forest increases, value of pollinating crops productivity decreases because of species richness of flying flower visitors also declined. In the study area, pollinator travels a distance of 0.5 kilometer on average from forest to crop plots.

Near to natural or semi natural grassland conserves much pollinator abundance that used to increase value of pollinating crops productivity. Pollinators travel 0.5 kilometer on average from grasslands to crop plots.

Increasing number of bee colony in the farm land leads to an increase in the value of pollinating crops productivity. Based on the survey result, 48 % and 79 % of the total households were agreed on the importance of pollination for crop production and environmental conservation respectively. Out of this, only 43% of the households want to increase pollinators in their farm land.

From the total sample respondents, 98.79 % were male headed household. The minimum and maximum ages of the respondents were 23 and 74 years respectively with mean age of 47years. The average household size in the study area was 5. The majority (58.06 %) of the respondents cannot read and write or they are illiterate while the remaining 41.94 % can read and write or they were literate.

Extension service Permits flows of information and transfer of knowledge and scientific findings to practice in agriculture to improve production and productivity.

From the sample of pollinating crop producers, about 78.63 % had access to extension advice while 21.37 % had not access to extension advice.

Crop rotation is growing of serious different types of crops in the same plot in sequential seasons. One of the most effective agricultural control strategies, as it comes with numerous advantages that are very important for reducing the use of chemicals on the farm and supporting long term soil fertility. The practice of crop rotation reduces the need for application of fertilizers and minimizes the risk of land and water pollution. Based on this, from the total farm households, about 81.45 % were practice crop rotation while, only 18.55 % were not practice crop rotation.

Training is an organized activity aimed at informing information and or instructions to improve receiver’s performance or to help them to attain a required level of knowledge. Or it is teaching /developing to others any skills and knowledge that relate to specific useful skills. It has specific goals of improving once ability, capacity, productivity and performance. But in the study area most of the households had not access to any training. out of the total farm households 73.39 % were not take any kind of training regarding how to being more productive. While only 26.61 % of the respondents had access to take training.

About 76.61 % of the total respondents were used inorganic fertilizer while only 23.39 % households were not used.

About 61.29 % of the total respondents were not take credit which used to bought inputs and improve value of productivity due to Borrowing is risky, Interest rate is high ,Too much procedures, did not try, have no collateral, and No credit providers in this area.

Mixed farming system was practiced in the study area. About 97.6 % of the total sample households were practice crop farming activity as their livelihood sources and about 33.5 % were practice livestock production. The most dominant pollinating crops grown in the study area were Niger seed, potato, Lupin, faba bean, pepper, sesame, vetch, Lin seed, Rape seed, pea, chickpea, lentils, haricot bean, onion, hopes, coffee, lemon, mango, apple, avocado, orange and banana. The household grow these crops either on meher or belg once in a year. The predictable farm households average production cost per hectare was Birr 1601birr for pollinating crops with average annual crop production of 274 kilogram per hectare. And the average total income of the household was 16329 birr.

The regression model has been used to identify the impact of the identified variables on value of pollinating crop productivity. The model result indicates that distance of crop plots to grassland, managed bee colony, educational status of the household, training access of the household, household size, sex of the household, access to extension service and crop rotation were statistically significant variables and had positive impact on value of pollinating crop productivity. As well as, distance of crop plots to forest and location dummy were statistically significant variables but had negative impact on value of pollinating crops productivity.

5.2. Recommendations

To create substantial improvement on crop production that leads for food security, the following events and actions should be taken by household heads, administration of region and woredas, as well as government. The possible areas of intervention that come from the results of this study are presented as follows:

Some farming methods that help to enhance agricultural production and reduce farmers’ costs like crop rotation and intercropping were ignored and practiced traditionally without the guidance of professionals. Therefore, to improve agricultural production and ensure sustainable agriculture, farmers should be guided in the series of crops in crop rotation and the pair of crops in the case of intercropping. As well, provision of extension service has to be strengthened to improve farmers’ access to information and extension advices through giving training and other related supports.

The assignment of crop, natural resource, livestock experts at the Kebele level is a good interchange to improve the production and productivity of the agricultural sector.

Distance was a significant variable which affect value of pollinating crop productivity. Based on this, the following 3 points were recommended.

1. To support pollination services and food security on farm-lands, the farm households need to provide food and nesting resources that are presently limiting to wild pollinators, while restricting conditions for their natural enemies. As well as improve conservation of natural and semi-natural habitats around agricultural landscapes should be taken.

2. Government should support and promote agricultural practices that benefit pollination services within agricultural systems, such as crop rotation, ecological focus areas at farm level, and organic farming system.

3. Almost less than a half percent (48 %) of the respondents is aware on the necessary of pollination and pollinators for their agricultural productivity especially for pollinating crops. But most of the households have not a clear understanding. So, Awareness creation on the importance of insect pollinators and pollination is now needed to farmers to conserve insect pollinators. Moreover, government need to strengthen attention pointed at education curriculum.

Education is also one factor for value of pollinator dependent crop productivity which affects positively and significantly. In case of production, household heads with very limited education encounter different problems like pollination in addition to usage of inputs. So, the government should increase the accessibility of education and create awareness how to being more productive.

Training is the other factor for production of pollinating crops which had positive impact. In order to being more productive, skills training about how, when and how much to produce should be given for the farm households.

Finally, it is important that researches should do on pollination service for crop productivity especially for pollinator dependent crops.

6. REFFERENCE

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7. APPENDIX

Appendix table 1 Test of multicollinearity

Variable VIF 1/VIF DFOREST 1.29 0.777603 DGRASLAN 1.23 0.777502 DWATER 1.55 0.646235 MANAGEDBEE~Y 1.72 0.580424 AGE 1.11 0.901808 EDU 1.11 0.901125 HHSIZE 1.05 0.949705 TFL 1.33 0.751736 TLU_sum 1.08 0.926614 EXTAD 1.33 0.749465 CRDACC 1.14 0.877248 TRANG 1.13 0.881547 Lnincome 1.28 0.780500 CROPRO 1.34 0.747110 FRTZR 1.11 0.897939 WOREDA 1 1.99 0.502245 2 2.02 0.494695 3 2.00 0.501123 Mean VIF 1.39

Appendix table 2 Test of Heteroscedasticity Breusch-Pagan / Cook-Weisberg test for heteroscedasticity

Ho: Constant variance

Variables: fitted values of YIELD

chi2 (1) = 1306.82

Prob > chi2 = 0.0000

Appendix table 3 Test of Endogeneity Instrumented: TFL Instruments: MANAGEDBEECOLONY DISTANC EDU FRTZR SEED CROPRO SEX HHSIZE TRANG CRDACC EXTAD PESTISD TLU_sum INCOME AGE . estat endog, forceweights

Tests of endogeneity Ho: variables are exogenous

Durbin (score) chi2 (1) = 1.19108 (p = 0.2751) Wu-Hausman F (1,232) = 1.11961 (p = 0.2911)

Appendix table 4 Correlation coefficient of pollinating crops variables | VALUEC~P MABCO DFOREST DWATER DGRASL~D INCOME AGE HHSIZE TFL TLU_sum ------+------VALUECROP 1.0000 MABCO 0.1332 1.0000 0.0360 DFOREST - 0.0999 -0.2510 1.0000 0.1168 0.0001 DWATER 0.0989 -0.1800 0.1007 1.0000 0.1202 0.0045 0.1137 DGRASLAND 0.1296 -0.2101 0.9432 0.0821 1.0000 0.0414 0.0009 0.0000 0.1974 INCOME 0.0350 -0.0459 0.0115 0.1120 0.0372 1.0000 0.5832 0.4721 0.8572 0.0783 0.5598 AGE -0.0901 -0.0404 -0.0400 0.0874 -0.0257 -0.0272 1.0000 0.1573 0.5263 0.5303 0.1701 0.6867 0.6703 HHSIZE 0.0889 -0.0950 0.0097 0.0275 0.0102 -0.0345 0.0857 1.0000 0.11628 0.1358 0.8798 0.6662 0.8734 0.5890 0.1786 TFL -0.0230 0.0627 0.0572 0.2436 0.1020 0.1531 0.2099 0.0824 1.0000 0.7185 0.3254 0.3695 0.0001 0.1091 0.0158 0.0009 TLU_sum 0.1105 -0.0218 -0.0225 0.0800 -0.0255 -0.0433 0.0115 -0.0200 -0.0418 1.0000 0.0825 0.7329 0.7239 0.2091 0.6898 0.4974 0.8572 0.7545 0.512 Sources: own survey result, 2018

Appendix table 5 Conversion factors used to compute tropical livestock units

Category of Animals Tropical livestock unit Ox 1.10 Cow 1.00 Heifer 0.50 Bull 0.60 Calves 0.20 Sheep 0.1 Goat 0.09 Donkey 0.50 Horse 0.80 Mule 0.70 Poultry 0.01 Source: Storck, et al. (1991) Appendix table 6 Perennial ownership of farmer respondents Crop owned Mean Standard Deviation Hopes 69.76852 134.5964 Apple 6.166667 4.875107 Lemon 1.96 1.240967 Coffee 93.29496 266.1569 Avocado 4.64 3.251666 Orange 3.6078443 3.779304 Banana 50.32609 101.8971 Mango 11.82759 31.33354 Source: own survey result, 2018

Appendix table 7 Asset ownership of farm respondent households

Recourse owned Mean Standard deviation Number of calves 2.125 1.070094 Number of sheep 5.140244 4.974644 Number of oxen 2.412017 1.134199 Number of horse 1.210526 0.4740792 Number of bull 1.480315 0.6405651 Number of cow 2.61674 1.542404 Number of heifer 1.63125 0.6693832 Number of goat 3.818182 2.857208 Number of poultry 5.746575 4.176802 Number of donkey 1.664179 0.7248959 Number of mule 1.142857 0.4483951 Number of bee colony 7.005 10.42778 Number of traditional hive 10.28646 7.188285 Number of transitional hive 2.615385 1.556624 Number of modern hive 2.867647 2.509114 Source: own survey result, 2018

Appendix table 8 Research questionnaire HH ID: ______International Centre for Insect Physiology and Ecology (icipe)

Thesis Prepared Questionnaire for farm household Bahir Dar University 2017

To be administered to household heads

Introduction and consent statement:

“Dear Sir/Madam, I work for the International Centre of Insect Physiology and Ecology (icipe). We are conducting a survey to study impact of pollinator habitats and managed bee pollination on yields of pollinating crops in this area. Taking part in this study is voluntary. Private information such as your name and address will not be disclosed to protect your privacy. If you choose not to take part, you have the right not to participate and there will be no consequences.

Do you and your family agree to provide information? 1=yes, 0=No.

“Thank you in advance for your kind co-operation”.

I. General Information Name of enumerator: ______Region: ______Cell phone of Zone: ______Enumerator:______Woreda: ______Checked by: ______Kebele: ______GPS latitude ° ' " N Intervention status: 1= Intervention, 2= Comparison GPS longitude ° ' " E Time the interview started (ET):______GPS elevation ______m Time the interview ended (ET):______Date of interview [GC](DD/MM/YYYY):______

Section A: Demographic Information [Respondent - Household Head2]

A-1 Name (s) of HH member (s) interviewed ______, A-2: Sex of respondent: 1=Male, 0=Female A-3 Respondents contact number______A-4: Type of Household: 1= Male Headed , 0 =Female Headed

Person A-5 Name of A-6 A-7 A-8 A-9 A-10 A-11 ID household Sex Age Relation to Highest grade Major primary Major secondary member 1=male in household head: completed job/activity job/activity (List of all 0=female Years See Code 1 See Code 2 Code 3 [skip for Code 3 [skip for persons in the under age children] under age children] household starting from head) 01 02 03 04 05 06 07 08 09 10

2 The respondent must be the person who can provide information on the (or responsible) agricultural activities of the household for most/ all activities related to agriculture. That could be the household head, the spouse or another adult household member.

Code 1 Code 2 Code 3 0=Head For grades 1-12 put the 0=none, 1=Spouse grade level, e.g. 3 for 3rd 1=farming; 2=head/spouse grade completed and so 2=farm assistant, parent on. 3=salaried employee; 3=Child 0=can’t read and write 4=student; 4=Grand child 13=10+1, 5=farm product trader, 5=Nephew/Niece 14=10+2 6=non-farm product trader; 6=Son/daughter- 15=10+3, 7=beverage sale, in-law 16=Diploma, 8=non-farm casual laborer; 7=Brother/Sister 17=Degree and above 9=too old to work (pensioner; 8=Other relative 18=Adult education (basic 10=handicraft, 9=Other non- education), 11=mining, relatives 19=religious education 12=carpentry, 13=house help, 14= farm laborer on others’ farm 15= cart or motorbike driving 16= weaving 17= others (specify)______

Section B1: Household Asset ownership

Code B-1 Asset B-2 B-3 B-4 How B-5 Who B-6 B-7 Who B-8 Who B-9 How did type Does the Num many of owns this Estimated makes makes the owner househo ber the hives asset in the current Value decision decision accessed the ld own owne have HH? (ETB) if the regarding to sell asset? [asset] d colony? Code 5 HH decides new [asset] Code 6 currentl curre [ask only to sell this purchase of Code 5 y? 1= ntly for code today. [asset]? Yes, 0= 01, 02 Code 5 No and 03] I: Honey production assets/tools Traditional 01 hives Transitiona 02 l hives Modern 03 hives

Code 5: 1=women only, 2=men only, 3=men and women. Code 6: 1= Allocated or given by government/NGO/Coop; 2= inherited; 3= given by relative or friend; 4 =purchased; 5=encroached/squatted; 6= catching swarms, 7=homemade, 8=home grown, 9=other (Specify) ______

Section B2: Household Livestock ownership

Code B-11 B-12 Does B-13 B-14 who B-15 what is B-16 Who B-17 Who B-18 Who B-19 How Asset the Number owns this the estimated makes the makes manages did the type household owned livestock in current Value decision decision to the owner own currentl the HH? (ETB) if the regarding sell [livestock livestock? accessed the [livestock y Code 5 HH decides new purchase name] Code 5 livestock? name] to sell this of [livestock Code 5 Code 6 currently? livestock name]? 1= Yes, 0= today. Code 5 No >> C 1 Oxen 2 Bulls 3 Cows 4 Heifer 5 Calves 6 Sheep 7 Goat 8 Poultry 9 Donkey 10 Horse 11 Mule Bee 12 colony

Section C1: Crop production inputs and costs

C-1Total farmland owned by the household currently (in hectares) ______C-2 Total farmland (own, rented in and shared in) cultivated by the household during 2017/18 cropping season (in hectares) ______C-3 Number of plots cultivated during 2017/18 cropping season? ______(this question is to set the number of rows, if u have other option ignore this).

Parc Plot Plot Plot Plot Plot C-4 C-5 C-6 If C-7 C-8 C-9 C-10 C-11 C-12 C-13 el nu loca loca locati size cropping Croppi intercro Main Main Type Total Amou Amoun Used num mbe tion tion on (ha) season ng mix: pping, Crop Sowing of cost of nt of t of organic ber r nam (N) (E) 1=Mehe 1=sole how grown type Seed seed in DAP Urea fertilize e r, croppin many A [use crop A used Birr used used r or 2=Belg g, 2= crops? code 7] 1=broa (Crop (Crop (kg) (kg), compos intercro (if 1 ask dcastin A) A) put put t? pping for crop g, 1=im (put 0 zero if zero if 1=Yes, A only, 2=line/r prove if not not not 0=No if 2 or ow d, purcha used used more plantin 0=loc sed) ask crop g al B) 0

Crop Code 7: 1=Teff, 2=wheat, 3=maize, 4= barley, 5=millet, 6= sorghum, 7=faba bean, 8= chickpea, 9= lentils, 10=pea, 11=haricot bean, 12= Masho, 13=oats, 14=soya bean, 15=Lupin (gibto), 16=Lin seed, 17=Niger seed, 18=sun flower, 19=potato, 20=onion, 21=vetch, 22=garlic, 23=pepper, 24=tomato, 25=sesame, 26=Cotton, 27=others (specify) _____ Code 5b: 1=Women only; 2=men only; 3=men and women jointly; Code 8: 1=2.4D, 2=Roundup, 3=Primagram, 4=Redomil, 5=Mankothem, 6=Malathion, 7=others, specify

Par Plot Plot C-14 C-15 C-16 C-17 C-18 If C-19 If C-20 If C-21 C-22 C-23 C-24 cel num na Appli Did Who Is any yes, how Yes, yes, what Amoun Unit Total Quantity ber me ed you manage no pestici many what is is the type t of (pesticide cost of harvested (sa (sam any rotat d this de pesticide the of pesticid all including ) 1=kg, me e as SWC e plot in used s used? name of pesticide es/herbi pesticid harvest & as c4) techni crop the on this 1=one, the used? cide 2=litre es used consumption abo ques? on househo plot? 2=two pesticide (pesticide used 3=sini/fin (Birr) at green ve) 1=Ye this ld? 1=Yes types, used? ) (pestici jal, stage Crop A s, plot? Code 5b , 0=No 3=more (pesticid 1=herbicid de) 4=others (Qt) 0=No 1=Y than two e) code e, (specify) es, 8a 2=insectic 0=N ide, o 3=fungici de,

Section C2: Crop utilization

Crop C-25 C-26 C-27 C-28 C-29 C-30 C-31 If sold, code Quantity Quantity Quantity quantity Quantity Quantity what is average (from harvested consumed sold (kg) retained for given out stored (kg) price per kg above) (kg) (kg) seed (kg) (kg)

C-32 Who usually makes decision regarding use of fertilizers in your household? Use code: 1=women only, 2-men only, 3=women and men C-33 Who usually makes decision regarding use of improved seed in your household? Use code: 1=women only, 2-men only, 3=women and men C-34: Who usually makes decision regarding crop utilization (sale, consumption, etc) in your household? Use code: 1=women only, 2-men only, 3=women and men C-35: Who usually makes decision regarding crop income use in the household? Use code: 1=women only, 2-men only, 3=women and men C-36: Have you/any member of the household received training on proper application of pesticides/herbicides over the last 2 years? 1=Yes, 0=No C-37: At which months of the year do you usually apply the pesticides? Months; Multiple responses possible C-38: At what time of the day do you usually apply pesticides/herbicides? 1=morning, 2=Afternoon, 3=night, 4=morning and afternoon, 5=morning and night, 6=afternoon and night, 7=morning, afternoon, and night

Section C3: Labor use and cost [Respondent-Household heads]

Plot Cro Size C39 C40 C41 C42 Cost C43 C44 C45 C46 C47 C48 C49 nu p (ha) Frequen Own Hired of hired Frequen Own Hired Cost of Own Hired Cost of mbe cod same cy of labor Labor labor for cy of Labor Labor hired labor for labor for hired r3 e as in plowing used for used for plowing weeding used for used for labor for harvesti harvesti labor for sam sam C6 plowing plowing (Birr) weeding weeding weeding ng ng harvesti e as e as (person (person (person (person (Birr) (person (person ng Crop in in days) days) days) days) days) days) A (Birr) C4 C7

3 A plot is a sub-unit of parcel with a single land use; it can hold a single crop type or intercropped and can be separated based on operator activities. 76

Section D: Timber Trees and Fruits Owned and Produced [Respondent-Household heads]

E-1: Name of tree or E-2: Did the E-3: E-4: Have E-5: Total harvest E-6 E-7 Unit E-8 fruit crop owned by the household Number you in 2017/18 in Amount 1=kg, Value household own any owned harvested in quintal/number sold 2=number sold [tree/fruit 2017/18 (Birr) crop]? production 1=Yes, season? 0=No 1=Yes, 0=No Eucalyptus Mango Banana Orange Avocado Papaya Coffee Sugarcane Lemon Apple Enset Khat Hops (Gesho) Improved bee forage trees (such as sespania, tree lucern, saligna, etc.)

Section E: household’s knowledge on pollination

E1: Are there flowering plants where the bees are kept (fruit trees or other plants)? 1=Yes, 0=No E2: Is there forest or other vegetation within 3 km where the bees are kept? 1=Yes 0=No E3 Did you plant bee forage species purposely for your bees? 1= Yes, 0= No E4 Do bees prefer flowering plants? 1=Yes 0=No E5 If yes what do you think the reason behind their preference? ------(string) E6 Do you also know other insects visiting the crops during flowering? 1=Yes, 0=No E7 Do you think flowering crops benefits from the bee or other insects’ visits? 1=Yes, 0=No E8 Do you think that, it is possible to get fruits or seeds without flowers? 1=Yes, 0=No E9 Would you like to increase pollinators in your farm? 1=Yes, 0=No E-10 Bees are useful for environmental conservation. (5) Strongly agree (4) Agree (3) Neutral (2) Disagree (1) strongly disagree E-11 Bee pollination is important for crop productivity. (5) Strongly agree (4) Agree (3) Neutral (2) Disagree (1) strongly disagree E-12 Bee management is men’s activity. (5) Strongly agree (4) Agree (3) Neutral (2) Disagree (1) strongly disagree

Section F: Information and knowledge support provided by the extension services [Respondent-Household heads]

F F-2: F -3: F -4: F -7: Did F -8: F-9: Did F-10: Why F-11: F-12 If the F-13: F-14: F-15: Did - Activity Did Did the household Did househo did Did HH did What was What household 1 househo househo get househo ld need household househo not the was receive : ld need ld informatio ld get credit not need ld receive source of the the c extensio receive n and marketi for credit for receive credit the amoun amount o n/inform extensio input on ng [activity [activity]? credit? why? credit? t of requested? d ation on n advice time for informa ]? 0=No> Code 16 credit e [activity on this tion 1=Yes Code 13 > skip to Code 15 receiv 1=Yes; ] in the [activity [activity)? [activity 0=No I12 ed? 0=No last 12 ]] over 1=Yes, ]? 1= No>>I1 1=Yes (ETB) months? the last 0=No Yes 0= 0 >>I13- 1= Yes 12 No 1=Yes> 15 0= No months? >I11 1= Yes 0= No 1 Apiculture 2 Sericulture 3 Crop production 4 Livestock production 5 Other agricultural commodity

Code 13 Code 15 Code 16: reason for not receiving credit. 1=Not cash 1=Money lender 1=Borrowing is risky constrained 2=Farmer group/coop 2=Interest rate is high 2=Activity is 3=microfinance 3=Too much paper work/ procedures not profitable 4= bank 4=Expected to be rejected, did not try 3=Never 5=Relative 5=I have no asset for collateral thought of this 6=Shopkeepers 6=No credit providers in this area for my investment 7=traders area of interest 4=Had own 8=Agricultural input 7=Lenders don’t provide the amount source dealers needed 5=Other, 9= Other, 8=No credit association in this area specify...... specify………… ....

Section G- Household income and activities [Respondent - Household Heads]

G-3: Did G-4 Rank in G-5 Percent G-6 Person who G-7: Total annual anyone in your order of contribution participated in income earned while household importance for to household the activity doing this work? engaged in household income (%) G-1: G-2: Livelihood Use code 5 (Birr per annum) [this activity] income Code Activities during the last contribution 12 months? (only for yes 1=yes 0=No response)

AGRICULTURE 1 Annual Crop production 2 Perennial crop production 3 Tree crops production 4 Livestock production 5 Honey production 9 Trading livestock or livestock products 10 Trading fruits and vegetables 11 Selling firewood or charcoal or selling wild 13 fruits,Land rent Code 5: 1=women only, 2=men only, 3=both men and women

End of Questionnaire!! Thank You very much for your Cooperation!!

Appendix table 9 Questions for the focus group discussions (FGD) 1. Do farmers in your village practice beekeeping activity? 2. Why do you think communities practice beekeeping in this village?

No Activity 1 Get additional income 2 Consumption 3 Pollination service 4 Health 5 Others

3. What type of beehives the communities used in this village? (Modern, traditional, transitional 4. What are the advantages of beekeeping in addition to generating income through selling honey and wax? 5. What are the farming systems of your beekeeping in this village? (Backyard, forest, under the roof, in the house…… 6. What are the main forage sources of bees?(onion, mango, papaya, cabbage, avocado, coffee, bean, orange, lemon, carrot, sun flower, apple, cotton pea, pepper…. 7. What are the main pollinator habitats in your area? (Forest grass land, shrubs, crops land … 8. How far is on average the distance between pollinator dependent crops farm land and pollinator habitats?(hours from the sources or habitats) 9. Do farmers receive an extension service for crop production activity? If Yes, how adequate is the information (adequate, not adequate) 10. If yes to Q 14, on average, how many times farmers receive an extension service during a year in relation to crop production? 11. In your view which crops depend on pollination service/insect (e.g. bees) 12. How many times do you harvest pollination dependent crops per annum? 13. What impact has the pollinator habitats and managed bees on the livelihood of the smallholder pollination dependent crop farmers?

Appendix table 10 Interview questions for key informant Checklists to Guide key informant interview 1. How many farmers in this village are involved in beekeeping activity? 2. What are the major pollinating crops grown in this village? 3. How the farmers use their land? 4. How many Agriculture experts in the districts and zonal level? 5. Who manages bee in your community (Male, female and both)? 6. Farming system of this kebele/ woreda

BIOGRAPHICAL SKETCH Sisay Getahun was born in Wofwasha kebele Tarmaber Woreda, North shewa Zone of Amhara Regional State in July 1994. She joined her primary education at Genet elementary school. She attended her secondary and preparatory school education at Debreina Secondary and Preparatory High School in North shewa zone.

After completion of her high school education, she joined Woldia University in November 2012 and graduated with BSc. Degree in Agricultural Economics in July 04/2015. Almost immediately after her graduation, Assistance Lecturer I employed her at Woldia University Mersa Campus. After one year experience the author joined Bahir Dar University College of Agriculture and Environmental Since in October 2016 to follow of her MSc degree in Agricultural Economics in regular program.