The contribution of diversified farming system to household farm income evidence from rural households in vietnam

The contribution of diversified farming system to household farm income: Evidence from rural households in Vietnam M.Sc Thesis “Sustainable International Agriculture” Specialization in “International Agribusiness and Rural Development Economics” Faculty of Agricultural Sciences Georg-August-University of Göttingen, Germany By Xuan Lam, DUONG Matriculation number: 21168870 Supervisors: Prof Dr M Qaim Dr T Gödecke Accomplished at the Department of Agricultural Economics and Rural Development Faculty of Agricultural Sciences Georg-August-Universität Göttingen 27 February 2014 STATUTORY DECLARATION I herewith declare that I composed my thesis submitted independently without having used any other sources or means than stated therein Date: 27th February, 2014 Signature: LIST OF TABLES Table 1: Definition of variables used in the models 21 Table 2: Descriptive statistics of sample rural households by wealth categories 28 Table 3: Determinants of crop diversification on rural household in Vietnam 33 Table 4: Determinants of livestock diversification of rural households in Vietnam 37 Tale 5: Tobit estimation of overall household farm income diversification 39 Table 6: Impact of diversification on household farm income 41 LIST OF FIGURES Figure 1: Conceptual model of a diversified farming system Figure 2: Components of farm household income .11 Figure 3: The location of the study 17 Figure 4: The proportion of crop income of sampled households .31 Figure 5: The proportion of livestock income of sampled households 32 LIST OF ABBREVIATIONS CIP DFS International Potato Center Diversified Farming Systems IFAD GSO FAO SID SWDI SEI OECD International Fund for Agricultural Development General Statistics Office (Vietnam) Food and Agriculture Organization (United Nations) Simpson Diversity Index Shannon-Weiner Diversity Index Shannon Equitability Index Organization for Economic Co-operation and VND Development Vietnamese Dong (Vietnamese currency) ABSTRACT Farm diversification, income and crop diversification have been identified as essential strategies for sustaining household income and reducing rural poverty The contribution of diversified farming systems was analyzed based on empirical data collected from two rural areas in northern uplands and north central coast of Vietnam under the support of the Food, Feed, Fiber and Fuel for a Greener Future (4FGF) project The aim of the paper is to examine the determinants of farm income diversification and investigate the impact of diversification on household farm income of rural household The determinants of income diversification were examined using the Simpson Index of Diversity, the Shannon Equitability Index, and number of farm income sources Our results suggested that the determinants of crop income diversification were gender and ethnicity of the household heads, number of crop grown and regional dummy for northern uplands area The determinants of livestock diversification were age of household head, ethnicity of the household head, number of livestock holding, access to agronomic-related training and regional dummy From the overall mix of farm income diversification, the education and ethnicity background of household heads together with some specific assets such as land, crops, and livestock owning and regional dummy are the main determinants of income diversity in sample area We also find that diversification has negative effect on household farm income per capita Furthermore, those households who residing in northern uplands region and possessing respective specified assets for agriculture production is likely to contribute to overall farm income per capita In order to promote diversified farming systems and income diversification targeting rural household, one of the first priorities is to improve capacity and enhance human resource management in agricultural production, especially taking into account the role of women and ethnic minority groups on different income generating opportunities ACKNOWLEDGEMENT The journey to attain my master degree has come to an end It is my pleasure to express my gratitude to all who supported me and were involved in one way or the other in this learning process The design, implementation and completion of this thesis would have been impossible without the help and contribution of my supervisors at the Department of Agricultural Economics and Rural Development, Faculty of Agricultural Science, University of Göttingen In the first place, I would like to express my deepest gratitude to Prof Dr Matin Qaim for his academic supervision from the start of my thesis My sincere gratitude also goes to Dr Theda Gödecke for her useful comments, remarks and engagement through the learning process of this master thesis In addition, my special thanks for Dr Stefan Schwarze for giving me advices and support at the very beginning of my thesis proposal Furthermore, I would like to express my gratitude to Dr Keith Fahrney from CIAT Asia for giving me a chance to involve in 4FGF and allowing me to use the dataset for the master thesis Also, I like to thank the participants in my survey, who have willingly shared their precious time during the process of interviewing I would also like to extend my thanks to my friends and colleagues: DUONG, Hoai An; CAO, Thi Hien; DO, Xuan Luan; LE, Thi Huong for commenting on my work and also excellent help for my data analysis Last but not least, I am deeply indebted to my family members: my grandmother, my parents, my brothers and other members for their understanding, provision of continuous encouragement and support during my study program Without their support, I would not have been able to finish this thesis DUONG, Xuan Lam Table of Contents CHAPTER ONE: INTRODUCTION 1.1 Problem statement 1.2 Objectives and Research Questions 11 1.3 Structure of the study .11 CHAPTER 2: LITERATURE REVIEW 12 2.1 Farming systems and diversified farming systems 12 2.2 The 4FGF project and diversified farming systems 14 2.3 Diversification in agriculture .15 CHAPTER 3: METHODOLOGY 22 Data collection 22 3.2 Location of the study 23 3.3 Data analysis 26 3.4 Limitation 32 CHAPTER 4: RESULTS 33 4.1 Rural households’ characteristics 33 4.2 On-farm livelihood strategies of sample rural households 36 4.3 Determinants income diversification 40 4.3.1 Determinants of crop income diversification 40 4.3.2 Determinants of livestock income diversification 43 4.3.3 Determinants of overall household farm income .45 4.4 Impact of diversification on household farm income 48 CHAPTER 5: CONCLUSION 51 5.1 Conclusion 51 5.2 Policies implication .52 REFERENCES 53 CHAPTER ONE: INTRODUCTION 1.1 Problem statement Vietnam has been one of the success stories in the attack on poverty and successfully concretized Millennium Development Goals The poverty rate has recently dropped from 58.1 percent in 1993 to 14.23 percent in 2010 (UNDP, 2012) Rural households in Vietnam depend largely on agriculture as the main source of income However, income from agriculture has a tendency to become unstable due to the increasing environmental risks and the economic risks incurred in accordance with Vietnam’s rapid development Therefore, achieving secure household income is generally assumed to be a fundamental step out of poverty and food insecurity To achieve a secure income, diversifying livelihood and income is considered the most important strategy There have been numerous agricultural economics literatures on diversification, particularly focus on issue of income diversification in the context of economic growth and poverty It has been identified that the increasing in returns of productive factors or reducing the risk of agricultural activities were the main reasons to diversify farm activities DEJANVRY et al (1991) showed that income diversification is not only positively correlated with wealth but also with increased ability to cope with shocks and diversification is a strategy rural household insuring themselves against the occurrence of such shocks ERSADO (2003) conducted a study in Zimbabwe and found that in rural areas, richer households had more diversified income sources, while in urban areas the reverse was true This is coincide with studies conducted by REARDON et al (1998); ABDULAI and CROLEREES (2001) who co-revealed evidences that poorer households have fewer opportunities in non-cropping activities such as livestock rearing and nonfarm work, and hence less diversified incomes They also indicated that households with educated heads are more likely to participate in the non-farm sector than those with illiterate heads Targeting to evaluate the role of land on income diversification and poverty reduction in rural Kenya, KARUGIA (2006) discovered that poorer households tend to depend more heavily on food-crop production and seasonal wage labor activities for their incomes and are therefore, likely to be vulnerable in face of personal and covariate shocks such as droughts ELLIS (2000) observed a linear negative relationship between non-farm income share and either total household income or landholding in Asia and Latin America A linear positive relationship, conversely found in rural Africa where livestock and human capital are the assets that separate the rural better-off from the rural poor A U-curve relationship was found where the nonfarm income share is relatively high for small farms and poor households In Vietnam, MINOT et al (2006) examined the trend of income diversification and poverty in northern upland of Vietnam They concluded that income diversification including crop diversification has increased over time Poorer household are more diversified in crop production than richer ones whereas rural households are more diversified than urban counterparts This contradicts to a study by BABATUNDE and QAIM (2009) which indicated that richer households tend to be more diversified Using the same dataset with MINOT et al (2006), VAN DE VALLE et al (2004) examined the role of the participation in rural nonfarm market economy on the poverty and found that it would not be the route out of poverty for every household They argued that education, ethnic minority and geographical characteristics were concurrently influencing on the consumption growth and level of diversification Some other factors have opposite effects such as household size is positive for diversification but negative for welfare, land size has positive impact on the welfare but negative on diversification PHUNG and WAIBEL (2009) analyzed the relationship between the allocation of labor and land, the number of crops grown and income sources of rural household in Vietnam and different types of shocks and risks The results show that the households diversify their portfolio into different income generating activities in order to cope with shocks Agriculture, economic shocks and risks are the main factors to explain the risk-coping strategies and the risk management of the households The number of crops grown and the number of income sources from the households experienced with shocks are higher than others Diversified farming systems (DFS) is defined as agricultural production systems that integrate agro-biodiversity at multiple temporal and spatial scales (KREMEN et al 2012) This farming system includes poly-culture, integration of livestock or fish with crops (mixed cropping systems), and rotation of crops or livestock over time, including 10 than the farmers in the comparison region The second strongest predictor, number of crops grown by household indicates that controlling for all the other variables in the model, larger total land holding is linked to a higher likelihood of crop diversification This is in line with MINOT et al (2006) who stated that farmers in the northern uplands have the most diverse cropping systems among other regions Ethnicity of the household head has significantly on crop diversification On other words, controlling for all the other predictors, the Kinh people are more likely to diversify their farms than other ethnic minority households When the household head is a woman, the households are likely to have more diverse income source from crop production than the men-headed households, indicating the importance of women in agriculture Other human capital factors positively affect crop diversification although they are not statistically significant Having participated in the 4FGF project and access to agricultural training/practice in the last two years does not have any impact on diversification of crop across rural households Surprisingly, age of household heads and number of household members in working age (household labors) have negative effect on crop diversification This is probably because of the participation of younger adults in off-farm generating activities Younger people attempt to find another chance to earn more money out of the farm to stabilize their household income 42 4.3.2 Determinants of livestock income diversification For livestock income diversity model, five out of the thirteen predictor variables have a statistically significant effect on the livestock income diversity The predictor variable that has the strongest effect was the regional dummy for Kim Lu commune The model showed that controlling for all the variables in the model, households in the Kim Lu are much more likely to diversify household income rely mainly on livestock production Number of livestock owning by household is the second strongest determinants that affect livestock income diversification This is very straightforward since by having more livestock, household will definitely have wider choice to diversify their livestock herds and therefore, obtain higher income Concerning human capital predictors, the age of the household heads has negatively affected on livestock income diversification This means that younger household heads are more likely to diversify their livestock income as compared to older one This is in line with crop income diversification pattern as discussed above Number of years in school of the household heads has negative effect on income diversification but it is not statistically significant Larger household is associated with higher likelihood that they will diversify their livestock income but number of adults in working age, however has negative influence on livestock income diversity This probably reflects the fact that household labor are looking forward to earning money from off-farm activities or working as hired employers, finding another means to generate income outside the farm gate In regards of agriculture specific-assets, total farm size and land-man ratio surprisingly have negative effect on livestock income diversity In other words, households with higher farm land per capita appear to specializing income on particular livestock income This reflect their relatively lack of capital, which makes it difficult for them to diversify away from subsistence agriculture 43 Table 4: Determinants of livestock diversification of rural households in Vietnam Variables Significant Coefficient t-value Age of household head -0.0051224 -2.07** 0.041 (years) (0.0024803) 0.79 0.432 2.64*** 0.009 0.03 0.976 -1.40 0.164 -0.43 0.669 -0.86 0.389 -0.40 0.688 2.04** 0.043 0.86 0.394 -1.33 0.187 2.14** 0.034 4.68*** 0.000 Men-headed household 0.0661017 (1 = yes) (0.0837749) Ethnicity 0.1645227 (1 = Kinh) (0.0624244) Household size 0.0008161 (persons) (0.0270123) Household labor -0.0424714 (persons) (0.0303128) Years of schooling of household -0.0049927 heads (years) (0.011651) Total farm size -0.047811 level (0.0553273) Land-man ratio -0.0224975 (0.0558085) Number of livestock 0.0383117 (0.018785) Area of fish pond 0.633726 (0.7411027) Participation in 4FGF project -0.0691047 (0.0520477) Access to agronomic training 0.1296432 (0.0605124) Dummy for Kim Lu 0.2898504 (0.0619319) 44 _cons 0.3484745 1.79* 0.062 (0.1951274) Note: standard errors in parentheses Log-likelihood = -47.481705; LR chi2 (12) = 47.71; Prob> chi2 = 0.0000; Pseudo R2 = 0.3344 There are 26 left-censored observations at SIDlivestock= *, **, *** denotes significant level at 10%, 5% and 1% respectively Source: Self collaboration With regard to ethnicity of the household heads, of which is substantially significant and positively affect livestock income diversification A statistically significant difference between minority households and other households, after controlling for other factors indicate that Kinh people are much likely to diversify their income from livestock production than ethnic group minorities It is argued that ethnic minorities are more subsistence oriented and resistant to new methods because of their personalities and/or cultural values Turning to other external factors expecting to affect livestock income diversification, access to agriculture-related training practice is positive related to level of income diversity This predictor is statistically significant at 5% level of significant However, being a participant in 4FGF project appears to lessen the likelihood of diversifying livestock income A reason for this might be that the project simply introduced small to medium on-field demonstration, farm trials, livelihood analysis meeting…, targeting at a limited number of participants There is a need to propagate what farmers learnt from project and spread them out to other farmers 4.3.3 Determinants of overall household farm income In this section, the overall mix attribute of household farm income will be investigated using two different measures: the Shannon Equitability Index (SEI) and number of farm income source (NIS) The results of our regression estimates are presented in Table showing the number of left-and right-censored observations in each equation as well as a likelihood ratio test as a goodness-of-fit indicator In general, results 45 derived from models using SEI and NIS as measures of income diversification are comparatively similar, except for age of the household heads While age of the household heads has positive influence on number of farm income sources, it has negative effect on SEI It means that the increase in household heads’ age decrease the diversity and evenness of farm income One possible reason would be an older household head with his experience will try to focus and specialize in particular on-farm income generating activities Table 5: Tobit estimates of overall household farm income diversification Variables Age of household head (years) Men-headed household (1=male) Ethnicity (1=non-minorities) Household labor (persons) Number of years in school of the household heads Total farm size Land-man ratio Participation in 4FGF Number of crop grown Number of livestock holdings Area of crop grown Area of fish pond Regional dummy for Kim Lu Household received agronomic training in last years (1=yes) Shannon Equitability Index (SEI) -0.0002042 (-0.33) -0.0186772 (-0.93) 0.0672324 (4.38***) -0.0054413 (-1.07) -0.0069142 (-2.35**) -0.0245738 (-1.72*) 0.0365176 (2.71**) -0.0176654 (-1.36) 0.0292257 (4.06***) 0.0195898 (4.20***) 0.0109951 (0.52) 0.7349678 (3.75***) 0.0623694 (4.13***) 0.0453873 (3.05**) 46 Number of on-farm income source (NIS) 0.0062952 (0.59) -0.1577461 (-0.45) 0.8237985 (3.09**) -0.1481307 (-1.69*) -0.0993369 (-1.94*) -0.3462426 (-1.40) 0.581226 (2.48**) -0.2466876 (-1.10) 0.6105542 (4.86***) 0.4249645 (5.25***) 0.1791997 (0.49) 13.71549 (3.97***) 1.126565 (4.29***) 0.6206142 (2.41**) Constant Log-likelihood LR chi2(14) Prob> chi2 Pseudo R2 Left-censored observation Right-censored observation 0.3755864 (8.12***) 170.55195 99.10 0.0000 -0.4095 1 2.528684 (3.14)** -228.89034 113.18 0.0000 0.1982 Note: t-statistics in parentheses Number of observations: 143 *, **, *** denotes coefficients statistically significant at 10%, 5% and 1%, respectively Source: Self collaboration As can be seen from our regression results in Table (column and 3), access to training plays a relatively important role for the household to move into different farm income generating activities This is statistically significant contribute to farm income diversification at percent level of significant.The number of laborers is also a significant determining factor on farm income diversification but the effect is negatively significant small An ethnic minority household has less income sources compared to the Kinh households This factor is statistically significant at percent level of significant Number of years in school and age of the household heads (as proxy for working experience) were expected to have strong significant effects on the number of farm income sources It is obvious that experience and education could give people more opportunities to move out of the agriculture sector However, our results shown that number of years in school is statistically significant but negatively affects number of income sources Age of household heads has expected sign but it is not statistically significant.Households having more land per adult could keep their laborers working on the agriculture sector and then have a significantly lower income diversification Nevertheless, although land-man ratio has positively significant influence on number of farm income sources but total farm size acts in opposite direction A reason for this might be due to unemployment in agriculture as a consequence of lacking land for farming, household labors try to find jobs in the non-farm sector to mitigate pressure on land The possession of farming assets, such as the number of crops, number of livestock holding, 47 and owning area of fish pond lead not only to increased participation in annual crop and livestock production, but also positively influence income obtained from these activities These three factors are highly significant at percent level of significant Finally, the households living in Kim Lu commune have higher number of income sources than Xuan Trach commune, which in part can be explained by higher availability of land for agricultural production and more favourable climatic conditions 4.4 Impact of diversification on household farm income This section estimates the impact of diversification on rural household farm income The relationship and interaction between diversification indexes will be examined Table 6: Impact of diversification on household farm income Variables Age of household head Gender of household head Ethnicity of household head Years of schooling of household head Household labor Total farm size Land-man ratio Participation in 4FGF project SID for crop SID for livestock Shannon equitability index Coefficients P-value 0.0298277 (2.10**) -0.0469245 (-0.15) -0.1749208 (-0.54) 0.0775927 (1.22) -0.131624 (-1.51) 0.3003798 (1.10) 0.4724861 (1.77*) 0.22445704 (0.95) -3.347062 (-2.32**) -1.309768 (-2.16**) 1.84292 0.039 48 0.885 0.592 0.227 0.135 0.272 0.080 0.343 0.022 0.033 0.485 Number of crops Number of livestock (0.70) -0.1347266 (-0.86) 0.5056769 (4.13***) 0.393 0.000 Access to training -0.0370074 0.912 (-0.11) Household reside in Kim Lu 0.9772539 0.001 (3.48***) Constant 6.560403 0.000 (4.89***) Dependent variable: Log annual household farm income per capita Note: Robust standard error in parentheses Number of observations: 109 F(15, 93) = 4.48; Prob> F = 0.0000; R-squared = 0.4094 *, **, *** denotes significant level at 10%, 5% and 1% respectively Source: Self collaboration From the regression analysis on Table 6, 40.94 percent of the variation in log household farm income per capita is explained by the model Age of the household heads is found to have a significant and positive influence on total farm income per capita One year increase in household heads’ age increase the total farm income per capita by 2.98 percent In other words, income per capita from farm activities increases with advancing age.This can be explained by first, experience increases with age; consequently, experienced persons have more prospects of investing and focusing on particular activities in agriculture sector The second explanation is that we have taken the age of household heads alone and out of 144 samples in our study; most of the household heads were middle aged The crop and livestock diversification index, representing the diversity of income sources in respective sub-sectors on farm have negative effect on household income per capita These two variables are all statistically significant at percent level of significant The educational level of the household heads, measured by number of years in school is found to have positive impact on dependent variable Household heads spend an additional year being in school increase farm income per capita by 7.76 percent.Household labor is found to be negatively related with the level of income diversification but the coefficient was not statistically significant.Land-man ratio and 49 number of livestock holding are two important factors that have positively influence on farm income per capita One unit increase in the number of livestock will increase 50.56 percent farm income per capita This sign effect is considerably large, suggesting that livestock production is the main contributing factor to farm income.Similarly, one unit increase in the ratio of land over household labor increase the farm household income by 47.24 percent change As can also be seen from Table 6, gender and ethnicity of the household heads have negative relationship with income per capita An ethnic minority woman become household heads would be contributed greatly to total farm income per capita There is evidence that households who had participated in 4FGF project will positively contribute to household farm income per capita However, access to agriculture training in the last two years appears to decrease the overall farm income per capita The sign of estimated coefficients for regional dummy was positive and statistically significant at percent level of significance which implies that ceteris paribus the households residing in the Kim Lu (northern upland) region of the study area have positive influence on farm income per capita than those in Xuan Trach region and it is because of difference in the location-specific agro-climatic and socio-economic factors 50 CHAPTER 5: CONCLUSION 5.1 Conclusion There have been numerous agricultural economics literatures on diversification, particularly focus on issue of income diversification in the context of economic growth and poverty Most of their literature have consensus that …In order to get away from poverty and ensure food security, rural household have no way other than adopt multiple income generating activities in response to risk management and to meet household consumption needs in the presence of high transaction costs and ensuring environmental soundness Diversified farming system is a production system taking into account concerns of sustainable development and better improves environmental soundness This study attempts to capture the extent of diversified farming system measured though crops income diversification Simpson diversity index, Shannon equitability index, and the number of income sources were used to measure the farm income diversification Tobit regression analyses find various determinants of farm income diversification Education and ethnicity of the household head, the share of farm land over household members in working age and their specific assets such as land, crop and livestock owning are key determinants of rural household farm income diversification in Vietnam Crop and livestock diversification are jointly determined by ethnic background of the household heads in conjunction with typical resource base and regional differences In the second part of the study, we adopt a simple log-linear regression model to examine the contribution of diversification on household farm income The estimates show that number 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The overall objective of this study is to examine the impact of a diversified farming system on households? ?? farm income of rural farmers in two provinces in Northern and North Central Coast of. .. using the following equation: 27 (2) Where: S is the number of income sources and incsharei the share of income from activity i in total farm household income The Shannon index Hincome takes into