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Food and Nutrition Security: What's the role of Agricultural Policy in Asia?

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Agricultural transformation in Cambodia has had mixed effects on food and nutrition security according to a study analyzing survey data. While per capita expenditure and commercialization indicators like the share of food purchases were positively associated with dietary diversity, some farm households saw worse nutrition outcomes. Subsistence farmers in particular experienced lower child weight-for-age z-scores compared to commercializing farmers, suggesting current agricultural policy may not benefit the most vulnerable. The results indicate Cambodia needs policies that ensure agricultural growth improves nutrition for all households, especially the poorest.

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Food and Nutrition Security: What’s the Role of Agricultural Policy in Asia? Siem Reap, Cambodia; September 25-27, 2013 International Conference on “Agricultural Transformation in Asia: Policy Options for Food and Nutrition Security” H.E. Srun Darith (moderator); Dr. Akhter Ahmed, Dr. Kamiljon Akramov, Dr. Olivier Ecker, Dr. Yanyan Liu
Introduction H.E. Srun Darith, Dr. Olivier Ecker
Motivation  Malnutrition slows economic growth (2-3% GDP lost) and deepens poverty through productivity losses (10% of lifetimes earnings) from poor physical performance and cognitive capacity as well as increased health care costs.  Poverty and malnutrition are closely associated and highly prevalent in rural areas.  Although agricultural growth has been shown to have high poverty reduction effects (Christiaensen et al. 2011; Diao et al. 2010; World Bank 2007), empirical evidence on its nutrition impact is limited and inconclusive (Pinstrup-Anderson 2013; Berti et al. 2004; Masset et al. 2011).  Nonetheless, development and agricultural policy is often based on the assumption that agricultural growth—particularly among smallholder farmers—improves household food security and thereby reduces malnutrition.
Pathways from Agricultural Transformation and Growth to Food and Nutrition Security Agricultural transformation and growth driven by  Demand increase  Productivity growth due to policy reform, investment, techno logical progress Purchasing power increase from  Income growth among farmers  Food price reduction Change in food self- sufficiency dependence among subsistence farmers Household food and nutrition security (in terms of food quantity and dietary quality) Nutrition outcomes Intra-household resource allocation, care, education, health environment
Dietary Quality-Growth Relationship Cambodia Global trend GDP per capita (constant 2005 US$) Share of calorie supply from staples (%) Bangladesh Nepal Tajikistan Source: O. Ecker based on data from FAO’s FSI and World Bank’s WDI, complemented with IMF’s WEO and UNSTAT data.
Undernutrition-Growth Relationship Source: O. Ecker based on data from World Bank’s WDI, complemented with IMF’s WEO, UNSTAT, and recent country survey data. Cambodia Global trend GDP per capita (constant 2005 US$) Prevalence of underweight among children under 5 years (%) Bangladesh Nepal Tajikistan
Dietary Diversity as FNS Indicator  Dietary quality contributes to an individual’s nutrition and health status and thereby to people’s economic productivity.  Households will only diversify their diets into higher-value micronutrient-rich foods when they have satisfied their basic calorie needs. For the poor, these foods are often unavailable or unaffordable.  Dietary diversity is a strong predictor of dietary quality in terms of (micro)nutrient intake and adequacy (Ruel et al. 2013).  Household dietary diversity is strongly correlated with per capita calorie consumption and dietary energy adequacy and is correlated with nutrition outcome indicators such as anthropometrics (Ruel 2003; Ruel et al. 2013).  Dietary diversity is responsive to welfare trends and sensitive to shocks and seasonality, indicating high inter-temporal validity (Headey & Ecker 2013).  Dietary diversity is measured as a count of different foods or food groups consumed over a specified reference period.  All country cases studies use 12-scale or 16-scale household Dietary Diversity Scores (DDS) as indicator of household food and nutrition security (FNS).
Evidence from 4 Country Case Studies 1. Cambodia: Does agricultural transformation slow progress toward achieving food and nutrition security? Presented by Dr. Olivier Ecker 2. Tajikistan: Agricultural biodiversity, dietary diversity and nutritional outcomes Presented by Dr. Kamiljon Akramov 3. Nepal: Nutritional Intake, Agricultural Production, and Conflict Presented by Dr. Yanyan Liu 4. Bangladesh: Pathways of impact of agriculture on nutrition Presented by Dr. Akhter Ahmed
Does Agricultural Transformation Slow Progress toward Achieving Food and Nutrition Security in Cambodia? Coauthor: Jean-Francois Trinh Tan Financial support: United States Agency for International Development (USAID) Dr. Olivier Ecker
Motivation and Research Questions Cambodia’s Rectangular Strategy (2009-2013) aims at achieving food and nutrition security through agricultural transformation and growth (p. 13):  The first Strategic Rectangle to promote broad-based economic growth is the “enhancement of the agricultural sector”, “especially in the high-potential agricultural and agro-industrial sectors”.  “The agricultural policy of the Royal Government is to improve agricultural productivity and diversification […] to bolster economic growth, create employment and generate income in the rural areas, thus ensuring nutritional improvement, food security and increased agricultural exports.”  This requires “shifting the direction from «expansionary» or «extensive» agriculture to «deepening» or «intensive» agriculture, especially by increasing the yields using the existing land through intensification”.  Does agricultural transformation and growth translate into improved food and nutrition security (FNS)?  What are the policy-relevant variables enabling this transmission?
Dietary Quality-Growth Relationship Cambodia Global trend GDP per capita (constant 2005 US$) Share of calorie supply from staples (%) 1992 2005 2009 SGDP=600 = -0.09 Annual change in share of calorie supply (%-points) Annual GDP per capita growth (%) Arc semi- elasticity 1992-2005 -0.54 5.30 -0.10 2005-2009 -0.50 5.33 -0.09 Source: Own estimation based on data from FAO’s FSI and World Bank’s WDI, complemented with IMF’s WEO and UNSTAT data.
Undernutrition-Growth Relationship Source: Own estimation based on data from World Bank’s WDI, complemented with IMF’s WEO, UNSTAT, and recent country survey data. Cambodia Global trend GDP per capita (constant 2005 US$) Prevalence of underweight among children under 5 years (%) 1996 2005 2010 SGDP=600 = -0.17 Annual change in child underweight (%-points) Annual GDP per capita growth (%) Arc semi- elasticity 1996-2005 -1.58 6.43 -0.25 2005-2010 0.12 5.13 0.02
Agricultural Transformation and Malnutrition 20 25 30 35 40 45 200 300 400 500 600 700 1996 1998 2000 2002 2004 2006 2008 2010 Constant 2005 US$ (PPP) Percent of children (<5 years) Child underweight GDP per capita Agriculture value added per worker Agricultural Transformation Source: Own estimation based on World Bank’s WDI data.
Measuring Agricultural Transformation  Agricultural transformation is characterized by at least four interlinked developments: 1. Commercialization: From subsistence to market-oriented production 2. Intensification: Total factor productivity (TFP) growth 3. Specialization: Reduction of production diversity on profitable activities 4. Deagrarianization: Moving-out of agriculture  The effects of agricultural transformation on FNS are likely to substantially differ across farm households, depending on their characteristics and stage in the agricultural transformation process.  Household surveys provide variables indicating farmers’ stage in transformation including the share of food sales on total food production, agricultural income, agricultural production diversity, and the share of off-farm income on total income.
Data and Methodology Data: Cambodia Socio-Economic Survey (CSES) 2009 Methodology: Linear regressions to explore (cross-sectional) correlation between agricultural transformation indicators and dietary diversity and child nutrition  Grouping of (farm) households acc. to their agricultural transformation stage  Dep. var:  Household Dietary Diversity Score, with a maximum of 16 food groups  Child weight-for-age z-score, measuring underweight  Indep. var.:  Per capita household expenditure; market distance  Ag. trans. var.: Share of food consumption from purchases, share of non- farm income, food crop diversity, livestock diversity  Household characteristics: Household size, gender of household head, female/mother’s education (primary, secondary), [child sex, age]  Controlling for district- and month-fixed effects
Household Classification Criterion: All 10,157 (100%) Farm 7,930 (78%) Non-farm 2,227 (22%) Subsistence farmer 1,379 (14%) Source: CSES 2009 data. Commercial farmer 2,378 (23%) Part-time farmer 4,173 (41%) Full-time farmer 3,757 (37%) Agricultural production Share of non-farm income >|< 50% Share of food consumption from purchases >|< 50%
Regression Results: Dietary Diversity Dep. var.: Household Dietary Diversity Score All Farm Full-time farmers Subsistence farmers Commercial farmers Part-time farmers Non-farm Per capita expenditure (log) 0.425*** 0.391*** 0.401*** 0.353*** 0.434*** 0.395*** 0.433*** Market distance (log) -0.163*** -0.166*** -0.140*** -0.085* -0.183*** -0.180*** -0.041 Farm household (=1) 0.241*** Share of non-farm income 0.156*** 0.358*** 0.513** 0.207 0.198 Share of food consumption from purchases 0.333*** 0.616*** 1.944*** -0.054 0.111 Food crop diversity (log) 0.008 -0.058 -0.179 0.017 0.005 Livestock diversity (log) 0.113*** 0.160*** 0.138* 0.177*** 0.086* Household size (log) 0.897*** 0.789*** 0.730*** 0.674*** 0.747*** 0.835*** 1.098*** Female-headed household (=1) 0.125*** 0.131*** 0.091** -0.058 0.140** 0.164*** 0.102 Female education, primary (=1) 0.060** 0.052** -0.009 -0.046 0.012 0.097** -0.004 Female education, secondary (=1) 0.007 0.035 -0.007 -0.009 0.016 0.079 -0.079 Constant 1.931*** 2.101*** 1.694*** 1.957*** 2.006*** 2.064*** 1.900*** Observations 10,157 7,930 3,757 1,379 2,378 4,173 2,227 Adjusted R-squared 0.380 0.371 0.410 0.482 0.376 0.353 0.416 Note: ***,**,* Coefficient is statistically significant at the 1%, 5%, and 10% level, respectively. Source: Own estimation based on CSES 2009 data.
Regression Results: Child Nutrition Dep. var.: Child weight-for-age z-score All Farm Full-time farmers Subsistence farmers Commercial farmers Part-time farmers Non-farm Per capita expenditure (log) 0.147*** 0.109** 0.113 0.172 -0.042 0.102* 0.166** Market distance (log) -0.059* -0.041 -0.037 0.097 -0.121 -0.093* 0.024 Farm household (=1) 0.093 Share of non-farm income -0.160* 0.273 -0.315 0.765** -0.158 Share of food consumption from purchases 0.160 0.234 0.312 0.511 -0.497** Food crop diversity (log) 0.014 0.120 0.253 0.138 0.008 Livestock diversity (log) 0.085 0.116 0.213 0.077 0.047 Age (log) -0.412*** -0.400*** -0.346*** -0.345*** -0.342*** -0.444*** -0.467*** Female (=1) 0.138*** 0.160*** 0.259*** 0.221** 0.278*** 0.056 0.051 Household size (log) 0.129** 0.163** 0.014 -0.091 0.084 0.255*** -0.014 Female-headed household (=1) -0.001 -0.014 0.085 -0.134 0.270* -0.145† 0.020 Mother's education, primary (=1) 0.096** 0.089* 0.040 0.163 0.017 0.112* 0.139 Mother's education, secondary (=1) 0.059 0.067 0.148 0.199 0.101 0.032 0.071 Constant -1.234** -0.782 -1.457 -2.771* -0.446 -0.190 -1.663 Observations 4,653 3,724 1,713 702 1,011 2 929 Adjusted R-squared 0.128 0.119 0.116 0.163 0.120 0.125 0.141 Note: ***,**,* Coefficient is statistically significant at the 1%, 5%, and 10% level, respectively. Source: Own estimation based on CSES 2009 data.
Conclusions and Policy Implications  Economic growth is good but is not enough for reducing (child) malnutrition.  Agricultural transformation may slow down progress toward achieving food and nutrition security, depending on the patterns of transformation and the adaptation capacity of the food and nutrition insecure farm households.  Market expansion benefits FNS overall.  Farm households tend to be more food and nutrition secure than non-farm households, while FNS—among subsistence farmers—increases with growing non-farm income.  Subsistence farmers’ FNS increases with higher shares of food consumption from purchases, whereas there is no evidence for positive effects from food production diversification—but, from livestock diversification.  Unlike for FNS, there is no evidence for positive effects of agricultural transformation on child nutrition.  To make agricultural transformation more nutrition-sensitive, complementary nutrition-specific interventions are needed.
Tajikistan: Agricultural Biodiversity, Dietary Diversity, and Nutritional Outcomes Dr. Kamiljon T. Akramov Coauthor: Mehrab Malek Financial support: United States Agency for International Development (USAID)
Motivation • Despite recent improvements, malnutrition in Tajikistan remains very high: stunting among children under 5 is about 30% • The current strategy of national government and development partners is to promote agricultural growth and diversification to ensure food security and nutritional outcomes – Agrarian policy concept, Food Security Strategy and Agricultural Investment Plan adopted by government in 2011 – USAID’s FFP and FTF programs and World Bank managed Global Agriculture and Food Security Program • These interventions could be very beneficial given the fact that Tajikistan has less diversified agricultural production system – About 75% of sown area is allocated to wheat and cotton • However, there is little evidence regarding the linkages between agricultural diversity, dietary diversity and nutrition in Central Asian context
Household diets are dominated by cereals (wheat) 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Percent Composition of calorie intake in Tajikistan, 1992-2007 Other Animal products Sugar Vegetable oil Vegetables Fruits Cereals Page 22
Research questions • This study aims to provide empirical evidence on agriculture-nutrition linkages in Tajikistan by investigating three interrelated questions – How is agricultural diversity associated with household dietary diversity? – Does allocation of more land to cotton and wheat affect dietary diversity? – How is dietary diversity correlated with nutritional outcomes? • Assumption: Agricultural biodiversity influences nutritional outcomes mainly by improving dietary diversity of households and individuals • What are the policy implications of main findings of the study?
Measuring agricultural and dietary diversity • Dietary diversity – Count based household DD score was developed using FAO’s (2011) guidelines (12 food groups) – Calorie intake and food expenditure weighted Berry indexes capture richness and evenness – Calorie intake and food expenditure based Log-abundance indexes captures richness and abundance • Agricultural diversity – Count based household level agricultural diversity score – Land allocation based and population-weighted log abundance diversity scores were calculated at the district level
Data and Methodology • Data sources – Tajikistan Living Standards Survey (TLSS) 2007 and 2009 – District level population and land allocation data (Regions of Tajikistan database, National Agency on Statistics, 2011) • Methodology: Multilevel mixed effects and control function models to examine relationships between agricultural diversity and dietary diversity and nutritional outcomes – Dependent variables: HH dietary diversity scores and child stunting, measuring chronic malnutrition – Key independent variables: agricultural diversity at HH and district levels and share of cotton and wheat in total land area; HH dietary diversity score, with a maximum of 12 food groups – Control variables: child, HH and community characteristics, and region fixed effects
Regression Results: Dietary diversity Count-based DD Calorie-weighted DD Expenditure- weighted DD Calorie-based log- abundance DD Expen.-based log- abundance DD Agricultural diversity (HH) 0.0500*** 0.0178 0.0235** 0.171*** 0.125*** (0.0127) (0.0119) (0.0118) (0.0623) (0.0411) Agricultural diversity (district) 0.0107* 0.0207*** 0.0238*** 0.0971*** 0.0902*** (0.0062) (0.0061) (0.0062) (0.0318) (0.0227) HH expenditure (log) 0.0901*** 0.0447*** 0.0544*** 0.732*** 0.499*** (0.00730) (0.00619) (0.00640) (0.0491) (0.0337) Poor -0.0178*** -0.00895* -0.0119** -0.140*** -0.122*** (0.00580) (0.00522) (0.00512) (0.0329) (0.0223) Location 0.0199** 0.00996* 0.0138** 0.0881** 0.0111 (0.00787) (0.00591) (0.00656) (0.0381) (0.0251) HH size 0.00756*** 0.00150 0.000866 0.000773 0.0989*** (0.00106) (0.000963) (0.000919) (0.00506) (0.00345) No of children under 14 0.00331** -0.000484 0.000925 0.0121 0.00303 (0.00159) (0.00139) (0.00137) (0.00756) (0.00501) HH head's gender 0.00336 -0.0107** -0.00617 0.00425 0.00916 (0.00600) (0.00494) (0.00510) (0.0286) (0.0177) Altitude (log) -0.00559 -0.00505 -0.00971** -0.0331 -0.0244* (0.00469) (0.00420) (0.00418) (0.0227) (0.0148) Distance to oblast center -0.00730*** -0.00263** -0.00591*** -0.0278*** -0.0113** (0.00168) (0.00134) (0.00138) (0.00814) (0.00520) Grain and Cotton share -0.0385** -0.004 0.0144 -0.340*** -0.263*** (0.0162) (0.0128) (0.0130) (0.0804) (0.0522) Constant 0.279*** 0.481*** 0.483*** -0.353 -1.326*** (0.0541) (0.0487) (0.0488) (0.321) (0.222) Observations 2,991 2,991 2,991 2,991 2,991 F-test 29.38 10.85 15.6 63.88 114.55 R-squared 0.248 0.127 0.138 0.421 0.587 Note: Robust standard errors in parentheses; All specifications control for HH and community characteristics, region fixed effects *** p<0.01, ** p<0.05, * p<0.1 Dependent variable
Regression Results: Child Nutrition Count-based DD Calorie-weighted DD Expenditure- weighted DD Calorie-based log- abundance DD Expen.-based log- abundance DD Dietary diversity -0.337 0.731 0.598 -0.0477 -0.0350 (0.320) (0.455) (0.499) (0.0595) (0.0804) Child age in months (log) 0.0458 0.261*** 0.262** 0.0819 0.0775 (0.0803) (0.0979) (0.113) (0.0608) (0.0472) Interaction of DD with children age 0.0495 -0.249* -0.237 0.00110 0.00401 (0.101) (0.138) (0.151) (0.0188) (0.0238) Child's gender 0.0302 0.0299 0.0295 0.0299 0.0299 (0.0195) (0.0196) (0.0196) (0.0196) (0.0196) HH expenditure (log) 0.00580 -0.0147 -0.00733 0.0214 -0.00287 (0.0426) (0.0408) (0.0418) (0.0456) (0.0481) Poor 0.00768 0.00615 0.00681 0.00413 0.00493 (0.0346) (0.0347) (0.0346) (0.0345) (0.0344) Location -0.0626* -0.0646** -0.0626** -0.0625* -0.0634** (0.0322) (0.0318) (0.0317) (0.0321) (0.0320) Altitude (log) 0.0264 0.0295 0.0273 0.0252 0.0270 (0.0318) (0.0313) (0.0314) (0.0319) (0.0316) Distance to oblast center 0.0294*** 0.0292*** 0.0290*** 0.0292*** 0.0295*** (0.00989) (0.00986) (0.00985) (0.00993) (0.00989) Chi-squared# 50.08 52.26 52.14 50.23 48.13 p-value 0.0000 0.0000 0.0000 0.0000 0.0000 Log-likelihood -1540.299 -1539.7904 -1539.5953 -1540.001 -1541.4897 Wald test (full model) 432.91 422.89 405.14 440.38 435.57 p-value 0.0000 0.0000 0.0000 0.0000 0.0000 Observations 2,291 2,291 2,291 2,291 2,291 Note: Robust standard errors in parentheses; All specifications control for HH and community characteristics, region fixed effects # Chi-squared tests for joint significance of dietary diversity, age of child (log), and their interactions *** p<0.01, ** p<0.05, * p<0.1 Key independent variable
Summary of Findings • Key empirical results suggest that – Agricultural diversity is positively associated with dietary diversity, and – Dietary diversity is in turn correlated with child nutritional outcomes and this relationship depends on child’s age • Findings also suggest that there is a negative association between household dietary diversity and share of land allocation to cotton and wheat • Households in communities located further away from urban centers tend to have lower dietary diversity • These results are robust – Across alternative measures of household dietary diversity – Changes in estimation techniques – Controls for key child, household and community characteristics
Policy implications • Further promotion of agricultural diversity may be necessary by allocating more land to horticulture and feed crops – In some districts up to 85% of arable land is still allocated to cotton and wheat • Investment in infrastructure is important to promote market integration across different regions of the country • Regional cooperation and trade is crucial to ensure food and nutritional security in the country
Nutritional Intake, Agricultural Production, and Conflict in Nepal Dr. Yanyan Liu Financial support: United States Agency for International Development (USAID)
Nepal: A Diverse Country • 3 ecological region – Mountain – Hill – Terai DOLPA MUGU JUMLA KAILALI BARDIYA HUMLA DOTI SURKHET NAWAL PARASI KAPIL- BASTU RUPAN- DEHI DANG BANKE ACHHAM KALIKOT JHAPA MORANG SIRAHA SAPTARI DARCHULA BAJHANG BAITADI DADEL- DHURA KANCHAN- PUR BAJURA PARSA BARA RAUT- AHAT DHANUSA MAHO- TARI SUNSARI SARLAHI DHADING MAKAWAN- PUR CHITWAN KASKI TANAHU PALPA SYANGJA PARBAT ARGHAK HACHI GULMI UDAYAPUR SINDHULI ILAM BHOJ- PUR DHAN- KUTA TAPLEJUNG OKHAL- DHUNGA TERHA- THUM KHOTANG LALIT BHAK KATHM SULUK- HUMBU DOLAKHA SANKHUWA- SABA NUWAKOT SINDHU- PALCHOK KAVRE RASUWA LAMJUN G GORKHA PYUT- HAN ROLPA SALYAN MYAGDI DAILEKH JAJARKOT RUKUM MUSTANG MANANG CHINA INDIA N Far western Midwestern Western Central Eastern
Motivation: Food Security in Nepal • Food insecurity remains a severe problem – 2% annual population growth rate – Stagnant arable land – Recovering from the 1996-2006 civil which caused 15,000 deaths • 44% population under poverty line (UNDP) • 30% underweight for children under 5
Nutritional Intake (NLSS) 1996 2003 2011 Total daily energy intake (Kcal p.c.) 2112 2118 2376 % energy intake from cereals, roots, or tubers 0.841 0.802 0.728 % energy intake from eggs, milk, or meat 0.058 0.076 0.096 % energy intake from vegetable or fruits 0.012 0.016 0.027
Research Questions 1. How is nutritional intake associated with income and agricultural production? (agriculture-nutrition relation) 2. How is children’s anthropometry associated with nutritional intake (nutrition-health relation) 3. How did the civil war affect household’s nutritional intake?
Q1: Agriculture-Nutrition Relation • Data: Nepal Living Standard Survey (NLSS) 2010/2011 • Dependent variables – Log total daily energy intake (Kcal p.c.) – % energy intake from cereals, roots, and tubers – % energy intake from eggs, milk, and meat – % energy intake from vegetable and fruits
Q1: Agriculture-Nutrition Relation (2) • Explanatory variables – Log total expenditure p. c. – Total amount of livestock p. c. – Whether produce cereals, tubers, or roots – Whether produce vegetable or fruits – Household characteristics including education, household size, female headship, age of head, number of female adults, and number of male adults – Community fixed effects to control for market price, access, etc.
Findings: Agriculture-Nutrition Relation Log energy intake % energy intake from Cereals, roots, tubers Milk, egg, meat Vegetable, fruits Log expend p.c. 0.102*** -0.0474*** 0.0257*** 0.00477*** Heads of livestock p.c. 0.0158*** -0.00195** 0.00182*** 0.000145 If produce cereals, roots, tubers 0.0856*** 0.00892 -0.00101 0.00155 If produce vegetable, fruits 0.00998 -0.0139*** 0.00837*** 0.00227*** • Livestock ownership and agricultural production have direct effects on nutritional intake
Q2: Nutrition-Health Relation • Data: NLSS 2010/11 • Dependent variables – Length/Height-for-age Z score (<5 yrs) – Weight-for-age Z score (<5 yrs) • Explanatory variables – Log expenditure p.c. – Total energy intake p.c. – % energy intake from cereals, roots, tubers – Household characteristics
Findings: Nutrition-Health Relation Z score Height-for-age Weight-for-age Log expenditure p.c. 0.137** 0.101** Log total energy intake -0.0251 -0.0927 % energy intake from cereal, roots, tubers -1.191*** -0.617** • Higher dietary diversity contributes to higher Z scores
Q3: Effect of Civil War on Nutrition • Data: – NLSS R1 (1995/96) and R2 (2002/03) – Number of people killed by year and district • Possible pathways through adverse effects on – agricultural production – agricultural assets (such as livestock) – market and transportation infrastructure
Q3: Effect of Civil War on Nutrition • Difference-in-difference (DID) method controlling for ecological region-year specific effects • Findings: the effects of the civil war – Total energy intake (no effect) – % energy intake from cereals, roots, or tubers (+) – % energy intake from eggs, milk, or meat (-) – % energy intake from vegetable or fruits (-)
Pathways: Effect of Civil War on Nutrition • Effects of civil war on – Total expenditure p.c. (no effects) – Total amount of livestock p.c. (no effects) – Total income from crops (no effects) • The pathway is not likely to be through the effects on agricultural production or assets. • Income elasticity on nutritional intake is lower in more conflict-intensive areas, indicating that markets being damaged may be the pathway.
Summary of findings • Dietary diversity is positively associated with livestock ownership and production of vegetable and fruits • Children’s length/height-for-age Z score and weight-for-age Z score are positively associated with household’s dietary diversity. • The civil war decreased households’ dietary diversity, likely through the disruption of the markets.
Pathways of impact of agriculture on nutrition: Evidence from Bangladesh Coauthor: Esha Sraboni Financial support: United States Agency for International Development (USAID) Dr. Akhter Ahmed
Pathways of impacts of agriculture on nutrition At household level:  Income  Education  Agricultural diversity  Dietary diversity  Gender equity At national level:  Agriculture research (biofortification, productivity increase)  Relative food prices  Policies
ISSUES
Food and Nutrition Security: What's the role of Agricultural Policy in Asia?
Overwhelming dominance of rice in diet: Share of rice in total nutrient intakes of Bangladeshis Source: IFPRI 2011-12 Bangladesh Integrated Household Survey
Rice-centric agriculture in Bangladesh: Share of crops on total cropped land Source: IFPRI 2011-12 Bangladesh Integrated Household Survey
Relative prices and diet quality  Real (inflation-adjusted) prices of rice has fallen by 36% over the past two decades. This has helped the rural landless and the urban poor who purchase the rice they consume  Despite a falling real price of rice, 17% of the population—about 27 million ultra poor—remain seriously underfed  Therefore, the level of technology and institutional innovations that made this price decline possible must be maintained  However, the real prices of some foods that are rich in micronutrients (fruits, vegetables, pulses, animal sourced foods) are increasing  If policies are not undertaken to increase supply of non-cereal, nutrient- rich foods, then their prices will continue to increase in the face of income and population growth  Consequently, the diet quality and nutritional status of the poor are likely to deteriorate further
EMPIRICAL EVIDENCE
Data and methodology Data: Bangladesh Integrated Household Survey (BIHS)a nationally representative household survey conducted by IFPRI in 2011-2012 Methodology  Instrumental variable regression to explore (cross-sectional) association between dietary diversity and agricultural diversity  Dep. var: Household dietary diversity score (DDS) with a maximum of 12 food groups (calculated from a 7-day food consumption recall)  Indep. var.: Number of food crops grown (instrumented by soil types and percentage of land irrigated), education, household size and demographic composition, assets, electricity connection, rice price, location fixed effects  OLS to explore association between food crop diversity and explanatory variables
Agricultural diversity leads to dietary diversity  Found statistically significant positive association between production diversity and dietary diversity  Other statistically significant factors influencing dietary diversity are:  Education of male hh head and female spouse (positive)  HH size (positive)  Electricity access (positive)  Farm size (positive)  Milk cow ownership (positive)  Hand tubewell ownership (positive)  Rice price (positive)
What factors affect crop diversity?  Crop diversity increases if:  Share of irrigated cropped area increases  Agricultural extension agents visit farm household  Household has access to loans  Household uses power tiller for land preparation  Crop diversity deceases if:  Household grows winter rice
WHAT ARE THE OPPORTUNITIES FOR LINKING AGRICULTURE AND NUTRITION IN BANGLADESH?
CGIAR Research Program (4) Agriculture for Nutrition and Health (A4NH) IFPRI-led, with 11 other CG centers
IFPRI’s work on agriculture-nutrition linkages in Bangladesh  Biofortification: High-zinc rice (HarvestPlus) released in Bangladesh in August 2013—the world’s first zinc-enriched rice variety  Community-level integrated programs:  Homestead food production (e.g. with HKI, BRAC)  Fish systems, fish ponds (with World Fish and partners)  Nutrition-sensitive value chains (fish with World Fish)  Policy research on integration of agriculture-health-nutrition in collaboration with:  other new nutrition-focused programs (e.g. Transform Nutrition, Leveraging Agriculture for Nutrition in South Asia (LANSA))  food security-focused policy research PRSSP  Agricultural Policy Support Program (APSU) supported by PRSSP with USAID funding
Photo: One Acre Fund Homestead Food Production to Improve Nutrition
HKI’s Homestead food production in Bangladesh Program: •: • Impact: Source: Millions Fed , IFPRI, 2009; www.ifpri.org/millionsfed Production-focused: micronutrient-rich vegetables, small livestock production Nutrition education to promote consumption  Focus on women: income generation, empowerment Nutrition objective: Improve diet diversity, micronutrient intake Integrating agriculture and nutrition at household and community level Tripled vegetable production; increased income  73% of gardens managed by women improved food security for 5 million people
Page 60 Agriculture Policies for Nutrition
Page 61 Agricultural policy and diet quality The solution to poor diet quality lies in balanced consumption of nutrient-rich foods (fruits, vegetables, fish, meat, milk) which the poor desire but cannot afford. Policies should be undertaken to:  Increase agricultural investments in non-staples that are high value added and high nutrition value added  Reduce production and market risks associated with these crops  Support the bio-fortification of staple crops with micronutrients  Recent agricultural research initiatives by CGIAR centers – the Bio- fortification Challenge Program HarvestPlus – and the Bangladesh Rice Research Institute (BRRI) have shown that it is possible to get the plants themselves to do the work of fortification. Zinc-enriched rice was developed and released. Bio-fortification is cost-effective and sustainable. Rice is an excellent vehicle for bio-fortification (with iron and zinc) in the sense that the entire population eats it.
Summary and Conclusions Dr. Akhter Ahmed, H.E. Srun Darith,
Take-Away Messages  We have opportunities and examples of success on how to bridge the agriculture-nutrition divide  Our challenge AND opportunity is to work together - cross-sectorally (how?)  We need to do much better at documenting successes – and failures; we need the evidence for advocacy, to stimulate investments  …  …

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Food and Nutrition Security: What's the role of Agricultural Policy in Asia?

  • 1. Food and Nutrition Security: What’s the Role of Agricultural Policy in Asia? Siem Reap, Cambodia; September 25-27, 2013 International Conference on “Agricultural Transformation in Asia: Policy Options for Food and Nutrition Security” H.E. Srun Darith (moderator); Dr. Akhter Ahmed, Dr. Kamiljon Akramov, Dr. Olivier Ecker, Dr. Yanyan Liu
  • 2. Introduction H.E. Srun Darith, Dr. Olivier Ecker
  • 3. Motivation  Malnutrition slows economic growth (2-3% GDP lost) and deepens poverty through productivity losses (10% of lifetimes earnings) from poor physical performance and cognitive capacity as well as increased health care costs.  Poverty and malnutrition are closely associated and highly prevalent in rural areas.  Although agricultural growth has been shown to have high poverty reduction effects (Christiaensen et al. 2011; Diao et al. 2010; World Bank 2007), empirical evidence on its nutrition impact is limited and inconclusive (Pinstrup-Anderson 2013; Berti et al. 2004; Masset et al. 2011).  Nonetheless, development and agricultural policy is often based on the assumption that agricultural growth—particularly among smallholder farmers—improves household food security and thereby reduces malnutrition.
  • 4. Pathways from Agricultural Transformation and Growth to Food and Nutrition Security Agricultural transformation and growth driven by  Demand increase  Productivity growth due to policy reform, investment, techno logical progress Purchasing power increase from  Income growth among farmers  Food price reduction Change in food self- sufficiency dependence among subsistence farmers Household food and nutrition security (in terms of food quantity and dietary quality) Nutrition outcomes Intra-household resource allocation, care, education, health environment
  • 5. Dietary Quality-Growth Relationship Cambodia Global trend GDP per capita (constant 2005 US$) Share of calorie supply from staples (%) Bangladesh Nepal Tajikistan Source: O. Ecker based on data from FAO’s FSI and World Bank’s WDI, complemented with IMF’s WEO and UNSTAT data.
  • 6. Undernutrition-Growth Relationship Source: O. Ecker based on data from World Bank’s WDI, complemented with IMF’s WEO, UNSTAT, and recent country survey data. Cambodia Global trend GDP per capita (constant 2005 US$) Prevalence of underweight among children under 5 years (%) Bangladesh Nepal Tajikistan
  • 7. Dietary Diversity as FNS Indicator  Dietary quality contributes to an individual’s nutrition and health status and thereby to people’s economic productivity.  Households will only diversify their diets into higher-value micronutrient-rich foods when they have satisfied their basic calorie needs. For the poor, these foods are often unavailable or unaffordable.  Dietary diversity is a strong predictor of dietary quality in terms of (micro)nutrient intake and adequacy (Ruel et al. 2013).  Household dietary diversity is strongly correlated with per capita calorie consumption and dietary energy adequacy and is correlated with nutrition outcome indicators such as anthropometrics (Ruel 2003; Ruel et al. 2013).  Dietary diversity is responsive to welfare trends and sensitive to shocks and seasonality, indicating high inter-temporal validity (Headey & Ecker 2013).  Dietary diversity is measured as a count of different foods or food groups consumed over a specified reference period.  All country cases studies use 12-scale or 16-scale household Dietary Diversity Scores (DDS) as indicator of household food and nutrition security (FNS).
  • 8. Evidence from 4 Country Case Studies 1. Cambodia: Does agricultural transformation slow progress toward achieving food and nutrition security? Presented by Dr. Olivier Ecker 2. Tajikistan: Agricultural biodiversity, dietary diversity and nutritional outcomes Presented by Dr. Kamiljon Akramov 3. Nepal: Nutritional Intake, Agricultural Production, and Conflict Presented by Dr. Yanyan Liu 4. Bangladesh: Pathways of impact of agriculture on nutrition Presented by Dr. Akhter Ahmed
  • 9. Does Agricultural Transformation Slow Progress toward Achieving Food and Nutrition Security in Cambodia? Coauthor: Jean-Francois Trinh Tan Financial support: United States Agency for International Development (USAID) Dr. Olivier Ecker
  • 10. Motivation and Research Questions Cambodia’s Rectangular Strategy (2009-2013) aims at achieving food and nutrition security through agricultural transformation and growth (p. 13):  The first Strategic Rectangle to promote broad-based economic growth is the “enhancement of the agricultural sector”, “especially in the high-potential agricultural and agro-industrial sectors”.  “The agricultural policy of the Royal Government is to improve agricultural productivity and diversification […] to bolster economic growth, create employment and generate income in the rural areas, thus ensuring nutritional improvement, food security and increased agricultural exports.”  This requires “shifting the direction from «expansionary» or «extensive» agriculture to «deepening» or «intensive» agriculture, especially by increasing the yields using the existing land through intensification”.  Does agricultural transformation and growth translate into improved food and nutrition security (FNS)?  What are the policy-relevant variables enabling this transmission?
  • 11. Dietary Quality-Growth Relationship Cambodia Global trend GDP per capita (constant 2005 US$) Share of calorie supply from staples (%) 1992 2005 2009 SGDP=600 = -0.09 Annual change in share of calorie supply (%-points) Annual GDP per capita growth (%) Arc semi- elasticity 1992-2005 -0.54 5.30 -0.10 2005-2009 -0.50 5.33 -0.09 Source: Own estimation based on data from FAO’s FSI and World Bank’s WDI, complemented with IMF’s WEO and UNSTAT data.
  • 12. Undernutrition-Growth Relationship Source: Own estimation based on data from World Bank’s WDI, complemented with IMF’s WEO, UNSTAT, and recent country survey data. Cambodia Global trend GDP per capita (constant 2005 US$) Prevalence of underweight among children under 5 years (%) 1996 2005 2010 SGDP=600 = -0.17 Annual change in child underweight (%-points) Annual GDP per capita growth (%) Arc semi- elasticity 1996-2005 -1.58 6.43 -0.25 2005-2010 0.12 5.13 0.02
  • 13. Agricultural Transformation and Malnutrition 20 25 30 35 40 45 200 300 400 500 600 700 1996 1998 2000 2002 2004 2006 2008 2010 Constant 2005 US$ (PPP) Percent of children (<5 years) Child underweight GDP per capita Agriculture value added per worker Agricultural Transformation Source: Own estimation based on World Bank’s WDI data.
  • 14. Measuring Agricultural Transformation  Agricultural transformation is characterized by at least four interlinked developments: 1. Commercialization: From subsistence to market-oriented production 2. Intensification: Total factor productivity (TFP) growth 3. Specialization: Reduction of production diversity on profitable activities 4. Deagrarianization: Moving-out of agriculture  The effects of agricultural transformation on FNS are likely to substantially differ across farm households, depending on their characteristics and stage in the agricultural transformation process.  Household surveys provide variables indicating farmers’ stage in transformation including the share of food sales on total food production, agricultural income, agricultural production diversity, and the share of off-farm income on total income.
  • 15. Data and Methodology Data: Cambodia Socio-Economic Survey (CSES) 2009 Methodology: Linear regressions to explore (cross-sectional) correlation between agricultural transformation indicators and dietary diversity and child nutrition  Grouping of (farm) households acc. to their agricultural transformation stage  Dep. var:  Household Dietary Diversity Score, with a maximum of 16 food groups  Child weight-for-age z-score, measuring underweight  Indep. var.:  Per capita household expenditure; market distance  Ag. trans. var.: Share of food consumption from purchases, share of non- farm income, food crop diversity, livestock diversity  Household characteristics: Household size, gender of household head, female/mother’s education (primary, secondary), [child sex, age]  Controlling for district- and month-fixed effects
  • 16. Household Classification Criterion: All 10,157 (100%) Farm 7,930 (78%) Non-farm 2,227 (22%) Subsistence farmer 1,379 (14%) Source: CSES 2009 data. Commercial farmer 2,378 (23%) Part-time farmer 4,173 (41%) Full-time farmer 3,757 (37%) Agricultural production Share of non-farm income >|< 50% Share of food consumption from purchases >|< 50%
  • 17. Regression Results: Dietary Diversity Dep. var.: Household Dietary Diversity Score All Farm Full-time farmers Subsistence farmers Commercial farmers Part-time farmers Non-farm Per capita expenditure (log) 0.425*** 0.391*** 0.401*** 0.353*** 0.434*** 0.395*** 0.433*** Market distance (log) -0.163*** -0.166*** -0.140*** -0.085* -0.183*** -0.180*** -0.041 Farm household (=1) 0.241*** Share of non-farm income 0.156*** 0.358*** 0.513** 0.207 0.198 Share of food consumption from purchases 0.333*** 0.616*** 1.944*** -0.054 0.111 Food crop diversity (log) 0.008 -0.058 -0.179 0.017 0.005 Livestock diversity (log) 0.113*** 0.160*** 0.138* 0.177*** 0.086* Household size (log) 0.897*** 0.789*** 0.730*** 0.674*** 0.747*** 0.835*** 1.098*** Female-headed household (=1) 0.125*** 0.131*** 0.091** -0.058 0.140** 0.164*** 0.102 Female education, primary (=1) 0.060** 0.052** -0.009 -0.046 0.012 0.097** -0.004 Female education, secondary (=1) 0.007 0.035 -0.007 -0.009 0.016 0.079 -0.079 Constant 1.931*** 2.101*** 1.694*** 1.957*** 2.006*** 2.064*** 1.900*** Observations 10,157 7,930 3,757 1,379 2,378 4,173 2,227 Adjusted R-squared 0.380 0.371 0.410 0.482 0.376 0.353 0.416 Note: ***,**,* Coefficient is statistically significant at the 1%, 5%, and 10% level, respectively. Source: Own estimation based on CSES 2009 data.
  • 18. Regression Results: Child Nutrition Dep. var.: Child weight-for-age z-score All Farm Full-time farmers Subsistence farmers Commercial farmers Part-time farmers Non-farm Per capita expenditure (log) 0.147*** 0.109** 0.113 0.172 -0.042 0.102* 0.166** Market distance (log) -0.059* -0.041 -0.037 0.097 -0.121 -0.093* 0.024 Farm household (=1) 0.093 Share of non-farm income -0.160* 0.273 -0.315 0.765** -0.158 Share of food consumption from purchases 0.160 0.234 0.312 0.511 -0.497** Food crop diversity (log) 0.014 0.120 0.253 0.138 0.008 Livestock diversity (log) 0.085 0.116 0.213 0.077 0.047 Age (log) -0.412*** -0.400*** -0.346*** -0.345*** -0.342*** -0.444*** -0.467*** Female (=1) 0.138*** 0.160*** 0.259*** 0.221** 0.278*** 0.056 0.051 Household size (log) 0.129** 0.163** 0.014 -0.091 0.084 0.255*** -0.014 Female-headed household (=1) -0.001 -0.014 0.085 -0.134 0.270* -0.145† 0.020 Mother's education, primary (=1) 0.096** 0.089* 0.040 0.163 0.017 0.112* 0.139 Mother's education, secondary (=1) 0.059 0.067 0.148 0.199 0.101 0.032 0.071 Constant -1.234** -0.782 -1.457 -2.771* -0.446 -0.190 -1.663 Observations 4,653 3,724 1,713 702 1,011 2 929 Adjusted R-squared 0.128 0.119 0.116 0.163 0.120 0.125 0.141 Note: ***,**,* Coefficient is statistically significant at the 1%, 5%, and 10% level, respectively. Source: Own estimation based on CSES 2009 data.
  • 19. Conclusions and Policy Implications  Economic growth is good but is not enough for reducing (child) malnutrition.  Agricultural transformation may slow down progress toward achieving food and nutrition security, depending on the patterns of transformation and the adaptation capacity of the food and nutrition insecure farm households.  Market expansion benefits FNS overall.  Farm households tend to be more food and nutrition secure than non-farm households, while FNS—among subsistence farmers—increases with growing non-farm income.  Subsistence farmers’ FNS increases with higher shares of food consumption from purchases, whereas there is no evidence for positive effects from food production diversification—but, from livestock diversification.  Unlike for FNS, there is no evidence for positive effects of agricultural transformation on child nutrition.  To make agricultural transformation more nutrition-sensitive, complementary nutrition-specific interventions are needed.
  • 20. Tajikistan: Agricultural Biodiversity, Dietary Diversity, and Nutritional Outcomes Dr. Kamiljon T. Akramov Coauthor: Mehrab Malek Financial support: United States Agency for International Development (USAID)
  • 21. Motivation • Despite recent improvements, malnutrition in Tajikistan remains very high: stunting among children under 5 is about 30% • The current strategy of national government and development partners is to promote agricultural growth and diversification to ensure food security and nutritional outcomes – Agrarian policy concept, Food Security Strategy and Agricultural Investment Plan adopted by government in 2011 – USAID’s FFP and FTF programs and World Bank managed Global Agriculture and Food Security Program • These interventions could be very beneficial given the fact that Tajikistan has less diversified agricultural production system – About 75% of sown area is allocated to wheat and cotton • However, there is little evidence regarding the linkages between agricultural diversity, dietary diversity and nutrition in Central Asian context
  • 22. Household diets are dominated by cereals (wheat) 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Percent Composition of calorie intake in Tajikistan, 1992-2007 Other Animal products Sugar Vegetable oil Vegetables Fruits Cereals Page 22
  • 23. Research questions • This study aims to provide empirical evidence on agriculture-nutrition linkages in Tajikistan by investigating three interrelated questions – How is agricultural diversity associated with household dietary diversity? – Does allocation of more land to cotton and wheat affect dietary diversity? – How is dietary diversity correlated with nutritional outcomes? • Assumption: Agricultural biodiversity influences nutritional outcomes mainly by improving dietary diversity of households and individuals • What are the policy implications of main findings of the study?
  • 24. Measuring agricultural and dietary diversity • Dietary diversity – Count based household DD score was developed using FAO’s (2011) guidelines (12 food groups) – Calorie intake and food expenditure weighted Berry indexes capture richness and evenness – Calorie intake and food expenditure based Log-abundance indexes captures richness and abundance • Agricultural diversity – Count based household level agricultural diversity score – Land allocation based and population-weighted log abundance diversity scores were calculated at the district level
  • 25. Data and Methodology • Data sources – Tajikistan Living Standards Survey (TLSS) 2007 and 2009 – District level population and land allocation data (Regions of Tajikistan database, National Agency on Statistics, 2011) • Methodology: Multilevel mixed effects and control function models to examine relationships between agricultural diversity and dietary diversity and nutritional outcomes – Dependent variables: HH dietary diversity scores and child stunting, measuring chronic malnutrition – Key independent variables: agricultural diversity at HH and district levels and share of cotton and wheat in total land area; HH dietary diversity score, with a maximum of 12 food groups – Control variables: child, HH and community characteristics, and region fixed effects
  • 26. Regression Results: Dietary diversity Count-based DD Calorie-weighted DD Expenditure- weighted DD Calorie-based log- abundance DD Expen.-based log- abundance DD Agricultural diversity (HH) 0.0500*** 0.0178 0.0235** 0.171*** 0.125*** (0.0127) (0.0119) (0.0118) (0.0623) (0.0411) Agricultural diversity (district) 0.0107* 0.0207*** 0.0238*** 0.0971*** 0.0902*** (0.0062) (0.0061) (0.0062) (0.0318) (0.0227) HH expenditure (log) 0.0901*** 0.0447*** 0.0544*** 0.732*** 0.499*** (0.00730) (0.00619) (0.00640) (0.0491) (0.0337) Poor -0.0178*** -0.00895* -0.0119** -0.140*** -0.122*** (0.00580) (0.00522) (0.00512) (0.0329) (0.0223) Location 0.0199** 0.00996* 0.0138** 0.0881** 0.0111 (0.00787) (0.00591) (0.00656) (0.0381) (0.0251) HH size 0.00756*** 0.00150 0.000866 0.000773 0.0989*** (0.00106) (0.000963) (0.000919) (0.00506) (0.00345) No of children under 14 0.00331** -0.000484 0.000925 0.0121 0.00303 (0.00159) (0.00139) (0.00137) (0.00756) (0.00501) HH head's gender 0.00336 -0.0107** -0.00617 0.00425 0.00916 (0.00600) (0.00494) (0.00510) (0.0286) (0.0177) Altitude (log) -0.00559 -0.00505 -0.00971** -0.0331 -0.0244* (0.00469) (0.00420) (0.00418) (0.0227) (0.0148) Distance to oblast center -0.00730*** -0.00263** -0.00591*** -0.0278*** -0.0113** (0.00168) (0.00134) (0.00138) (0.00814) (0.00520) Grain and Cotton share -0.0385** -0.004 0.0144 -0.340*** -0.263*** (0.0162) (0.0128) (0.0130) (0.0804) (0.0522) Constant 0.279*** 0.481*** 0.483*** -0.353 -1.326*** (0.0541) (0.0487) (0.0488) (0.321) (0.222) Observations 2,991 2,991 2,991 2,991 2,991 F-test 29.38 10.85 15.6 63.88 114.55 R-squared 0.248 0.127 0.138 0.421 0.587 Note: Robust standard errors in parentheses; All specifications control for HH and community characteristics, region fixed effects *** p<0.01, ** p<0.05, * p<0.1 Dependent variable
  • 27. Regression Results: Child Nutrition Count-based DD Calorie-weighted DD Expenditure- weighted DD Calorie-based log- abundance DD Expen.-based log- abundance DD Dietary diversity -0.337 0.731 0.598 -0.0477 -0.0350 (0.320) (0.455) (0.499) (0.0595) (0.0804) Child age in months (log) 0.0458 0.261*** 0.262** 0.0819 0.0775 (0.0803) (0.0979) (0.113) (0.0608) (0.0472) Interaction of DD with children age 0.0495 -0.249* -0.237 0.00110 0.00401 (0.101) (0.138) (0.151) (0.0188) (0.0238) Child's gender 0.0302 0.0299 0.0295 0.0299 0.0299 (0.0195) (0.0196) (0.0196) (0.0196) (0.0196) HH expenditure (log) 0.00580 -0.0147 -0.00733 0.0214 -0.00287 (0.0426) (0.0408) (0.0418) (0.0456) (0.0481) Poor 0.00768 0.00615 0.00681 0.00413 0.00493 (0.0346) (0.0347) (0.0346) (0.0345) (0.0344) Location -0.0626* -0.0646** -0.0626** -0.0625* -0.0634** (0.0322) (0.0318) (0.0317) (0.0321) (0.0320) Altitude (log) 0.0264 0.0295 0.0273 0.0252 0.0270 (0.0318) (0.0313) (0.0314) (0.0319) (0.0316) Distance to oblast center 0.0294*** 0.0292*** 0.0290*** 0.0292*** 0.0295*** (0.00989) (0.00986) (0.00985) (0.00993) (0.00989) Chi-squared# 50.08 52.26 52.14 50.23 48.13 p-value 0.0000 0.0000 0.0000 0.0000 0.0000 Log-likelihood -1540.299 -1539.7904 -1539.5953 -1540.001 -1541.4897 Wald test (full model) 432.91 422.89 405.14 440.38 435.57 p-value 0.0000 0.0000 0.0000 0.0000 0.0000 Observations 2,291 2,291 2,291 2,291 2,291 Note: Robust standard errors in parentheses; All specifications control for HH and community characteristics, region fixed effects # Chi-squared tests for joint significance of dietary diversity, age of child (log), and their interactions *** p<0.01, ** p<0.05, * p<0.1 Key independent variable
  • 28. Summary of Findings • Key empirical results suggest that – Agricultural diversity is positively associated with dietary diversity, and – Dietary diversity is in turn correlated with child nutritional outcomes and this relationship depends on child’s age • Findings also suggest that there is a negative association between household dietary diversity and share of land allocation to cotton and wheat • Households in communities located further away from urban centers tend to have lower dietary diversity • These results are robust – Across alternative measures of household dietary diversity – Changes in estimation techniques – Controls for key child, household and community characteristics
  • 29. Policy implications • Further promotion of agricultural diversity may be necessary by allocating more land to horticulture and feed crops – In some districts up to 85% of arable land is still allocated to cotton and wheat • Investment in infrastructure is important to promote market integration across different regions of the country • Regional cooperation and trade is crucial to ensure food and nutritional security in the country
  • 30. Nutritional Intake, Agricultural Production, and Conflict in Nepal Dr. Yanyan Liu Financial support: United States Agency for International Development (USAID)
  • 31. Nepal: A Diverse Country • 3 ecological region – Mountain – Hill – Terai DOLPA MUGU JUMLA KAILALI BARDIYA HUMLA DOTI SURKHET NAWAL PARASI KAPIL- BASTU RUPAN- DEHI DANG BANKE ACHHAM KALIKOT JHAPA MORANG SIRAHA SAPTARI DARCHULA BAJHANG BAITADI DADEL- DHURA KANCHAN- PUR BAJURA PARSA BARA RAUT- AHAT DHANUSA MAHO- TARI SUNSARI SARLAHI DHADING MAKAWAN- PUR CHITWAN KASKI TANAHU PALPA SYANGJA PARBAT ARGHAK HACHI GULMI UDAYAPUR SINDHULI ILAM BHOJ- PUR DHAN- KUTA TAPLEJUNG OKHAL- DHUNGA TERHA- THUM KHOTANG LALIT BHAK KATHM SULUK- HUMBU DOLAKHA SANKHUWA- SABA NUWAKOT SINDHU- PALCHOK KAVRE RASUWA LAMJUN G GORKHA PYUT- HAN ROLPA SALYAN MYAGDI DAILEKH JAJARKOT RUKUM MUSTANG MANANG CHINA INDIA N Far western Midwestern Western Central Eastern
  • 32. Motivation: Food Security in Nepal • Food insecurity remains a severe problem – 2% annual population growth rate – Stagnant arable land – Recovering from the 1996-2006 civil which caused 15,000 deaths • 44% population under poverty line (UNDP) • 30% underweight for children under 5
  • 33. Nutritional Intake (NLSS) 1996 2003 2011 Total daily energy intake (Kcal p.c.) 2112 2118 2376 % energy intake from cereals, roots, or tubers 0.841 0.802 0.728 % energy intake from eggs, milk, or meat 0.058 0.076 0.096 % energy intake from vegetable or fruits 0.012 0.016 0.027
  • 34. Research Questions 1. How is nutritional intake associated with income and agricultural production? (agriculture-nutrition relation) 2. How is children’s anthropometry associated with nutritional intake (nutrition-health relation) 3. How did the civil war affect household’s nutritional intake?
  • 35. Q1: Agriculture-Nutrition Relation • Data: Nepal Living Standard Survey (NLSS) 2010/2011 • Dependent variables – Log total daily energy intake (Kcal p.c.) – % energy intake from cereals, roots, and tubers – % energy intake from eggs, milk, and meat – % energy intake from vegetable and fruits
  • 36. Q1: Agriculture-Nutrition Relation (2) • Explanatory variables – Log total expenditure p. c. – Total amount of livestock p. c. – Whether produce cereals, tubers, or roots – Whether produce vegetable or fruits – Household characteristics including education, household size, female headship, age of head, number of female adults, and number of male adults – Community fixed effects to control for market price, access, etc.
  • 37. Findings: Agriculture-Nutrition Relation Log energy intake % energy intake from Cereals, roots, tubers Milk, egg, meat Vegetable, fruits Log expend p.c. 0.102*** -0.0474*** 0.0257*** 0.00477*** Heads of livestock p.c. 0.0158*** -0.00195** 0.00182*** 0.000145 If produce cereals, roots, tubers 0.0856*** 0.00892 -0.00101 0.00155 If produce vegetable, fruits 0.00998 -0.0139*** 0.00837*** 0.00227*** • Livestock ownership and agricultural production have direct effects on nutritional intake
  • 38. Q2: Nutrition-Health Relation • Data: NLSS 2010/11 • Dependent variables – Length/Height-for-age Z score (<5 yrs) – Weight-for-age Z score (<5 yrs) • Explanatory variables – Log expenditure p.c. – Total energy intake p.c. – % energy intake from cereals, roots, tubers – Household characteristics
  • 39. Findings: Nutrition-Health Relation Z score Height-for-age Weight-for-age Log expenditure p.c. 0.137** 0.101** Log total energy intake -0.0251 -0.0927 % energy intake from cereal, roots, tubers -1.191*** -0.617** • Higher dietary diversity contributes to higher Z scores
  • 40. Q3: Effect of Civil War on Nutrition • Data: – NLSS R1 (1995/96) and R2 (2002/03) – Number of people killed by year and district • Possible pathways through adverse effects on – agricultural production – agricultural assets (such as livestock) – market and transportation infrastructure
  • 41. Q3: Effect of Civil War on Nutrition • Difference-in-difference (DID) method controlling for ecological region-year specific effects • Findings: the effects of the civil war – Total energy intake (no effect) – % energy intake from cereals, roots, or tubers (+) – % energy intake from eggs, milk, or meat (-) – % energy intake from vegetable or fruits (-)
  • 42. Pathways: Effect of Civil War on Nutrition • Effects of civil war on – Total expenditure p.c. (no effects) – Total amount of livestock p.c. (no effects) – Total income from crops (no effects) • The pathway is not likely to be through the effects on agricultural production or assets. • Income elasticity on nutritional intake is lower in more conflict-intensive areas, indicating that markets being damaged may be the pathway.
  • 43. Summary of findings • Dietary diversity is positively associated with livestock ownership and production of vegetable and fruits • Children’s length/height-for-age Z score and weight-for-age Z score are positively associated with household’s dietary diversity. • The civil war decreased households’ dietary diversity, likely through the disruption of the markets.
  • 44. Pathways of impact of agriculture on nutrition: Evidence from Bangladesh Coauthor: Esha Sraboni Financial support: United States Agency for International Development (USAID) Dr. Akhter Ahmed
  • 45. Pathways of impacts of agriculture on nutrition At household level:  Income  Education  Agricultural diversity  Dietary diversity  Gender equity At national level:  Agriculture research (biofortification, productivity increase)  Relative food prices  Policies
  • 48. Overwhelming dominance of rice in diet: Share of rice in total nutrient intakes of Bangladeshis Source: IFPRI 2011-12 Bangladesh Integrated Household Survey
  • 49. Rice-centric agriculture in Bangladesh: Share of crops on total cropped land Source: IFPRI 2011-12 Bangladesh Integrated Household Survey
  • 50. Relative prices and diet quality  Real (inflation-adjusted) prices of rice has fallen by 36% over the past two decades. This has helped the rural landless and the urban poor who purchase the rice they consume  Despite a falling real price of rice, 17% of the population—about 27 million ultra poor—remain seriously underfed  Therefore, the level of technology and institutional innovations that made this price decline possible must be maintained  However, the real prices of some foods that are rich in micronutrients (fruits, vegetables, pulses, animal sourced foods) are increasing  If policies are not undertaken to increase supply of non-cereal, nutrient- rich foods, then their prices will continue to increase in the face of income and population growth  Consequently, the diet quality and nutritional status of the poor are likely to deteriorate further
  • 52. Data and methodology Data: Bangladesh Integrated Household Survey (BIHS)a nationally representative household survey conducted by IFPRI in 2011-2012 Methodology  Instrumental variable regression to explore (cross-sectional) association between dietary diversity and agricultural diversity  Dep. var: Household dietary diversity score (DDS) with a maximum of 12 food groups (calculated from a 7-day food consumption recall)  Indep. var.: Number of food crops grown (instrumented by soil types and percentage of land irrigated), education, household size and demographic composition, assets, electricity connection, rice price, location fixed effects  OLS to explore association between food crop diversity and explanatory variables
  • 53. Agricultural diversity leads to dietary diversity  Found statistically significant positive association between production diversity and dietary diversity  Other statistically significant factors influencing dietary diversity are:  Education of male hh head and female spouse (positive)  HH size (positive)  Electricity access (positive)  Farm size (positive)  Milk cow ownership (positive)  Hand tubewell ownership (positive)  Rice price (positive)
  • 54. What factors affect crop diversity?  Crop diversity increases if:  Share of irrigated cropped area increases  Agricultural extension agents visit farm household  Household has access to loans  Household uses power tiller for land preparation  Crop diversity deceases if:  Household grows winter rice
  • 55. WHAT ARE THE OPPORTUNITIES FOR LINKING AGRICULTURE AND NUTRITION IN BANGLADESH?
  • 56. CGIAR Research Program (4) Agriculture for Nutrition and Health (A4NH) IFPRI-led, with 11 other CG centers
  • 57. IFPRI’s work on agriculture-nutrition linkages in Bangladesh  Biofortification: High-zinc rice (HarvestPlus) released in Bangladesh in August 2013—the world’s first zinc-enriched rice variety  Community-level integrated programs:  Homestead food production (e.g. with HKI, BRAC)  Fish systems, fish ponds (with World Fish and partners)  Nutrition-sensitive value chains (fish with World Fish)  Policy research on integration of agriculture-health-nutrition in collaboration with:  other new nutrition-focused programs (e.g. Transform Nutrition, Leveraging Agriculture for Nutrition in South Asia (LANSA))  food security-focused policy research PRSSP  Agricultural Policy Support Program (APSU) supported by PRSSP with USAID funding
  • 58. Photo: One Acre Fund Homestead Food Production to Improve Nutrition
  • 59. HKI’s Homestead food production in Bangladesh Program: •: • Impact: Source: Millions Fed , IFPRI, 2009; www.ifpri.org/millionsfed Production-focused: micronutrient-rich vegetables, small livestock production Nutrition education to promote consumption  Focus on women: income generation, empowerment Nutrition objective: Improve diet diversity, micronutrient intake Integrating agriculture and nutrition at household and community level Tripled vegetable production; increased income  73% of gardens managed by women improved food security for 5 million people
  • 61. Page 61 Agricultural policy and diet quality The solution to poor diet quality lies in balanced consumption of nutrient-rich foods (fruits, vegetables, fish, meat, milk) which the poor desire but cannot afford. Policies should be undertaken to:  Increase agricultural investments in non-staples that are high value added and high nutrition value added  Reduce production and market risks associated with these crops  Support the bio-fortification of staple crops with micronutrients  Recent agricultural research initiatives by CGIAR centers – the Bio- fortification Challenge Program HarvestPlus – and the Bangladesh Rice Research Institute (BRRI) have shown that it is possible to get the plants themselves to do the work of fortification. Zinc-enriched rice was developed and released. Bio-fortification is cost-effective and sustainable. Rice is an excellent vehicle for bio-fortification (with iron and zinc) in the sense that the entire population eats it.
  • 62. Summary and Conclusions Dr. Akhter Ahmed, H.E. Srun Darith,
  • 63. Take-Away Messages  We have opportunities and examples of success on how to bridge the agriculture-nutrition divide  Our challenge AND opportunity is to work together - cross-sectorally (how?)  We need to do much better at documenting successes – and failures; we need the evidence for advocacy, to stimulate investments  …  …

Editor's Notes

  • #5: Conceptional framework (simplified) of this sessionTwo MAJOR pathways: (1) changing purchasing power, (2) changing self-sufficiency level  Second pathway is unique to subsistence farm households
  • #6: Data from 1992 to 2009Share of calories: 3-year average
  • #7: Data from … to …Cambodia: 1996-2010Bangladesh: 1983-2007Nepal: 1975-2011Tajikistan: 2005-2007
  • #8: Diversification into micronutrient-rich foods: meats, fish, eggs, dairy products, and to a lesser extent fruits and vegetablesThough, the DDS does not capture the consumed quantity of nutritious food.
  • #10: As a stimulation of the following discussion, I raise a provocative question and show evidence supporting the hypothesis.Presentation of preliminary findings from ongoing work
  • #11: Can we empirically show the effects of agriculture on food security and nutrition?  Absence of evidence of the impact of agriculture on nutrition outcomes doesn’t mean absence of impact!
  • #13: Cambodia has a remarkable record in generating economic growth and in utilizing growth for reducing malnutrition until the mid-2000s.Since 2005, progress in reducing malnutrition has stagnated, despite continuing, high growth.Has the structure of growth been unfavorable for reducing malnutrition?Indeed, Cambodia’s economic growth has been narrowly based, largely on garments and tourism (and rendering the economy relatively vulnerable to external shocks).The Royal Government has recognized this problem and addressed it in the Rectangular Strategy (2009-2013), where agricultural growth is deemed a key role as a driver of broad-based and inclusive growth.
  • #14: Agriculture is critical for achieving food and nutrition security in Cambodia:Agriculture adds up to about one-third of total GDP80 percent of the population lives in rural areas, and the vast majority is engaged in agriculture.Child underweight is much higher in rural areas (30.6 percent) than in urban areas (19.8 percent).Unlike in many other countries, the majority of the rural poor in Cambodia are net food SELLERS, and 70-80 percent of the poor live in good agricultural potential areas (WB 2007: 2008 WDR “Agriculture for Development”).Hence, agricultural transformation matters for a large share of the population and an even larger share of the malnourished people.Since the early 2000s, major agricultural activities in Cambodian upland have changed from mainly swidden and shifting cultivation of smallholders to large-scale economic land concessions and the restitution of already existing plantations.Substantial investments into modern agricultural production techniques as well as a conversion of subsistence farming methods into market-oriented crops have been transforming the land use systems. Other drivers of Cambodia’s agricultural growth and transformation include land reforms and productivity-enhancing policy incentives.
  • #16: Use of regression approach (instead of pairwise correlation) to control for confounding effects. For example, we are interested in the effects of agricultural transformation, independent of the households’ wealth.HH DDS is calculated as monthly median count from a repeated 24-hour food consumption recall.HH expenditure is proxy for HH income.The proximity to farmer markets (and input markets) matters for ag. trans.Share of food consumption from purchases (as opposed to from own production) is a measure of ag. commercialization.Share of non-farm income is a measure of deagrarianization.Food crop diversity and livestock diversity are measures of agricultural production diversity or specialization. Food crop diversity and livestock diversity are the counts of different food crops and livestock types (for food production).
  • #17: Survey sample observations (and proportions) [including households with complete data only]
  • #18: Dietary diversity …Increases with household incomeDecreases with the distance to markets among all farmersIs higher among farmers than non-farmersIncreases with the share of non-farm income among subsistence farmersIncreases with growing food consumption from market purchases, or with declining consumption of own-produced foods, among subsistence farmers (which may be a bit surprising)Increases with the diversity of livestock holding (number of different livestock types) among all farmers, but no statistical effect for the diversity of cultivated crops
  • #19: Unlike for dietary diversity, we find very few statistically significant effects of ag. trans. on child nutrition. And, the significant coefficients for the share of food consumption from purchases provide no clear picture.
  • #20: Farmers adaptation capacity depends on their endowments which enables or disables them to benefit from the transformation process.In other words, we do not find that agricultural specialization compromise FNS or specifically dietary diversity. This finding may provide a rationale for improving FNS through agricultural transformation—particularly specialization—as intended by the Rectangular Strategy.As said before: Absence of evidence of the impact of agriculture on nutrition outcomes doesn’t mean absence of impact!Akhter Ahmed will present some evidence from Bangladesh on how to better integrate agriculture and nutrition and hence how to make agricultural growth more nutrition-sensitive.