Does the adoption of legume diversification and modern seeds affect nutritional security in Ethiopia? Evidence from panel data analysis

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1 Does the adoption of legume diversification and modern seeds affect nutritional security in Ethiopia? Evidence from panel data analysis Menale Kassie With Paswel Marenya, Moti Jaleta, Hailemariam Teklewold, Olaf Erenstein Conference on Impacts of International Agricultural Research: Rigorous evidence for policy 6-8 July 2017, ICRAF, Nairobi, Kenya

2 Introduction Many published studies on adoption and impact to date largely focused on: individual technology adoption and impact analysis Farmers adopt more than one technologies at farm and/or household level Adoption rate, % Kenya Malawi Ethiopia Tanzania Number of SIPs Maize-legume diversification, MT, Varieties, chemical fertilizer, manure, SWC Source: Kassie et al, Land use Policy (2015) 42, pp

3 Introduction We consider 2 technologies/practices; this gives 4 technology set choices (2 2 ) D1V0 V1D1 V1D0 V0DO V=improved maize varieties; D=legume-maize diversification (1=denotes adoption and zero otherwise)

4 Objective We estimate the adoption of individual and combination of legume diversification (D) and improved maize varieties (V) on: Per capita calories consumption Per capita protein consumption Per capita iron consumption Household dietary diversity (Simpson index) nn SSSS = 1 ii=1 ww ii 2 ww ii =calorie share of food group ii, (ii =cereals, pulses, oil crops, vegetables, fruits, meat/eggs, fish, dairy products, and beverages/sugar/honey)

5 Pathways through which adoption can influence nutritional outcomes Production pathway: Increases consumption of own-produced crop due to higher yields Income pathway: adopting hhlds may have access to more food and variety with higher income from sales of additional production and production cost reduction Control over and use of income Price/adoption effects Diversification pathway: increases number of food crops grown on a plot Production stability pathway: diversification increases production stability and smooth out consumption Substitution effect: Adoption of varieties may lead more acreage under maize and shrinking acreage available of other foods crop households who consume more own-produced maize may purchase less foods (Herforth & Harris 2014, Teklewold et al. 2013; Kassie et al. 2015a; Kassie et al. 2015b, Kassie et al. 2017; Sauer et al. 2016; Zeng et al. 2017)

6 Methodology The sample Sample covers all maize growing areas of the country (see map) 39 districts and 183 villages Survey of 2400 and 2289 farmers conducted in 2010 and 2013, respectively Comprehensive household and plot level data collected

7 Methodology Nutrition data Data on calories, protein, iron & dietary diversity are based on food consumption in the past 12 months Used food conversion tables for Ethiopia to obtain amounts of calorie, protein and iron consumed per adult equivalent (AE) per day.

8 Methodology Adoption data Modern maize Legume Choice Year Combination variety (V) diversification (D) (j) V 0 V 1 D 0 D A B C D E F G H 1 V 0 D V 1 D V 0 D V 1 D V=improved maize varieties; D=legume-maize diversification (1=denotes adoption and zero otherwise)

9 Empirical strategy: Multinomial Endogenous Switching Regression 1) Multinomial logit model: three adoption equations PPPPPPPP jj XX, ZZ, XX = JJ kk=1 exp XX hiiii ββ jj +ZZ htt αα jj + XX hii, jj = 0,1,2,3 exp XX hiiii ββ kk +ZZ htt αα kk + XX hii h indexes individual farmer, ii indexes plot jj indexes technology set tt indexes time period XX is vector of controls (household, plot and village characteristics) including time period dummy ZZ is a vector of exclusion restriction XX is average of time varying explanatory variables; a proxy to unobserved (motivation, risk preference and entrepreneurial/managerial ability) Carter and Milon 2005; Bourguignon et al. 2007; Kassie et al. 2017

10 Multinomial Endogenous Switching Regression 2) Four Outcome equations: Establishing actual and counterfactual outcomes Actual outcome a) EE QQ htt,jj jj = JJ = ββ JJ XX htt,jj + σσ JJ λλ htt,jj, jj = 1,2,3 (aaaaaaaaaaaaaaaa) b) EE QQ htt,0 jj = 0 = ββ 0 XX htt,0 + σσ JJ λλ htt,0 (nnnnnn aaaaaaaaaaaaaaaa) Counterfactual outcome c) EE QQ htt,0 jj = JJ = ββ 0 XX htt,jj + σσ 0 λλ htt,jj jj = 0 denotes that neither of the technology nor their combinations were adopted jj = 1, 7 represents adoption of either technology or their combinations QQ 11 and QQ 00 are outcomes with and without adoption of technology set jj XX vector of controls (household, plot and village level characteristics, including a time period dummy (TT)), λλ inverse Mills ratios derived from equation 1 to capture time-varying individual effects, Outcome equations estimated using fixed effects estimator

11 Average adoption effect Impact of adoption of technology set jj on adopters (a - c) AAAAAA = EE QQ htt,jj jj = JJ EE QQ htt,0 jj = JJ = ββ JJ ββ 0 XX htt,jj + σσ JJ σσ 0 λλ htt,jj Potential impact of adoption on non-adopters can be derived following similar procedure

12 Results Technology adoption Variables Description None Improved maize variety Legume diversification Both All Gender 1=if gender of household head is male Age Age of household head (years) Hhsize Total family size (number) Educhd Education level of household head (years) Totfarmsize Farm size, Ha Propcereal Proportion of cereal crops area Proplegum Proportion of legume crops area Propoil Proportion of oil crops area Credit 1=if credit constraint (credit is needed but unable to get) Tlu Livestock size (in tropical livestock unit) Assetval Value of farm & household assets ( 000 Birr) Ox0 1=if household owns no oxen Ox1 1=if household owns only one ox Vilmktdist Walking distance to village markets. km Manmktdist Walking distance to main markets. km Distinput Walking distance to input markets. km Group 1=if member in input/marketing/group Kinship Number of close relatives living outside the village Trader Number of grain traders that farmers know and trust Connections 1=if household has relative in leadership position Yearlived Number of years the household has lived in the village Distext Walking distance to extension agents office. km Extenskill 1=if confident with skills of extension workers Govtsup 1=if believe in government support in case of crop failure Pests 1=if pest is a key problem Disease 1=if disease is a key problem Waterlog 1=if waterloging is a key problem Drought 1=if drought is a key problem Plotdist Plot distance from home. minutes Tenure 1=if owned and cultivated by the household Shalwdepplt a 1=if plot has shallow depth soil Moddepplt a 1=if plot has moderately deep soil Godfertplt b 1=if plot has good fertile soil Modfertplt b 1=if plot has moderately fertile soil Flatslop c 1=if plot has flat slop Modslpplt c 1=if plot has moderately steep slop Manureuse 1=if manure use Altitude Variables used in the analysis Altitude (meter above sea level)

13 Results Distribution of food groups consumption by year % of 0.6 hhlds Cereal Pulse Oil crops Vegetable Fruits Food groups Meat/egg Fish Milk and dairy Beverages

14 Results Household nutrition and food diversity by adoption status Consumption per adult equivalent per day Calorie Protein Practices (Kcal) (gm) Iron (mg) V 0 D (7.042) (0.403) (0.112) V 1 D (6.247) (0.298) (0.072) V0D (9.969) (0.469) (0.118) V1D (9.493) (0.429) (0.105) Average (4.047) (0.203) (0.055) Food diversity (Simpson index) 0.77 (0.003) 0.84 (0.002) 0.91 (0.002) 0.92 (0.002) 0.85 (0.001) V=improved maize varieties; D=legume-maize diversification (1=denotes adoption and zero otherwise)

15 Results CDF Calorie consumption with V0D0 Calorie consumption with V1D0 Calorie consumption with V0D1 Calorie consumption with V1D1 CDF Protein consumption with V0D0 Protein consumption with V1D0 Protein consumption with V0D1 Protein consumption with V1D Calorie consumption per adult equivalent per day (Kcal) Protein consumption per adult equivalent per day (gm) CDF Iron consumption with V0D0 Iron consumption with V1D0 Iron consumption with V0D1 Iron consumption with V1D1 CDF Food diversity with V0D0 Food diversity with V1D0 Food diversity with V0D1 Food diversity with V1D Iron consumption per adult equivalent per day (mg) Food diversity (Simpson index based on calorie share)

16 Results Adoption effects: per capita calorie consumption Adoption status Average adoption Nutrition Indicator Outcome Adopting Non-adopting effects (Actual) (counterfactual) A B C D E Calorie(C) EE CC jj jj = (1.36) (1.18) (1.79)*** consumption per EE CC jj jj = (3.35) (1.75) (3.78)*** adult equivalent (Kcal/day) EE CC jj jj = (2.61) (1.76) (3.15)*** Where 2, 3 and 4 refers to adoption of varieties, LD, ands both, respectively Individual adoption of technology increases per capita calorie consumption by % and combination of adoption by 13.2%v

17 Results Adoption effects: per capita protein consumption Adoption status Average adoption Nutrition Indicator Outcome Adopting Non-adopting effects (Actual) (counterfactual) A B C D E Protein(P) EE PP jj jj = (0.094) (0.134) 7.39 (0.163)*** consumption per EE PP jj jj = (0.209) (0.189) 8.93 (0.282)*** adult equivalent (gm/day) EE PP jj jj = (0.145) (0.190) 9.55 (0.237)*** Where 2, 3 and 4 refers to adoption of varieties, LD, ands both, respectively Individual adoption of technology increases per capita calorie consumption by % and combination of adoption by 26.5%.

18 Results Adoption effects: per capita iron consumption Nutrition Indicator Outcome Adoption status Average adoption Adopting Non-adopting effects (Actual) (counterfactual) A B C D E Iron(I) consumption EE II jj jj = (0.023) (0.028) 1.36 (0.036)*** per adult equivalent EE II jj jj = (0.047) (0.041) 2.37 (0.062)*** (mg/day) EE II jj jj = (0.049) (0.041) 3.55 (0.064)*** Where 2, 3 and 4 refers to adoption of varieties, LD, ands both, respectively Individual adoption of technology increases per capita calorie consumption by % and combination of adoption by 28.5%.

19 Results Adoption effects: dietary diversity consumption Nutrition Indicator Outcome Adoption status Average adoption Adopting Non-adopting effects (Actual) (counterfactual) A B C D E Food diversity EE SS jj jj = (0.001) 0.78 (0.001) (0.001)*** (Simpson index-s) EE SS jj jj = (0.001) 0.81 (0.001) (0.002)*** EE SS jj jj = (0.001) 0.81 (0.001) (0.002)*** Where 2, 3 and 4 refers to adoption of varieties, LD, ands both, respectively Individual adoption of technology increases per capita calorie consumption by % and combination of adoption by 12.8%.

20 Conclusion Legume diversification and improved maize varieties adoption not only enhances farm household s economic wellbeing, as found in previous literature, but also enhances nutrition and food security Switching from single technology to multiple technology adoption enhance more household nutrition security status Strengthening smallholder legume diversification can be considered as a nutrition-smart in subsistence based agrarian production systems. Agricultural programs and policies aiming to have impacts on rural household nutrition should promote on-farm cereal-legume diversification rather than focus only on increasing the total quantity of cereal alone.

21 Thank you SPIA CRP maize ACIR Farmers

22 Supplementary data

23 Conditional probabilities of technology adoption (JAE) D D F F V V P(A T =1) P(A T =1/A D =1) P(A T =1/A F =1) P(A T =1/A V =1) P(A T =1/A D =1 & A F =1) P(A T =1/A D =1 & A V =1) P(A T =1/A F =1 & A V =1)

24 Adoption yield effects (JAE 2017) Adopters Technology set Actual outcome (maize yield if household adopt technology set choice j Counterfactual outcome (maize yield if household did not adopt any technology set choice) ATT A B C D=B-C F 1 V 0 D (33.75)*** F 0 V 1 D (43.63)*** F 0 V 0 D (79.85)** F 1 V 1 D (53.07)*** F 1 V 0 D (148.31)*** F 0 V 1 D (155.40)*** F 1 V 1 D (54.28) ***

25 Adoption cost effects (JAE 2017) Adopters Technology set Actual outcome (maize production cost If household adopt technology set choice j) Counterfactual outcome (maize production cost If household did not adopt any technology set) ATT F 1 V 0 D (77.09)*** F 0 V 1 D (59.14)** F 0 V 0 D (168.54) F 1 V 1 D (65.52)*** F 1 V 0 D (258.54)*** F 0 V 1 D (273.30) F 1 V 1 D (90.14)***

26 Methodology Nutrition and Dietary data Data on calories, protein, iron & household level dietary diversity are based on consumption levels of food items in the past 12 months Used food conversion tables for Ethiopia to obtain amounts of calorie, protein and iron consumed per adult equivalent (AE) per day. Child stunting data based on weight and height data of 1814 children up to 60 months of age (2013) Women dietary diversity data based on 24 hrs recall (FAO 2011)

27 Mean household dietary intake, food diversity and stunting by year year Total Calorie intake per adult equivalent, Kcal per day (412.78) (430.93) (423.49) Protein intake per adult equivalent, gm per day (19.96) b (19.57) (20.09) Iron intake per adult equivalent, mg per day (5.54) (5.76) (5.73) Simpson index (based on calorie share) (0.14) (0.12) (0.14) Height-for-age Z-score (HAZ) (2.59) (2.59) Stunted (%) [Height-for-age Z-score <-2] Number of households

28 Adoption Determinants All Households V 1 D 0 V 0 D 1 V 1 D 1 Variables Coeff. Std. Error Coeff. Std. Error Coeff. Std. Error Gender ln(age) ln(hhsize) ln(educhd) ln(totfarmsize) Credit 1.931*** *** ln(tlu) 0.582*** *** *** ln(assetval) 0.197*** *** *** Ox * *** Ox *** ** ln(vilmktdist) ln(manmktdist) ln(distinput) Buyer * ** Seller *** Group *** Kinship Trader 0.057*** Connections Yearlived * ln(distext) ** ** * Extenskill Govtsup 0.384*** *** *** Pests *** Disease Waterlog Drought * Year (2013) 0.858*** *** *** ln(plotdist) *** *** Tenure *** Shalwdepplt a *** Moddepplt a ** Godfertplt b 0.410** ** * Modfertplt a ** Flatslop c 0.441** * Modslpplt c 0.421* Manureuse *** *** * ln(altitude) ** *** ** Amhara d 4.983*** *** *** Oromia d 2.846*** *** *** SNNP d 3.043*** *** *** Constant Joint significance of: time varying covariates χ 2 (18) *** 52.48*** ***