Gitonga etal impact of metal silo iaae presentation brazilv3 slideshare
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Maize is the most important food staple in Eastern and Southern Africa, with a highly seasonal production but relatively constant consumption over the year. Farmers have to store maize to bridge seasons, for food security and to protect against price fluctuations. However, the traditional storage methods do not protect grain well, resulting in large postharvest losses. Hermetically sealed metal silos kill storage pests by oxygen deprivation without pesticides. Popular in Central America, they are now being promoted in Africa, but their impact here has not yet been studied. This study used propensity score matching to evaluate the impact of metal silos on duration of maize storage, loss abatement, cost of storage, and household food security. Metal silo adopters (N=116) were matched with non-adopting farmers from a representative sample of 1340 households covering the major maize-growing zones in Kenya. The major effect of the metal silos was an almost elimination of losses due to insect pests, saving farmers an average of 150- 200 kg of grain, worth KSh 9750 (US$130). Metal silo adopters also spent about KSh 340 less on storage insecticides. Adopters were able to store their maize for 1.8 to 2.4 months longer, and sell their surplus after five months at good prices, instead of right after the harvest. The period of inadequate food provision among adopters was reduced by more than one month. We conclude that metal silos are effective in reducing grain losses due to maize-storage insects and have a large impact on the welfare and food security of farm households. The initial cost of metal silos is high (KSh 20,000/ 1.8 ton) and therefore policies to increase access to credit, to reduce the cost of sheet metal and to promote collective action can improve their uptake by smallholder farmers.
Gitonga etal impact of metal silo iaae presentation brazilv3 slideshare
- 1. Can metal silo technology offer solution to grain storage and food security problem in developing countries? An Impact evaluation from Kenya Zachary M. Gitonga, Hugo de Groote, Kassie Menale and Tadele Tefera 28th International Conference of Agricultural Economics Foz do Iguacu, Brazil, 1824 August 2012. Published as: Gitonga, Z.M., Hugo De Groote, Kassie Menale and Tadele Tefera. 2013. Measuring the impact of metal silos on household maize storage and food security in Kenya using propensity score matching. Food Policy 43:44-55.
- 2. Introduction
- 3. Introduction Farmers suffer huge losses due to insect damage and poor storage. sell shortly after harvest at low prices Buy later in the market at higher prices Food insecurity and poverty cycle Many using storage facilities that do not protect against storage pest Hermetically sealed Metal silo can kill pest through oxygen deprivation w/o pesticides Has worked in Latin America (Postcosecha) Elsalvador, Guatemala,Honduras, Nicaragus Impact of metal silo at farm level not studied in ESA
- 4. Objectives We therefore evaluated the impact of metal silos on: maize storage loss due to pests, cost of storage, duration of storage food security. Hypotheses: Households that use metal silos: incur negligible losses due to pests spend less on storage chemicals delay selling their maize, are more food secure than non adopters
- 5. Methods: Sampling: Multi stage random sampling for control Sub-locations from KNBS & grouped according to AEZ Proportionate to size random sampling used to select 120 sub-locations across the six AEZ. a sampling frame of all households in each sub-location 1468 household interviewed Same questionnaire used for adopters and non adopters of metal silos
- 6. Impact evaluation Assessing the impact of metal silos requires making an inference about the outcomes that would have been observed for adopters had they not adopted. Impact evaluation is a problem of missing data it is impossible to measure outcomes for someone in two states of nature at the same time Propensity score matching technique used to match adopters and non adopters on observable characteristics Propensity score or balancing test used to check for selection bias
- 7. Impact evaluation Sensitivity analysis conducted to check hidden bias Endogenous switching regression used to check the robustness of PSM results The model simultaneously estimates adoption and outcome equations the estimated coefficients of correlation (rho) between the adoption equation and the outcome equations not significantly different from zero. unobserved factors did not influence the decision to adopt metal silos.
- 8. Results: Benefit of maize storage Embu is a maize deficit area Prices low in during harvest time & high before next harvest Harvest in Rift valley around August but green maize in July
- 9. Results: Maize sale after storage adopters & non-adopters sell within the first month of harvest Non adopters sell more soon after harvest at low prices Adopters sell very little initially Adopters sell more in the fifth month to take advantage of improved prices
- 10. The propensity to adopt Variable Coef. Std. Err. P>z Moist transitional 2.63 0.78 0.00 Moist mid altitude 3.24 0.79 0.00 Household size 0.05 0.04 0.24 Gender of the household head 0.37 0.38 0.33 Age of the household head 0.02 0.01 0.20 Literacy of the household head 1.08 0.53 0.04 Having mobile phone account 1.33 0.56 0.02 Having bank account 0.79 0.27 0.00 Total cultivated land 0.04 0.02 0.08 Hosting big social events 0.21 0.24 0.39 Farming experience -0.02 0.01 0.09 Distance to a passable road (km) -0.07 0.03 0.03 Natural log shelled maize stored 0.09 0.09 0.32 Constant -8.64 1.32 0.00 Number of observations 892.00 LR chi2 (17) 178.01 Prob > chi2 0.00 Pseudo R2 0.25
- 11. Test for selection bias Mean t-test Variable Treated (124) Control (768) %bias t p-value Moist transitional 0.5 0.4 19 1.5 0.127 Moist mid altitude 0.5 0.6 -20 -1.5 0.128 Decision maker, female15-64yr 0.3 0.2 14 1.2 0.242 Decision maker, male>64yr 0.1 0.2 -10 -0.7 0.492 Decision maker, female >64ys 0.0 0.1 -17 -1.3 0.198 Household size 6.9 6.8 1 0.1 0.951 Gender of the household head 1.1 1.2 -15 -1.2 0.233 Age of the household head 53.3 54.7 -10 -0.9 0.385 Literacy of the household head 1.0 1.0 0 0.0 1.000 Having mobile phone account 1.0 0.9 7 0.9 0.357 Having bank account 0.8 0.8 -4 -0.3 0.756 Experience 24.6 25.5 -6 -0.5 0.602 Hosting big social events 0.3 0.3 3 0.3 0.794 Distance to a passable road (km) 1.5 1.6 -2 -0.2 0.874 Land owned (ha) 9.2 6.6 18 1.4 0.158 Ln shelled maize before storage 1.8 1.8 2 0.1 0.886 Total cultivated land (ha) 8.3 8.5 -4 -0.2 0.828 Matching method Pseudo R2 LR chi2 p>chi2 Mean Bias Median Bias Before matching 0.244 175.16 0.000 32.9 29.42 Neighbor matching 0.043 14.89 0.604 8.9 7.0 Radius matching 0.019 6.35 0.991 4.3 2.6 Kernel matching 0.015 5.01 0.998 4.1 2.9 Mean t-test Variable Treated (124) Control (768) %bias t p-value Moist transitional 0.5 0.4 19 1.5 0.127 Moist mid altitude 0.5 0.6 -20 -1.5 0.128 Decision maker, female15-64yr 0.3 0.2 14 1.2 0.242 Decision maker, male>64yr 0.1 0.2 -10 -0.7 0.492 Decision maker, female >64ys 0.0 0.1 -17 -1.3 0.198 Household size 6.9 6.8 1 0.1 0.951 Gender of the household head 1.1 1.2 -15 -1.2 0.233 Age of the household head 53.3 54.7 -10 -0.9 0.385 Literacy of the household head 1.0 1.0 0 0.0 1.000 Having mobile phone account 1.0 0.9 7 0.9 0.357 Having bank account 0.8 0.8 -4 -0.3 0.756 Experience 24.6 25.5 -6 -0.5 0.602 Hosting big social events 0.3 0.3 3 0.3 0.794 Distance to a passable road (km) 1.5 1.6 -2 -0.2 0.874 Land owned (ha) 9.2 6.6 18 1.4 0.158 Ln shelled maize before storage 1.8 1.8 2 0.1 0.886 Matching method Pseudo R2 LR chi2 p>chi2 Mean Bias Median Bias Before matching 0.244 175.16 0.000 32.9 29.42 Neighbor matching 0.043 14.89 0.604 8.9 7.0 Radius matching 0.019 6.35 0.991 4.3 2.6 Kernel matching 0.015 5.01 0.998 4.1 2.9
- 12. Common support y-axis: frequency of the propensity score distribution propensity scores for the adopters and non- adopters after matching greatly overlap there is a high chance of getting good matches
- 13. Impact evaluation: Storage loss abatement Loss abatement range from US$ 95 to US$135 adopters lost US$ 1.99 adopters keep some maize aside for regular consumption The net effect of MS adoption is a 98.54% reduction in storage losses of maize due to pest.
- 14. Impact evaluation: Reduction in storage cost Metal silo adopters saved between US$10 & US$18 per season. Some adopters used pesticides because: adopters keep some maize in bags for regular consumption they were told to do so during the installation by the artisans Maize damage is negligible in metal silo with or without pesticides (Kimenju and De Groote, 2010).
- 15. Impact evaluation: Length of storage and food security Metal silos Adopters: Store maize by between 1.8 months and 2months longer. are food secure by > a month more than non-adopters.
- 16. Conclusion Metal silo technology can effectively help smallholder farmers to: reduce storage cost and losses due to storage pests earn better incomes by delaying sale of their produce improve their household food security situation. Reduce health risks
- 17. Recommendations Promotion and awareness creation Address supply side issues Initial investment in metal silo is high for smallholder farmers Collective action can address technical (handling and maintenance) financial constraints Provision of subsidized credit (Kilomo biashara) Lobbing for zero-rating of metal sheets Training more artisans near farmers localities: increase access to metal silos, reduce transport costs enable the artisans to offer aftersales service to the farmers.
- 18. ObrigadoBueno, 臓gracias por escuchar mi presentaci坦n! 臓El pastel est叩 servido!