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.

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, 18–24 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, ¡gracias por escuchar
mi presentación! ¡El pastel
está servido!