Annex 1 – Extended abstract for Contributed Paper session
Impacts of Push-pull technology on food security and aggregate
Paper Title poverty: A case study in Kenya
Contributed Paper abstract prepared for presentation at the 91st Annual
Conference of the Agricultural Economics Society, Royal Dublin Society in
Dublin, Ireland
24 - 26 April 2016
200 words max
Abstract
This study presents the farm level and aggregate welfare impacts of push pull
technology(PPT) adoption. We use comprehensive farm household survey data collected in
2016, from eight counties in western Kenya. We combine the endogenous switching
regression treatment model with the economic surplus model to compute supply shift
parameter while controlling for the endogeneity of adoption decisions. We find significant
impacts of PPT adoption on farm level yield and costs of production. The change in maize
yield and costs of production due to adoption result in a 21-39% reduction in cost per
kilogram of maize (supply shift parameter). This increases the total change in economic
surplus by US$ 49-114 million per annum at the current level of PPT adoption. The number
of poor people escaping poverty due to the additional economic gains is about 0.04-0.10
million per year. Finally, adoption decisions are correlated with neighbourhood effects, field
days’ attendance, quality of extension services, plot ownership, rural institutions, credit
constraint, family size, and diary animal ownership. Results from this study support
investment in PPT research and dissemination to tackle important development challenges.
Reaching the poor with PPT however requires policy for improving extension efforts, access
to information, and complimentary inputs and services.
Keywords
JEL Code
Introduction
Push pull technology, endogenous switching regression,
economic surplus, poverty, impact, Kenya
O12, Q12, Q16
100 – 250 words
Agriculture is vital for Africa’s economy, contributing to 15% of its GDP on average and
employing 60% of the African labor force (Moyo et al., 2015). However, the productivity of
this sector remains low, undermining food security and poverty reduction in the continent.
Insect pests such as cereal stemoborers, striga weed, poor soil fertility and climate change are
among the major factors contributing to the poor performance of the sector (Khan et al., 2008;
2014). For instance, stemborers and striga infestations respectively cause grain yield losses
of US$1.5 billion and US$ 40.8 million in Africa (Kfir et al., 2002; Kanampiu et al., 2002).
The International Center of Insect Physiology and Ecology (icipe) and its partners
developed push pull technology (PPT) which address the above major constraints of cereallivestock production. Push-pull technology (PPT) is a planting system in which cereals are
intercropped with a legume (desmodium) that repels stemborers and suppresses Striga and
surrounded by a border grass (Napier or Brachiaria) that attracts stemborers away from the
cereal crops. The fodder crops have the added benefits of reducing soil erosion and improving
soil fertility through nitrogen fixation and improved organic matter content and of providing
high quality forage for livestock consumption or sale. The improved forage increases
livestock productivity, particularly milk productivity, which can increase income and
nutritional security.
Despite these benefits, there is lack of systematic study that investigates the welfare
impacts of the technology based on farm household level data. The objective of this paper is
to answer the following empirical questions. First, does adoption of PPT impact farm level
yield and costs of production? Second, if so, how does PPT adoption impacts economic
surplus gains and population poverty?
Methodology
100 – 250 words
To answer these questions, we combine economic surplus model with endogenous switching
treatment regression to compute supply shift parameter (K-shift) while controlling for the
endogeneity of adoption decisions. We use endogenous switching treatment regression
(Kassie et al. 2015) to estimate the research induced supply shift parameter by estimating the
direct effects, farm level yield and cost changes, associated with adoption of PPT. We use the
estimated supply shift parameter in the economic surplus model to compute the total
economic surplus gains derived from adoption of PPT. Finally, we estimate the aggregate
poverty impact of adoption as a function of the change in total economic surplus estimates,
adoption rate, agricultural gross domestic product (AgGDP), poverty elasticity with respect to
AgGDP and number of poor people in the study areas. This approach of poverty estimation
captures both direct and indirect beneficiaries from agricultural productivity growth due to
technology adoption.
Results
100 – 250 words
Farmers’ decisions to adopt are affected by quality of extension service, participation in PPT
demonstration field days, neighbourhood effect, number of rural institutions in a village,
credit constraint, diary animal ownership, family size, household age, and plot ownership.
The adoption of PPT increases maize yield (yield ATT) on average by 55% after
controlling for other determinants of yield. Results suggest that the cost adoption effect (cost
ATT) is Kenya Shilling(Ksh) 10, 608 per acre without considering fodder revenue generated
by the PPT. This is equivalent to increasing costs of production by 31%. However, the cost
ATT considering the fodder benefit is Ksh 31, 675 per acre. This is equivalent to a 94% costs
of production reduction. These results suggest that policy and program that aim to promote
PPT can improve rural household food security as increasing agricultural productivity
enhance both food availability and accessibility.
The combination of a 55% yield increases and a 31% cost increases result in a 21%
cost reduction per kg of maize (K-shift parameter) at the current level of adoption (23%),
whereas a combination of 55% yield increases and a 94% cost decreases result in a 39% cost
reduction per kg of maize.
Assuming a small open economy with a base adoption rate of 23% and a 0.5%
elasticity of supply, a 21% shift in the supply curve generate a change in total economic
surplus of US$ 49 million per annum. The change in economic surplus associated with a 39%
shift in supply curve is US$ 97 million per annum. The number of poor people escaping
poverty because of the change in economic surplus are 0.04 and 0.08 million per year.
The economic surplus gains under closed economy market assumption is $US53-US$
114 million per year and the number of poor people escaping poverty is estimated to be 0.050.10 million per annum.
Discussion and Conclusion
100 – 250 words
We investigate the impacts of PPT adoption on farm level and aggregate welfare using survey
data collected in maize growing counties of Kenya. We combine the economic surplus model
with econometric method to control for the endogeneity of adoption decisions and compute
the K-shift parameter.
The study shows the adoption of PPT has a significant impact on farm level yield and
costs of production. This contributes to increase in economic surplus gains and poverty
reduction. The economic surplus gains are US$ 49-114 million per annum under the current
level of adoption and both under open and closed economy assumptions. The number of
people escaping poverty are estimated in the range of 0.04-0.10 million per year.
The implications of these results are the following; firstly, investing in information
delivery mechanisms are crucial in the success of PPT adoption. Secondly, the significant
impact of household age, family size, rural institutions, and credit point out the need to
strengthening the local labour exchange system, farmers’ association, and availing affordable
credit service in a village. Thirdly, the significant economic and poverty reduction effects of
fodder crops suggest strengthening fodder market and facilitating easy access to
complementary technologies such as diary animals. Overall, policy attention for upscaling
PPT should focus on solid information-delivery through strong extension services, better
access to markets and provision of inclusive finance.
While this study provides important evidence on adoption and impacts of PPT, we
recognize this study is based on cross-sectional data which does not answer important policy
questions such as adoption and welfare mobility over time. We recommend future study to
undertake adoption and welfare dynamic analysis using panel data sets.
Acknowledgment
The authors are grateful to the Department for International Development(DFID), UK, for its
generous funding of this research.
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