Using Extension and Subsidy Polices to predict the Adoption of components of conservation Agriculture in Eastern and Southern Africa Paswel Marenya, Kassie Menale and Colleagues
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Using Extension and Subsidy Policesto predict the Adoption of components of
conservation Agriculture in Eastern and Southern Africa
Paswel Marenya, Kassie Menale and Colleagues
Background • Two main policy levers for facilitating widespread
adoption and upscaling of conservation agriculture (CA):
– greater investments in agricultural extension to increase the availability of information
– policies designed to relieve financial and credit constraints to adoption (subsidies programs and credit schemes).
Country Policy Background• Ethiopia: the government has increased extension-
personnel to farmer ratio (EFR) to globally comparable levels
• Malawi: the government’s input subsidy program has attracted global attention.
• Kenya: most liberalized agricultural sector• Tanzania: lowest extension personnel-to-farmer ratios
Malawi and Ethiopia offer two important and contrasting policy experiences.
Extension personnel per 10,000 farmers (EFR)
Ethiopia Kenya Malawi Tanzania Average
16.0 10.0 6.2 4.0 9.0Period 2010 2012 2008 2010 2008-2012
Sourcea Davis et al. (2010) GoK (2012) Pablo et al. (2008) Davis et al. (2010)
Input subsidy expenditure as a percent of public agriculture spending (%) (SER)
Ethiopia Kenya Malawi Tanzania Average
10.4 19.0 58.9 46.0 33.6Period 2009-2011 2009-2011 2009-2011 2009-2011 2009-2012
Source Jayne and Rashid (2013)
Jayne and Rashid (2013)
Jayne and Rashid (2013)
Jayne and Rashid (2013)
Extension workers/ 10000 Farmers in a few countries
India Nigeria Tanzania Indonesia Ethiopia China
23
4
6
16 16
Extension workers/ 10000 Farmers in a few countries
India Nigeria Tanzania Indonesia Ethiopia China
23
4
6
16 16
Pye-Smith (2012) suggests 33 extension workers per 10, 000 farmers as near adequate
Objective and Method of Policy Study• We examine the impact of EFR, input subsidies and
percent households who accessed some credit on adoption of CA
• We conduct a series of virtual policy experiments to assess the impact of these policy options in facilitating CA adoption
• Based on econometric models which use farm household data and country policy information as covariates.
•
Ethiopia Kenya Malawi Tanzania
0.26
0.04
0.34
0.10
Adoption of minimum tillage and or/residue use in 2010 (%)
Ethiopia Kenya Malawi Tanzania
26%
4%
34%
10%7%
50%
21%
Increasing Extension Farmer Ratio to Ethiopia Levels
Base case EFR at Ethiopian mean
Ethiopia Kenya Malawi Tanzania
26%
4%
34%
10%
30%
9%
31%
14%
Can high subsidy make up for low of Extension? Base case EFR at Tanzania level and SER at Malawi level
Ethiopia Kenya Malawi Tanzania
26%
4%
34%
10%
30%
9%
31%
14%
Can high subsidy make up for low extension? Base case EFR at Tanzania level and SER at Malawi level
Ethiopia Kenya Malawi Tanzania
26%
4%
34%
10%
5%
20%
8%
Can high extension-farmer ratios make up for low subsidies?
Base case At Ethiopia’s EFR and Ethiopia’s SER
Ethiopia Kenya Malawi Tanzania
26%
4%
34%
10%
18%
6%
47%
18%
Can high extension make up for lack of credit?
Base case No credit available and EFR at Ethiopia level
SUMMARY• Both extension and subsidy policies had high predictive
power on adoption of elements of CA`
• When the extension is compared to subsidy, subsidy appears to have the stronger effect on CA adoption
• With high extension, adoption increased even in the complete absence of credit
• Information availability can go a long way in enabling adoption even under severe credit limitations.
Implications• Power of input subsidies implies that lowering costs of
complementary inputs is central in encouraging CA adoption
• investing in agricultural extension and expanding the reach of public and private providers is crucial for diffusion of CA
• Take Home Message: Policy attention for upscaling CA should remain focused on:– solid information delivery through strong agricultural extension– better access to markets for affordable inputs – Build infrastructures for inclusive finance.
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