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.

Suggestions, Comments, and Questions

are welcome

p.marenya@cgiar.org