This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.

1. Bio-economics of Conservation Agriculture and Soil
Carbon Sequestration in Developing Countries
Anders Ekbom, Focali (www.focali.se), Dept of Economics, University of Gothenburg, Sweden
Co-author: Wisdom Akpalu, Department of History, Economics and Politics, State University of New York, USA
ABSTRACT : Improvement in soil carbon through conservation agriculture in developing
countries may generate some private benefits to farmers as well as sequester carbon
emissions, which is a positive externality to society. Leaving crop residue on the farm has
become an important option in conservation agriculture practice. However, in developing
countries, using crop residue for conservation agriculture has the opportunity cost of say
feed for livestock. In this paper, we model and develop an expression for an optimum
economic incentive that is necessary to internalize the positive externality. A crude value of
the tax is calculated using data from Kenya. We also empirically investigated the
determinants of the crop residue left on the farm and found that it depends on cation
exchange capacity (CEC) of the soil, the prices of maize, whether extension officers visit the
plot or not, household size, the level of education of the household head and alternative cost
of soil conservation.
DISCUSSION AFTER PRESENTATION: Questions raised related to how payment
systems could be organised. The need to be aware of all the competing uses of crop residues
was also emphasised.

2. Points of departure
 Agriculture and other land use contribute
substantially to the world’s GHG emissions
 Conservation agriculture (CA) increases soil
carbon concentrations
 CA generates private benefits to farmers as
well as public goods (carbon sequestration)
 To provide public good, CA farmers may need
incentives (e.g. compensation)

3. Outline, Content
 Conservation agriculture in Kenya
 The conceptual, theoretical model
 Model results
 Empirical investigation – determinants of
integrated crop residue management
 Empirical results and policy implications

4. The Kenyan context
Crop
residues
Burnt
CO2 + other
GHGs
Livestock
Manure
Agric.
production

6. Conceptual model
 Farmers optimally allocate crop residues
between improving soil - which mitigates CO2-
emissions - and providing fodder to livestock.
 => derive optimum amount of residue that
farmer will leave on the farm, and
 => identify optimum incentive (subsidy)
necessary to internalize externality if residue
allotted to feed livestock

7. Theoretical model
q(s, L) = prod. function (s=soil, L=labour)
iR = total biomass of stovers generated on farm i
i iR R− = biomass deposited on field => improves soil
iR = biomass used to feed livestock
ρR = total benefit of R as livestock fodder
( ) ( )( )
0
, rt
i i iV q s L R wL R R e d tρ σ
∞
−
= + − − −∫

8. Theoretical model (c’ed)
Soil-quality evolution equation:
( )i iR R−
=> Biomass deposited on the field
builds up soil quality
=>Ag. labor (L) depletes soil quality
( )i is R R Lα β= + − −

9. Design an optimum economic incentive that encourages
farmers to internalize the positive externality (carbon
sequestration) generated by integrating crop residues
The Social Planner’s Problem
( ) ( )
( )( ) 2
1 ,
( )
i
i i i i
q s L R wL
R R L R R
τ ρ
λ α β γ
Η= + + −
+ + − − + −
Incentive
External benefit
from crop residue
Shadow value of
soil capital
Benefit of
R as livestock
fodder

10. Results: Comparative statics
 The optimal subsidy necessary to promote global env.
benefits via ICRM should be:
- increasing in the marginal net benefit of livestock fodder
(ie discourage removal of crop residues)
- increasing in total biomass of crop residue generated
- decreasing with increased labour wages (due to
substitution between labour and soil quality)
- decreasing if marginal benefit from carbon sequestration
increases (reduced need for subsidy)
( ) ( )
( )( ) 2
1 ,
( )
i
i i i i
q s L R wL
R R L R R
τ ρ
λ α β γ
Η= + + −
+ + − − + −
*
( )τ

11.  Objective: Identify determinants of ICRM in
agricultural production
 Assumptions:
 Rate of ICRM depends on soil & socio-economic factors
 Crop residues left in the field not uniform across farms
(due to differences in marg. net benefits);
 Study area: Kenya’s central highlands
 Data: Soil sample data and socio-economic data
from HH questionnaire (+250 HHs)
Empirical analysis
( ) ( )
( )( ) 2
1 ,
( )
i
i i i i
q s L R wL
R R L R R
τ ρ
λ α β γ
Η= + + −
+ + − − + −
*
( )τ

12. The Study Area

13. Figure A2. Distribution of Carbon (C) (%)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0 20 40 60 80 100 120 140 160 180 200 220 240 260
Households
%
Distribution of Carbon (C) (%)

14. Descriptive statistics
Variable Nr. Obs. Mean SD
Residue left on the farm (kg per hectare) 233 713 1368
Soil pH 243 5.6 0.67
Extension officers visit farm (=1, 0
otherwise)
246 0.24 0.42
Householdsize 246 4.1 2.2
Education ( in years ) 244 5.6 4.4
Cost of alternative soil cons. (Ksh) 243 240 600
Age of householdhead 246 55.1 13.8
Cation exchange capacity (CEC) 243 15.7 5.4
Price of maize ( in 1000 Ksh) 236 0.042 0.059

15. Regression results: Determinants of Crop Residues Deposited on Plots
Variable t-stats#
Soil pH 1.26
Ext.officers visit farm (=1, 0 otherw.) 3.84
Household size 2.35
Education ( in years ) 3.70
Cost of alternative soil conservation 4.28
Age of household head 0.87
Cation exchange capacity (CEC) -2.13
Price of maize ( in 1000sh) 4.29
Constant 6.70
R-Squared
Observations
* significant at 10%; ** significant at 5%; *** significant at 1%.
# Robust and absolute values of t-statistics
0.27
227
Coeff.
-0.041 -0.634 **
4.196 0.184***
4.278
0.063 0.357***
0.423 0.103***
0.005 0.249
Elasticity
0.138 0.770
0.626 0.146***
0.069 0.288**

16. Conclusions
 Agricultural soils – large reservoirs of carbon;
Agriculture – large potentials for expanded
carbon sequestration & GHG mitigation.
 Conservation agriculture may be a desirable
option for increased carbon sequestration
 Necessary to: i) explore, understand trade-offs,
ii) identify optimal incentives; iii) explore
determinants of integrated crop residue
management (conservation agriculture)