Bio-economics of Conservation Agriculture and Soil Carbon Sequestration in Developing CountriesAnders 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 developingcountries may generate some private benefits to farmers as well as sequester carbonemissions, which is a positive externality to society. Leaving crop residue on the farm hasbecome an important option in conservation agriculture practice. However, in developingcountries, using crop residue for conservation agriculture has the opportunity cost of sayfeed for livestock. In this paper, we model and develop an expression for an optimumeconomic incentive that is necessary to internalize the positive externality. A crude value ofthe tax is calculated using data from Kenya. We also empirically investigated thedeterminants of the crop residue left on the farm and found that it depends on cationexchange capacity (CEC) of the soil, the prices of maize, whether extension officers visit theplot or not, household size, the level of education of the household head and alternative costof soil conservation.

DISCUSSION AFTER PRESENTATION: Questions raised related to how paymentsystems could be organised. The need to be aware of all the competing uses of crop residueswas also emphasised.

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)

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

The Kenyan context

Crop residues

BurntCO2 + other GHGs

Livestock

Manure

Agric. production

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

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 livestockρR = total benefit of R as livestock fodder

( ) ( )( )

0

, rti i iV q s L R wL R R e d tρ σ

∞−= + − − −∫

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= + − −

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

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

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

The Study Area

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 260Households

%

Distribution of Carbon (C) (%)

Descriptive statisticsVariable Nr. Obs. Mean SDResidue left on the farm (kg per hectare) 233 713 1368Soil pH 243 5.6 0.67

Extension officers visit farm (=1, 0 otherwise)

246 0.24 0.42

Household size 246 4.1 2.2

Education ( in years ) 244 5.6 4.4

Cost of alternative soil cons. (Ksh) 243 240 600

Age of household head 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

Regression results: Determinants of Crop Residues Deposited on PlotsVariable t-stats#Soil pH 1.26Ext.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) 4.29Constant 6.70 R-SquaredObservations* significant at 10%; ** significant at 5%; *** significant at 1%. # Robust and absolute values of t-statistics

0.27227

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**

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)