Your SlideShare is downloading. ×
Bioeconomics of Conservation Agriculture and Soil Carbon Sequestration in Developing Countries
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Bioeconomics of Conservation Agriculture and Soil Carbon Sequestration in Developing Countries

224

Published on

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

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

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
224
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
11
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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
  • 5. 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
  • 6. 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 ∞ − = + − − −∫
  • 7. 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= + − −
  • 8. 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
  • 9. 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
  • 10.  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
  • 11. The Study Area
  • 12. 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) (%)
  • 13. 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
  • 14. 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**
  • 15. 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)

×