Economic of Climate change adaptation among Sweet Potato producers In Uganda. John Ilukor, Bernard Bashaasha, Fred Bagamba 2011 February 26 th

Introduction Climate change threatens to intensify food insecurity problems in Africa (Water insecurity, floods, drought, pest and diseases out break) Crop yields may fall by 10 to 20% by the year 2050 because of warming and drying (Jones and Thornton, 2003; Thornton et al., 2006). Uganda’s agricultural sector, which is the backbone of Uganda’s economy contributing 42% of the GDP, over 90% to exporting earnings and employing 80% of the population, is highly vulnerable.

Introduction (cont) Uganda’s vulnerability can be clearly seen based on macro level indicators Weak institutional capacity, Limited skills and equipment for disaster management Heavy dependence on rain fed agriculture, limited financial resources and increasing population.

Introduction (cont) The affects on agriculture in Uganda are experienced in two ways; First, there has been more erratic, unreliable rainfall during first rainy season in March to June, and this has been followed by drought affecting crop yields. Second, the rainfall especially, in the second rains, is reported to be intense and destructive resulting into floods, landslides and soil erosion (Oxfam 2008)

Introduction (cont) A graph showing means maximum monthly temperatures in Soroti district

Introduction (cont) A graph showing mean monthly rainfall trends in Soroti district

Climate change and Sweet Potato Temperature and rainfall changes influences out break of pest and diseases in sweet potato. Rising temperatures is increasing spread of sweet potato virus disease (SPVD) (Tairo et al., 2004, Claudia et al 2007) The Sweet potato virus disease can cause 65% to 72% reduction in yields from different cultivars (Gutiérrez et al, 2003). Results from NARO sweet potato programme indicate that the yield decline resulting from sweet potato virus ranges from 56 to 100%.

Motivation New technologies have been developed to meet climate change related challenges. These include cleaning of vines for viruses, pest and disease resistant varieties, tolerant to drought, tolerant to heat and nutrient depletion, These are varieties are NASPOT 1 (Gibson, 2005), and New Kawongo, Dimbuka-Bukulula, NK259L, NK103M (Mwanga, 2007) Cleaning of the planting material of the SPVD also increase yields by over 56 percent in Uganda (Mukasa, et al 2006).

Motivation Understanding what farms adopt, where,and why? What incentives are required to achieve a target adoption rate is necessary if we are minimize climate change effects

Modeling process: Minimum data Tradeoff Analysis Model (MD-TOA Model) Public stakeholders Policy makers Scientists Indicators, tradeoffs and scenariosCoordinated Disciplinary Research Communicate results to stakeholders A participatory process, not a model

Methodology Modeling Adoption Rates in Heterogeneous Populations Farmers choose practices to max expected returns v (p, s, z) ($/ha) p = output & input prices, s = location, z = system 1, 2 Farmers earn v (p, s, 1) for current system Farmers can adopt system 2 and earn v (p, s, 2) – TC – A where TC = transaction cost, A = other adoption costs

Methodology The farmer will choose system 2 if v (p, s, 1) < v (p, s, 2) – TC – A The opportunity cost of switching from 1 to 2 is  = v (p, s, 1) – v (p, s, 2) + TC + A  adopt system 2 if  < 0. Suppose Government or NGO wants to encourage adoption by providing incentive payment PAY (e.g., to reduce negative externalities of syst 1, or encourage positive externalities of syst 2)  adopt system 2 if  < PAY. Opportunity cost varies spatially, so at some sites farms adopt system 1 and at other sites adopt system 2

Analysis of Adaptation to CC Impacts of climate change: Productivity of traditional system declines more than resilient with new crops technology, e.g., Pest Resistant variety vs traditional variety, Virus free vines + pest and disease resistant variety vs traditional variety PAY is amount needed to compensate for loss Adaptation is adoption of practices that are relatively less vulnerable under the changed climate Reduces loss due to climate change, or increases gains

Minimum Data Methods to Simulate Adoption Rates (Antle and Valdivia, AJARE 2006) How to estimate the spatial distribution of opportunity cost of changing practices? Use “complete” data to estimate site-specific inherent- productivities (Inprods) and simulate site-specific land management decisions to construct spatial distribution of returns MD approach: estimate mean, variance, covariance of net returns distributions using available data Need to know mean and variance of  = v (p, s, 1) – v (p, s, 2) + TC + A

MD approach: use available data to estimate mean and variance of  Mean: E (  ) = E (v 1 ) – E (v 2 ) + TC + A Suppose system 1 has one activity, then: E (v 1 ) = p 11 y 11 – C 11 is usually observed E (v 2 ) = p 21 y 21 – C 21 is estimated using In prods* and cost data: y 21 = y 11 {1+ (INP 21 – INP 11 )/INP 11 } * In prod = inherent productivity = expected yield at a site with “typical” management C 21 is estimated using C 11 and other information on changes in practices TC and A are estimated using available data, if relevant

Variance of returns: Observation: cost of production c   y where  is a constant and y is yield Then v = py – c  (p -  ) y and CV of v is equal to CV of y Recall:  = v (p, s, 1) – v (p, s, 2) + TC + A so we know   2 =    12 Usually observe  1 2, can assume  1 2   2 2  12 difficult to observe. Can assume correlation is positive and high in most cases. If  1 2   2 2 =  2 then   2  2   12    2 = 2  2 (1 –  12 )

Most systems involve multiple activities (crops, livestock).  1 2 and  2 2 depend on variances and covariance's of returns to each activity. In the MD model, we assume all correlations between activities within system 1 are equal (  1 ), and make the same assumption for system 2 (  2 ). In general, incentive payments are calculated as PAY = PES * ES Where PES = $/unit of ES, ES = services / ha For adoption analysis, set ES = 1, then PAY = PES ($/ha)

Conclusion: to implement MD approach we need: Mean yields for system 1 Either mean yields for system 2, or Inprods for each activity in each system Output prices and cost of production for each activity Variances (or CVs) of returns (yields) for each system Correlation of returns to activities within each system (  1 and  2 ) Correlation of returns between systems 1 and 2 (  12 )

Data for modeling Kabale application RegionsCrop Activities Base systemSystem 1System 2 Flat slopes Cost/ha Yields/h a Price/k gArea/haSDCVWeights Drought Resistant Variety (%) Drought Resistant Variety + Clean Vines (%) Beans Potatoes Sweet- potatoes Sorghum Moderate slopesBeans Potatoes Sweet- potatoes Sorghum Steep slopesBeans Potatoes Sweet- potatoes Sorghum Source: Field Survey Data (May 2010)

Data for modeling Soroti application Regions Crop Activities Base systemSystem 1 Cost/haYields/haPrice/kgArea/haSDCVWeights Drought Resistant Variety + Clean Vines (%) Better-off Sweet- potatoes Sorghum Millet Cassava G/nuts Maize Cowpeas Worse-off Sweet- potatoes Sorghum Millet Cassava G/nuts Maize Cowpeas Source: Field Survey Data (March 2010)

Results from Stakeholders workshop Farmers experience Unpredictable rainfall Increased pest and disease Declining soil fertility Adaptation mechanism Swamp cultivation Disease and pest resistant crop varieties Mixed and multiple cropping Short duration crops (vegetables) Water Harvesting Flood and micro irrigation Adaptation mechanism Cont Spraying for pest Crop rotation and migration Note: 1) Farmers noted that only those with money and information can acquire some of technologies like resistant varieties 2) If provided under govt (NAADS), gainers are the politically powerful and the rich, even when the target is the poor.

Traditional System Vs Resistant Variety and Virus free Vines Adoption rate of planting pest and disease resistant varieties that are virus free is 65% without compensation 57% of the households would plant resistant varieties without compensation. To raise adoption level by 20% (from 65% to 85% and 57% to 80%), farmers should be compensated by about 250,000 Uganda shillings per hectare ($110) These results indicate that farmers are rational because they do not adopt the technology as long as benefits do not exceed the costs.

Subsidy Vs No subsidy case 63.8% will adopt virus free planting material without subsidy 65% adopt planting material planting material if subsidy is provided Results show small difference in adoption rates implying that a sweet potato vine subsidy would achieve little in terms of promoting the adoption of pest and disease resistant virus free planting materials. Subsidization in order to increase adoption climate change adaptation strategies is not sustainable

Agro –ecological zones The adoption rate on flat land is 65.3% The adoption rate on moderate slopes is 60.7% The adoption rate on the steep slopes is 64.4% The production of sweet potatoes under new improved sweet potato technologies varies with the slope agro- ecological zones Variations in adoption is depends on Competing uses and opportunity cost of allocating land to new technology

Better off Vs Worse off The adoption potential for those sweet potato farmers with endowed with land (better off) is 65.4% whereas it is 53.85% for those farmers less endowed with land (worse off). This result implies that those farmers endowed with land have a stronger resource base and better capacity to bear the risks associated with the new sweet potato technology while those farmers less endowed with land tend to be risk averse and is hence hesitant to take chances with the new sweet potato technology.

Conclusion and Recommendation Households are adapting to climate change Some adaptation strategies are not affordable by some farmers. Subsidy provision is not sustainable in climate change adaptation. Opportunity cost of land is one of the critical determinants of sustainable adoption of improved agricultural technologies Adoption CC adaptation strategies varies base different agro-ecological zones Climate change policy needs to target particular households based agro-ecological zone or Poverty The institutional framework and systems should be strengthened to improve on accountability in the implementation of climate change adaptation strategies of a public nature Climate change policy should focus on reducing opportunity costs and transaction cost involved in adopting these CC adaptation strategies. END