This study evaluated climate change adaptation strategies for small-scale farmers in Kenya using survey data, crop modeling, and an impact assessment model. The study areas were Vihiga and Machakos-Makueni provinces. Climate projections predicted higher temperatures and variable rainfall. Adaptation strategies included improved crop varieties, introducing sweet potatoes, and improving livestock feed. The model assessed the economic impacts of climate change and adaptations on poverty rates and farm incomes under different socioeconomic scenarios. It found that most farms would be negatively impacted by climate change but that certain adaptations could offset these impacts, though different strategies were needed for the two regions. The modeling framework provided insights into how adaptations could help smallholder farmers despite limitations in data and assumptions.

1. A Method for Evaluating Climate Change Adaptation Strategies for
Small-Scale Farmers Using Survey, Experimental and Modeled Data
L. Claessens ,*, J.M. Antle , J.J. Stoorvogel , R.O. Valdivia , P.K. Thornton , M. Herrero
1,2 3 2 3 4 4
1
ICRISAT, 2Wageningen University, 3Oregon State University, 4ILRI
* ICRISAT, Nairobi, Kenya, l.claessens@cgiar.org
Agricultural Systems 111 (2012) 85–95
Introduction
Figure 1.
Aggregated
economic
• SSA predicted to experience considerable negative effects of CC (IPCC 4AR).
impact of
• Adaptation is essential but hard to assess in the context of small scale subsistence agriculture.
climate
• New methodologies needed for ex ante impact assessment of adaptation strategies.
change and
• Data needs for system level analysis usually high (bio-physical, socio-economic).
simulated
• Application of the Tradeoff Analysis Model for Multi-Dimensional Impact Assessment (TOA-MD).
adaptation
strategies on
farmers in
Vihiga and
Study Areas Machakos-
Makueni,
VIHIGA, western province Kenya (pop. 500,000, 60% below poverty line of 1 US$/day) Kenya.
• 1,300-1,500 masl, 419 km2 arable land, 1,800-2,000 mm prec., 14-32 °C. Notation of
• Well drained nitisols, Nitrogen and Phosphorus deficient. legend as in
• Food crops: maize, beans, sorghum, groundnuts, bananas, vegetables. Cash crop: tea. Table 1
• Livestock: dairy production , local Zebu. Napier grass for fodder.
MACHAKOS-MAKUENI, eastern province Kenya
• 400-1,200 masl, 6,615 km2 arable land, 500-1,300 mm prec., 15-25 °C.
• Deep, friable soils, Nitrogen and Phosphorus deficient (sharp yield declines, no inputs).
• Food crops: maize, beans, millet, sorghum, vegetables, fruits, cassava. Cash crops: coffee,
cotton.
Table 2. Impacts of climate change, simulated adaptation strategies and socio-
economic scenarios on farmers in Vihiga and Machakos.
Vihiga Machakos
Poverty Rate (% of farm population living on <$1 per day)
Scenario No Dairy Dairy Total No Dairy Dairy Irrigated Total
base 85 38 62 85 43 54 73
CC 89 49 69 89 51 57 78
imz 87 42 65 85 44 50 73
dpsplw 88 42 66 85 44 50 73
dpsp 85 41 63 83 43 50 71
dpsp1 85 36 60 83 41 49 71
dpsp12 85 30 58 83 38 48 70
RAP1 base 65 17 41 72 30 46 60
RAP1 CC 71 18 44 77 33 47 64
RAP1 imz 66 15 41 70 27 40 58
RAP1 dpsp 65 15 40 69 27 40 57
RAP2 base 89 48 68 91 50 57 79
RAP2 CC 91 50 71 93 53 57 81
Net Loss (percentage of mean agricultural income in base system)
SURVEY DATA (Table 1) CC 26 27 27 32 31 33 32
imz 8 11 11 -16 6 -50 -20
• Vihiga: PROSAM project (Waithaka et al., 2005). dpsplw 13 12 12 -23 5 -49 -23
• Machakos: NUTMON project (Gachimbi et al., 2005). dpsp -7 9 6 -31 3 -51 -27
• Survey data (quantities and prices) on inputs (such as seeds, labor, fertilizer, manure and land dpsp1 -7 -5 -6 -31 -7 -65 -34
allocation), outputs (crop yields, milk production), and farm management. dpsp12 -7 -23 -21 -31 -19 -80 -43
TOA-MD RAP1 CC 30 5 8 35 11 12 19
RAP1 imz 4 -5 3 -23 -8 -44 -27
• Previous applications: technology adoption, payments for environmental services (Antle and RAP1 dpsp 2 -6 -5 -27 -8 -42 -28
Validivia, 2006, Antle and Stoorvogel, 2008, Immerzeel et al., 2008, Claessens et al., 2009).
RAP2 CC 26 7 10 25 14 8 16
• Extension of the technology adoption model to include calculation of poverty rates associated with
Adoption Rate (percentage of farm population)
adoption. Technical details in Antle (2011). imz 62 52 56 54 51 51 53
• Economic analysis of base system versus alternative system (under CC, with or without dpsplw 52 51 51 58 53 50 56
adaptation). dpsp 74 57 64 61 55 51 59
dpsp1 74 77 77 61 65 55 61
CLIMATE PROJECTIONS & EFFECTS ON PRODUCTIVITY dpsp12 74 90 84 61 74 59 63
• 2050, IPCC 4AR, WorldCLIM, combinations of GCMs HadCM3 & ECHam4 with SRES scenarios RAP1 imz 71 56 62 57 54 52 56
A1FI & B1 (very high and low emissions). RAP1 dpsp 73 58 64 60 55 51 58
• Downscaling described in Thornton et al. (2009, 2010) and Jones et al. (2009).
• DSSAT crop growth models for maize and beans. Estimations for other crops.
• Livestock: decline in milk yield due to increased heat stress and decreased farm produced feed.
RESULTS
ADAPTATION STRATEGIES (Table 1)
• Fig.1: where curve crosses X axis = percentage of farms that gain; left = percentage with gains greater
• Improved, drought tolerant maize variety. than amount on Y axis; right = percentage with losses less than or equal amount on Y axis.
• Introduction of dual-purpose sweet potato (DPSP) with varying yield levels. • 76% of Vihiga farms and 62% of Machakos farms negatively impacted by CC.
• Improved livestock breeds and feed quality (high crude protein content sweet potato vines). • Improved maize variety or low yielding dpsp sufficient to offset negative effects in Machakos.
• Improved feed quantity & quality and improved livestock breeds needed for Vihiga.
REPRESENTATIVE AGRICULTURAL PATHWAYS (RAPs, Table 1)
• Dairy farmers in Vihiga benefit relatively more than Machakos. Same trend for adoption rates.
• Socio-economic scenarios along lines of RCPs and SSPs but more relevant for agricultural • RAPs scenarios have major effect on impact indicators like poverty rates.
models DISCUSSSION
• RAP 1: low adaptation challenges, high economic growth
• Inherent tradeoff between using limited data and the amount of uncertainty in model outcomes.
• Assumptions had to be made in climate change and adaptation scenarios.
• Uncertainty in GCMs, SRES and RAPs scenarios, downscaling and crop growth simulation models.
• Climate variability, pests and diseases not included.
• Adoption rates based on economic feasibility, providing upper bound for real adoption.
CONCLUSION
• Despite the limitations of the methodology, this study yielded insights into the way different adaptation
strategies could assist in improving the livelihoods of smallholder farmers operating in the mixed crop-
livestock systems in East Africa.
• TOA-MD offers a flexible, generic framework that can use available and modeled data to evaluate climate
impact and adaptation strategies under a range of socio-economic scenarios.
REFERENCES
Antle, J.M. 2011. Parsimonious multi-dimensional impact assessment. Am. J. Agric. Econ. 93 (5), 1292–1311.
Antle, J.M., Stoorvogel, J.J. 2006. Predicting the supply of ecosystem services from agriculture American Journal of Agricultural Economics,
88(5):1174-1180.
Antle, J.M., Valdivia, R.O., 2006. Modelling the supply of ecosystem services from agriculture: a minimum-data approach. The Australian Journal
of Agricultural and Resource Economics 50: 1–15.
Claessens, L., Stoorvogel, J.J., Antle, J.M. 2009. Ex ante assessment of dual-purpose sweet potato in the crop-livestock system of western
Kenya: A minimum-data approach. Agricultural Systems, 99(1):13-22.
Gachimbi, L. N., H. van Keulen, E. G. Thuranira, A. M. Karuku, A. Jager, S. Nguluu, B. M. Ikombo, J. M. Kinama, J. K. Itabari, Nandwa, S. M.,
2005. Nutrient balances at farm level in Machakos (Kenya), using a participatory nutrient monitoring (NUTMON) approach, Land Use Policy 22(1),
13-22.
Immerzeel, W., J. Stoorvogel, Antle, J., 2008. Can payments for ecosystem services secure the water tower of Tibet? Agricultural Systems 96(1-
3), 52-63.
Jones, P. G., Thornton, P. K., Heinke, J., 2009. Software to generate characteristic daily weather data at any resolution using downscaled climate
model data from the IPCC’s Fourth Assessment. BMZ Project Discussion Paper, ILRI, Nairobi.
Thornton, P.K., Jones, P.G., Alagarswamy, G., Andresen, J. 2009a. Spatial variation of crop yield response to climate change in East Africa. Global
Environmental Change, 19(1):54-65.
Thornton, P.K., Jones, P.G., Alagarswamy, G., Andresen, J., Herrero, M., 2010. Adapting to climate change: Agricultural system and household
impacts in East Africa. Agricultural Systems 103, 73-82.
Waithaka, M.M., Thornton, P.K., Herrero, M., Shepherd, K.D., Stoorvogel, J.J., Salasya, B., Ndiwa, N., Bekele, N., Croucher, M., Karanja, S.,
Booltink, H., 2005. System prototyping and impact assessment for sustainable alternatives in mixed farming systems in high-potential areas of
Eastern Africa. Final Program Report to the Ecoregional Fund to Support Methodological Initiatives.