Climatic Change

, Volume 147, Issue 3–4, pp 491–506 | Cite as

How will East African maize yields respond to climate change and can agricultural development mitigate this response?

  • Frank Davenport
  • Chris Funk
  • Gideon Galu


We analyze the response of Kenyan maize yields to near-term climate change and explore potential mitigation options. We model county level yields as a function of rainfall and temperature during a period of increased regional warming and drying (1989–2008). We then do a counter factual analysis by comparing existing maize yields from 2000 to 2008 to what yields might have been if observed warming and drying trends had not occurred. We also examine maize yields based on projected 2026–2040 climate trends. Without the observed warming and drying trends, Eastern Kenya would have had an 8% increase in maize yields, which in turn would have led to a net production increase of 500,000 metric tons. In Western Kenya, the magnitude of change is higher but the relative changes in predicted values are smaller. If warming and drying trends continue, we expect future maize yields to decline by 11% in Eastern Kenya (vs. 7% in Western Kenya). We also examine whether these future losses might be offset through agricultural development. For that analysis, we use a household panel dataset (2000, 2005) with measurements of individual farm plot yields, inputs, and outputs. We find that under a scenario of aggressive adoption of hybrid seeds and fertilizer usage coupled with warming and drying trends, yields in Western Kenya might increase by 6% while those in Eastern Kenya could increase by 14%. This increase in yields might be larger if there is a corresponding increase in usage of drought-tolerant hybrids. However, wide prediction intervals across models highlight the uncertainty in these outcomes and scenarios.



This work was primarily supported by USGS cooperative agreement #G14AC00042 and NASA grant #NNX16AM02G. Chris Funk is supported under the USGS Drivers of Drought program. Sari Blakeley provided valuable feedback on an earlier version of this paper. We also thank the three anonymous reviewers for their comments and critiques. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Supplementary material

10584_2018_2149_MOESM1_ESM.docx (8.2 mb)
ESM 1 (DOCX 8389 kb)


  1. Abate T, Shiferaw B, Menkir A, Wegary D, Kebede Y, Tesfaye K, Kassie M, Bogale G, Tadesse B, Keno T (2015) Factors that transformed maize productivity in Ethiopia. Food Sec 7:965–981CrossRefGoogle Scholar
  2. Ainembabazi JH, van Asten P, Vanlauwe B, Ouma E, Blomme G, Birachi EA, Nguezet PMD, Mignouna DB, Manyong VM (2016) Improving the speed of adoption of agricultural technologies and farm performance through farmer groups: evidence from the Great Lakes region of Africa. Agricultural Economics, n/a-n/aGoogle Scholar
  3. Argwings-Kodhek G, Kiiru MW, Tschirley D, Ochieng BA, Landan BW (2000) Measuring income and the potential for poverty reduction in rural Kenya. Nakuru: Egerton University, Tegemeo Institute of Agricultural Policy and Development TAMPAGoogle Scholar
  4. Ariga J, Jayne TS, (2010) Factors driving the increase in fertilizer use by smallholder farmers in Kenya, 1990–2007. World BankGoogle Scholar
  5. Bänziger M, Edmeades GO, Lafitte HR (1999) Selection for drought tolerance increases maize yields across a range of nitrogen levels. Crop Sci 39(4):1035–1040CrossRefGoogle Scholar
  6. Bänziger M, Edmeades GO, Lafitte HR (2002) Physiological mechanisms contributing to the increased N stress tolerance of tropical maize selected for drought tolerance. Field Crop Res 75(2):223–233CrossRefGoogle Scholar
  7. Bezemer D, Headey D (2008) Agriculture, development, and urban bias. World Dev 36:1342–1364CrossRefGoogle Scholar
  8. Bot A and Benites J (2005) Drought-resistant soils: optimization of soil moisture for sustainable plant production: Proceedings of the Electronic Conference Organized by the FAO Land and Water Development Division, Food & Agriculture OrgGoogle Scholar
  9. Chen B-L, Liao S-Y (2015) The role of agricultural productivity on structural change. Rev Dev Econ 19:971–987CrossRefGoogle Scholar
  10. Davenport F, Husak G, Jayanthi H (2015) Simulating regional grain yield distributions to support agricultural drought risk assessment. Appl Geogr 63:136–145CrossRefGoogle Scholar
  11. Davenport F, Grace K, Funk C, Shukla S (2017) Child health outcomes in sub-Saharan Africa: a comparison of changes in climate and socio-economic factors. Glob Environ Chang 46:72–87CrossRefGoogle Scholar
  12. Diao X, Hazell P, Thurlow J (2010) The role of agriculture in African development. World Dev 38:1375–1383CrossRefGoogle Scholar
  13. Droppelmann KJ, Snapp SS, Waddington SR (2017) Sustainable intensification options for smallholder maize-based farming systems in sub-Saharan Africa. Food Sec 9(1):133–150CrossRefGoogle Scholar
  14. Duflo E, Kremer M, Robinson J (2008) How high are rates of return to fertilizer? Evidence from field experiments in Kenya. Am Econ Rev 98:482–488CrossRefGoogle Scholar
  15. Duflo E, Kremer M, Robinson J (2011) Nudging farmers to use fertilizer: theory and experimental evidence from Kenya. Am Econ Rev 101:2350–2390CrossRefGoogle Scholar
  16. FAOSTAT (2015) FAOSTATGoogle Scholar
  17. Funk CC, Brown ME (2006) Intra-seasonal NDVI change projections in semi-arid Africa. Remote Sens Environ 101:249–256CrossRefGoogle Scholar
  18. Funk C, Brown M (2009) Declining global per capita agricultural production and warming oceans threaten food security. Food Sec 1:271–289CrossRefGoogle Scholar
  19. Funk C, Dettinger MD, Michaelson J, Verdin J, Brown ME (2008) The warm ocean dry Africa dipole controls decadal moisture transports threatening food insecure Africa. Proc Natl Acad Sci 105:11081–11086CrossRefGoogle Scholar
  20. Funk C, Hoell A, Shukla S, Bladé I, Liebmann B, Roberts JB, Robertson FR, Husak G (2014) Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices. Hydrol Earth Syst Sci 18:4965–4978CrossRefGoogle Scholar
  21. Funk C, Nicholson SE, Landsfeld M, Klotter D, Peterson P, Harrison L (2015a) The Centennial Trends Greater Horn of Africa precipitation dataset. Scientific Data 2Google Scholar
  22. Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A (2015b) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data 2Google Scholar
  23. Funk C, Shukla S, Hoell A, Livneh B (2015c) Assessing the contributions of East African and west Pacific warming to the 2014 boreal spring East African drought. Bull Am Meteorol Soc 96:S77–S82CrossRefGoogle Scholar
  24. Funk C, Verdin A, Michaelsen J, Peterson P, Pedreros D, Husak G (2015d) A global satellite assisted precipitation climatology. Earth Syst Sci Data Discuss 7:1–13CrossRefGoogle Scholar
  25. Funk C, Davenport F, Harrison L, Magadzire T, Galu G, Artan G, Shukla S, Korecha D, Indeje M, Pomposi C, Macharia D, Husak G (2017) Anthropogenic enhancement of moderate-to-strong El Niños likely contributed to drought and poor harvests in Southern Africa during 2016. Bull Am Meteorol Soc 37:S1–S3.
  26. Gollin D, Parente S, Rogerson R (2002) The role of agriculture in development. Am Econ Rev 92:160–164CrossRefGoogle Scholar
  27. Grace K, Davenport F, Funk C, Lerner AM (2012) Child malnutrition and climate in Sub-Saharan Africa: an analysis of recent trends in Kenya. Appl Geogr 35:405–413CrossRefGoogle Scholar
  28. Grace K, Davenport F, Hanson H, Funk C, Shukla S (2015) Linking climate change and health outcomes: examining the relationship between temperature, precipitation and birth weight in Africa. Glob Environ Chang 35:125–137CrossRefGoogle Scholar
  29. Hansen JW, Indeje M (2004) Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya. Agric For Meteorol 125:143–157CrossRefGoogle Scholar
  30. Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int J Climatol 34:623–642CrossRefGoogle Scholar
  31. Hatfield JL, Prueger JH (2015) Temperature extremes: effect on plant growth and development. Weather Clim Extremes 10(Part A):4–10CrossRefGoogle Scholar
  32. Hicks DR, Thomison PR (2004) Corn management, in: Smith CW, Beltran J, Runge ECA (Eds), Corn: origin, history, technology, and production. Wiley, pp. 481–522Google Scholar
  33. Jose VRR, Winkler RL (2008) Simple robust averages of forecasts: some empirical results. Int J Forecast 24:163–169CrossRefGoogle Scholar
  34. Li G, Zhao B, Dong S, Zhang J, Liu P, Vyn TJ (2017) Impact of controlled release urea on maize yield and nitrogen use efficiency under different water conditions. PLoS One 12(7):e0181774CrossRefGoogle Scholar
  35. Lobell DB, Bänziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Clim Chang 1:42–45CrossRefGoogle Scholar
  36. Magrini E, Vigani M (2016) Technology adoption and the multiple dimensions of food security: the case of maize in Tanzania. Food Sec 8:707–726CrossRefGoogle Scholar
  37. Makridakis S (1983) Averages of forecasts: some empirical results. Manag Sci 29:987–996CrossRefGoogle Scholar
  38. Makridakis S, Wheelwright S, Hyndman R (1998) Forecasting: methods and applicationsGoogle Scholar
  39. Mason NM, Wineman A, Kirimi L, Mather D (2017) The effects of Kenya’s ‘smarter’ input subsidy programme on smallholder behaviour and incomes: do different quasi-experimental approaches lead to the same conclusions? J Agric Econ 68:45–69CrossRefGoogle Scholar
  40. Novick K, Williams C, Phillips R, Oishi A, Sulman B, Bohrer G and Ficklin D (2015). Vapor pressure deficit is as important as soil moisture in determining limitations to evapotranspiration during drought. AGU Fall Meeting AbstractsGoogle Scholar
  41. Nyakudya IW, Stroosnijder L (2011) Water management options based on rainfall analysis for rainfed maize (Zea mays L.) production in Rushinga district, Zimbabwe. Agric Water Manag 98(10):1649–1659CrossRefGoogle Scholar
  42. Salasya B, Mwangi W, Mwabu D, Diallo A (2007) Factors influencing adoption of stress-tolerant maize hybrid (WH 502) in western Kenya. Afr J Agric Res 2(10):544–551Google Scholar
  43. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci 106:15594–15598CrossRefGoogle Scholar
  44. Sheffield J, Goteti G, Wood EF (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J Clim 19:3088–3111CrossRefGoogle Scholar
  45. Short C, Mulinge W, Witwer M, (2012) Analysis of incentives and disincentives for maize in Kenya, in: FAO (Ed), Technical Notes Series MAFAP. FAO, RomeGoogle Scholar
  46. Slegers MFW, Stroosnijder L (2008) Beyond the desertification narrative: a framework for agricultural drought in semi-arid East Africa. AMBIO 37(5):372–380CrossRefGoogle Scholar
  47. Smale M and Olwande J (2011). Is older better?: Maize hybrid change on household farms in Kenya, Michigan State University, Department of Agricultural, Food, and Resource Economics and Department of EconomicsGoogle Scholar
  48. Stroosnijder L (2007) Rainfall and land degradation. Climate and land degradation. M. V. K. Sivakumar and N. Ndiang’ui. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 167–195Google Scholar
  49. Stroosnijder L (2009) Modifying land management in order to improve efficiency of rainwater use in the African highlands. Soil Tillage Res 103(2):247–256CrossRefGoogle Scholar
  50. Westgate M E, Otegui M, Andrade FH, (2004) Physiology of the corn plant, in: Smith CW, Betrán J, Runge ECA (Eds), Corn: origin, history, technology, and production. Wiley, pp. 235–272Google Scholar
  51. Wood S (2007) The mgcv package. www.r-project.orgGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Climate Hazards Center, Department of GeographyUniversity of California Santa BarbaraSanta BarbaraUSA
  2. 2.US Geological Survey, Earth Resources Observation and Science Center (EROS), Climate Hazards Center, Department of GeographyUniversity of California Santa BarbaraSanta BarbaraUSA
  3. 3.Climate Hazards Center, Department of GeographyUniversity of California Santa Barbara, Santa Barbara and Famine Early Warning System Network (FEWSNET)Santa BarbaraUSA

Personalised recommendations