Climatic Change

, Volume 106, Issue 2, pp 267–283 | Cite as

Sensitivity of southern African maize yields to the definition of sowing dekad in a changing climate

  • Olivier Crespo
  • Sepo Hachigonta
  • Mark Tadross


Most African countries struggle with food production and food security. These issues are expected to be even more severe in the face of climate change. Our study examines the likely impacts of climate change on agriculture with a view to propose adaptation options, especially in hard hit regions. We use a crop model to evaluate the impact of various sowing decisions on the water satisfaction index (WSI) and thus the yield of maize crop. The crop model is run for 176 stations over southern Africa, subject to climate scenarios downscaled from 6 GCMs. The sensitivity of these simulations is analysed so as to distinguish the contributions of sowing decisions to yield variation. We compare the WSI change between a 20 year control period (1979–1999) and a 20 year future period (2046–2065) over southern Africa. These results highlight areas that will likely be negatively affected by climate change over the study region. We then calculate the contribution of sowing decisions to yield variation, first for the control period, then for the future period. This contribution (sensitivity) allows us to distinguish the efficiency of adaptation decisions under both present and future climate. In most countries rainfall in the sowing dekad is shown to contribute more significantly to the yield variation and appears as a long term efficient decision to adapt. We discuss these results and additional perspectives in order to propose local adaptation directions.


Future Climate Future Period Adaptation Option Crop Model Crop Failure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akpalu W, Hassan RM, Ringler C (2008) Climate variability and maize yield in South Africa. Tech. rep., International Food Policy Research Institute (IFPRI)Google Scholar
  2. Allen R, Pereira L, Raes D, Smith M (1998) Crop evapotranspiration–guidelines for computing crop water requirements. In: FAO irrigation and drainage paper, vol 56. Food and Agriculture Organization (FAO), Rome, ItalyGoogle Scholar
  3. Annandale J, Jovanic N, Benade N, Allen R (2002) Software for missing data error analysis of penman-monteith reference evapotranspiration. Irrig Sci 21(2):57–67CrossRefGoogle Scholar
  4. Brown M, Funk C (2008) Food security under climate change. Science 319(5863):580–581CrossRefGoogle Scholar
  5. Christensen J, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli R, Kwon WT, Laprise R, Rueda VM, Mearns L, Menéndez C, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp 847–940Google Scholar
  6. Clarke H (2008) Classical decision rules and adaptation to climate change. Aust J Agric Resour Econ 52:487–504CrossRefGoogle Scholar
  7. Doorenbos J, Kassam A (1979) Yield response to water. In: FAO irrigation and drainage paper, vol 33. Food and Agriculture Organization (FAO), RomeGoogle Scholar
  8. FAO (2007) Adaptation to climate change in agriculture, forestry and fisheries: perspective, framework and priorities. Tech. Rep., Food and Agriculture Organization (FAO), Rome, ItalyGoogle Scholar
  9. Fischer G, Shah M, van Velthuizen H, Nachtergaele F (2001) Global agro-ecological assessment for agriculture in the 21st century. In: IIASA research report, international institute for applied systems analysis (IIASA) and food and agriculture organization (FAO), Laxenburg, AustriaGoogle Scholar
  10. Frère M, Popov G (1986) Early agrometeorological crop yield assessment. In: FAO plant production and protection paper, vol 73. Food and Agriculture Organization (FAO), Rome, ItalyGoogle Scholar
  11. Gbetibouo G, Hassan R (2005) Measuring the economic impact of climate change on major South African field crops: a ricardian approach. Glob Planet Change 47:143–152CrossRefGoogle Scholar
  12. Gibbons J, Ramsden S (2008) Integrated modelling of farm adaptation to climate change in East Anglia, UK: scaling and farmer decision making. Agric Ecosyst Environ 127:126–134CrossRefGoogle Scholar
  13. Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Eng 108:225–230Google Scholar
  14. Hewitson B, Crane R (2006) Consensus between gcm climate change projections with empirical downscaling: precipitation downscaling over South Africa. Int J Climatol 26(10):1315–1337CrossRefGoogle Scholar
  15. Jones J, Hoogenboom G, Porter C, Boote K, Batchelor W, Hunt L, Wilkens P, Singh U, Gijsman A, Ritchie J (2003) Dssat cropping system model. Eur J Agron 18:235–265CrossRefGoogle Scholar
  16. Keating B, Carberry P, Hammer G, Probert M, Robertson M, Holzworth D, Huth N, Hargreaves J, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J, Silburn M, Wang E, Brown S, Bristow K, Asseng S, Chapman S, McCown R, Freebairn D, Smith C (2003) An overview of apsim, a model designed for farming systems simulation. Eur J Agron 18:267–288CrossRefGoogle Scholar
  17. Leavy J, Lussier K, Solomon I (2008) Time is now–lessons from farmers adapting to climate change. Tech. rep., ActionAid International, The Hague, The NetherlandsGoogle Scholar
  18. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319:607–610CrossRefGoogle Scholar
  19. Mukhala E, Hoefsloot P (2004) AgroMetShell manualGoogle Scholar
  20. Parry M, Arnell N, McMichael T, Nicholls R, Martens P, Kovats S, Livermore M, Rosenzweig C, Iglesias A, Fischer G (2001) Millions at risk: defining critical climate change threats and targets. Glob Environ Change 11(3):181–183CrossRefGoogle Scholar
  21. Priestley C, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92CrossRefGoogle Scholar
  22. Pyke CR, Bierwagen BG, Furlow J, Gamble J, Johnson T, Julius S, West J (2007) A decision inventory approach for improving decision support for climate change impact assessment and adaptation. Environ Sci Policy 10:610–621CrossRefGoogle Scholar
  23. R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0Google Scholar
  24. Reason C, Hachigonta S, Phaladi R (2005) Interannual variability in rainy season characteristics over the limpopo region of southern Africa. Int J Climatol 25(14):1835–1853CrossRefGoogle Scholar
  25. Risbey J, Kandlikar M, Dowlatabadi H, Graetz D (1999) Scale, context, and decision making in agricultural adaptation to climate variability and change. Mitig Adapt Strategies Glob Chang 4:137–165CrossRefGoogle Scholar
  26. Saltelli A, Tarantola S (1999) A quantitative model-independent method for global sensitivity analysis of model output. Technometrics 41(1):39–56CrossRefGoogle Scholar
  27. Smale M, Jayne T (2003) Maize in Eastern and southern Africa: seeds of success in retrospect. In: Environment and production technology division (EPTD) discussion paper, vol 97. International Food Policy Research Institute, Washington, USAGoogle Scholar
  28. Tadross M, Hewitson B, Usman M (2003) Calculating the onset of the maize growing season over southern Africa using gts and cmap data. CLIVAR Exchanges 27:48–50Google Scholar
  29. Walker N, Schulze R (2008) Climate change impacts on agro-ecosystem sustainability across three climate regions in the maize belt of South Africa. Agric Ecosyst Environ 124:114–124CrossRefGoogle Scholar
  30. Ziervogel G, Cartwright A, Tas A, Adejuwon J, Zermoglio F, Shale M, Smith B (2008) Climate change and adaptation in African agriculture. Tech. rep., Rockefeller FoundationGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Climate Systems Analysis Group, Department of Environmental and Geographical ScienceUniversity of Cape TownCape TownSouth Africa

Personalised recommendations