Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2755


Climate change impact on maize (Zea mays L.) yield using crop simulation and statistical downscaling models: A review

Charles B. Chisanga
  • Charles B. Chisanga
  • Ministry of Agriculture, Box 70232, Ndola, Ndola, Zambia.
  • Google Scholar
Elijah Phiri
  • Elijah Phiri
  • Department of Soil Science, School of Agricultural Sciences, University of Zambia, Box 32379, Lusaka, Zambia.
  • Google Scholar
Vernon R. N. Chinene
  • Vernon R. N. Chinene
  • Department of Soil Science, School of Agricultural Sciences, University of Zambia, Box 32379, Lusaka, Zambia.
  • Google Scholar

  •  Received: 13 June 2017
  •  Accepted: 06 September 2017
  •  Published: 30 September 2017


Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.


Key words: Review, AgMIP, climate scenario, climate change, variability, crop simulation model, bias correction, dynamical downscaling, Global Climate Model (GCM), statistical downscaling.


 Atmospheric Oceanic Global Circulation Models (AOGCM). United Kingdom Transient climate experiment (UKTR). European Centre-Hamburg model version 1 (ECHAM1). Global  Sea  Ice  and  Sea  Surface Temperature (GISSTR). Canadian Centre for Climate (Modelling and Analysis) (CCC). Intergovernmental Panel on Climate Change and Task Group on Data and Scenario Support for Impact and Climate Analysis (IPCC-TGICA)