Abstract
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.
Highlights
Energy, water, transportation, wildlife, health, and agriculture sectors are being affected by climate change
The literature review suggests that many crop models such as agricultural production simulator (APSIM)-maize and decision support system for agrotechnology (DSSAT)-Crop Environmental Resource Synthesis (CERES)-maize have been employed in many applications such as precision agriculture and on-farm management and regional assessments of the impact of climate variability and change
There is significant uncertainty in future climate scenarios used in simulating crop response due to climate change
Summary
Water, transportation, wildlife, health, and agriculture sectors are being affected by climate change. A holistic use of GCMs, statistical downscaling, and crop simulation models are vital in assessing the site-specific climate change impact on crop growth and yield. The combined use of GCMs, crop simulation models and statistical downscaling techniques are the primary tools available to assess climate change impact on maize growth and yield.
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