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

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.

Climate change impacts on maize and soybean yields in Zambia

“…In the far future under both RCPs simulated, maize yields are often projected to experience a large decrease in both provinces studied, whereas soybean most often experiences marginal variations or increases. This evidence is supported by several studies that also project maize vulnerability to climate change as compared with soybean (Adhikari et al., 2015; Chisanga et al., 2017; Jones & Thornton, 2003; Mulenga et al., 2015; Thornton et al., 2011; Wang et al., 2015; Wineman & Crawford, 2017; Zipper et al., 2016).…”

“…In complement to a number of valuable climate change studies on Zambia (e.g., Adhikari et al., 2015; Chisanga et al., 2017; Jones & Thornton, 2003; Mulenga et al., 2015; Thornton et al., 2011; Wang et al., 2015; Wineman & Crawford, 2017; Zipper et al., 2016), this study used higher spatial resolution to assess maize and soybean changes at the district level. Larger maize yield declines occur more frequently in Central Province's districts compared with Eastern Province's districts where yield changes mostly remain within historical deviation (Figure 2).…”

“…For example, 70–80% of maize crop losses in sub‐Saharan Africa are attributed to climate change‐enhanced droughts and floods, which makes maize one of the most susceptible crops to climate change (Mulungu & Ng'ombe, 2020). Several studies have reported that maize will be among the most negatively affected crops due to anticipated future climate patterns with measurable consequences on food security (Adhikari et al., 2015; Chisanga et al., 2017; Jones & Thornton, 2003; Mulenga et al., 2015; Thornton et al., 2011; Wang et al., 2015; Wineman & Crawford, 2017; Zipper et al., 2016). Despite a recent assessment of land soybean suitability in Central Zambia (Munene et al., 2017), the effect of climate change on soybean growth and yields in the country remain largely unclear.…”

“…The resulting local scale evidence offers scientists, planners, and policy makers information of greater meaning to support farmers in making better‐informed decisions, including selection of crops and cultivars, planting dates, and irrigation water application and scheduling (Rauff & Bello, 2015). In addition, identifying spatial variations of future climate impacts is necessary to infer future food security and livelihoods and provides potential to reduce the increasing risk of future food insecurity (Chisanga et al., 2017).…”

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

“…With the growing concern about climate change and food security, process-based crop models have been used to aid decisions at local, regional and global scales [1,2]. Traditional agronomic experiments are time-consuming and expensive, but crop performance simulations allow evaluation of the potential of crops and crop varieties in different geographic locations under different agro-climatic environments [3]. Crop modelling approaches are also used in on-farm decision making, studying nutrient dynamics [4], plant breeding [5], sensitivity of crops to changing climates [1] and policy-making [2].…”