Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

Abstract

Climate change and variability are projected to alter the geographic suitability of lands for crop cultivation. In many developing countries, such as Kenya, information on the mean changes in climate is limited. Therefore, in this study, we model the current and future changes in areas suitable for rainfed maize production in the country using a maximum entropy (MaxENT) model. Maize is by far a major staple food crop in Kenya. We used maize occurrence location data and bioclimatic variables for two climatic scenarios-Representative Concentration Pathways (RCP) 4.5 and 8.5 from two general circulation models (HadGEM2-ES and CCSM4) for 2070. The study identified the annual mean temperature, annual precipitation and the mean temperature of the wettest quarter as the major variables that affect the distribution of maize. Simulation results indicate an average increase of unsuitable areas of between 1.9–3.9% and a decrease of moderately suitable areas of 14.6–17.5%. The change in the suitable areas is an increase of between 17–20% and in highly suitable areas of 9.6% under the climatic scenarios. The findings of this study are of utmost importance to the country as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain maize production under future climates.

Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models

“…To estimate the human population exposed at different levels of risk, we overlaid the reclassified final suitability map derived from the ensemble technique using the natural breaks [43] in ARC GIS with human population raster data retrieved from WorldPop () geoportal.…”

Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach

“…Given the many environmental variables available, choosing those really critical to crop growth and keeping the choice objective is difficult. Correlation analysis is commonly used for eliminating highly correlated variables and thus minimizing multicollinearity, avoiding overfitting, and ensuring more accurate predictions [16,41,42]. Nevertheless, prior knowledge, often locally applicable and possibly subjective, is still required, for example to choose from among those within the same correlation cluster [16].…”

“…Based on correlations between known geo-referenced crop locations and data on environmental variables, SDMs can characterize a given environment in terms of its fit with crop requirements and project such suitable conditions into environmental scenarios at different times to model current and future suitability [15]. The approach based on SDMs has been applied successfully in several assessments including those for maize [15][16][17] and wheat [18][19][20]. Nevertheless, the use of SDMs is considered inherently risky and limited [21], beset with such problems as appropriate selection of the environmental variables that serve as inputs for the models.…”

Potential Risks of Plant Invasions in Protected Areas of Sri Lanka under Climate Change with Special Reference to Threatened Vertebrates

“…These impacts on vertebrate fauna may result in a number of important implications for their conservation in the future. This highlights the requirements of climate change adaptation strategies and focused research that can minimize the vulnerability of these species to climate change [97,98]. Given the importance of IAPS as an agent of global environmental change, the role they play in degrading the habitat quality of native vertebrates in PAs remains poorly understood.…”