首页|MODELLING THE IMPACTS OF CLIMATE CHANGE ON AGRICULTURAL CROPS IN ZIMBABWE
MODELLING THE IMPACTS OF CLIMATE CHANGE ON AGRICULTURAL CROPS IN ZIMBABWE
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NETL
Climate in general plays a significant role on planet Earth. It determines the natural distribution of species as well as how their facets will change over time. Climate change is a serious global issue affecting countries at different degrees. Projections on climate change and variability have indicated a probably alteration in the geographically suitable land areas for crop cultivation. As such, many African countries will be affected given their great dependence on agriculture and Zimbabwe is no exception. Understanding crops’ geographical or climate suitability is very essential for avoiding yield losses due to unfavorable growth environments in the changing climatic circumstances yet in Zimbabwe information on change in crop suitability due to climate change is limited. Hence, the objective in this study is to model the current and future potential distribution of rain-fed food and cash crops that are cultivated in Zimbabwe namely groundnuts, maize, millet, sorghum, soybean, cotton and tobacco. The study seeks to determine how current crop suitability will shift under the changing climate in the 2050s. The study used Maximum Entropy model (Maxent) which is a correlative machine learning model with the ability to generate correlation between values of explanatory variables and recognized occurrence points of species. Bioclimatic variables for two Shared Socioeconomic pathways (SSP) 2.4-5 and 5.8-5 from three General Circulation Models (ACCESS-CM2, IPSL-CM6A-LR and MRI-ESM2-0) were selected for predicting potential future distribution. A total of 22 environmental variables were used in the study including 19 bioclimatic variables. The other contributing variables are soil texture, organic carbon and land use–land cover. The other contributing variables are soil texture, organic carbon and land use–land cover. The jackknife performance, based on the Area Under Curve (AUC) values wasamp;nbsp;used as evaluation of the model. AUC value of 0.638, 0.669, 0.664, 0.679, 0.698, 0,745 and 0.718 were obtained for maize, groundnut, millet, sorghum, soybean, tobacco and cotton respectively. The areas were classified as having no suitability, marginal suitability, moderate and high suitability. Cotton suitability was mainly influenced by Bio 7, soil texture and Bio 13. Increase in temperature caused decrease in land suitability while increase in precipitation increased suitability. The model shows high suitability concentration in Mashonaland west, Mashonaland East, Mashonaland central, Manicaland and Masvingo provinces. as well as Midlands. The areas of high suitability are mostly in the farming region II, III and IV. Matabeleland North and Matabeleland South provinces in region IV have the greatest land portions classified as having no suitability. The future climate change follows the eastward trend. After calculating land size, it was observed that the areas of high suitable land will significantly change. Prediction of groundnut suitability was mostly influenced by Bio 3, Bio 7, Bio 10 soil texture and organic carbon. The current high suitability lands are distributed in the south and southeast part of the country in as we as the North part or the country Areas of no suitability are widely distributed in the North western side with small areas in the south and north eastern. Future change will cause suitability to shrink towards the east. Unsuitable lands are predicted to continue to increase while the high suitable areas will decrease as a result of climate change. For maize suitability was determined by, soil type, Bio 10, Bio 16 and Bio 18 thus, the maize suitability can be linked to the increase in precipitation. Maize suitability was predicted in all the provinces. Matabeleland North and Matabeleland south show the smallest land areas of high suitability for maize.
NETL
