Abstract
Climate change, characterised by long-term shifts in temperature, precipitation, and extreme weather events, poses significant challenges to agricultural sustainability. This study aims to mitigate the impact of climate change on wheat production in Oman by identifying optimal cultivation areas for four temporal periods. Utilising the maximum entropy (MaxEnt) model, the study assessed the suitability of wheat cultivation across four periods: 1970-2020 (reference period), 2021-2040, 2041-2060 and 2061-2080. The model considered environmental variables, such as temperature and precipitation, to predict wheat distribution for the present and future climate scenarios. The MaxEnt model demonstrated robust predictive performance, with area under curve (AUC) scores consistently above 0.8 across all periods. The model achieved an AUC of 0.82 for the reference period (1970-2020) and accurately identified the regions suitable for wheat cultivation. The AUC for the immediate future (2021-2040) decreased marginally to 0.81, reflecting potential shifts in environmental conditions that might influence wheat distribution, and returned to 0.82 for the 2041-2060 period, indicating the model's resilience in predicting wheat suitability despite the projected climate change impacts. Notably, the AUC increased to 0.83 for the 2061-2080 period, suggesting that wheat distribution patterns might become more distinct under future climate scenarios or that the environmental variables driving the model gain greater significance as climate change intensifies. These results highlight the effectiveness of the MaxEnt model in identifying suitable wheat cultivation areas in varying climate conditions. The results provide critical insights into Oman's long-term agricultural planning and sustainable practices. Given the historical wheat cultivation in different regions of Oman, it is crucial to identify optimal areas for future production under climate change to ensure food security and support strategic decision-making. This study emphasises the importance of integrating predictive modelling into agricultural planning and calls for further research to refine strategies for climate-resilient wheat production.