ArticlePloS one2025
Climate change and its impact on wheat distribution in semi-arid ecosystems: A case study from the Sultanate of Oman.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.