Warming-induced spatial shifts in single and double cropping rice habitat suitability across China

• Bridging spatial-scale gaps between satellite crop mapping and suitability modelling. • Compared with RF, the XGBoost model is more suitable for crop suitability assessment. • Transferable framework for multi-crop suitability under climate change scenarios. • Double-cropping rice showed suitability expansion coupled with quality degradation. • Single-cropping rice stayed stable short term but shifted poleward with long-term warming. As a staple food for more than half of the global population, rice is crucial for world food security, especially in developing economies. However, there is a knowledge gap concerning the spatial extent of rice habitat suitability in China − the world’s largest rice producer – particularly regarding actual cultivation feasibility beyond purely climatic factors. Here, we assessed historical (2017–2020) and future (2021–2100) shifts in the suitability of single (SR) and double (DR) cropping rice systems, and established a research framework for rice suitability (Machine Learning-Ecological Niche Modeling frameworks, ML-ENM). We use the MaxEnt model to evaluate potential habitat suitability under natural constraints and the XGBoost algorithm to assess potential cultivation feasibility. The study reveals that the proposed framework that combines ecological niche modeling with agronomic feasibility analysis generates actionable insights for climate-resilient crop system design, with direct implications for global adaptation planning. The results showed that high-suitability areas for DR (11.1%) were concentrated in central and southern China, with suitability decreasing progressively toward concentric zones. In contrast, high-suitability areas for SR (11.3%) were more widely distributed across the Sichuan Basin, Yangtze River Basin, southern North China Plain, and Northeast Plain. Notably, the SR suitability was more climate-sensitive than DR. Warming drove DR expansion northward and westward, though with reduced suitability, whereas SR remained stable in the short term but shifted toward higher latitudes under prolonged warming. These findings provide spatially explicit insights for optimizing rice cultivation strategies, mitigating warming impacts, and enhancing climate resilience in China’s rice production.

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Publication Details

Journal
Ecological Indicators
Published
2025-11-14
DOI
https://doi.org/10.1016/j.ecolind.2025.114410
Primary Topic
Soil and Land Suitability Analysis
Type
article
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article

Warming-induced spatial shifts in single and double cropping rice habitat suitability across China

Junyi Zhang, Fengtai Zhang, Vijay P. Singh, Richard Iestyn Woolway et al.
Ecological Indicators
Soil and Land Suitability Analysis
article

Warming-induced spatial shifts in single and double cropping rice habitat suitability across China

Junyi Zhang, Fengtai Zhang, Vijay P. Singh, Richard Iestyn Woolway, Haiying Ma, Xiaoying Qin, Qiang Zhang, Jianfeng Li, Chong‐Yu Xu, Xiaoqing Wu, Deliang Sun
article en

Abstract

• Bridging spatial-scale gaps between satellite crop mapping and suitability modelling. • Compared with RF, the XGBoost model is more suitable for crop suitability assessment. • Transferable framework for multi-crop suitability under climate change scenarios. • Double-cropping rice showed suitability expansion coupled with quality degradation. • Single-cropping rice stayed stable short term but shifted poleward with long-term warming. As a staple food for more than half of the global population, rice is crucial for world food security, especially in developing economies. However, there is a knowledge gap concerning the spatial extent of rice habitat suitability in China − the world’s largest rice producer – particularly regarding actual cultivation feasibility beyond purely climatic factors. Here, we assessed historical (2017–2020) and future (2021–2100) shifts in the suitability of single (SR) and double (DR) cropping rice systems, and established a research framework for rice suitability (Machine Learning-Ecological Niche Modeling frameworks, ML-ENM). We use the MaxEnt model to evaluate potential habitat suitability under natural constraints and the XGBoost algorithm to assess potential cultivation feasibility. The study reveals that the proposed framework that combines ecological niche modeling with agronomic feasibility analysis generates actionable insights for climate-resilient crop system design, with direct implications for global adaptation planning. The results showed that high-suitability areas for DR (11.1%) were concentrated in central and southern China, with suitability decreasing progressively toward concentric zones. In contrast, high-suitability areas for SR (11.3%) were more widely distributed across the Sichuan Basin, Yangtze River Basin, southern North China Plain, and Northeast Plain. Notably, the SR suitability was more climate-sensitive than DR. Warming drove DR expansion northward and westward, though with reduced suitability, whereas SR remained stable in the short term but shifted toward higher latitudes under prolonged warming. These findings provide spatially explicit insights for optimizing rice cultivation strategies, mitigating warming impacts, and enhancing climate resilience in China’s rice production.

Ecological IndicatorsVol. 181
Chongqing Normal University (CN), Hong Kong Baptist University (HK), Bangor University (GB), University of Oslo (NO), United Arab Emirates University (AE), Beijing Normal University (CN), Norsk Hydro (Norway) (NO), Chinese Academy of Agricultural Sciences (CN), Institute of Environment and Sustainable Development in Agriculture (CN), Chongqing University of Technology (CN), Texas A&M University (US)
Openalex Percentile: Top 55%
Soil and Land Suitability Analysis
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