Dynamic patterns and driving factors of productive cropland in Ukraine before and after Russia-Ukraine conflict

Abstract

Ukraine, as one of the world’s largest agricultural producers and exporters, plays a critical role in global food security. It is essential to understand the spatiotemporal dynamics and drivers of productive cropland in Ukraine, particularly in the context of the 2022 Russia-Ukraine conflict. We provide the first comprehensive assessment of both conflict- and non-conflict-related factors that influenced the distribution and productivity of Ukraine’s cropland from 2013 to 2023. In addition, we propose a novel method using machine learning models to isolate the impact of conflict on cropland. Our findings reveal that, prior to the conflict, the spatial pattern of Ukraine’s mean cultivation rate was primarily shaped by natural factors—such as climate, soil properties, and elevation—whereas socio-economic factors (e.g., GDP and population size) exerted a weaker influence. Interannual dynamics in productive cropland area were largely driven by climate variability. The onset of conflict in 2022 dramatically altered this landscape, with nearly half of the cropland grid cells experiencing a conflict-induced reduction. Notably, almost half of the interannual reduction in productive cropland in 2022 was attributed to climate change. Remarkably, in 2023, the return of displaced populations and favorable climatic conditions in many oblasts contributed to a positive trend in cropland reclamation. Despite this, the total area of productive cropland in 2023 remained below expected levels, due to ongoing conflict and localized droughts. Finally, we highlight the urgent need to adopt a two-pronged approach that addresses both the immediate impacts of conflict and the ongoing threats posed by climate change to ensure the resilience and sustainability of agricultural systems in post-conflict areas.

Keywords

Ukraine’s cropland dynamics / Driving factors analysis / Time-series remote sensing / Russia-Ukraine conflict

Data availability

Data will be made available on request.

CRediT authorship contribution statement

Yiliang Li: Writing - review & editing, Writing - original draft, Visualization, Methodology, Investigation. Kaixuan Yao: Writing - original draft, Visualization, Validation, Investigation, Data curation. Qingxiang Meng: Visualization, Investigation. Yujie Wang: Validation, Writing - review & editing. Rui Xiao: Methodology, Conceptualization. Yuhang Liu: Investigation, Data curation. Sensen Wu: Writing - review & editing, Methodology. Yansheng Li: Writing - review & editing, Supervision, Methodology, Funding acquisition, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grants No. 41971284 and 42371321), and the Key Research and Development Program of Hubei Province (Grant No. 2025BAB024). Additionally, we thank Google Earth Engine for providing satellite data and computational resources (https://earthengine.google.com/).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100401.

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