In the wake of the Russia-Ukraine conflict, Ukraine’s agricultural landscape has been dramatically altered, with vast tracts of cropland left abandoned due to war damage, infrastructure destruction, and mass refugee outflows. A groundbreaking study led by Shike Zhang, a researcher at the School of Geoscience and Technology, Zhengzhou University, has shed new light on the spatial distribution and changes of these abandoned croplands, offering crucial insights for agricultural assessments and international aid efforts.
The study, published in ‘Scientific Reports’ (translated to English from ‘Nature Scientific Reports’), introduces a novel Dual-period Change Detection method that leverages time-series NDVI (Normalized Difference Vegetation Index) data to fit crop curves on a pixel-by-pixel basis. This innovative approach allows for the distinction between different types of abandoned cropland, such as unused and unattended fields, which have been challenging to identify using common methods.
“Our method not only detects abandoned cropland but also differentiates between unused and unattended fields, providing a more nuanced understanding of the situation on the ground,” Zhang explains. This differentiation is crucial for targeted interventions and resource allocation, especially in conflict-affected areas where agricultural infrastructure has been severely compromised.
The research reveals that before the conflict, the national average unused cropland rate in Ukraine was around 1.6%, with fluctuations year by year. However, in 2022, the unused cropland area doubled compared to pre-conflict averages, with a significant portion of this land becoming unattended, totaling approximately 462,000 hectares, primarily in the eastern regions. By 2023, the unused cropland area decreased by 67.8%, while unattended cropland increased by 116.7%, highlighting the dynamic nature of land use changes in conflict zones.
The spatial clustering of both types of abandoned cropland in regions like Crimea, Kherson Oblast, Zaporizhzhia Oblast, and Donetsk Oblast underscores the need for targeted interventions. “Understanding these spatial patterns can help in directing aid and resources more effectively, ensuring that the most affected areas receive the support they need,” Zhang notes.
The implications of this research extend beyond immediate agricultural assessments. For the energy sector, which relies heavily on biofuels and agricultural byproducts, understanding the extent and distribution of abandoned cropland can inform strategic planning and investment decisions. As the global push for renewable energy sources continues, the availability of agricultural land for biofuel production becomes increasingly important. This study provides a critical tool for monitoring and predicting land use changes, which can guide sustainable energy policies and investments.
The high overall accuracy of the abandoned cropland extraction, ranging from 83 to 96% during the study period, validates the effectiveness of the Dual-period Change Detection method. This method could be adapted for use in other conflict-affected regions, providing a valuable tool for global agricultural monitoring and intervention.
As the conflict in Ukraine continues to evolve, so too will the agricultural landscape. This research not only offers a snapshot of the current situation but also lays the groundwork for future developments in spatiotemporal analysis and agricultural monitoring. By providing a more detailed and accurate picture of abandoned cropland, this study paves the way for more effective and targeted interventions, ultimately contributing to the resilience and sustainability of agricultural systems in conflict-affected areas.