Planting area and production decreased for winter-triticeae crops but increased for rapeseed in Ukraine with climatic impacts dominating

Abstract

On-time mapping dynamics of crop area, yield, and production is important for global food security. Such information, however, is often not available. Here, we used satellite information, a spectral-phenology integration approach for mapping crop area, and a machine learning model for predicting yield in the war-stricken Ukraine. We found that in Ukraine crop area and production declined in 2022 relative to 2017–2021 and 2021 for winter-triticeae crops, which was invaded before the cropping season in February of that year. At the same time, crop area and production for rapeseed increased in Ukraine, with yields consistently lower by 6.5 % relative to 2021. The low precipitation and the Russian-Ukrainian conflict-related factors contributed to such yield variations by -1.3 % and -0.9 % for winter-triticeae crops and -4.2 % and -0.5 % for rapeseed in 2022. We demonstrate a robust framework for monitoring country-wide crop production dynamics in near real-time, serving as an early-food-security-warning system.

Keywords

Food security / Conflict / Machine learning / Production / Satellite

CRediT authorship contribution statement

Jichong Han: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Yuchuan Luo: Validation, Software, Methodology, Formal analysis, Data curation. Zhao Zhang: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. Jialu Xu: Project administration, Funding acquisition. Yi Chen: Investigation. Senthold Asseng: Resources. Jonas Jägermeyr: Resources. Christoph Müller: Resources. Jørgen E Olesen: Resources. Reimund Rötter: Resources. Fulu Tao: Writing – review & editing, Supervision, 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.

Data availability

MCD12Q1 data is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1.

MOD09A1 is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09A1.

MOD11A2 data is available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2.

Nighttime light is available at https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG#description.

ACLED is available at https://acleddata.com/.

ERA5-Land is available at https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY.

HWSD is available at https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/.

Agricultural statistics are available at https://ukrstat.gov.ua/.

GADM is available at https://gadm.org/.

Acknowledgements

This research has been supported by the National Natural Science Foundation of China (Grant No. 42061144003).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.08.006.

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