非表示:
キーワード:
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要旨:
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.
