High-resolution global maps of yield potential with local relevance for targeted crop production improvement

IF 23.6 Q1 FOOD SCIENCE & TECHNOLOGY
Fernando Aramburu-Merlos, Marloes P. van Loon, Martin K. van Ittersum, Patricio Grassini
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引用次数: 0

Abstract

Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km2 at the Equator) and accuracy for maize, wheat and rice. These maps serve as a robust reference to benchmark farmers’ yields in the context of current cropping systems and water regimes and can help to identify areas with large room to increase crop yields. High-resolution global maps of yield potential were created through crop modelling and machine learning. These maps can help orient agricultural research and development programmes and assess food security and land use from local to regional levels.

Abstract Image

Abstract Image

高分辨率全球产量潜力图,与地方相关,用于有针对性地改进作物生产
确定当前耕地中尚未开发的作物增产机会对于指导粮食供应干预措施至关重要。在此,我们将农学上稳健的自下而上方法与机器学习相结合,生成了高分辨率(赤道地区约 1 平方公里)、高精度的全球玉米、小麦和水稻产量潜力图。这些地图可作为当前耕作制度和水制度背景下农民产量基准的可靠参考,并有助于确定作物产量有较大提高空间的地区。
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CiteScore
28.50
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0.00%
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