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.
Original language | English |
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Pages (from-to) | 667-672 |
Number of pages | 6 |
Journal | Nature Food |
Volume | 5 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2024 |
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Global gridded maps of yield potential of the Global Yield Gap Atlas (GYGA)
Aramburu-Merlos, F. (Producer), van Loon, M. (Contributor), van Ittersum, M. (Supervisor) & Grassini, P. (Supervisor), University of Nebraska–Lincoln, 29 Jul 2024
Dataset