TY - JOUR
T1 - High-resolution global maps of yield potential with local relevance for targeted crop production improvement
AU - Aramburu-Merlos, Fernando
AU - van Loon, Marloes P.
AU - van Ittersum, Martin K.
AU - Grassini, Patricio
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Limited 2024.
PY - 2024/8
Y1 - 2024/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85199986633&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85199986633&partnerID=8YFLogxK
U2 - 10.1038/s43016-024-01029-3
DO - 10.1038/s43016-024-01029-3
M3 - Article
C2 - 39075160
AN - SCOPUS:85199986633
SN - 2662-1355
VL - 5
SP - 667
EP - 672
JO - Nature Food
JF - Nature Food
IS - 8
ER -