TY - JOUR
T1 - Deteriorating weed control and variable weather portends greater soybean yield losses in the future
AU - Landau, Christopher A.
AU - Hager, Aaron G.
AU - Williams, Martin M.
N1 - The authors greatly appreciate the work of the many individuals who contributed to the Herbicide Evaluation Program at the University of Illinois Urbana-Champaign, particularly Doug Maxwell. This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the DOE or USDA. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer.
The authors greatly appreciate the work of the many individuals who contributed to the Herbicide Evaluation Program at the University of Illinois Urbana-Champaign, particularly Doug Maxwell. This research was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the DOE or USDA. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
PY - 2022/7/15
Y1 - 2022/7/15
N2 - Since the 1950's much of the US soybean growing region has experienced rising temperatures, more variable rainfall, and increased carbon emissions. These trends are predicted to continue throughout the 21st century. Variable weather and weed interference influence crop performance; however, their combined effects on soybean yield are poorly understood. Using machine learning techniques on a database of herbicide trials spanning 28 years and 106 weather environments we modeled the most important relationships among weed control, weather variability, and crop management on soybean yield loss. When late-season weeds were poorly controlled, average soybean yield losses of 48% were observed. Additionally, when weeds were not completely controlled, low rainfall and high temperatures during seed fill exacerbated soybean yield loss due to weeds. Since much of the US soybean growing region is heading towards drier, warmer conditions, coupled with growing herbicide resistance, future soybean yield loss will increase without significant improvements in weed management systems.
AB - Since the 1950's much of the US soybean growing region has experienced rising temperatures, more variable rainfall, and increased carbon emissions. These trends are predicted to continue throughout the 21st century. Variable weather and weed interference influence crop performance; however, their combined effects on soybean yield are poorly understood. Using machine learning techniques on a database of herbicide trials spanning 28 years and 106 weather environments we modeled the most important relationships among weed control, weather variability, and crop management on soybean yield loss. When late-season weeds were poorly controlled, average soybean yield losses of 48% were observed. Additionally, when weeds were not completely controlled, low rainfall and high temperatures during seed fill exacerbated soybean yield loss due to weeds. Since much of the US soybean growing region is heading towards drier, warmer conditions, coupled with growing herbicide resistance, future soybean yield loss will increase without significant improvements in weed management systems.
KW - (Glycine max)
KW - Climate change
KW - Machine learning
KW - Weed interference
UR - http://www.scopus.com/inward/record.url?scp=85127152043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127152043&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.154764
DO - 10.1016/j.scitotenv.2022.154764
M3 - Article
C2 - 35341841
AN - SCOPUS:85127152043
SN - 0048-9697
VL - 830
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 154764
ER -