Journal
GLOBAL CHANGE BIOLOGY
Volume 27, Issue 23, Pages 6156-6165Publisher
WILEY
DOI: 10.1111/gcb.15857
Keywords
climate change; herbicide efficacy; machine learning; maize (Zea mays); weather variability; weed interference
Ask authors/readers for more resources
The study found that late-season control of all weed species is the most important factor affecting maize yield loss, and lack of weed control will result in increased yield loss. In the future, maize-growing regions in the US will experience warmer, drier summers, with increasing prevalence of herbicide resistance, which will increase the risk of maize yield loss due to weeds.
Both weed interference and adverse weather can cause significant maize yield losses. However, most climate change projections on maize yields ignore the fact that weeds are widespread in maize production. Herein, we examine the effects of weed control and weather variability on maize yield loss due to weeds by using machine learning techniques on an expansive database of herbicide efficacy trials spanning 205 weather environments and 27 years. Late-season control of all weed species was the most important driver of maize yield loss due to weeds according to multiple analyses. Average yield losses of 50% were observed with little to no weed control. Furthermore, when the highest levels of weed control were not achieved, drier, hotter conditions just before and during silking exacerbated maize yield losses due to weeds. Current climate predictions suggest much of the US maize-growing regions will experience warmer, drier summers. This, coupled with the growing prevalence of herbicide resistance, increases the risk of maize yield loss due to weeds in the future without transformational change in weed management systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available