Diminishing weed control exacerbates maize yield loss to adverse weather. (8th September 2021)

• Record Type: Journal Article
• Authors: Landau, Christopher A.; Hager, Aaron G.; Williams, Martin M.
• Is Part Of: Global change biology. Volume 27:Number 23(2021)
• Page Start: 6156
• Page End: 6165
• Publication Date: 2021-09-08
• Subjects: climate change -- herbicide efficacy -- machine learning -- maize (Zea mays) -- weather variability -- weed interference; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.15857
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: 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. Abstract : Individually, adverse weather and weed interference cause significant maize yield losses; however, the combined effects of the two on maize is poorly understood. Using machine learning techniques on a database of herbicide trials spanning 27 years, we identified poor late‐season weed control, high temperatures, and low water availability during silking as the major drivers of maize yield loss.