Deteriorating weed control and variable weather portends greater soybean yield losses in the future

Landau, Christopher A.; Hager, Aaron G.; Williams, Martin M.

doi:10.1016/j.scitotenv.2022.154764

Cited by 14 publications

(12 citation statements)

References 47 publications

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“…Studies, such as [98], show that by combining information from WOFOST crop model outputs data, weather data, crop data, soil data, and yield statistics, they were able to accurately predict soft wheat, spring barley, sunflower, grain maize, sugar beets, and potato yields. Data on water, earlyseason weed control, late-season weed control, season-long weed control, temperature, and crop management were used to predict soybean yield by Landau et al [24]. Soil properties, different types of fertiliser application rate, and yield data from historical yields were used to predict corn yields by Du et al [21].…”

Section: Analysis and Discussionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.

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Section: Analysis and Discussionmentioning

confidence: 99%

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

et al. 2022

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“…Across experimental studies, ACSL scientists found weeds to have similar effects on crops with and without warming or drought (Vilà et al 2021). In contrast, analysis of decades of herbicide evaluation trials by scientists at the GCPRU in Urbana, IL, showed that very high temperatures or low rainfall can increase yield losses in both corn and soybean (Landau et al 2021b(Landau et al , 2022.…”

Section: Climate Change Effects On Weeds and Invasive Plantsmentioning

confidence: 99%

Agricultural Research Service Weed Science Research: Past, Present, and Future

Young¹,

Anderson²,

Baerson

et al. 2023

Weed Sci

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The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

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“…Weed management is essential in row crops. In corn and soybean, weed interference can reduce yields by 50% (Landau et al 2022;Soltani et al 2016Soltani et al , 2017. Fertilization, especially nitrogen, is compulsory in corn but can be partly tailored based on crop uptake (Martins et al 2020;Ziadi et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Weed communities after decades of mineral fertilization and tillage treatments in a corn–soybean rotation

Simard,

Ziadi

2024

Weed Technol

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Soil fertility and disturbance can potentially modify weed dynamics in a corn-soybean rotation. Knowing how added mineral fertilizers and tillage influence weed populations in the long term can provide insight into weed community shifts, which may affect future weed management requirements. The first objective of this study was to evaluate the effect of 24-25 years of nitrogen and phosphorus fertilization treatments, and tillage (moldboard vs. no tillage) on weeds (density, biomass and composition) before and after herbicide applications in 2016/corn and 2017/soybean. The second objective was to evaluate the effect of the same treatments six years after no post-emergence weed management in 2022/corn. Since the last evaluation performed in 2004, weed density, richness, and diversity increased, and more annual grasses were observed under both tillage regimes. No fertilization effect was observed on any weed variables, including composition, except for increased biomass when left to grow all season after crop planting. In managed plots, the density and biomass of annuals and perennials was generally higher in no-till, and discrepancies were highest for annual grass densities before herbicide application. Weed species richness and diversity based on counts were equivalent between tillage regimes, but total biomass was distributed between more species in no-till. Higher weed densities and concurrent weed biomass, observed in no-till, reduced crop yields in 2016/soybean only. The combination of low crop diversity and low use of residual herbicides during the trial potentially led to the observed species shifts and increased weed density.

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Deteriorating weed control and variable weather portends greater soybean yield losses in the future

Cited by 14 publications

References 47 publications

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Agricultural Research Service Weed Science Research: Past, Present, and Future

Weed communities after decades of mineral fertilization and tillage treatments in a corn–soybean rotation

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