Monitoring the temporal changes in herbicide-resistant Amaranthus tuberculatus: a landscape-scale probability-based estimation in Iowa.

BACKGROUND: A landscape-scale probability-based sampling of Iowa soybean [Glycine max (L.) Merr.] fields was conducted in 2013 and 2019; Amaranthus tuberculatus [Moq.] J.D. Sauer seed was collected from 97 random geospatial selected fields. The objectives were to evaluate the prevalence and distribution of herbicide-resistant A. tuberculatus (waterhemp) in soybean fields and evaluate temporal changes over 6 years. Amaranthus tuberculatus seedlings were evaluated for resistance to imazethapyr, atrazine, glyphosate, lactofen and mesotrione at 1× and 4× label rates. RESULTS: Resistance to imazethapyr, glyphosate, lactofen and mesotrione at the 1× rate increased significantly from 2013 to 2019 and was found in 99%, 97%, 16% and 15% of Iowa A. tuberculatus populations in 2019, respectively. Resistance to atrazine at the 4× rate increased over time; atrazine resistance was found in 68% of populations in 2019. Three-way multiple herbicide-resistant A. tuberculatus was the most frequent and increased significantly to 4× rates from 16% in 2013 to 43% of populations in 2019. All A. tuberculatus populations resistant to HPPD-inhibitor herbicides also were resistant to atrazine. CONCLUSION: To the best of our knowledge, this is the first probability-based study that presented evolution of A. tuberculatus herbicide resistance over time. The results demonstrated that imazethapyr, atrazine and glyphosate resistance in Iowa A. tuberculatus populations was frequent whereas resistance to lactofen and mesotrione was less frequent. Most Iowa A. tuberculatus populations evolved resistance to multiple sites of action over time. The results of our study are widely applicable given the similarities in weed management practices throughout the Midwest United States. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis.

A delayed harvest of maize and soybean crops is associated with yield or revenue losses, whereas a premature harvest requires additional costs for artificial grain drying. Accurately predicting the ideal harvest date can increase profitability of US Midwest farms, but today's predictive capacity is low. To fill this gap, we collected and analyzed time-series grain moisture datasets from field experiments in Iowa, Minnesota and North Dakota, US with various maize (n = 102) and soybean (n = 36) genotype-by-environment treatments. Our goal was to examine factors driving the post-maturity grain drying process, and develop scalable algorithms for decision-making. The algorithms evaluated are driven by changes in the grain equilibrium moisture content (function of air relative humidity and temperature) and require three input parameters: moisture content at physiological maturity, a drying coefficient and a power constant. Across independent genotypes and environments, the calibrated algorithms accurately predicted grain dry-down of maize (r2 = 0.79; root mean square error, RMSE = 1.8% grain moisture) and soybean field crops (r2 = 0.72; RMSE = 6.7% grain moisture). Evaluation of variance components and treatment effects revealed that genotypes, weather-years, and planting dates had little influence on the post-maturity drying coefficient, but significantly influenced grain moisture content at physiological maturity. Therefore, accurate implementation of the algorithms across environments would require estimating the initial grain moisture content, via modeling approaches or in-field measurements. Our work contributes new insights to understand the post-maturity grain dry-down and provides a robust and scalable predictive algorithm to forecast grain dry-down and ideal harvest dates across environments in the US Corn Belt.

Effect of Glyphosate Application on Sudden Death Syndrome of Glyphosate-Resistant Soybean Under Field Conditions.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important yield limiting disease of soybean. Glyphosate is used to control weeds in soybean; however, its effect on SDS is not clearly understood. The objective of this study was to examine the impact of glyphosate on SDS, yield, and plant nutrition under field conditions. Fourteen field experiments were conducted in Iowa, Illinois, Indiana, Michigan, Wisconsin, and Ontario, Canada during 2011 to 2013. The experiment consisted of six treatment combinations of glyphosate and herbicides not containing glyphosate. Disease index was significantly different across the location-years, ranging from 0 to 65. The highest disease was noted in locations with irrigation, indicating that high soil moisture favors development of SDS. There were no effects of herbicide treatments or interactions on disease. The foliar disease index among the treatments over all years ranged from 9 to 13. Glyphosate-treatments also tended to yield more than treatments of herbicides not containing glyphosate. There were no interactions between glyphosate-treatments and total manganese in plant tissue. The interaction of glyphosate with other nutrients in plant tissue was inconclusive. This 14 location-year study demonstrated that glyphosate application did not increase SDS severity or adversely affect soybean yield under field conditions.