AbstractAtrazine carryover from application to a monocot crop may adversely affect soybean (Glycine max (L.) Merr.) grown in rotation. Here, we tested the hypothesis that genotype selection could reduce atrazine carryover damage to soybean. Five commercially relevant soybean varieties were evaluated for differences in sensitivity to a range of atrazine rates using visual ratings, photosynthetic, and biomass parameters. Visual injury ratings and photosynthetic gas exchange, chlorophyll fluorescence, relative chlorophyll content (SPAD), and aboveground biomass were measured during vegetative stages. Under 9.0 g a.i. ha−1 atrazine, SH 5515 LL exhibited visual injury and aboveground fresh biomass reduction but was unaffected in net photosynthesis rate (A) and effective quantum yield of photosystem II (ϕPSII) compared to controls. By 21 days after emergence (DAE), P53A67X recovered in A and ϕPSII, and AG56X8 recovered in SPAD. S52RS86 remained unaffected visually and photosynthetically at this atrazine rate. All genotypes treated with 179.2 g a.i. ha−1 atrazine showed higher injury ratings and lower SPAD, A, and ϕPSII after 7 DAE and lower aboveground biomass 21 DAE except S52RS86, which was similar in SPAD throughout. Atrazine at 358.4 and 716.8 g a.i. ha−1 caused plant death in all genotypes 14 DAE. Visual ratings were strongly correlated with photosynthetic measurements and aboveground biomass at each sampling. Soybean atrazine sensitivity is a function of atrazine concentration in the soil and genotype‐specific tolerance or recovery ability, indicating that growers can select soybean genotypes that reduce risk when atrazine carryover is suspected.

Recent years have seen significant technological advancements in precision farming and plant phenotyping. Remote sensing along with deep learning (DL) techniques can increase phenotyping efficiency and help on-farm decision making with rapid stress detection. In this work, we use these techniques to evaluate drought stress in soybean plants, a crop whose yield is significantly affected by water availability. Images were taken from a high vantage in the field at various times throughout the day. Each image is given a wilting score ranging from 0 to 4 by expert scorers. We implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image. Given the significant overlap between adjacent classes in our dataset, we were able to achieve an overall classification accuracy of 64% and a one-off accuracy of 96% on our holdout test set. Our model outperformed DenseNet121 in most metrics, and provided comparable performance to a vision transformer (ViT) while having fewer parameters overall, less complexity (useful for edge implementations), and some interpretability. Furthermore, we were able to show that our model outperformed expert human annotators by predicting more consistent and accurate wilt levels when considering single-image re-annotation. The results show that our proposed methodology can be a useful approach in detecting drought stress in soybean fields to facilitate efficient crop management and aid selection of drought-resilient varieties.

Elevated temperatures resulting from climate change adversely affect natural and crop ecosystems, necessitating the development of heat-tolerant crops. We established a framework to precisely identify wheat protein-phosphorylated sites associated with varying temperature sensitivities. We identified specific kinases primarily associated with a specific temperature, but our results also suggest a striking overlap between cold and heat signaling. We furthermore demonstrated that the phosphorylation state of a specific set of proteins creates a unique signature for heat stress tolerance. These findings can potentially aid in the identification of targets for breeding or genome editing to enhance sub/supra optimal temperature tolerance. ### Competing Interest Statement The authors have declared no competing interest.

Abstract This strategic plan summarizes the major accomplishments achieved in the last quinquennial by the soybean [Glycine max (L.) Merr.] genetics and genomics research community and outlines key priorities for the next 5 years (2024–2028). This work is the result of deliberations among over 50 soybean researchers during a 2‐day workshop in St Louis, MO, USA, at the end of 2022. The plan is divided into seven traditional areas/disciplines: Breeding, Biotic Interactions, Physiology and Abiotic Stress, Functional Genomics, Biotechnology, Genomic Resources and Datasets, and Computational Resources. One additional section was added, Training the Next Generation of Soybean Researchers, when it was identified as a pressing issue during the workshop. This installment of the soybean genomics strategic plan provides a snapshot of recent progress while looking at future goals that will improve resources and enable innovation among the community of basic and applied soybean researchers. We hope that this work will inform our community and increase support for soybean research.

Soybean physiology, seed yield and composition can vary greatly and are a function of genetics, environment, and management practices. N fertilizer is not often applied to soybeans, but it can affect yield and seed composition, and the outcome of N fertilization has varied widely among studies. To test if certain physiological responses might be linked with positive agronomic responses to N fertilizer, a field study was conducted in varied environments unique to the Southeast United States (US). Five genotypes from maturity groups V-VII were evaluated. Although the environment and genotype had significant effects on most of the physiological characteristics and yield components measured, nitrogen application interacted significantly with environment for seed protein concentration, seed oil concentration, yield, and percent of nitrogen derived from the atmosphere in leaves during the seed fill period. The direction of the nitrogen effect on protein concentration differed among environments, increasing protein concentration in one environment and reducing protein concentration in another environment. Nitrogen application effects did not vary among the genotypes included in this study. Photosynthetic and nitrogen fixation responses to nitrogen application were not clearly linked with seed protein concentration response. A tailored nitrogen recommendation for environment is the best option for improving seed protein in this region with diverse environmental conditions.

With projected increases in global temperatures and changes in regional climate, understanding the impact of soil management choices on yield stability is critical for farmer decision-making and agricultural resiliency. No-till and conservation tillage have had variable yield effects depending on crop and location, requiring long-term system-specific studies to gauge potential benefits. Yield and weather data from a 28-year tillage study in the southeastern U.S. piedmont region were analyzed to determine the effect of various conservation tillage practices on maize and soybean productivity and stability under a variety of growth conditions. Growing seasons were grouped by soil moisture and temperature during crop growth stages, and mean crop yields and yield coefficient of variation for the tillage treatments were calculated within the year clusters. Probability density estimates were also used to predict the likelihood of obtaining yields at low and high percentiles. No-till and conservation tillage increased maize yields 42–93% and no-till decreased coefficient of variation of maize yields when soil moisture was low by 10–32%, but had a less pronounced effect on soybean yields. However, the probability of reaching the 90th yield percentile was greater in no-till than conventional tillage in both maize and soybean, by 15% and 10%, respectively. Yield differentiation occurred early in the study, before there was likely substantial differentiation of soil properties from tillage treatments. Previous reports from the site have likewise indicated little differentiation in soil health between tillage systems over the life of the study. Results suggest that surface residue management may be an important driver of system performance, possibly more so than overall soil health.

Public concern regarding the use of herbicides in urban areas (e.g., golf courses, parks, lawns) is increasing. Thus, there is a need for alternative methods for weed control that are safe for the public, effective against weeds, and yet selective to turfgrass and other desirable species. New molecular tools such as ribonucleic acid interference (RNAi) have the potential to meet all those requirements, but before these technologies can be implemented, it is critical to understand the perceptions of key stakeholders to facilitate adoption as well as regulatory processes. With this in mind, turfgrass system managers, such as golf course superintendents and lawn care providers, were surveyed to gain insight into the perception and potential adoption of RNAi technology for weed management. Based on survey results, turfgrass managers believe that cost of weed management and time spent managing weeds are the main challenges faced in their fields. When considering new weed management tools, survey respondents were most concerned about cost, efficacy, and efficiency of a new product. Survey respondents were also optimistic toward RNAi for weed management and would either use this technology in their own fields or be willing to conduct research to develop RNAi herbicides. Although respondents believed that the general public would have some concerns about this technology, they did not believe this to be the most important factor for them when choosing new weed management tools. The need for new herbicides to balance weed control challenges and public demands is a central factor for turfgrass managers' willingness to use RNAi-based weed control in turfgrass systems. They believe their clientele will be accepting of RNAi tools, although further research is needed to investigate how a wider range of stakeholders perceive RNAi tools for turfgrass management more broadly.

Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks.

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max . We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa , Zea mays , Sorghum bicolor , and Triticum aestivum . Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

BACKGROUNDThe potential of weed species to respond to selection forces affecting the evolution of weedy traits such as competitive ability is poorly understood. This research characterized evolutionary growth changes in a single Abutilon theophrasti Medik. population comparing multiple generations collected from 1988 to 2016. A competition study was performed to understand changes in competitive ability, and a herbicide dose-response study was carried out to assess changes in sensitivity to acetolactate synthase-inhibiting herbicides and glyphosate over time. RESULTSWhen grown in monoculture, A. theophrasti biomass production per plant increased steadily across year-lines while leaf number decreased. In replacement experiments, A. theophrasti plants from newer year-lines were more competitive and produced more biomass and leaf area than the oldest year-line. No clear differences in sensitivity to imazamox were observed among year-lines. However, starting in 1995, this A. theophrasti population exhibited a progressive increase in growth in response to a sublethal dose of glyphosate (52 g a.e. ha(-1)), with the 2009 and 2016 year-lines having more than 50% higher biomass than the nontreated control. CONCLUSIONThis study demonstrates that weeds can rapidly evolve increased competitive ability. Furthermore, the results indicate the possibility of changes in glyphosate hormesis over time. These results highlight the importance of the role that rapid (i.e., subdecadal) evolution of growth traits might have on the sustainability of weed management strategies. & COPY; 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Management decisions like planting density and tillage could influence soybean seed composition through their impacts on competition and soil properties. To determine if either of these management decisions could help improve soybean [Glycine max (L.) Merr.] seed quality in the southeastern United States, field experiments were conducted to evaluate the impact of planting density and tillage on seed composition. Five soybean genotypes were examined in multiple environments across three plant densities from 204,000 to 476,000 plants ha(-1), and in conventional tillage compared to no-till. In two of the three environments, seed protein concentration was higher at higher plant densities. Tillage did not affect seed protein concentration, but conventional tillage improved yield when compared with no-till in two of four environments, resulting in higher protein yield under conventional tillage in these environments. Plant density may be an important management decision to consider for improving soybean seed protein in specific environments in the southeastern United States, and further research could help determine the specific environmental attributes that lead to a density benefit for seed protein.

Herbicide carryover injury to rotational crops can vary in severity depending on the influence of soil properties on herbicide bioavailability. Greenhouse bioassays were conducted with soybean, radish, and canola to evaluate differences in the bioavailability of three herbicides with carryover risk, atrazine, mesosulfuron-methyl, and topramezone. Bioassays were conducted in three varying regional soil types with nine herbicide treatment rates including a control. Plant visual injury was evaluated weekly, and aboveground dry biomass was weighed after harvest of soybean 28 days after emergence (DAE) and radish and canola 21 DAE. A log-logistic dose-response regression model was used to quantify herbicide-effective concentrations for 30% (EC30), 50% (EC50), and 80% (EC80) visual injury and aboveground dry biomass reduction in each soil type. Relative herbicide-soil bioavailability was determined through comparisons of herbicide-effective concentrations among soil types. Pearson correlation revealed that atrazine, mesosulfuron-methyl, and topramezone EC30 for all species were positively correlated to soil organic matter (OM) content (r = 0.56, 0.48, and 0.40, respectively) and cation exchange capacity (CEC) (r = 0.43, 0.41, and 0.45). Topramezone EC80 for soybean and radish was positively correlated to soil clay content (r = 0.51) and silt content (r = 0.51) and negatively correlated to sand content (r = -0.51) and pH (r = -0.52). Decreased atrazine, mesosulfuron-methyl, and topramezone bioavailability in soil with high OM and CEC, decreased topramezone bioavailability in coarse-textured soil and at high soil pH, and differential herbicide sensitivity of crop species can inform grower decisions on herbicide selections and rotational crop plans.

AbstractCompetition between genotypes within a plant population can result in the displacement of the least competitive by more competitive genotypes. Although evolutionary processes in plants may occur over thousands and millions of years, it has been suggested that changes in key fitness traits could occur in as little as decades, with herbicide resistance being a common example. However, the rapid evolution of complex traits has not been proven in weeds. We hypothesized that changes in weed growth and competitive ability can occur in just a few years because of selection in agroecosystems. Seed of multiple generations of a single natural population of the grassy weed giant foxtail (Setaria faberi Herrm.) were collected during 34 yr (i.e., 1983 to 2017). Using a “resurrection” approach, we characterized life-history traits of the different year-lines under noncompetitive and competitive conditions. Replacement-series experiments comparing the growth of the oldest year-line (1983) versus newer year-lines (1991, 1996, 1998, 2009, and 2017) showed that plant competitive ability decreased and then increased progressively in accordance with oscillating selection. The adaptations in competitive ability were reflected in dynamic changes in leaf area and biomass when plants were in competition. The onset of increased competitive ability coincided with the introduction of herbicide-resistant crops in the landscape in 1996. We also conducted a genome-wide association study and identified four loci that were associated with increased competitive ability over time, confirming that this trait changed in response to directional selection. Putative transcription factors and cell wall–associated enzymes were linked to those loci. This is the first study providing direct in situ evidence of rapid directional evolution of competitive ability in a plant species. The results suggest that agricultural systems can exert enough pressure to cause evolutionary adaptations of complex life-history traits, potentially increasing weediness and invasiveness.

The reduction of row spacing and increase of crop population density are important tools for maximizing crop yield. For this strategy to be effective, the crop population should not create intraspecific crop competition that penalizes yield. Thus, planting arrangements that increase light interception throughout the canopy without increasing row spacing might be needed to maintain yield. In this study, heterogeneous planting arrangements on evenly spaced rows were analyzed for maize (Zea mays L.), cotton (Gossypium hirsutum L.), and soybean (Glycine max (L.) Merr.). Each crop had four planting arrangements: (1) normal density in all rows, considered the control, (2) doubled density in all rows, (3) a sequential arrangement of normal and tripled densities (each in every other row; NTNT), and (4) normal-tripled-tripled-normal (NTTN). Maize and cotton did not exhibit changes in growth and architecture when comparing uniform and variable planting arrangements. Soybeans were more adaptable and increased biomass production by 44% to 45% in variable arrangements. None of the crops showed differences in yield due to planting arrangement, so the use of rows with different densities might not be needed when using high densities to maximize yield.

Investigating the effects of soil properties on herbicide persistence can aid in evaluating the carryover potential of herbicides in soil and the consequent injury risk to rotational crops. Laboratory incubation experiments were conducted to quantify the persistence of atrazine, mesosulfuron-methyl, and topramezone in five regional soils under aerobic conditions at 23 degrees C. Additionally, mesosulfuron-methyl persistence was tested at 7 degrees C, which is representative of regional average winter soil temperature. Herbicide half-life was calculated with the logarithmic form of first-order rate of degradation using linear regression and was correlated with soil properties. Half-lives of atrazine (37-73 d) and topramezone (15-19 d) varied among soil types at 23 degrees C. Mesosulfuron-methyl half-life varied among soils at 7 degrees C (8.8-9.8 d) and 23 degrees C (5.4-5.8 d) and between temperatures. Atrazine and topramezone half-lives were shortest in Candor sand (4% clay, 1.8% organic matter [OM], pH 5.1) and longest in Portsmouth sandy loam (13% clay, 5.3% OM, pH 4.3). Mesosulfuron-methyl half-life was longer at lower soil temperature. Half-lives of atrazine, mesosulfuron-methyl, and topramezone were correlated with soil OM content (r = .83, -.53, and .63, respectively) and pH (r = -.86, .55, and -.57). Additionally, atrazine and topramezone half-lives were positively correlated with soil clay content (r = .83 and .71), and mesosulfuron-methyl half-life was negatively correlated with temperature (r = -.97). Correlations between soil OM content, clay content, and pH among soil types may have influenced herbicide persistence.

An average temperature increase between 2.6 and 4.8 degrees C, along with more frequent extreme temperatures, will challenge crop productivity by the end of the century. To investigate genotypic variation in soybean response to elevated temperature, six soybean (Glycine max) genotypes were subjected to elevated air temperature of + 4.5 degrees C above ambient for 28 days in open-top field chambers. Gas exchange and chlorophyll fluorescence were measured before and during heating and yield as well as seed composition were evaluated at maturity. Results show that long-term elevated air temperature increased nighttime respiration, increased the maximum velocity of carboxylation by Rubisco, impacted seed protein concentration, and reduced seed oil concentration across genotypes. The genotypes in this study varied in temperature responses for photosynthetic CO2 assimilation, stomatal conductance, photosystem II operating efficiency, quantum efficiency of CO2 assimilation, and seed protein concentration at maturity. These diverse responses among genotypes to elevated air temperature during seed development in the field, reveal the potential for soybean heat tolerance to be improved through breeding and underlines the importance of identifying efficient selection strategies for stress-tolerant crops.

High crop densities are valuable to increase weed suppression, but growers might be reluctant to implement this practice due to increased seed cost. Because it is also possible to lower planting densities in areas with no or low weed interference risk, the area allocated to each planting density must be optimized considering seed cost and productivity per plant. In this study, the growth and yield of maize (Zea mays L.), cotton (Gossypium hirsutum L.), and soybean [Glycine max (L.) Merr.] were characterized in response to low planting densities and arrangements. The results were used to develop a bioeconomic model to optimize the area devoted to high- and low-density plantings to increase weed suppression without increasing seed cost. Physiological differences seen in each crop varied with the densities tested; however, maize was the only crop that had differences in yield (per area) between densities. When a model to optimize low and high planting densities was used, maize and cotton showed the most plasticity in yield per planted seed (g seed(-1)) and area of low density to compensate for high-density area unit. Maize grown at 75% planting density compared with the high-planting density (200%) increased yield (g seed(-1)) by 229%, return by 43%, and profit by 79% while decreasing the low-density area needed to compensate for high-density area. Cotton planted at 25% planting density compared with the 200% planting density increased yield (g seed(-1)) by 1,099%, return by 46%, and profit by 62% while decreasing the low-density area needed to compensate for high-density area. In contrast, the high morphological plasticity of soybean did not translate into changes in area optimization, as soybean maintained return, profit, and a 1:1 ratio for area compensation. This optimization model could allow for the use of variable planting at large scales to increase weed suppression while minimizing costs to producers.

Plants respond to mild warm temperature conditions by increased elongation growth of organs to enhance cooling capacity, in a process called thermomorphogenesis. To this date, the regulation of thermomorphogenesis has been exclusively shown to intersect with light signalling pathways. To identify regulators of thermomorphogenesis that are conserved in flowering plants, we map changes in protein phosphorylation in both dicots and monocots exposed to warm temperature. We identify MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE KINASE4 (MAP4K4)/TARGET OF TEMPERATURE3 (TOT3) as a regulator of thermomorphogenesis that impinges on brassinosteroid signalling in Arabidopsis thaliana . In addition, we show that TOT3 plays a role in thermal response in wheat, a monocot crop. Altogether, the conserved thermal regulation by TOT3 expands our knowledge of thermomorphogenesis beyond the well-studied pathways and can contribute to ensuring food security under a changing climate.