Soybean Populations Research Articles

Combined BLUP selection indexes with parents and F2 populations in soybean (Glycine max) breeding

Combined BLUP selection indexes with parents and F2 populations in soybean (Glycine max) breeding

Natural immunity stimulation using ELICE16INDURES® plant conditioner in field culture of soybean

Recently, climate change has had an increasing impact on the world. Innate defense mechanisms operating in plants - such as PAMP-triggered Immunity (PTI) - help to reduce the adverse effects caused by various abiotic and biotic stressors. In this study, the effects of ELICE16INDURES® plant conditioner for organic farming, developed by the Research Institute for Medicinal Plants and Herbs Ltd. Budakalász Hungary, were studied in a soybean population in Northern Hungary. The active compounds and ingredients of this product were selected in such a way as to facilitate the triggering of general plant immunity without the presence and harmful effects of pathogens, thereby strengthening the healthy plant population and preparing it for possible stress effects. In practice, treatments of this agent were applied at two different time points and two concentrations. The conditioning effect was well demonstrated by using agro-drone and ENDVI determination in the soybean field. The genetic background of healthier plants was investigated by NGS sequencing, and by the expression levels of genes encoding enzymes involved in the catalysis of metabolic pathways regulating PTI. The genome-wide transcriptional profiling resulted in 13 contigs related to PAMP-triggered immunity and activated as a result of the treatments. Further analyses showed 16 additional PTI-related contigs whose gene expression changed positively as a result of the treatments. The gene expression values of genes encoded in these contigs were determined by in silico mRNA quantification and validated by RT-qPCR. Both - relatively low and high treatments - showed an increase in gene expression of key genes involving AOC, IFS, MAPK4, MEKK, and GST. Transcriptomic results indicated that the biosyntheses of jasmonic acid (JA), salicylic acid (SA), phenylpropanoid, flavonoid, phytoalexin, and cellular detoxification processes were triggered in the appropriate molecular steps and suggested that plant immune reactions may be activated also artificially, and innate immunity can be enhanced with proper plant biostimulants.

Mutant Tof11 alleles are highly accumulated in early planting-adaptable Japanese summer type soybeans.

To avoid crop failure because of climate change, soybean (Glycine max (L.) Merrill) cultivars adaptable to early planting are required in western Japan. Because current Japanese cultivars may not be adaptable, genetic resources with high early-planting adaptability, and their genetic information must be developed. In the present study, summer type (ST) soybeans developed for early planting were used as plant materials. We examined their phenological characteristics and short reproductive period as an indicator of early planting adaptability and performed genetic studies. Biparental quantitative trait loci (QTL) analysis of a representative ST cultivar revealed a principal QTL for the reproductive period duration on chromosome 11. The results of resequencing analysis suggested that circadian clock-related Tof11 (soybean orthologue of PRR3) is a candidate QTL. Additionally, all 25 early planting-adaptable germplasms evaluated in this study possessed mutant alleles in Tof11, whereas 15 conventional cultivars only had wild-type alleles. These results suggest that mutant alleles in Tof11 are important genetic factors in the high adaptability to early planting of these soybeans, and thus, these alleles were acquired and accumulated in the ST soybean population.

Identification of major quantitative trait loci and candidate genes for seed weight in soybean

Key messageFour major quantitative trait loci for 100-seed weight were identified in a soybean RIL population under five environments, and the most likely candidate genes underlying these loci were identified.Seed weight is an important target of soybean breeding. However, the genes underlying the major quantitative trait loci (QTL) controlling seed weight remain largely unknown. In this study, a soybean population of 300 recombinant inbred lines (RILs) derived from a cross between PI595843 (PI) and WH was used to map the QTL and identify candidate genes for seed weight. The RIL population was genotyped through whole genome resequencing, and phenotyped for 100-seed weight under five environments. A total of 38 QTL were detected, and four major QTL, each explained at least 10% of the variation in 100-seed weight, were identified. Six candidate genes within these four major QTL regions were identified by analyses of their tissue expression patterns, gene annotations, and differential gene expression levels in soybean seeds during four developmental stages between two parental lines. Further sequence variation analyses revealed a C to T substitution in the first exon of the Glyma.19G143300, resulting in an amino acid change between PI and WH, and thus leading to a different predicted kinase domain, which might affect its protein function. Glyma.19G143300 is highly expressed in soybean seeds and encodes a leucine-rich repeat receptor-like protein kinase (LRR-RLK). Its predicted protein has typical domains of LRR-RLK family, and phylogenetic analyses reveled its similarity with the known LRR-RLK protein XIAO (LOC_Os04g48760), which is involved in controlling seed size. The major QTL and candidate genes identified in this study provide useful information for molecular breeding of new soybean cultivars with desirable seed weight.

Classification of soybean genotypes for industrial traits using UAV multispectral imagery and machine learning

Soybean genotypes have distinct physicochemical characteristics, mainly regarding the oil and protein contents in the grains. The use of high-throughput phe-notyping technologies allied to data processing by machine learning algorithms facili-tates and can make it faster and more precise to obtain information about the charac-teristics of the grains. Thus, the objective of the study was to identify machine learning algorithms and inputs with better performance for classifying genotypes clustered based on industrial traits. The experiment was implemented in a randomized block design with two replicates. 103 F2 soybean populations were evaluated. Red, green, near-infrared, and infrared spectral bands and the vegetation indices NDVI, NDRE, GNDVI, SAVI, MSAVI, MCARI, EVI, and SCCCI were measured using UAV multispectral imagery. The industrial traits evaluated were: crude protein content, oil yield, and ash and fiber contents. Data were subjected to Pearson correlation analysis expressed by a correlation network. A genotype clustering based on industrial traits was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while three input configurations were tested: only spectral bands (B), only vegetation indices (VIs), and B + VIs. ML algorithms tested were: artificial neural net-work (ANN), decision tree algorithms J48 (J48), REPTree (DT), and RandomTree (Rt), random forest (RF), Support Vector Machine (SVM), and logistic regression (LR, used as control). Statistical metrics used to evaluate the accuracy of the models were per-centage of correct classification (CC) and F-score. ML algorithms that achieved the highest classification accuracies were ANN, DT and SVM. As for the inputs tested, the best results were obtained using only spectral bands.

A Dynamic Detection Method for Phenotyping Pods in a Soybean Population Based on an Improved YOLO-v5 Network

Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.

Genetic dissection of and genomic selection for seed weight, pod length, and pod width in soybean

A biparental soybean population of 364 recombinant inbred lines (RILs) derived from Zhongdou 41 × ZYD 02.878 was used to identify quantitative trait loci (QTL) associated with hundred-seed weight (100-SW), pod length (PL), and pod width (PW). 100-SW, PL, and PW showed moderate correlations among one another, and 100-SW was correlated most strongly with PW (0.64–0.74). Respectively 74, 70, 75 and 19 QTL accounting for 38.7%–78.8% of total phenotypic variance were identified by inclusive composite interval mapping, restricted two-stage multi-locus genome-wide association analysis, 3 variance-component multi-locus random-SNP-effect mixed linear model analysis, and conditional genome-wide association analysis. Of these QTL, 189 were novel, and 24 were detected by multiple methods. Six loci were associated with 100-SW, PL, and PW and may be pleiotropic loci. A total of 284 candidate genes were identified in colocalizing QTL regions, including the verified gene Seed thickness 1 (ST1). Eleven genes with functions involved in pectin biosynthesis, phytohormone, ubiquitin-protein, and photosynthesis pathways were prioritized by examining single nucleotide polymorphism (SNP) variation, calculating genetic differentiation index, and inquiring gene expression. The prediction accuracies of genomic selection (GS) for 100-SW, PL, and PW based on single trait-associated markers reached 0.82, 0.76, and 0.86 respectively, but selection index (SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy. These results shed light on the genetic basis of 100-SW, PL, and PW and provide GS models for these traits with potential application in breeding programs.

Cereal rye seeding rate does not affect magnitude of weed suppression when planting green within Mid-Atlantic United States

AbstractIn the Mid-Atlantic United States, there is increasing interest in delaying cereal rye termination until after soybean planting (i.e., planting green). Improved understanding of cereal rye seeding rate effects is needed to balance weed and agronomic management goals. We investigated the effects of cereal rye seeding rates on weed control and crop performance when planting green in complementary experiments in two Mid-Atlantic regions. The Pennsylvania experiment was replicated at three site-years and the Delaware experiment at two site-years. In both experiments, population-level weed responses were evaluated across four cereal rye seeding rates: 0, 51, 101, and 135 kg ha−1. The Delaware experiment also implemented a nitrogen treatment factor (0 and 34 kg N ha−1; spring applied). Both experiments showed that integrating cereal rye in the fall significantly improved winter- and summer-annual weed suppression compared with the fallow control, but no differences in total cereal rye biomass production or weed suppression were found among alternative cereal rye seeding rates (51 to 135 kg ha−1). Soybean yield did not differ among treatments in any of the studies. These results show there is no reason to increase cereal rye seeding rates for weed suppression services or to decrease seeding rates for agronomic reasons (i.e., soybean population and yield) when employing planting-green tactics in no-till soybean production within the Mid-Atlantic region of the United States.

Fitness and Rhizobacteria of F2, F3 Hybrids of Herbicide-Tolerant Transgenic Soybean and Wild Soybean.

The introduction of herbicide-tolerant (HT) transgenic soybeans (Glycine max (L.) Merr.) into farming systems raises great concern that transgenes may flow to endemic wild soybeans (Glycine soja Sieb. et Zucc.) via pollen, which may increase the ecological risks by increasing the fitness of hybrids under certain conditions and threaten the genetic diversity of wild soybean populations. In order to demonstrate the potential risk of gene flow from the HT soybean to the wild soybean, the fitness of F2 and F3 hybrids obtained from two wild soybean populations (HLJHRB-1, JSCZ) collected from China and the HT soybean was measured under farmland and wasteland soil conditions, as well as with or without weed competition. Compared with their wild progenitors, the F2 and F3 hybrids of HLJHRB-1 displayed a higher emergence rate, higher aboveground dry biomass, more pods and filled-seed plants, as well as better composite fitness under four planting conditions. The F2 and F3 hybrids of JSCZ also displayed a higher emergence rate, higher aboveground dry biomass, more pods, and more filled seeds per plant under mixed planting, whereas these characteristics were lower under pure planting conditions in wasteland and farmland soil. Therefore, the composite fitness of JSCZ hybrids was higher or lower depending on the planting conditions. Furthermore, the soil microbial communities of the F3 of HLJHRB-1, JSCZ, and the wild soybean were investigated with 16S rDNA sequencing, which showed that low alpha diversity of rhizobacteria was relative to high fitness, and Rhizobium played an important role in promoting F3 plant growth.

Efficiency of visual selection of agronomic traits for soybean production in a protected environment

AbstractThe objectives of this work were to evaluate the efficiency of visual selection in soybean under a protected environment, to determine if the ability of an evaluator to select visually depends on their experience with the crop, and to study the efficiency of visual selection of traits associated with productivity to obtain income gains. For that, three soybean populations (F3, F4, and F5) were used, composed of 20 families each, conducted in a greenhouse in a randomized block design, with three replicates and experimental units, two plants per pot. In these, five evaluators with different levels of experience with the crop made the visual selection for each plant. Evaluators differ in their ability to identify more productive plants, but this ability is not associated with the evaluator's experience with the crop. In the search for more productive soybean plants, the number of pods per plant is the characteristic that most assists visual selection, provided there is uniformity of experimental conditions and genetic variability.

Genome-wide association study uncovers major genetic loci associated with flowering time in response to active accumulated temperature in wild soybean population

Flowering time and active accumulated temperature (AAT) are two key factors that limit the expanded production especially for soybean across different regions. Wild soybean provides an important germplasm for functional genomics study in cultivar soybean. However, the studies on genetic basis underlying flowering time in response to AAT especially in wild soybean were rarely reported. In this study, we used 294 wild soybean accessions derived from major soybean production region characterized by different AAT in Northeast of China. Based on genome-wide association study (GWAS), we identified 96 SNPs corresponded to 342 candidate genes that significantly associated with flowering time recorded in two-year experiments. Gene Ontology enrichment analysis suggests that the pathways of photosynthesis light reaction and actin filament binding were significantly enriched. We found three lead SNPs with -log10(p-value) > 32 across the two-year experiments, i.e., Chr02:9490318, Chr04:8545910 and Chr09:49553555. Linkage disequilibrium block analysis shows 28 candidate genes within the genomic region centered on the lead SNPs. Among them, expression levels of three genes (aspartic peptidase 1, serine/threonine-protein kinase and protein SCAR2-like) were significantly differed between two subgroups possessing contrasting flowering time distributed at chromosome 2, 4 and 9, respectively. There are 6, 7 and 3 haplotypes classified on the coding regions of the three genes, respectively. Collectively, accessions with late flowering time phenotype are typically derived from AAT zone 1, which is associated with the haplotypic distribution and expression levels of the three genes. This study provides an insight into a potential mechanism responsible for flowering time in response to AAT in wild soybean, which could promote the understanding of genetic basis for other major crops.

Yield trade‐off and the role of parental selection based on seed size when breeding for soybean seed protein

AbstractAlternative physiological strategies can increase protein concentration in soybean: (i) more‐than‐proportional increases in seed protein content (mg seed−1) relative to increases in carbohydrate and oil content in large‐seeded genotypes or (ii) more‐than‐proportional reductions in carbohydrate and/or oil content relative to protein content reductions in small‐seeded genotypes. Because these strategies differentially affect crop growth and development, we hypothesized that populations developed from high‐protein (HP) parents with contrasting seed sizes will present differences in how seed yield and protein concentration correlate. To test this, three breeding strategies were developed by mating high‐yielding cultivars and HP ones that differ in seed sizes, reflecting the alternative strategies mentioned above. Neither tested crossing strategies showed differences in their correlation values between seed yield and protein concentration, as initially expected. Nevertheless, populations developed from crossing a HP‐small‐seeded parent to a HP‐large‐seeded one showed the highest number of transgressive segregants for protein yield. Our results showed that parent selection based on seed size has no beneficial effects on the development of high‐yielding, HP soybean populations, but it might affect the number of transgressive segregants for protein yield.

Identification of QTLs and joint QTL segments of leaflet traits at different canopy layers in an interspecific RIL population of soybean.

A leaflet trait on different canopy layers may have different QTLs; leaflet trait QTLs may cluster to form joint QTL segments; all canopy layer QTLs form a complete QTL system for a leaflet trait. As the main part of the plant canopy structure, leaf/leaflet size and shape affect the plant architecture and yield. To explore the leaflet trait QTL system, a population composed of 199 recombinant inbred lines derived from Changling (annual wild, narrow leaflet) and Yiqianli (landrace, broad leaflet) with their parents was tested for leaflet length (LL), width (LW) and length to width (LLW). The population was genotyped with specific-locus amplified fragment sequencing (SLAF-seq) and applied for linkage mapping of the leaflet traits. The results showed that the leaflet traits varied greatly even within a plant, which supported a stratified leaflet sampling strategy to evaluate these traits at top, middle and bottom canopy layers. Altogether, 13 LL, 10 LW and 9 LLW in a total of 32 plus 3 duplicated QTLs were identified, in which, 17 QTLs were new ones, and 48.6%, 28.6% and 22.8% of QTLs were from the top, middle and bottom layers, respectively, indicating the genetic importance of the top layer leaves. Since a leaflet trait may have layer-specific QTLs, all layer QTLs form a complete QTL system. Five QTL clusters each with their QTL supporting intervals overlapped were designated as joint QTL segments (JQSs). In JQS-16, with its linkage map further validated using PCR markers, two QTLs, qLW-16-1 and qLLW-16-1 of the top layer leaflet, were identified six QTL·times. Six candidate genes were predicted, with Glyma.16G127900 as the most potential one for LW and LLW. Three PCR markers, Gm16PAV0653, BARCSOYSSR_16_0796 and YC-16-3, were suggested for marker-assisted selection for LW and LLW in JQS-16.

Seed production of wild soybean (Glycine soja Sieb. et Zucc.) under favorable, ruderal, and natural growing conditions.

Field trials were conducted in Japan under different growing conditions to better understand seed production of wild soybean (Glycine soja Seib. et Zucc.). The objectives of these trials were to evaluate yield and yield components of wild soybean: (1) across 11 diverse populations grown under favorable conditions to assess seed production potential, (2) under different planting densities (112, 208, 416, and 832 plants/m2) to assess intra-specific competition, and (3) across growing conditions (favorable, ruderal, and natural) to assess the impacts of environmental stress and inter-specific competition. Significant differences in yield and seed number observed among the evaluated wild soybean populations were predominantly due to environmental effects and genetic by environmental interaction. Seed production was impacted by both intra- and inter-specific competition. Wild soybean grown without plant competition had 51-fold and 247-fold higher yield compared to when grown in ruderal and natural environments, respectively. Under favorable growing conditions, wild soybean had substantial yield potential due to the ability to produce a high number of seeds. In nature, yield potential is severely limited because of plant competition and other environmental stressors. The results of this research are useful to inform environmental risk assessment when considering the potential impact of soybean biotechnology traits that increase or protect yield. If such traits were to be inadvertently transferred from imported soybean into wild soybean, this research indicates that the effects would likely have little overall impact on wild soybean seed production.

Identification of Loci Governing Agronomic Traits and Mutation Hotspots via a GBS-Based Genome-Wide Association Study in a Soybean Mutant Diversity Pool.

In this study, we performed a genotyping-by-sequencing analysis and a genome-wide association study of a soybean mutant diversity pool previously constructed by gamma irradiation. A GWAS was conducted to detect significant associations between 37,249 SNPs, 11 agronomic traits, and 6 phytochemical traits. In the merged data set, 66 SNPs on 13 chromosomes were highly associated (FDR p < 0.05) with the following 4 agronomic traits: days of flowering (33 SNPs), flower color (16 SNPs), node number (6 SNPs), and seed coat color (11 SNPs). These results are consistent with the findings of earlier studies on other genetic features (e.g., natural accessions and recombinant inbred lines). Therefore, our observations suggest that the genomic changes in the mutants generated by gamma irradiation occurred at the same loci as the mutations in the natural soybean population. These findings are indicative of the existence of mutation hotspots, or the acceleration of genome evolution in response to high doses of radiation. Moreover, this study demonstrated that the integration of GBS and GWAS to investigate a mutant population derived from gamma irradiation is suitable for dissecting the molecular basis of complex traits in soybeans.

Field Studies on the Effect of Rye Cover Crop on Soybean Root Disease and Productivity

Cover crops improve soil and water quality in annual cropping systems, but knowledge of their impact on soybean ( Glycine max L.) seedling and root diseases is limited. The effects of winter rye cover crops ( Secale cereale L.) on soybean population, biomass, root morphology, seedling and root diseases, pathogen incidence, canopy reflectance, and yield were assessed over 2 years in Iowa and Missouri, U.S.A. Plots without a rye cover crop were compared with plots with early-kill rye and late-kill rye cover crops, which were terminated 34 to 49 days or 5 to 17 days before soybean planting, respectively. Soybean shoot dry weight, root rot severity, and incidence of Fusarium spp. and Pythium spp. on roots were not influenced by the treatments. Soybean grain yield and plant population were reduced in the presence of rye in 2 site years, increased in 1 site year, and unchanged in the remaining site years. Soybean canopy reflectance was measured at 810 nm, and measurements were first made at 70 to 80 days after planting (DAP). At least five measurements were obtained at 7- to 15-day intervals, ending at 120 to 125 DAP. Measurements at approximately 120 to 125 DAP differed by treatments but were not consistently associated with the presence or absence of a rye cover crop. Our field studies suggest that Iowa and Missouri soybean farmers can use winter rye as a cover crop in soybean fields with low seedling disease pressure without increasing the risk of seedling and root diseases or suppressing yield. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Genome wide association study to detect genetic regions related to isoflavone content in a mutant soybean population derived from radiation breeding.

Isoflavones are major secondary metabolites that are exclusively produced by legumes, including soybean. Soy isoflavones play important roles in human health as well as in the plant defense system. The isoflavone content is influenced by minor-effect quantitative trait loci, which interact with polygenetic and environmental factors. It has been difficult to clarify the regulation of isoflavone biosynthesis because of its complex heritability and the influence of external factors. Here, using a genotype-by-sequencing-based genome-wide association mapping study, 189 mutant soybean genotypes (the mutant diversity pool, MDP) were genotyped on the basis of 25,646 high-quality single nucleotide polymorphisms (SNPs) with minor allele frequency of >0.01 except for missing data. All the accessions were phenotyped by determining the contents of 12 isoflavones in the soybean seeds in two consecutive years (2020 and 2021). Then, quantitative trait nucleotides (QTNs) related to isoflavone contents were identified and validated using multi-locus GWAS models. A total of 112 and 46 QTNs related to isoflavone contents were detected by multiple MLM-based models in 2020 and 2021, respectively. Of these, 12 and 5 QTNs were related to more than two types of isoflavones in 2020 and 2021, respectively. Forty-four QTNs were detected within the 441-Kb physical interval surrounding Gm05:38940662. Of them, four QTNs (Gm05:38936166, Gm05:38936167, Gm05:38940662, and Gm05:38940717) were located at Glyma.05g206900 and Glyma.05g207000, which encode glutathione S-transferase THETA 1 (GmGSTT1), as determined from previous quantitative trait loci annotations and the literature. We detected substantial differences in the transcript levels of GmGSTT1 and two other core genes (IFS1 and IFS2) in the isoflavone biosynthetic pathway between the original cultivar and its mutant. The results of this study provide new information about the factors affecting isoflavone contents in soybean seeds and will be useful for breeding soybean lines with high and stable concentrations of isoflavones.

Residual Herbicides and Cover Crops Interactions for Soybean Weed Control

Residual herbicides and cover crops are important tools inside an integrated weed management program. The straw produced in crop rotation can interacts with herbicides. The aim of this study was to evaluate the interaction of diclosulam, sulfentrazone, imazethapyr, flumioxazin, s-metolachlor and pyroxasulfone with black oat, cereal rye, common vetch and oilseed radish cover crops and it&amp;acute;s reflection on weed control and soybean production. Were evaluated the biomass production of cover crops and its influence on the soybean population. A phytosociological survey of the weed community was carried out, further evaluating the control provided by the herbicides and its effects on soybean productivity. Diclosulam was the more efficient herbicide tested, reducing both weed density and biomass (68% and 89%, respectively) compared to the fallow, independently of cover crop straw. The best control levels for the population identified were provided by the combination of the herbicides diclosulam with black oat, radish or fallow. We observed that herbicide efficacy in this case was more related with control spectrum than with herbicide-straw interation. This research demonstrates that the integration of cover crops and residual herbicides is efficient in the suppression and control of weeds in the soybean crop in the no-tillage system.

Effects of neonicotinoid seed treatments on wild bee populations in soybean and corn fields in eastern Ontario

Abstract Neonicotinoid‐coated corn and soybean seeds are a common crop in Canada and the US. A growing body of research is demonstrating that, through various exposure routes, neonicotinoids can impact a suite of nontarget organisms including beneficial insects such as bees. However, to date, only a few studies have examined the effects of neonicotinoids in field settings. We assessed the relationship between agricultural crop soil neonicotinoid levels and wild bee abundance and diversity at 16 agricultural sites representing different soil neonicotinoid levels. We detected clothianidin at 11 sites, thiamethoxam at three sites; imidacloprid was not detected. Hedgerow and crop soils were consistent in terms of where clothianidin was detected; thiamethoxan was not detected in hedgerow soils. Based on model outcomes, fields with higher levels of soil neonicotinoids exhibited significantly lower wild bee abundance and diversity than those with low or no neonicotinoids detected. Crop soil neonicotinoid level, hedgerow floral resource abundance and crop type were consistent predictors of bee abundance across models; only neonicotinoid level and crop type were significant predictors of diversity. Our results are consistent with recent findings in the midwestern US, and underscore the potential risk of soil neonicotinoids to wild bee populations across regions and crop systems.

Microbiota Communities of Healthy and Bacterial Pustule Diseased Soybean.

Soybean is an important source of protein and for a wide range of agricultural, food, and industrial applications. Soybean is being affected by Xanthomonas citri pv. glycines, a causal pathogen of bacterial pustule disease, result in a reduction in yield and quality. Diverse microbial communities of plants are involved in various plant stresses is known. Therefore, we designed to investigate the microbial community differentiation depending on the infection of X. citri pv. glycines. The microbial community’s abundance, diversity, and similarity showed a difference between infected and non-infected soybean. Microbiota community analysis, excluding X. citri pv. glycines, revealed that Pseudomonas spp. would increase the population of the infected soybean. Results of DESeq analyses suggested that energy metabolism, secondary metabolite, and TCA cycle metabolism were actively diverse in the non-infected soybeans. Additionally, Streptomyces bacillaris S8, an endophyte microbiota member, was nominated as a key microbe in the healthy soybeans. Genome analysis of S. bacillaris S8 presented that salinomycin may be the critical antibacterial metabolite. Our findings on the composition of soybean microbiota communities and the key strain information will contribute to developing biological control strategies against X. citri pv. glycines.