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Pantana phyllostachysae Chao is a leaf-eating pest that poses a significant threat to bamboo forest health. Current research mainly focuses on statically identifying damage using remote sensing images. However, the mechanism behind the damage's traceability remains unclear, making it difficult to pinpoint early infestation sources accurately. Additionally, our understanding of the pest's spreading laws is limited. This study leverages Sentinel-2A/B images from February to November 2021 to investigate P. phyllostachysae infestation traceability through the dynamic age algorithm and indicator analysis method. The results shed light on the distribution of early pest sources over the study period. By analyzing both the overall pest infestation “cluster” and its center of gravity, we dissect P. phyllostachysae infestation characteristics and paths monthly throughout the study period. Our findings reveal three zones with strong spreading momentum, three with slow spreading momentum, and two transitional zones during the February-November period, aligning with P. phyllostachysae occurrence patterns. However, the direction of P. phyllostachysae spreading varies, likely due to a combination of meteorological, topographical, vegetative biochemical, and human activity factors. This study introduces innovative approaches for identifying early pest source points and understand their spreading laws, contributing to more effective pest prevention and control in forest ecosystems.

Microbial communities play an important role in the growth and development of plants, including plant immunity and the decomposition of complex substances into absorbable nutrients. Hence, utilizing beneficial microbes becomes a promising strategy for the optimization of plant growth. The objective of this research was to explore the root bacterial profile across different soybean genotypes and the change in the microbial community under soybean cyst nematode (SCN) infection in greenhouse conditions using 16S rRNA sequencing. Soybean genotypes with soybean cyst nematode (SCN) susceptible and resistant phenotypes were grown under field and greenhouse conditions. Bulked soil, rhizosphere, and root samples were collected from each replicate. Sequencing of the bacterial 16S gene indicated that the bacterial profile of soybean root and soil samples partially overlapped but also contained different communities. The bacterial phyla Proteobacteria, Actinobacteria, and Bacteroidetes dominate the soybean root-enriched microbiota. The structure of bacteria was significantly affected by sample year (field) or time point (greenhouse). In addition, the host genotype had a small but significant effect on the diversity of the root microbiome under SCN pressure in the greenhouse test. These differences may potentially represent beneficial bacteria or secondary effects related to SCN resistance.

Moso bamboo (Phyllostachys pubescens) stands as a pivotal economic bamboo species globally, holding substantial potential for carbon sequestration. Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is crucial due to its close ties with the ecosystem's carbon cycle. Despite the maturation of monitoring techniques for Pantana phyllostachysae Chao, a significant pest of Moso bamboo, its interplay with AGB in these forests remains enigmatic. This study addressed this gap by categorizing P. phyllostachysae's impact on Moso bamboo forests into four levels: healthy, mild damage, moderate damage, and severe damage. By scrutinizing field data, we delved into the shifts in Moso bamboo leaf biomass under P. phyllostachysae stress. Leveraging Sentinel-2A/B imagery, we extracted diverse correlation factors, including original wave bands, vegetation indices, texture attributes, and vegetation's physical and chemical parameters. Subsequently, machine learning algorithms-namely, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) were employed to achieve remote sensing inversion of AGB in Moso bamboo forests, accounting for the presence of insect pests. We analyzed the response of Moso bamboo biomass sensitive factors and to further clarify the changes of AGB of Moso bamboo forests under insect pest stress at the remote sensing level ultimately. The results showed that (1) the degree of Moso bamboo leaf biomass damage was positively related to the damage level, which gradually increased from 15.15 % to 59.42 %; (2) the RF algorithm excelled in estimating Moso bamboo forest AGB, particularly in May, and inclusion of insect pest considerations enhanced AGB estimation accuracy; (3) among the four factor types, Band information and vegetation indices emerged as most impactful, and Band5, Band11, Band12, NDVI68a and MSAVI were selected the most often; (4) at the remote sensing level, AGB in Moso bamboo forests significantly varies under P. phyllostachysae stress. Healthy areas demonstrate an AGB of 66.9037 Mg ha−1, while heavily affected regions drop to 52.6591 Mg ha−1. It can be seen that combining pest factors for Moso bamboo biomass estimation solves the problem of rough biomass estimation, and this study provides a more promising method for forest growth monitoring.

Abstract Genomic selection has been utilized for genetic improvement in both plant and animal breeding and is a favorable technique for quantitative trait development. Within this study, genomic selection was evaluated within a breeding program, using novel validation methods in addition to plant materials and data from a commercial soybean (Glycine max) breeding program. A total of 1501 inbred lines were used to test multiple genomic selection models for multiple traits. Validation included cross‐validation, inter‐environment, and empirical validation. The results indicated that the extended genomic best linear unbiased prediction (EGBLUP) model was the most effective model tested for yield, protein, and oil in cross‐validation with accuracies of 0.50, 0.68, and 0.64, respectively. Increasing marker number from 1000 to 3000 to 6000 single nucleotide polymorphism markers leads to statistically significant increases in accuracy. Cross‐environment predictions were statistically lower than cross‐validation with accuracies of 0.24, 0.54, and 0.42 for yield, protein, and oil, respectively, using the extended genomic BLUP model. Empirical validation, predicting the yield of 510 soybean lines, had a prediction accuracy of 0.34, with the inclusion of a maturity covariate leading to a notable increase in accuracy. Genomic selection identified high‐performance lines in inter‐environment predictions: 34% of lines within the upper quartile of yield, and 51% and 48% of the highest quartile protein and oil lines, respectively. Statistically similar results occurred comparing rankings in empirical validation and selection for advancements in yield trials. These results indicate that genomic selection is a useful tool for selection decisions.

Soybean meal is a major component of livestock feed due to its high content and quality of protein. Understanding the genetic control of protein is essential to develop new cultivars with improved meal protein. Previously, a genomic region on chromosome 20 significantly associated with elevated protein content was identified in the cultivar Danbaekkong. The present research aimed to introgress the Danbaekkong high-protein allele into elite lines with different genetic backgrounds by developing and deploying robust DNA markers. A multiparent population consisting of 10 F5-derived populations with a total of 1,115 recombinant inbred lines (RILs) was developed using “Benning HP” as the donor parent of the Danbaekkong high-protein allele. A new functional marker targeting the 321-bp insertion in the gene Glyma.20g085100 was developed and used to track the Danbaekkong high-protein allele across the different populations and enable assessment of its effect and stability. Across all populations, the high-protein allele consistently increased the content, with an increase of 3.3% in seed protein. A total of 103 RILs were selected from the multiparent population for yield testing in five environments to assess the impact of the high-protein allele on yield and to enable the selection of new breeding lines with high protein and high yield. The results indicated that the high-protein allele impacts yield negatively in general; however, it is possible to select high-yielding lines with high protein content. An analysis of inheritance of the Chr 20 high-protein allele in Danbaekkong indicated that it originated from a Glycine soja line (PI 163453) and is the same as other G. soja lines studied. A survey of the distribution of the allele across 79 G. soja accessions and 35 Glycine max ancestors of North American soybean cultivars showed that the high-protein allele is present in all G. soja lines evaluated but not in any of the 35 North American soybean ancestors. These results demonstrate that G. soja accessions are a valuable source of favorable alleles for improvement of protein composition.

Bamboo groves predominantly thrive in tropical or subtropical regions. Assessing the efficacy of remote sensing data of various types in extracting bamboo forest information from bright and shadow areas is a critical issue for achieving precise identification of bamboo forests in complex terrain. In this study, 34 features were obtained from Sentinel-1 SAR and Sentinel-2 optical images using the Google Earth Engine platform. The normalized shaded vegetation index (NSVI) was then employed to segment the bright and shadow woodlands. Different features from diverse data sources were evaluated to extract bamboo forest information in the bright and shadow areas, then use the random forest (RF) classification algorithm to extract bamboo forest. The results showed that (1) the red-edge and short-wave infrared bands of Sentinel-2 optical images and their corresponding vegetation indices are significant in bamboo forest information extraction. (2) The dissimilarity and homogeneity of Sentinel-2 texture features in the bright area and dissimilarity in the shadow area, the Sentinel-1 backscatter features in the bright area and the VV and VH in the bright area and VV-VH in the shadow area have some variability between bamboo and nonbamboo forests, which can be used as effective features for bamboo forest extraction. (3) The combination of spectral, texture and backscatter features yields the highest overall classification accuracy and Kappa coefficient, at 87.96% and 0.7435, respectively. This study has the potential for remote sensing refinement of bamboo forest identification in complex terrain areas by utilizing subregion classification methods combined with optical and radar image features.

Abstract Background Frogeye leaf spot is a disease of soybean, and there are limited sources of crop genetic resistance. Accurate quantification of resistance is necessary for the discovery of novel resistance sources, which can be accelerated by using a low-cost and easy-to-use image analysis system to phenotype the disease. The objective herein was to develop an automated image analysis phenotyping pipeline to measure and count frogeye leaf spot lesions on soybean leaves with high precision and resolution while ensuring data integrity. Results The image analysis program developed measures two traits: the percent of diseased leaf area and the number of lesions on a leaf. Percent of diseased leaf area is calculated by dividing the number of diseased pixels by the total number of leaf pixels, which are segmented through a series of color space transformations and pixel value thresholding. Lesion number is determined by counting the number of objects remaining in the image when the lesions are segmented. Automated measurement of the percent of diseased leaf area deviates from the manually measured value by less than 0.05% on average. Automatic lesion counting deviates by an average of 1.6 lesions from the manually counted value. The proposed method is highly correlated with a conventional method using a 1–5 ordinal scale based on a standard area diagram. Input image compression was optimal at a resolution of 1500 × 1000 pixels. At this resolution, the image analysis method proposed can process an image in less than 10 s and is highly concordant with uncompressed images. Conclusion Image analysis provides improved resolution over conventional methods of frogeye leaf spot disease phenotyping. This method can improve the precision and resolution of phenotyping frogeye leaf spot, which can be used in genetic mapping to identify QTLs for crop genetic resistance and in breeding efforts for resistance to the disease.

Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean’s profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross’s offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.

Image dehazing models are critical in improving the recognition and classification capabilities of image-related artificial intelligence systems. However, existing methods often ignore the limitations of receptive field size during feature extraction and the loss of important information during network sampling, resulting in incomplete or structurally flawed dehazing outcomes. To address these challenges, we propose a multi-level perception fusion dehazing network (MPFDN) that effectively integrates feature information across different scales, expands the perceptual field of the network, and fully extracts the spatial background information of the image. Moreover, we employ an error feedback mechanism and a feature compensator to address the loss of features during the image dehazing process. Finally, we subtract the original hazy image from the generated residual image to obtain a high-quality dehazed image. Based on extensive experimentation, our proposed method has demonstrated outstanding performance not only on synthesizing dehazing datasets, but also on non-homogeneous haze datasets.

Biochemical components of Moso bamboo (Phyllostachys pubescens) are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem. The coupled PROSPECT with SAIL (PROSAIL) radiative transfer model is widely used for vegetation biochemical component content inversion. However, the presence of leaf-eating pests, such as Pantana phyllostachysae Chao (PPC), weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered. Therefore, this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables (LUTs) based on the PROSAIL framework by setting different parameter ranges according to infestation levels. Quantitative inversions of leaf area index (LAI), leaf chlorophyll content (LCC), and leaf equivalent water thickness (LEWT) were derived. The scale conversions from LCC to canopy chlorophyll content (CCC) and LEWT to canopy equivalent water thickness (CEWT) were calculated. The results showed that LAI, CCC, and CEWT were inversely related with PPC-induced stress. When applying multiple LUTs, the p-values were

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Optimization of plant architecture by modifying stem termination and timing of flowering and maturity of soybean is a promising strategy to improve its adaptability to specific production environments. Therefore, it is important to choose a proper stem termination type and to understand morphological differences between each stem termination type under various environmental conditions. Variations in abruptness of stem termination have been generally classified into three classical genetic types, indeterminate (Dt1), determinate (dt1), and semi-determinate (Dt2). However, an additional stem termination type, termed tall determinate, and its genetic symbol, dt1-t, were introduced about 25 years ago. The tall determinate soybean lines show delayed cessation of apical stem growth and about 50% taller plant heights than the typical determinate soybeans, even though the genetic control of the tall determinate phenotype was found to be allelic to dt1. Despite the potential agronomic merits of the alternative stem termination type, knowledge about the tall determinate soybean remains limited. We clarified the molecular basis of the tall determinate stem termination type and examined potential agronomic merits of the alternative stem type under three different production environments in the US. Sequence analysis of the classical tall determinate soybean lines revealed that the dt1-t allele responsible for tall determinate stem architecture is caused by two of the identified independent missense alleles of dt1, dt1-t1 (R130K), and dt1-t2 (R62S). Also, from the comparison among soybean accessions belonging to each of the genotype categories for stem termination types, soybean accessions with tall determinate alleles were found to have a high discrepancy rate in phenotyping. Newly developed tall determinate late-maturing soybean germplasm lines had taller plant heights and a greater number of nodes with a similar stem diameter and similar pod density at the apical stem compared to typical determinate soybeans having dt1 (R166W) alleles in Southern environments in the US. The phenotype of increased pod-bearing nodes with lodging resistance has the potential to improve yield, especially grown in high yield environments. This study suggests an alternative strategy to remodel the shape of soybean plants, which can possibly lead to yield improvement through the modification of soybean plant architecture.

Leaf water content (LWC) is very important in the growth of vegetation. LWC and leaf spectra change when the leaves are under pest stress; exploring the change mechanism between LWC, leaf spectra, and pest stress can lay the foundation for pest detection. In this study, we measured the LWC and leaf spectra of moso bamboo leaves under different damage levels, used the Pearson–Lasso method to screen the features, and established a multiple linear regression (MLR) and random forest regression (RFR) model to estimate the LWC. We analyzed the relationship between LWC and spectral features of moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and their changes. The results showed that: (1) the LWC showed a decreasing trend as the pest level increased. (2) The spectra changed substantially when the leaves were under pest stress. (3) The number and significance of response features associated with the LWC were diverse under different damage levels. (4) The estimation of LWC under different damage levels differed significantly. LWC, leaf spectra, response features, and the model estimation effect were diverse under different damage levels. The correlation between LWC and features was higher for healthy leaves than for damaged and off-year leaves. The two models were more effective in estimating the LWC of healthy leaves but less effective for damaged and off-year leaves. This study provides theoretical support for the prediction of PPC stress and lays the foundation for remote sensing monitoring.

Drought stress causes the greatest soybean [Glycine max (L.) Merr.] yield losses among the abiotic stresses in rain-fed U.S. growing areas. Because less than 10% of U.S. soybean hectares are irrigated, combating this stress requires soybean plants which possess physiological mechanisms to tolerate drought for a period of time. Phenotyping for these mechanisms is challenging, and the genetic architecture for these traits is poorly understood. A morphological trait, slow or delayed canopy wilting, has been observed in a few exotic plant introductions (PIs), and may lead to yield improvement in drought stressed fields. In this study, we visually scored wilting during stress for a panel of 162 genetically diverse maturity group VI-VIII soybean lines genotyped with the SoySNP50K iSelect BeadChip. Field evaluation of canopy wilting was conducted under rain-fed conditions at two locations (Athens, GA and Salina, KS) in 2015 and 2016. Substantial variation in canopy wilting was observed among the genotypes. Using a genome-wide association mapping approach, 45 unique SNPs that tagged 44 loci were associated with canopy wilting in at least one environment with one region identified in a single environment and data from across all environments. Several new soybean accessions were identified with canopy wilting superior to those of check genotypes. The germplasm and genomic regions identified can be used to better understand the slow canopy wilting trait and be incorporated into elite germplasm to improve drought tolerance in soybean.

Abstract Background Drought stress is a major limiting factor of soybean [Glycine max (L.) Merr.] production around the world. Soybean plants can ameliorate this stress with improved water-saving, sustained N2 fixation during water deficits, and/or limited leaf hydraulic conductance. In this study, carbon isotope composition (δ13C), which can relate to variation in water-saving capability, was measured. Additionally, nitrogen isotope composition (δ15N) and nitrogen concentration that relate to nitrogen fixation were evaluated. Decrease in transpiration rate (DTR) of de-rooted soybean shoots in a silver nitrate (AgNO3) solution compared to deionized water under high vapor pressure deficit (VPD) conditions was used as a surrogate measurement for limited leaf hydraulic conductance. A panel of over 200 genetically diverse soybean accessions genotyped with the SoySNP50K iSelect BeadChips was evaluated for the carbon and nitrogen related traits in two field environments (Athens, GA in 2015 and 2016) and for transpiration response to AgNO3 in a growth chamber. A multiple loci linear mixed model was implemented in FarmCPU to perform genome-wide association analyses for these traits. Results Thirty two, 23, 26, and nine loci for δ13C, δ15N, nitrogen concentration, and transpiration response to AgNO3, respectively, were significantly associated with these traits. Candidate genes that relate to drought stress tolerance enhancement or response were identified near certain loci that could be targets for improving and understanding these traits. Soybean accessions with favorable breeding values were also identified. Low correlations were observed between many of the traits and the genetic loci associated with each trait were largely unique, indicating that these drought tolerance related traits are governed by different genetic loci. Conclusions The genomic regions and germplasm identified in this study can be used by breeders to understand the genetic architecture for these traits and to improve soybean drought tolerance. Phenotyping resources needed, trait heritability, and relationship to the target environment should be considered before deciding which of these traits to ultimately employ in a specific breeding program. Potential marker-assisted selection efforts could focus on loci which explain the greatest amount of phenotypic variation for each trait, but may be challenging due to the quantitative nature of these traits.

Abstract Soybean [Glycinemax (L.) Merr.] maturity determines the growing region of a given soybean variety and is a primary factor in yield and other agronomic traits. The objectives of this research were to identify the quantitative trait loci (QTL) associated with maturity groups (MGs) and determine the genetic control of soybean maturity in each MG. Using data from 16,879 soybean accessions, genome‐wide association (GWA) analyses were conducted for each paired MG and across MGs 000 through IX. Genome‐wide association analyses were also performed using 184 genotypes (MGs V–IX) with days to flowering (DTF) and maturity (DTM) collected in the field. A total of 58 QTL were identified to be significantly associated with MGs in individual GWAs, which included 12 reported maturity loci and two stem termination genes. Genome‐wide associations across MGs 000–IX detected a total of 103 QTL and confirmed 54 QTL identified in the individual GWAs. Of significant loci identified, qMG‐5.2 had effects on the highest number (9) of MGs, followed by E2, E3, Dt2, qMG‐15.5, E1, qMG‐13.1, qMG‐7.1, and qMG‐16.1, which affected five to seven MGs. A high number of genetic loci (8–25) that affected MGs 0–V were observed. Stem termination genes Dt1 and Dt2 mainly had significant allele variation in MGs II–V. Genome‐wide associations for DTF, DTM, and reproductive period (RP) in the diversity panel confirmed 15 QTL, of which seven were observed in MGs V–IX. The results generated can help soybean breeders manipulate the maturity loci for genetic improvement of soybean yield.

Nitrogen is an important indicator of vegetation health, but the relationship between changes in the leaf nitrogen content of Moso bamboo leaves under Pantana phyllostachysae Chao (PPC) stress and leaf spectra remains unclear. We analyzed the relationship between the leaf nitrogen content and leaf spectra of Moso bamboo leaves under PPC stress to investigate whether the relationship could be used to detect pests and prevent their spread. We measured the nitrogen content and leaf spectra of Moso bamboo leaves under different damage levels, identified spectral indicators that were correlated with leaf nitrogen content (by removing the envelope and first-order differentiation of the raw spectra), and estimated leaf nitrogen content from the spectral data using regression models. Leaf nitrogen content decreased with increasing pest damage, and the leaf spectral curves changed, with the “green peak” and “red valley” in the visible range disappearing and the slope of the spectral curve decreasing. The wavelength region with the strongest correlation between the nitrogen content and spectral characteristics changed significantly with increasing pest damage, and the correlation in the red-edge region gradually decreased. The fits of nitrogen-content estimation models tended to decrease and then increase with increasing pest damage and were worst among leaves in the moderate damage state (Mo). A disordered relationship between nitrogen content and spectral characteristics indicated possible PPC damage. The degree of disorder was greatest in the Mo state. This study provides theoretical support for remote sensing monitoring of PPC hazards.

Flooding is a frequent environmental stress that reduces soybean (Glycine max) growth and grain yield in many producing areas in the world, such as, e.g., in the United States, Southeast Asia and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas by rotating with rice (Oryza sativa), which provides numerous technical, economic and environmental benefits. Given these realities, this work aimed to characterize physiological responses, identify genes differentially expressed under flooding stress in Brazilian soybean genotypes with contrasting flooding tolerance, and select SNPs with potential use for marker-assisted selection. Soybean cultivars TECIRGA 6070 (flooding tolerant) and FUNDACEP 62 (flooding sensitive) were grown up to the V6 growth stage and then flooding stress was imposed. Total RNA was extracted from leaves 24 h after the stress was imposed and sequenced. In total, 421 induced and 291 repressed genes were identified in both genotypes. TECIRGA 6070 presented 284 and 460 genes up- and down-regulated, respectively, under flooding conditions. Of those, 100 and 148 genes were exclusively up- and down-regulated, respectively, in the tolerant genotype. Based on the RNA sequencing data, SNPs in differentially expressed genes in response to flooding stress were identified. Finally, 38 SNPs, located in genes with functional annotation for response to abiotic stresses, were found in TECIRGA 6070 and absent in FUNDACEP 62. To validate them, 22 SNPs were selected for designing KASP assays that were used to genotype a panel of 11 contrasting genotypes with known phenotypes. In addition, the phenotypic and grain yield impacts were analyzed in four field experiments using a panel of 166 Brazilian soybean genotypes. Five SNPs possibly related to flooding tolerance in Brazilian soybean genotypes were identified. The information generated from this research will be useful to develop soybean genotypes adapted to poorly drained soils or areas subject to flooding.

Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities (rMP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in rMP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, rMP for the AP method (0.55, 0.30) approached rMP for the WP method (0.60, 0.52). Though comparable, rMP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in rMP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.

In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo.

The genetic diversity of North American soybean cultivars has been largely influenced by a small number of ancestors. High yielding breeding lines that possess exotic pedigrees have been developed, but identifying beneficial exotic alleles has been difficult as a result of complex interactions of yield alleles with genetic backgrounds and environments as well as the highly quantitative nature of yield. PI 416937 has been utilized in the development of many high yielding lines that have been entered into the USDA Southern States Uniform Tests over the past ~20 years. The primary goal of this research was to identify genomic regions under breeding selection from PI 416937 and introduce a methodology for identifying and potentially utilizing beneficial diversity from lines prevalent in the ancestry of elite cultivars. Utilizing SoySNP50K Infinium BeadChips, 52 high yielding PI 416937-derived lines as well as their parents were genotyped to identify PI 416937 alleles under breeding selection. Nine genomic regions across three chromosomes and 17 genomic regions across seven chromosomes were identified where PI 416937 alleles were under positive or negative selection. Minimal significant associations between PI 416937 alleles and yield were observed in replicated yield trials of five RIL populations, highlighting the difficulty of consistently detecting yield associations.

Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes. The RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. The RhizoVision Analyzer was physically validated using copper wire, and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating that differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.

Soybean cyst nematode (Heterodera glycines Ichinohe) (SCN) is the most destructive pest affecting soybeans [Glycine max (L.) Merr.] in the U.S. To date, only two major SCN resistance alleles, rhg1 and Rhg4, identified in PI 88788 (rhg1) and Peking (rhg1/Rhg4), residing on chromosomes (Chr) 18 and 8, respectively, have been widely used to develop SCN resistant cultivars in the U.S. Thus, some SCN populations have evolved to overcome the PI 88788 and Peking derived resistance, making it a priority for breeders to identify new alleles and sources of SCN resistance. Toward that end, 461 soybean accessions from various origins were screened using a greenhouse SCN bioassay and genotyped with Illumina SoySNP50K iSelect BeadChips and three KASP SNP markers developed at the Rhg1 and Rhg4 loci to perform a genome-wide association study (GWAS) and a haplotype analysis at the Rhg1 and Rhg4 loci. In total, 35,820 SNPs were used for GWAS, which identified 12 SNPs at four genomic regions on Chrs 7, 8, 10, and 18 that were significantly associated with SCN resistance (P < 0.001). Of those, three SNPs were located at Rhg1 and Rhg4, and 24 predicted genes were found near the significant SNPs on Chrs 7 and 10. KASP SNP genotyping results of the 462 accessions at the Rhg1 and Rhg4 loci identified 30 that carried PI 88788-type resistance, 50 that carried Peking-type resistance, and 58 that carried neither the Peking-type nor the PI 88788-type resistance alleles, indicating they may possess novel SCN resistance alleles. By using two subsets of SNPs near the Rhg1 and Rhg4 loci obtained from SoySNP iSelect BeadChips, a haplotype analysis of 461 accessions grouped those 58 accessions differently from the accessions carrying Peking or PI 88788 derived resistance, thereby validating the genotyping results at Rhg1 and Rhg4. The significant SNPs, candidate genes, and newly characterized SCN resistant accessions will be beneficial for the development of DNA markers to be used for marker-assisted breeding and developing soybean cultivars carrying novel sources of SCN resistance.

Crop improvement represents a long-running experiment in artificial selection on a complex trait, namely yield. How such selection relates to natural populations is unclear, but the analysis of domesticated populations could offer insights into the relative role of selection, drift, and recombination in all species facing major shifts in selective regimes. Because of the extreme autogamy exhibited by soybean (Glycine max), many “immortalized” genotypes of elite varieties spanning the last century have been preserved and characterized using ∼50,000 single nucleotide polymorphic (SNP) markers. Also due to autogamy, the history of North American soybean breeding can be roughly divided into pre- and posthybridization eras, allowing for direct interrogation of the role of recombination in improvement and selection. Here, we report on genome-wide characterization of the structure and history of North American soybean populations and the signature of selection in these populations. Supporting previous work, we find that maturity defines population structure. Though the diversity of North American ancestors is comparable to available landraces, prehybridization line selections resulted in a clonal structure that dominated early breeding and explains many of the reductions in diversity found in the initial generations of soybean hybridization. The rate of allele frequency change does not deviate sharply from neutral expectation, yet some regions bare hallmarks of strong selection, suggesting a highly variable range of selection strengths biased toward weak effects. We also discuss the importance of haplotypes as units of analysis when complex traits fall under novel selection regimes.

After analyzing the enterprise’s production strategy under the constraints of carbon quota, this paper proposes new mathematical models aiming for the optimal choice of enterprise’s production strategy under the monopoly and competitive environments respectively. Combining the neural network optimization theory, then the methods of Nonlinear Programming and Nash Equilibrium in Static Games are used to solve the models to obtain the enterprise’s equilibrium quantity, the optimal carbon emission, and the profit of unit product in the low carbon technology. The study found that: under a monopoly environment, enterprises choose technology innovation strategy; under a competitive environment, enterprises use carbon trading strategies whenever carbon trading prices are low or high; however, there is no pure strategy Nash equilibrium when carbon trading prices are in the middle, in this case enterprises prefer to use the production strategies different from the competitor.

Frogeye leaf spot (FLS), caused by the fungus Cercospora sojina K. Hara, may cause a significant yield loss to soybean growers in regions with a warm and humid climate. Two soybean accessions, PI 594891 and PI 594774, were identified to carry a high level of resistance similar to that conditioned by the Rcs3 gene in 'Davis'. Previously, we reported that the resistance to FLS in these two plant introductions (PIs) was controlled by a novel gene (s) on chromosome 13 that is different from Rcs3. To fine-map the novel FLS resistance gene(s) in these two PIs, F2: 3 seeds from the crosses between PI 594891 and PI 594774, and the FLS susceptible genotype 'Blackhawk' were genotyped with SNP markers that were designed based on the SoySNP50k iSelect BeadChip data to identify recombinant events and locate candidate genes. Analysis of lines possessing key recombination events helped narrow down the FLS-resistance genomic region in PI 594891 from 3.3 Mb to a 72.6 kb region with five annotated genes. The resistance gene in PI 594774 was fine-mapped into a 540 kb region that encompasses the 72.6 kb region found in PI 594891. Sequencing five candidate genes in PI 594891 identified three genes that have several mutations in the promoter, intron, 5', and 3' UTR regions. qPCR analysis showed a difference in expression levels of these genes in both lines compared to Blackhawk in the presence of C. sojina. Based on phenotype, genotype and haplotype analysis results, these two soybean accessions might carry different resistance alleles of the same gene or two different gene(s). The identified SNPs were used to develop Kompetitive Allele Specific PCR (KASP) assays to detect the resistance alleles on chromosome 13 from the two PIs for marker-assisted selection.

Microbial communities play an important role in the growth and development of plants, including plant immunity and the decomposition of complex substances into absorbable nutrients. Hence, utilizing beneficial microbes becomes a promising strategy for the optimization of plant growth. The objective of this research was to explore the root bacterial profile across different soybean genotypes and the change in the microbial community under soybean cyst nematode (SCN) infection in greenhouse conditions using 16S rRNA sequencing. Soybean genotypes with soybean cyst nematode (SCN) susceptible and resistant phenotypes were grown under field and greenhouse conditions. Bulked soil, rhizosphere, and root samples were collected from each replicate. Sequencing of the bacterial 16S gene indicated that the bacterial profile of soybean root and soil samples partially overlapped but also contained different communities. The bacterial phyla Proteobacteria, Actinobacteria, and Bacteroidetes dominate the soybean root-enriched microbiota. The structure of bacteria was significantly affected by sample year (field) or time point (greenhouse). In addition, the host genotype had a small but significant effect on the diversity of the root microbiome under SCN pressure in the greenhouse test. These differences may potentially represent beneficial bacteria or secondary effects related to SCN resistance. [ABSTRACT FROM AUTHOR]

Soybean meal is a major component of livestock feed due to its high content and quality of protein. Understanding the genetic control of protein is essential to develop new cultivars with improved meal protein. Previously, a genomic region on chromosome 20 significantly associated with elevated protein content was identified in the cultivar Danbaekkong. The present research aimed to introgress the Danbaekkong high-protein allele into elite lines with different genetic backgrounds by developing and deploying robust DNA markers. A multiparent population consisting of 10 F5-derived populations with a total of 1,115 recombinant inbred lines (RILs) was developed using “Benning HP” as the donor parent of the Danbaekkong high-protein allele. A new functional marker targeting the 321-bp insertion in the gene Glyma.20g085100 was developed and used to track the Danbaekkong high-protein allele across the different populations and enable assessment of its effect and stability. Across all populations, the high-protein allele consistently increased the content, with an increase of 3.3% in seed protein. A total of 103 RILs were selected from the multiparent population for yield testing in five environments to assess the impact of the high-protein allele on yield and to enable the selection of new breeding lines with high protein and high yield. The results indicated that the high-protein allele impacts yield negatively in general; however, it is possible to select high-yielding lines with high protein content. An analysis of inheritance of the Chr 20 high-protein allele in Danbaekkong indicated that it originated from a Glycine soja line (PI 163453) and is the same as other G. soja lines studied. A survey of the distribution of the allele across 79 G. soja accessions and 35 Glycine max ancestors of North American soybean cultivars showed that the high-protein allele is present in all G. soja lines evaluated but not in any of the 35 North American soybean ancestors. These results demonstrate that G. soja accessions are a valuable source of favorable alleles for improvement of protein composition. [ABSTRACT FROM AUTHOR]