Soybean Fields Research Articles

Viruses infecting soybean and molecular characterization of soybean mosaic virus isolates in the Black Sea Region of Türkiye

Many viruses are known to infect and damage soybean (Glycine max) around the world. To detect viruses and determine their incidence, surveys were conducted in soybean fields in Samsun province located in the northern part of Türkiye for four consecutive years from 2014-2017. Soybean mosaic virus (SMV) and cucumber mosaic virus (CMV) were detected in soybeans after analyzing 444 leaf samples by enzyme linked immunosorbent assay (ELISA). SMV was the most prevalent virus, with an average of 13.9%, followed by CMV (3.6%), and SMV+CMV (0.9%) in this study. However, alfalfa mosaic virus (AMV), tobacco ringspot virus (TRSV), and tomato spotted wilt virus (TSWV) were not detected in any of the soybean samples tested. In this study, four SMV-infected samples were selected according to their location and the SMV isolates were molecularly analyzed based on P3 cistrons. Results showed that all SMV isolates were the same at the amino acid level in terms of P3 amino acid sequence when those isolates were compared. The BLAST analysis of the P3 cistron of the Turkish SB20, SC25, and STK1 isolates showed that they were most closely related to the German Salzlandkreis-2_17 isolate (99.71 - 99.62% nucleotide identity; 100% amino acid identity, respectively), while the other isolate, STR2, was more similar to the Iranian Ar33 and Lo3 isolates, the German Salzlandkreis-2_17, and the Dutch Summer Shell isolates (99.62% nucleotide identity; 100% amino acid identity, respectively). To our knowledge, this is the first report for SMV and CMV infection in soybean plants in the Black Sea Region and the first molecular characterization of SMV isolates in Türkiye.

Field-evolved cross-resistance to ALS inhibiting herbicides in redroot pigweed (Amaranthus retroflexus L.) populations and alternative chemical options for effective control

Abstract Twelve putative-resistant (R) redroot pigweed populations were collected in sunflower and soybean fields located in northeastern Greece, after repeated exposure to the acetolactate synthase (ALS)-inhibiting herbicides imazamox and tribenuron-methyl. Studies were conducted to determine the resistance status to these two ALS-inhibiting herbicides and evaluate alternative post- and preemergence herbicides for effective control. Two susceptible (S) populations were also included for comparison. Eleven out of the 12 putative-R populations studied in the whole plant dose response pot experiments were characterized as cross-resistant (R) to the imidazolinone imazamox and the sulfonylurea tribenuron-methyl. In contrast, the putative R5 and the two reference populations (S1, S2) populations were found susceptible. Sequencing of the ALS gene revealed that a point mutation (TGG to TTG at position 574) was selected in domain B, where in combination with domain A the majority of point mutations conferring resistance have been detected, resulting in an amino acid substitution from tryptophan (Trp) to leucine (Leu) in the 11 R populations. By contrast, all sequenced plants of the three susceptible populations were found with the wild-type allele encoding Trp574. The labeled rate of the postemergence herbicides tembotrione and dicamba provided fair to excellent control of the populations with ALS cross-resistance. In contrast, at this rate the preemergence herbicides S-metolachlor+terbuthylazine, isoxaflutole, aclonifen, metribuzin, and pendimethalin provided excellent control. These findings strongly suggest that 11 redroot pigweed populations have evolved cross-resistance to ALS-inhibiting herbicides, but viable options for chemical control of this weed still exist.

Bacillus-inoculated organomineral fertilizer in soybean crop during short-term: comparing sources and doses with mineral fertilizer

Bacillus species and organomineral fertilizers can improve various crop responses. The objective of this study was to evaluate the efficiency of organomineral fertilizer with B. subtilis and B. licheniformis, in comparison to mineral fertilizer, on soybean growth, leaf nutrient content, grain yield, and soil characteristics. Two soybean field experiments (305 km apart) were set up in southeastern Brazil. Two sources of fertilizers (inoculated granulated organomineral, 5-26-0 + 0-0-32 N-P2O5-K2O, and coated mineral fertilizer, 10-52-0 + 0-0-60 N-P2O5-K2O), and four doses of P plus K (no fertilizer, 50, 75, 100 kg ha−1 of each P2O5 and K2O) were applied. At 60 days after sowing, plant height and leaf analysis were assessed. Weight of 1000 seeds, grain yield, and soil analysis were evaluated (120 days after sowing). In both fields, there was interaction between source and P plus K dose factors for soybean grain yield. In Site 1, the highest grain yield (4,543.69 kg ha−1) was observed for 100 kg ha−1 of P2O5 and K2O dose of coated mineral fertilizer; in Site 2, the highest grain yield (4,638.55 kg ha−1) was observed for 75 kg ha−1 of P2O5 and K2O dose of organomineral fertilizer. These factors also affected N, P, and K leaf contents. Less effects were observed on plant height, weight of 1000 seeds, and soil characteristics. Inoculated organomineral fertilizer can be used for total or partial supply of nutrients without a clear difference from mineral fertilization. Smaller doses of inoculated organomineral fertilizer could be used without compromising soybean grain yield.

Investigation of in-field heterogeneities in the development of soybean according to remote sensing data and properties of the plow horizon (on the example of the south of the Far East)

Assessment of crop heterogeneities is one of the key conditions for predicting crop yield and increasing the economic efficiency of farming. The aim of the study is to develop methods for assessing in-field heterogeneities of soybean based on remote sensing data and to determine the relationship between soybean productivity indicators and soil characteristics. The work was carried out on meadow-brown heavy loamy soil, 10 plots of soybean field with a total area of 36.9 ha (Khabarovsk Krai) were selected for sampling in May and August 2023. The research was based on the results of remote sensing data processing (Sentinel-2 satellite, DJI Mavic3M quadrocopter), assessment of soybean productivity indicators, agrochemical, physical and chemical characteristics of soils and micro- and macroelement composition. The spatial distribution of NDVI modeled from Sentinel-2 data (early August) corresponded to the distribution of NDVI from DJI Mavic3M data. NDVI index values in August 2023 were found to be significantly correlated with soybean height (R = 0.64) and number of beans (R = 0.64). Soil moisture has a positive correlation with NDVI (R = 0.87) and soybean height (R = 0.68) for the entire growing season of the crop. NDVI was positively correlated with Hg (R = 0.79) and negatively correlated with pH (R = –0.79). The content of N-NO3 ranged from 2.51 to 6.84 mg/kg (V = 35.12 %), mobile forms of Р2О5 – from 2.47 to 6.07 mg/100 g (V = 33.13 %), К2О – from 4.98 to 9.37 mg/100 g (V = 20.07 %). The variability of N-NO3 and Р2О5 content decreased to 11.61 % and 21.99 % by August. No significant changes in soil bulk composition were observed between the first and second sampling. Variation of content of rare-earth elements (Sc, Y, lanthanides) in the selected sites did not exceed 5 %. By the date of the second sampling a significant decrease by 4–10.5 % (p 0.05) in the content of rare earth elements in soil was noted.

First Report of metaplexis yellow mottle-associated virus Infecting Metaplexis japonica (Thunb.) Makino in Shandong, China.

Metaplexis japonica (Thunb.) Makino, commonly known as rough potato, has a wide distribution in China, Japan, Korea, and adjacent Russia. In China, M. japonica is a traditional herbal medicinal plant, which is also cultivated as a vegetable (Shi et al. 2020; Wei et al. 2019). In July 2023, leaves of M. japonica plants growing near a soybean field in Qingdao, Shandong province, exhibited leaf crinkling, mosaic and distorting symptoms of probable virus infection (Supplementary Figure 1). The disease incidence in a 50 m2 area was approximately 40%. To identify the suspected viral etiological agents, symptomatic leaves from 10 M. japonica plants were collected and pooled to perform small RNA deep sequencing (sRNA-Seq). TransZol Up Total RNA Extraction Kit (TransGen Biotech, Beijing, China) was used to extract total RNA. Small RNA library construction and high-throughput sequencing (HTS) were performed on Illumina NovaSeq platform by Genepioneer (Nanjing, China) (Li et al. 2024). A total of 17,384,311 raw reads were obtained. Redundant reads were removed by cutadapt software (version 1.18) to obtain 11,580,876 clean reads with 18 to 26 nucleotide (nt) sizes. The clean reads were assembled using velvet software (version 1.1.07). A total of forty-six small contigs from 42 to 283 nt were identified, with 85 to 100% nucleotide sequence identities, respectively, to metaplexis yellow mottle-associated virus (MeYMaV, genus Caulimovirus, family Caulimoviridae, accession numbers: NC_077108.1). Finally, 1,355,955 reads (11.71% mapped ratio of total reads, cover 56.7% over the MeYMaV genome) were mapped to the genome of MeYMaV by bwa software (version 0.7.17-r1188). To confirm the sRNA-Seq results, PCR was performed with specific primers MeYMaV-N-F/MeYMaV-N-R (5'-TGGTATCAGAGCCTAGTTAA-3'/5'-GGAGTTGGTAATGTATTACC-3') and MeYMaV-C-F/MeYMaV-C-R (5'-AATGGAACGGCTGTTAGTAT3'/TTAATTTCTAGCCCTTGGCTACTTAC). Both the primer pairs were designed using GenBank accession numbers: NC_077108.1 (Yang et al. 2021) to obtain the N and C terminals genome fragments of 10 MeYMaV plants. Two amplicons approximately in 4000-, and 3900-bp sizes were amplified (Supplementary Figure 2), sequenced (tsingke, Beijing, China) and aligned to obtain 7,742-nt complete MeYMaV genome sequence (Accession no. PP892524). BLASTn analysis revealed 90.16% and 92.18% sequence identity, respectively, with the MeYMaV isolate LM-Cau-A (NC_077108.1) based on complete genome and coat protein sequences, respectively. Previously, cucumber mosaic virus and MeYMaV were reported in M. japonica from Jiangsu and Liaoning provinces in China, respectively (Yang et al. 2018; 2021). To our knowledge, this is the first natural infection report of MeYMaV in M. japonica in Shandong, China. The natural occurrence of MeYMaV is not only affects the quality of M. japonica, but also poses a potential threat to surrounding crops. This study enriches information on the disease distribution of MeYMaV and will be helpful for disease management.

The impact of combined application of biochar and fertilizer on the biochemical properties of soil in soybean fields.

Heilongjiang Province is a major soybean production area in China. To improve soil structure and increase soybean yield, this study examined the effects of combined biochar and chemical fertilizer application on the biochemical properties of soil in a maize-soybean rotation system. The research were conducted from 2021 to 2022 at Heshan Farm Science Park in Heilongjiang Province, this field plot experiment utilized two soybean varieties, Heihe 43 (a high-protein variety) and Keshan 1 (a high-oil variety). In 2021, two plots with similar fertility levels were selected for planting soybeans and maize. In 2022, a maize-soybean rotation was implemented with five treatments: conventional fertilization (CK), increased biochar+reduced fertilizer 1 (F1+B), reduced fertilizer 1 (F1), increased biochar+reduced fertilizer 2 (F2+B), and reduced fertilizer 2 (F2). The study systematically analyzed the effects of combined biochar and chemical fertilizer application on soil chemical properties and microbial characteristics. Over 2 years, results showed that combined application effectively improved soil chemical traits. Compared to conventional fertilization (CK) and reduced fertilization (F1, F2), t he combined application of biochar and chemical fertilizer (F1+B, F2+B) increased soil pH, EC and the absolute value of zeta potential of soil surface, the CEC of soil significantly increased by 15.6-44.3%, the soil surface charge density and the soil surface charge quantity significantly increased by 16.4-73.5%. The combined application of biochar and chemical fertilizer also effectively enhanced the abundance and diversity of soil microbes. Dominant bacterial groups in soybean field soils under different treatments included Actinobacteria, Acidobacteria, Chloroflexi, and Proteobacteria; while dominant fungal groups were Ascomycota, Basidiomycota, and Mortierellomycota. Alpha and Beta diversity analyses revealed that the F1+B treatment significantly enhanced the species richness and diversity of bacteria and fungi in the soil, increasing the proportion and evenness of dominant and beneficial genera.

A study on hyperspectral soil total nitrogen inversion using a hybrid deep learning model CBiResNet-BiLSTM

Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R2 of 0.937, with the R2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content.Graphical abstract

Effect of postemergence applications of aminocyclopyrachlor, aminopyralid, 2,4-D, and dicamba on non–auxin resistant soybean

Abstract Soybean [Glycine max (L.) Merr.] that lack resistance to auxin herbicides [i.e., not genetically modified for resistance] have well-documented responses to those particular herbicides, with yield loss being probable. When a soybean field is injured by auxin herbicides, regulatory authorities often collect a plant sample from that field. This research attempted to simulate soybean exposures due to accidental mixing of incorrect herbicides, tank contamination, or particle drift. This research examined whether analytical testing of herbicide residues on soybean to aminocyclopyrachlor (ACP), aminopyralid, 2,4-D, or dicamba would be related to the visual observations and yield responses from these herbicides. ACP and aminopyralid were applied to R1 soybean at 0.1, 1, and 10 g ae ha−1; 2,4-D and dicamba were applied at 1, 10, and 100 g ae ha−1. Visual evaluations and plant sample collections were undertaken at 1, 3, 7, 14, and 21 d after treatment (DAT), and yield was measured. The conservative limits of detection for the four herbicides in this project were 5, 10, 5, and 5 ng g−1 fresh weight of soybean for ACP, aminopyralid, 2,4-D, and dicamba, respectively. Many of the plant samples were non-detects, especially at lower application dosages. All herbicide concentrations rapidly declined soon after application, and many reached nondetectable limits by 14 DAT. All herbicide treatments caused soybean injury, although the response to 2,4-D was markedly lower than the responses to the other three herbicides. There was no apparent correlation between herbicide concentrations (which were declining over time) and the observed soybean injury (which was increasing over time or staying the same). This research indicated that plant samples should be collected as soon as possible after soybean exposure to auxin herbicides.

Advanced drone-based weed detection using feature-enriched deep learning approach

This research addresses the pressing challenge of weed identification in agriculture, crucial for ensuring food security in anticipation of a global population exceeding 9.7 billion by 2050. Utilizing drone imagery, we collected a dataset and proposed a customized model to achieve optimal performance. Our proposed model uses strategically modified backbone, neck, and head components, leveraging elements such as Ghost Convolution, BottleNeckCSP, and ECA (Efficient Channel Attention) layers. These modifications enhance the model’s capability to discern intricate patterns in drone imagery, ultimately leading to improved precision in weed detection. We introduce a purposefully crafted dataset to complement the model’s training, and our experiments demonstrate superior performance compared to the baseline models. Our model achieves a precision of 72.5%, recall of 68.0%, and mAP@0.5 of 73.9, showcasing the effectiveness of our approach in enhancing detection accuracy. Leveraging a unique blend of feature extraction mechanisms, our model achieves remarkable accuracy in real-time soybean detection, outperforming established models like RT-DETR (Real-Time DEtection TransfoRmer) and YOLOv10. A detailed ablation study and comparative analysis with different YOLO versions and the transformer-based RT-DETR showcase the effectiveness of the proposed enhancements. Our work signifies a significant step toward advancing the field of precision agriculture, offering a model that is not only adaptive but also robust in identifying and localizing weeds in soybean fields.

Triple impact: Biochar, no-tillage, and cover crops for soil carbon enhancement and climate resilience in soybean farming

With the dissemination of conservation agriculture, no-tillage (NT) and cover crops have widely been adopted globally. However, their effects on greenhouse gas (GHG) emissions are controversial. Biochar is posited to mitigate climate change by increasing carbon (C) sequestration and decreasing GHG emission in soil. To investigate the comprehensive effect of NT, cover crops, and biochar on soil organic carbon (SOC) sequestration and net global warming potential (GWP), a split-split-plot experiment was conducted. The experiment involved various combinations of two tillage methods (NT; Moldboard plowing, MP), two cover crop treatments (Fallow, FA; Rye, RY), and two biochar treatments (with biochar application, WB; no biochar application, NB). NT and RY demonstrated a trend of increasing N2O emission, while WB tended to reduce the N2O emission in NT plots. NT–RY–WB increased the SOC stock (0–30 cm) by 23.2% in 2020 and 30.2% in 2021 compared with MP–FA–NB, indicating that this combination promoted C sequestration. Due to the heightened SOC stock, the net carbon dioxide (CO2) retention effectively compensated the GWP arising from non-CO2 emissions. Consequently, the triple combination of NT, RY and WB positively contributed to a decreased net GWP in the soybean field (−1231 kg CO2 equivalent ha−1 year−1 in 2020 and −2767 kg CO2 equivalent ha−1 year−1 in 2021). These findings highlight the considerable potential of the combined NT–RY–WB for SOC sequestration and net GWP decrease, positioning it as an environmentally beneficial agricultural system for mitigating climate change in Asia’s long-term food production.

Development of a Recombinase Polymerase Amplification Assay for Rapid Detection of the New Root-lesion Nematode Pratylenchus dakotaensis on Soybean.

Root-lesion nematodes, Pratylenchus spp. are reported to cause serious yield losses in various crops including soybean. A new root-lesion nematode species was recently detected in a soybean field in North Dakota (ND) and named Pratylenchus dakotaensis. Nematode detection and differentiation from other species are critical in management strategies. Thus, a recombinase polymerase amplification (RPA) assay was developed for rapid detection of this nematode from field soils under isothermal conditions. New primers and probes were designed from ITS-rDNA region of the nematode genome and tested for both specificity and sensitivity. The RPA assay was able to detect DNA from a single adult nematode at 39.5°C in 20 minutes using both Basic and Exo kits. The specificity of the primers was initially confirmed through in silico analyses, followed by laboratory tests. The assay successfully amplified DNA from the target species, while no amplification occurred for other Pratylenchus spp. and non-Pratylenchus control species. Sensitivity testing with real-time RPA revealed its ability to detect DNA in dilutions equivalent to 1/32 of a single nematode from DNA extracted from inoculated sterile soil. To further validate the assay, it was tested with 19 field soil samples collected in ND. This assay amplified soil DNA extracts of all P. dakotaensis-infested field samples confirmed through conventional PCR. It did not amplify DNA from 13 other field soils infested with other Pratylenchus spp. This is the first report of RPA develoment for detecting a root-lesion nematode species. The RPA assay developed can help in the rapid detection of this nematode species for effective nematode management.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

Assessment of Soil Enzyme Activity in Soybean Fields under Long-Term Fertilization and Manuring Practices

The experiment carried out in this field study was between at theAICRP Research Farm in the outward-bound farm of the College of Agriculture, Indore. Indore is a small city in the western part of Madhya Pradesh, Central India, situated on the Malwa Plateau. An experiment was conducted with three replications by using different combination of N, P levels and crop residues in RBD design. Experiment field was medium to black soil with pH of 7.63 and medium in organic carbon 0.58%. The soil was low in available nitrogen (208.0 kgha-1) medium in available phosphorus (21.0 kgha-1) and high in potassium (585 kg ha-1) and divided into gross plot size of 10 x 7.2 m2 and a net plot size of 9 x 6.4 m2, respectively. During crop harvest four different depths were used to gather the soil samples: 0–10, 10–20, 20–30, and 30–40 cm. All the collected samples are examined separated for Dehydrogenase, Urease enzyme, Acid & alkaline phosphate, Arysulphatase activity. The finding showed that maximum soil DHA, Urease, Acid & alkaline phosphate, Arysulphataseactivitywas observed with the application of treatment T6 treatment (FYM 6 t ha-1 + N20 P13.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Fungicide Sensitivity and Non-Target Site Resistance in Rhizoctonia zeae Isolates Collected from Corn and Soybean Fields in Nebraska.

Rhizoctonia zeae was recently identified as the major Rhizoctonia species in corn and soybean fields in Nebraska and was shown to be pathogenic on corn and soybean seedlings. Fungicide seed treatments commonly used to manage seedling diseases include prothioconazole (demethylation inhibitor), fludioxonil (phenylpyrrole), sedaxane (succinate dehydrogenase inhibitor), and azoxystrobin (quinone outside inhibitor; QoI). To establish the sensitivity of R. zeae to these fungicides, we isolated this pathogen from corn and soybean fields in Nebraska during 2015 to 2017 and estimated the relative effective concentration for 50% inhibition (EC50) of a total of 91 R. zeae isolates from Nebraska and Illinois. Average EC50 for prothioconazole, fludioxonil, sedaxane, and azoxystrobin was 0.219, 0.099, 0.078, and > 100 µgml-1, respectively. In planta assays showed that azoxystrobin did not significantly reduce the disease severity on soybean (P > 0.05). The cytochrome b gene of R. zeae did not harbor any mutation known to confer QoI resistance and had a type-I intron directly after codon 143 suggesting that a G143A mutation is unlikely to evolve in this pathogen. For prothioconazole, fludioxonil, and sedaxane, EC50 of isolates did not differ significantly among years of collection (P > 0.05) and their single discriminatory concentrations were identified as 0.1 µgml-1. This is the first study to establish non-target site resistance of R. zeae to azoxystrobin and the sensitivity of R. zeae to commonly used seed treatment fungicides in Nebraska. This information will help to guide strategies for chemical control of R. zeae and monitor sensitivity shifts in future.

A conserved protein family in mirid bug Riptortus pedestris plays dual roles in regulating plant immunity.

The mirid bug (Riptortus pedestris), a major soybean pest, migrates into soybean fields during the pod filling stage and causes staygreen syndrome, which leads to substantial yield losses. The mechanism by which R. pedestris elicits soybean (Glycine max) defenses and counter-defenses remains largely unexplored. In this study, we characterized a protein family from R. pedestris, designated Riptortus pedestris HAMP 1 (RPH1) and its putative paralogs (RPH1L1, 2, 3, 4, and 5), whose members exhibit dual roles in triggering and inhibiting plant immunity. RPH1 and RPH1L1 function as herbivore-associated molecular patterns (HAMPs), activating pattern-triggered immunity (PTI) in tobacco (Nicotiana benthamiana) and G. max. Furthermore, RPH1 stimulates jasmonic acid and ethylene biosynthesis in G. max, thereby enhancing its resistance to R. pedestris feeding. Additionally, RPH1 homologs are universally conserved across various herbivorous species, with many homologs also acting as HAMPs that trigger plant immunity. Interestingly, the remaining RPH1 putative paralogs (RPH1L2-5) serve as effectors that counteract RPH1-induced PTI, likely by disrupting the extracellular perception of RPH1. This research uncovers a HAMP whose homologs are conserved in both chewing and piercing-sucking insects. Moreover, it unveils an extracellular evasion mechanism utilized by herbivores to circumvent plant immunity using functionally differentiated paralogs.

Insight into the differential response of functional denitrifiers to novel formulated organic amendments in soybean agroecosystem

Fertilizer application has been known to cause substantial changes in the microbial composition of agricultural soil. Therefore, there is a need for more fertilizer management practices that will improve nitrogen (N) content, which is the key restrictive factor for microbial growth. To elucidate the characteristics of these fertilizers in the soil, samples were collected from a soybean field of control (S0) with no addition of organic amendment, biochar made from rice straw (S1), compost made from cattle manure and maize straw at a ratio of 5:1 (S2), composting S2 + 10 % S1 (S3), and mixture of S2 + 10 % S1 without composting (S4). The soil functional denitrifiers (nirS and nirK) were unravelled using Illumina high-throughput sequencing. It was observed that S3 (66.56 %) and S4 (61.14 %) increased the NO3−-N, while S2 increased the total Kjeldahl nitrogen (TKN) by 15.79 % compared to S0. OTU847_norank_p_environmental_samples in nirS and OTU112_unclassified_f_Bradyrhizobiaceae in nirK were the most abundant genera in S1-S4 while S2 and S3 had the highest unique OTUs in nirK and nirS communities, respectively. The canonical correspondence analysis (CCA) showed that NO2−-N and nitrate reductase (NIR) enzyme-shaped nirS and nirK denitrifiers. Also, from the structural equation model (SEM), TKN showed a higher negative significant effect on nirK alpha and beta diversities, while S4 showed the lowest positive network in nirS and nirK- denitrifiers. Meanwhile, Bradyrhizobium was observed as a common genus in the multivariate co-occurrence network in nirS- and nirK-type denitrifiers. This study provides the theoretical basis and technical support that single and combined fertilizers could influence nirS and nirK denitrifiers in soybean-grown soil.

Monitoring and zoning soybean maturity using UAV remote sensing

Timely and accurate determination of the maturity status during the soybean harvest period is crucial for devising strategic harvesting plans, significantly enhancing the intelligent management of soybean production and minimizing losses. To explore the maturation dynamics of soybeans and pinpoint the precise maturity stage for optimal harvesting, we collected Unmanned Aerial Vehicle (UAV) remote sensing data, along with physical and chemical parameters, during the Beginning maturity(R7)-Full maturity(R8) stages from 2021 to 2022. We analyzed the variations in these parameters throughout the maturation period. Employing UAV multispectral technology and correlation analysis algorithms, we established a relationship between the spectral and physicochemical parameters during the maturation period, which led to the formulation of a soybean maturity evaluation index. Furthermore, we incorporated the concept of precision management zoning. In line with actual field production requirements, this index, combined with real-time moisture content, established a standard for grading soybean maturity. Additionally, our study examined the impact of spatial scale on maturity assessments by conducting a theoretical analysis of regionalized variables and analyzing the spatial correlations and variability of soybean maturity across different field plots. We determined the optimal theoretical model for various sampling distances, thereby establishing feasible zoning ranges. Comparative analysis of zoning algorithms, including Hierarchical Clustering (HC), K-Means, and its optimized versions K-Means++ and Mini Batch K-Means, was conducted using the partition performance index as the criterion. This analysis identified the most suitable algorithm for assessing soybean field maturity, which was subsequently verified through field tests. Results indicated that soybean leaf chlorophyll content and photosynthesis decreased sharply initially, then stabilized at a low rate. Water content in both soybean plants and seeds decreased rapidly before slowing, with plant dehydration rates exceeding those of the seeds. Green Normalized Difference Vegetation Index(GNDVI) showed a positive correlation with various physiological indicators, and soybean maturity was classified into three levels using the unit gridding management method, considering actual moisture content. Spatial correlation analysis of maturity variations in small and large test fields revealed Moran indices ranging from 0.78 to 0.95 and 0.70–0.90, respectively. Optimal detection ranges were determined to be 2.00–30.00 m for small fields and 2.00–67.00 m for large fields. The zoning categories for small and large fields were three and four, respectively. Comparative studies showed that the Mini Batch K-Means algorithm, an optimization of K-Means, achieved Calinski-Harabasz Index(CH) scores and silhouette coefficients of 10521.97, 109508.27, and 0.57, 0.54, respectively, indicating comparable zoning effectiveness to K-Means and K-Means++ and superiority over the HC algorithm. The zoning speeds were 12.50 s and 191.68 s, respectively, underscoring its efficiency in maturity zoning. Furthermore, zoning results corresponded well with actual soybean maturity. Terrain had little impact on soybean maturity, which was more susceptible to slope aspect. This study provides a theoretical foundation and practical guidelines for applying UAV remote sensing technology in determining soybean maturity and optimal harvesting periods, thereby reducing potential harvest losses.

First Report of Seed Decay Caused by Diaporthe ueckeri on soybean in Brazil.

A survey of Diaporthe/Phomopsis Complex (DPC) species was carried out on 479 asymptomatic soybean (Glycine max (L.) Merrill) seed samples collected from commercial soybean fields in the states of Santa Catarina (20 counties) and Rio Grande do Sul (41 counties), in the 2020/21 (n=186), 2021/22 (n=138) and 2022/23 (n=155) seasons from 120 cultivars. The seeds were provided by seed producers who collected according to the sampling standard of the Ministry of Agriculture, Livestock and Food Supply. From each sample received, 200 symptomless seeds were randomly sorted out. The seeds were surface disinfected by immersion in a sodium hypochlorite solution (1%) for two minutes and placed on Potato Dextrose Agar (PDA). The plates were incubated for 7 days at 23°C with a photoperiod of 12-h. The average prevalence of 73.7% of DPC-infected seeds. Colonies were isolated by transferring mycelial tips to PDA and incubating for 14 days at 25ºC in a 12-h photoperiod. One colony (isolate MEMR0500) had morphological characteristics similar to those reported in Lopez-Cardona (2021). This isolate had a floccose, dense colony ranging from grayish beige to brown with greenish regions and black globose pycnidia (3 to 4 pycnidia/cm²). Alpha-conidia, 5.1 to 7.0 µm x 1.5 to 2.8 µm, were observed after 30 days and were hyaline, aseptate and fusiform (Figure S1). No beta-conidia were observed. Soybean plants of cultivars BMX Cromo IPRO, BMX Zeus IPRO, BRS 5804 RR, FPS 1867 IPRO and NEO 750 IPRO were tested for pathogenicity using the toothpick inoculation method (Siviero and Menten 1995). Non-colonized toothpicks served as a negative control. Plants were incubated for four days at 25°C and 90% relative humidity. Elongated 1.0 to 2.5 cm x 0.5 to 0.9 cm lesions gray-brown/reddish-brown with a depressed center were observed in all inoculated cultivars. The fungus was reisolated and the characteristics of the colonies were identical to those previously isolated. For molecular characterization, DNA was extracted from the mycelia using the CTAB method (Doyle and Doyle 1990). End-point PCR was performed using GoTaq® Flexi DNA Polymerase (Promega, USA) and primer pairs, ITS-4F/ITS-5, T2/Bt2b and EF1-728F/EF1-986R to amplify the internal transcribed spacer (ITS) (Costamilan et al. 2008), β-tubulin (TUB2) (Glass and Donaldson 1995), and translation elongation factor 1-α (TEF1) (Carbone and Kohn 1999) genes, respectively. The amplified fragments were sequenced and submitted to blast search (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the sequences available in GenBank. The fragment from ITS (accession number OR912979) showed 99.8% (549/582 bp) identity with Diaporthe ueckeri Udayanga & Castl. [as 'ueckerae'] [syn. D. miriciae R.G. Shivas, S.M. Thomps. & Y.P. Tan] isolate FAU656 (Ac. N. KJ590726). The sequence of TEF (Ac. N. PP372869) showed 99.7% (339/355 bp) identity with D. ueckeri FAU656 (Ac. N. KJ590747), and of TUB (Ac. N. PP372870) showed 98.9% (436/536 bp) identity with D. ueckeri FAU656 (Ac. N. KJ610881). A phylogenetic tree with amplified sequences of each gene and the corresponding representative sequences from the DPC was constructed in MEGA X (Kumar et al. 2018). The MEMR0500 isolate was clustered only with the D. ueckeri clade, confirming the identity of the fungus (Figure S2). In Brazil, this is the first report of the association of this pathogen with soybean seeds. In other countries, this pathogen has been identified as the causal agent of stem canker (Mena et al. 2020; Lopez-Cardona et al. 2021). Further research is needed to analyze the risk of this seed-associated pathogen.

The population genomics of Conyza spp. in soybean macroregions suggest the spread of herbicide resistance through intraspecific and interspecific gene flow

Herbicide-resistant Conyza spp. are a threat to many crops. These widespread weeds are closely related species and often cooccur. To characterize the origins of their resistance and the mechanisms underlying their spread, we assessed the genomic variation in glyphosate-resistant Conyza spp. in Brazil. Twenty populations were sampled from soybean fields across four macroregions (MRSs). A genotyping-by-sequencing study resulted in 2,998 single-nucleotide polymorphisms (SNPs) obtained for C. bonariensis (L.) and the closely related C. sumatrensis (Retz) E. Walker. Higher genomic diversity (π) and heterozygosity (HO/HE) and lower inbreeding coefficient (FIS) values were detected in populations of Conyza spp. from MRS 1 (southern) than in those from other MRSs. Strong genomic structure clustered individuals into three groups (FST = 0.22; p value = 0.000) associated with the MRSs. Thus, resistance to glyphosate originated from independent selection in different MRSs across Brazil. Our dataset supports the occurrence of intraspecific gene flow in Brazil and identified individuals of C. bonariensis that did not group within species. These findings suggest that allelic introgressions within and among species have impacted the evolution and spread of resistance to glyphosate in Conyza spp. We discuss how to mitigate new resistance cases, particularly for the released stacked traits herbicide tolerance in soybeans.