Soybean Fields Research Articles

Application of UAV Multispectral Imaging to Monitor Soybean Growth with Yield Prediction through Machine Learning

The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, green, red, red edge, and near-infrared), instead of satellite-captured data, to monitor soybean growth in a field. The field experiment was conducted in a soybean field at the Mississippi State University Experiment Station near Pontotoc, MS, USA. The experiment consisted of five cover crops (Cereal Rye, Vetch, Wheat, Mustard plus Cereal Rye, and native vegetation) planted in the winter and three fertilizer treatments (Fertilizer, Poultry Liter, and None) applied before planting the soybean. During the soybean growing season in 2022, eight UAV imaging flyovers were conducted, spread across the growth season. UAV image-derived vegetation indices (VIs) coupled with machine learning (ML) models were computed for characterizing soybean growth at different stages across the season. The aim of this study focuses on monitoring soybean growth to predict yield, using 14 VIs including CC (Canopy Cover), NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2), and others. Different machine learning algorithms including Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are used for this purpose. The stage of the initial pod development was shown as having the best predictability for earliest soybean yield prediction. CC, NDVI, and NAVI (Normalized area vegetation index) were shown as the best VIs for yield prediction. The RMSE was found to be about 134.5 to 511.11 kg ha−1 in the different yield models, whereas it was 605.26 to 685.96 kg ha−1 in the cross-validated models. Due to the limited number of training and testing samples in the K-fold cross-validation, the models’ results changed to some extent. Nevertheless, the results of this study will be useful for the application of UAV remote sensing to provide information for soybean production and management. This study demonstrates that VIs coupled with ML models can be used in multistage soybean yield prediction at a farm scale, even with a limited number of training samples.

Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery

Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

Exploring Competitive Dynamics, Technological Innovations, and Sustainable Strategies for X Plant-Based Meat Health Technology Co., Ltd.

The research objectives were as follows: 1) to analyze the competitive environment of X Plant-Based Meat Health Technology Co., Ltd.; 2) to determine the technologies and innovation capabilities of X Plant-Based Meat Health Technology Co., Ltd.; and 3) to formulate sustainable strategies for X Plant-Based Meat Health Technology Co., Ltd. This research adopted qualitative research and document study. Twelve key informants, including top-level management, product development teams, head of marketing departments, and consumer groups within the company, were involved. The data analyzed the current market competition status of X Plant-Based Meat Technology Co., Ltd. using PEST, Porter’s Five Forces, and SWOT models, thus proposing sustainable development strategies. The research findings revealed that: 1) X Plant-Based Meat Health Technology Co., Ltd. operated in a competitive market driven by government initiatives and increasing environmental awareness. Challenges included intensified competition and price sensitivity. However, the company had opportunities for growth in China's expanding plant-based food sector. Success depended on strategic differentiation, technological innovation, and adaptability to evolving market trends amidst competition from diverse players; 2) X Plant-Based Meat Health Technology Co., Ltd. excelled in technology and innovation, operating eight cutting-edge soybean processing facilities and maintaining extensive non-GMO soybean fields. With over 250 patents, it was a global leader, prioritizing environmental sustainability and quality control. Its integrated supply chain, diverse product portfolio, and strategic partnerships contributed to its ongoing success and growth; and 3) X Plant-Based Meat Health Technology Co., Ltd. pursued a comprehensive strategy for sustainable growth in a competitive market. Key elements included niche market penetration, organizational excellence, channel expansion, production capacity enhancement, technical expertise development, and market protection through innovation. These strategies aimed to position the company for long-term success in the plant-based food industry.

Egg parasitoid assemblages and their effectiveness against the Lepidoptera soybean defoliators in Argentina's subtropical production region

AbstractSoybean, Glycine max (L.) Merr. (Fabaceae), is one of the most important crops worldwide, but caterpillars of various species (Lepidoptera) may greatly reduce its annual yield. Nowadays, the main method of controlling these pest species is based on the use of broad‐spectrum insecticides, which are harmful to human health and increase environmental contamination. Therefore, other sustainable methods of control, such as biological control, are in increasing demand. The present study aimed to identify the egg parasitoid species that attack lepidopterans defoliating soybeans in South America's subtropical regions, as well as evaluate their effectiveness at various stages of soybean development. The research was conducted in a commercial soybean field (80 ha) located in Tucumán, Argentina, which was surrounded by wild vegetation as a way to promote biological control. Every week, throughout three consecutive soybean seasons, 20 soybean plants were randomly selected and brought to the laboratory in search of lepidopteran eggs. Over the course of the three crop seasons, the soybean plants were attacked by Anticarsia gemmatalis Hübner (Erebidae), Rachiplusia nu (Guenée), and Chrysodeixis includens (Walker) (both Noctuidae). Six egg parasitoid species were identified, but only three of them were frequently found during the current study. Trichogramma pretiosum Riley (Hymenoptera: Trichogrammatidae) was the most predominant species attacking all lepidopteran pests across all soybean phenological stages. Encarsia porteri (Mercet) (Hymenoptera: Aphelinidae) preferred to attack R. nu eggs, whereas Telenomus cyamophylax Polaszek (Hymenoptera: Scelionidae) parasitized only A. gemmatalis and R. nu eggs. Despite the use of insecticides in the soybean field, the parasitism rates recorded were high. This suggests that using conservation tactics such as native vegetation patches and surrounding alternative crops may result in higher rates of natural control.

Effects of insecticide spray drift on arthropod prey resources of birds in grasslands in Minnesota

AbstractSoybean aphid (Aphis glycines) insecticides are used throughout the Upper Midwest and Great Plains regions of North America, including the farmland region of Minnesota, USA, to combat insect pests. These broad‐spectrum, foliar spray insecticides have the potential to drift beyond target fields into nearby grassland cover where birds and other insectivores forage. Arthropods serve important roles in grassland ecology and are susceptible to mortality and sublethal effects from exposure to these pesticides. Our objective was to assess effects of soybean aphid insecticides on grassland arthropods, especially those that are important in grassland bird diets. We measured the abundance, consumable biomass, and family richness of insects and spiders in grasslands adjacent to soybean fields in an agricultural landscape. Soybean fields were treated with chlorpyrifos and lambda‐cyhalothrin, which were the 2 most common foliar pesticides used to control soybean aphids in Minnesota. We compared measures at focal sites to samples collected at reference sites adjacent to corn fields not sprayed for aphids during 3 periods in mid‐to‐late summer: 1–3 days before spraying, 3–5 days post‐spraying, and 19–21 days post‐spraying. The abundance of arthropods in focal grasslands was lower 3–5 days after pesticide applications. Coleoptera family richness at focal sites was also lower than at reference sites 3–5 days after pesticide applications. These measures 19–21 days after application were similar to pre‐spraying levels, indicating that arthropod populations rebounded during this period. Measures of consumable dry biomass, bird prey abundance, bird prey biomass, family richness of Araneae, family richness of Hemiptera, and family richness of Orthoptera were not different between focal and reference sites after spraying. Our results reveal that reductions in arthropod food abundance for grassland birds are associated with pesticide applications up to 5 days after spraying. We suggest that natural resource managers factor proximity to row crop fields and susceptibility to pesticide drift into decisions about where to add grassland cover to landscapes.

Metarhizium rileyi with broad-spectrum insecticidal ability confers resistance against phytopathogens and insect pests as a phytoendophyte.

Entomophagous fungi (EPF) not only directly kill insect pests, but also colonize plants and improve their resistance against pests. However, most previous research has focused on Beauveria bassiana and Metarhizium anisopliae, and there are few reports on whether other EPF can enhance resistance against pests via endogenous colonization. Herein, an EPF strain was isolated from diseased larvae of Spodoptera litura in a soybean field, and subjected to genome-wide sequencing at the chromosomal level. The pathogenicity of the isolate toward various pest insects was evaluated, and the ability to colonize plants and induce resistance against phytopathogens and insect pests was tested. The purified isolate was identified as M. rileyi and designated MrS1Gz1-1. Biological assays revealed its strong pathogenicity toward five insect pests belonging to Lepidoptera and Hemiptera. Furthermore, the strain inhibited the growth of soil-borne plant disease caused by Sclerotinia sclerotiorum in vitro. It colonized plants as an endophyte via soil application, thereby inducing plant resistance-related genes against phytopathogen infection, and it disrupted the feeding selectivity of S. litura larvae. M. rileyi MrS1Gz1-1 has potential as a broad-spectrum microbial control agent that can induce resistance against phytopathogens and insect pests feeding as an endotype. The complete genome provides a valuable resource for exploring host interactions. © 2024 Society of Chemical Industry.

A lightweight and enhanced model for detecting the Neotropical brown stink bug, Euschistus heros (Hemiptera: Pentatomidae) based on YOLOv8 for soybean fields

Insect pest detection and monitoring are vital in an agricultural crop to help prevent losses and be more precise and sustainable regarding the consequent actions to be taken. Deep learning (DL) approaches have attracted attention, showing triumphant performance in many image-based applications. In the adult stage, this research considers detecting a vital insect pest in soybean crops, the Neotropical brown stink bug (Euschistus heros), from field images acquired by drones and cellphones. We develop and test an improved YOLO-model convolutional neural network (CNN) with fewer parameters than other state-of-the-art models and demonstrate its superior generalization and average precision on public image datasets and the new field data provided here. Considering the proposal's precision and time of response, the possibility of deploying this technology for automatic monitoring and pest management in the near future is promising. We provide open code and data for all the experiments performed.

The PGPR Bacillus aryabhattai promotes soybean growth via nutrient and chlorophyll maintenance and the production of butanoic acid.

Plant growth-promoting rhizobacteria (PGPR) colonize plant roots, establish a mutualistic relationship with the plants and help them grow better. This study reports novel findings on the plant growth-promoting effects of the PGPR Bacillus aryabhattai. Soil was collected from a soybean field, PGPR were isolated, identified, and characterized for their ability to promote plant growth and development. The bacterium was isolated from the soybean rhizosphere and identified as B. aryabhattai strain SRB02 via 16s rRNA sequencing. As shown by SEM, the bacterium successfully colonized rice and soybean roots within 2 days and significantly promoted the growth of the GA-deficient rice cultivar Waito-C within 10 days, as well as the growth of soybean plants with at least six times longer shoots, roots, higher chlorophyll content, fresh, and dry weight after 10 days of inoculation. ICP analysis showed up to a 100% increase in the quantity of 18 different amino acids in the SRB02-treated soybean plants. Furthermore, the 2-DE gel assay indicated the presence of several differentially expressed proteins in soybean leaves after 24 hrs of SRB02 application. MALDI-TOF-MS identified β-conglycinin and glycinin along with several other proteins that were traced back to their respective genes. Analysis of bacterial culture filtrates via GCMS recorded significantly higher quantities of butanoic acid which was approximately 42% of all the metabolites found in the filtrates. The application of 100 ppm butanoic acid had significantly positive effects on plant growth via chlorophyll maintenance. These results establish the suitability of B. aryabhattai as a promising PGPR for field application in various crops.

Suppression of entireleaf morning glory (Ipomoea hederacea Jacq. var. integriuscula A. Gray) growth by lawngrass (Zoysia japonica Steud.) on soybean field levees in southwestern Japan

AbstractThe spread of morning glory (Ipomoea spp.) in Japanese soybean (Glycine max (L.) Merr.) fields has severely decreased soybean yields. Field levees are believed to be a source of this invasion. The introduction of Zoysia japonica Steud. is expected to suppress weed growth and reduce the required labor for weed management on the levees. In this study, field experiments were conducted on the soybean levees of turf (Z. japonica), bare, and weed (mainly broad leaf weeds such as white clover) from 2017 to 2019 to compare its effects on the occurrence, growth, and seed production of I. hederacea (L.) Jacq. var. integriuscula A. Gray. The canopy height in the turf levee was the lowest, and the vegetation cover rate was the highest. At the flowering and fruiting stage of I. hederacea var. integriuscula (September), the largest number of I. hederacea var. integriuscula that survived were on the turf levee; however, the plant length, dry weight, and the number of fruits were the least. Additionally, the stems of the morning glory on the turf levee were slight and spindly, and their growth and seed productivity were suppressed. Consequently, vegetation conversion to Z. japonica on the soybean field levees can be an effective strategy to suppress the colonization and growth of morning glory, thereby, improving soybean yields.

Dynamics of N2O production and reduction processes in a soybean field revealed by isotopocule analyses

Agricultural soils are the largest anthropogenic source of atmospheric nitrous oxide (N2O) that causes global warming and stratospheric ozone depletion. In addition to the well-known emission associated with fertilization, significant N2O emission during the harvest season has been reported for soybean fields. Because soybean production is increasing, it is important to understand the production and consumption mechanisms of N2O in soybean fields. This study aimed to identify the microbial production processes using the ratios of isotopically substituted molecules, isotopocules. We also investigated the effectiveness of inoculating soybean with symbiotic nitrogen-fixing soil bacteria (rhizobia) with a high N2O-reducing ability, which was suggested as a mitigation option for N2O emission from soybean fields, on the basis of the characteristic isotope effect during N2O reduction.Along with semi-continuous flux measurements, weekly sampling and analysis of soil-emitted gas and soils were conducted in an experimental soybean field for two periods, after fertilization and before harvest, in two successive years. The isotopocule ratios of N2O emitted after fertilization suggested that it was produced by bacterial denitrification and nitrifier denitrification with smaller contributions from nitrification and fungal denitrification. Those for preharvest emission showed that N2O was produced by bacterial denitrification, including the process mediated by nodule rhizobia, with little progress of N2O reduction. We could not detect any difference between soybeans inoculated with rhizobia with different N2O reducing activity (nosZ− dominant, nosZ+, and nosZ++) because of the weak N2O reduction, probably due to relatively aerobic soil conditions. Since temporal changes in the N2O isotopocule ratios further implied that the rate of N2O reduction decreased in the later phase of autumn emission, suitable soil conditions are crucial for effective N2O reduction by inoculation of high nosZ-expressing rhizobia.

First Report of Foliar Nematode (Aphelenchoides pseudobesseyi) on Soybean in Colombia

Soybean (Glycine max L.) is produced in over 70,000 ha in the Altillanura Region, eastern Colombia (Agronet 2023). From 2018 to 2020, foliar symptoms like green stem and foliar retention of soybean, which in Brazil can cause up to 100% soybean yield losses (Meyer et al. 2017), were observed in soybean fields in Colombia. During 2020, samples from symptomatic plants in reproductive stages (R1-R8) were collected from different commercial soybean fields in the Altillanura Region. Over 200 samples were processed, using an incubation method described in Coyne et al. (2014). Nematodes were recovered from photosynthetic leaf tissues and enlarged nodes/buds with population densities ranging from 13 to 132 and 36 to 936 nematodes/10g, respectively. Adult females were morphologically and molecularly characterized as Aphelenchoides pseudobesseyi (Oliveira et al. 2019; Subbotin et al. 2020). Female body length (n = 20) ranged from 653.3 to 806.3 μm (mean = 723 μm ± 52.7), stylet length from 11.0 to 12.3 μm (11.8 μm ± 0.3), body diameter from 14.8 to 17.9 μm (16.3 μm ± 1.1), post-uterine sac length from 38.7 to 51.9 μm (44.6 μm ± 5.1), vulva to anus from 145.5 to 223.2 μm (172.2 μm ± 22.4), and 26% of the vulva-anus distance. Genomic DNA was extracted (QIAGEN DNeasy® Blood & Tissue kit) from a pool of nematodes. The D2A/D3B (Tenente et al. 2004) primers were used to amplify and sequence the D2/D3 expansion region of the 28S rRNA gene. PCR product (~759 bp) was purified, sequenced, deposited in GenBank (OQ930285), and compared to previously deposited sequences (e.g., KX356756, KY510840, KY510839, KY510841, KT692694, KY510842, MH187565) by means of the BLAST algorithm. Similarly, 988F and 18SR-Burs (De Jesus et al. 2016) primers were used to amplify and sequence the near full-length 18S RNA gene (SSU). PCR product was purified, sequenced, deposited in GenBank (OQ954344), and compared to previously deposited sequences (e.g., KT454962, KT943534, KT943535, KY510835, KY510836, KY510837, KY510838, MH187565). Phylogenetic Bayesian analysis (Ronquist et al. 2012) of the of the D2/D3 and 18S regions placed this nematode from Colombia in the A. pseudobesseyi clade (PP = 100). To fulfill a modified Koch’s postulates, the A. pseudobesseyi population described above was used in a greenhouse assay. In total, 120 soybean plants (cv. Flor Blanca) were infected with 200 A. pseudobesseyi (females + males)/plant. Briefly, at cotyledon stage (VC), 50 µl aliquot containing 50 A. pseudobesseyi was delivered onto each cotyledon and unifoliolate leaves (200 nematodes/plant). Sterile water was delivered to 80 plants which served as control. Plants were kept in the greenhouse at approximately 25°C and covered with clear plastic bag for 72 h to maintain over 90% relative humidity. After 15, 30, 45, and 60 days, soybean plants (n = 20) were processed, A. pseudobesseyi quantified, and the average reproduction factor (final population/initial population) was 0.1, 2.9, 14.0, and 1.8, respectively. Infected plants showed symptoms of blistering leaves with malformation (midrib vein twist), and A. pseudobesseyi was not observed in control plants. To our knowledge, this is the first report of A. pseudobesseyi parasitizing soybean buds and leaves in Colombia. Soybean is an important commodity for the Altillanura Region, and it is important to monitor the risk posed by this nematode. Furthermore, a better understanding of the nematode-host interaction and epidemiology in Colombia soybean producing regions is needed.

Biochar application alters soil metabolites and nitrogen cycle-related microorganisms in a soybean continuous cropping system

Biochar application is a promising practice to enhance soil fertility. However, it is unclear how field-aged biochar affects the soil metabolites and microbial communities in soybean fields. Here, the rhizosphere soil performance after amending with biochar addition rates at 0 (CK), 20 (B20), 40 (B40), and 60 t ha−1 (B60) was examined via a five-year in-situ field experiment based on a soybean continuous cropping system. Untargeted metabolomics and metagenomics analysis techniques were applied to study the regulatory mechanism of biochar on soybean growth from metabolomics and N cycle microbiology perspectives. We found that the contents of soil total N (TN), available N (Ava N), NH4+‐N, and NO3−‐N were significantly increased with biochar addition amounts by 20.0–65.7 %, 3.6–10.7 %, 29.5–57.1 %, and 24.4–46.7 %, respectively. The B20, B40, and B60 triggered 259 (236 were up-regulated and 23 were down-regulated), 236 (220 were up-regulated and 16 were down-regulated), and 299 (264 were up-regulated and 35 were down-regulated) differential metabolites, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and topology analysis demonstrated that differential metabolites were highly enriched in seven metabolic pathways such as Oxidative phosphorylation and Benzoxazinoid biosynthesis. Moreover, ten differential metabolites were up-regulated in all three treatments with biochar. Biochar treatments decreased the Nitrospira abundance in soybean rhizosphere soil while increasing Bradyrhizobium abundance significantly in B60. Mantel test revealed that as the biochar addition rate grows, the correlation between Nitrospira and soil properties other than NO3−‐N became stronger. In conclusion, the co-application of biochar with fertilizers is a feasible and effective way to improve soil N supply, even though biochar has undergone field aging. This work offers new insights into the variations in soil metabolites and microbial communities associated with N metabolism processes under biochar addition in soybean continuous cropping soils.

First Report of Charcoal Rot on Soybean Caused by Macrophomina phaseolina in Manitoba, Canada.

First Report of Charcoal Rot on Soybean Caused by Macrophomina phaseolina in Manitoba, Canada.

Fast and Precise Detection of Dense Soybean Seedlings Images Based on Airborne Edge Device

During the growth stage of soybean seedlings, it is crucial to quickly and precisely identify them for emergence rate assessment and field management. Traditional manual counting methods have some limitations in scenarios with large-scale and high-efficiency requirements, such as being time-consuming, labor-intensive, and prone to human error (such as subjective judgment and visual fatigue). To address these issues, this study proposes a rapid detection method suitable for airborne edge devices and large-scale dense soybean seedling field images. For the dense small target images captured by the Unmanned Aerial Vehicle (UAV), the YOLOv5s model is used as the improvement benchmark in the technical solution. GhostNetV2 is selected as the backbone feature extraction network. In the feature fusion stage, an attention mechanism—Efficient Channel Attention (ECA)—and a Bidirectional Feature Pyramid Network (BiFPN) have been introduced to ensure the model prioritizes the regions of interest. Addressing the challenge of small-scale soybean seedlings in UAV images, the model’s input size is set to 1280 × 1280 pixels. Simultaneously, Performance-aware Approximation of Global Channel Pruning for Multitask CNNs (PAGCP) pruning technology is employed to meet the requirements of mobile or embedded devices. The experimental results show that the identification accuracy of the improved YOLOv5s model reached 92.1%. Compared with the baseline model, its model size and total parameters were reduced by 76.65% and 79.55%, respectively. Beyond these quantitative evaluations, this study also conducted field experiments to verify the detection performance of the improved model in various scenarios. By introducing innovative model structures and technologies, the study aims to effectively detect dense small target features in UAV images and provide a feasible solution for assessing the number of soybean seedlings. In the future, this detection method can also be extended to similar crops.

Why does identification matter? Thrips species (Thysanoptera: Thripidae) found in soybean in southern Brazil show great geographical and interspecific variation in susceptibility to insecticides

Thrips have historically been considered secondary soybean pests in Brazil and neighboring countries. However, since the 2010s they have gained importance in soybean in central and northern Brazil. From 2017 onwards, high infestations of thrips have also been detected in soybean in southern Brazil. Here, we collected and identified thrips species from soybean fields in southern Brazil throughout the 2022/2023 season and conducted laboratory bioassays to assess their susceptibility to selected insecticides. Thrips were exposed to the field concentrations of selected insecticides in leaf-dip bioassays using soybean leaves. Our results indicated that Frankliniella schultzei (Trybom, 1910) and Caliothrips phaseoli (Hood, 1912) are the major thrips species found in soybean in southern Brazil. These species exhibited high mortality and relatively low geographic and interspecific variation in susceptibility to the field concentrations of spinetoram, methomyl, spinetoram + methoxyfenozide and profenofos + cypermethrin. In general, most populations of C. phaseoli were more susceptible to acephate, chlorfenapyr and abamectin than F. schultzei, whereas bifenthrin + carbosulfan showed high lethality against most populations of F. schultzei, however, both species had wide geographic variation in susceptibility to these chemistries. In contrast, populations of both species have low mortality when exposed to the field concentrations of imidacloprid, lambda-cyhalothrin and lambda-cyhalothrin + sulfoxaflor. In summary, our study documents significant variations in the susceptibility of F. schultzei and C. phaseoli to selected insecticides, both among species and among geographical areas across southern Brazil. These results highlight the importance of species identification for the effective control of thrips in soybean in Brazil and other South American countries.

Response of Evapotranspiration, Photosynthetic Characteristics and Yield of Soybeans to Groundwater Depth

To clarify the physiological mechanism of different groundwater depths affecting soybean evapotranspiration, photosynthetic characteristics and yield, a field experiment with four groundwater depth levels (1 m (D1), 2 m (D2), 3 m (D3) and 4 m (D4)) was conducted through the groundwater simulation system in 2021 and 2022. In this study, a quantitative analysis was conducted on the groundwater recharge and irrigation water demand and evapotranspiration (ET) of soybean fields with different treatments, and the effects of different treatments on soybean leaf area index (LAI), chlorophyll content index (SPAD), intercepted photosynthetic active radiation (IPAR), photosynthetic gas exchange parameters, dry matter accumulation (DMA) and yield were explored. The results showed the following: (1) Groundwater depth affected soybean ET and the source of ET. With the increase in groundwater depth, groundwater recharge and its contribution to ET gradually decreased, but the amount of irrigation required gradually increased, resulting in the ET as D1 > D4 > D2 > D3. (2) Soybean LAI, SPAD and IPAR were significantly affected by the different groundwater depths, of which the D1 treatment always maintained the maximum, followed by the D4 treatment, and the D3 treatment was the minimum. The photosynthetic gas exchange parameters under different treatments changed synergistically, showing significant differences in the flowering and podding stages, notably D1 > D4 > D2 > D3. Soybean DMA and yield first decreased and then increased with the increase in groundwater depth, and the average DMA and yield under the D1 treatment increased by 27.71%, 46.80% and 22.82% and 20.29%, 29.91% and 12.83% in the two years, respectively, compared to the D2, D3 and D4 treatments. (3) The structural equation model demonstrated that the groundwater depth indirectly affected the growth of soybean leaf area by affecting groundwater recharge, which in turn regulated soybean ET and photosynthetic capacity and ultimately affected DMA and yield. The above results showed that in the case of shallow groundwater depth (D1), the largest groundwater recharge promoted the growth of soybean leaf area and chlorophyll synthesis and increased the absorption and utilization of solar radiation. And it improved the leaf stomata conditions, accelerated the gas exchange between the plant and atmosphere, enhanced the photosynthetic production capacity and ET and achieved maximum DMA and yield. Soybean leaf growth and photosynthesis diminish with the increase in groundwater depth. In the case of deep groundwater depth (D4), the maximum irrigation improved the growth and photosynthetic performance of soybean leaves, which was favorable to ET, and ultimately led to increases in DMA and yield.

The Throughfall, Stemflow, and Canopy Interception Loss in Corn and Soybean Fields in Northeast China

Information about throughfall, stemflow, and canopy interception loss is essential for the water use efficiency of crops and the dynamic processes of water erosion. A two-year field experiment was conducted under natural rainfall conditions to observe the characteristics and factors that affect throughfall, stemflow, and canopy interception loss in corn (Zea mays L.) and soybean (Glycine max L.) fields in northeast China from 2019 to 2020. Nine measurement sites (A, B, C, D, E, F, G, H, and I) were distributed horizontally between two planting rows under the crop canopy. The mean value of the throughfall volume (TF) in measurement locations B, C, and G under the corn canopy and measurement locations B and C could represent the mean level of TF of corn and soybean fields, respectively. The volume of TF, stemflow (SF), and canopy interception loss (CI) of two growing seasons from 2019 to 2020 accounted for approximately 58.5%, 30.1%, and 11.4% of the gross rainfall (GR) of two growing seasons in corn fields, and 78.0%, 7.5%, and 14.5% of the GR in the soybean field, respectively. The TF and TFR of each rainfall event in the corn and soybean fields could be predicted by linear regression models with a normalized root mean square error (NRMSE) lower than 25.0%. These results and prediction models will be used in water management and soil erosion control in northeast China.

Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy.

With the increasing environmental awareness and the demand for sustainable agriculture, herbicide reduction has become an important goal. Accurate and efficient weed detection in soybean fields is the key to test the effectiveness of herbicide application, but current technologies and methods still have some problems in terms of accuracy and efficiency, such as relying on manual detection and poor adaptability to some complex environments. Therefore, in this study, weeding experiments in soybean fields with reduced herbicide application, including four levels, were carried out, and an unmanned aerial vehicle (UAV) was utilized to obtain field images. We proposed a weed detection model-YOLOv7-FWeed-based on improved YOLOv7, adopted F-ReLU as the activation function of the convolution module, and added the MaxPool multihead self-attention (M-MHSA) module to enhance the recognition accuracy of weeds. We continuously monitored changes in soybean leaf area and dry matter weight after herbicide reduction as a reflection of soybean growth at optimal herbicide application levels. The results showed that the herbicide application level of electrostatic spraying + 10% reduction could be used for weeding in soybean fields, and YOLOv7-FWeed was higher than YOLOv7 and YOLOv7-enhanced in all the evaluation indexes. The precision of the model was 0.9496, the recall was 0.9125, the F1 was 0.9307, and the mAP was 0.9662. The results of continuous monitoring of soybean leaf area and dry matter weight showed that herbicide reduction could effectively control weed growth and would not hinder soybean growth. This study can provide a more accurate, efficient, and intelligent solution for weed detection in soybean fields, thus promoting herbicide reduction and providing guidance for exploring efficient herbicide application techniques.

Understanding Phakopsora pachyrhizi in soybean: comprehensive insights, threats, and interventions from the Asian perspective.

Soybean (Glycine max L.) is an important crop in Asia, accounting for 17% of global soybean cultivation. However, this crop faces formidable challenges from the devastating foliar disease, Asian Soybean Rust (ASR), caused by Phakopsora pachyrhizi, a biotrophic fungus with a broad host range, causing substantial yield losses (10-100%) in Asia. This comprehensive review consolidates knowledge on ASR, encompassing its impact, historical perspectives, genetic diversity, epidemic drivers, early detection, risk assessment, and sustainable management strategies of ASR in the region. ASR has expanded globally from Asia, reaching Africa and Americas, driven by wind-dispersed urediniospores. Genetic diversity studies reveal the complexity of P. pachyrhizi, with distinct populations exhibiting varying virulence patterns. Factors affecting ASR epidemics in Asia include host susceptibility, landscape connectivity, climate, and environmental conditions. Understanding the interplay of these factors is essential for early intervention and control of ASR in soybean fields. Effectively managing ASR can exploit the utilization of diverse intervention strategies, encompassing disease forecasting, automated early detection, disease resistance, fungicide application, and biological control. A pivotal aspect of successful, sustainable disease management lies in reducing the ASR pathogen virulence and preventing it from developing fungicide resistance, while the highpoint of effectiveness in disease control is attained through a synergistic approach, integrating various strategies. In summary, this comprehensive review provides insights into multifaceted approaches that contribute to the development of sustainable and economically impactful soybean production in the face of the persistent threat of ASR in Asia.

Interactive dynamics between rhizosphere bacterial and viral communities facilitate soybean fitness to cadmium stress revealed by time-series metagenomics

The distinctive symbiotic relationship between rhizobacteria and soybean (Glycine max) can enhance soil fertility and assist soybean defense heavy metal stress, such as cadmium (Cd), in soils. Soil viruses are critical for modulating microbial activities and enhancing host adaptability to soil contamination by encoding auxiliary metabolic genes (AMGs). However, the community structure, functional diversity and ecological roles of soil viruses and their dynamic interactions with soil-dwelling microbes under Cd stress remain unclear. Here, we characterized the bulk and rhizosphere bacterial community in a Cd-contaminated soybean field at five growth stages and investigated the rhizosphere microbial functional traits and the viral community after flowering using metagenomics. We showed that the rhizosphere bacterial community exhibited a stronger soybean development-dependent diversity pattern relative to bulk soil and had a more critical role in predicting Cd accumulation. The relative abundance of Actinobacteria in the rhizosphere increased with soybean development, particularly in the families Nocardioidaceae and Intrasporangiaceae, which were further identified as the main contributors of amino acid transport E and secondary metabolites Q genes. Viral analysis suggested an interactive and complementary assembly between viral and bacterial communities. Importantly, viruses exhibited a broader host range during the flowering period, and infected specific functional taxa such as Bradyrhizobium and Nocardioides, highlighting their potential impact on the rhizobia-soybean symbiosis and soybean growth and fitness. Furthermore, AMGs related to element cycling and microbial metabolism were identified and functionally and phylogenetically characterized, suggesting the contribution of these virus-encoded genes to biogeochemical cycles and soybean fitness under Cd stress. Together, our results expand our understanding of the ecological functions of rhizosphere viruses in Cd pollution-stressed soils, and provide insights in developing new strategies for bioremediation of contaminated soils.