Soybean Crop Research Articles

Productive characteristics of integrated agricultural systems with or without winter grazing and different fertilization strategies

This study aimed to evaluate of productive characteristics in integrated livestock-crop systems with or without winter grazing and fertilization strategies. The experimental design used was a randomized complete block design with split plots (3 replications per treatment). Treatments were composed of four fertilization strategies (COMB = combination of fertilization for both soybeans and winter pasture applied during the pasture phase; FSBP = fertilization for soybeans applied during the pasture phase; REC = recommended fertilizations for pasture and soybean applied in their respective phases and TRAD = traditional recommendation, with N fertilization applied during the pasture phase and P and K fertilizers applied during soybean phase) and two managements of black oat/ryegrass pasture (with or without grazing cattle). Forty crossbred beef cattle were used in an intermittent stocking. Black oat/ryegrass forage production was greater in the presence of grazing animals. When grazed, the winter pasture production was greater under COMP and FSBP fertilization strategies compared to the REC and TRAD. The number of pods per plant and number of grains per pod were higher in grazed areas. The soybean grain productivity was not affected when grazing animals were included into the system. The COMB strategy resulted in the highest soybean productivity. The application of fertilization for soybean crops in the winter pasture increased soybeans productivity regardless of the addition of grazing animals into the system.

Impact Assessment of Climate Change on Soybean Crop Using CROPGRO-Soybean Model in Central India

Impact Assessment of Climate Change on Soybean Crop Using CROPGRO-Soybean Model in Central India

ЗАЩИТА СОИ ОТ СОРНЫХ РАСТЕНИЙ ДВУХКОМПОНЕНТНЫМ ГЕРБИЦИДОМ

The paper provides information on the effectiveness of using a two-component herbicide on soybean crops. The effectiveness of its use at different rates of application and introduction, both with and without adjuvants, as well as the species sensitivity of weeds to the active ingredients of the drug have been studied. Options for chemical weeding have been identified with the most effective and safe consumption rates of herbicide with adjuvants, in a tank mixture with which it was possible to achieve maximum suppression of dicotyledonous and cereal weeds and ensure a high yield of soybean grain up to 3.35 t/ha. The species composition and number of weeds in the crop before the protective measure differed from year to year, but in all years of testing, the predominant annual weed species were Amaranthus retroflexus, Chenopodium album, and Echinochloacrus galli. In 2018, the total number of annual dicotyledonous weeds reached 29 ind/m2, with the largest numbers of upturned acorn weed and white pigweed. Perennial dicotyledonous weeds were represented by thistle (Cirsium arvense) and thistle (Sonchus arvensis). Among the cereal species, common barnyard grass and gray bristle grass (Setaria glauca) were observed in the experiment. Before treatment with the herbicide, they were in the phase of 1–3 leaves, which made their species identification difficult, so their total number was taken into account. The total number of weeds before the experiment was 64 ind/m2. The year 2019 had the highest number of weeds, when the crop was sown with 496 specimens of unwanted plants with a predominance of upturned acorn and white goosefoot.

A user-friendly software to accurately count and measure cysts from the parasitic nematode Heterodera glycines

The soybean-cyst nematode (SCN; Heterodera glycines) is one of the most destructive pests affecting soybean crops. Effective management of SCN is imperative for the sustainability of soybean agriculture. A promising approach to achieving this goal is the development and breeding of new resistant soybean varieties. Researchers and breeders typically employ exploratory methods such as Genome-Wide Association Studies or Quantitative Trait Loci mapping to identify genes linked to resistance. These methods depend on extensive phenotypic screening. The primary phenotypic measure for assessing SCN resistance is often the number of cysts that form on a plant’s root system. Manual counting hundreds of cysts on a given root system is not only laborious but also subject to variability due to individual assessor differences. Additionally, while measuring cyst size could provide valuable insights due to its correlation with cyst development, this aspect is frequently overlooked because it demands even more hands-on work. To address these challenges, we have created Nemacounter, an intuitive software designed to detect, count, and measure the size of cysts autonomously. Nemacounter boasts a user-friendly graphical interface, simplifying the process for users to obtain reliable results. It enhances productivity by delivering annotated images and compiling data into csv files for easy analysis and reporting.

Alternative Sources of Potassium for Soybean Crops

Over the last three decades, soybean production in the Brazilian agricultural sector has experienced significant yield growth. The soil must have adequate fertility to provide nutrients for good agricultural yield. This study aimed to evaluate the effect of different sources and doses of potassium on the soybean grain yield and its components. The experiment was conducted in the 2022/2023 crop season at the experimental field of the Goiás State University in Ipameri, GO. The experimental design used was randomized blocks arranged in a 3 x 5 factorial scheme, with three potassium sources potassium chloride (KCl: 58% K2O), Phonolite (Ph1:8% K2O and 25% Si), and Hydrothermalized Phonolite (HPh2:12% K2O and 25% Si) and five doses (0, 50, 100, 150, and 200 kg ha-1), with four replications. The management used the no-till system on sorghum straw planting the NEO 750 IPRO soybean cultivar. According to the results, there was an influence of the interaction between the factors only on the number of pods per plant. The potassium doses influenced the first pod insertion height, hectoliter mass, and grain yield. Based on the results, potassium fertilization using the alternative sources, Ph1 and HPh2, reached satisfactory grain yield levels compared to KCl, with the maximum grain yield value, regardless of the source, occurring at a dose of 138.18 kg ha-1 of potassium.

Agronomic Efficiency of Compost Extracts and Nitrogen-Fixing Bacteria in Soybean Crops

Regenerative agriculture and the use of bioinputs have been gaining prominence in the global agribusiness sector, driven by the growing demand for healthier foods produced with minimal impact on ecosystems. In this context, compost and its derivatives (compost extracts and teas) are used to provide effective microorganisms to crops, although production processes affect the efficiency of compost extracts, as well as the soil microbiota. Thus, the hypothesis raised was that the organic matter source used for compost formation affects the agronomic efficiency of compost extracts. The objective of this study was to evaluate the effect of compost extracts based on litterfall of angiosperm (AC) and gymnosperm (GC) species, and the use of inoculation with the nitrogen-fixing bacteria Bradyrhizobium japonicum and Azospirillum brasilense (Bra+Azo), on soil quality, crop growth, grain yield, and disease control in soybean (Glycine max L.) crops. Using AC and GC resulted in varying effects on soybean growth and soil microbial biomass carbon (SMBC), confirming the hypothesis that the organic matter source affects the agronomic efficiency of compost extracts. Plants inoculated with Bra+Azo exhibited higher chlorophyll contents, resulting in a higher photochemical yield than for those treated with compost extracts (AC and GC). However, plants inoculated with AC and GC exhibited high plasticity in mitigating photochemical stress, reaching similar photosynthetic and transpiration rates to those observed in plants inoculated with Bra+Azo. Additionally, inoculation with Bra+Azo, overall, improved the photosynthetic efficiency of soybean plants, and the compost extracts (AC and GC) were more effective than the inoculation with Bra+Azo in increasing soybean 1000-grain weight, probably due to improvements in root development. The growth promotion observed with AC and GC is likely attributed to increases in SMBC by these compounds, denoting improvements in soil quality and biocontrol of damage caused by insect attacks.

Optimizing the thermostability of triketone dioxygenase for engineering tolerance to mesotrione herbicide in soybean and cotton

Optimized triketone dioxygenase (TDO) variants with enhanced temperature stability parameters were engineered to enable robust triketone tolerance in transgenic cotton and soybean crops. This herbicide tolerance trait, which can metabolize triketone herbicides such as mesotrione and tembotrione, could be useful for weed management systems and provide additional tools for farmers to control weeds. TDO has a low melting point (~39°C–40°C). We designed an optimization scheme using a hypothesis-based rational design to improve the temperature stability of TDO. Temperature stabilization resulted in enzymes with Kcat values less than half of wild-type TDO. The best variant TDO had a Kcat of 1.2 min−1 compared to wild-type TDO, which had a Kcat of 2.7 min−1. However Km values did not change much due to temperature stabilization. Recovery of the Kcat without losing heat stability was the focus of additional optimization. Multiple variants were found that had better heat stability in vitro and efficacies against mesotrione equaling the wild-type (WT) TDO in greenhouse and field tests.

Early Leaf Disease Detection of Soybean Plants using Convolution Neural Network Algorithm

Background: Agricultural specialists usually examine leaves closely to check for plant diseases, that process takes a long time and can have errors. To overcome these problems, nowadays, machine learning methods, specifically sequential convolution neural networks (CNN) are widely used, because of their extensive potential to extract features and patterns from the image dataset. Methods: This study introduces a complete methodology for detecting diseases in soybean plants by employing Convolutional Neural Networks (CNNs). A dataset sourced from the Mendeley database, containing images of soybean legume crops affected by caterpillars, Diabrotica Speciosa diseases and undamaged (healthy) foliage is used., The research explores the efficacy of the CNN Model in accurately classifying these diseases. The CNN algorithm is trained in such a way that it can handle the complex nature of soybean leaf imaging by utilizing convolutional layers for extracting features, pooling layers for reducing dimensionality and softmax layers for classification. Result: The training and validation results exhibit remarkable accuracy rates (97.64% accuracy) after 150 epochs. The evaluation metrics, such as precision, recall and F1-score, demonstrate the model’s performance across different leaf diseases of soybean, suggesting its ability to accurately identify instances inside each category. The classification matrices provide complete knowledge of the accuracy of the prediction of different diseases. The overall accuracy of the proposed model is 94.05%. This study can be utilized as a reference to increase the progress of agricultural disease detection, in turn, can enable enhance the reduction of crop losses and ensure food security.

Evaluation of Commercial Fertilizer Based Medium for Desmodesmus abundans Cultivation and the Use of Microalgal Biomass as Biostimulant in Soybean Glycine max (L.) Merr.

Microalgae have emerged as a promising renewable resource due to their biomass production and cell composition. This study aimed to evaluate a commercial nutrient source for Desmodesmus abundans L2B Bold cultivation, analyzing its metabolome, photosynthetic pigments, and biomass potential as a biostimulant for soybean crops. Samples underwent metabolomics analysis via GC/MS after derivatization. Microalgal biomass produced in a commercial fertilizer medium (CF) was applied as a biostimulant at three concentrations in soybean crops at 30, 50, and 70 days post-sowing. Pigments and dry biomass levels were similar in BG11 and CF media, but CF reduced production costs by 31.8%. Distinct metabolite profiles were observed, with major classes being organic acids (19%), amines (14%), sugars (12%), fatty acids (11%), and esters (10%). Foliar application of 20% (v/v) D. abundans L2B Bold increased stem diameter (5.88 mm), leaf fresh and dry mass, and stem mass. Additionally, there were increases in nitrogen (26%) and protein (38.1%) in seeds, alongside higher pod (10.9) and seed numbers (25.2) and seed mass (3.43 g) per plant−1. These findings demonstrate the feasibility of using CF as a culture medium and the potential of D. abundans L2B Bold as a biostimulant for soybean production.

Effect of molybdenum supply on crop performance through rhizosphere soil microbial diversity and metabolite variation

Molybdenum (Mo) deficiency is a global problem in acidic soils, limiting plant growth, development, and nutrient availability. To address this, we carried out a field study with two treatments, i.e., Mo applied (+Mo) and without Mo (−Mo) treatment to explore the effects of Mo application on crop growth and development, microbial diversity, and metabolite variations in maize and soybean cropping systems. Our results indicated that the nutrient availability (N, P, K) was higher under Mo supply leading to improved biological yield and nutrient uptake efficiency in both crops. Microbial community analysis revealed that Proteobacteria and Acidobacteria were the dominant phyla in Mo treated (+Mo) soils for both maize and soybean. Both these phyla accounted together 39.43% and 57.74% in −Mo and +Mo, respectively, in soybean rhizosphere soil, while they accounted for 44.51% and 46.64% in maize rhizosphere soil. This indicates more variations among the treatments in soybean soil compared to maize soil. At a lower taxonomic level, the diverse responses of the genera indicated the specific bacterial community adaptations to fertilization. Candidatus Koribacter and Kaistobacter were commonly significantly higher in both crops under Mo-applied conditions in both cropping systems. These taxa, sharing similar functions, could serve as potential markers for nutrient availability and soil fertility. Metabolite profiling revealed 8 and 10 significantly differential metabolites in maize and soybean, respectively, under +Mo treatment, highlighting the critical role of Mo in metabolite variation. Overall, these findings emphasize the importance of Mo in shaping soil microbial diversity by altering metabolite composition, which in turn may enhance the nutrient availability, nutrient uptake, and plant performance.

РАЗРАБОТКА ОПТИМАЛЬНОЙ СХЕМЫ СЫРЬЕВОГО КОНВЕЙЕРА ДЛЯ ЗАСУШЛИВЫХ УСЛОВИЙ АСТРАХАНСКОЙ ОБЛАСТИ

The purpose of research is to scientifically substantiate the optimal layout of the raw material conveyor in the Astrakhan Region. Objectives: development of a raw material conveyor scheme, study of the duration of interphase periods of joint crops of grain and leguminous crops, assessment of the yield of raw material conveyor crops. Research was carried out in the Kamyzyaksky District of the Astrakhan Region in 2020–2022. The raw material conveyor scheme included three groups of crops, differing in terms of harvesting. Research results have shown that when harvesting winter grain crops in the first ten days of May, you can get up to 25.5 t/ha of green mass. When sowing alfalfa in the second ten days of September, the first cutting can be done in the third ten days of May with a yield of 27.2 t/ha of green mass. Millet is usually ready for cutting in the second ten days of June; the yield of green mass on average over 3 years was 9.5 t/ha. From crops of Sudanese grass, an early harvest was obtained from 1 cutting – 19.0 t/ha of green mass in the third ten days of July. Soybean reached mowing ripeness by the end of the third decade of July, the average yield for three years was 23.1 t/ha. Joint crops of corn and soybeans were harvested in the waxy ripeness phase in the 1st ten days of August, the yield of green mass reached 51 t/ha. The yield of joint crops of sweet sorghum and soybeans was at the level of 40.8 t/ha. When sown in the second ten days of May, the yield of safflower by the end of the first ten days of August on average over 3 years amounted to 21.1 t/ha of green mass, 4.4 t/ha of chickpea grain was obtained by the beginning of the second ten days of August. When sowing melons in the first half of May, harvesting was carried out in the second ten days of August. On average, over three years, the maximum yield was obtained from fodder pumpkin – 40.2 t/ha.

Soybean Leaf Disease Identification Through Smart Detection using Machine Learning-convolutional Neural Network Model

Background: Soybean, a vital global crop, faces threats from diverse leaf diseases impacting yield and quality. By utilizing cutting-edge Machine Learning-Convolutional Neural Network (CNN) models, this study develops a Smart Detection System for the precise identification of soybean leaf diseases. Methods: Convolutional Neural Networks (CNNs) are used in this study to identify soybean leaf diseases. Different images that depict different diseases are used to train the CNN model. The dataset obtained from Mendeley includes three essential categories: Diabrotica Speciosa, Caterpillar and Healthy soybean leaves. Labeling, grayscale conversion and scaling are all part of image processing. 80% of the dataset is used for training, 20% is used for validation and the accuracy of the model is assessed. Result: The CNN model showcases exceptional capabilities, achieving an impressive 95 per cent accuracy in precise soybean leaf disease classification. The Smart Detection System emerges as a powerful and timely tool for disease identification, holding significant implications for advancing precision agriculture. This study underscores the transformative potential of advanced machine learning in reshaping sustainable soybean crop management practices.

The possibility of using a natural drug as an antistressant to herbicides in soybean crops

The results of research on the use of a drug of natural origin as an antistressant to herbicides are presented. Soybean plants, like other crops, are exposed to stress not only from abiotic (spring-summer and summer droughts), but also from anthropogenic factors (pesticide treatments). Experiments were conducted in the northern forest-steppe of the Novosibirsk region in 2020-2021 on leached chernozem. The objects of the study were the humic preparation Cytohumate and soy varieties SibNIIK-315 and Zolotaya. It has been shown that the Cytohumate brown coal antistressant at a rate of 0.4 l/ha improves the physiological state of soybean plants, provides rapid biomass growth and increases crop yield. The humic preparation contributed to the timely passage of the phenological phases of soybean development. Due to the reduction of the stress effect against the background of Cytohumate in 2020, the phenological phases occurred 2–3 days earlier than in the control. In the dry conditions of July 2021, an acceleration of the budding and flowering phases was noted. Against the background of a natural antistress agent in 2020, an increase in soybean height was statistically proven. The use of the humic preparation had a stimulating effect on the biomass of the cultivated plant. This was most noticeable in the wetter 2020, when the biomass in the experimental variant was significantly higher than the control by 16.8 %. In 2021, in the case of using Cytohumate, only a tendency to increase biomass was observed and the differences from the control were 7 %. The yield increase when applying Cytohumate as an antistress agent to herbicides amounted to 0.4–0.5 t / ha of grain.

The Effects of Potassium Dose, Timing, and Source in Soybean Crops in Brazilian Savannah Oxisol

In Brazil, several silicic rocks can be used as powder-based K sources, which can reduce production costs in agriculture. The optimized supply of K not only increases yield but also contributes to soil fertility preservation and long-term sustainability by curtailing nutrient losses and reducing the risk of nutrient imbalances. Therefore, this study aimed to investigate the effects of K application timing, source, and doses on nodulation, productive components, and productivity of soybeans in a not-tillage system in the Savannah. The experiment was carried out in the field, for two years, in an Oxisol, with a clayey texture. The experimental design was in random blocks, in a 2 × 4 × 3 factorial scheme, as follows: two application timings (early and sowing), four K2O rates (0, 40, 80, and 120 kg ha−1), and three sources (KCl, Potasil, and Ekosil), with four replicates. Potassium fertilizer was broadcasted on the soil without incorporation into the soil. Due to the interactions between doses and K2O sources, there was a linear adjustment for KCl sources, the higher the dose, the lower the nodule mass. Also, for nodule mass, the interaction between dose and application time was significant for the early application of the Ecosil and Potasil sources for 80 kg ha−1. The highest estimated soybean grain productivity was 3262 kg ha−1 with 78 kg ha−1 of K2O, being the most suitable for growing soybeans under a no-tillage system.

Potential of Lambda-Cyhalothrin for Controlling the Black Armyworm Spodoptera cosmioides (Walker) (Lepidoptera: Noctuidae): Toxicity and Midgut Histopathological Effects.

Caterpillars of the genus Spodoptera are the main pests in soybean and cotton crops and Spodoptera cosmioides causes more severe losses than other caterpillars in these agricultural crops. However, there are few recommended insecticides for controlling this pest. Lambda-cyhalothrin is a pyrethroid used to control a wide spectrum of arthropods including lepidopterans. Therefore, the objective of this study was to evaluate the potential of lambda-cyhalothrin for the control of S. cosmioides. Specifically, toxicity and histopathological changes in the midgut were evaluated. The effectiveness of the insecticide was determined by estimating the different lethal concentrations (LCs) ​​in the laboratory upon S. cosmioides. Lambda-cyhalothrin was found toxic to S. cosmioides (LC50 = 23.03mg L-1 and LC90 = 174.8mg L-1), with the survival reduced from 83.33% in the control to 37.89%, 16.66%, 0%, and 0% after 72h of exposure to the LC25, LC50, LC75, and LC90 of lambda-cyhalothrin, respectively. Histopathological studies revealed that lambda-cyhalothrin caused damage to midgut cells, including epithelial disorganization, increased cytoplasmic vacuolization, brush border degeneration, nuclear chromatin condensation, and cell fragmentation, indicating cell death by apoptosis. It was concluded that lambda-cyhalothrin, a neurotoxic insecticide, caused damage to the midgut of S. cosmioides, compromising its physiology and indicating that it has potential to be used to control this pest.

Environmental Covariates for Sampling Optimization and Pest Prediction in Soybean Crops

Insect pest infestations can vary due to spatial differences in microclimates and food availability within agroecosystems. Covariates can reflect these environmental conditions. This study tested whether using environmental covariates in two-phase sample optimization improved the spatial predictions for soybean insect pests. During the 2021–2022 crop season, insect pest samples were collected at 50 georeferenced points in a commercial soybean field in Brazil, alongside data on environmental covariates such as vegetation indices, soil properties, terrain topography, and distances from riparian areas. Three covariates were selected using correlation and principal component analysis (PCA). In the 2022–2023 crop season, sample designs were optimized using the iterative algorithm optimization of sample configurations using spatial simulated annealing (SPSANN) using the selected covariates, resulting in two optimized designs that were compared to a regular grid. Data from the three sampling designs comprising 50 points were evaluated using geostatistical methods, regression analysis (pest abundance), and classification (pest presence or absence) via the random forest algorithm. The data showed no spatial dependence, making using geostatistical interpolators inappropriate. However, a multi-objective optimized sampling design, tailored to refine configurations for identifying and estimating variograms and spatial trends essential for spatial interpolation, produced the most accurate predictions. Therefore, a two-phase sample optimization with prior in situ selection of environmental covariates improves pest predictions in agricultural systems, contributing to more efficient and sustainable agricultural management.

Preparation of a Self-Assembled Nanoantiviral Pesticide with Strong Adhesion Performance for Efficiency Control of Destructive Soybean Mosaic Virus.

In this context, we reported for the first time the design and development of a self-assembled nanoantiviral pesticide based on the star polycation (SPc) and the broad-spectrum fungicide/antiviral agent seboctylamine for field control of Soybean mosaic virus (SMV), a highly destructive plant virus in soybean crops. The SPc could self-assemble with seboctylamine through hydrogen bonds and van der Waals forces, and the complexation with SPc reduced the particle size of seboctylamine to form a spherical seboctylamine/SPc complex. In addition, the contact angle of seboctylamine decreased, and its retention increased with the aid of SPc, indicating excellent wetting properties and strong leaf surface adhesion performance. The incorporation of SPc significantly inhibited viral accumulation in soybean plants and enhanced the field control efficacy of seboctylamine against SMV, resulting in improved agronomic traits. Overall, our study would contribute to facilitating a new strategy for the effective control of SMV in soybeans.

Environmental impact of lead in soybeans (Glycine max L.) marketed in the canton of Quevedo, Ecuador

The bioaccumulation of lead in soybean crops, originating in contaminated soils, represents a significant environmental problem and a risk to public health due to the inherent toxic metal. The objective of this study was to evaluate the environmental impact of lead in samples of soybeans (Glycine max L.) marketed in the canton of Quevedo, Ecuador. Grain samples were collected randomly, in triplicate, from four local markets during a period of [indicate period] and a control sample from a non-contaminated cultivation area. The samples were subjected to a preparation process that included drying, grinding, acid digestion with concentrated HNO₃ and HCl, and dilution. Lead determination was carried out by electrothermal atomization atomic absorption spectrometry (GFAAS). The calculation of the lead contamination index was performed using a standardized equation, contrasting the results obtained with the maximum allowable limits established by national (NTE INEN 452:2013) and international (Codex Alimentarius, European Union) regulations. The results evidenced an average concentration of lead in the soybean samples of 1.15 ± 0.13 mg.kg-1, significantly exceeding the maximum allowable limits. The calculated contamination index indicated a high level of contamination in the analyzed grains, which represents a risk to both the ecosystem and human health when consumed. These findings underscore the need to implement environmental and food management strategies to reduce lead contamination in soybean crops, thus ensuring food safety and public health protection.

A vision-language model for predicting potential distribution land of soybean double cropping

A vision-language model for predicting potential distribution land of soybean double cropping

História do Ensino Religioso nas escolas brasileiras

Este artigo oferece uma análise histórica e temporal da evolução do Ensino Religioso no Brasil, concentrando-se em aspectos relacionados à laicidade e à diversidade religiosa presentes em nossa nação. A pesquisa adota uma abordagem qualitativa, fundamentada em estudos documentais e bibliográficos sobre o assunto. A discussão abrange desde a influência da Igreja Católica, que começou com a chegada dos jesuítas e a catequização dos povos indígenas, até o presente, analisando assim, qual a relação entre a religiosidade e as políticas educacionais públicas. Os resultados obtidos permitiram uma compreensão de que a prática do Ensino Religioso no Brasil está intimamente ligada à formação de caráter e valores, baseada no respeito, na liberdade de expressão e na diversidade religiosa que se mantém dentro da perspectiva de um país laico.