Soybean Yield Research Articles

Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population's genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

Enhancing soil quality and grain yields through fertilization strategies in integrated crop-livestock system under no-till in Brazilian Cerrado

The study aimed to evaluate the effects of P and K fertilization strategies with and without N-fertilization on soil quality and crop yields in integrated crop-livestock system (ICLS) in the Brazilian Cerrado. The treatments included various fertilization strategies, such as applying P and K during either the cropping phase (conventional fertilization) or the pasture phase (system fertilization), with or without N-fertilization during the pasture phase. Soil samples were collected two years after the experiment was initiated from the 0–10 cm layer, and soybean yields were determined at the end of the crop cycle. Soil carbon (C) stocks remained largely unaffected by the different fertilization strategies. However, soil nitrogen (N) stocks, as well as C and N within the microbial biomass, were lower when conventional fertilization and 0 kg ha−1 of N were used in pastures compared to other treatments. Enzyme activity also decreased with conventional fertilization and 0 kg ha−1 of N in pastures. Scores for Nutrient Cycling and Nutrient Storage were higher with 100 kg ha−1 compared to 0 kg ha−1 of N in the case of conventional fertilization, marking an increase of 22% and 18% for Nutrient Cycling and Nutrient Storage, respectively. For soil function associated with Nutrient Supply, there was no difference between the treatments. Within the 0 kg ha−1 of N group, system fertilization was 12% and 24% higher compared to conventional fertilization. The soil quality index (SQIFERTBIO) was higher with 100 kg ha−1 (0.83) compared to 0 kg ha−1 of N (0.77), showing an 8% increase. Soybean yield was 3% higher with system fertilization and 7% higher with 100 kg ha−1 of N, compared to conventional fertilization with 0 kg ha−1 of N. In conclusion, even in the short term, adopting a system fertilization strategy and applying N-fertilization in pastures benefits soybean yields in ICLS under no-till.

Intraspecific competition in row spacings in soybean

The aim of this study is to assess the impact of various row spacings on the morphology, components of grain yield, and overall grain yield of soybean. The experiments were conducted over two planting seasons (2014/2015 and 2015/2016) using two planting dates (November and December) and two cultivars, FPS Urano RR and BMX Tornado RR. The row spacings tested were: 45 cm (wide row), 45 × 45 cm (cross row), 22.5 × 45 cm (twin row), and 22.5 cm (narrow row). Measurements included leaf area index and sunlight interception at R2 stage, as well as grain yield components and overall grain yield. Narrow rows, in addition to achieving canopy closure, also demonstrated enhanced sunlight interception at the R2 stage. However, the positive effect of increased sunlight interception on grain yield was only significant for the December planting date, with no notable difference observed for the November planting date. These findings imply that while narrow rows may offer advantages, particularly in later planting dates, their implementation should be carefully considered and may not consistently lead to increased grain yield, reinforcing the continued preference for conventional row spacing.

GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data

Accurately estimating large-area crop yields, especially for soybeans, is essential for addressing global food security challenges. This study introduces a deep learning framework that focuses on precise county-level soybean yield estimation in the United States. It utilizes a wide range of multi-variable remote sensing data. The model used in this study is a state-of-the-art CNN-BiGRU model, which is enhanced by the GOA and a novel attention mechanism (GCBA). This model excels in handling intricate time series and diverse remote sensing datasets. Compared to five leading machine learning and deep learning models, our GCBA model demonstrates superior performance, particularly in the 2019 and 2020 evaluations, achieving remarkable R2, RMSE, MAE and MAPE values. This sets a new benchmark in yield estimation accuracy. Importantly, the study highlights the significance of integrating multi-source remote sensing data. It reveals that synthesizing information from various sensors and incorporating photosynthesis-related parameters significantly enhances yield estimation precision. These advancements not only provide transformative insights for precision agricultural management but also establish a solid scientific foundation for informed decision-making in global agricultural production and food security.

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.

Effect of combined use of supplementary irrigation, manure and P fertilization on grain yield and profitability of soybean in northern Nigeria

Declining soil fertility particularly phosphorus deficiency, low organic carbon, moisture stress and high cost of input are factors limiting soybean yield in the Nigeria savanna. Supplementary irrigation, nutrient application and inoculation with Bradyrhizobium could increase the grain yield of soybean. We evaluated the effects of Rhizobia inoculant, phosphorus fertilization, manure, and supplementary irrigation on the nodulation and productivity of a tropical soybean variety in two locations in northern Nigeria in the 2017 and 2018 cropping seasons. The treatments consisted of five input bundles: Supplementary irrigation +17.5 kg P ha−1 + 4 t ha−1 poultry manure + nodumax inoculant (S + P + M + I); 17.5 kg P ha−1 + 4 t ha−1 poultry manure + nodumax inoculant (P + M + I); 17.5 kg P ha−1 + nodumax inoculant (P + I); 17.5 kg PP ha−1 (P); and nodumax inoculant (I). Economic analysis was done to determine the benefit-cost ratio (BCR) for each input bundle. In Kano, the input bundle S + P + M + I produced mean number of nodules that were 38, 102, 200 and 352% higher than that of input bundles P + M + I, P + I, P and I, respectively. At Lere, the application of input bundle S + P + M + I increased mean number of nodules by 33, 81, 93 and 182% over that of input bundles P + M + I, P + I, P and I, respectively. Mean grain yield in Kano was greater for input bundle S + P + M + I over P + M + I, P + I, P and I bundles by 31, 50, 64 and 223%, respectively. In Lere, grain yield for input bundle S + P + M + I was higher than that of input bundles P + M + I, P + I, P and I only, by 27, 47, 41 and 184% respectively. The input bundle P + M + I produced the highest BCR (1.4) in Kano and application only of P produced the highest BCR (1.3) in Lere. Supplementary irrigation was not found to be profitable due to the high cost of supplementary irrigation.The application of P with or without manure/inoculant is recommeded for profitable soybean production in the savannas of Nigeria.

Influence of mineral nutrition and predecessors on the yield and quality of soybean seed in the crop rotation link

Studies conducted in the Ryazan region have shown that both the presence of mineral nutrition and the nature of the predecessors in the crop rotation are extremely important for the soybean yield. The experiment was carried out on six-field crop rotations with two backgrounds of fertilizers — without fertilizers and with the application of (NPK)90. The soybean of the Georgiy variety, which is the fifth link in crop rotations, was used as an object of study. Barley, winter wheat, and various perennial grasses were present in each of the crop rotations. To replenish organic matter in the soil, the organic mass of plants in the form of crop residues after harvesting the main part of the crop and perennial grasses included in the crop rotation was crushed and incorporated into the soil. In each crop rotation at the time of soybean cultivation, soil fertility, yield and quality of soybean seed were studied in order to identify the best predecessors. It was revealed that the highest yield is achieved in a crop rotation with legume-grass grasses using (NPK)90 — the largest increase in relation to unfertilized options was 14%, 11% and 22.8%. Variants with fertilizers also demonstrate the best indicators of soybean seed quality in terms of protein and oil content, where crop rotations with legumes and grasses also reach the highest values. It was revealed that all fertilized variants with were characterized by better indicators of soil fertility compared to unfertilized ones. It is concluded that the presence of perennial sideral grasses has a positive effect on soil fertility and contributes to an increase in soybean yields.

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

Overexpression of GmPAP4 Enhances Symbiotic Nitrogen Fixation and Seed Yield in Soybean under Phosphorus-Deficient Condition.

Legume crops establish symbiosis with nitrogen-fixing rhizobia for biological nitrogen fixation (BNF), a process that provides a prominent natural nitrogen source in agroecosystems; and efficient nodulation and nitrogen fixation processes require a large amount of phosphorus (P). Here, a role of GmPAP4, a nodule-localized purple acid phosphatase, in BNF and seed yield was functionally characterized in whole transgenic soybean (Glycine max) plants under a P-limited condition. GmPAP4 was specifically expressed in the infection zones of soybean nodules and its expression was greatly induced in low P stress. Altered expression of GmPAP4 significantly affected soybean nodulation, BNF, and yield under the P-deficient condition. Nodule number, nodule fresh weight, nodule nitrogenase, APase activities, and nodule total P content were significantly increased in GmPAP4 overexpression (OE) lines. Structural characteristics revealed by toluidine blue staining showed that overexpression of GmPAP4 resulted in a larger infection area than wild-type (WT) control. Moreover, the plant biomass and N and P content of shoot and root in GmPAP4 OE lines were also greatly improved, resulting in increased soybean yield in the P-deficient condition. Taken together, our results demonstrated that GmPAP4, a purple acid phosphatase, increased P utilization efficiency in nodules under a P-deficient condition and, subsequently, enhanced symbiotic BNF and seed yield of soybean.

Weed Recognition at Soybean Seedling Stage Based on YOLOV8nGP + NExG Algorithm

The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings.

Identification and Genetic Dissection of Resistance to Red Crown Rot Disease in a Diverse Soybean Germplasm Population.

Red crown rot (RCR) disease caused by Calonectria ilicicola negatively impacts soybean yield and quality. Unfortunately, the knowledge of the genetic architecture of RCR resistance in soybeans is limited. In this study, 299 diverse soybean accessions were used to explore their genetic diversity and resistance to RCR, and to mine for candidate genes via emergence rate (ER), survival rate (SR), and disease severity (DS) by a multi-locus random-SNP-effect mixed linear model of GWAS. All accessions had brown necrotic lesions on the primary root, with five genotypes identified as resistant. Nine single-nucleotide polymorphism (SNP) markers were detected to underlie RCR response (ER, SR, and DS). Two SNPs colocalized with at least two traits to form a haplotype block which possessed nine genes. Based on their annotation and the qRT-PCR, three genes, namely Glyma.08G074600, Glyma.08G074700, and Glyma.12G043600, are suggested to modulate soybean resistance to RCR. The findings from this study could serve as the foundation for breeding RCR-tolerant soybean varieties, and the candidate genes could be validated to deepen our understanding of soybean response to RCR.

Can gypsum and organic amendments achieve sustainability, productivity and maintain soil health under soybean-mustard cropping in sodic soils of western India

Soil salinity is a major environment stress impairing crop production and accelerating soil degradation. Use of soil amendments are practical solutions for altering soil quality to enhance crop productivity in these soils. Hence, we systematically evaluated the impact of soil amendments practices on crop productivity, nutrient use efficiency, soil properties, soil quality index, economics and energetics on soybean-mustard cropping system in sodic Vertisol. In this study, eight treatments comprising of various combinations of soil amendments such as gypsum (@2.5 t ha−1), farmyard manure (FYM@10 t ha−1), crop residue (CR@1.5 t ha−1 of soybean residue during rabi and 3 t ha−1 of mustard residue during kharif season) with recommended fertilizer doses (RNPK) was evaluated in randomized block design with three replications for four consecutive years in soybean-mustard cropping sequences (2016–2019). Results demonstrated that application of gypsum with CR and FYM recorded a significant drop in exchangeable sodium percentage (ESP) (45–48 %), and bulk density (BD) (3–6 %) than that of control. Organic amendment in conjunction with gypsum and chemical fertilizer significantly improved soil chemical, physical and biological properties than that under control and inorganic fertilizer alone treatments. Based on principal component analysis and correlation matrix, minimum data set identified ESP, pH, BD, organic carbon, available nutrients, biomass carbon, calcium content, dehydrogenase as the most important properties controlling soil quality. Integration of RNPK+Gypsum+CR and RNPK+Gypsum+FYM are found superior leading to higher crop yield in soybean (1.23 and 1.21 Mg ha−1 respectively), mustard (1.43 and 1.39 Mg ha−1 respectively), better nutrient recovery efficiency (77 and 53 % respectively), improved soil quality index (0.90 and 0.93 respectively), and higher economic return (benefit: cost ratio-2.88 and 2.1 respectively).Therefore, this study findings highlighted the conjunctive use of gypsum with organic amendments is effective in reclaiming salt stress, improving soil health and crop productivity under oilseed cropping sequence in degraded soils of semi-arid tropics.

Quantifying High-Temperature and Drought Stress Effects on Soybean Growth and Yield in the Western Guanzhong Plain

High-temperature and drought events significantly impact crop growth and development. In the soybean-producing region of the Guanzhong Plain in China, understanding the dynamics of these climatic phenomena is vital for soybean yield preservation. Through a fixed-position observation experiment that analyzed four growth stages, nine agronomic traits, and soybean yield per unit area from 1998 to 2023, this research evaluated the characteristics of high-temperature and drought processes in various growth stages. It also examined the influence of high-temperature processes, drought processes, and their combined effects on agronomic traits and yield. The results indicate the following: (1) High temperature was a constant factor during the soybean growth period, with temperature-related indices markedly surpassing those related to drought. Notably, the occurrence of high-temperature and drought events was more prevalent during the flowering–podding stage than at the podding or grain-filling stages. (2) High temperature profoundly affected soybean yield components, primarily through a decrease in the number of grains per plant during the flowering–podding stage, subsequently impacting the grain weight per plant and yield. In years with extremely high temperatures, the soybean plant height was reduced by 6.1 to 15 cm, the main stem node number decreased by 0.1 to 2.9, the branch number decreased by 0.2 to 0.6, the number of pods per plant decreased by 4.8 to 13.7, the number of grains per pod decreased by 0.1 to 0.3, the number of grains per plant decreased by 13.5 to 32.6, the grain weight per plant decreased by 3.8 to 6.9 g, and the 100-grain weight decreased by 0.1 to 4.5 g. The common impact of high temperature combined with drought processes in different growth stages was reflected in the reduction in the number of branches by 0.1 to 1.4 and the reduction in the number of grains per pod by 0.02 to 13.7. This study underscores the importance of addressing the quantitative effects of climate change and extreme weather on soybean yield, which could help to develop effective adaptation and mitigation strategies.

Comparative analysis of soil quality and enzymatic activities under different tillage based nutrient management practices in soybean–wheat cropping sequence in Vertisols

In the modern era, intensive agricultural practices such as agrochemicals are applied in excessive amounts to enhance agricultural production. However, imbalanced adoption of these chemicals has arisen in the dwindling of agriculture factor productivity and soil quality. To maintain soil fertility and production, these chemical fertilizers must be supplemented with organic inputs. Keeping this in the backdrop, a research trail was established during 2018–19 and 2019–20 years at Research Farm of Agriculture University, Kota, India. The treatment setup was comprised of 5 treatment modules viz., conservation tillage + organic management (CAOM), conservation tillage + chemical management (CACM), conventional tillage + chemical management (CTCM), conventional tillage + organic management (CTOM) and the package of practices (PoPs) with four replications. Results indicated that the highest organic carbon (0.68%), bacterial (29.11 × 107 cfu g−1), fungal (4.77 × 104 cfu g−1), actinomycetes populations (5.67 × 104 cfu g−1), acid phosphatase (44.1 µg g−1 h−1), urease (45.3 µg g−1 h−1) and dehydrogenase (23.3 µg triphenylformazan [TPF] g−1 h−1) activity in soil were found in the treatment of conservation organic system during both the years of study at each soil depth. In contrast to other parameters, the highest system productivity was observed with conservation chemical crop management approaches, with a soybean equivalent yield of 4615 kg ha−1 in a soybean–wheat system of production. Furthermore, the soil quality index (SQI) significantly varied from the lowest score (0.30) at 45–60 cm layer of soil in the package of practices to the highest score (0.92) at 0–15 cm layer of soil with regards to the conservation organic which shows, 206.67 percent enhancement through the soil profile of various crop management practices. The SQI variation from 0–15 to 45–60 cm soil depth was 130.0, 81.08, 60.0, 175.0 and 83.33 percent, respectively, for CAOM, CACM, CTCM, CTOM and PoPs. Amongst, different systems, the highest mean performance was noticed under the conservation organic systems for physical and biological properties. Hence, in line with the salient outcome, we may propose that the conservation chemical system needs to be followed to improve crop productivity, whereas, conservation organic seems a good option for soil health with long-term viability.

Integrated Transcriptomic and Proteomic Characterization of a Chromosome Segment Substitution Line Reveals the Regulatory Mechanism Controlling the Seed Weight in Soybean

Soybean is the major global source of edible oils and vegetable proteins. Seed size and weight are crucial traits determining the soybean yield. Understanding the molecular regulatory mechanism underlying the seed weight and size is helpful for improving soybean genetic breeding. The molecular regulatory pathways controlling the seed weight and size were investigated in this study. The 100-seed weight, seed length, seed width, and seed weight per plant of a chromosome segment substitution line (CSSL) R217 increased compared with those of its recurrent parent ‘Suinong14’ (SN14). Transcriptomic and proteomic analyses of R217 and SN14 were performed at the seed developmental stages S15 and S20. In total, 2643 differentially expressed genes (DEGs) and 208 differentially accumulated proteins (DAPs) were detected at S15, and 1943 DEGs and 1248 DAPs were detected at S20. Furthermore, integrated transcriptomic and proteomic analyses revealed that mitogen-activated protein kinase signaling and cell wall biosynthesis and modification were potential pathways associated with seed weight and size control. Finally, 59 candidate genes that might control seed weight and size were identified. Among them, 25 genes were located on the substituted segments of R217. Two critical pathways controlling seed weight were uncovered in our work. These findings provided new insights into the seed weight-related regulatory network in soybean.

Unveiling synergistic QTLs associated with slow wilting in soybean (Glycine max [L.] Merr.)

Key messageA stable QTL qSW_Gm10 works with a novel locus, qSW_Gm01, in a synergistic manner for controlling slow-wilting traits at the early vegetative stage under drought stress in soybean.Drought is one of the major environmental factors which limits soybean yield. Slow wilting is a promising trait that can enhance drought resilience in soybean without additional production costs. Recently, a Korean soybean cultivar SS2-2 was reported to exhibit slow wilting at the early vegetative stages. To find genetic loci responsible for slow wilting, in this study, quantitative trait loci (QTL) analysis was conducted using a recombinant inbred line (RIL) population derived from crossing between Taekwangkong (fast-wilting) and SS2-2 (slow-wilting). Wilting score and leaf moisture content were evaluated at the early vegetative stages for three years. Using the ICIM-MET module, a novel QTL on Chr01, qSW_Gm01 was identified, together with a previously known QTL, qSW_Gm10. These two QTLs were found to work synergistically for slow wilting of the RILs under the water-restricted condition. Furthermore, the SNP markers from the SoySNP50K dataset, located within these QTLs, were associated with the wilting phenotype in 30 diverse soybean accessions. Two genes encoding protein kinase 1b and multidrug resistance-associated protein 4 were proposed as candidate genes for qSW_Gm01 and qSW_Gm10, respectively, based on a comprehensive examination of sequence variation and gene expression differences in the parental lines under drought conditions. These genes may play a role in slow wilting by optimally regulating stomatal aperture. Our findings provide promising genetic resources for improving drought resilience in soybean and give valuable insights into the genetic mechanisms governing slow wilting.

Rotational complexity across US counties is currently insufficient to observe yield gains in major crops

Landscape complexity promotes ecosystem services and agricultural productivity, and often encompasses aspects of compositional or configurational land cover diversity across space. However, a key agricultural diversification practice, crop rotation, extends crop land cover complexity concurrently across space and time. Long-term experiments suggest that complex crop rotations can facilitate yield increases in major crops. Using a compiled county-annual panel dataset, we examined whether yield benefits of crop rotational complexity were apparent on a landscape scale in the conterminous US for four major crops between 2008 and 2020. We found that the benefit of rotational complexity was only apparent for cotton and winter wheat, and that the benefit for wheat was driven by one region. Corn exhibited the opposite pattern, wherein higher yields were consistently obtained with lower rotational complexity, while soybean yield appeared relatively insensitive to rotational complexity. Effects of rotational complexity were sometimes influenced by agrochemical usage. Positive effects of rotational complexity were only apparent with high fertilizer for soybean and wheat, and with low fertilizer for cotton. Corn yield in high-complexity, low-yielding counties appeared to improve with high fertilizer inputs. For the overwhelming majority of acres growing these major crops, crop rotation patterns were quite simple, which when combined with the short time span of available data, may explain the apparent discrepancy between long-term experiments and nationwide data. Current demand and incentives that promote highly intensified and specialized agriculture likely hinder realization of the benefits of rotational complexity for production of key crops in the US. Increasing rotational complexity where major crops are grown thus remains an underutilized approach to mitigate landscape simplification and to promote ecosystem services and crop yields.

The Impact of fertilisation, seed treatment, and meteorological conditions on soybean yield in the right-Bank Forest-Steppe

The Impact of fertilisation, seed treatment, and meteorological conditions on soybean yield in the right-Bank Forest-Steppe

Artificial intelligence applied to estimate soybean yield

The application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.

Effect of Plant Growth Promoting Rhizobacteria (PGPR) on Growth and Yield of Soybean

This study aimed to (1) examine the effect of giving PGPR (Plant Growth Promoting Rhizobacteria) to increase soybean growth and production and (2) obtain the best dose of PGPR to increase soybean growth and production. The research was carried out in Tontalete Village, Kema District, North Minahasa Regency, North Sulawesi from June 2021 to October 2021. The field experiment was arranged in a Randomized Block Design (RBD). The treatment tested was the concentration of PGPR, which consisted of P0 = 0 ml/l (control), P1 = 5 ml/l, P2 = 10 ml/l, P3 = 15 ml/l, and P4 = 20 ml/l. The treatment was repeated three times, so there were 15 experimental units. Parameters observed were: plant height, number of branches, number of pods per plant, percentage of damaged pods per plant, the weight of 100 dry seeds, and seed yield/plot. The data were analyzed using the Analysis of Variance (ANOVA) and continued with the mean difference test using the Least Significant Difference Test (LSD) at the 5% level. The results showed that the application of PGPR affected the growth and yield of soybeans by increasing plant height, the number of pods per plant, and dry seed yield. The highest dry seed yield was achieved at PGPR concentrations of 10 ml/l and 15 ml/l, which were 1.34 g and 127.87 g, equivalent to 1.34 tons ha-1 and 1.28 tons ha-1, respectively.