Explore the vast range of titles available.

Background The present study aims to study the effects of biofertilizers potential of Arbuscular Mycorrhizal Fungi (AMF) and Bradyrhizobium japonicum ( B. japonicum ) strains on yield and growth of drought stressed soybean (Giza 111) plants at early pod stage (50 days from sowing, R3) and seed development stage (90 days from sowing, R5). Results Highest plant biomass, leaf chlorophyll content, nodulation, and grain yield were observed in the unstressed plants as compared with water stressed-plants at R3 and R5 stages. At soil rhizosphere level, AMF and B. japonicum treatments improved bacterial counts and the activities of the enzymes (dehydrogenase and phosphatase) under well-watered and drought stress conditions. Irrespective of the drought effects, AMF and B. japonicum treatments improved the growth and yield of soybean under both drought (restrained irrigation) and adequately-watered conditions as compared with untreated plants. The current study revealed that AMF and B. japonicum improved catalase (CAT) and peroxidase (POD) in the seeds, and a reverse trend was observed in case of malonaldehyde (MDA) and proline under drought stress. The relative expression of the CAT and POD genes was up-regulated by the application of biofertilizers treatments under drought stress condition. Interestingly a reverse trend was observed in the case of the relative expression of the genes involved in the proline metabolism such as P5CS , P5CR , PDH, and P5CDH under the same conditions. The present study suggests that biofertilizers diminished the inhibitory effect of drought stress on cell development and resulted in a shorter time for DNA accumulation and the cycle of cell division. There were notable changes in the activities of enzymes involved in the secondary metabolism and expression levels of GmSPS1 , GmSuSy, and GmC-INV in the plants treated with biofertilizers and exposed to the drought stress at both R3 and R5 stages. These changes in the activities of secondary metabolism and their transcriptional levels caused by biofertilizers may contribute to increasing soybean tolerance to drought stress. Conclusions The results of this study suggest that application of biofertilizers to soybean plants is a promising approach to alleviate drought stress effects on growth performance of soybean plants. The integrated application of biofertilizers may help to obtain improved resilience of the agro ecosystems to adverse impacts of climate change and help to improve soil fertility and plant growth under drought stress.

Applying Biochar (BC) or biofertilizers (BF) are potential approaches to reduce the nitrogen input and mitigate soil degradation in the maize soybean relay strip intercropping system (IS). In 2019 and 2020, a two-factor experiment was carried out to examine the effects of BC and BF on soil productivity and yield production in IS. 4 N input levels (8.4, 22.5, 45 kg, and 67.5 kg ha − 1 ) referred to as N0, N1, N2, and N3 were paired with various organic treatments, including BC (150 kg ha − 1 ), BF (300 kg ha − 1 ), and without organic amendments (CK). The results demonstrated that, despite BF decreasing the biomass and N distribution into grains, BF performed better on improved soybean yield (5.2–8.5%) by increasing the accumulation of soybean biomass (7.2 ~ 11.6%) and N (7.7%). Even though BC and BF have a detrimental effect on soybean nitrogen fixation by reducing nodule number and weight, the values of soybean nitrogenase activity and nitrogen fixation potential in BF were higher than those in BC. Additionally, BF performs better at boosting the soil’s nitrogen content and nitrate reductase and urease activity. BF increased the concentration of total N, soil organic matter, Olsen-phosphorus, and alkaline hydrolyzable N in the soil by 13.0, 17.1, 22.0, and 7.4%, respectively, compared to CK. Above all, applying BF combination with N2 (45 kg ha − 1 N) is a feasible strategy to raise crop grain output and keep soil productivity over the long term in IS.

Crop yield prediction which provides critical information for management decision-making is of significant importance in precision agriculture. Traditional manual inspection and calculation are often laborious and time-consuming. For yield prediction using high-resolution images, existing methods, e.g., convolutional neural network, are challenging to model long range multi-level dependencies across image regions. This paper proposes a transformer-based approach for yield prediction using early-stage images and seed information. First, each original image is segmented into plant and soil categories. Two vision transformer (ViT) modules are designed to extract features from each category. Then a transformer module is established to deal with the time-series features. Finally, the image features and seed features are combined to estimate the yield. A case study has been conducted using a dataset that was collected during the 2020 soybean-growing seasons in Canadian fields. Compared with other baseline models, the proposed method can reduce the prediction error by more than 40%. The impact of seed information on predictions is studied both between models and within a single model. The results show that the influence of seed information varies among different plots but it is particularly important for the prediction of low yields.

High-dimensional climate data collected on a daily, monthly, or seasonal time step are now commonly used to predict crop yields worldwide with standard statistical models or machine learning models. Since the use of all available individual climate variables generally leads to calculation problems, over-fitting, and over-parameterization, it is necessary to aggregate the climate data used as predictors. However, there is no consensus on the best way to perform this task, and little is known about the impacts of the type of aggregation method used and of the temporal resolution of weather data on model performances. Based on historical data from 1981 to 2016 of soybean yield and climate on 3447 sites worldwide, this study compares different temporal resolutions (daily, monthly, or seasonal) and dimension reduction techniques (principal component analysis (PCA), partial least square regression, and their functional counterparts) to aggregate climate data used as inputs of machine learning and linear regression (LR) models predicting yields. Results showed that random forest models outperformed and were less sensitive to climate aggregation methods than LRs when predicting soybean yields. With our models, the use of daily climate data did not improve predictive performance compared to monthly data. Models based on PCA or averages of monthly data showed better predictive performance compared to those relying on more sophisticated dimension reduction techniques. By highlighting the high sensitivity of projected impact of climate on crop yields to the temporal resolution and aggregation of climate input data, this study reveals that model performances can be improved by choosing the most appropriate time resolution and aggregation techniques. Practical recommendations are formulated in this article based on our results.

Continuous cropping has a negative effect on soybean yield. In this study, a positioning experiment was conducted starting in 2015, with three treatments: maize–soybean rotation (SMR), 2-year maize, 2-year soybean rotation cropping (SC2), and 8-year soybean continuous cropping (SC8). We determined soybean yields (2015–2022) and analyzed soil microbial communities, functions, and metabolites composition in the 0–20 cm tillage layer using metagenomics technology and GC–MS technology during soybean flowering in 2022. Results indicated that continuous cropping (SC8) significantly reduced soybean yield compared to crop rotation (SMR) during the experimental period, while SC8 showed higher yield than SC2 in 2022. Compared to SMR, SC8 significantly increased soil N content and significantly decreased pH and TP, AP, and AK content. However, the pH and AK contents of SC8 were significantly higher than those of SC2. LeFSe analysis showed that Friedmanniella, Microlunatus, Nitrososphaera, Rubrobacter, Geodermatophilus, Nitriliruptor were enriched in SC8. Gaiella, Sphaerobacter, Methyloceanibacter were enriched in SC2. Sphingomonas, Cryobacterium, Marmoricola, Haliangium, Arthrobacter, Ramlibacter, Rhizobacter, Pseudolabrys, Methylibium, Variovorax were enriched in SMR. And the relative abundance of Cryobacterium, Marmoricola, Haliangium, Arthrobacter, Ramlibacter, Rhizobacter, Methylibium, Variovorax was significantly positively correlated with yield, while the relative abundance of Gaiella and Sphaerobacter was significantly negatively correlated with yield. SC8 significantly increased the abundance of genes in nitrogen metabolism and significantly decreased the abundance of genes related to phosphorus and potassium metabolism compared with SMR. However, the abundance of genes in potassium metabolism was significantly higher in SC8 than in SC2. Metabolomic analysis showed that compared to SMR, SC8 decreased the abundance of carbohydrates, ketones, and lipid. However, the abundance of carbohydrates, ketones, and lipid was significantly higher in SC8 than in SC2. Mantel test showed that soil pH and AK significantly affected soil microbial community, function, and metabolite composition. Correlation analysis showed significant correlation between soil metabolites and microorganisms, metabolic functions.

This study aimed to investigate the influence of water availability and nitrogen fertilization on plant growth, nutrient dynamics, and variables related to soybean crop yield. Trials were performed in Teresina, Piauí, Brazil, using randomized blocks in a split-split plot arrangement. The plots corresponded to water regimes (full and deficient), the split plots to N fertilization (0 and 1000 kg ha−1 N-urea), and the split-split plots to harvest times of soybean plants (16, 23, 30, 37, 44, 58, 65, 79 and 86 days after emergence), with three replicates. In general, the accumulation and partitioning of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulphur (S), copper (Cu), iron (Fe), manganese (Mn), zinc (Zn) and boron (B) were decreased in plants subjected to water deficit and without N fertilization. Although nitrogen fertilization promoted elevated N accumulation in tissues, it did not result in any significant yield gain, and the highest seed yields were found in plants under full irrigation, regardless of N supplementation. However, deficient irrigation decreased the seed oil content of N-fertilized plants. In conclusion, N fertilization is critical for nutrient homeostasis, and water availability impairs biomass and nutrient accumulation, thereby limiting soybean yield performance.

Background Phosphorus (P) plays a vital role in plant growth. The pqqC and phoD genes serve as molecular markers for inorganic and organic P breakdown, respectively. However, the understanding of how P-mobilizing bacteria in soil respond to long-term fertilization and rhizobium application is limited. Herein, soil that had been treated with fertilizer and rhizobium for 10 years was collected to investigate the characteristics of P-mobilizing bacterial communities. Five treatments were included: no fertilization (CK), phosphorus fertilizer (P), urea + potassium fertilizer (NK), NPK, and PK +  Bradyrhizobium japonicum 5821 (PK + R). Results The soybean nodule dry weight was highest in the P treatment (1.93 g), while the soybean yield peaked in the PK + R treatment (3025.33 kg ha − 1 ). The abundance of the pqqC gene increased in the rhizosphere soil at the flowering-podding stage and in the bulk soil at the maturity stage under the P treatment, while its abundance increased in the bulk soil at the flowering-podding stage and in the rhizosphere soil at the maturity stage under the PK + R treatment. The abundance of the phoD gene was enhanced in the bulk soil at the flowering-podding stage under the PK + R treatment. The Shannon and Ace indexes of pqqC - and phoD -harboring bacteria were higher in the rhizosphere soil at maturity under the PK + R treatment compared to other treatments. Furthermore, a comprehensive analysis of the neutral community model and co-occurrence pattern demonstrated that the application of P fertilizer alone led to an increase in the distribution and dynamic movement of pqqC -harboring bacteria, but resulted in a decrease in complexity of network structure. On the other hand, rhizobium inoculation enhanced the distribution and dynamic movement of phoD -harboring bacteria, as well as the stability and complexity of the network structure. Pseudomonas and Nitrobacter , as well as Steptomyces , Stella , and Nonomuraea , may be crucial genera regulating the composition and function of pqqC - and phoD -harboring communities, respectively. Conclusions These findings affirm the crucial role of fertilization and rhizobium inoculation in regulating pqqC - and phoD -harboring bacterial communities, and highlight the significance of long-term phosphate-only fertilization and rhizobium inoculation in enhancing dissolved inorganic phosphorus and mineralized organophosphorus, respectively.

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.

Background: Soybean is one of major crop plants cultivated in numerous parts of the world, which is due to an increasing demand for plant protein. Both in Europe and Poland, much attention is paid to enhancing the production of their own fodder protein, as to reduce the import of soybean meal produced from genetically modified plants. Climate warming and breeding progress have made it possible to grow soybeans in central Europe. The yield potential of plants, including soybeans, can be enhanced by an application of biostimulants, which alleviate negative effects of stresses disturbing the life processes of plants. The objective of the present work was to evaluate, under the climatic conditions of central-eastern Poland, the yielding of three non-modified soybean cultivars treated with biostimulants. Methods: A field experiment was conducted in the years 2017–2019 in eastern Poland (central Europe). The soil of the experimental field belonged to the Haplic Luvisol group. The experimental factors included three non-GMO soybean cultivars (Abelina, Merlin, and SG Anser) and two biostimulants (Asahi SL and Improver). Results: Soybean seed yields were affected by the climatic conditions during the growing season, cultivars, and biostimulant applications. Regardless of cultivars and biostimulants, the highest yields were produced by plants grown in 2017 (on average, 3.41 Mg∙ha−1), them being slightly lower in 2019 (on average, 3.0 Mg∙ha−1) and the lowest in the dry 2018 (on average, 2.48 Mg∙ha−1). Significant differences were recorded between cv. SG Anser (the average yield 2.73 Mg∙ha−1) and Merlin (the average yield 3.31 Mg∙ha−1). An application of biostimulants resulted in a significant increase in soybean seed yield compared with the control. Biostimulants contributed to a significant increase in the values of the remaining characteristics, i.e., 1000-seed weight, seed number per pod, and average number of seeds per pod.

The commercial launch of dicamba-tolerant (DT) crops has resulted in increased dicamba usage and a high number of dicamba off-target movement complaints on sensitive soybeans (Glycine max L.). Dicamba is a synthetic auxin and low dosages as 0.028 g ae ha−1 can induce injury on sensitive soybean. Tank contamination has been identified as one of the sources for unintended sensitive crop exposure. The labels of new dicamba formulations require a triple rinse cleanout procedure following applications. Cleanout efficacy might vary based on the sprayer type and procedure followed. This study was performed to quantify dicamba retention in commercial sprayers and assess the risk for crop injury from remaining contaminants. The results indicate triple rinse with water was comparable to cleanout procedures utilizing ammonium, commercial tank cleaners, and glyphosate in rinses. Dicamba contaminants in final rinsates resulted in <15% visual injury and no yield response when applied to sensitive soybeans at R1 stage. A survey of 25 agricultural sprayers demonstrated a cleanout efficacy of 99.996% by triple rinsing with water following applications of dicamba at 560 g ae ha−1, with concentrations of less than 1 ug mL−1 detected rinsates from the fourth rinse. A dose response experiment predicted dosages causing 5% visual injury and the yield losses were 0.1185 and 2.8525 g ae ha−1. However, symptomology was observed for all tested dosages, including the rate as low as 0.03 g ae ha−1. The results from this study suggest triple rinsing with sufficient amount of water (≥10% of tank volume) is adequate for the removal of dicamba residues from sprayers to avoid sensitive soybean damage. This study can provide producers with confidence in cleanout procedures following dicamba applications, and aid in minimizing risk for off-target movement through tank contamination.