The objective of this study was to analyze the response of antioxidant parameters in soybean plants inoculated with newly isolated Bradyrhizobium japonicum and Bacillus subtilis strains as single and co-inoculants under drought stress. Bacterial strains were selected according to osmotic stress tolerance (in the presence of 36% PEG 6000) in appropriate liquid media. The effect of soybean inoculation was examined in a soil pot experiment in water deficit conditions (0 and 7 days withholding water). The influence of water stress and inoculation was evaluated in soybean leaves, roots, and nodules through guaiacol peroxidase (POX), ionically cell-wall-bound peroxidase (POD) activity, and ABTS˙⁺ radical cation scavenging capacity, as well as parameters of N-fixation efficiency. The results showed a significant influence of inoculation on constitutive and drought-induced antioxidant and N-fixation parameters. Inoculation increased the activity of POX (up to 116, 169, and 245%), POD (up to 116, 102, and 159%), and antioxidant capacity (up to 74, 76, and 81%) in soybean leaves, roots, and nodules under water deficit, respectively. Application of bacterial strains resulted in higher shoot, root, and nodule weight and nitrogen content both in non-stressed and drought stress conditions. Overall, co-inoculation had better effects on the investigated soybean parameters compared to single inoculation. Selection and application of bacterial strains with improved tolerance to drought stress is necessary in developing inoculants that would result in enhanced crop production under unfavorable environmental conditions.

Background: The effectiveness of soybean inoculants in field conditions may be improved through the joint application of rhizobia and plant growth-promoting bacteria (PGPB). Plant nutrients may also contribute to the higher efficacy of microbial inoculants as well as the growth and development of plants. Methods: A field experiment was performed to evaluate the responses of the rhizomicrobiome and soybean to seed treatments with multiple bacterial strains (Bradyrhizobium japonicum, Bacillus subtilis, Bacillus megaterium and Azotobacter chroococcum), applied individually or in consortia, with and without nutrient complex (S, Mg, Mn, Fe, Zn, Cu, B and Mo). Result: Seed treatments with microbial consortia of Br. japonicum, B. subtilis/B. megaterium, A. chroococcum strains and nutrients had the highest effect on the abundance of total bacteria, nitrogen-fixing bacteria, actinomycetes and activity of dehidrogenase in the soybean rhizosphere. The highest effect on plant height, plant weight, pod number, pod weight, seed number and seed weight was obtained from treatment with Br. japonicum strains with nutrients, followed by co-inoculation with B. megaterium and A. chroococcum. In comparison with the control and Bradyrhizobium single inoculation, a statistically significant increase in the seed yield was recorded in treatment with Br. japonicum, B. megaterium, A. chroococcum and nutrients, reflecting the highest increase in protein and oil yield. The interactive effects of microbial consortia and nutrients could be used as promising seed technology for sustainable soybean cropping.

Biostimulants based on Bradyrhizobium japonicum are commonly used in soybean production. However, the effect of nitrogen-fixing bacteria in consortia with other plant growth-promoting rhizobacteria (PGPR) and their integration with mineral nutrients on soybean seed quality has not been explored. The study aimed to examine the effects of five treatments on seed germination and initial seedling growth of two soybean cultivars (‘NS Apolo’, ‘NS Rubin’): control (untreated seeds); Br. japonicum (BJ), BJ and nutrient complex (NC), BJ, Azotobacter chroococcum (AC), Bacillus subtilis (BS), and NC; BJ, AC, Bacillus megaterium (BM), and NC. Seed treatments significantly enhanced germination energy, seedling vigor index, root length, fresh shoot weight, fresh root weight, dry shoot weight, and dry root weight of both cultivars, as well as final germination, shoot length, and shoot elongation rate of ‘NS Rubin’, as compared to the control. The highest effect on the investigated parameters was achieved by integrated use of PGPR and nutrients (BJ + BM + AC + NC), indicating that integration of PGPR with a targeted NC represents an innovative approach with practical implications for improving early soybean establishment and field performance.

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.

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R² in the final model remained high (R² = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation.

The aim of this study was to evaluate the effects of winter cover crops (CCs) on soybean agronomic performance and their implications for different physiological groups of rhizosphere microorganisms in two sustainable production systems. The production techniques for rye, peas, and oats are well known, but their suitability as CCs for soybean (organic and low-input) production needs to be examined. After two years of trials, soybean yields among the two tested winter CCs (peas and oats (P + O) and rye (R)) were statistically significant only for P + O. The soybean yield in succession to P + O as winter CCs was 3.0 t ha⁻¹, whereas in succession to R, it was 2.7 t ha⁻¹, and in the control plot, it was 2.6 t ha⁻¹. The average soybean grain protein content was in the range of 40 to 41% dry matter (DM), while the oil content ranged from 20 to 22% DM. Protein and oil content primarily depends on the selected soybean variety and it is confirmed through this study that, in the studied system, we can obtain adequate grain nutritional quality. The results indicate an increase in the abundance of total bacteria, ammonifiers, and free N₂-fixing bacteria in the rhizosphere, depending on the selected CCs, and differences between the tested production systems. According to this study, winter cover crops (CCs), including peas and oats (P + O) and rye (R), can be included in crop rotation for soybean. CCs can be the answer to agro-biodiversity empowerment in less diverse soybean cropping systems, along with other benefits that CCs can provide at the level of crop rotation. In addition, in almost all aspects of the study, organic production was ahead of low input. Low input is an adequate production system if there are no opportunities for organic certification and for producers who are aware of the advantages of sustainable systems, and it can also represent a transitional path towards regenerative agriculture or organic production.