Due to the observed climate warming, water deficiency in soil is currently one of the most important stressors limiting the size and quality of plant crops. Drought stress causes a number of morphological, physiological, and biochemical changes in plants, limiting their growth, development, and yield. Innovative methods of inducing resistance and protecting plants against stressors include the inoculation of crops with beneficial microorganisms isolated from the rhizosphere of the plant species to which they are to be applied. The aim of the present study was to evaluate 12 different strains of rhizosphere bacteria of the genera Pantoea, Bacillus, Azotobacter, and Pseudomonas by using them to inoculate strawberry plants and assessing their impact on mitigating the negative effects of drought stress. Bacterial populations were assessed by estimates of their size based on bacterial counts in the growth substrate and with bioassays for plant growth-promoting traits. The physiological condition of strawberry plants was determined based on the parameters of chlorophyll fluorescence. The usefulness of the test methods used to assess the influence of plant inoculation with rhizosphere bacteria on the response of plants growing under water deficit was also evaluated. A two-factor experiment was performed in a complete randomization design. The first experimental factor was the inoculation of plant roots with rhizosphere bacteria. The second experimental factor was the different moisture content of the growth substrate. The water potential was maintained at −10 to −15 kPa under control conditions, and at −40 to −45 kPa under the conditions of water deficit in the substrate. The tests on strawberry plants showed that the highest sensitivity to water deficiency, and thus the greatest usefulness for characterizing water stress, was demonstrated by the following indices of chlorophyll “a” fluorescence: F_M, F_V, F_V/F_M, PI, and Area. Based on the assessment of the condition of the photosynthetic apparatus and the analysis of chlorophyll “a” fluorescence indices, including hierarchical cluster analysis, the following strains of rhizosphere bacteria were found to have favorable effects on strawberry plants under water deficit: the Bacillus sp. strains DLGB2 and DKB26 and the Pantoea sp. strains DKB63, DKB70, DKB68, DKB64, and DKB65. In the tests, these strains of Bacillus sp. exhibited a common trait—the ability to produce siderophores, while those of Pantoea sp. were notable for phosphate mobilization and ACCD activity.

With climate change escalating to global proportions over the past decade, along with a growing population, methods are being sought to help the natural and cultivated environment function in an ecologically balanced manner. Over the past few years, there has been a significantly increased interest in research on the use of natural substances for sustainable agriculture and horticulture. One of the most effective solutions to the emerging need is biochar, which has been the subject of environmental research for years due to its potential to increase soil carbon sequestration, reduce greenhouse gas emissions, remediate contaminated soil, and alleviate anthropogenic pressures. There is evidence of improved soil fertility and increased crop yields in agricultural production after biochar application. Our work comprehensively describes the effects of biochar on soil properties, crop productivity, and mitigating environmental stresses, and its remediation potential in heavy metal-contaminated soils. We analyzed a wide range of the literature on the most important properties of biochar for various potential uses. We summarized the results of research work over the past two decades to analyze soil and plant responses to biochar application.

Drought conditions pose significant challenges to sustainable agriculture and food security. Identifying microbial strains that can mitigate drought effects is crucial to enhance crop resilience and productivity. This study presents a comprehensive comparison of several machine learning models, including Random Forest, Decision Tree, XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN), to predict optimal microbial strains for this purpose. Models were assessed on multiple metrics, such as accuracy, standard deviation of results, gains, total computation time, and training time per 1000 rows of data. Notably, the Gradient Boosted Trees model outperformed others in accuracy but required extensive computational resources. This underscores the balance between accuracy and computational efficiency in machine learning applications. Leveraging machine learning for selecting microbial strains signifies a leap beyond traditional methods, offering improved efficiency and efficacy. These insights hold profound implications for agriculture, especially concerning drought mitigation, thus furthering the cause of sustainable agriculture and ensuring food security.

One of the main causes of climate change is the emission of GHGs, and one of the sources for the generation of such gasses is agriculture via plant production. Considering the foregoing, a study was conducted to assess PGPRs in strawberry cultivation which were able to limit GHG emissions. The first experimental factor was the inoculation of plant roots with the Bacillus sp. strains DLGB3, DKB26, DKB58, and DKB 84; the Pantoea sp. strains DKB63, DKB64, DKB65, and DKB68; Azotobacter sp. AJ 1.2; and Pseudomonas sp. PJ 1.1. The second experimental factor constituted the different moisture levels of the growth substrate. In the experiment, emissions of NH₃, CO₂, N₂O, and CH₄ were measured. In light of the conducted research, five strains were selected (Azotobacter sp. AJ 1.2; Pantoea sp. DKB64, DKB63, and DKB68; and Pseudomonas sp. strain PJ 1.1) that showed the greatest potential for reducing GHG emissions depending on the prevailing environmental conditions. The application of the tested bacterial strains under different moisture conditions in the substrate either reduced or did not affect GWP. This research on PGPR, which was conducted to select strains of rhizosphere bacteria that would be able to reduce GHG emissions, may form the basis for creating an inoculum and can be employed as an effective strategy for mitigating certain abiotic stresses.

Drought presents a critical challenge to global crop production, exacerbated by the effects of global warming. This study explores the role of rhizospheric bacteria (Bacillus, Pantoea, and Pseudomonas) in enhancing the drought resistance and nutrient absorption of strawberry plants. The experimental approach involved inoculating plant roots with various strains of rhizobacteria and assessing their impact under different water potential conditions in two substrates: optimal moisture and water deficit. The results showed significant changes in the nutrient content of strawberry plants, influenced by the type of bacterial strain and moisture conditions. Phosphorus and potassium content in the leaves varied considerably, with the highest levels observed in plants inoculated with specific bacterial strains under both optimal and water-deficit conditions. Similarly, calcium and magnesium content in the leaves also changed notably, depending on the bacterial strain and moisture level. The water deficit cluster, featuring the PJ1.1, DKB63, and DKB65 strains, showed PGPR’s role in maintaining nutrient availability and plant resilience. The study demonstrates that inoculation with PGPR can markedly influence the nutrient profile of strawberry plants. These findings underscore the potential of using rhizobacteria to enhance crop resilience and nutritional status, especially in the context of increasing drought conditions due to climate change.

Drought mitigation by plant growth-promoting rhizobacteria (PGPR) is commonly assessed using individual physiological traits, although plant responses to water deficit are intrinsically multivariate. We developed a Multitrait Physiological Drought Mitigation Index (PDMI) to summarize PGPR-specific physiological responses in strawberry (Fragaria × ananassa) while separating drought-specific effects from general performance under optimal moisture. The balanced two-season experiment comprised 240 plant-level observations and 10 complete matched replication sets per season. The primary PGPR analysis excluded the non-bacterial magnesium-sulfate comparator (CMg) and included four PGPR strains, the uninoculated control, 200 plant-level observations, and 80 replication-level difference-in-differences (DiD) profiles. Nine nonredundant physiological traits were direction-aligned and standardized using the 2021 development season and aggregated with equal weights; all parameters were then applied unchanged to the 2022 holdout season. Block-adjusted MANOVA confirmed a joint inoculation variant × moisture interaction (Pillai’s trace = 0.533, F(36,664) = 2.835, p < 0.001). Block-restricted PERMANOVA detected a small multivariate strain effect (pseudo-F = 0.995, R² = 0.0378, p = 0.0289), whereas PERMDISP was nonsignificant (p = 0.273). In 2021, DLGB 2 obtained the highest PDMI (0.160) and had a 66.3% bootstrap probability of rank 1 and an 88.3% probability of inclusion in the top two. In the 2022 holdout, AJ 1.2 ranked first; cross-season rank agreement was positive but uncertain (Spearman’s ρ = 0.60, p = 0.40). The best individual trait, water-use efficiency, showed greater within-season rank stability than PDMI (P(rank 1) = 0.998), demonstrating that the composite did not universally outperform single measurements. PDMI was not significantly associated with holdout yield mitigation (R² = 0.0458; block-clustered p = 0.127; within-block permutation p = 0.341). PDMI should, therefore, be interpreted as a transparent integrative descriptor of coordinated physiological response rather than a definitive classifier, a universally superior screening metric, or a standalone yield predictor.