Agricultural areas exhibiting numerous abiotic stressors, such as elevated water stress, temperatures, and salinity, have grown as a result of climate change. As such, abiotic stresses are some of the most pressing issues in contemporary agricultural production. Understanding plant responses to abiotic stressors is important for global food security, climate change adaptation, and improving crop resilience for sustainable agriculture, Over the decades, explorations have been made concerning plant tolerance to these environmental stresses. Plant growth-promoting rhizobacteria (PGPR) and their phytohormones are some of the players involved in developing resistance to abiotic stress in plants. Several studies have investigated the part of phytohormones in the ability of plants to withstand and adapt to non-living environmental factors, but very few have focused on rhizobacterial hormonal signaling and crosstalk that mediate abiotic stress tolerance in plants. The main objective of this review is to evaluate the functions of PGPR phytohormones in plant abiotic stress tolerance and outline the current research on rhizobacterial hormonal communication and crosstalk that govern plant abiotic stress responses. The review also includes the gene networks and regulation under diverse abiotic stressors. The review is important for understanding plant responses to abiotic stresses using PGPR phytohormones and hormonal signaling. It is envisaged that PGPR offer a useful approach to increasing plant tolerance to various abiotic stresses. However, further studies can reveal the unclear patterns of hormonal interactions between plants and rhizobacteria that mediate abiotic stress tolerance.

Dessureault-Rompré, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N ₀) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R ²=0.17–0.18) of SNS when compared with a field-based measure of SNS (PASNS) if values of N ₀ were predicted from the regression equation. We also examined a two-pool zero- plus first-order (ZF) prediction function. A regression model was developed including soil organic C and Pool I and explained 66% of the variance in k _S , the rate constant of the zero-order pool. In addition, a regression equation was developed which explained 86% of the variance in the size of the first-order pool, N _L , from Pool I. The ZF prediction function provided satisfactory prediction of SNS (R ²=0.41–0.49) using both measured and predicted values of k _S and N _L . This study demonstrated a simple prediction function can be used to estimate SNS over a growing season where the mineralizable N parameters are predicted from simple soil properties using regression equations.