The indiscriminate use of hazardous chemical fertilizers can be reduced by applying eco-friendly smart farming technologies, such as biofertilizers. The effects of five different types of plant growth-promoting rhizobacteria (PGPR), including Fla-wheat (F), Barvar-2 (B), Nitroxin (N1), Nitrokara (N2), and SWRI, and their integration with chemical fertilizers (50% and/or 100% need-based N, P, and Zn) on the quantitative and qualitative traits of a rainfed wheat cultivar were investigated. Field experiments, in the form of randomized complete block design (RCBD) with four replications, were conducted at the Qamloo Dryland Agricultural Research Station in Kurdistan Province, Iran, in three cropping seasons (2016–2017, 2017–2018, and 2018–2019). All the investigated characteristics of rainfed wheat were significantly affected by the integrated application of PGPR chemical fertilizers. The grain yield of treated plants with F, B, N1, and N2 PGPR plus 50% of need-based chemical fertilizers was increased by 28%, 28%, 37%, and 33%, respectively, compared with the noninoculated control. Compared with the noninoculated control, the grain protein content was increased by 0.54%, 0.88%, and 0.34% through the integrated application of F, N1, and N2 PGPR plus 50% of need-based chemical fertilizers, respectively. A combination of Nitroxin PGPR and 100% of need-based chemical fertilizers was the best treatment to increase the grain yield (56%) and grain protein content (1%) of the Azar-2 rainfed wheat cultivar. The results of this 3-year field study showed that the integrated nutrient management of PGPR-need-based N, P, and Zn chemical fertilizers can be considered a crop management tactic to increase the yield and quality of rainfed wheat and reduce chemical fertilization and subsequent environmental pollution and could be useful in terms of sustainable rainfed crop production.

Increasing global food requirements and global warming are two challenges of future food security. Water availability and nutrient management are two important factors that affect high-yield and high-quality wheat production. The main and interactive effects of nitrogen and potassium fertilizers on quantitative-qualitative properties and drought tolerance of an Iranian rainfed cultivar of wheat, Azar-2, were evaluated. Four rates of nitrogen (N0, N30, N60, and N90 kg/ha), along with four concentrations of potassium (K0, K30, K60, and K90 kg/ha), were applied in rainfed (drought stress) and non-stress conditions. The interactive effect of N × K was significant on nitrogen and protein contents of grains at 5% and 1% probability levels, respectively. Different trends of SSI, STI, K1STI, and K2STI indexes were observed with the interactive levels of nitrogen and potassium. The lowest SSI index (0.67) was observed in N30K30, whereas the highest STI (1.07), K1STI (1.46), and K2STI (1.51) indexes were obtained by N90K60 and N90K90. The obtained results could be useful to increase yield and quality of winter rainfed wheat cultivars under drought stress with cool-rainfed areas. N60K30 and N90K60 can be recommended to increase the grain yield and protein content of rainfed wheat under drought stress and non-stress conditions, respectively.

Digital Soil Mapping (DSM) provides a modern framework for assessing soil variability in complex landscapes. This study evaluated the performance of Random Forest (RF) and Support Vector Machine (SVM) classifiers for predicting soil subgroups in a semi-arid mountainous area of 3,000 ha, based on 81 soil profiles and multiple environmental covariates. Two predictor sets were tested: (i) topographic indices and spectral data, and (ii) an extended set including subsurface soil properties. Results showed that both models benefited from the integration of soil profile variables, with RF outperforming SVM. The most influential predictors were soil depth, clay content of second horizon, and a sentinel carbonate index derived from satellite data, which capture key pedogenic processes in semi-arid conditions. The final soil map indicated that Typic Calcixerepts and Lithic Xerorthents were the most widespread subgroups. Overall, the study demonstrates that combining pedological information with environmental covariates significantly improves DSM performance, offering more reliable soil maps for land evaluation and sustainable management in mountainous semi-arid regions.