Sustainable soil management with the appropriate understanding of soil characteristics is vital in maintaining and improving agriculture soil management. The objectives of the present study are to characterize the spatial variability of soil using the GIS technique and used agglomerative hierarchical clustering (AHC) for the delineation of management zones (MZs) for precision agriculture. A total of 111 soil samples were collected from 37 soil profiles in systematic depths (0–50, 50–100, and 100–150 cm) from the South Hail region, KSA. Samples were analyzed for pH, ECe, CaCO₃, available macro and micronutrients, and hydrological properties. The best fit models, using ArcGIS software, were J-Bessel for pH, Clay, bulk density (BD), and available water (AW); K-Bessel for EC and available N; Stable for CaCO₃, P, K, Fe, Zn, Sand, field capacity (FC) and saturated hydraulic conductivity (Ks); Spherical for Mn and Cu; Gaussian for saturation percentage (SP); whereas exponential for permanent wilting point (PWP). The principal component analysis (PCA) resulted in six principal components (PCs) explaining 79.75% of the total variance of soil properties. The PC1 was strongly influenced by soil BD, FC, clay, PWP, Ks, and sand. PC2 was dominated by N, ECe, and CaCO₃; PC3 was dominated by pH; PC4 was dominated primarily by K and P, PC5 was mainly dominated by Fe; Mn, and Cu, and PC6 was mainly dominated by SP and Zn. Based on AHC, four soil management zones (MZs) cover 77.94, 14.10, 7.11 and 0.85% of the studied area. Management zone 1 (MZ1) and Management zone 3 (MZ3) are classified as moderately saline while Management zone 2 (MZ2) is classified as highly saline soils, greater than the limiting critical value for the sensitive crops. The potential solutions to reduce salinization in the area include: reducing irrigation, moving to salt-tolerant crops or applying humic acids to fix anions and cations and eliminate them from the root zone of the plants. Treating the area with diluted sulfuric acid to remove salts and reduce ECe to less than 2 dSm⁻¹, to get maximum productivity. This finding is diagnostic for determining the amount of fertilizer and irrigation water to be applied to soils in different management zones. Its emphasis’s the importance of site-specific management for long-term crop productivity and, as a result, reducing environmental hazards caused by uneven fertilizers and water applications.

Photosynthetica covers wide range of photosynthesis research or research including photosynthetic parameters of both experimental and theoretical nature.

Sustainable agriculture in arid and semi-arid regions critically depends on the interactions between soil physical properties, water dynamics, and mechanized field operations. In this context, soil physical attributes, such as texture, bulk density, aggregate stability, and soil water potential, play a crucial role in determining soil–water–machine interactions. Soil attributes such as texture, bulk density, aggregate stability, and soil water potential govern both water movement and retention, as well as traction efficiency, draft energy, and compaction under mechanized traffic. Deviations from the optimal soil moisture range in sandy or calcareous soils increase wheel slip, energy consumption, and soil structural degradation, resulting in uneven infiltration and reduced water-use efficiency. This review synthesizes recent research on these coupled processes, emphasizing how soil mechanics and hydraulics collectively influence irrigation performance and mechanization energy requirements. The novelty of this study lies in presenting an integrated soil–machine–water conceptual framework that captures the continuous interactions and interdependencies among soil physical state, machine behavior, and water movement. By highlighting these dynamic relationships, this review provides a systems-level perspective on energy and water interactions in dryland agroecosystems, offering a foundation for predicting the environmental implications of mechanized operations under arid conditions. Overall, the review demonstrates that sustainable mechanized agriculture in arid regions requires integrated management of soil physical state, machine operation, and irrigation timing, where maintaining soil moisture within an optimal operational range is the key factor for reducing energy losses, preventing soil compaction, and improving water productivity.

Plant growth-promoting bacteria (PGPB) have a wide range of applications in agriculture and environmental management. They act as biostimulants and biofertilizers to enhance crop quality and yields in a more sustainable way. The present research aimed at isolating three active strains from the arid rhizosphere soil to act as biofertilizer. The plant growth-promoting features were evaluated in vitro and their implementation on pepper growth and yield were assessed and measured. Regarding IAA and ammonia production, the three designated isolates (P21, P22-1 and P58) showed patterns of high IAA production, producing 154.47 µg/mL, 155.03 µg/mL, and 188.65 µg/mL, respectively. Furthermore, considerable amounts of ammonia were detected in the supernatant of peptone water medium after 72 h of growth. Isolate P21 produced the maximum amount and generated 17.38 μmol/mL, whereas both P22-1 and P58 displayed lower amounts (15.47 and 15.92, respectively), without significant differences. P-solubilization efficacy calculated 18.7% (isolate P21), 64% (isolate P22-2), and 54% (isolate P58) when compared with un-inoculated medium. The molecular identification by 16S rRNA displayed that the three isolates belonged to Pseudomonas alkylphenolica strain P21 (PX257452), Pantoea agglomerans strain P22-1 (PX257453), and Pantoea brenneri strain P58 (PX257454). Applying the selected strains with sweet pepper in the presence of rock phosphate (RP) was assessed under greenhouse conditions. Three treatments (adding bacterial suspension at 0, 10 and 20 days after transplanting) from P21, P22-1, and P58 strains revealed that P21(3), P21(2), P22-1(3), and P58(3) treatments are considered the most promising treatments related to plant height, root length, leaf area, number of leaves per plant, leaf P-uptake, and stem P-uptake in addition to total plant P-uptake. In addition, the PCA biplot showed that MSP (mono-super phosphate), P22-1(3), and P58(3) are closely associated with high phosphorus uptake, indicating their effectiveness in enhancing phosphorus absorption by solubilizing insoluble forms. Eventually, PGPB will help the environment by improving soil fertility and structure, decreasing the need for toxic chemical fertilizers, and improving ecosystem health overall.

The assessment of soil suitability for sustainable intensive agriculture is important for the selection of land suitable for agricultural production with the least economic and environmental costs. This study was directed to evaluate agricultural soil suitability in the south of Hail province, Saudi Arabia to suggest alternative crops of alfalfa using the Agricultural Land Evaluation System for arid regions (ALES-Arid) model and Geographic Information System (GIS). The study area covers about 77558 ha and 111 soil samples were collected from 37 profiles and 37 groundwater samples were analyzed for physical, chemical and fertility properties. To evaluate land suitability for selected crops, the FAO guideline was used and also to analyze and map soils within the study area, the GIS was employed. The main research goals were to characterize the soil and water resources of the study area, in order to plan the best alternative crops of alfalfa using land evaluation facilities for different purposes (capability and suitability). The results indicated that the soils of the area are characterized by sandy loam texture, low fertility, low total carbonate and most of the area have moderate to high salinity soil. Capability classes of the area are C3 and C4 which covers about 89.19% and 10.81% of the total area, respectively. The most fertile classes were situated in C2 and C3 which covers 37.84 and 40.54% of the area with phosphorus and potassium limitation. The land suitability results show that 99.68, 97.18 and 85.37% of the area is classified as S1 (highly suitable) for wheat, barley and alfalfa, respectively; while 98.91% of the area is classified as S3 (marginally suitable) for maize. From the obtained data, growers are advised to cultivate barley or wheat in winter and maize in summer instead of alfalfa, in order to save irrigation water. This is also because the expected productivity of either wheat or barley is high. It is important for decision makers to determine the best way of using land for agricultural purposes, since it serves as a decision and planning support.

Soil health refers to the combined physical, chemical, and biological properties of soils that allow them to function as a living system that sustains life. While numerous reviews summarize several methods to measure soil health, this review critically evaluates how the Soil Management Assessment Framework (SMAF) performs across diverse ecosystems, including croplands, agroforestry, coastal mangrove, and rangelands. We explore the evolutionary shift from single indicators to integrated assessment frameworks, tracing how targeted management practices, such as conservation tillage, crop rotation, cover cropping, organic amendments, and water management, alter physical, chemical, and biological indicator performance. The SMAF framework and scoring system are outlined using examples from around the world. This synthesis concludes that while SMAF provides an exceptionally rigorous, non-linear platform for quantitative Soil Quality Index (SQI), its practical execution remains deeply constrained by data-intensive requirements, selection of appropriate indicators, and adaptation to local contexts. To close this gap, we outline critical future directions, integrating the framework with AI and machine learning, real-time Internet of Things (IoT) field sensors, and dynamic digital twins. Ultimately, this work aims to shift soil monitoring from descriptive reporting to predictive, automated intelligence to provide the exact data needed for sustainable land management decisions.