Soil salinity and sodicity severely degrade soil structure and hydraulic properties in arid regions, limiting agricultural productivity. This study investigated the effects of reduced fertilizer combined with humic acid (HA) and biochar (BC) on the physical properties of sodic soil. A three-year field experiment included five treatments: an untreated control (CK), full fertilizer application (F, 700 kg/ha compound fertilizer with 1:1:1 NPK ratio), reduced fertilizer combined with HA (FA, 350 kg/ha fertilizer + 3000 kg/ha HA), reduced fertilizer combined with BC (FB, 350 kg/ha fertilizer + 3000 kg/ha BC), and reduced fertilizer combined with both HA and BC (FAB, same rates). Results demonstrated that the FAB treatment synergistically enhanced soil aggregation, increasing the content of >2 mm macroaggregates by 7.10-fold in the surface layer and improving aggregate stability (mean weight diameter increased by 2.62-fold) compared to CK. It also reduced soil bulk density by 17% and increased saturated water-holding capacity by 47%. Strong correlations indicated that aggregate stability governed improvements in porosity and water retention. These findings provide a mechanistic basis for using combined organic amendments with reduced fertilizer to rehabilitate sodic soils in terms of soil structure and hydraulic properties.

Soil degradation in Northwest Ethiopia, driven by acidity and nutrient depletion, severely limits maize productivity. This study evaluated the integrated use of biochar (BC), vermicompost (VC; applied on a nitrogen-equivalent basis), and inorganic nitrogen-phosphorus (N-P) nutrients to rehabilitate acidic soils over two growing seasons (2023/24–2024/25). Treatments markedly improved key soil physical properties: bulk density decreased by up to 10.8%, while gravimetric moisture content increased substantially. These improvements directly enhanced maize growth, increasing leaf area index by 26% and harvest index to over 50%. The highest grain yield (12.13 t ha−1) was achieved with high amendment rates (8 t BC + 10.04 t VC + 120–30.2 kg N-P ha−1), representing a 176% increase over the control. However, economic analysis revealed that moderate integrated application (4 t BC + 5.02 t VC + 120–30.2 kg N-P ha−1) delivered near-optimal yield (12.09 t ha−1) with superior economic returns, optimizing the cost-benefit ratio. A strong predictive model coefficient of determination (R 2 = 0.906) confirmed soil and crop parameters as reliable yield indicators. We recommend this moderate, integrated package as a suitable agronomic and economically viable strategy for sustainable maize intensification in acidic and drought-prone agroecosystems.

Due to limited daily weather data, reanalysis data like NASA/POWER and ERA5 are used for simulation models and decision tools. This study evaluates the accuracy of these two reanalysis products in Iran (2013–2023) and their effectiveness in simulating wheat growth using the CSM-CERES-Wheat model, comparing them against simulations based on observed data. The study found that reanalysis data from NASA/POWER and ERA5 generally matched observational data well for solar radiation and temperature, with high correlations and low errors. NASA/POWER generally outperformed ERA5 in predicting both precipitation and solar radiation throughout the study area and during the winter wheat growing season. A comparison of simulated maturity dates and evapotranspiration during winter wheat growth based on observed and reanalysis data, revealed a mean NRMSE of 6.5% and 20.6%, respectively, across the study area. Simulated wheat yields using reanalysis data showed an NRMSE of 23.2–27.2% and an agreement of 52–64% compared to observed data under irrigated systems. Reanalysis datasets serve as a vital resource for crop simulation studies, especially in regions with limited availability of high-quality meteorological station data. However, their spatiotemporal variability must be thoroughly evaluated to ensure reliability and suitability for specific applications.

Saravan County, a significant date-producing region in Iran, has experienced an increasing frequency and intensity of dust storms in recent years. These events pose substantial threats to the regional economy, agricultural productivity, and public health. Prior research indicates that dust storms in this area are associated with a 30-40% reduction in Mazafati date yields, a notable decrease in fruit total sugar content, and a 40% increase in respiratory-related medical center visits. This study investigates the spatiotemporal variations of dust Aerosol Optical Depth (AOD) in Saravan County, Sistan and Baluchestan Province, over a period (2000-2022). MODIS satellite data (1 km resolution AOD product) and climatic and hydrological indices were employed to analyze trends and spatial correlations within a GIS environment. The Mann-Kendall test revealed significant increasing trends (p < 0.05) for the Palmer Drought Severity Index (PDSI), precipitation, evapotranspiration, maximum temperature, wind speed, and AOD, whereas minimum temperature exhibited a decreasing trend. Spatial correlation analysis indicated a negative correlation (r = −0.45 to −0.68) between AOD and precipitation, runoff, and soil moisture indices. Conversely, evapotranspiration, maximum temperature, minimum temperature, and wind speed indices displayed a positive correlation (r = 0.52 to 0.79) with AOD. The novelty of this study lies in mechanistically explaining the climatic paradox of how the region is becoming dustier despite a trend toward wetter conditions, identifying escalating thermal stress as the dominant driver over precipitation in this hyper-arid, economically sensitive environment.

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

Water erosion poses a significant threat to the sustainability of natural resources and hydro-agricultural infrastructure, particularly in the Mediterranean region, including Morocco. The Allal El Fassi watershed faced such challenges and underwent anti-erosion measures between 2008 and 2014. This study aims to evaluate the effectiveness of these measures in mitigating dam siltation within the watershed. The methodologies employed include the utilization of the MUSLE model, optimized with bathymetric surveys of the dam, to estimate the amount of sediment reaching the dam and derive the P-factor associated with the implemented anti-erosion measures. Additionally, the radioisotope technique was utilized to assess erosion rates at managed and unmanaged sites within the watershed. The results obtained using the MUSLE model and bathymetric data revealed a noteworthy reduction in mean annual soil loss within the timeframe of 2002–2010 to 2010–2013, resulting in a 13.9% decrease in sediment reaching the dam. Furthermore, variations in the P-factor of erosion control measures were observed, ranging from 0.31 to 0.52 during the respective periods. Utilizing the radionuclide 7Be, erosion rates ranging from 6.8 to 25.3 t.ha−1 were determined for the studied plots, facilitating the calculation of P-factors for specific erosion control measures. Significantly, gabion check dams displayed a P-factor of 0.47, dry stone barriers had a P-factor of 0.41, fruit trees with basins showed a P-factor of 0.51, and enclosures exhibited a P-factor of 0.67. These findings suggest a causal relationship between the implementation of erosion control measures and the reduction in sedimentation rates at the Allal El Fassi Dam, highlighting the interdependence between the two phenomena.

The degradation of salinized soils is imposing a steady decline in productivity, leading to significant socio-economic and environmental repercussions. As a promising solution for revitalizing depleted soil potential, the cultivation of salt-tolerant plants such as Casuarina emerges as a viable approach. This study delves into this prospect by evaluating six Casuarina species to discern their levels of tolerance to varying concentrations of NaCl, particularly in the initial stages of germination and seedling growth. The imposition of salt stress instigated a reduction in both germination capacity and speed. High variability in salt tolerance was observed between the tested species. Notably, the final germination percentages at 100 mM NaCl unveiled considerable disparities: C. torulosa, C. tenuissima, and C. stricta exhibited percentages of 33%, 19%, and 10%, respectively, while C. cunninghamiana, C. equisetifolia, and C. glauca showcased higher germination rates at 63%, 62%, and 55%, respectively. Furthermore, the increase of the recovery rate with high NaCl concentration confirmed that the salinity caused only an osmotic effect and not a toxic one on the studied species. Additionally, the adoption of seed priming showcased marked enhancements in germination rates across all NaCl concentrations, effectively expediting the germination process. In response to salinity stress, reductions were noted in root length (RL), and shoot length (SL). All germination parameters and the computation of the salt tolerance index (STI) facilitated the classification of the six Casuarina species in a descending order of salinity tolerance: C. cunninghamiana Miq. > C. equisetifolia L. > C. glauca Sieb. > C. torulosa Ait. > C. tenuissima Sieb. > C. stricta Ait.

The investigation of unsaturated soil through numerical modeling requires the prior information on soil properties including Soil Water Characteristic (SWC) equation. Acquiring these data for different soil texture classes is laborious and time-consuming. Consequently, there is a need for predefined dataset numerical modeling of unsaturated zone in different soil texture classes. In this study, 32 soil samples were collected from different locations, and soil physical properties like particle size distribution, particle density, bulk density, porosity, and saturated hydraulic conductivity were determined. Six different soil texture classes have been found in this study and average properties were computed for each soil texture class. SWC curves were developed for all soil samples using pressure plate apparatus. Based on average SWC curves, optimal parameters of four different SWC equations (Brooks and Corey, Campbell, Van Genuchten, and Fredlund and Xing models) were determined using non-linear regression with the Trust Region Optimization algorithm with R 2 and RMSE values ranging from 0.9650 to 0.9983 and from 0.0018 to 0.0219 cm3/cm3, respectively. All the four SWC models were compared using Taylor diagrams, which show that Fredlund and Xing models are the best models among the four. Further, the performance of developed SWC equations was compared with the moisture content of UNSODA datasets and results were satisfactory as RMSE ranges from 0.0381 to 0.1265 cm3/cm3. Thus, this study provides a reliable SWC equation for different soils in Bihar.

Land degradation (LD) is a significant environmental issue that needs to be spatially mapped in different areas. This research provides a framework to prioritize the geo-environmental and anthropogenic factors on LD in a semi-arid region, Qazvin Plain, in Iran based on the maximum entropy model for the first time and also to produce the LD hazard map. The LD inventory map that shows the occurrence points of LD was prepared based on the decreasing trend of net primary productivity (NPP) over 20 years. 120 random points were selected as degraded sites where NPP showed a decreasing trend. Several environmental and anthropogenic factors including temperature, rainfall, land use, normalized difference vegetation index (NDVI), vegetation soil salinity index (VSSI), visible and shortwave infrared drought index (VSDI), electrical conductivity (EC) and sodium adsorption ratio (SAR) of groundwater, groundwater table, annual depletion of groundwater resources, slope, aspect, and altitude were considered as predictive factors of LD and their relative contributions were determined using the maximum entropy model. According to the findings, 49.74% of the region was in the class of low degradation. Also, 22.57%, 17.87%, and 9.82% of the plain were related to the moderate, high, and very high degradation classes, respectively. The model specified altitude as the most significant variable influencing LD followed by land use, slope, and VSSI. The least important variable affecting LD in the study area was the aspect. The results of modeling LD also showed that the areas most sensitive to LD were frequently rangelands which are mostly found on steep slopes and highlands. Based on the UAC value, the model illustrated a very good efficiency with a value of 0.8. According to the results, the maximum entropy model successfully mapped the LD hazard in the Qazvin plain.

Water resources pose a considerable limitation in arid and semi-arid regions like Iran, primarily due to the scarcity of rainfall and heightened evaporation rates. Within open hydroponic systems, a significant concern arises from the depletion of nutrient solutions utilized for crop fertigation. This predicament subsequently gives rise to environmental issues of note and a discernible surge in production expenses. Application of humic acid (HA), as a plant biostimulant, enhances plant tolerance to reduced water supply and counterbalances the effects of diluted nutrient solutions. A greenhouse experiment was conducted to investigate the impact of applying HA (600 ppm) in conjunction with three nutrient solution concentrations (complete nutrient solution (NSc), 75% NSc (NS1), and 50% NSc (NS2)), as well as two irrigation levels (100 and 50% of plant water requirement, IL100 and IL50, respectively). The treatment under IL50 was referred to as ½ NSc, ½ NS1, and ½ NS2. The findings showed that almost all of the traits studied were significantly affected by the applied treatments. The highest yield (409.4 g m−2), leaf area (34.67 cm−2 plant), biomass (1.092 g−1 plant), membrane stability index (90.47%), chlorophyll a (0.99 mg g−1 FW), chlorophyll b (0.413 mg g−1 FW), total chlorophyll (1.54 mg g−1 FW), carotenoid (0.437 mg g−1 FW), vitamin C (730.7 mg ascorbic acid kg−1 FW), amino acid (236.8 µg g−1 FW) and nitrate reductase activity (102.8 µg NO2 - mg Protein−1 h−1) were observed in NSc + HA. In addition, the highest carbohydrate (46.99 mg g−1 FW), phenol (1129 mg GAE Kg−1 FW), and flavonoid content (1133 mg Catechin Kg−1 FW) and antioxidant activity (74.75%) were observed in ½ NS1 + HA. In addition, the highest relative water content (86.62%) and nitrate content (1106 mg kg−1 FW) were recorded in NS2 + HA and NSc, respectively. The application of HA combined with different concentrations of the nutrient solution under both irrigation levels caused an increase in most of the parameters studied in rocket plants compared to respective treatments without HA application. In addition, the results obtained confirmed that HA could compensate for the stress caused by a shortage of nutrients and water in the nutrient solution in this crop.