Fertilizers application are widely used to get a higher yield in agricultural fields. Nutrient management can be improved by cultivating leguminous species in order to obtain a better understanding of the mechanisms that increase the amount of available phosphorus (P) and potassium (K) through fertilizer treatments. A pot experiment was conducted to identify the leguminous species (i.e., chickpea and pea) under various fertilizer treatments. Experimental design is as follows: T0 (control: no fertilizer was applied), T1: P applied at the level of (90 kg ha⁻¹), T2: (K applied at the level of 90 kg ha⁻¹), and T3: (PK applied both at 90 kg ha⁻¹). All fertilizer treatments significantly (p < 0.05) improved the nutrient accumulation abilities and enzymes activities. The T3 treatment showed highest N uptake in chickpea was 37.0%, compared to T0. While T3 developed greater N uptake in pea by 151.4% than the control. However, T3 treatment also increased microbial biomass phosphorus in both species i.e., 95.7% and 81.5% in chickpeas and peas, respectively, compared to T0 treatment. In chickpeas, T1 treatment stimulated NAGase activities by 52.4%, and T2 developed URase activities by 50.1% higher than control. In contrast, T3 treatment enhanced both BGase and Phase enzyme activities, i.e., 55.8% and 33.9%, respectively, compared to the T0 treatment. Only the T3 treatment improved the activities of enzymes in the pea species (i.e., BGase was 149.7%, URase was 111.9%, Phase was 81.1%, and NAGase was 70.0%) compared to the control. Therefore, adding combined P and K fertilizer applications to the soil can increase the activity of enzymes in both legume species, and changes in microbial biomass P and soil nutrient availability make it easier for plants to uptake the nutrients.

Soil salinization is a serious environmental issue that significantly influences crop yield and soil fertility, especially in coastal areas. Numerous studies have been conducted on the salinity status in Pakistan. Information about the geospatial and temporal distribution of salinity in the Sujawal district is still lacking. The present study examines the soil salinity status and the impact of seawater intrusion in the entire district from 1990 to 2017 using field and remote sensing (RS) data. In addition, 210 soil samples at different depths (0–20, 20–40, and 40–60 cm) were collected from randomly selected locations for lab measurements of physiochemical properties. The results showed that the soil texture classes were mainly fine to medium particles. The samples collected at the 0–20 cm depth were mostly dominated by three textural classes of soil: clay at 19.5%, clay loam at 25.6%, and loam at 32.9%. The electrical conductivity (EC) of 65.7% soil samples collected from the top layer exceeded the normal range. The quantitative results indicated that the exchangeable sodium percentage (ESP) ranged between 1.38 and 64.58, and 72.2% of the top layer soil samples had ESP >15, while 81.5% of soil samples were in the normal range of soil pH. Furthermore, the results indicated that the vegetation decreased by 8.6% from 1990 to 2017, while barren land and water bodies increased significantly, by approximately 4.4% and 4.2%, respectively. The extreme and high salinity classes were characterized by high contents of soluble salt on the surface in the Jati and Shah Bandar subdistricts. In addition, the soil EC values at the 0–20 cm depth were significantly correlated with the salinity index (S1). Therefore, it was concluded that more than 50% of the top layer of soil was affected by salinity due to seawater intrusion, low rainfall, climate change, and erratic river flow. It is suggested that remote sensing (RS) data are more suitable for the detection of the soil salinity status of a region and impose a lower cost compared to other conventional approaches. However, this study could provide significant knowledge to land managers, policymakers, and government officials to allow them to take action to implement salinity control measures in the study area.

Cultivation of green manure crops is important strategy for improving soil fertility in South China. Therefore, it is important to obtain plant varieties that can better accumulate nutrients during the green manuring phase. The present study evaluated the phosphorus (P) and potassium (K) uptake efficiencies of various winter leguminous species at two different sites. Varieties tested included six Chinese milk vetch cultivars (i.e., Minzi No.6, Ningbodaqiao, Wanzi No.1, Xiangzi No.1, Yijiangzi, and Yujiangdaye), as well as hairy vetch and common vetch, while ryegrass was planted as a control. All leguminous species showed higher ability for P and K absorption in the two sites compared to the ryegrass. Hairy vetch and common vetch performed better than all six Chinese milk vetch cultivars, and the highest biomass and P and K uptake capacities were observed in hairy vetch. Green manuring had different effects on soil enzymes. Phosphatase (87.0%) and leucine-aminopeptidase (163.8%) were increased by hairy vetch. β-glucosidase (143.4%) and N-acetyl-glucosaminidase (283.3%) were increased by Yijiangzi and Yujiangdaye, respectively, in Guangxi compared to the control. Xiangzi No.1 increased N-acetyl-glucosaminidase (352.6%), leucine-aminopeptidase (477.5%), phosphatase (591%), and β-glucosidase (786.0%) in Hunan compared to the ryegrass. Enzyme activities increased nutrient availability at both experimental sites. P and K uptake efficiencies significantly related with soil enzymes in Guangxi. It is suggested that hairy vetch has the largest capability for P and K uptake and might be a potential choice for improving P and K management in sustainable agriculture.

The potential nutrient uptake abilities of a plant are essential for improving the yield and quality. Green manures can take up a huge amount of macronutrients from the soil. The mechanisms underlying the differences in nutrient uptake capacity among different nonlegume species remain unclear. The plot experiments were conducted to investigate the performance of nonlegume species including forage radish (Raphanus raphanistrum subsp. sativus), oil radish (Raphanus sativus var. Longipinnatus), February orchid (Orychophragmus violaceus L), and rapeseed (Baricca napus), while a ryegrass (Lolium perenne L.) species was used as a control. The study results showed that forage radish had the highest nutrient uptake (N and P), i.e., 322 and 101% in Hunan and 277 and 469% in the Sichuan site, respectively, compared with the control. While the greatest K uptake was found in forage radish, i.e., 123%, and February orchid, 243%, in the Hunan and Sichuan sites. Forage radish also presented higher phosphorus use efficiency in both experimental areas: Hunan by 301% and Sichuan by 633% compared to the control. Significant modifications were found in nutrient availability and enzyme activities after the cultivation of various species. The oil radish enhanced the β-glucosidase (BG) and leucine-aminopeptidase enzyme activities by 324 and 367%, respectively, while forage radish developed the highest phosphatase (Phase) and N-acetyl-glucosaminidase (NAG) activities compared to the ryegrass in Hunan. In the Sichuan site, the oil radish promotes enzyme activities such as Phase (126%), BG (19%), and NAG (17%), compared to the control. It is concluded that forage radish, oil radish, and February orchid can easily improve soil nutrient quality in green manuring practices and provide valuable nutrient management systems.

Winter green manure potentially uptake large amount of phosphorus (P) and potassium (K) from the soil. However, the mechanism underlying nutrient uptake in green manure varieties remain elusive. This study illustrates the P and K uptake potential in different leguminous and non-leguminous species through integrated approaches. The designed studies evaluated the 10 green manure crops included five leguminous species (i.e smooth vetch hairy vetch, common vetch, lathyrus sativus, and milk vetch) and five non-leguminous species (i.e February orchid, chinese radish, forage radish, rapeseed and ryegrass) were planted. Experimental results indicated that the smooth vetch had highest P and K uptake capabilities among all leguminous species. While, rapeseed and chinese radish greatly P and K obtain among all non-leguminous treatments. Therefore various species also affected soil P and K availability and soil enzymes. Soil enzymatic activities including phosphatase (150.7%) and N-acetyl-glucosaminidase (95.4%) were elevated in lathryus sativus compared to the hairy vetch and common vetch respectively. The highest β-glucosidase (95.2%) and leucine-aminopeptidase (107.6%) activities accumulated in lathyrus sativus greater than the smooth vetch in all legumes species. In non-leguminous treatments phosphatase (500.5%), β-glucosidase (424.4%), and N-acetylglucosaminidase (256.3%) activities were improved in chinese radish as compared to the rapeseed. In constrast ryegrass was promoted greatest leucine-aminopeptidase activity by 182.7% higher than forage radish. However soil enzymes significanlty improve soil nutrient status. As a conclusion smooth vetch, rapeseed, and Chinese radish varieties have more P and K obtain capacity and forage radish could enhance soil enzyme activities. It is suggested these varieties might be a better choice in green manuring practice for improving nutrient management and soil nutrient quality in agro-ecosystems.

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis.