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Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. The measured soil loss tolerance was compared to the risk of soil erosion (T value).In this study, GIS and remote sensing techniques have been integrated with the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss in the Mayurakshi river basin of eastern India. To determine soil erosion-prone areas, rainfall, land use, and land cover maps, as well as a digital elevation model (DEM), were used as input. The annual soil loss in the basin area is estimated to be 4,629,714.8 tons. Accordingly, the study basin was categorized into five soil loss severity classes: very low (40.92), low (49), moderate (6.5), high (2.4) and very high (1.18) risk classes. Soil erosion rates ranged from very slight to slight throughout the majority of the region. The section of the basin’s lower plain has been discovered to be least affected by soil loss. The results of study area can be helpful to conservation of soil management practices and watershed development program in the basin area. © 2022 by the authors.

Natural dyes are gaining a great deal of attention due to their eco-friendly and sustainable properties for advanced apparel applications. However, the reproducibility and accessibility of various colors using natural dyes remain challenging. In this study, plant-extracted fluorescent protein C-phycocyanin (CP) is used as a natural dye source and doped in polyvinyl alcohol (PVA) nanofibers via electrospinning for advanced apparel applications. The prepared nanofibers show a smooth and bead-free surface morphology. The FTIR results confirmed the formation of PVA nanofibers followed by a major peak at 3304 cm−1 due to the stretching of hydroxyl groups. Subsequently, CP-doping in PVA nanofibers is observed by the N–H deformation peaks at 1541 cm−1; C–N stretching vibrations at 1250 cm−1 and 1092 cm−1; and the C=O stretching vibrations of the carboxyl group at 1722 cm−1, respectively. Thus, CP-doped PVA nanofibers exhibit a good color strength (K/S) of 0.2 having a blue color tune and good color fastness properties. The mechanical strength of PVA nanofibers increased from 6 MPa to 18 MPa, due to crystalline characteristics endowed by the dope dyeing technique. Further, CP-doped PVA nanofibers exhibit homogeneous bright red fluorescence in individual nanofibers. Therefore, the proposed CP-doped PVA nanofibers can be used for flexible advanced apparel and biosensor applications.

of the current research was to use plant growth promoting rhizobacteria for sequestration and biotransformation of the toxic form of Cr and As into non-toxic form. Remediating these contaminants using microbes is a common technique and rhizo-microbiota not only relieves metal stress but also acts as biofertilizers. Role of plant growth-promoting rhizobacterial (PGPR) strains Acinetobacter beijerinckii (C5) and Raoultella planticola (C9) in counteracting chromium and arsenic stress in soybean seedlings was assessed. The isolated rhizobacteria were able to tolerate excessive quantities (up to 1200 ppm) of chromate and arsenate in liquid media. Beside their growth in heavy metal containing media, the strains were able to bio-transform chromate and arsenate to their least toxic form. They released significant quantities of stress related metabolites including phenols, flavonoids, proline, sugars and protein even in the presence of 1200 ppm of the heavy metals. They also released several plant hormones together with indole-3-acetic acid (IAA), salicylic acid (SA) and gibberellins. Another important feature of the isolates was their ability to solubilize phosphate and release siderophores and exposure to different levels of the selected heavy metals enhanced phosphate solubilization potential of both the isolates by up to 2-fold. Release of siderophore in A. beijerinckii C5 was enhanced by increasing heavy metals concentration in the media but in case of R. planticola C9 a decline was noted. When inoculated on soybean seedlings, the isolates modulated several metabolites of the hos plant enabling them to combat heavy metal toxicity at different levels. The PGPR strains boosted host's antioxidants production which minimized the oxidative damage by scavenging excessive ROS produced under stress. Control plants showed upregulation of stress response metabolites compared to PGPR application, whereas, IAA and SA were significantly higher in PGPR associated seedlings. In conclusion, PGPR alters the physiological and metabolic responses of soybean enabling it to cope better with chromate and arsenic toxicity and grow well under the stress.

Irrigation with sewage-contaminated water poses a serious threat to food security, particularly in developing countries. Heavy metal tolerant bacteria are sustainable alternatives for the removal of wastewater contaminants. In the present study, four lead (Pb)-tolerant strains viz. Bacillus megaterium (N8), Bacillus safensis (N11), Bacillus sp. (N18), and Bacillus megaterium (N29) were inoculated in spinach and grown in sewage water treated earthen pots separately and in combination with canal water. Results showed that Pb-tolerant strains significantly improved plant growth and antioxidant activities in spinach and reduces metal concentration in roots and leaves of spinach plants irrigated with treated wastewater. Strain Bacillus sp. (N18) followed by B. safensis (N11) caused the maximum increase in shoot length, root length, shoot fresh weight, root fresh weight, shoot dry weight, root dry weight, and leaf area compared to the uninoculated control of sewage water treated plants. These strains also improved antioxidant enzymatic activity including catalase, guaiacol peroxidase dismutase, superoxide dismutase, and peroxidases activities compared to the uninoculated control under sewage water conditions. Strain Bacillus sp. (N18) followed by B. safensis (N11) showed the highest reduction in nickel, cadmium, chromium, and Pb contents in roots and leaves of spinach compared to the uninoculated control plants treated with the sewage water. Such potential Pb-tolerant Bacillus strains could be recommended for the growth promotion of spinach after extensive evaluation under field conditions contaminated with wastewater.

A field experiment was conducted to investigate the effectiveness of different herbicides for controlling wild onion (Asphodelus tenuifolius) in cumin (Cuminum cyminum L.) during the rabi seasons (2018–2019 and 2019–2020) at Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan. The experiment comprised eight herbicidal weed management treatments for wild onion applied to cumin in a three-replication randomized block design. Among the herbicidal weed management treatments, early post-emergence (8 DAS) application of oxyfluorfen 200 g/ha resulted in the lowest weed density and dry matter of Asphodilus tenuifolius, with maximum weed (Asphodilus tenuifolius) control efficiency at 40 days after sowing (DAS) during both experimental years. Likewise, the highest total efficiency of weed control was recorded with the application of oxyfluorfen 200 g/ha at 8 DAS. Oxyflourfen 200 g/ha used early post emergence (8 DAS) reduced the weed index more effectively than the other herbicides. It also recorded the highest number of branches/plant, plant height, umbels/plant, umbellates/umbel, seeds/umbellates, and seed yield. However, application of oxyflourfen @ 200 g/ha 8 DAS–early POE and pendimethalin 38.7 CS 500 g/ha + oxyfluorfen @ 150 g/ha 8 DAS–early POE were statistically similar in terms of plant growth, yield, and yield attributes. The net returns (366.49 USD/ha in 2018–2019 and 175.72 USD/ha in 2019–2020) and B:C ratio (1.70 and 1.33 in 2018–2019 and 2019–2020, respectively) were also superior, with oxyfluorfen 200 g/ha applied early post emergence.

The center pivot irrigation system is a type of irrigation technology used to apply water effectively and uniformly over a wide variety of areas and topographies. These irrigation systems’ uniformity of water application greatly affects water use, energy consumption, and crop production. Performance tests of the standard lateral galvanized and modified polyethylene plastic pipes in the center pivot irrigation systems were conducted in different regions of Saudi Arabia. Water distribution depths along the laterals, coefficient of uniformity (CU), and distribution uniformity of the low quarter (DU) were determined. The results revealed that profiles of water distribution ranged from 4 to 14 mm for the standard-center pivot irrigation systems, while those for the modified-center pivot irrigation systems ranged from 6.5 to 50 mm. Standard-center pivot irrigation systems’ CU values ranged from 74 to 90%, with an average of 86%. In comparison, the modified-center pivot irrigation systems’ CU values ranged from 62 to 83%, with an average of 78%. The DU values ranged from 60 to 82% for the standard-center pivot irrigation systems, with an overall average of 77%. For the modified-center pivot irrigation systems, the DU values, in contrast, ranged from 31 to 75%, with an average of 65%. Thus, the accuracy and uniformity of the standard-center pivot irrigation systems are superior to those that have been modified. Additionally, a statistical model was developed to investigate the relationship between the water losses and the main climatic factors under field operating conditions. Therefore, the study results are expected to draw attention to standard lateral pipes’ value on the one hand and demonstrate the detrimental consequences of growers’ incorrect practices in pivot irrigation systems, motivating them to take strong action against these activities, on the other hand.

Documentation the relative influence of ecological dynamics on species diversity patterns can help us better understand spatial distribution patterns and devise a systematically comprehensive base for carrying out environmental explorations. The current attempt aimed at exploring the distribution patterns, diversity and richness of wild grasses with respect to climatic dynamics in the Gujrat district of Punjab, Pakistan. For this purpose, we applied the randomized sample method and sampled a total of 90 sites across the Gujrat district between 2019 and 2021 to document data on wild grasses and related ecological conditions. After assessment of the significant value index of each grass species with ecological records, we evaluated the data by ordination and cluster analysis. A total of 57 wild grasses from 37 genera were documented from the Gujrat district. The leading genera were Brachiaria, Cenchrus and Setaria, each accounting for 7.02% of all documented species, followed by Aristida and Panicum, each representing 5.76% of the species. Dactyloctenium, Dichanthium, Eragrostis,Polypogon,Poa and Saccharum each accounted for 5.26% of the species, and Digitaria, Pennisetum, Eragrostis, Chrysopogon Poa and Setaria each accounted for 3.51% of the species. Other genera each had a single species. Among all grasses, 75.44% of the species were native and 24.56% species were exotic and introduced to the study area. The leading life forms were therophytes (56.14%), followed by hemicryptophytes (42.11%) and geophytes (1.75%). Microphylls, with a 54.39% share, dominated the leaf size spectra of the wild grasses flora in this research. Other frequent classes included nanophylls (21.05%), macrophylls (19.3%) and leptophylls (5.26%). Flowering phenology of wild grasses showed that mainly species at the flowering phase were recorded during the months of June to August (40.35%) and July to September (19.29%). By applying Ward’s agglomerative clustering method, we classified the ninety transects into four major groups. Ordination analysis showed that different ecological factors had significant (p ≤ 0.002) effects on vegetation relations. The present endeavor provides a basic way to understand the impacts of ecological variables on the structure, diversity, composition and associations of wild grasses, which are helpful to improve the scientific-informed conservation and management measures for the environmental reestablishment of degraded habitat in the studied region.

In recent times, with the globalization of markets, shrinking of land and climate change, food basket diversification, increase in demand for nutrient-rich food, the protected cultivation of high-value crops (HVCs) have assumed a pivotal role in augmenting higher crop productivity and profitability and enhancing nutritional security of the growing population. In this context, a study was undertaken to analyze the impact of protected cultivation in horticultural crops in the districts of Almora and Dehradun in the Uttarakhand state. It was mainly based on primary data obtained through a primary survey and focus group discussion with the 96 farmers practicing protected cultivation by using a well-structured and pre-tested questionnaire. In economic analysis, the project analysis tools were used to assess the feasibility of the protected cultivation. The study clearly demonstrated that the cultivation of vegetables and flowers under protected cultivation is a highly profitable enterprise. However, the findings of the study indicated that the subsidy scheme needs to be continued to encourage maximum farmers to adopt protected cultivation and farmers need to be encouraged to form farmers producers organizations (FPOs), which would help them in seeking better quality of inputs and enhancing negotiating power in the market to realize maximum returns for their farm produce.

The aim of this study was to explore the effect of sowing methods and deficit irrigation on weed dynamics, yield and water-use efficiency (WUE) of the maize–cowpea system during the summer and monsoon seasons, respectively. The field experiment was carried out for two years (2015 and 2016) using a split design with three replicates under irrigated (maize) and rainfed (cowpea) conditions on vertisols of a semi-arid region. Treatments included three sowing methods [i.e., broad bed and furrow (BBF), corrugated furrow (CF) and ridges and furrow (RF)] and four irrigation levels [i.e., irrigation once in 10 days (I10D), irrigation at 40% (I40), at 50% (I50) and at 60% (I60) depletion]. The results indicated that, regardless of weed flora (monocots, dicots and sedges), the RF method produced higher weed density (2.09–2.98 No. m−2) compared to CF (2.00–2.80 No. m−2) and BBF (1.85–2.64 No. m−2) in maize at 30 and 60 days after sowing (DAS). The RF method with irrigation at I40 and I50 recorded significantly higher weed density, followed by the CF and BBF method. A similar trend was also observed with dry weight of weeds (monocot, 24.19%, dicot, 25.52%, and sedges, 29.80%) in maize at 30 and 60 DAS. Higher weed density and dry weight of weeds in the RF method with I40 was due to higher soil moisture availability and higher nutrient uptake due to larger lateral wetting of the soil and greater water use (29.27%). However, the BBF method favoured the growth of weeds (9.33–16.60%) in cowpea at 55 DAS and coped under rain-fed situation over CF and RF. The CF with moderate depletion (I50) method produced significantly higher maize equivalent yield (MEY) of cowpea (10,000 kg ha−1) with considerable reduction in the total water usage (19.33%). Therefore, under a water scarcity situation, growers can practice CF and I50 for higher yield and WUE of maize–cowpea sequence cropping.

Recently, the development of eco-friendly mechanochemical approaches for the preparation of novel catalysts with enhanced activity and selectivity has gained considerable attention. Herein, we developed a rapid and solvent-less mechanochemical method for the preparation of mixed metal oxide (Ag2O&ndash, MnO2) decorated graphene oxide (GRO)-based nanocomposites (Ag2O&ndash, MnO2/(X wt.%)GRO), as the Ag2O&ndash, MnO2/(X wt.%)GRO nanocomposite was fabricated by the physical grinding of freshly prepared GRO and pre-annealed (300 &deg, C) mixed metal oxide nanoparticles (NPs) (Ag2O&ndash, MnO2) using an eco-friendly milling procedure. The as-prepared nanocatalysts were characterized by using various techniques. Furthermore, the nanocomposites were applied as a heterogeneous catalyst for the oxidation of alcohol by employing gaseous O2 as an eco-friendly oxidant under base-free conditions. The mechanochemically obtained GRO-based composite exhibited noticeable enhancement in the surface area and catalytic performance compared to the pristine Ag2O&ndash, MnO2. The results revealed that (1%)Ag2O&ndash, MnO2/(5 wt.%)GRO catalyst exhibited higher specific performance (13.3 mmol.g&minus, 1.h&minus, 1) with a 100% conversion of benzyl alcohol (BnOH) and &gt, 99% selectivity towards benzaldehyde (BnH) within 30 min. The enhancement of the activity and selectivity of GRO-based nanocatalyst was attributed to the presence of various oxygen-containing functional groups, a large number of defects, and a high specific surface area of GRO. In addition, the as-prepared nanocatalyst also demonstrated excellent catalytic activity towards the conversion of a variety of other alcohols to respective carbonyls under optimal conditions. Besides, the catalyst ((1%)Ag2O&ndash, MnO2/(5 wt.%)GRO) could be efficiently recycled six times with no noticeable loss in its performance and selectivity.

Development of sustainable and efficient irrigation strategies is a priority for producers faced with water shortages. A promising management strategy for improving irrigation water use efficiency (IWUE) is deficit irrigation, which attempts to optimize yield and IWUE. Soil water use, crop yield and IWUE of tomato were evaluated for two consecutive years under two types of irrigation methods (subsurface and surface drip irrigation) and three irrigation strategies: 1.0 of full irrigation supply (T1), 0.8 of full irrigation supply (T2) and 0.6 of full irrigation supply (T3). The results showed that the highest yields were found in the plots irrigated by subsurface drip irrigation at T1 (94.1 ton/ha) and T2 (81.4 ton/ha). Conversely, the fully stressed treatment (T3) reduced the amount of irrigation water by 40%, but significantly decreased mean tomato yield by 25.6% and 26.1% under subsurface and surface drip irrigation, respectively, as compared to T1. The maximum IWUE tended to be higher for subsurface drip than for surface drip irrigation system. The greatest IWUEs were obtained from subsurface drip and surface drip at T3 (19.7 kg/m3 and 18.3 kg/m3), whereas the lowest IWUEs were those estimated in T1 (15.9 kg/m3 and 14.8 kg/m3, respectively). The primary conclusion is that deficit irrigation strategies present certain advantages to crop water management with minimal effects on production and quality, thus contributing to crop sustainability.

Wind drift and evaporation losses (WDEL) play a significant role in the development of water conservation strategies in sprinkler irrigation. In this study, artificial neural network (ANN) and multiple linear regression (MLR) models were developed by taking data collected from published studies on predicted WDEL for several design, operational, and meteorological conditions of variables in sprinkler irrigation. Five combinations of input variables, including riser height, operating pressure, main nozzle diameter, auxiliary nozzle diameter (da), water discharge by main nozzle, water discharge by auxiliary nozzles, wind speed (WS), air temperature, and relative humidity were used to create prediction models for WDEL. The ANN and MLR models were trained and tested on 70% and 30% of the data points, respectively. The accuracy of the models was assessed by the coefficients of correlation (r), overall indices of model performance (OI), root mean square errors (RMSE), and mean absolute errors (MAE). Statistical results showed that the ANN and MLR models with all input variables had the best predicting capabilities. When comparing the results of different ANN and MLR models, it was seen that the ANN models had more success in predicting WDEL. The ANN models gave higher r (0.843–0.956) and OI (0.794–0.909) values, and lower RMSE (2.662%–4.886%) and MAE (2.197%–3.729%) values compared to the MLR models in the training stage. The MLR models’ r values ranged from 0.794 to 0.864, OI values ranged from 0.747 to 0.816, RMSE values ranged from 4.562% to 5.514%, and MAE values ranged from 3.513% to 4.414%. Furthermore, a contribution analysis found that the design parameter da and the climatic parameter WS were considered to obtain the most robust estimation model. It can be stated that the ANN model is a more suitable tool than the MLR model for the prediction of WDEL from sprinkler-irrigation.

Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs.

An in-situ field study on two types of irrigation methods and three irrigation regimes was conducted in a sandy loam soil located at King Saud University, Riyadh, Saudi Arabia in 2015 and 2016. The study was to assess the effects of different irrigation methods on physiological and yield responses of tomato crops under water shortage conditions. The tested irrigation methods were surface drip irrigation (SDI) and subsurface drip irrigation (SSDI) systems. Irrigation treatments consisted of three strategies: (1) plants were irrigated with a water depth of 100% of the full irrigation supply; (2) plants were irrigated with a water depth of 80% of the full irrigation supply; and (3) plants were irrigated with a water depth of 60% of the full irrigation supply. Results indicated that water shortage significantly affected yield and quality response for each season. Over a 2-year average, yield increase was greatest in T1-SSDI followed by T2-SSDI and then T1-SDI. The yield response factor was 0.95 and 1.05 for SSDI and SDI, respectively. The highest water use efficiency values were obtained in T2-SSDI (16.3 kg m−3) and T1-SSDI (15.6 kg m−3), and the lowest ones, those estimated in T1-SDI (10.9 kg m−3) and T3-SDI (9.5 kg m−3).

The prediction of the soil infiltration rate is advantageous in hydrological design, watershed management, irrigation, and other agricultural studies. Various techniques have been widely used for this with the aim of developing more accurate models; however, the improvement of the prediction accuracy is still an acute problem faced by decision makers in many areas. In this paper, an intelligent model based on a fuzzy logic system (FLS) was developed to obtain a more accurate predictive model for the soil infiltration rate than that generated by conventional methods. The input variables that were considered in the fuzzy model included the silt and clay contents. The developed fuzzy model was tested against both the observed data and multiple linear regression (MLR). The comparison of the developed fuzzy model and MLR model indicated that the fuzzy model can simulate the infiltration process quite well. The coefficient of determination, root mean square error, mean absolute error, model efficiency, and overall index of the fuzzy model were 0.953, 1.53, 1.28, 0.953, and 0.954, respectively. The corresponding MLR model values were 0.913, 2.37, 1.92, 0.913, and 0.914, respectively. The sensitivity results indicated that the clay content is the most influential factor when the FLS-based modelling approach is used for predicting the soil infiltration rate. Keywords: FLS, infiltration rate, MLR, modelling, sensitivity analysis

An increasing scarcity of water, as well as rapid global climate change, requires more effective water conservation alternatives. One promising alternative is rainwater harvesting (RWH). Nevertheless, the evaluation of RWH potential together with the selection of appropriate sites for RWH structures is significantly difficult for the water managers. This study deals with this difficulty by identifying RWH potential areas and sites for RWH structures utilizing geospatial and multi-criteria decision analysis (MCDA) techniques. The conventional data and remote sensing data were employed to set up needed thematic layers using ArcGIS software. The soil conservation service curve number (SCS-CN) method was used to determine surface runoff, centered on which yearly runoff potential map was produced in the ArcGIS environment. Thematic layers such as drainage density, slope, land use/cover, and runoff were allotted appropriate weights to produced RWH potential areas and zones appropriate for RWH structures maps of the study location. Results analysis revealed that the outcomes of the spatial allocation of yearly surface runoff depth ranging from 83 to 295 mm. Moreover, RWH potential areas results showed that the study areas can be categorized into three RWH potential areas: (a) low suitability, (b) medium suitability, and (c) high suitability. Nearly 40% of the watershed zone falls within medium and high suitability RWH potential areas. It is deduced that the integrated MCDA and geospatial techniques provide a valuable and formidable resource for the strategizing of RWH within the study zones.

Field experiments have been conducted in King Saud University, Riyadh- Saudi Arabia to study the weather-based irrigation controllers (WBICs) affecting agronomical characteristics and water use of irrigated. The assessed WBICs technologies were Weathermatic SL1600 and Hunter pro C controllers under subsurface drip irrigation system. The study investigated the effect of these technologies and its suitability for agricultural applications. The main results in this study can be summarized as the WBICs had significantly affected the tomato yield, water use efficiency and water savings. The WBICs could save irrigation water by 32.4 % and increased water use efficiency greatly up to 50.8 %, while maintaining competing yield as compared to a time-based irrigation schedules (control). Furthermore, the agronomical characteristics (vegetative growth, fruit quality and fruit yield traits) confirmed the priority of the WBICs when they compared with various time-based irrigation schedules.

CoxOy&ndash, manganese carbonate (X%)(CoxOy&ndash, MnCO3 catalysts (X = 1&ndash, 7)) were synthesized via a straightforward co-precipitation strategy followed by calcination at 300 &deg, C. Upon calcination at 500 &deg, C, these were transformed to CoxOy&ndash, dimanganese trioxide i.e., (X%)CoxOy&ndash, Mn2O3. A relative catalytic evaluation was conducted to compare the catalytic efficiency of the two prepared catalysts for aerial oxidation of benzyl alcohol (BzOH) to benzaldehyde (BzH) using O2 molecule as a clean oxidant without utilizing any additives or alkalis. Amongst the different percentages of doping with CoxOy (0&ndash, 7% wt./wt.) on MnCO3 support, the (1%)CoxOy&ndash, MnCO3 catalyst exhibited the highest catalytic activity. The influence of catalyst loading, calcination temperature, reaction time, and temperature and catalyst dosage was thoroughly assessed to find the optimum conditions of oxidation of benzyl alcohol (BzOH) for getting the highest catalytic efficiency. The (1%)CoxOy&ndash, MnCO3 catalyst which calcined at 300 &deg, C displayed the best effectiveness and possessed the largest specific surface area i.e., 108.4 m2/g, which suggested that the calcination process and specific surface area play a vital role in this transformation. A 100% conversion of BzOH along with BzH selectivity &gt, 99% was achieved after just 20 min. Notably, the attained specific activity was found to be considerably larger than the previously-reported cobalt-containing catalysts for this transformation. The scope of this oxidation reaction was expanded to various alcohols containing aromatic, aliphatic, allylic, and heterocyclic alcohols without any further oxidation i.e., carboxylic acid formation. The scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and Brunauer&ndash, Emmett&ndash, Teller (BET) specific surface area analytical techniques were used to characterize the prepared catalysts. The obtained catalyst could be easily regenerated and reused for six consecutive runs without substantial decline in its efficiency.

The proper planning of storage structures, waterways, irrigation schemes, water harvesting, erosion control structures, and groundwater development strategies requires accurate estimation of surface runoff. However, hydrologists in Saudi Arabia face serious challenges, specifically due to the rare availability of surface runoff data. In this study, the soil conservation service-curve number (SCS-CN) method integrated with geographic information system (GIS) and remote sensing (RS) was utilized to estimate the surface runoff in Wadi-Uranah basin, in the western region of Saudi Arabia. Different thematic maps such as slope, hydrologic soil group (HSG), land use/land cover (LULC), and daily rainfall have been created in GIS environment and processed to generate the curve number (CN) and surface runoff maps. Based on the soil classification results, the study area was categorized into two HSGs (B and C). The dominant HSG was group C, representing about 98.8% of the total area. The LULC analysis showed four main land use types in the study region: urban, rocks, barren soil, and agricultural areas. Furthermore, the finding results showed that CN values for the normal conditions (CNII) ranged between 74 and 93 in agricultural and both urban and rock areas, respectively. The CNII values were further corrected using slope data to derive slope-adjusted CNII. Moreover, the rainfall-runoff results showed an increase in the daily runoff of the study region with a minimum of 15 mm to a maximum of 74 mm. Another interesting result was rainfall-runoff linear regression analysis that showed a good correlation of 0.98. Additionally, the peak runoff hydrograph flows for 10-, 50-, and 100-year return periods obtained from the SCS-based dimensionless unit hydrograph were 828, 1353, and 1603 m3/s, respectively. Therefore, this study highlights that the SCS-CN method integrated with RS and GIS deserves further attention for estimating runoff of ungauged basins for better basins management and conservation purposes.

The determination of the water requirements and crop coefficient (Kc) of agricultural crops helps to create an appropriate irrigation schedule, and with the effective management of irrigation water. The aim of this research was to estimate the water requirement, Kc, and water-use efficiency (WUE) of potato using non-weighing-type lysimeters in four regions of the Kingdom of Saudi Arabia (Qassiem, Riyadh, Al-Jouf, and Eastern). Our results clearly show that the accumulated values of the measured crop evapotranspiration of potato derived from the lysimeters were 573, 554, 592, and 570 mm, while the accumulated values of the predicted crop evapotranspiration from Penman-Monteith equation based on FAO (Food and Agriculture Organization) were 651, 632, 672, and 647 mm for the Qassiem, Riyadh, Al-Jouf, and Eastern regions, respectively. The Kc values of potato obtained from the lysimeters were Kc initial (0.58, 0.54, 0.50, and 0.52), Kc middle (1.02, 1.05, 1.13, and 1.10), and Kc end (0.73, 0.74, 0.74, and 0.75) for the Qassiem, Riyadh, Al-Jouf, and Eastern regions, respectively. Based on the amount of water used and the yield achieved, the highest WUE (3.6 kg m&minus, 3) was observed in the Riyadh region, while the lowest WUE (1.5 kg m&minus, 3) was observed in the Al-Jouf region.

Automated irrigation systems (AISs) are critical for the sustainability of irrigated farming systems, considering the present water crisis in Saudi Arabia. This study investigated whether electronic controllers in irrigation systems effectively save water. The study also assessed the effect of these controllers on crop yield using drip and sprinkler irrigation systems in severely arid climate conditions. Evapotranspiration (ET) controllers were installed in experimental fields of wheat (Triticum aestivum) and tomato (Solanum lycopersicum Mill.) crops for 2 successive seasons. The results revealed that the water use efficiency (WUE) and irrigation water use efficiency (IWUE) were typically higher in the AIS than in the conventional irrigation control system (CIS). Under the AIS treatment, the WUE and IWUE values were 1.64 and 1.37 k·gm-3 for wheat, and 7.50 and 6.50 kg·m-3 for tomato crops; under the CIS treatment the values were 1.47 and 1.21 kg·m-3 for wheat and 5.72 and 4.70 kg·m-3 for tomatoes, respectively. Therefore, the AIS provided significant advantages in both water savings and crop yields by utilising up to 26% less water than the CIS, and simultaneously generating higher total yields. The automated irrigation system technique may be a valuable tool for conserving water and scheduling irrigation for wheat and tomato crops, and may be extendable to other similar agricultural crops.Keywords: autonomous control, evapotranspiration controller, water conservation, sprinkler and drip irrigation, water-use efficiency, arid regions