Land degradation has become one of the major threats throughout the globe, affecting about 2.6 billion people in more than 100 countries. The highest rate of land degradation is in Asia, followed by Africa and Europe. Climate change coupled with anthropogenic activities have accelerated the rate of land degradation in developing nations. In India, land degradation has affected about 105.48 million hectares. Thus, modeling and mapping soil loss, and assessing the vulnerability threat of the active erosional processes in a region are the major challenges from the land and water conservation aspects. The present study attempted rigorous modeling to estimate soil loss from the Banas Basin of Rajasthan state, India, using GIS-integrated Revised Universal Soil Loss Equation (RUSLE) equation. Priority ranking was computed for different watersheds in terms of the degree of soil loss from their catchments, so that appropriate conservation measures can be implemented. The total area of Banas basin (68,207.82 km²) was systematically separated into 25 watersheds ranging in area from 113.0 to 7626.8 km². Rainfall dataset of Indian Meteorological Department for 30 years (1990–2020), FAO based Soil map for soil characterization, ALOS PALSAR digital elevation model for topographic assessment, and Sentinal-2 based land use and land cover map were integrated for modeling and mapping soil erosion/loss risk assessment. The total annual soil loss in the Banas basin was recorded as 21,766,048.8 tons. The areas under very low (0–1 t ha^-1 year^-1), low (1–5 t ha^-1 year^-1), medium (5–10 t ha^-1 year^-1), high (10–50 t ha^-1 year^-1) and extreme (>50 t ha^-1 year^-1) soil loss categories were recorded as 24.2, 66.8, 7.3, 0.9, and 0.7%, respectively, whereas the respective average annual soil loss values were obtained as 0.8, 3.0, 6.0, 23.1, and 52.0 t ha^-1 year^-1. The average annual soil loss among different watersheds was recorded in the range of 1.1–84.9 t ha^-1 year^-1, being highest (84.9 t ha^-1 year^-1) in WS18, followed by WS10 (38.4 t ha^-1 year^-1), SW25 (34.7 t ha^-1 year^-1) and WS23 (17.9 t ha^-1 year^-1), whereas it was lowest for WS8 (1.1 t ha^-1 year^-1). Thus, WS18 obtained the highest/top priority rank in terms of the average annual soil loss (84.9 t ha^-1 year^-1) to be considered as the first priority for land and water conservation planning and implementation. The quantitative results of this study would be useful for implementation of land and water conservation measures in the problematic areas of the Banas basin for controlling soil loss through water erosion.

Identifying the most critical sub-watershed or reservoir catchment in relation to water spread, pattern of soil erosion and aquifer recharge in a basin can be highly useful for implementing conservation measures. Thus, a study was undertaken to (i) review the various parameters used in watershed morphometric analysis with their appropriate mathematical expressions, (ii) discuss their importance in watershed prioritization and conservation planning, (iii) formulate mathematical relationship between important morphometric parameters, and (iv) discuss the different tools and techniques used for sub-watershed prioritization. All the morphometric parameters are important for hydrologic characterization and watershed prioritization. However, some of them are highly significant for better understanding of the watershed drainage network, geometry, drainage texture and relief parameters. The smaller values of shape parameters viz. form factor (Ff < 0.78), elongation ratio (Re < 0.80) and circularity ratio (Rc < 0.50) indicate a basin having elongated shape and flatter peak for extended period and permit a greater soil erosion/loss as these are inversely related with erodibility. Whereas, the greater values of Ff (>0.78), Re (>0.80) and Rc (>0.50) indicate a basin having circular shape and higher peak for smaller period. The higher values of drainage density (Dd > 0.60), drainage texture (Dt > 0.6) and stream frequency (Fs > 10) also indicate higher erosion due to their direct relationship with erodibility. As per this review, watersheds can be prioritized by several techniques including morphometric analysis, LULC change analysis and soil loss estimation. However, the morphometric analysis-based watershed prioritization is found to be laborious and time consuming as it involves numerous parameters, and hence can be replaced by modern data reduction methods like principal component analysis (PCA) using suitable software's like R, SPSS and XLSTAT. Thus, geospatial techniques-based catchment/watershed prioritization supported with an appropriate data dimension reduction technique (e.g. PCA) would be highly significant for planning conservation measures and management in a watershed.'

A study was carried out to develop and evaluate the performance of different machine learning (ML) models for predicting reference evapotranspiration (ET0). The models included multiple linear regression (MLR), least square-support vector machine (LS-SVM), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The daily meteorological data for 50 years (1970–2019) were used to estimate ET0 using FAO-ET calculator. The FAO-ET calculator was compared with ML models to investigate the best-fit ML model for predicting ET. Thereafter, ET predicted by the best-fit ML model was compared with satellite (Moderate Resolution Imaging Spectroradiometer – MODIS) ET, which was finally mapped to a larger landscape (over entire Punjab and Haryana). Modeling of ET0 was best performed through LS-SVM followed by ANN2, ANN1, ANFIS10, ANFIS2, MLR and ANFIS9 models. Among developed models, coefficient of determination (R2) value varied from 0.800 to 0.998, being highest (0.998) under LS-SVM model. MODIS overestimated ET when compared with LS-SVM having R2 and root mean square error (RMSE) values of 0.73 and 3.95 mm, respectively. After applying the bias correction factor, R2 and RMSE were 0.74 and 1.19 mm, respectively. The ML and satellite-based ET estimation would be useful for timely water budgeting to manage the water scarcity problems from local to regional levels.

A study was carried out in a split plot design in three replicates during 2016–17 to monitor the performance of soilless cucumbers in relation to differential fertigation. The treatments included fertigation levels viz. 100.0% (F1), 85.0% (F2) and 70.0% (F3) in main plots and varieties viz. Kafka (V1), Multistar (V2) and PBRK-4 (V3) in sub plots. Fertigation levels and varieties significantly affected the fruit yield being highest and lowest values for F1V2 and F3V3, respectively. The average fruit yield with and without fruit thinning was obtained in the range of 2.4–3.5 kg plant⁻¹ and 3.3–4.4 kg plant⁻¹, respectively. The crop water use efficiency (CWUE) was computed to be in the range of 60.4–86.3 kg m⁻³ and 84.6–108.9 kg m⁻³ with and without fruit thinning. Overall, the soilless cucumber cultivation in relation to differential fertigation under naturally ventilated greenhouse conditions proved to be a success in terms of plant growth, yield, quality and economic viability.