A field experiment on green pea (Pisum Sativum L.) was conducted under drip irrigation to determine the irrigation schedule based on real-time soil moisture measurements with irrigation treatments (main plots) and fertilizer treatments (sub-plots) in a split-plot design with three replications. Main plots consisted of fourirrigation levels at different matric potential ranges (I₁: −20 kPa; I₂: −30 kPa; I₃: −35 kPa; and I₄: −40 kPa), while the sub-plots consisted of three fertigation levels (F₁: 120%, F₂: 100% and F₃: 80%) of recommended dose of fertilizers (40:60:50 kg/ha of NPK). The tensiometer with digital pressure transducer transferred the soil matric potential data to the irrigation controller, which activated the solenoid valves for irrigation. Observations were collected on plant growth parameters, pod yield, and quality parameters. Descriptive statistics of different plant growth parameters were made. The higher SMP threshold (−20 kPa) and lower SMP threshold (−40 kPa) greatly reduced the yield and water-use efficiency. Considering the results, real-time soil moisture-based irrigation at the soil matric potential threshold level of −30 kPa with 120% of recommended dose of fertilizers through fertigation was recommended for attaining maximum green pea pod yield and water-use efficiency under semi-arid Inceptisols.

A field experiment was conducted on okra (Abelmoschus esculentus L.) for assessing the sustainability of yield with optimum irrigation schedule based on soil moisture depletion. Four irrigation treatments: Irrigation at I₁:20%, I₂:30%, I₃:40% and I₄:50% of soil moisture depletion rate in main plots and three fertilizer treatments: Fertigation at F₁:100%, F₂:80% and F₃:60% of recommended NPK (100:25:40 kg/ha) in subplots were tested. Soil matric potential was recorded continuously using electronic tensiometers. The soil moisture characteristics curve was derived for various soil matric potential value sand the soil water content. The irrigation controller triggered solenoid valves for irrigation when soil moisture depletion reached a prespecified level in each treatment. Soil moisture depletion values were significantly predicted based on a regression model calibrated for each treatment over the crop growing period. The model gave minimum prediction error (PE) for I₁, followed by I₂, I₃ and I₄, respectively. Plant growth and yield parameters were significantly influenced by the soil moisture availability under each treatment. It is recommended that irrigation be scheduled at 20% soil moisture depletion rate together with 100% NPK fertilizer application for attaining sustainable yield of okra (12.3 t/ha), apart from maximum WUE (3.5 kg/m³) and plant growth parameters under semiarid inceptisols.

Greengram (Vigna radiata L.) plays a crucial role in India’s nutritional security and agroecosystem sustainability, though constrained by climatic variability, moisture stress, and suboptimal crop management. To identify pathways for sustainable intensification, the APSIM model was employed to simulate potential yield (Yp), quantify yield gaps, and assess water productivity across twenty major greengram growing districts of India during 2003–2023. Simulations integrated weather, soil profiles, region-specific management practices, and cultivar-specific genetic coefficients, while physical (PWP), irrigation (IWP), and economic (EWP) water productivity indices were evaluated. Observed yields (162–611 kg ha⁻¹) remained lower than simulated potential yields (386–1220 kg ha⁻¹), resulting in yield gaps of 27–66% (average ~ 389 kg ha⁻¹). Central Zone districts exhibited relatively higher EWP. Model performance was robust during calibration and validation (R² and NSE ≈ 0.97–0.99; RMSE ≤ 20%; NMSE < 0.05). Carbon budgeting revealed carbon efficiency ranged from 0.65 to 1.98 and a mean Carbon Sustainability Index (CSI) of 0.32. Principal Component Analysis identified irrigation, soil fertility, and crop geometry as dominant yield drivers, while correlation-density matrix highlighted the dependence of grain yield on irrigation. The results demonstrate that improving irrigation management, optimizing nutrient use, and adopting region-specific agronomic practices can substantially reduce yield gaps and enhance water productivity in greengram.

Floods are one of the most damaging natural disasters in the world, causing significant socioeconomic disruptions, especially in countries like India that are controlled by the monsoon. This study used an integrated method that included remote sensing (RS), Geographic Information Systems (GIS), and the analytical hierarchy process (AHP) to identify regions in the Subarnarekha River Basin (SRB) that are susceptible to flooding. Seventeen flood-conditioning factors including elevation, slope, drainage density, distance from river, rainfall, land-use land cover, normalized difference vegetation index, soil texture, geomorphology, and topographic indices were used for comprehensive analysis. The AHP-derived weights achieved a consistency ratio of 0.0089, indicating highly consistent expert judgments. The map was validated using the area under the curve (AUC) method against an independent Sentinel-1 synthetic aperture radar (SAR)-derived flood inventory. The AUC value of 0.770 was obtained, which indicates a moderate-to-good level of prediction performance. The most significant conditioning elements were found to be elevation (17%) and slope (15%), followed by drainage density (11%), distance from river (11%), and rainfall (9%). A significant area of the basin is classified as moderate to high susceptibility, with high-risk zones primarily located in low-lying downstream areas, according to spatial analysis. Unlike previous GIS–AHP flood susceptibility studies in India that typically employed fewer conditioning factors or relied mostly on expert-based validation, this study integrates 17 flood-conditioning factors at a uniform 30 m spatial resolution with independent Sentinel-1 SAR-derived flood inventory validation. In the SRB and other monsoon-dominated river basins, the suggested GIS-RS-AHP framework improves the dependability of basin-scale flood susceptibility mapping and offers a strong, transferable decision-support tool for flood-risk management, land-use planning, and disaster preparedness.