Energy use efficiency is a key indicator of sustainability in pulse-based agricultural systems. This study evaluated the energy input–output performance of five major pulse crops of India—chickpea, redgram, lentil, greengram, and blackgram-across representative pulse-growing districts using standard energy equivalents and APSIM simulations. Total energy input ranged from 4579 MJ ha ⁻¹ in greengram to 10,048 MJ ha ⁻¹ in lentil, while energy output varied considerably across crops and regions. Redgram recorded the highest mean total energy output (110,346 MJ ha ⁻¹ ), net energy (101,868 MJ ha ⁻¹ ), and energy use efficiency (13.02). Lentil showed the lowest efficiency (5.02) and the highest specific energy (8.30 MJ kg ⁻¹ ). Chickpea exhibited relatively low specific energy (3.93 MJ kg ⁻¹ ) and stable performance under semi-arid conditions. Significant spatial variability in energy indicators reflected regional differences in climate and management practices. Overall, redgram and chickpea emerged as energetically superior pulse crops under Indian conditions. Quadratic regression analysis of the water use-yield relationship revealed crop-specific optima, with redgram requiring 600–900 mm seasonal water use for maximum yield, whereas chickpea, greengram, lentil, and blackgram showed optima between 100 and 550 mm. These findings highlight the importance of region-specific crop and water management strategies to improve productivity, energy efficiency, and sustainability in pulse-based cropping systems.

Graphical abstract of variability and trends of rainfall and its extremes. This study investigates rainfall trends, variability, and extreme precipitation events across 11 districts in the northwestern region of Uttar Pradesh, India, over the period 1970–2021. Utilizing daily rainfall data, the Mann–Kendall trend test and Sen's slope estimator were applied to detect temporal trends. The results indicated a widespread decline in rainfall and rainy days across the regions. The strongest declines were observed at Moradabad, Meerut, and Gautam Budh Nagar, where annual rainfall decreased by –5.93 to –7.58 mm year−1, accompanied by significant reductions in monsoon and post-monsoon rainy days. The analysis of rainfall extreme indices revealed significant decreasing trends for consecutive wet days (CWD) and total annual rainfall (PRCPTOT) at nearly 63.6% (7 out of 11) and 45.5% (5 out of 11) of districts, respectively. Heavy precipitation indices also showed declining patterns, with significant decreases recorded at 36.4% (4 out of 11) of districts for R10, 45.5% (5 out of 11) for R20, and 36.4% (4 out of 11) for RX5day. District-wise analysis indicated significant reductions in extreme rainfall events at Baghpat (RX1day: Q = –0.70; RX5day: Q = –1.26), Meerut (R20: Q = –0.11; R95p: Q = –3.35), and Moradabad (R20: Q = –0.10; RX5day: Q = –1.02). Significant declining trends in total annual rainfall (PRCPTOT) were further observed at Baghpat (Q = –4.42), Gautam Budh Nagar (Q = –4.86), Ghaziabad (Q = –4.95), Meerut (Q = –6.13), and Moradabad (Q = –4.86), highlighting an overall reduction in rainfall intensity and frequency across several locations. These findings underscore the growing impact of climate variability on regional hydrological regimes and highlight the importance of adaptive water resource management strategies in the face of changing precipitation extremes.

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.

India faces large, persistent yield gaps across eight major crops—wheat, rice, maize, soybean, pulses, mustard, cotton, and sugarcane—that collectively limit food security and rural income. This review synthesises evidence from over 100 peer-reviewed studies (1990–2024) to provide the first comprehensive, multi-crop, agro-climatically resolved yield gap analysis for India. Applying the three-tier framework of potential yield ($Y_p$), attainable yield ($Y_a$), and farmer yield ($Y_f$), exploitable yield gaps are quantified at national and agro-climatic zone level, ranked limiting factors are identified, and projected climate change impacts are assessed. Yield gaps range from 20 to 35% in irrigated rice of the north-western Indo-Gangetic Plain to 60–75% in rainfed cotton and pulses of the semi-arid tropics. Yield gaps of blackgram across 20 major districts average 515 kg ha⁻¹, driven by moisture stress, biotic pressure, and management constraints. Nitrogen management, irrigation access, variety adoption, and sowing-date optimisation collectively explain 80–85% of exploitable gaps, while nitrogen and irrigation alone account for 40–58%. Closing 50% of exploitable yield gaps could add approximately 100 million tonnes of food grain annually without expanding cultivated area, directly supporting Sustainable Development Goal (SDG 2). However, climate change is projected to widen yield gaps of rainfed rice by 15–30% by the 2040s under Representative Concentration Pathway (RCP) 8.5. Beyond this crop-by-crop synthesis, the review proposes a constraint-transition framework in which the dominant type of limiting factor shifts predictably from biophysical (water, climate) in the largest-gap rainfed systems to agronomic management (nitrogen, sowing date, variety) at intermediate gap levels and to institutional and socioeconomic constraints (credit, extension, land tenure, gender) as gaps narrow in the best-resourced irrigated systems, thus offering a conceptual lens for prioritising interventions by development stage rather than by crop alone.