‘Participatory-mode’ adaptive research was conducted in wheat in north-western Himalayas (NWH) during 2008–2014 to develop an improved chemical weed management (ICWM) technology. First of all, two years ‘on-farm experimentation’ was performed in a randomized block design at 10 locations in NWH using seven treatments (Clodinafop @ 60 g a.i./ha (Clod); Clod followed by 2,4-D (Na-salt) @ 1.0 kg a.i./ha (Clod-fb-D); Isoproturon 75 WP @ 1.0 kg a.i./ha (Iso); Iso + D; Sulfosulfuron 75% WG @ 25 g a.i./ha + Metsulfuron 5% WG @ 2 g a.i./ha (Sulf + Met); weed-free-check; and un-weeded-check). In this study, the post-emergence application of Sulf + Met reported the lowest weed-index and NPK depletion by weeds with higher weed control efficiency (86.4%), weed control index (81.1%) and herbicide efficiency index (2.62) over other herbicides. Sulf + Met exhibited significantly higher wheat productivity (3.57 t/ha), protein yield, net-returns and water-productivity, which was followed by Iso + D and Clod-fb-D, all of which remained statistically at par with each other. An impact assessment of intensive technology-transfer programme (2008–2014) revealed a higher technology adoption rate (71–98%) of ICWM leading to higher wheat productivity (~22%) and net income gains (2.8–26.4%) in NWH. Overall, Sulf + Met proved highly effective against mixed weed flora in wheat to boost wheat productivity, profitability, quality and water productivity in addition to a higher technology adoption rate and NIGs to transform rural livelihoods in NWH.

An experiment was conducted to assess the effects of irrigation systems, irrigation and N levels on nutrient uptake of rabi maize and summer groundnut during 2021-22 and 2022-23 at the College Farm, College of Agriculture, Rajendranagar, PJTAU, Hyderabad. The experiment composed of 18 treatment combinations (3 irrigation systems as main plot, 3 irrigation levels as sub-plot and 2 nitrogen levels as sub-sub-plot) in a split plot design replicated thrice. The study revealed that irrigation systems, irrigation and N levels and the interaction of irrigation systems and irrigation levels influenced the nutrient uptake of maize and groundnut crops during both the years of experimentation. Significantly higher NPK uptake was observed with sub-surface drip irrigation (M₂), irrigation level at 1.2 Epan;IW/CPE (S₁) and N level at 100 per cent RDN (N₁). The interaction of irrigation systems and irrigation levels (M x S) was significant while the interaction of N levels with irrigation systems (M x N) and irrigation levels (N x S) was not significant for both rabi maize and summer groundnut. The interaction of irrigation systems and irrigation levels revealed that in both maize and groundnut crops at earlier crop growth period the M₃S₁ (surface irrigation with 1.2 IW/CPE) resulted in significantly higher NPK uptake followed by M₂S₁ and M₂S₂ (sub-surface drip irrigation with 1.2 and 0.9 Epan). However at later crop growth stages due to prolonged fertigation schedules in micro irrigation systems, the higher NPK uptake was observed with M₂S₁ and M₂S₂ followed by surface drip irrigation system with 1.2 Epan (M₁S₁) and surface irrigation system with 1.2 IW/CPE (M₃S₁) while M₃S₃ resulted in lowest NPK uptake at all crop stages and in grains & stover/haulm during both the consecutive years (2021-22 and 2022-23).

An experiment was conducted to study the effects of irrigation systems, irrigation, and N levels on the economics of maize and groundnut during 2021-22 and 2022-23 at the College Farm, College of Agriculture, Rajendranagar, PJT Agricultural University, Hyderabad. The experiment consisted of 18 treatment combinations (3 irrigation systems as the main plot, 3 irrigation levels as the sub-plot, and 2 nitrogen levels as the sub-sub-plot) in a split plot design replicated thrice. The experiment revealed that among irrigation systems, irrigation, and N levels in both crops (maize and groundnut) significantly higher gross returns, net returns, and BCR were recorded with M₂ (sub-surface irrigation system), S₁ (1.2 Epan; IW/CPE) and N₁ (100 percent RDN). In both crops, the interaction between irrigation system and irrigation levels (M x S) revealed significantly higher returns and BCR with sub-surface drip irrigation levels@ 1.2 Epan & 0.9 Epan ratios (M₂S₁ and M₂S₂) followed by surface drip irrigation levels @ 1.2 Epan & 0.9 Epan ratios (M₁S₁ and M₁S₂), surface irrigation @ 1.2 IW/CPE ratio (M₃S₁). In contrast, surface irrigation @ 0.6 IW/CPE ratio (M₃S₃) resulted in the least returns and BCR during both the years. The interaction effects of irrigation systems and N levels (M x N), irrigation, and N levels (S x N) were non-significant in the groundnut crop whereas they were significant in maize crop. The M₂N₁ recorded significantly higher mean gross returns (₹. 1,46,852 ha^-1), net returns (₹. 90,960 ha^-1), and BCR (2.62) whereas M₃N₂ recorded the lowest mean gross return (₹. 97,052 ha^-1), net return (₹. 45,305 ha^-1) and BCR (1.87). Among S x N interaction effects, the S₁N₁ (1.2 Epan; IW/CPE with 100 percent RDN) resulted in significantly higher gross returns (₹. 1,44,385 ha^-1), net returns (₹. 89,256 ha^-1) and BCR (2.61) followed by S₂N₁. In contrast, S₃N₂ resulted in significantly least returns and BCR. The interaction effects of irrigation systems, irrigation, and N levels (M x S x N) were non-significant in the groundnut crop while showing a significant impact on the economics of maize. The sub-surface irrigation system (M₂) with 1.2 Epan (S₁) and 100 percent RDN (N₁) recorded significantly higher mean gross returns (₹. 1,58,749 ha^-1), net return (₹. 1,10,243 ha^-1) and BCR (2.82) followed by M₂S₂N₁ (₹. 1,56,106 ha^-1, ₹. 1,14,542 ha^-1 and 2.79), M₁S₁N₁ and M₁S₂N₁ while M₃S₃N₂ (surface irrigation system with 0.6 IW/CPE and 75 per cent RDN) recorded least mean gross returns (₹. 65,253 ha^-1), net returns (₹. 14,106 ha^-1) and BCR (1.28) thus making it the least remunerative among all treatment combinations studied.

Evapotranspiration (ET) is a key component of the hydrological cycle and is critical for determining crop water requirements. Accurate ET estimation is essential for improving irrigation efficiency, particularly under increasing water scarcity and climate variability. Conventional approaches such as the soil water balance, empirical formulations, the FAO Penman-Monteith method, eddy covariance flux towers, lysimeters, and scintillometers each have limitations related to spatial representativeness, accuracy, or operational cost. Unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors offer a high spatial resolution and cost-effective alternative for field-scale assessment of surface energy balance components and ET. In this study, a field experiment was conducted on maize during rabi season of 2022-23 under two irrigation regimes based on depletion of available soil moisture (20% DASM and 40% DASM). UAV-based multispectral (0.05 m) and thermal imagery (0.33 m) were acquired at five crop growth stages and processed using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to estimate actual evapotranspiration (ETa) and surface energy fluxes. Spatiotemporal analysis showed that the 20% DASM treatment (400 mm) resulted in a 1.7 °C lower land surface temperature, a 16.5% higher NDVI, and an 11% increase in daily ETa compared with the 40% DASM treatment (316 mm), which experienced water stress and a 20% reduction in seasonal ETa. The UAV-based METRIC estimates of daily ETa showed strong agreement with that of Penman-Monteith (PM) combination approach (R² = 0.84; RMSE = 0.22 mm day⁻¹; MAPE = 6.1%), with a slight underestimation of seasonal ETa (-7%). Agreement with the soil water balance method ranged from-3% to + 3%, demonstrating the capability of the approach to capture irrigation-induced variability in ETa and surface energy fluxes. Overall, the results highlight the potential of UAV-based METRIC for spatiotemporal assessment of crop evapotranspiration and surface energy dynamics to support precision irrigation management.

Soil erosion poses a growing threat to watershed sustainability under changing climatic conditions, particularly in monsoon-dominated regions. This study integrates the Soil and Water Assessment Tool (SWAT) with bias-corrected CORDEX–South Asia climate projections to quantify present and future sediment yield and to evaluate the effectiveness of structural best management practices (BMPs) in the Kesinga catchment, eastern India. The watershed was delineated into 33 sub-catchments, and model calibration and validation (2003–2020) demonstrated very good performance for streamflow during calibration (NSE = 0.86) and good performance during validation (NSE = 0.72). Sediment-yield performance was satisfactory during calibration (NSE = 0.52) but unsatisfactory during validation (NSE = 0.49), according to the performance criteria adopted in this study. Future projections under RCP4.5 and RCP8.5 scenarios indicate increases in streamflow (26–73%) and sediment yield (5–36%), with the greatest amplification under end-century RCP8.5 conditions; these sediment projections should be interpreted in light of the unsatisfactory validation performance. Spatial analysis revealed persistent sediment hotspots in central and eastern sub-catchments, where high slope gradients and projected precipitation intensification interact to enhance erosion risk. Across the future periods and scenarios, 40.2–51.2% of the catchment area was projected to fall within the severe-to-extreme erosion categories, with the highest proportion occurring under end-century RCP8.5. Structural BMPs, including grassed waterways (GWW) and vegetative filter strips (VFS), were implemented in priority sub-catchments. Under historical conditions, VFS and GWW reduced soil loss by 13% and 25%, respectively, while their combined application achieved up to 38% reduction. Although absolute sediment loads increased under future climates, combined BMP implementation consistently mitigated erosion by 27–36%, demonstrating potential for reducing sediment losses under intensified rainfall regimes. By explicitly linking projected climatic changes with sediment response and BMP performance at the sub-catchment scale, this study provides a spatially targeted framework for climate-resilient watershed management in monsoon-driven agro-ecosystems. The results highlight the need for proactive and spatially targeted implementation of integrated soil and water conservation measures in erosion-prone sub-catchments.

Spatially explicit soil fertility diagnosis is still difficult in data sparse paddy cultivated areas because single soil test indicators cannot separate soil chemical condition, primary nutrient supply, micronutrient balance and yield relevance. This study developed a soil-test driven, domain-balanced Soil Fertility Index (SFI) framework using 64 georeferenced long-term paddy-field samples from Hanumakonda district, Telangana, India. Ten laboratory indicators (pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, zinc, iron, copper, and manganese) were transformed into agronomic scores and aggregated into Chemical Condition, Macronutrient, Micronutrient, and Overall Soil Fertility indices. Regularized regression models were used to approximate the derived fertility-index targets from the original soil-test variables and to identify the conditional contribution of individual indicators within each index formulation. Ridge, Lasso and ElasticNet regression models were evaluated using five-fold cross-validation within the training dataset ( n = 51) and independent test-set evaluation ( n = 13). Among the evaluated models, Lasso provided the best prediction of Overall SFI (R ² = 0.594, RMSE = 0.033, RPD = 1.63), whereas ElasticNet performed best for the Micronutrient Index (R ² = 0.683, RPD = 1.85) and showed stable prediction of the Macronutrient Index (R ² = 0.577, RPD = 1.60) Relative zone mapping showed that low to moderate priority classes dominated the predicted overall SFI surface, while the Micronutrient Index produced the sharpest local management contrast. The predicted fertility surfaces were mapped across approximately 140,387 ha within the empirical point-covered sampling domain using a 30 m spatial analysis grid. Independent yield validation supported the agronomic relevance of the framework. Available nitrogen and organic carbon were strongly correlated with yield. Yield validation showed that predicted MNI relative zones significantly separated productivity classes, with mean yield increasing from 22.52 to 26.81 q acre ⁻¹ (Kruskal-Wallis H = 17.52, p < 0.001), whereas Overall SFI, MiNI and SFI 5core zones did not show significant yield separation. The proposed framework provides an interpretable and reproducible decision-support approach for prioritizing soil testing and site-specific nutrient management in data-sparse paddy landscapes.