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Bioavailability, enrichment, and risk of phosphorus (P) and its fraction composition were monitored in the paddy soils of Kunnukara during the pre-cultivation and post-harvest periods in the years 2020 and 2021. Iron-bound P (≥ 105.56 ± 0.05 mg/kg) was found highest among the P fractions. The bioavailability of P was recorded at peak value during the post-harvest period, contributed by organic P, Iron bound P, and loosely bound P. Principal component analysis inferred that loosely bound P was pH-dependent and significantly influenced by cation exchange, particle density, soil aggregate stability, and total organic carbon (TOC) in the post-harvest soil, whereas TOC, aluminium-bound P, and calcium-bound P in the pre-cultivation soils. Additionally, physico-chemical parameters like electrical conductivity, bulk density, specific gravity, TOC, and soil aggregate stability have a significant impact on the composition of P fractions in the soil. Bioavailable phosphorus (BAP) ranged from 642.78 ± 0.49 to 594.20 ± 1.23 mg/kg during the post-harvest period. Moreover, the contribution of BAP to total P ranged from 99.45 to 99.54%, indicating the fact that soil is sufficient in BAP. Pollution indices revealed that the paddy soils are at risk of eutrophication. Phosphorus Pollution Index (PPI) > 1 exhibited moderate pollution (1.06 to 1.07) at the topsoil (0-15 cm) and PPI < 1, mild pollution (0.92 to 0.99) at 15-30 cm depths. The organic nitrogen index ≥ 0.133 indicates severe soil pollution in the study site. An extended fertilizer application in the field contributes to nutrient enrichment and warrants the risk of contamination in nearby riverine systems (River Periyar and River Chalakkudy)., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4 years) and 4 to 5 consecutive drought years (once in 120 years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R 2 (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R 2 (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

Changes in the pattern of electric power consumption in India have influenced energy utilization processes and socio-economic development to greater extent during the last few decades. Assessment of spatial distribution of electricity consumption is, thus, essential for projecting availability of energy resource and planning its infrastructure. This paper makes an attempt to model the future electricity demand for sustainable energy and its management in India. The nighttime light database provides a good approximation of availability of energy. We utilized defense meteorological satellite program-operational line-scan system (DMSP-OLS) nighttime satellite data, electricity consumption (1993-2013), gross domestic product (GDP) and population growth to construct the model. We also attempted to examine the sensitiveness of electricity consumption to GDP and population growth. The results revealed that the calibrated DMSP and model has provided realistic information on the electric demand with respect to GDP and population, with a better accuracy of r 2  = 0.91. The electric demand was found to be more sensitive to GDP (r = 0.96) than population growth (r = 0.76) as envisaged through correlation analysis. Hence, the model proved to be useful tool in predicting electric demand for its sustainable use and management.