• Energy Research

Initial evaluations of the System of Rice Intensification in India and elsewhere focused mainly on its impacts on yield and income, and usually covered just one or two seasons. Researchers at the ICAR-Indian Institute of Rice Research have conducted a more comprehensive evaluation of SRI methods over six years (six wet and six dry seasons), comparing them with three alternatives: modified, partially mechanized SRI (MSRI) to reduce labor requirements; direct-seeded rice (DSR) as an alternative method for growing rice; and conventional transplanting of rice with flooding of fields (CTF). Grain yield with SRI methods was found to be about 50% higher than with CTF (6.35 t ha−1 vs. 4.27 t ha−1), while the MSRI yield was essentially the same (6.34 t ha−1), 16% more than with DSR (5.45 t ha−1). Water productivity with SRI methods was 5.32–6.85 kg ha-mm−1, followed by 4.14–5.72 kg ha-mm−1 for MSRI, 5.06–5.11 kg ha-mm−1 for DSR, and 3.52–4.56 kg ha-mm−1 for CTF. In comparison with CTF, SRI methods significantly enhanced soil microbial populations over time: bacteria by 12%, fungi by 8%, and actinomycetes by 20%. Biological activity in the rhizosphere was also higher as indicated by 8.5% greater dehydrogenase and 16% more FDA enzymes in soil under SRI management. Similarly, an indicator of soil organic matter, glucosidase activity, was 78% higher compared to CTF. SRI enhanced the relative abundance of beneficial microbial-feeding nematodes by 7.5% compared to CTF, while that of plant-pathogenic nematodes was 7.5% lower under SRI. Relative to conventional methods, SRI management reduced GHG emissions by 21%, while DSR reduced them by 23%, and MSRI by 13%, compared to standard rice-growing practice. Economic analysis showed both gross and net economic returns to be higher with SRI than with the other management systems evaluated. While the six-year study documented many advantages of SRI crop management, it also showed that MSRI is a promising adaptation that provides similar benefits but with lower labor requirements.

In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.

Soil is a crucial component for plant growth, as it provides water, nutrients, and mechanical support. Various factors, such as crop cultivation, microflora, nutrient addition, and water availability, significantly affect soil properties. Maintaining soil health is important, and one approach is the introduction of native organisms with multifaceted activities. The study evaluates the effects of introducing these microbes (Trichoderma asperellum strain TAIK1, Bacillus cabrialesii strain BIK3, Pseudomonas putida strain PIK1, and Pseudomonas otitidis strain POPS1) and their consortium, a combination of four bioagents, on soil health, plant growth, and the incidence of stem rot disease caused by Sclerotium oryzae in rice. Upon treatment of soil with the consortium of the four native bioagents mentioned above through seed treatment or soil application, variations/increases in the chemical properties of the soil were observed, viz., pH (8.08 to 8.28), electrical conductivity (EC) (0.72 to 0.75 d S m−1), organic carbon (OC) (0.57 to 0.68 %), available soil nitrogen (SN) (155 to 315 kg/ha), soil phosphorus (SP) (7.87 to 24.91 kg/ha), soil potassium (SK) (121.29 to 249.42 kg/ha), and soil enzymes (urease (0.73 to 7.33 µg urea hydrolyzed g−1 soil h−1), acid and alkaline phosphatase (0.09 to 1.39 and 0.90 to 1.78 µg of p-nitrophenol released g−1 soil h−1), and dehydrogenase (0.14 to 16.44 mg triphenyl formazan (TPF) produced g−1 soil h−1)), compared to untreated soil. Treatment of seeds with the consortium of four native bioagents resulted in a significant increase in plant height (39.16%), the number of panicles (30.29%), and average grain yield (41.36%) over control plants. Under controlled conditions, the bioagent-treated plants showed a 69.37% reduction in stem rot disease. The findings of this study indicate a positive correlation between soil properties (pH, EC, OC, SN, SP, SK, and soil enzymes) and plant growth (shoot and root length, fresh and dry weight) as well as a highly negative association of soil properties with stem rot disease severity. The results suggest that using native bioagents as a management strategy can control stem rot disease and enhance crop productivity, while reducing reliance on chemical management. These findings provide valuable insights into the development of sustainable agricultural practices that maximize productivity by minimizing negative environmental impacts, which promotes soil health, plant growth, and disease management.