Management of soil cover and tillage regimes in upland rice-sweet corn systems for better system performance, energy use and carbon footprints.

This study investigates the effects of tillage and mulching regimes on rice-sweet corn systems in the lower Gangetic plains, focusing on region-specific and crop-specific impacts on soil-crop-environmental parameters. The experiment consisted of three levels of tillage: conventional (CT), minimum (MT), and zero (ZT), and four levels of mulching: live, leaf litter, paddy straw, and no mulching. The results show that ZT tillage resulted in higher bulk density (BD) compared to other treatments, despite an increase in soil organic carbon (SOC). Live and leaf litter mulching led to slight reductions in BD in the upper soil layers. CT resulted in net depletion of SOC whereas ZT registered a positive sequestration rate of 1.19 Mg ha-1 yr-1. Live and leaf litter mulching increased SOC sequestration by 42.6% and 38.8% compared to paddy straw mulching, respectively. Initially, ZT resulted in a 10.3% reduction in system productivity compared to CT, while MT yields were comparable to CT. However, mulching regimes consistently improved production by 16.4%-25.2% as compared to no mulch. ZT and MT were found to be more affordable than CT, with cost savings of 18.2% and 6.8%, respectively. ZT had the highest B: C ratio, indicating better economic efficiency. Among the mulching treatments, live mulching was the most economical. Both ZT and MT saved input energy by approximately 22.9% and 13.5%, respectively compared to CT. Live mulching resulted in the highest net energy and energy output. Compared to CT, ZT reduced carbon footprint (CF) by 41.5 and 22.2% in rice and sweet corn, respectively. MT scored midway between ZT and CT in all parameters. CT exhibited several limitations, including high input energy requirements, high cost of cultivation, poor economic efficiency, negative environmental impacts, and loss of SOC. ZT initially experienced yield reduction and lower net returns in the early years. Therefore, MT was identified as the best alternative in the initial years before transitioning completely to ZT, as it provided comparable yields to CT with better overall benefits. Among the soil cover regimes, live mulching was found to be the most favorable option across all dimensions.

Genetic characterization and diversity analysis of indigenous aromatic rice.

Gangetic old alluvial zone in India has conserved many locally adapted aromatic rice landraces. In order to determine the extent of genetic divergence of ten morphological characters, the study was conducted to examine forty-eight aromatic rice genotypes for six Kharif seasons (2016-2021) at the Instructional Farm of Regional Research Station (Old Alluvial Zone), Uttar Banga Krishi Viswavidyalaya, Majhian, West Bengal, India. The experiment was laid out in Randomized Complete Block Design (RCBD) with three replications. A considerable degree of variation was noted for all the traits being investigated. It was found that the total number of tillers per plant, panicle numbers per plant, number of grains per panicle, fertility percentage, test weight, and grain length/breadth ratio had significantly positive correlated with seed yield per plant. Based on D2 analysis values, all the genotypes were grouped into six clusters. Cluster III (Tulaipanji, Patnai, Basmati 1121, Jugal, and Bahurupi) and Cluster VI (Kanakchur), containing genotypes were found most divergent with maximum inter-cluster distance (6941.51). According to the cluster means, Cluster II had the largest intra-cluster distance (1937.52), and important attributes including test weight, number of grains per panicle, seed yield per plant, and fertility percentage made remarkably significant contributions to this cluster. In terms of principal component analysis, maximum variability was found in PC1 (23.88 %), with high positive loading values for tillers per plant (0.459), panicle number per plant (0.441), seed yield per plant (0.408), fertility percentage (0.364), test weight (0.264), and grain length/breadth (L/B) ratio (0.263). On the basis of biplot analysis, four genotypes, namely Shakbhati, Sugandhi, Bahurupi and Kanakchur, were identified as the most divergent types for the yield-attributing traits of aromatic rice. The diverse genotypes could be used as potential donors in future breeding programmes.

Enhancing aromatic rice production through agronomic and nutritional management for improved yield and quality.

To meet the growing international demand for aromatic rice, this study, conducted at Uttar Banga Krishi Viswavidyalaya in Cooch Behar, West Bengal, aimed to enhance the yield and quality of the 'Tulaipanji' rice cultivar through advanced establishment methods and the use of organic nutrients over two years. The research tested three planting techniques: mechanical transplanting, wet direct seeding (using a drum seeder), and traditional methods, alongside four nutrient management strategies: vermicompost, farmyard manure, a mix of both, and conventional fertilizers. Findings revealed that mechanical transplanting significantly increased yield by over 31.98% and 71.05% compared to traditional methods and wet direct seeding, respectively. Using vermicompost alone as a nutrient source not only boosted yields by 21.31% over conventional fertilizers but also enhanced the rice's nutritional value and cooking quality. Moreover, soils treated with vermicompost showed higher dehydrogenase activity, indicating better soil health. Economically, mechanical transplanting with vermicompost was the most beneficial, yielding the highest net returns and benefit-cost ratios in both years studied. This approach presents a viable model for improving the sustainability of aromatic rice production globally, emphasizing the economic and environmental advantages of adopting mechanical planting techniques and organic fertilization methods.

Hybrid time series models with exogenous variable for improved yield forecasting of major Rabi crops in India.

Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)-TDNN (Time-Delay Neural Network), ARIMAX-NLSVR (Non-Linear Support Vector Regression), ARIMAX-WNN (Wavelet Neural Network), ARIMAX-CNN (Convolutional Neural Network), ARIMAX-RNN (Recurrent Neural Network) and ARIMAX-LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX-LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures.