Browsing by Author "Santosha Rathod"

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Correlation of the Effect of Native Bioagents on Soil Properties and Their Influence on Stem Rot Disease of Rice
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Sowmya Vanama; Maruthi Pesari; Gobinath Rajendran; Uma Devi Gali; Santosha Rathod; Rajanikanth Panuganti; Srivalli Chilukuri; Kannan Chinnaswami; Sumit Kumar; Tatiana Minkina; Estibaliz Sansinenea; Chetan Keswani
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. © 2023 by the authors.
Extreme temperature and rainfall event trends in the Middle Gangetic Plains from 1980 to 2018
(Indian Academy of Sciences, 2023) S. Vijayakumar; Sudhir Kumar Rajpoot; N. Manikandan; R. Jayakumara Varadan; J.P. Singh; Dibyendu Chatterjee; Sumanta Chatterjee; Santosha Rathod; Anil Kumar Choudhary; Adarsh Kumar
Regional-level studies aimed at identifying and assessing various types of extreme weather events and comprehending their effects on various sectors are crucial. In the present study, we have utilized the RClimDex software to compute the trend in temperature and precipitation extreme events in the Varanasi district of Uttar Pradesh, India, from 1980 to 2018. We employed both Mann–Kendall test and linear regression to test the statistical significance of the computed trend. Out of 13 temperature indices, 8 showed a significant trend while the remaining showed a non-significant trend. The annual mean maximum temperature, warm days, diurnal temperature range and a monthly minimum of maximum temperature had decreased significantly by 0.029ºC, 0.159 days, 0.032ºC and 0.122ºC/yr respectively, whereas cool days and cold spell duration had increased significantly by 0.264ºC and 0.372 days/yr respectively, indicating an increased cooling effect over the study area. Similarly, out of the 11 rainfall indices, only two showed a significant trend, while the remaining showed a nonsignificant trend. The increasing drought over the study area is evident as the number of rainy days and consecutive wet days have decreased significantly by 0.262 days and 0.058 days/yr respectively, with a non-significant increase in consecutive dry days during the same period. The weak negative non-significant trend of a maximum of five consecutive days of rainfall, very heavy rainfall days and total annual precipitation indicate the decreasing trend of floods. This study stresses the development of adaptation plans to overcome the adverse consequences of extreme weather events in Varanasi district. © 2023, Current Science. All Rights Reserved.
Transformer-based deep learning architecture for time series forecasting[Formula presented]
(Elsevier B.V., 2024) G.H. Harish Nayak; Md Wasi Alam; G. Avinash; Rajeev Ranjan Kumar; Mrinmoy Ray; Samir Barman; K.N. Singh; B. Samuel Naik; Nurnabi Meherul Alam; Prasenjit Pal; Santosha Rathod; Jaiprakash Bisen
Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like RNNs, LSTMs, and GRUs process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. © 2024