Dr. Dharm Nath Jha is a Principal Scientist in Agricultural Statistics at the ICAR–Central Inland Fisheries Research Institute (ICAR-CIFRI), Prayagraj, where he has been serving since 2010.  He completed his graduation from Banaras Hindu University (BHU), Varanasi, followed by his Master’s and Ph.D. from the Indian Agricultural Research Institute (IARI), New Delhi. His postgraduate research focused on analytical techniques for on-farm trial data, while his doctoral research, titled “A Study on Spatial Regression Models Under Measurement Errors Framework,” addressed statistical modelling under measurement-error conditions.

Research Expertise

Dr. Jha has developed specialized expertise in statistical analysis of agricultural, ecological and fisheries data. His research encompasses time-series analysis, spatial analysis, regression modelling, multivariate analysis, descriptive statistics, deep learning and machine-learning approaches. His work integrates quantitative methods with fisheries science and aquatic ecosystem research to understand patterns, trends and relationships in complex biological and environmental datasets.

Research Projects & Scientific Contributions

As Principal Investigator and Co-Principal Investigator, Dr. Jha has completed 2 and 5 ICAR-CIFRI projects, respectively, in addition to 3 externally funded Government of India projects. His research has addressed environmental flow requirements of the Sone and Tamas rivers, impacts of river interlinking on the Ken–Betwa ecosystem, fish production and water-resource estimation, and ecological conditions affecting fish and fisheries.

He has also contributed to the systematic collection and maintenance of monthly fish-landing data from the Prayagraj stretch of the Ganga River, supporting estimation of annual fish production and providing quantitative information relevant to fisheries planning and resource management.

Publications & Research Impact

Dr. Jha has contributed to 64 research publications spanning fisheries statistics, aquatic ecology, environmental assessment, fish population dynamics, water quality, biodiversity, fisheries production and statistical modelling. His recent research increasingly incorporates advanced computational approaches, including machine learning, for environmental prediction and risk assessment. His 2026 publication on machine-learning-driven prediction of sediment contamination and ecological and human-health risk indices demonstrates the application of advanced data-analytics techniques to aquatic environmental management.

His publications have appeared in journals including Current Science, Environmental Monitoring and Assessment, Marine Pollution Bulletin, Water Air Soil Pollution, Aquatic Ecosystem Health & Management, Indian Journal of Fisheries, and Journal of Water and Climate Change.

Academic Guidance & Professional Engagement

Dr. Jha has served on advisory committees for Ph.D. scholars at institutions including Sam Higginbottom University of Agriculture, Technology & Sciences and Nehru Gram Bharati University. He has also successfully guided M.Sc. students in their dissertation research. His professional involvement includes editorial responsibilities and membership in scientific organizations, including the Inland Fisheries Society of India.

Future Research Vision

His future research focuses on integrating big-data analytics, Artificial Neural Networks, Support Vector Machines, machine learning and genetic algorithms with ecological, biological and socioeconomic datasets collected across time and space. His objective is to improve prediction and forecasting of complex aquatic ecosystem processes and contribute to sustainable management of water resources, fisheries and livelihoods dependent upon aquatic ecosystems.

Eligibility for Emerging Scientist Award 

Dr. Dharm Nath Jha is eligible for the Emerging Scientist Award in Scientific Laurels based on his sustained scientific contributions in agricultural statistics, fisheries research and aquatic ecosystem management. His combination of advanced statistical expertise, interdisciplinary research, project leadership, extensive publication record, postgraduate mentoring and application of emerging computational methods such as machine learning to fisheries and environmental challenges demonstrates a strong research trajectory. His work connecting statistical science with sustainable aquatic-resource management further highlights his potential for continued scientific advancement and emerging leadership in his field.