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Bappa Das

Researcher at Indian Council of Agricultural Research

Publications -  75
Citations -  990

Bappa Das is an academic researcher from Indian Council of Agricultural Research. The author has contributed to research in topics: Environmental science & Medicine. The author has an hindex of 11, co-authored 51 publications receiving 462 citations. Previous affiliations of Bappa Das include Indian Agricultural Research Institute.

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Effect of integrated nutrient management practice on soil aggregate properties, its stability and aggregate-associated carbon content in an intensive rice–wheat system

TL;DR: In this article, the impact of integrated nutrient management practices on the physical properties and structural stability of soil aggregates, and the associated C contents after 18 years of rice-wheat rotation on a sandy loam soil at Project Directorate for Farming Systems Research, Modipuram.
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Optimization of energy consumption and environmental impacts of arecanut production through coupled data envelopment analysis and life cycle assessment

TL;DR: In this article, the authors investigated the environmental impacts of arecanut production with a cradle to gate perspective; using, raw materials extraction, manufacture, use, and supply of inputs to the farm.
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Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India

TL;DR: This investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net) for rice yield prediction using long-term weather data.
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Status, Prospects, Threats, and the Way Forward for Sustainable Management and Enhancement of the Tropical Indian Reservoir Fisheries: An Overview

TL;DR: In this article, the authors synthesize the updated information available on Indian reservoirs and the developments during the last decade with reference to fisheries, biodiversity, potential, threats, and recommends strategies for sustainable management of the reservoir fisheries in India.
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Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice

TL;DR: The results obtained showed potential of hyperspectral remote sensing for non-destructive phenotyping of salinity stress in rice and revealed that PLSR-combined models was the best followed by PCA-based model while indices based model was found to be least accurate.