Optimisation of Anaerobic Digestate and Chemical Fertiliser Application to Enhance Rice Yield—A Machine-Learning Approach

Binoy Kumar Show, Suraj Panja, Richik GhoshThakur, Aman Basu, Apurba Koley, Anudeb Ghosh, Kalipada Pramanik, Shibani Chaudhury, Amit Kumar Hazra, Narottam Dey, Andrew B. Ross and Srinivasan Balachandran ()
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Binoy Kumar Show: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Suraj Panja: Department of Biotechnology, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Richik GhoshThakur: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Aman Basu: Department of Biology, York University, Toronto, ON M3J 1P3, Canada
Apurba Koley: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Anudeb Ghosh: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Kalipada Pramanik: Department of ASEPAN Institute of Agriculture, Visva-Bharati, Sriniketan 731236, West Bengal, India
Shibani Chaudhury: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Amit Kumar Hazra: Department of Adult, Continuing Education and Extension, Palli-Samgathana Vibhaga, Visva-Bharati, Sriniketan 731236, West Bengal, India
Narottam Dey: Department of Biotechnology, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
Andrew B. Ross: School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK
Srinivasan Balachandran: Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India

Sustainability, 2023, vol. 15, issue 18, 1-13

Abstract: The present study evaluates the synergistic application of an anaerobic digestate for enhanced rice yield. The study utilised the digestate as a fertiliser with various inoculum-to-substrate (IS) ratios of anaerobic digestion from cow dung and water hyacinth (CW–BF) with combinations of NPK (16-22-22) fertiliser for rice yield optimisation. The outcome of the combined digestate and fertiliser application on rice cultivation was observed in terms of parameters such as the number of tillers, panicle number, panicle length, fertile panicles, and 1000-grain weight. The digestate combination of CW–BF:NPK (3:1:1) resulted in the highest grain yield (7521 kg/hectare) with increased panicle length, test weight, and more filled grains than the other combinations. Moreover, various machine-learning approaches were used to study the efficacy of the different combinations of applied fertiliser (cow dung, water hyacinth, and NPK). The gradient-boosting machine-learning model was appropriate for predicting the modelling based on the measured data. Principal component analysis revealed NPK as the first principal component with high loading values and the digestate as the second principal component, which indicates its crucial role in fertiliser preparation. Therefore, deploying such hybridised fertilisers using the proper statistical analysis and machine-learning approaches can improve rice yield, which would be essential for the socio-economic uplifting of marginal rice farmers.

Keywords: water hyacinth; cow dung; biofertiliser; NPK; rice yield; model comparison; gradient boosting; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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