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

Author

Listed:
  • 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
    These authors contributed equally to this work.)

  • Richik GhoshThakur

    (Department of Environmental Studies, Siksha-Bhavana, Visva-Bharati, Santiniketan 731235, West Bengal, India
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • 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 & Srinivasan Ba, 2023. "Optimisation of Anaerobic Digestate and Chemical Fertiliser Application to Enhance Rice Yield—A Machine-Learning Approach," Sustainability, MDPI, vol. 15(18), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13706-:d:1239595
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    References listed on IDEAS

    as
    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Yongjian Sun & Yunxia Wu & Yuanyuan Sun & Yinghan Luo & Changchun Guo & Bo Li & Feijie Li & Mengwen Xing & Zhiyuan Yang & Jun Ma, 2022. "Effects of Water and Nitrogen on Grain Filling Characteristics, Canopy Microclimate with Chalkiness of Directly Seeded Rice," Agriculture, MDPI, vol. 12(1), pages 1-20, January.
    3. Can Zhao & Guangming Liu & Yue Chen & Yan Jiang & Yi Shi & Lingtian Zhao & Pingqiang Liao & Weiling Wang & Ke Xu & Qigen Dai & Zhongyang Huo, 2022. "Excessive Nitrogen Application Leads to Lower Rice Yield and Grain Quality by Inhibiting the Grain Filling of Inferior Grains," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
    4. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    5. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    6. Rattan Lal, 2015. "Restoring Soil Quality to Mitigate Soil Degradation," Sustainability, MDPI, vol. 7(5), pages 1-21, May.
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