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Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data

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  • De Clercq, Djavan
  • Mahdi, Adam

Abstract

Yield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning.

Suggested Citation

  • De Clercq, Djavan & Mahdi, Adam, 2024. "Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data," Agricultural Systems, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:agisys:v:220:y:2024:i:c:s0308521x2400249x
    DOI: 10.1016/j.agsy.2024.104099
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    1. Begoña Vitoriano & M. Ortuño & Gregorio Tirado & Javier Montero, 2011. "A multi-criteria optimization model for humanitarian aid distribution," Journal of Global Optimization, Springer, vol. 51(2), pages 189-208, October.
    2. Timsina, Jagadish & Dutta, Sudarshan & Devkota, Krishna Prasad & Chakraborty, Somsubhra & Neupane, Ram Krishna & Bishta, Sudarshan & Amgain, Lal Prasad & Singh, Vinod K. & Islam, Saiful & Majumdar, Ka, 2021. "Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency," Agricultural Systems, Elsevier, vol. 192(C).
    3. Lenaers, Ian & De Moor, Lieven, 2023. "Exploring XAI techniques for enhancing model transparency and interpretability in real estate rent prediction: A comparative study," Finance Research Letters, Elsevier, vol. 58(PA).
    4. Ziaul Haq Adnan & Abdul Hai Ashik & Muntasina Rahman & Sherajus Shalahin Bhuiyan & Anirban Ganguly, 2022. "Applying linear programming for logistics distribution of essential relief items during COVID-19 lockdown: evidence from Bangladesh," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 9(3), pages 191-204.
    5. Koen Peters & Sérgio Silva & Tim Sergio Wolter & Luis Anjos & Nina van Ettekoven & Éric Combette & Anna Melchiori & Hein Fleuren & Dick den Hertog & Özlem Ergun, 2022. "UN World Food Programme: Toward Zero Hunger with Analytics," Interfaces, INFORMS, vol. 52(1), pages 8-26, January.
    6. Maximilian Auffhammer & V. Ramanathan & Jeffrey Vincent, 2012. "Climate change, the monsoon, and rice yield in India," Climatic Change, Springer, vol. 111(2), pages 411-424, March.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. Samal, Parshuram & Pandey, Sushil & Kumar, G.A.K. & Barah, B.C., 2011. "Rice Ecosystems and Factors Affecting Varietal Adoption in Rainfed Coastal Orissa: A Multivariate Probit Analysis," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 24(01), June.
    9. Ishfaq, Muhammad & Farooq, Muhammad & Zulfiqar, Usman & Hussain, Saddam & Akbar, Nadeem & Nawaz, Ahmad & Anjum, Shakeel Ahmad, 2020. "Alternate wetting and drying: A water-saving and ecofriendly rice production system," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    11. Ruixue Wang & Roderick M Rejesus & Serkan Aglasan, 2021. "Warming Temperatures, Yield Risk and Crop Insurance Participation [Federal crop insurance and the disincentive to adapt to extreme heat]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(5), pages 1109-1131.
    12. Gutjahr, Walter J. & Nolz, Pamela C., 2016. "Multicriteria optimization in humanitarian aid," European Journal of Operational Research, Elsevier, vol. 252(2), pages 351-366.
    13. Nickell, Erin Burrell & Schwebke, Jason & Goldwater, Paul, 2023. "An introductory audit data analytics case study: Using Microsoft Power BI and Benford’s Law to detect accounting irregularities," Journal of Accounting Education, Elsevier, vol. 64(C).
    14. Andrijana Horvat & Vincenzo Fogliano & Pieternel A Luning, 2020. "Modifying the Bass diffusion model to study adoption of radical new foods–The case of edible insects in the Netherlands," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-23, June.
    15. İbrahim Miraç Eligüzel & Eren Özceylan & Gerhard-Wilhelm Weber, 2023. "Location-allocation analysis of humanitarian distribution plans: a case of United Nations Humanitarian Response Depots," Annals of Operations Research, Springer, vol. 324(1), pages 825-854, May.
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