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Impact of economic indicators on rice production: A machine learning approach in Sri Lanka

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  • Sherin Kularathne
  • Namal Rathnayake
  • Madhawa Herath
  • Upaka Rathnayake
  • Yukinobu Hoshino

Abstract

Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study’s findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection.

Suggested Citation

  • Sherin Kularathne & Namal Rathnayake & Madhawa Herath & Upaka Rathnayake & Yukinobu Hoshino, 2024. "Impact of economic indicators on rice production: A machine learning approach in Sri Lanka," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-20, June.
    • Handle: RePEc:plo:pone00:0303883
    DOI: 10.1371/journal.pone.0303883
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    References listed on IDEAS

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    1. Anurag Satpathi & Parul Setiya & Bappa Das & Ajeet Singh Nain & Prakash Kumar Jha & Surendra Singh & Shikha Singh, 2023. "Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India," Sustainability, MDPI, vol. 15(3), pages 1-18, February.
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    5. repec:uwe:journl:v:8:y:2013:i:1:p:5-11 is not listed on IDEAS
    6. Namal Rathnayake & Upaka Rathnayake & Tuan Linh Dang & Yukinobu Hoshino, 2023. "Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-21, April.
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