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Analyzing Spatial Dependence of Rice Production in Northeast Thailand for Sustainable Agriculture: An Optimal Copula Function Approach

Author

Listed:
  • Suneerat Srisopa

    (Department of Mathematics, Faculty of Education, The Eastern University of Management and Technology, Ubon Ratchathani 34000, Thailand)

  • Peerapong Luamka

    (Regional Office of Agricultural Economics Section 4, Tha Phra, Mueang, Khon Kaen 40000, Thailand)

  • Saowanee Rattanawan

    (Department of Mathematics, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham 44150, Thailand)

  • Khanitta Somtrakoon

    (Department of Biology, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham 44150, Thailand)

  • Piyapatr Busababodhin

    (Department of Mathematics, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham 44150, Thailand
    DSSA Research Unit, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham 44150, Thailand)

Abstract

Rice is not only central to Thailand’s economy and dietary consumption but also plays a significant role in global food security. Northeast Thailand, in particular, is a principal region for rice cultivation. However, with the mounting concerns of climate change, it becomes paramount to understand the interplay between regional weather patterns and rice yields, aiming to develop effective adaptive agricultural strategies. The current study aimed to fill the research gap by investigating an optimal copula for the spatial dependence of rice production and related meteorological variables in this area. The objective of this study is to understand how rice production in different areas relates to each other in order to improve farming practices and address challenges such as suitable weather. To achieve this goal, we apply three families of copulas—elliptical, Archimedean, and extreme—to analyze crop and meteorological variables across the watershed in the northeastern region of Thailand. With a data foundation extending from 1981 to 2021 from the Regional Office of Agricultural Economics Sector 4, Thailand, this study offers a comprehensive analysis of the spatial dynamics driving rice production across twenty provinces in Northeast Thailand. Using a piecewise linear model, we dissected rice yield trends, revealing distinct slopes in production and yield across various periods. The analysis leaned on elliptical, Archimedean, and extreme copula families, using the maximum likelihood estimation to discern marginal distribution residuals. Through rigorous bootstrap goodness-of-fit tests and cross-validation, the most appropriate copula for each province was identified. Key findings demonstrate pronounced spatial interdependencies in rice yields, with the Frank copula prominently capturing the product relationship between provinces such as Maha Sarakham and Roi-Et. Conversely, the Clayton copula better characterized regions such as Srisaket and Ubon Ratchathani. Moreover, the results underscore the considerable influence of meteorological factors, notably rainfall and temperature, on rice production, especially in regions like Ubon Ratchathani. In distilling these multifaceted relationships, the study charts a pathway for crafting sustainable, localized agricultural strategies. As the world grapples with climate change’s ramifications, the insights from this research stand crucial, offering direction for fostering resilience, adaptation, and optimizing rice productivity across Thailand’s diverse agrarian landscapes.

Suggested Citation

  • Suneerat Srisopa & Peerapong Luamka & Saowanee Rattanawan & Khanitta Somtrakoon & Piyapatr Busababodhin, 2023. "Analyzing Spatial Dependence of Rice Production in Northeast Thailand for Sustainable Agriculture: An Optimal Copula Function Approach," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14774-:d:1257926
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    References listed on IDEAS

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    1. Genest, Christian & Rémillard, Bruno & Beaudoin, David, 2009. "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 199-213, April.
    2. Steffen Grønneberg & Nils Lid Hjort, 2014. "The Copula Information Criteria," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 436-459, June.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    4. Yi Yang & Rongling Ye & Mallika Srisutham & Thanyaluck Nontasri & Supranee Sritumboon & Masayasu Maki & Koshi Yoshida & Kazuo Oki & Koki Homma, 2022. "Rice Production in Farmer Fields in Soil Salinity Classified Areas in Khon Kaen, Northeast Thailand," Sustainability, MDPI, vol. 14(16), pages 1-10, August.
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