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The Diffusion of Greenhouse Agriculture in Northern Thailand: Combining Econometrics and Agent‐Based Modeling

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  • Pepijn Schreinemachers
  • Thomas Berger
  • Aer Sirijinda
  • Suwanna Praneetvatakul

Abstract

This paper studies the diffusion of greenhouse agriculture in a watershed in the northern uplands of Thailand by applying econometrics and agent‐based modeling in combination. Adoption has been rapid by farmers in the central valley of the watershed, while farmers at higher altitudes, lacking transferable land titles that could serve as mortgage collateral, have been unable to obtain loans for greenhouse investment. The objectives of the paper are both methodological and empirical. On the methodological side, it shows that econometrically estimated models of farm household behavior are useful to design and to parameterize an agent‐based model. On the empirical side, simulation results show that if mortgage collateral would not be required, then adoption in the upper part of the watershed could reach nearly 77% of farm households by 2020, as compared to about 36% under current conditions. Furthermore results suggest a significant increase in incomes related to the innovation and a substantially greater irrigation water use, especially in the central part. As bell pepper under greenhouses has replaced pesticide‐intensive chrysanthemum, it has declined average levels of pesticide use. Nevertheless, pesticide use is high and farmers are struggling to control pests, which raises questions about the long‐term sustainability of the innovation. Dans le présent article, nous avons analysé, à l'aide d'un modèle économétrique et d'un modèle multi‐agent, l'expansion de la culture en serre dans un bassin versant des hautes terres du Nord de la Thaïlande. Les agriculteurs de la vallée centrale du bassin versant ont adopté rapidement cette forme d'agriculture, tandis que les agriculteurs installés dans les hautes altitudes n’ont pu, faute de titres fonciers transférables pouvant servir de garantie, obtenir de prêts pour construire des serres. Les objectifs du présent article étaient à la fois méthodologiques et empiriques. Sur le plan méthodologique, notre étude a montré que les modèles de comportement des ménages agricoles estimés économétriquement sont utiles pour concevoir et paramétrer un modèle multi‐agent. Sur le plan empirique, les résultats de simulation ont montré que, si des garanties de prêt n’étaient pas exigées, 77 p. 100 des ménages agricoles adopteraient la culture en serre dans les hautes terres du bassin versant d'ici 2020, comparativement à environ 36 p. 100 dans les conditions actuelles. De nouveaux résultats ont indiqué que cette innovation ainsi qu’un usage accru de l'eau pour l'irrigation, particulièrement dans la partie centrale, pourraient générer une hausse substantielle des revenus. Depuis que la culture en serre du poivron vert a remplacé la culture du chrysanthème exigeante en pesticides, l'usage des pesticides a beaucoup diminué, mais demeure tout de même élevé. Les agriculteurs ont de la difficultéà lutter contre les ravageurs, ce qui soulève des questions sur la viabilitéà long terme de l'innovation.

Suggested Citation

  • Pepijn Schreinemachers & Thomas Berger & Aer Sirijinda & Suwanna Praneetvatakul, 2009. "The Diffusion of Greenhouse Agriculture in Northern Thailand: Combining Econometrics and Agent‐Based Modeling," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 513-536, December.
  • Handle: RePEc:bla:canjag:v:57:y:2009:i:4:p:513-536
    DOI: 10.1111/j.1744-7976.2009.01168.x
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    1. McCarl, Bruce A. & Apland, Jeffrey, 1986. "Validation Of Linear Programming Models," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 18(2), pages 1-10, December.
    2. Thomas Berger & Regina Birner & Nancy Mccarthy & JosÉ DíAz & Heidi Wittmer, 2007. "Capturing the complexity of water uses and water users within a multi-agent framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(1), pages 129-148, January.
    3. Prasnee Tipraqsa & Pepijn Schreinemachers, 2009. "Agricultural commercialization of Karen Hill tribes in northern Thailand," Agricultural Economics, International Association of Agricultural Economists, vol. 40(1), pages 43-53, January.
    4. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    5. Janssen, Sander & van Ittersum, Martin K., 2007. "Assessing farm innovations and responses to policies: A review of bio-economic farm models," Agricultural Systems, Elsevier, vol. 94(3), pages 622-636, June.
    6. Balmann, Alfons, 1997. "Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 24(1), pages 85-108.
    7. Letcher, R.A. & Croke, B.F.W. & Merritt, W.S. & Jakeman, A.J., 2006. "An integrated modelling toolbox for water resources assessment and management in highland catchments: Sensitivity analysis and testing," Agricultural Systems, Elsevier, vol. 89(1), pages 132-164, July.
    8. Berger, Thomas & Schreinemachers, Pepijn & Woelcke, Johannes, 2006. "Multi-agent simulation for the targeting of development policies in less-favored areas," Agricultural Systems, Elsevier, vol. 88(1), pages 28-43, April.
    9. Letcher, R.A. & Croke, B.F.W. & Jakeman, A.J. & Merritt, W.S., 2006. "An integrated modelling toolbox for water resources assessment and management in highland catchments: Model description," Agricultural Systems, Elsevier, vol. 89(1), pages 106-131, July.
    10. Happe, Kathrin & Kellermann, Konrad & Balmann, Alfons, 2006. "Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11(1).
    11. Schreinemachers, Pepijn & Berger, Thomas & Aune, Jens B., 2007. "Simulating soil fertility and poverty dynamics in Uganda: A bio-economic multi-agent systems approach," Ecological Economics, Elsevier, vol. 64(2), pages 387-401, December.
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    Cited by:

    1. Chen, Assaf, 2017. "Spatially explicit modelling of agricultural dynamics in semi-arid environments," Ecological Modelling, Elsevier, vol. 363(C), pages 31-47.
    2. Quang, Dang Viet & Schreinemachers, Pepijn & Berger, Thomas, 2014. "Ex-ante assessment of soil conservation methods in the uplands of Vietnam: An agent-based modeling approach," Agricultural Systems, Elsevier, vol. 123(C), pages 108-119.
    3. James Nolan & Dawn Parker & G. Cornelis Van Kooten & Thomas Berger, 2009. "An Overview of Computational Modeling in Agricultural and Resource Economics," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 417-429, December.
    4. Grovermann, Christian & Schreinemachers, Pepijn & Riwthong, Suthathip & Berger, Thomas, 2017. "‘Smart’ policies to reduce pesticide use and avoid income trade-offs: An agent-based model applied to Thai agriculture," Ecological Economics, Elsevier, vol. 132(C), pages 91-103.
    5. Laura Schmitt Olabisi & Ryan Qi Wang & Arika Ligmann-Zielinska, 2015. "Why Don’t More Farmers Go Organic? Using A Stakeholder-Informed Exploratory Agent-Based Model to Represent the Dynamics of Farming Practices in the Philippines," Land, MDPI, vol. 4(4), pages 1-24, October.
    6. Shang, Linmei & Heckelei, Thomas & Börner, Jan & Rasch, Sebastian, 2020. "Adoption and Diffusion of Digital Farming Technologies – Integrating Farm-Level Evidence and System-Level Interaction," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305586, German Association of Agricultural Economists (GEWISOLA).
    7. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    8. Grovermann, Christian & Schreinemachers, Pepijn & Berger, Thomas, 2015. "Evaluation of IPM adoption and financial instruments to reduce pesticide use in Thai agriculture using econometrics and agent-based modeling," 2015 Conference, August 9-14, 2015, Milan, Italy 211690, International Association of Agricultural Economists.
    9. Marohn, Carsten & Troost, Christian & Warth, Benjamin & Bateki, Christian & Zijlstra, Mink & Anwar, Faizan & Williams, Benjamin & Descheemaeker, Katrien & Berger, Thomas & Asch, Folkard & Dickhoefer, , 2022. "Coupled biophysical and decision-making processes in grassland systems in East African savannahs – A modelling framework," Ecological Modelling, Elsevier, vol. 474(C).
    10. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    11. Holderieath, Jason, 2016. "Spatiotemporal management under heterogeneous damage and uncertain parameters. An agent-based approach," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235850, Agricultural and Applied Economics Association.
    12. Grovermann, Christian & Schreinemachers, Pepijn & Berger, Thomas, 2012. "Private and Social Levels of Pesticide Overuse in Rapidly Intensifying Upland Agriculture in Thailand," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126341, International Association of Agricultural Economists.
    13. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    14. Kopp, Thomas & Salecker, Jan, 2020. "How traders influence their neighbours: Modelling social evolutionary processes and peer effects in agricultural trade networks," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    15. Thomas Berger & Christian Troost & Tesfamicheal Wossen & Evgeny Latynskiy & Kindie Tesfaye & Sika Gbegbelegbe, 2017. "Can smallholder farmers adapt to climate variability, and how effective are policy interventions? Agent-based simulation results for Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 48(6), pages 693-706, November.
    16. Tesfamicheal Wossen & Thomas Berger & Salvatore Di Falco, 2015. "Social capital, risk preference and adoption of improved farm land management practices in Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 81-97, January.
    17. Marius Eisele & Christian Troost & Thomas Berger, 2021. "How Bayesian Are Farmers When Making Climate Adaptation Decisions? A Computer Laboratory Experiment for Parameterising Models of Expectation Formation," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 805-828, September.
    18. Schreinemachers, Pepijn & Potchanasin, Chakrit & Berger, Thomas & Roygrong, Sithidech, 2009. "The declining profitability of litchi orchards in northern Thailand: Can innovations reverse the trend?," 2009 Conference, August 16-22, 2009, Beijing, China 50954, International Association of Agricultural Economists.
    19. Jaap Sok & Egil A J Fischer, 2020. "Farmers' heterogeneous motives, voluntary vaccination and disease spread: an agent-based model," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 1201-1222.
    20. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    21. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
    22. Thomas Kopp & Jan Salecker, 2018. "Modelling Social Evolutionary Processes and Peer Effects in Agricultural Trade Networks: the Rubber Value Chain in Indonesia," Papers 1811.11476, arXiv.org.

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