IDEAS home Printed from
   My bibliography  Save this article

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


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


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

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. 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.
    2. McCarl, Bruce A. & Apland, Jeffrey, 1986. "Validation of Linear Programming Models," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 18(2), pages 155-164, December.
    3. 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.
    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. 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.
    6. 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.
    7. Balmann, Alfons, 1997. "Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 24(1), pages 85-108.
    8. 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.
    9. 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, ZBW - Leibniz Information Centre for Economics.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. 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, Open Access Journal, vol. 4(4), pages 1-24, October.
    2. 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.
    3. 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.
    4. 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.
    5. Jaap Sok & Egil A J Fischer, 2020. "Farmers’ heterogeneous motives, voluntary vaccination and disease spread: an agent-based model," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 47(3), pages 1201-1222.
    6. Chen, Assaf, 2017. "Spatially explicit modelling of agricultural dynamics in semi-arid environments," Ecological Modelling, Elsevier, vol. 363(C), pages 31-47.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:canjag:v:57:y:2009:i:4:p:513-536. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.