IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4326-d976894.html
   My bibliography  Save this article

A Bayesian Causal Model to Support Decisions on Treating of a Vineyard

Author

Listed:
  • Federico Mattia Stefanini

    (Department of Environmental Science and Policy, University of Milan, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Lorenzo Valleggi

    (Department of Statistics, Computer Science, Applications, University of Florence, 50134 Florence, Italy
    These authors contributed equally to this work.)

Abstract

Plasmopara viticola is one of the main challenges of working in a vineyard as it can seriously damage plants, reducing the quality and quantity of grapes. Statistical predictions on future incidence may be used to evaluate when and which treatments are required in order to define an efficient and environmentally friendly management. Approaches in the literature describe mechanistic models requiring challenging calibration in order to account for local features of the vineyard. A causal Directed Acyclic Graph is here proposed to relate key determinants of the spread of infection within rows of the vineyard characterized by their own microclimate. The identifiability of causal effects about new chemical treatments in a non-randomized regime is discussed, together with the context in which the proposed model is expected to support optimal decision-making. A Bayesian Network based on discretized random variables was coded after quantifying the expert degree of belief about features of the considered vineyard. The predictive distribution of incidence, given alternative treatment decisions, was defined and calculated using the elicited network to support decision-making on a weekly basis. The final discussion considers current limitations of the approach and some directions for future work, such as the introduction of variables to describe the state of soil and plants after treatment.

Suggested Citation

  • Federico Mattia Stefanini & Lorenzo Valleggi, 2022. "A Bayesian Causal Model to Support Decisions on Treating of a Vineyard," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4326-:d:976894
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4326/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/22/4326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lavik, Ming Su & Hardaker, J. Brian & Lien, Gudbrand & Berge, Therese W., 2020. "A multi-attribute decision analysis of pest management strategies for Norwegian crop farmers," Agricultural Systems, Elsevier, vol. 178(C).
    2. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Martin Ravallion, 2022. "On the Gains from Tradable Benefits‐in‐kind: Evidence for Workfare in India," Economica, London School of Economics and Political Science, vol. 89(355), pages 770-787, July.
    3. Peter Abell & Ofer Engel, 2021. "Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?," Sociological Methods & Research, , vol. 50(4), pages 1842-1862, November.
    4. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    5. Salvatore Bimonte & Antonella D’Agostino, 2021. "Tourism development and residents’ well-being: Comparing two seaside destinations in Italy," Tourism Economics, , vol. 27(7), pages 1508-1525, November.
    6. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    7. Antonio R. Linero, 2022. "Simulation‐based estimators of analytically intractable causal effects," Biometrics, The International Biometric Society, vol. 78(3), pages 1001-1017, September.
    8. Berger, Marius & Hottenrott, Hanna, 2021. "Start-up subsidies and the sources of venture capital," Journal of Business Venturing Insights, Elsevier, vol. 16(C).
    9. Sahar Saeed & Erica E. M. Moodie & Erin C. Strumpf & Marina B. Klein, 2018. "Segmented generalized mixed effect models to evaluate health outcomes," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(4), pages 547-551, May.
    10. Hodula, Martin & Melecký, Martin & Pfeifer, Lukáš & Szabo, Milan, 2023. "Cooling the mortgage loan market: The effect of borrower-based limits on new mortgage lending," Journal of International Money and Finance, Elsevier, vol. 132(C).
    11. Manuel S. González Canché, 2017. "Financial Benefits of Rapid Student Loan Repayment: An Analytic Framework Employing Two Decades of Data," The ANNALS of the American Academy of Political and Social Science, , vol. 671(1), pages 154-182, May.
    12. Damian Clarke & Daniel Paila~nir & Susan Athey & Guido Imbens, 2023. "Synthetic Difference In Differences Estimation," Papers 2301.11859, arXiv.org, revised Feb 2023.
    13. Almer, Christian & Winkler, Ralph, 2017. "Analyzing the effectiveness of international environmental policies: The case of the Kyoto Protocol," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 125-151.
    14. Sanford C. Gordon & Hannah K. Simpson, 2020. "Causes, theories, and the past in political science," Public Choice, Springer, vol. 185(3), pages 315-333, December.
    15. Lechner, Michael, 2008. "A note on endogenous control variables in causal studies," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 190-195, February.
    16. Angelov, Nikolay & Eliason, Marcus, 2014. "The effects of targeted labour market programs for job seekers with occupational disabilities," Working Paper Series 2014:27, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    17. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    18. Slutskin, L., 2017. "Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 12-30.
    19. Yiyan Huang & Cheuk Hang Leung & Siyi Wang & Yijun Li & Qi Wu, 2024. "Unveiling the Potential of Robustness in Evaluating Causal Inference Models," Papers 2402.18392, arXiv.org.
    20. Tianmeng Lyu & Björn Bornkamp & Guenther Mueller‐Velten & Heinz Schmidli, 2023. "Bayesian inference for a principal stratum estimand on recurrent events truncated by death," Biometrics, The International Biometric Society, vol. 79(4), pages 3792-3802, December.

    Corrections

    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:gam:jmathe:v:10:y:2022:i:22:p:4326-:d:976894. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.