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Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach

Author

Listed:
  • Helder Fraga

    (Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal)

  • Teresa R. Freitas

    (Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal)

  • Marco Moriondo

    (National Research Council of Italy, IBE-CNR, Institute of BioEconomy, via Madonna del Piano 10, 50019 Sesto Fiorentino FI, Italy
    Department of Agriculture, Food, Environment, and Forestry, University of Florence, P.le delle Cascine 18, 50144 Firenze, Italy)

  • Daniel Molitor

    (LIST—Luxembourg Institute of Science and Technology, Environmental Research and Innovation (ERIN) Department, 41, rue du Brill, L-4422 Belvaux, Luxembourg)

  • João A. Santos

    (Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), Universidade de Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal)

Abstract

The Côa region in inner-northern Portugal heavily relies on viticulture, which is a cornerstone of its economy and cultural identity. Understanding the intricate relationship between climatic variables and wine production (WP) is crucial for adapting management practices to changing climatic conditions. This study employs machine learning (ML), specifically random forest (RF) regression, to predict grapevine yields in the Côa region using high-resolution climate data for 2004–2020. SHAP (SHapley Additive exPlanations) values are used to potentially explain the non-linear relationships between climatic factors and WP. The results reveal a complex interplay between predictors and WP, with precipitation emerging as a key determinant. Higher precipitation levels in April positively impact WP by replenishing soil moisture ahead of flowering, while elevated precipitation and humidity levels in August have a negative effect, possibly due to late-season heavy rainfall damaging grapes or creating more favorable conditions for fungal pathogens. Moreover, warmer temperatures during the growing season and adequate solar radiation in winter months favor higher WP. However, excessive radiation during advanced growth stages can lead to negative effects, such as sunburn. This study underscores the importance of tailoring viticultural strategies to local climatic conditions and employing advanced analytical techniques such as SHAP values to interpret ML model predictions effectively. Furthermore, the research highlights the potential of ML models in climate change risk reduction associated with viticulture, specifically WP. By leveraging insights from ML and interpretability techniques, policymakers and stakeholders can develop adaptive strategies to safeguard viticultural livelihoods and stable WP in a changing climate, particularly in regions with a rich agrarian heritage, such as the Côa region.

Suggested Citation

  • Helder Fraga & Teresa R. Freitas & Marco Moriondo & Daniel Molitor & João A. Santos, 2024. "Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach," Land, MDPI, vol. 13(6), pages 1-16, May.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:749-:d:1403306
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    References listed on IDEAS

    as
    1. van Leeuwen, Cornelis & Darriet, Philippe, 2016. "The Impact of Climate Change on Viticulture and Wine Quality," Journal of Wine Economics, Cambridge University Press, vol. 11(1), pages 150-167, May.
    2. Fraga, Helder & Santos, João A., 2018. "Vineyard mulching as a climate change adaptation measure: Future simulations for Alentejo, Portugal," Agricultural Systems, Elsevier, vol. 164(C), pages 107-115.
    3. Oiliam Stolarski & João A. Santos & André Fonseca & Chenyao Yang & Henrique Trindade & Helder Fraga, 2023. "Climate Change Impacts on Grassland Vigour in Northern Portugal," Land, MDPI, vol. 12(10), pages 1-18, October.
    4. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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