Vintage Port prediction and climate change scenarios

Abstract

The Douro region is renowned for its quality wines, particularly for the famous Port Wine. Vintage years, declared approximately 2–3 times per decade, signify exceptional quality linked to optimum climatic conditions driving grape quality attributes. Climate change poses challenges, as rising temperatures and extreme weather events impact viticulture. This study uses machine learning algorithms to assess the climatic influence on vintage years and climate change impacts for the next decades. Historical vintage data were collected from 1850 to 2014. Monthly climatic data for the same period were obtained, including temperature, precipitation, humidity, solar radiation, and wind components. Various machine-learning algorithms were selected for classification, and a statistical analysis helped identify relevant climate variables for differentiation. Cross-validation was used for model training and evaluation, with the hits and misses (confusion matrix) as the performance metric. The best-performing model underwent hyperparameter tuning. Subsequently, future climate projections were acquired for four regional climate models from 2030 until 2099 under different socio-economic scenarios (IPCC SSP2, SSP3, and SSP5). Quantile mapping bias adjustment was applied to correct future climate data and reduce model biases. Past data revealed that vintages occurred 23.6 % of the years, with an average of two vintage years per decade, with a slightly positive trend. Climate variables such as precipitation in March, air temperatures in April and May, humidity in March and April, solar radiation in March, and meridional wind in June were identified as important factors influencing vintage year occurrence. Machine-learning models were employed to predict vintage years based on the climate variables, with the XGBClassifier achieving the highest performance with 76 %/88 % hits for the vintage/non-vintage classes, respectively, and an ROC score of 0.86, demonstrating strong predictive capabilities. Future climate change scenarios under different socio-economic pathways were assessed, and the results indicated a decrease in the occurrence vintage years until 2099 (10.3 % for SSP2, 9.1 % for SSP3, and 5.8 % for SSP5). The study provides valuable insights into the relationship between climate variables and wine vintage years, enabling winemakers to make informed decisions about vineyard management and grape cultivation. The predictions suggest that climate change may challenge the wine industry, emphasising the need for adaptation strategies.