Predicting Wine Prices Based on the Weather: Bordeaux Vineyards in a Changing Climate

39 Pages Posted: 7 May 2022

See all articles by Aymeric Roucher

Aymeric Roucher

University of Cambridge

Leonidas Aristodemou

University of Cambridge

Professor Frank Tietze

University of Cambridge

Abstract

In the twentieth century, high summer temperatures were beneficial to grape and wine quality in Bordeaux. However, owing to global warming gradually raising temperature closer to the optimum needed by the regional grape varieties, the positive effect of higher-than-usual summer temperatures has vanished over the last decades. Therefore, it is unknown whether any weather variable is still impactful enough to quantify future wine quality.Here we provide a predictive model of wine prices, based only on weather data. We establish that it predicts more accurately a vintage’s long-term quality than a world-class expert rating this same vintage in the year following its production.We first design a corpus of features suited to the grapevine lifecycle, so as to distinguish the grapevine’s contrasting needs across the different stages of its growth.  Using Bayesian inference with a Hamiltonian Monte Carlo (HMC) algorithm, we then select the most powerful drivers of wine quality. Finally, we build a predictive model that leverages Local Least Squares kernel regression (LLS) to allow model coefficients to change over the vintages, thus factoring in the time-varying nature of climate impact on the grapevine.Using the phenology-adapted features, and letting coefficients vary through time with LLS, both significantly improve performance. The proposed model thus achieves state-of-the-art predictive accuracy, and it even provides a better predictive ranking of successive vintages than the grades given by world-famous wine critic Robert Parker.This demonstrates that weather is still a very efficient predictor of wine quality in Bordeaux. This study provides strong support for the usage of weather-based models as auxiliaries in the pricing of premium agricultural products. The HMC algorithm, both flexible and robust to noise, can be used in the feature selection step of many modelling problems. Finally, the novel usage of a LLS architecture, allowing the input-output relationship to smoothly vary over time, would have exciting development in the modelling of other agricultural systems, in face of the changes introduced by global warming and adaptation of production methods.

Keywords: Climate change, Grapevine, machine learning, Hamiltonian Monte Carlo, Local Least Squares kernel regression

Suggested Citation

Roucher, Aymeric and Aristodemou, Leonidas and Tietze, Frank, Predicting Wine Prices Based on the Weather: Bordeaux Vineyards in a Changing Climate. Available at SSRN: https://ssrn.com/abstract=4102696 or http://dx.doi.org/10.2139/ssrn.4102696

Aymeric Roucher (Contact Author)

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

Leonidas Aristodemou

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

Frank Tietze

University of Cambridge ( email )

Institute for Manufacturing (IfM)
17 Charles Babbage Road
Cambridge, CB3 0FS
United Kingdom
00441223338083 (Phone)

HOME PAGE: http://www.ifm.eng.cam.ac.uk/people/ft263/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
101
Abstract Views
420
Rank
487,043
PlumX Metrics