Local influence of climate on grapevine: an analytical process involving a functional and Bayesian exploration of farm data time series synchronised with an eGDD thermal index

Abstract

Climate influence on grapevine physiology is prevalent and this influence is expected to increase with climate change. Climate influence on grapevine physiology can vary depending on the terroir. A better understanding of these local terroir variations is likely to be achieved with analyses that use local data; i.e., farm/vineyard data. Thus, the challenge lies in exploiting farm data to enable grape growers to understand their own terroir and consequently adapt their practices to the local conditions. In such a context, this article proposes an analytical process to site-specifically study climate influence on grapevine physiology by focusing on time series of the weather data often contained in farm data sets. This article focuses on temperature and precipitation influence on yield in the form of a case study. The analytical process includes the Extended Growing Degree Days (eGDD) and the Bayesian functional Linear regression with Sparse Steps functions (BLiSS) methods in order to detect site-specific periods of strong climate influence on grapevine yield. It uses data from three commercial vineyards situated in the Bordeaux region (France), California (USA) and Israel. In general, the periods of climate influence on grapevine yield detected for the three vineyards identified the same stages of yield development, which have already been studied in the scientific literature. However, some vineyard differences were observed, including: i) different periods of influence associated with a given stage of yield development between the vineyards, ii) different influential weather variables between the three vineyards for a given period, and iii) differing duration of the period of influence associated with a given stage of yield development between the vineyards. These results show the potential of the proposed analytical process for analysing the time series of farm weather data in order to extract site-specific climate indicators of grapevine yield.

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

“…Throughout critical phenological stages, including budburst and the subsequent development of shoots and inflorescences, water availability within a moderate range becomes a critical determinant for optimal vine growth [34]. Several studies have investigated the effect of precipitation on grapevine yields, indicating that higher rainfall at early stages is beneficial [35], promoting vegetative growth and berry and wine quality benefits [36], which are in agreement with the present study findings. Adequate water availability during this phase enables proper shoot growth and promotes a successful berry set [37].…”

Climatic records and within field data on yield and harvest quality over a whole vineyard estate

“… The dataset can be used for any research in vine ecophysiology aimed at identifying potential relationships between yield and harvest quality parameters for different grape varieties [3] . It can also be used to explore, test and validate modeling approaches that aim at understanding or predicting yield and/or grape quality parameters according to the year's climatic data [4] . Another value of this data set is the spatial footprint of the georeferenced data which makes it possible to relate multispectral time series of satellite images such as Sentinel to identify potential links between yield and harvest quality parameters and remote sensed canopy changes [5] .…”

“…It can also be used to explore, test and validate modeling approaches that aim at understanding or predicting yield and/or grape quality parameters according to the year's climatic data [4] .…”

“…Its unique feature is to provide these data with a small spatial footprint and therefore a high spatial accuracy. As a complement to datasets acquired in other conditions, it therefore offers potential for gaining a better understanding of the determinants of climate on fruit yield and quality, as in stochastic modelling approaches like those proposed by Laurent et al [4] which aimed at seeking links between climatic time series and observed yield.…”