Big climate data assessment of viticultural conditions for wine quality determination in France

Grapevine is one of the economically most important crops especially in Europe. Although its investment value has been widely recognized, the complex interactions between climate and viticulture remain immaturely understood and modeled, which largely limits a reliable investing strategy by using the observed climate conditions to estimate the wine quality. Therefore, with the aim of comprehensively analyzing the climate-viticulture relationship, compared to most previous studies which employed a few climate factors derived from sparsely located meteorological stations, in the present study, we include 22 climate factors, including temperature, water balance, atmosphere, and radiation data provided by a global land assimilation system covering a period of 40 years (1970 to 2010) as well as two large-scale atmospheric teleconnection indices to establish a holistic climate-wine quality model. Moreover, instead of the conventionally used simple regression methods, to deal with the comprehensive but volume climate dataset, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, which excels in ingesting a massive amount of variables having complex collinearities. In the pre-analysis of correlations between utilized climate factors, it is found that sunlight has the strongest connections with other factors as it correlates with the most number of climate factors. On the contrary, temperature, the conventionally most commonly employed factor, correlates with much fewer factors. Finally, via validation with wine vintage scores derived from two authoritative rating systems, it is ensured that our proposed approach can accurately establish the climate-wine quality models for four well-known wine-growing regions in France, including Alsace, Bordeaux, Burgundy, and Champagne. Due to the more complex climate pattern of Bordeaux compared to other regions, two bank-wise models instead of a bank-merged model is vital for Bordeaux to achieve a similar modeling accuracy. Eventually, a satisfactory vintage deviance explaining accuracy with one standard deviation score residual within ± 6 points can be achieved in all regions. Therefore, based on the established climate-wine quality model together with the observed climate conditions, the wine quality of each region can be reliably predicted, which provides a reliable reference for wine investment.