Must quality estimation based on climate data in the Upper Moselle region

Abstract

Environmental climate conditions have a high impact on must quality expressed in must density and acidity. In view of the observed climate warming and changes in wine quality, finding the interacting factors between climate and must quality are of high interest to estimate trends in must quality for future periods. Cross validated statistical models based on forward and backward regression are derived using 35 years of climatological and viticultural data observed in the Upper Moselle region. For seven white wine varieties as well as for groups of these varieties, which are detected by hierarchical cluster analysis, models for must density and acidity are developed using 160 different predictors. In general, temperature has the highest impact on must quality. The most important predictor is the accumulation of degree days from April to October, but also temperature means for different periods, climate indices, sunshine duration and precipitation contribute to the explained variance of must quality. Parameterisations of the phenological phases budburst and flowering are also included in the must quality models. The models reproduce must density with an explained variance between 59 % and 79 %, and acidity with 62 %-88 % explained variance depending on vine variety. The explained variance for grouped varieties is higher: 70 %-80 % for must density and 82 %-89 % for acidity. The model in the considered region has an error of about 3°Oe for must density and less than 1 g/l for acidity and is consequently well suited for further application.