摘要：Understanding the role of the climate on the wine production is one of the major concerns of this
sector since the environment usually determines the output of this industry. There are only a few previous
studies that attempted to compile these environmental effects as an index, usually considering the temperature
and the precipitation as their core variables. The present study suggests a new climate index which is based on
descriptive statistics. Our index tries to mimic the target region characteristics and avoid the past studies
premise of imposing previously conceived restrictions such as a fixed optimal climate. We then used yearly
production and daily temperature data (1950-2016) from the Portuguese Minho wine region to test our
proposed index and compare it with Ribéreau-Gayon and Peynaud (RGP, Ribéreau-Gayon et al., 2003) and
Growing Degree-Days (GDD, Winkler et al., 1974) indexes. Our results showed that the newly proposed index
may outperform the explanatory power of the other indexes and, in addition, may output interesting and
unknown characteristics such as the different ideal temperatures regarding the studied region.
其他摘要：Understanding the role of the climate on the wine production is one of the major concerns of this sector since the environment usually determines the output of this industry. There are only a few previous studies that attempted to compile these environmental effects as an index, usually considering the temperature and the precipitation as their core variables. The present study suggests a new climate index which is based on descriptive statistics. Our index tries to mimic the target region characteristics and avoid the past studies premise of imposing previously conceived restrictions such as a fixed optimal climate. We then used yearly production and daily temperature data (1950-2016) from the Portuguese Minho wine region to test our proposed index and compare it with Ribéreau-Gayon and Peynaud (RGP, Ribéreau-Gayon et al., 2003) and Growing Degree-Days (GDD, Winkler et al., 1974) indexes. Our results showed that the newly proposed index may outperform the explanatory power of the other indexes and, in addition, may output interesting and unknown characteristics such as the different ideal temperatures regarding the studied region.
