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Analyzing Weather Patterns to Predict Wine Quality for Sonoma County Pinot Noir.

机译:分析天气模式以预测索诺玛县黑皮诺的葡萄酒质量。

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摘要

Every year the average quality of wine produced in any particular region rises or falls to some degree. Differences in climate conditions each year are generally considered to be the main cause of this annual variation. In an effort to correlate weather with wine quality, software was developed to analyze weather patterns and make predictions on wine scores. Various weather data including temperature highs and lows, precipitation, temperature range and degree day were correlated to regional vintage wine scores using a genetic algorithm, linear regression, and an artificial neural network. Different calculations of weather data within a wide range of time periods throughout the growing season were evaluated in order to find ones that had a mathematical effect on the wine scores.;Pinot Noir from Sonoma County and neighboring areas was focused on, due to its thin skin and climate-sensitive nature. Sonoma County was selected for its large Pinot Noir production, numerous small AVA's, and climate differences among these distinct regions. Climate data was acquired from 14 different weather stations around Sonoma County up through 2007, and wine quality was represented by wine scores from Wine Spectator magazine. Thirteen separate regions were differentiated, and weather data and vintage scores were created for each one. Each region was individually analyzed for correlations between its weather data and vintage wine scores. Along with this, all of the regions were combined together and evaluated as a single area. Each region had approximately 17 years of data, with the total combination consisting of a set of 164 points, one for each year of each region.;High correlations between specific weather periods and vintage scores were found for each region, and accurate wine score predictions were made based on these. The weather periods and calculations used to make these predictions were studied, and consistent trends were found that linked them to different stages of berry development.
机译:每年,在任何特定地区生产的葡萄酒的平均质量都会在某种程度上上升或下降。通常认为,每年气候条件的差异是造成这种年度变化的主要原因。为了使天气与葡萄酒质量相关联,开发了软件来分析天气模式并做出葡萄酒分数的预测。使用遗传算法,线性回归和人工神经网络,将各种天气数据(包括温度的高低,降水,温度范围和度日)与区域性葡萄酒评分相关联。为了找到对葡萄酒评分有数学影响的方法,评估了整个生长季节在不同时间段内的天气数据的不同计算方式。索诺玛县和附近地区的黑皮诺由于其薄而着重皮肤和气候敏感性质。索诺玛县因其黑皮诺产量大,众多小型AVA以及这些不同地区之间的气候差异而被选中。截止2007年,从索诺玛县的14个不同气象站获取了气候数据,《葡萄酒观察家》杂志的葡萄酒评分代表了葡萄酒的质量。区分了13个不同的区域,并为每个区域创建了气象数据和年份得分。每个地区都经过单独分析,以了解天气数据与葡萄酒评分之间的相关性。同时,将所有区域组合在一起,并作为一个区域进行评估。每个地区大约有17年的数据,其总组合由164个点组成,每个地区每年一次。;每个地区的特定天气周期与年份得分之间存在高度相关性,并且准确的葡萄酒得分预测是基于这些。研究了用于做出这些预测的天气时段和计算,发现了一致的趋势,这些趋势将它们与浆果发育的不同阶段联系在一起。

著录项

  • 作者

    Mattis, Nathaniel Spencer.;

  • 作者单位

    University of California, Davis.;

  • 授予单位 University of California, Davis.;
  • 学科 Agriculture Horticulture.;Agriculture Plant Culture.
  • 学位 M.S.
  • 年度 2011
  • 页码 81 p.
  • 总页数 81
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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