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A GIS-based multivariate clustering for characterization and ecoregion mapping from a viticultural perspective

机译:一种基于GIS的多变量聚类,用于葡萄栽培视角的表征和eCoregion映射

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摘要

In wine-growing regions, zoning studies define areas according to their potential to produce specific wines and also identify the key drivers behind their variability and optimize vineyard management for sustainable viticulture. However, delineation of homogeneous zones is difficult because of the complex combination of factors which could affect zone classifications. One possibility to capture potential variability is the use of natural environmental properties as they are related to success in grape growing. With the aim of characterizing the spatial variability of the main vine-related environmental variables and determining different zones, climate and topographical data were obtained for Extremadura (southwestern Spain), an important wine region. Firstly, accurate maps of all climate indices were generated by using regression-kriging as the most suitable algorithm in which exhaustive secondary information on elevation was incorporated, and maps of topography-derived variables were obtained using GIS (Geographical Information System) tools. Secondly, principal component analysis and multivariate geographic classification were used to define homogeneous classes, resulting in three zones. Each zone was further characterized by overlaying the zonation map with a geology map and all enviromental layers. It was obtained that although a wide part of the Extremaduran territory has warm climate characteristics, the zones have different viticultural potential and a high proportion of the region lays on suitable substrate. This zonation in Extremadura is the basis for further zoning studies at more detailed field scale and the modeling of vineyard response to climate change.
机译:在葡萄酒生长区域,分区研究根据其生产特定葡萄酒的潜力,确定了各个地区,并确定了其可变性背后的关键驱动因素,并优化可持续葡萄栽培的葡萄园管理。然而,由于可能影响区域分类的复杂因素组合,众所周知区域的描绘很困难。捕获潜在变异性的一种可能性是利用自然环境属性,因为它们与葡萄种植成功有关。目的是表征主要葡萄藤相关的环境变量的空间变异,并确定不同区域,为重要葡萄酒区(西班牙西班牙西班牙)获得气候和地形数据。首先,通过使用回归-kriging作为最合适的算法来生成所有气候指标的准确图,其中包含有关高程的穷举次要信息,并且使用GIS(地理信息系统)工具获得地形推导变量的映射。其次,使用主成分分析和多变量地理分类来定义均匀类,导致三个区域。每个区域进一步表征,通过用地质图和所有环境层覆盖分区图。获得了尽管极端的极端区域具有温暖的气候特征,但区域具有不同的葡萄栽培电位,并且高比例的区域位于合适的基材上。 Extremadura的这种分区是在更详细的场比例下进一步分区研究的基础和对气候变化的葡萄园反应的建模。

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