Oger Baptiste, Zhang Yulin, Gras Jean-Philippe, Valloo Yoann, Faure Pauline, Brunel Guilhem, Tisseyre Bruno
ITAP, Univ. Montpellier, INRAE, Institut Agro, 2 Place Pierre Viala, 34060 Montpellier, France.
Data Brief. 2023 Sep 15;50:109580. doi: 10.1016/j.dib.2023.109580. eCollection 2023 Oct.
In order to enhance the understanding of vine yield development and facilitate the design of innovative agricultural practices in viticulture (i.e., new estimation methods), it is essential to have accurate and detailed data on vine yield components, including unproductive vines, number of bunches, and bunch weight. However, obtaining accurate and high spatial resolution yield data at the vine scale is costly and difficult to have for the main yield components (number of bunches, weight of bunch, missing plants, etc.). As a result, existing vine yield data are frequently estimated or measured at the field level. Unfortunately, the accuracy of these vine yield data is insufficient to study the intricate relationships between different yield components and their spatial distribution within vineyards. In this context, this article proposes a complete vine yield dataset that was specifically collected to develop and to test new sampling protocols in precision viticulture. This dataset comprises a comprehensive mapping of vine yield at the plant scale over two vine fields located in the southern region of France. Both vine fields were planted with the : cv. Syrah. The first field (Field 1) occupies 0.8 ha and data were collected in 2022, while the second field (Field 2) has an area of 0.5 ha and data were collected in 2008. Throughout the growing season, information regarding unproductive vines, inflorescence number, and bunch weight was collected for both vine fields. For both fields, at the flowering stage, the location of each productive and unproductive vines (dead and missing vines) was georeferenced, and the number of inflorescences was manually counted for all productive vines. For Field 1, at harvest, all bunches of the field were manually weighed with an accuracy of ±1 gram and georeferenced precisely (one point per vine). For each vine, total yield (grams per vine) was then computed as as the sum of the weight of its bunches. For Field 2, at harvest, the total yield per vine was estimated based on the weighing of representative bunches obtained from several regularly spaced set of 5 vines. In addition to the yield data, two ancillary data, including soil apparent resistivity measurements and common vegetative index derived from remote sensed imagery, are provided for both vine fields. Overall, the dataset consists of 3644 vines, with 2151 being productive, along with a total count of 33354 inflorescences and 19635 manually weighed bunches at harvest. This dataset is of interest as it contains information on grape yield organization at the within-field level. This dataset could be used to assess the impact of unproductive vines on neighbouring vines yield, as well as the correlations between available ancillary data and all yield components.
为了增强对葡萄产量形成的理解，并促进葡萄栽培中创新农业实践（即新的估算方法）的设计，获取关于葡萄产量构成要素的准确而详细的数据至关重要，这些要素包括不结果的葡萄藤、果穗数量和果穗重量。然而，在葡萄藤尺度上获取准确且高空间分辨率的产量数据成本高昂，并且对于主要产量构成要素（果穗数量、果穗重量、缺失植株等）而言很难获得。因此，现有的葡萄产量数据通常是在田间层面估算或测量的。不幸的是，这些葡萄产量数据的准确性不足以研究不同产量构成要素之间的复杂关系及其在葡萄园中的空间分布。在此背景下，本文提出了一个完整的葡萄产量数据集，该数据集是专门为开发和测试精准葡萄栽培中的新采样协议而收集的。这个数据集包括位于法国南部地区的两个葡萄园地块在植株尺度上的葡萄产量综合图谱。两个葡萄园地块均种植了西拉品种。第一个地块（地块1）占地0.8公顷，数据于2022年收集，而第二个地块（地块2）面积为0.5公顷，数据于2008年收集。在整个生长季节，收集了两个葡萄园地块关于不结果葡萄藤、花序数量和果穗重量的信息。对于两个地块，在开花阶段，对每株结果和不结果的葡萄藤（死亡和缺失的葡萄藤）的位置进行地理定位，并人工统计所有结果葡萄藤上的花序数量。对于地块1，在收获时，对该地块的所有果穗进行人工称重，精度为±1克，并精确地理定位（每株葡萄一个点）。然后，对于每株葡萄，将其果穗重量之和计算为总产量（克/株）。对于地块2，在收获时，根据从几组间隔均匀的5株葡萄中获得的代表性果穗的称重来估算每株葡萄的总产量。除了产量数据外，还为两个葡萄园地块提供了两个辅助数据，包括土壤表观电阻率测量值和从遥感影像中得出的常见植被指数。总体而言，该数据集由3644株葡萄组成，其中2151株结果，收获时共有33354个花序和19635个经过人工称重的果穗。这个数据集很有价值，因为它包含了田间层面葡萄产量构成的信息。该数据集可用于评估不结果葡萄藤对邻近葡萄藤产量的影响，以及可用辅助数据与所有产量构成要素之间的相关性。
