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High spatial resolution dataset of grapevine yield components at the within-field level

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,...

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Autores principales: Oger, Baptiste, Zhang, Yulin, Gras, Jean-Philippe, Valloo, Yoann, Faure, Pauline, Brunel, Guilhem, Tisseyre, Bruno
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539884/
https://www.ncbi.nlm.nih.gov/pubmed/37780465
http://dx.doi.org/10.1016/j.dib.2023.109580
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author Oger, Baptiste
Zhang, Yulin
Gras, Jean-Philippe
Valloo, Yoann
Faure, Pauline
Brunel, Guilhem
Tisseyre, Bruno
author_facet Oger, Baptiste
Zhang, Yulin
Gras, Jean-Philippe
Valloo, Yoann
Faure, Pauline
Brunel, Guilhem
Tisseyre, Bruno
author_sort Oger, Baptiste
collection PubMed
description 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 Vitis vinifera: 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.
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spelling pubmed-105398842023-09-30 High spatial resolution dataset of grapevine yield components at the within-field level Oger, Baptiste Zhang, Yulin Gras, Jean-Philippe Valloo, Yoann Faure, Pauline Brunel, Guilhem Tisseyre, Bruno Data Brief Data Article 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 Vitis vinifera: 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. Elsevier 2023-09-15 /pmc/articles/PMC10539884/ /pubmed/37780465 http://dx.doi.org/10.1016/j.dib.2023.109580 Text en © 2023 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Oger, Baptiste
Zhang, Yulin
Gras, Jean-Philippe
Valloo, Yoann
Faure, Pauline
Brunel, Guilhem
Tisseyre, Bruno
High spatial resolution dataset of grapevine yield components at the within-field level
title High spatial resolution dataset of grapevine yield components at the within-field level
title_full High spatial resolution dataset of grapevine yield components at the within-field level
title_fullStr High spatial resolution dataset of grapevine yield components at the within-field level
title_full_unstemmed High spatial resolution dataset of grapevine yield components at the within-field level
title_short High spatial resolution dataset of grapevine yield components at the within-field level
title_sort high spatial resolution dataset of grapevine yield components at the within-field level
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539884/
https://www.ncbi.nlm.nih.gov/pubmed/37780465
http://dx.doi.org/10.1016/j.dib.2023.109580
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