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A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards
A web-based app was developed and tested to provide predictions of phenological stages of budburst, flowering and veraison, as well as warnings for meteorological drought. Such predictions are especially urgent under a climate change scenario where earlier phenology and water scarcity are increasing...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556084/ https://www.ncbi.nlm.nih.gov/pubmed/37798342 http://dx.doi.org/10.1038/s41598-023-44019-4 |
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author | Canavera, Ginevra Magnanini, Eugenio Lanzillotta, Simone Malchiodi, Claudio Cunial, Leonardo Poni, Stefano |
author_facet | Canavera, Ginevra Magnanini, Eugenio Lanzillotta, Simone Malchiodi, Claudio Cunial, Leonardo Poni, Stefano |
author_sort | Canavera, Ginevra |
collection | PubMed |
description | A web-based app was developed and tested to provide predictions of phenological stages of budburst, flowering and veraison, as well as warnings for meteorological drought. Such predictions are especially urgent under a climate change scenario where earlier phenology and water scarcity are increasingly frequent. By utilizing a calibration data set provided by 25 vineyards observed in the Emilia Romagna Region for two years (2021–2022), the above stages were predicted as per the binary event classification paradigm and selection of the best fitting algorithm based on the comparison between several metrics. The seasonal vineyard water balance was calculated by subtracting daily bare or grassed soil evapotranspiration (ET(s)) and canopy transpiration (T(c)) from the initial water soil reservoir. The daily canopy water use was estimated through a multiple, non-linear (quadratic) regression model employing three independent variables defined as total direct light, vapor pressure deficit and total canopy light interception, whereas ET(S) was entered as direct readings taken with a closed-type chamber system. Regardless of the phenological stage, the eXtreme Gradient Boosting (XGBoost) model minimized the prediction error, which was determined as the root mean square error (RMSE) and found to be 5.6, 2.3 and 8.3 days for budburst, flowering and veraison, respectively. The accuracy of the drought warnings, which were categorized as mild (yellow code) or severe (red code), was assessed by comparing them to in situ readings of leaf gas exchange and water status, which were found to be correct in 9 out of a total of 14 case studies. Regardless of the geolocation of a vineyard and starting from basic in situ or online weather data and elementary vineyard and soil characteristics, the tool can provide phenology forecasts and early warnings of meteorological drought with no need for fixed, bulky and expensive sensors to measure soil or plant water status. |
format | Online Article Text |
id | pubmed-10556084 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105560842023-10-07 A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards Canavera, Ginevra Magnanini, Eugenio Lanzillotta, Simone Malchiodi, Claudio Cunial, Leonardo Poni, Stefano Sci Rep Article A web-based app was developed and tested to provide predictions of phenological stages of budburst, flowering and veraison, as well as warnings for meteorological drought. Such predictions are especially urgent under a climate change scenario where earlier phenology and water scarcity are increasingly frequent. By utilizing a calibration data set provided by 25 vineyards observed in the Emilia Romagna Region for two years (2021–2022), the above stages were predicted as per the binary event classification paradigm and selection of the best fitting algorithm based on the comparison between several metrics. The seasonal vineyard water balance was calculated by subtracting daily bare or grassed soil evapotranspiration (ET(s)) and canopy transpiration (T(c)) from the initial water soil reservoir. The daily canopy water use was estimated through a multiple, non-linear (quadratic) regression model employing three independent variables defined as total direct light, vapor pressure deficit and total canopy light interception, whereas ET(S) was entered as direct readings taken with a closed-type chamber system. Regardless of the phenological stage, the eXtreme Gradient Boosting (XGBoost) model minimized the prediction error, which was determined as the root mean square error (RMSE) and found to be 5.6, 2.3 and 8.3 days for budburst, flowering and veraison, respectively. The accuracy of the drought warnings, which were categorized as mild (yellow code) or severe (red code), was assessed by comparing them to in situ readings of leaf gas exchange and water status, which were found to be correct in 9 out of a total of 14 case studies. Regardless of the geolocation of a vineyard and starting from basic in situ or online weather data and elementary vineyard and soil characteristics, the tool can provide phenology forecasts and early warnings of meteorological drought with no need for fixed, bulky and expensive sensors to measure soil or plant water status. Nature Publishing Group UK 2023-10-05 /pmc/articles/PMC10556084/ /pubmed/37798342 http://dx.doi.org/10.1038/s41598-023-44019-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Canavera, Ginevra Magnanini, Eugenio Lanzillotta, Simone Malchiodi, Claudio Cunial, Leonardo Poni, Stefano A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title | A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title_full | A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title_fullStr | A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title_full_unstemmed | A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title_short | A sensorless, Big Data based approach for phenology and meteorological drought forecasting in vineyards |
title_sort | sensorless, big data based approach for phenology and meteorological drought forecasting in vineyards |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10556084/ https://www.ncbi.nlm.nih.gov/pubmed/37798342 http://dx.doi.org/10.1038/s41598-023-44019-4 |
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