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Multiseasonal grapevine water consumption – Drivers and forecasting
Year:
2020
Authors :
Ben-Gal, Alon
;
.
Ohana-Levi, Noa
;
.
Volume :
280
Co-Authors:

Sarel Munitz, Amnon Schwartz, Aviva Peeters, Yishai Netzer

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

The interactions between temperature, relative humidity, radiation, wind speed and their effect on plant transpiration in the context of water consumption for irrigation purposes have been studied for over a century. Leaf area has also been established as an important factor affecting water consumption. We analyzed a multivariable time series composed of both meteorological and vegetative variables with a daily temporal resolution for the growing seasons of 2013–2016 for Vitis vinfera ‘Cabernet Sauvignon’ vineyards in the mountainous region in Israel. Time-series analysis of this data was used to characterize seasonal patterns affecting water consumption (ETc) of vines and to quantify interrelations between meteorological and vegetative factors affecting vine water consumption. Moreover, we applied a machine learning regression model to determine the relative influence of meteorological and vegetative factors on ETc during four growing seasons. Finally, we developed an ensemble model for temporally forecasting vine ETc for an additional season using a training dataset of multiple variables. Our findings show that decomposing the time-series dataset uncovered a wider variety of underlying temporal patterns, and enabled quantification of seasonal and daily relationships. Leaf area had a substantial impact on ETc and was found to have a relative influence ranging between 62 and 86% for the different growing seasons. Mean temperature was ranked second followed by minor effects of relative humidity, solar radiation and wind speed that were interchangeably ordered. The ensemble model produced reliable results, with cross validation coefficients ~ 0.9. Incorporating leaf area measurements into the regression model improved both the performance of the model and the training data correlation. Using time-series statistics to explore meteorological and vegetative temporal characteristics, patterns, interrelations and relative effect on evapotranspiration may facilitate the understanding of water consumption processes and assist in generating more effective and skillful irrigation models. © 2019 Elsevier B.V.

Note:
Related Files :
Drainage lysimeters
evapotranspiration
Machine learning
time series analysis
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More details
DOI :
10.1016/j.agrformet.2019.107796
Article number:
107796
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
44414
Last updated date:
02/03/2022 17:27
Creation date:
29/10/2019 10:40
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Scientific Publication
Multiseasonal grapevine water consumption – Drivers and forecasting
280

Sarel Munitz, Amnon Schwartz, Aviva Peeters, Yishai Netzer

Multiseasonal grapevine water consumption – Drivers and forecasting

The interactions between temperature, relative humidity, radiation, wind speed and their effect on plant transpiration in the context of water consumption for irrigation purposes have been studied for over a century. Leaf area has also been established as an important factor affecting water consumption. We analyzed a multivariable time series composed of both meteorological and vegetative variables with a daily temporal resolution for the growing seasons of 2013–2016 for Vitis vinfera ‘Cabernet Sauvignon’ vineyards in the mountainous region in Israel. Time-series analysis of this data was used to characterize seasonal patterns affecting water consumption (ETc) of vines and to quantify interrelations between meteorological and vegetative factors affecting vine water consumption. Moreover, we applied a machine learning regression model to determine the relative influence of meteorological and vegetative factors on ETc during four growing seasons. Finally, we developed an ensemble model for temporally forecasting vine ETc for an additional season using a training dataset of multiple variables. Our findings show that decomposing the time-series dataset uncovered a wider variety of underlying temporal patterns, and enabled quantification of seasonal and daily relationships. Leaf area had a substantial impact on ETc and was found to have a relative influence ranging between 62 and 86% for the different growing seasons. Mean temperature was ranked second followed by minor effects of relative humidity, solar radiation and wind speed that were interchangeably ordered. The ensemble model produced reliable results, with cross validation coefficients ~ 0.9. Incorporating leaf area measurements into the regression model improved both the performance of the model and the training data correlation. Using time-series statistics to explore meteorological and vegetative temporal characteristics, patterns, interrelations and relative effect on evapotranspiration may facilitate the understanding of water consumption processes and assist in generating more effective and skillful irrigation models. © 2019 Elsevier B.V.

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