Maria P. Diago, Juan Fernández-Novales, Salvador Gutiérrez, Miguel Marañón, Javier Tardaguila
Abstract
Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψs) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction ([Formula: see text]) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture.
Pharmacology, Toxicology and Pharmaceutical Science
1
1%
Nursing and Health Professions
1
1%
Other
7
8%
Unknown
28
33%
Attention Score in Context
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2018.
All research outputs
#3,397,849
of 24,323,943 outputs
Outputs from Frontiers in Plant Science
#1,745
of 22,870 outputs
Outputs of similar age
#76,168
of 448,391 outputs
Outputs of similar age from Frontiers in Plant Science
#49
of 447 outputs
Altmetric has tracked 24,323,943 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,870 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 92% of its peers.
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We're also able to compare this research output to 447 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.
