Development and validation of a new methodology to assess the vineyard water status by on-the-go near infrared spectroscopy

  1. Diago, M.P. 1
  2. Fernández-Novales, J. 1
  3. Gutiérrez, S. 1
  4. Marañón, M. 1
  5. Tardaguila, J. 1
  1. 1 Instituto de Ciencias de la Vid y del Vino
    info

    Instituto de Ciencias de la Vid y del Vino

    Logroño, España

    ROR https://ror.org/01rm2sw78

Journal:
Frontiers in Plant Science

ISSN: 1664-462X

Year of publication: 2018

Volume: 9

Type: Article

DOI: 10.3389/FPLS.2018.00059 SCOPUS: 2-s2.0-85043352456 WoS: WOS:000423533200001 GOOGLE SCHOLAR

More publications in: Frontiers in Plant Science

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 (R2 P) 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. © 2018 Diago, Fernández-Novales, Gutiérrez, Marañón and Tardaguila.