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Using the fraction of transpirable soil water to estimate grapevine leaf water potential: comparing the classical statistical regression approach to machine learning algorithms
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Resumo(s)
Context and purpose of the study – Weather uncertainty is forcing Mediterranean winegrowers to adopt new
irrigation strategies to cope with water scarcity while ensuring a sustainable yield and improved berry and wine
quality standards. Therefore, more accurate and high-resolution monitoring of soil water content and vine water
status is a major concern. Leaf water potential measured at pre-dawn (YPD) is considered to be in equilibrium
with soil water potential and is highly correlated with soil water content at the soil depth where roots extract
water.
The aim of this study is to evaluate a dataset of eco-physiological data collected in a 3-year vineyard irrigation
trial to assess the explanatory power of the fraction of transpirable soil water (FTSW) to predict YPD by comparing
the classical statistical regression approach with a machine learning algorithm (MLA).
Material and methods – Deficit irrigation trials were conducted from 2013 to 2015 in a commercial vineyard in
the Alentejo (southern Portugal). Trial plot was planted with Vitis vinifera (L.) cv. Aragonez (ARA)(syn.
Tempranillo), grafted onto 1103 Paulsen rootstock and spaced 1.5 m within and 3.0 m between N-S oriented
rows. The experimental layout was a randomized complete block design with two treatments: sustained deficit
irrigation (SDI – control; ~30% Etc) and regulated deficit irrigation (RDI; ~15% Etc) and 4 replicates per treatment.
The YPD and soil water content were measured the day before and the day after each irrigation event by using a
capacitance probe down to a soil depth of 1 m and a Scholander pressure chamber. Models predicting YPD from
FTSW were trained on 600 data cases and validated on an independent dataset (10% of all available data) using
MATLAB R2022b (Mathworks, USA) and STATISTICA 13 (Tibco, USA).
Results – Our results show that 87.6% of the observed YPD variability is explained by the FTSW using a linear
regression model (LRM) with a linear-logarithmic transformation of the independent variables. The accuracy of
the prediction model, as measured by root mean squared error (RMSE), in the independent validation dataset,
was 0.08 MPa. These results were compared to the estimation accuracy of a set of MLAs. Two support vector
machine (SVM) algorithms with a quadratic and a medium Gaussian kernel function, and three Gaussian process
regression (GPR) algorithms with an exponential, a squared exponential and a rational quadratic kernel functions
were tested. All trained MLAs showed an accuracy in explaining the variability of the YPD (86-87%) similar to the
LRM. An increase in the model explained variability of the independent dataset from 89 to 91% was observed in
all MLAs, with an accuracy of 0.087 to 0.096 MPa as measured by the RMSE.
Both statistical methods indicate that YPD can be estimated with good accuracy using FTSW as an explanatory
variable. Regarding the comparative performance of the two types of statistical models no differences were found
in the ability of the tested models to estimate YPD.
Descrição
Palavras-chave
deficit irrigation soil water content machine learning algorithms
Contexto Educativo
Citação
Egipto R, Costa J.M., Silvestre J. & Lopes C.M. (2023). Using the fraction of transpirable soil water to estimate grapevine leaf water potential: comparing the classical statistical regression approach to machine learning algorithms. IVES Conference Series, GiESCO 2023.