Upgrading and validating a soil water balance model to predict stem water potential in vineyards

Efficient water management is pivotal for viticulture sustainability. Decision support tools can advise on how to optimize irrigation or on the feasibility of growing grapes in rainfed conditions, but reliable algorithms for assessing vine water status are required. In this context, the aim of the current study was to upgrade a soil water balance model specific for vineyards by incorporating meteorological, soil and vine vigor in equations that transform the fraction of transpirable soil water into midday stem water potential (Ψ_stem). The model's sensitivity to variations in the magnitude of input parameters was analyzed. Furthermore, the model was tested in a broad scope of Spanish vineyards with different grapevine cultivars (both red and white), rootstocks, plant age, soil and climatic conditions, and water regimes, totaling 129 scenarios. The model was only slightly sensitive to variations in the magnitude of most inputs, except for the fraction of transpirable water at which leaf stomatal conductance begin to decline. Moreover, the model satisfactorily reproduced the evolution of Ψ_stem over the growing season, although it slightly overestimated the measured Ψ_stem values, as the slopes of the fitted regression lines were lesser than 1 on most occasions, 76 out of 129. Nonetheless, the coefficients of determination for these relationships were greater than 0.9, except for 21 datasets. Mean errors averaged 0.024 ± 0.015 MPa, while root mean square errors averaged 0.27 ± 0.01 MPa. The index of agreement was greater than 0.75 in 51 datasets, with only three datasets showing an index of agreement lower than 0.5. Nevertheless, the deviations between observed and simulated Ψ_stem values did not alter the classification of the water stress undergone by grapevines. This upgraded model could constitute the core of a decision support system for water management in vineyards, applicable to both rainfed and irrigated conditions.