An Index for User-Friendly Proximal Detection of Water Requirements to Optimized Irrigation Management in Vineyards

We propose an index for proximal detection of water requirements to optimize the use of water resources in arid and semi-arid wine growing regions. To test the accuracy and representativeness of the proposed irrigation need index (IIN), plant water status and physiological performances were monitored during seasons 2019 and 2020 in two grapevine varieties with different anisohydric degree (Vermentino and Cannonau) grown in 3 sites in Sardinia (Italy). Daily leaf gas exchange curves and stem water potential were recorded. Canopy temperature was monitored, using both thermistor sensors (Tc) and infrared thermometry (IR). Meteorological data, including dry and wet bulb temperatures were collected to compute and parametrize IIN, based on energy balance equation. Vineyard water balance, thermal time and irrigation water productivity were characterized. Linear regression analysis allowed to validate IIN for both varieties and to establish target thresholds for mild, moderate and severe water deficit to optimize irrigation for high yield and quality objectives. IIN well represents plant water status, using either Tc or IR, and allows rapid and easy detection of water and heat stress condition, even when a stricter stomatal control determines slighter variation and lower response of stem water potential, as in plants with low anisohydric degree.