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Evaluation of a Remote Sensing-based toolkit to monitor vine water use and status for irrigation scheduling in California vineyards.

Evaluation of a Remote Sensing-based toolkit to monitor vine water use and status for irrigation scheduling in California vineyards.

Dr. Maria Mar Alsina
E&J Gallo, Modesto, California, USA

Grapevines are one of the major drivers of agriculture in California, where close to three hundred and seventy-five thousand hectares of agricultural land are planted to vineyards, and about 70% of those are wine grapes. Climate models predict that with global warming there is a strong likelihood that there will be more frequent and prolonged droughts and associated heat waves in the western US. Under these conditions, irrigation becomes even more crucial to maintain profitable yield and quality. Furthermore, grapevines are grown under regulated deficit irrigation, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete samples that do not represent the often-high spatial variability of vine water status in vineyard blocks. Remote sensing techniques can provide spatial information to account for heterogeneity in vine water use and water status, and if this information is provided in near real-time, it can become a decision-making tool for irrigation.

We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project into a practical tool that growers can use for irrigation management. We ran this pilot in a commercial vineyard located in Cloverdale, Sonoma, CA, during two growing seasons, 2019 and 2020. During these seasons, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation, and Normalized Vegetation Water Indices, local weather data, forecasted ET, and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. In this work, we analyzed the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks by evaluating the vine water status responses and comparing the irrigation prescription based on the provided data with the grower's normal irrigation decisions.