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Research Project: Improving the Quality of Grapes, Other Fruits, and their Products through Agricultural Management

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Title: Use of canopy temperature for precision irrigation of wine grapes

item Shellie, Krista
item King, Bradley - Brad

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/5/2015
Publication Date: 5/31/2015
Citation: Shellie, K., King, B.A. 2015. Use of canopy temperature for precision irrigation of wine grapes. Meeting abstract from Les 19èmes Journées GiESCO in Montpellier, France 5/31/15-6/5/15.

Interpretive Summary:

Technical Abstract: Application of precision irrigation practices has been hindered by the lack of a remote method for monitoring vine water status with high spatial and temporal resolution. The objectives of this research were to develop and validate a method for calculating a crop water stress index (CWSI) that could be used remotely to monitor vine water status for precision irrigation scheduling. Data were collected from ‘Syrah’ grapevines grown under arid conditions with high solar radiation in a field trial located in Parma, ID USA (43.78N, 116.94W; 750 m asl). Plots consisted of three vine rows with 6 vines per row and were drip-irrigated either weekly or three times per week to supply 70 or 35% of their estimated evapotranspiration-based water demand (ETc). Border vines in the trial perimeter were irrigated to meet or exceed vine water demand. Infrared radiometers were used to measure the canopy temperature of well-watered and deficit-irrigated vines and sensors were installed in the vineyard to monitor environmental parameters. Measured canopy temperatures and environmental parameters were used to train, test and validate a neural network model for predicting well-watered canopy temperature. The linear regression of model predicted versus measured well-watered canopy temperature had a correlation coefficient of 0.89 with a root mean square error of 1.06 °C. The canopy temperature of non-transpiring vines used to calculate the CWSI was estimated from the cumulative probability distribution of measured temperature differences between ambient air and the canopy temperature of vines irrigated at 35% ETc. The CWSI had a strong linear correlation with midday leaf water potential and its slope and intercept varied with irrigation frequency. Results from this research demonstrated that neural network modeling combined with infrared thermography provided remote, real-time monitoring of vine water status that could be used for application of precision irrigation practices.