Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index

Authors: King, Bradley - Brad; Shellie, Krista

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2017
Publication Date: 1/3/2018
Publication URL: http://handle.nal.usda.gov/10113/6330215
Citation: King, B.A., Shellie, K. 2018. Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index. Computers and Electronics in Agriculture. 145:122-129. https://doi.org/10.1016/j.compag.2017.12.025.
DOI: https://doi.org/10.1016/j.compag.2017.12.025

Interpretive Summary: Precision irrigation management in wine grape production is hindered by the lack of a reliable method to easily quantify and monitor vine water status. Mild to moderate water stress is desirable in wine grape for controlling vine vigor and optimizing fruit yield and quality. A crop water stress index (CWSI) that effectively monitors plant water status has not been widely adopted in wine grape because of the need to measure well-watered and non-transpiring leaf temperature under identical environmental conditions. In this study canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc were measured under well-watered conditions over multiple years and modeled as a function of climatic parameters solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in non-water stressed canopy temperature, both models provided good prediction results when all cultivars were collectively modeled. All predictive models had an uncertainty of plus or minus 0.1 in calculation of the CWSI despite significantly different prediction error variance between models.

Technical Abstract: The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions. The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict canopy temperature non-water stressed wine vines. The canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc were measured under well-watered conditions over multiple years and modeled as a function of climatic parameters solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in non-water stressed canopy temperature, both models provided good prediction results when all cultivars were collectively modeled. All predictive models had an uncertainty of plus or minus 0.1 in calculation of the CWSI despite significantly different prediction error variance between models.