Prediction of Grape Berry Temperature Using Wireless Dataloggers Contained Within a Grape Mimic

Abstract

Fruit zone leaf removal effects on grapevine (Vitis sp.) productivity and fruit quality have been widely researched. Many fruit zone leaf removal studies state that grape temperature influences grape composition; however, few studies have quantified grape berry temperature fluctuations over time, likely because of technical challenges. An efficient, simple, and economical way to estimate grape berry temperature would be valuable for researchers and industry. Consistent quantification of grape temperature would allow researchers to compare the effects of leaf removal on grape composition across varying climates and regions. A cost-effective means to quantify berry temperature would also provide industry members site-specific information on berry temperature patterns and guide leaf removal practice. Our goals were to develop a method and model to estimate berry temperature based on air temperature and berry mimics, thereby precluding the need to measure solar radiation or obtain expensive equipment. We evaluated the ability of wireless temperature sensors, submerged in various volumes of water within black or white balloons, to predict berry temperature. Treatments included 0-, 10-, 30-, 50-, and 70-mL volumes of deionized water in black and white balloons and a clear plastic bag with no water. Regression analysis was used to determine the relationship between sensor-logged temperatures and ‘Camminare noir’ berry temperatures recorded with hypodermic thermocouples. Nighttime berry temperatures were close to air temperature in all treatments. Using a piecewise regression model, the 30-mL white- and 30-mL black-balloon treatments predicted berry temperature with the greatest accuracy (R2 = 0.98 and 0.96, respectively). However, during daytime hours only, the 30-mL white-balloon treatment (R2 = 0.91) was more effective at estimating temperature than the 30-mL black-balloon treatment (R2 = 0.78). Housing temperature sensors in balloons proved to be an accurate, practical, and cost-effective solution to estimate berry temperature. Further refinement of this method in different regions, row orientations, training systems, and cultivars is necessary to determine applicability of this approach under a wide range of conditions.