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Fruit zone leaf removal is a vineyard management practice used to manage bunch rots, fruit composition, and crop yield. We were interested in evaluating fruit zone leaf removal effects on bunch rot, fruit composition, and crop yield in ‘Chardonnay’ grown in the U.S. state of Georgia. The experiment consisted of seven treatments: no leaf removal (NO); prebloom removal of four or six leaves (PB-4, PB-6), post–fruit set removal of four or six leaves (PFS-4, PFS-6), and prebloom removal of two or three leaves followed by post–fruit set removal of two or three leaves (PB-2/PFS-2, PB-3/PFS-3). Although leaf removal reduced botrytis bunch rot and sour rot compared with NO, effects were inconsistent across the two seasons. Fruit zone leaf removal treatments reduced titratable acidity (TA) and increased soluble solids compared with NO. PB-6 consistently reduced berry number per cluster, cluster weight, and thus crop yield relative to PFS-4. Our results show that post–fruit set fruit zone leaf removal to zero leaf layers aids in rot management, reduces TA, increases soluble solids, and maintains crop yield compared with no leaf removal. We therefore recommend post–fruit set leaf removal to zero leaf layers over no leaf removal if crops characterized by relatively greater soluble solids-to-TA ratio and reduced bunch rot are desirable for winemaking goals.

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 (R ² = 0.98 and 0.96, respectively). However, during daytime hours only, the 30-mL white-balloon treatment (R ² = 0.91) was more effective at estimating temperature than the 30-mL black-balloon treatment (R ² = 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.