Air temperature is the key driver of grapevine phenology and a significant environmental factor impacting yield and quality for a winegrape growing region. In this study the optimal downscaled CMIP5 ensemble for computing thegrowing season average temperature (GST) viticulture climate classification index was determined to spatially compute on a decadal basis predictions of the GST climate index and the grapevine sugar ripeness (GSR) model for Pinot Noir throughout the Willamette Valley (WV) American Viticultural Area (AVA). Forecasts for average temperature and a 220 g/L target sugar concentration level were computed using daily Localized Constructed Analogs (LOCA) downscaled CMIP5 historic and Representative Concentration Pathways (RCP) future climate projections of minimum and maximum daily temperature. We explore spatiotemporal trends of the GST climate classification index and Pinot Noir specific applications of the GSR phenology model for the WV AVA. Spatiotemporal computations of the GST climate index and Pinot Noir specific applications of the GSR model enable the opportunity to explore relationships between their computed values with one intent being to provide updated GST ranges that better align with current temperature-based modeling understanding of Pinot Noir grapevine phenology and the viticultural application of LOCA CMIP5 climate projections for the WV AVA. The Pinot Noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot Noir production is currently at or near its peak value in the upper 80s to lower 90s of this century.

Three locally-adapted native plants were evaluated to determine their capacity to provide insectary benefits to predatory arthropods in association with vineyards, and thereby to enhance biological control of insect pests. Native plants are preferred as supplementary flora, as they are naturally adapted to Australia’s climatic conditions.

Forest fires in the vicinity of vineyards have significantly increased in the last decade and are a concern for grapegrowers and winemakers in many wine producing countries. The fires cause smoke drift throughout vineyards which cannot be avoided and may result in the production of wines described as ‘smoke tainted’. Such wines are characterized by undesirable sensory characters described as ‘smoky’, ‘burnt’, ‘ash’ aromas and flavours, and also may cause a lingering, unpleasant ashy aftertaste [1; 2].

Light-induced reactions can be responsible for detrimental changes of white and rosé wines. This is associated to the photo-degradation of riboflavin (RF) and of methionine (Met) causing the appearance of light-struck taste (LST).

In the context of sustainable viticulture, modern and efficient techniques to determine water status are required to optimize irrigation practices. Proximal techniques such as thermography and spectroscopy have shown promising results. When these techniques are incorporated into mobile systems is possible to evaluate the water status on-the-go, offering the possibility to generate variability maps. However, in most cases, complex protocols of data acquisition and analysis are required. Also, the inherent physiological behaviour of the plants under certain water stress conditions needs to be considered. Therefore, the aim of this study was to evaluate the effect of scion-rootstock combinations on the performance of a predefined plant-based method based on proximal near-infrared (NIR) spectroscopy.