Taylor, Peter Kevin2025-02-172025-02-172024https://hdl.handle.net/10182/18114The quality of Pinot noir is intricately linked to climatic conditions experienced in season, and in particular the influence of temperature within phenological stages. Many studies have modelled the impact of climatic parameters, including temperature, on wine quality with wine score often used as proxy. However, these approaches are limited to regional spatial resolution, with a single climate dataset applied to all vineyards. The aim of this research was to model the relationship between temperature-base climatic indicators to phenological stages and phases, creating a set of ecoclimatic indicators,and assessing Pinot noir quality at the single-vineyard scale against these indices. Utilising modelled temperature-related climate parameters, derived through thin-plate spline interpolation, for individually mapped vineyards in New Zealand, a climate record was constructed for Pinot noir vineyards for the vintages 2015 to 2022. This climate record was used to simulate phenological timing of flowering, veraison, and time to target sugar concentration as a proxy of ‘ripening’(defined as 210g/L), then to determine the influence of frosts, incidence of hot days and stress days (maximum temperature >25oC and >30oC respectively), and diurnal temperature range on Pinot noir quality using scores (100point scale) using individual linear, then multiple linear regression models. Both an increasing incidence of hot days and increasing diurnal temperature ranges were positively and significantly related to Pinot noir quality between both flowering and veraison and veraison to ripening. Conversely, the relationship between frosts and Pinot noir quality was convoluted by covariance with a regional effect leading to an inability to determine its influence on wine quality. This research has provided a novel platform for assessing at a very high spatial resolution the impacts of ecoclimatic indicators on the quality of Pinot noir. Potential applications of this research include modelling how future climate scenarios may impact wine quality, simulating potential impacts and phenological timing under climate change scenarios, and applications in training neural network models to predict potential quality based on weather data inputs.enwine qualitybioclimatic indicatorsclimate changePinot noirDissertationANZSRC::470205 Cultural studies of agriculture, food and wineANZSRC::300805 Oenology and viticultureANZSRC::460207 Modelling and simulationANZSRC::410199 Climate change impacts and adaptation not elsewhere classifiedhttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 International