Modelling grape phenology and yield

Part A: The high resolution grape phenology model

Common cumulative degree day models used to forecast grape growth stages often are only of local validity, restricted to a limited number of phenological stages, or do not take into consideration that the forcing effect of temperature is limited at higher temperatures. A new model was developed to simulate all 26 phenological stages (according to the BBCH scheme) of Vitis vinifera L. Müller-Thurgau between budburst and harvest. Sixty time series of grape phenology from four European countries were used to set up and validate the model. Three cumulative degree day models (starting with budburst: BBCH 09) with one, two, or three optimized temperature threshold values were compared. The incorporation of an upper threshold temperature, above which a further increase of the temperature will not accelerate plant development, and of a heat threshold, above which a further increase of the temperature leads to a development deceleration, significantly improved the accuracy of the model compared to previous cumulative degree day approaches. The threshold triplet 5°C, 20°C, and 22°C for lower (base), upper, and heat threshold temperature, respectively, allowed the most precise forecast. In 70.5 or 95.8% of the cases, phenological stages were correctly predicted in 3 or 7 days (assuming daily mean temperatures of 20°C), respectively, around the predicted cumulative degree day. The model can be used for a range of applications in viticultural research and practical viticulture and could further be parameterized for other varieties

Part B: The yield model

Grape yields show distinct interannual fluctuations caused by environmental conditions. Statistical investigations based on a 22-year data set (1993-2015) of annual yields of two grape cultivars grown in Luxembourg aimed at (i) investigating the impact of meteorological conditions during specific phases of yield formation, (ii) identifying meteorological conditions with predictive value for annual grape yield, and (iii) developing models to simulate yield based on meteorological data. Window pane analysis showed that pre-bloom and bloom minimum temperatures and precipitation sums in the preceding year, winter temperatures, spring temperatures, and post-veraison minimum temperatures in the current year were positively correlated with annual yield; early spring and post-harvest temperatures in the preceding year, and, for Riesling, pre-bloom precipitation sums and post-bloom maximum temperatures in the current year were negatively correlated with annual yield. Models developed from these data simulated annual yield with high accuracy (R2adj = 0.88 for Riesling, and R2adj = 0.92 for Müller-Thurgau). Meteorological conditions during distinct periods of yield formation are correlated with annual yield. Yield models can be used in practical viticulture as well as in climate change impact studies.