New multiple linear regression models for predicting the European grapevine moth (Lobesia botrana) in Austria


Abstract: Global warming may promote the spread, accelerate the development and
overwintering capacities of thermophilic insect pests and thus increase climate induced risks
for regional crop production systems. To limit these future risks, improved or new forecasting
models could help to optimize the timing of monitoring and control measures. In this context,
prediction models for the first seasonal occurrence of the different developmental stages (egg,
larvae, adult) of the 1st and 2nd generation of European grapevine moth, Lobesia botrana (Denis & Schiffermüller) (Lepidoptera: Tortricidae) were calibrated by applying stepwise multiple linear regression (MLR) analysis. For the development of the MLR models long-term monitoring data from 1980 to 2022 at 60 monitoring sites in 4 federal provinces in Austria and measured weather data from reference weather stations near the monitoring sites were used. The validation showed that the prediction accuracy of five out of the six newly generated MLR models was high (at least: R² > 0.6; RMSE < 3.6 or BIAS < 2.5). The use of the new MLR models for impact assessments under regional climate scenarios can help to determine the potential future risks of L. botrana occurrence in Austrian wine-growing regions.

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