Fig 1.
Grape downy mildew (GDM) outbreaks temporal variability in monitored untreated plots.
(A) Last value of GDM incidence and severity on leaves and bunches after bunch closing in monitored plots. The color of the point represents the health status of each plot at the end of the season (green = last observation < median; red = last observation ≥ median). (B) Imputed disease onset dates in monitored plots. Median contamination levels and median disease onset date are represented by vertical dashed lines in panel (A) and panel (B), respectively. In both panel, the lower and upper hinges of the boxes correspond to the first and third quartiles (the 25th and 75th percentiles) and horizontal segment represent the range between min and max values.
Fig 2.
Climatic variability in March, April, May and June during the 2010–2018 period in the 153 untreated monitored plots.
(A) Mean monthly precipitation amount (in mm day-1) and (B) mean monthly temperature (in °C). Median precipitation amount and median temperature are represented by vertical dashed lines in panel (A) and (B), respectively. In both panel, the lower and upper hinges of the boxes correspond to the first and third quartiles (the 25th and 75th percentiles) and horizontal segment represent the range between min and max values.
Fig 3.
Illustration of the modeling framework implemented in this study.
This modeling framework included 6 steps. (I) Extraction of epidemiological and climatic data from two different databases (model input and outputs are written in bold and in italic, respectively). (II) GDM onset date imputation by a semi-parametric survival model. (III) Models fitting. (IV) Models assessment based on a ROC analysis; models with higher area under the ROC curve (AUC) were selected. (V) Sensitivity analysis of the model outputs to weather inputs. (VI) Estimated reduction in GDM fungicide application obtained by delaying the date of the first fungicide application compared to current practices in the Bordeaux vineyards; calculations were based on GLM model predictions using dates of appearance of GDM as inputs.
Fig 4.
Area under the ROC curve (AUC) of several models used to predict occurrence of high level of GDM incidence and severity on leaves and bunches.
Higher AUC represents higher model performance.
Fig 5.
Importance of the inputs used in random forest and gradient boosting models predicting the risk of high GDM severity on leaves at bunch closing stage.
Models presented in A and B include all inputs (date and climate) and models presented in C and D include climate inputs only. The importance metric reflects the gain in the model performance resulting from the use of each input.
Fig 6.
Probability of high severity on leaves according to different precipitation variations and to different levels of temperature increase between March and June.
Each graphic shows the effect of precipitation change (from -15% to +15%) for a fixed level of temperature increase (from +0°C to +4°C) on predicted probability that GDM severity on leaves will be higher than regional median at the end of the season. Probabilities are forecasted by a gradient boosting algorithm that includes all climatic features. Each boxplot represents the distribution of the probability values over the vineyard plots of our dataset; the shaded boxplot corresponds to initial precipitation and temperatures (precipitation and temperature kept unchanged compared to actual conditions) and the median probability obtained with this scenario is indicated by a red dotted line. The lower and upper hinges of the boxes correspond to the first and third quartiles (the 25th and 75th percentiles) and vertical segment represent the range between min and max values.
Fig 7.
Partial dependence plots of the relationships between probability of high GDM severity on leaves and the four most important climate variables of gradient boosting (according to Fig 5D).
Each graph represents the marginal effect of one variable on the probability computed by the gradient boosting model. (A) Partial dependence plot for the average amount of rainfall in May (in mm/day). (B) Partial dependence plot for the average amount of rainfall in June (in mm/day). (C) Partial dependence plot for the mean temperature in April (in °C). (D) Partial dependence plot for the mean temperature in June (in °C).
Fig 8.
Response of probability of high severity on leaves to date of disease onset estimated with the GLM and its 95% confidence interval (in green), and partial dependence plot obtained with the gradient boosting algorithm including climate inputs and date of disease onset (in red).
Median, minimum, 1st and 3rd quartiles, and maximum of observed onset dates are represented by a dot and four crosses, respectively.
Fig 9.
Impact of the model-based decision rule on GDM pesticide use in Bordeaux vineyards as a function of a predefined triggering probability threshold (probability of high GDM severity on leaves).
(A) The black curve indicates the average numbers of fungicide treatments in the vineyard plots of our dataset computed while assuming that the first treatment is triggered only when the GLM probability of high severity exceeds the value given in the x-axis. Blue line represents the number of treatments for threshold = 0, i.e. when the first treatment is applied in all plots as soon as GDM symptoms are detected. Red and orange lines correspond to the average numbers of treatments recorded by the SSP in 2013 and 2010, respectively. (B) Potential reduction of GDM treatments compared to other treatment scenarios, represented by the color of each curve, and computed according to the predefined triggering probability threshold. Blue curve represents the reduction induced by the application of the decision rules compared to the strategy where first treatment is triggered at disease onset. Orange and red lines represent the treatment reduction compared to the results of SSP study in 2010 and 2013, respectively.