A Weather-Driven Model for Predicting Infections of Grapevines by Sporangia of Plasmopara viticola

Abstract

A mechanistic model was developed to predict secondary infections of Plasmopara viticola and their severity as influenced by environmental conditions; the model incorporates the processes of sporangia production and survival on downy mildew (DM) lesions, dispersal and deposition, and infection. The model was evaluated against observed data (collected in a 3-year vineyard) for its accuracy to predict periods with no sporangia (i.e., for negative prognosis) or with peaks of sporangia, so that growers can identify periods with no/low risk or high risk. The model increased the probability to correctly predict no sporangia [P(P−O−) = 0.67] by two times compared to the prior probability, with fewer than 3% of the total sporangia found in the vineyard being sampled when not predicted by the model. The model also correctly predicted peaks of sporangia, with only 1 of 40 peaks unpredicted. When evaluated for the negative prognosis of infection periods, the model showed a posterior probability for infection not to occur when not predicted P(P−O−) = 0.87 with only 9 of 108 real infections not predicted; these unpredicted infections were mild, accounting for only 4.4% of the total DM lesions observed in the vineyard. In conclusion, the model was able to identify periods in which the DM risk was nil or very low. It may, therefore, help growers avoid fungicide sprays when not needed and lengthen the interval between two sprays, i.e., it will help growers move from calendar-based to risk-based fungicide schedules for the control of P. viticola in vineyards.

Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology

“…The most important weather parameter is the total amount of rainfall during the month of May [28]. In addition, the wind intensity is also important for sporangia dispersion [20]. Although the statistical analyses [36] demonstrated that the wind is not one of the main parameters influencing infection risk, we strongly believe that this claim needs to be more thoroughly investigated.…”

“…For both RS and VV locations, the anemometer was missing and the dataset does not have a wind speed parameter. iMetos ® alarms for primary or secondary infections as well as mechanistic model 2 [20] alarms for secondary infections are compared with the alarms provided by the proposed model in the bottom part of the figures. The iMetos ® system sends alarms for light, moderate, and severe infections, while our model and mechanistic model 2 provide alarms at a single level.…”

“…However, the incubation period was set to two discrete values in correlation with the levels of temperature (cold and warm) and humidity (wet, partly wet, dry, and very dry). Temperature is considered cold at below 13 • C and warm at above 13 • C. The daily precipitation is added for the wet and partly wet parts in the next model [20] (we designate it as mechanical model 2).…”

“…Wind and FNC as crucial parameters for sporangia dispersion are reported in several works. The latest is in [20]. However, the wind is not a part of their model.…”

“…In addition, the proposed model is compared with the latest mechanistic model [20]). The correlation ranges between 0.55 and 0.71 for the secondary alarms.…”