This review gives a perspective of selected advances made since the middle of the 20th century in plant disease modeling, and the associated increase in the number of models published during that time frame. This progress can be mainly attributed to advances in (i) sensors and automatic environmental data collection technology, (ii) instrumentation and methods for studying botanical epidemiology, and (iii) data analytics and computer science. We review the evolution of techniques for developing data-based (empirical) models and process-based (mechanistic) models using the wheat rusts as a case study. We also describe the increased importance of knowledge about biological processes for plant disease modeling by using apple scab as a second case study. For both wheat rusts and apple scab, we describe how the models have evolved over the last 50 years by considering certain milestones that have been achieved in disease modeling. Finally, we describe how plant disease models are used as part of a multi-modeling approach to develop decision-making tools in the application of integrated pest management.

Six commercial biocontrol agents (BCAs, containing Aureobasidium pullulans, Bacillus amyloliquefaciens, Bacillus amyloliquefaciens plantarum, Bacillus subtilis, Pythium oligandrum, or Trichoderma atroviride) were applied to ripening berries that were then incubated at one of four temperatures (T, 15, 20, 25, and 30°C) and one of four relative humidity levels (RH, 60, 80, 90, and 100%). After 1 to 13 days of incubation (BCA colonization period), the berries were inoculated with conidia of Botrytis cinerea and kept at 25°C and 100% RH for 7 days, at which time Botrytis bunch rot (BBR) was assessed. The response of BBR control to T/RH conditions and BCA colonization period differed among BCAs; the coefficients of variation among the BCAs ranged from 44.7 to 72.4%. An equation was developed that accounted for the combined effects of T, RH, and BCA colonization period on BBR control. The equation, which had an R²>0.94, could help farmers select the BCA to be used for a specific application based on weather conditions at the time of treatment and in the following days.

Agriculture needs to reduce its current dependence toward pesticides while reducing crop losses caused by pests and ensuring food security; Integrated Pest Management (IPM) is considered the most appropriate approach to achieve the goal. More specifically, growers should use tools that enable informed decisions on whether and when crop protection is needed, and which methods should be used. These tools include risk algorithms, decision rules, intervention thresholds, and decision support systems (DSSs), collectively named decision tools (DTs). A large number of DTs have been developed and made available to advisors and farmers, mainly through Internet-based systems. The adoption rate of these systems, however, has been low because of technical limitations and farmer perceptions. Fragmentation of the DTs offered, poor local implementation, and restriction to particular users are among the causes for low adoption. If properly mobilised, the use and effects of DTs could substantially be increased. Sharing of IPM DTs has a strong potential for providing wider access to the existing knowledge, for fostering IPM implementation, and for supporting plant health policies. In this article, we outline an overall approach to develop a web-based platform, in which DTs are shared and made widely available. Such a platform can include a range of ready-to-use DTs, i.e. DTs which are currently available, that have been field-validated, and which are already being used in some agricultural contexts. The platform also provides open, full documentation of DTs, makes DTs available for validation and adaptation in different agricultural contexts, and makes DTs easily available for multiple kinds of end-users involved in IPM (farmers, IPM experts, public and private information and service providers, and policy makers). We also consider how DT sharing can reduce both the technological and behavioural limitations of existing plant health management systems.

The use of biocontrol agents (BCAs) represents a promising alternative to conventional methods for the management of gray mold in vineyards during the berry ripening stage. The main advantages of BCAs are the short preharvest interval and lack of chemical fungicide residues in wine. In this study, eight commercial BCAs (based on different Bacillus or Trichoderma species and strains, Aureobasidium pullulans, Metschnikowia fructicola, and Pythium oligandrum) and a reference fungicide (boscalid) were applied to a vineyard during berry ripening over three seasons to evaluate the dynamics over time in terms of their relative efficacies in gray mold control. At 1–13 days after application of BCAs to the berry surfaces in field conditions, the berries were collected and artificially inoculated with conidia of Botrytis cinerea under controlled laboratory conditions, and gray mold severity was observed after 7 days of incubation. Significant differences were observed in gray mold severity among years, according to the number of days the BCAs grew on the berry surface before B. cinerea inoculation, and the season by day interaction (altogether accounting for >80% of the experimental variance). The variability in BCA efficacy was closely related to the environmental conditions at the time of application and in the following days. Overall, the BCA efficacy increased with the degree days accumulated between BCA application in the vineyard and B. cinerea inoculation in the dry (no rain) periods (r = 0.914, P = 0.001). Rainfall and the associated drop in temperature caused a relevant reduction of BCA efficacy. These results demonstrate that BCAs are an effective alternative to conventional chemicals for the preharvest control of gray mold in vineyards. However, environmental conditions can considerably affect the BCA efficacy.

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.