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.

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.

Ripe rot caused by Colletotrichum spp. is a serious threat in many vineyards, and its control relies mainly on the repeated use of fungicides. A mechanistic, dynamic model for the prediction of grape ripe rot epidemics was developed by using information and data from a systematic literature review. The model accounts for (i) the production and maturation of the primary inoculum; (ii) the infection caused by the primary inoculum; (iii) the production of a secondary inoculum; and (iv) the infection caused by the secondary inoculum. The model was validated in 19 epidemics (vineyard × year combinations) between 1980 and 2014 in China, Japan, and the USA. The observed disease incidence was correlated with the number of infection events predicted by the model and their severity (ρ = 0.878 and 0.533, respectively, n = 37, p ≤ 0.001). The model also accurately predicted the disease severity progress during the season, with a concordance correlation coefficient of 0.975 between the observed and predicted data. Overall, the model provided an accurate description of the grape ripe rot system, as well as reliable predictions of infection events and of disease progress during the season. The model increases our understanding of ripe rot epidemics in vineyards and will help guide disease control. By using the model, growers can schedule fungicides based on the risk of infection rather than on a seasonal spray calendar.

Grapevine trunk diseases (GTDs) are serious threats in all viticultural areas of the world, and their management is always complex and usually inadequate. Fragmented and inconsistent information on the epidemiology and environmental requirements of the causal fungi is among the reasons for poor disease control. Therefore, we conducted a quantitative analysis of literature data to determine the effects of temperature on mycelial growth and the effects of temperature and moisture duration on spore germination. Using the collected information, we then developed mathematical equations describing the response of mycelial growth to temperature, and the response of spore germination to temperature and moisture for the different species and disease syndromes. We considered 27 articles (selected from a total of 207 articles found through a systematic literature search) and 116 cases; these involved 43 fungal species belonging to three disease syndromes. The mycelial growth of the fungi causing Botryosphaeria dieback (BD) and the esca complex (EC) responded similarly to temperature, and preferred higher temperatures than those causing Eutypa dieback (ED) (with optimal temperature of 25.3, 26.5, and 23.3°C, respectively). At any temperature, the minimal duration of the moist period required for 50% spore germination was shorter for BD (3.0 h) than for EC (17.2 h) or ED (15.5 h). Mathematical equations were developed accounting for temperature–moisture relationships of GTD fungi, which showed concordance correlation coefficients ≥0.888; such equations should be useful for reducing the risk of infection.

Ascochyta blight caused by Ascochyta rabiei is an important disease of chickpea. By using systems analysis, we retrieved and analyzed the published information on A. rabiei to develop a mechanistic, weather-driven model for the prediction of Ascochyta blight epidemics. The ability of the model to predict primary infections was evaluated using published data obtained from trials conducted in Washington (USA) in 2004 and 2005, Israel in 1996 and 1998, and Spain from 1988 to 1992. The model showed good accuracy and specificity in predicting primary infections. The probability of correctly predicting infections was 0.838 and the probability that there was no infection when not predicted was 0.776. The model’s ability to predict disease progress during the growing season was also evaluated by using data collected in Australia from 1996 to 1998 and in Southern Italy in 2019; a high concordance correlation coefficient (CCC = 0.947) between predicted and observed data was obtained, with an average distance between real and fitted data of root mean square error (RMSE) = 0.103, indicating that the model was reliable, accurate, and robust in predicting seasonal dynamics of Ascochyta blight epidemics. The model could help growers schedule fungicide treatments to control Ascochyta blight on chickpea.

Grapevine trunk diseases are caused by a complex of fungi that belong to different taxa, which produce different spore types and have different spore dispersal mechanisms. It is commonly accepted that rainfall plays a key role in spore dispersal, but there is conflicting information in the literature on the relationship between rain and spore trapping in aerobiology studies. We conducted a systematic literature review, extracted quantitative data from published papers, and used the pooled data for Bayesian analysis of the effect of rain on spore trapping. We selected 17 papers covering 95 studies and 8,778 trapping periods, concerning a total of 26 fungal taxa causing Botryosphaeria dieback (BD), Esca complex (EC), and Eutypa dieback (ED). Results confirmed the role of rain in the spore dispersal of these fungi but revealed differences among the different fungi. Rain was a good predictor of spore trapping for ED (AUROC = 0.820) and BD (0.766) but not for the ascomycetes involved in EC (0.569) and not for the only basidiomycetes, Fomitiporella viticola, studied as for spore discharge (AUROC not significant). Prediction of spore trapping was more accurate for negative prognosis than for positive prognosis; a rain cutoff of ≥0.2 mm provided an overall accuracy of ≥0.61 for correct prognoses. Spores trapped in rainless periods accounted for only <10% of the total spores. Our analysis had some drawbacks, which were mainly caused by knowledge gaps and limited data availability; these drawbacks are discussed to facilitate further research.