Modelling the effect of partial resistance on epidemics of downy mildew of grapevine

Abstract

The cultivation of grape varieties with partial resistance to disease may become an important component for disease management in the future. The impact of partial resistance on downy mildew epidemics according to its components have not been explored so far. This work aims to model, understand, and quantitatively analyse the effect of partial resistance against dual disease epidemics (foliage and clusters) caused by Plasmopara viticola, and rank the efficiency of different resistance components in disease suppression. We use an epidemiological simulation model to integrate the effect of four components of partial resistance, expressed as relative resistance parameters, i.e. infection efficiency (RRIE), latency period (RRLP), sporulation (RRSP), and infectious period (RRIP). Both the individual and combined effect of these components of resistance on downy mildew epidemics are evaluated through a sensitivity analysis. A comparison of simulation runs in different scenarios of disease conduciveness using experimental measurements of components of partial resistance for 16 different grapevine varieties is also performed. Increasing values of RR parameters led to a suppression of disease progress on foliage. The strongest reduction of epidemics on foliage is generated by increases in RRIE, followed by RRSP, RRIP, and last by RRLP. The effect of partial resistance on epidemics is more conspicuous in a scenario of limited disease conduciveness. The strongest suppressive effect of simulated epidemics on clusters is associated with RRIE, and the lowest effect with RRLP, with similar effects of increasing values of RRIP and RRSP. The use of experimentally measured relative resistance parameters to run simulated epidemics shows a reduction of the area under the disease progress curve from 4 × 105 (on a susceptible reference grapevine variety) to 4 × 102 (on cv. Bronner), i.e. a reduction of disease by 1000. The simulation of the varietal effect in intermediate and less favourable scenarios of disease conduciveness strongly suppresses the epidemic on foliage and limits disease on clusters to very low levels. Deploying partial host plant resistance in environments that are not strongly conducive to downy mildew epidemics could represent an effective use of partial resistance.

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants

“…Our model is flexible in both design and calibration. For instance, the model can easily incorporate components for lesion expansion, plant growth, senescence, or host susceptibility by integration of additional model components ( Loomis and Adams, 1983 ) and of modifiers accounting for resistance components, as has been done with similar models ( Savary et al., 2015 ; Bove et al., 2021 ). Model flexibility also enables easy incorporation of new information on pathogen biology and epidemiology, so that inserting species-specific or host-specific calibrations may improve the prediction accuracy especially in hosts or in regions in which anthracnose disease is caused by a dominant Colletotrichum species, or for clades not considered in this work such as the boniense clade, which is economically important on several Citrus spp.…”

Plant Resistance Inducers Affect Multiple Epidemiological Components of Plasmopara viticola on Grapevine Leaves

“…This is an important finding because it illustrates a broader and longer effect of PRIs on DM epidemic dynamics, which is not limited to the prevention of an infection event and the consequent reduction of disease severity. In epidemiological terms, reduced production of sporangia on lesions adversely affects the basic infection rate of the epidemic [ 87 ] and, thus, how the disease progresses over time [ 88 ]. Epidemiological components other than sporulation are altered in partially resistant grapevine varieties [ 40 ], namely, the infection frequency (i.e., the proportion of sporangia able to cause another infection) and lesion size (which together determine disease severity), as well as the latency period (i.e., the time between infection and the start of sporulation on lesions).…”

Development and Validation of a Mechanistic, Weather-Based Model for Predicting Puccinia graminis f. sp. tritici Infections and Stem Rust Progress in Wheat

“…Nevertheless, the design of the model makes it easy to implement further modifications and improvements based on new scientific evidence. For instance, the use of less susceptible hosts can be easily addressed by incorporating modifiers (Loomis and Adams, 1983) accounting for resistance components, as previously implemented in similar model structures (Savary et al, 2012(Savary et al, , 2015Bove et al, 2021). A daily time step was used for the main structure so that the rate of transfer (Rtrans) and rate of removal (Rrem) are calculated using the mean daily temperature.…”