Tiphaine Vidal

Effects of wheat varietal resistance level and rainfall characteristics on splash dispersal of Septoria tritici blotch

Septoria tritici blotch is an important splash-dispersed disease, causing high yield losses in Europe. Plant disease propagation results from spore dispersal and susceptibility of plant tissues. An experiment was performed in order to study differents aspects of the disease dispersal cycle. Three wheat varieties with contrasted resistance levels were grown in greenhouse conditions until flowering. Plant canopies of each variety received rains of two different raindrop diameter distributions generated by a rain simulator. A linear inoculum source consisting of an aqueous suspension of spores was placed in the middle of each canopy. Horizontal and vertical spore fluxes were measured using traps composed of microscope slides. Varietal resistance was assessed in parallel. After incubation, leaves sampled in canopies were collected and scanned. Spore traps slides were photographed using a microscope combined with a digital camera. Disease area measurement and automatic spore counting were achieved using an image analysis software. Both disease and spore fluxes decreased with the distance from the inoculum source and lower mean raindrop diameter. Disease levels depended on variety and leaf level. Vertical and horizontal gradients of spore fluxes and disease varied in function of rain type and variety. Combining all these results made it possible to disentangle components of splash-dispersed disease propagation for a single dispersal event.

Cultivar architecture modulates spore dispersal by rain splash: A new perspective to reduce disease progression in cultivar mixtures

Cultivar mixtures can be used to improve the sustainability of disease management within farming systems by growing cultivars that differ in their disease resistance level in the same field. The impact of canopy aerial architecture on rain-splash dispersal could amplify disease reduction within mixtures. We designed a controlled conditions experiment to study single splash-dispersal events and their consequences for disease. We quantified this impact through the spore interception capacities of the component cultivars of a mixture. Two wheat cultivars, differing in their aerial architecture (mainly leaf area density) and resistance to Septoria tritici blotch, were used to constitute pure stands and mixtures with 75% of resistant plants that accounted for 80% of the canopy leaf area. Canopies composed of 3 rows of plants were exposed to standardized spore fluxes produced by splashing calibrated rain drops on a linear source of inoculum. Disease propagation was measured through spore fluxes and several disease indicators. Leaf susceptibility was higher for upper than for lower leaves. Dense canopies intercepted more spores and mainly limited horizontal spore transfer to the first two rows. The presence of the resistant and dense cultivar made the mixed canopy denser than the susceptible pure stand. No disease symptoms were observed on susceptible plants of the second and third rows in the cultivar mixture, suggesting that the number of spores intercepted by these plants was too low to cause disease symptoms. Both lesion area and disease conditional severity were significantly reduced on susceptible plants within mixtures on the first row beside the inoculum source. Those reductions on one single-splash dispersal event, should be amplified after several cycle over the full epidemic season. Control of splash-dispersed diseases within mixtures could therefore be improved by a careful choice of cultivars taking into consideration both resistance and architecture.

Sensitivity analysis to help individual plant model parameterization for winter oilseed rape

Functional Structural Plant models can help to understand plant plasticity. Such a model was developed for winter oilseed rape, based on the GreenLab model. However, the model had a large number of parameters, which made its calibration difficult. In order to simplify the model, we applied sensitivity analysis method to select the most important parameters. Analyses were performed on the following outputs: biomasses of the whole plant, of leaves, internodes and pods. First, the model linearity was computed with the Standardized Regression Method, revealing two phases of high nonlinearity. Second, total sensitivity indices were computed for each parameter with the Sobol method. We deduced that five parameters out of 46 had a prevalent impact on model outputs, while the other had a limited influence and could be fixed to reference values.

Contrasting plant height can improve the control of rain-borne diseases in wheat cultivar mixture: modelling splash dispersal in 3-D canopies

Background and Aims Growing cultivars differing by their disease resistance level together (cultivar mixtures) can reduce the propagation of diseases. Although architectural characteristics of cultivars are little considered in mixture design, they could have an effect on disease, in particular through spore dispersal by rain splash, which occurs over short distances. The objective of this work was to assess the impact of plant height of wheat cultivars in mixtures on splash dispersal of Zymoseptoria tritici, which causes septoria tritici leaf blotch. Methods We used a modelling approach involving an explicit description of canopy architecture and splash dispersal processes. The dispersal model computed raindrop interception by a virtual canopy as well as the production, transport and interception of splash droplets carrying inoculum. We designed 3-D virtual canopies composed of susceptible and resistant plants, according to field measurements at the flowering stage. In numerical experiments, we tested different heights of virtual cultivars making up binary mixtures to assess the influence of this architectural trait on dispersal patterns of spore-carrying droplets. Key Results Inoculum interception decreased exponentially with the height relative to the main inoculum source (lower diseased leaves of susceptible plants), and little inoculum was intercepted further than 40 cm above the inoculum source. Consequently, tall plants intercepted less inoculum than smaller ones. Plants with twice the standard height intercepted 33 % less inoculum than standard height plants. In cases when the height of suscpeptible plants was doubled, inoculum interception by resistant leaves was 40 % higher. This physical barrier to spore-carrying droplet trajectories reduced inoculum interception by tall susceptible plants and was modulated by plant height differences between cultivars of a binary mixture. Conclusions These results suggest that mixture effects on spore dispersal could be modulated by an adequate choice of architectural characteristics of cultivars. In particular, even small differences in plant height could reduce spore dispersal.

A new methodology based on sensitivity analysis to simplify the recalibration of functional–structural plant models in new conditions

Background and Aims Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. Methods First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the models quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Key Results Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Conclusions Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.