Genome Structure and Reproductive Behaviour Influence the Evolutionary Potential of a Fungal Phytopathogen

Modern agriculture favours the selection and spread of novel plant diseases. Furthermore, crop genetic resistance against pathogens is often rendered ineffective within a few years of its commercial deployment. Leptosphaeria maculans, the cause of phoma stem canker of oilseed rape, develops gene-for-gene interactions with its host plant, and has a high evolutionary potential to render ineffective novel sources of resistance in crops. Here, we established a four-year field experiment to monitor the evolution of populations confronted with the newly released Rlm7 resistance and to investigate the nature of the mutations responsible for virulence against Rlm7. A total of 2551 fungal isolates were collected from experimental crops of a Rlm7 cultivar or a cultivar without Rlm7. All isolates were phenotyped for virulence and a subset was genotyped with neutral genetic markers. Virulent isolates were investigated for molecular events at the AvrLm4-7 locus. Whilst virulent isolates were not found in neighbouring crops, their frequency had reached 36% in the experimental field after four years. An extreme diversity of independent molecular events leading to virulence was identified in populations, with large-scale Repeat Induced Point mutations or complete deletion of AvrLm4-7 being the most frequent. Our data suggest that increased mutability of fungal genes involved in the interactions with plants is directly related to their genomic environment and reproductive system. Thus, rapid allelic diversification of avirulence genes can be generated in L. maculans populations in a single field provided that large population sizes and sexual reproduction are favoured by agricultural practices.     

Are penetrometer measurements useful in predicting emergence of sugar beet (Beta vulgaris L.) seedlings through a crust?

Soil crusting strongly affects seedling emergence. Laboratory experiments were carried out to analyse the emergence of sugar beet (Beta vulgarisL.) seedlings from beneath a crust, and to determine whether mechanical models using penetrometer measurements could predict the final emergence rates. Wet and dry crusts were created under a rainfall simulator. An ascending micropenetrometer was used to measure the crust strength in conditions as close as possible to those encountered by seedlings. The mode of the emergence was observed for both micropenetrometer probe and seedlings. While 94% of the emerged seedlings penetrated the wet crusts, only 6% broke it. The probe penetrated the wet crust in only 46% of cases, and broke it in the other 54%. In contrast, 39% of the seedlings emerged directly through a crack in the dry crust and 55% emerged by breaking (5% lifted a detached fragment and 1% penetrated the crust). In the same conditions, the probe emerged directly through a crack in only 4% of cases; 67% by breaking and 29% by lifting a crust fragment. The force recorded by the penetrometer was 0.05 – 0.80 N, and it varied with the mode of emergence. The seedling growth force distribution was 0 – 0.30 N (mean = 0.09 N). Mechanical models comparing the force exerted by the micropenetrometer probe to the seedling force distribution according to the mode of the emergence were used to predict final emergence rates. Penetrometer measurements appeared to overestimate the mechanical resistance encountered by the seedlings, leading to an underestimation of emergence rates under all emergence conditions. This overestimation of crust strength was attributed to the rigidity of the probe as opposed to the flexibility of hypocotyls.     

Simulating the impact of genetic diversity of Medicago truncatula on germination and emergence using a crop emergence model for ideotype breeding

Background and Aims

Germination and heterotrophic growth are crucial steps for stand establishment. Numerical experiments based on the modelling of these early stages in relation to major environmental factors at sowing were used as a powerful tool to browse the effects of the genetic diversity of Medicago truncatula, one of the model legume species, under a range of agronomic scenarios, and to highlight the most important plant parameters for emergence. To this end, the emergence of several genotypes of M. truncatula was simulated under a range of sowing conditions with a germination and emergence simulation model.

Methods

After testing the predictive quality of the model by comparing simulations to field observations of several genotypes of M. truncatula, numerical experiments were performed under a wide range of environmental conditions (sowing dates × years × seedbed structure). Germination and emergence was simulated for a set of five genotypes previously parameterized and for two virtual genotypes engineered to maximize the potential effects of genetic diversity.

Key Results

The simulation results gave an average value of 5–10 % difference in final emergence between genotypes, which was low, but the analysis underlined considerable inter-annual variation. The effects of parameters describing germination and emergence processes were quantified and ranked according to their contribution to the variation in emergence. Seedling non-emergence was mainly related to mechanical obstacles (40–50 %). More generally, plant parameters that accelerated the emergence time course significantly contributed to limiting the risk of soil surface crusting occurring before seedling emergence.

Conclusions

The model-assisted analysis of the effects of genetic diversity demonstrated its usefulness in helping to identify the parameters which have most influence that could be improved by breeding programmes. These results should also enable a deeper analysis of the genetic determinism of the main plant parameters influencing emergence, using the genomic tools available for this model plant.     

Emergence of seedlings of sugar beet (Beta vulgaris L.) as affected by the size,roughness and position of aggregates in the seedbed

Laboratory and field experiments were carried out to quantify the effects of the size and roughness of aggregates placed in the seedling path of sugar beet, in order to help in decision making for soil tillage and sowing operations. Graded aggregates (10, 15, 20, 30, 40, 50, 60 and 70 mm longest axis) were either laid on the soil surface or included in the soil over the seeds. The percent emergence decreased exponentially with aggregate size over 10 mm when the aggregates were included in the seedbed. The result was the same with aggregates laid on the soil surface, but for aggregates over 30 mm (mass>10 g). Aggregates on the soil surface could be lifted by the seedlings until their weight exceeded the seedling emergence force. Larger aggregates or aggregates in the soil layer could not be moved. Seedlings which did not emerge remained blocked in small cavities in the aggregate surface. No seedlings were blocked under smooth aggregates or glass beads. The experimental results fits with a model giving the probability to meet a hole according the distance covered and the diameter and density of holes. The results obtained under controlled conditions were similar to those obtained in field experiments for a wide range of aggregate sizes. These results will be incorporated into a computerised seedbed generator to simulate the effects of seedbed structure on seedling emergence.     

Injury Profile SIMulator,a Qualitative Aggregative Modelling Framework to Predict Injury Profile as a Function of Cropping Practices,and Abiotic and Biotic Environment. II. Proof of Concept: Design of IPSIM-Wheat-Eyespot

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM''s modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM''s modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.