Lo-Pelzer, Elise (2008) Modelling the effects of cropping systems and their spatial distribution on phoma stem canker on Winter OilSeed Rape and the adaptation of fungal pathogen populations responsible for the disease (Leptosphaeria maculans) to cultivar resistances. PhD thesis Agronomie, UMR 211 Agronomie, INRA Grignon, AgroParistech 2008AGPT0030 p.246.

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Abstract

One of the main control methods to manage phoma stem canker is the use of cultivars with specific or

quantitative resistances. However, specific resistances lack durability. Other control methods can be used to

manage the disease: chemical and cultural control methods (tillage to limit the quantity of primary inoculum on

infected stubble, adaptation of the sowing date and rate, organic nitrogen supply to limit crop’s receptivity to

infections). The spatial and temporal combination of genetic, cultural and chemical control methods in a region

makes it possible to better control the disease, to preserve the efficiency of specific resistant cultivars as well as

economic profit for farmers, and to satisfy environmental and toxicological exigencies of integrated crop

management. Given the considered scales and the number of technical operations that have to be taken into

account, experiments are difficult, or simply impossible to set up to test control strategies. A model has been

developed to test cropping systems that address these issues: SIPPOM-WOSR, a Simulator for Integrated

Pathogen Population Management, for Winter OilSeed Rape. SIPPOM consists of 5 sub-models simulating i)

primary inoculum production, ii) ascospore dispersal, iii) crop growth and attainable yield, iv) dynamics of

pathogen population genetic structure, and v) infection and relative yield loss. Output variables are disease

severity index and the associated yield loss, yield, gross margin, energetic cost of cultural practices and

Treatments Frequency Index, as well as the genetic structure of pathogen populations, under the effect of four

evolutionary forces (migration, selection, recombination, and Allee effect). An experiment has been set up to

collect data on disease recurrence, and formalisms, as effect of quantitative resistance on disease severity or

attainable yield calculation, have been completed with data from other experimentations. A sensitivity analysis

has been carried out to study the sensitivity of sub-models to parameter variations. Examples of simulation

illustrate how SIPPOM can be used to test integrated and durable disease control strategies.

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Table of content

Table de matières

Introduction générale 3

Chapitre 1. Problématique 3

1. Analyse bibliographique 4

1.1. Conception-évaluation de stratégies de production intégrée 4

1.2. Importance économique de la culture de colza d’hiver et risques liés au phoma 4

1.2.1. Le colza d’hiver 7

1.2.2. Le phoma du colza 8

1.2.3. Méthodes de lutte 10

1.2.3.1. Contrôle génétique 10

1.2.3.2. Contrôle cultural et chimique 11

1.2.3.3. Contrôle biologique et physique 12

2. Objectif de la thèse 13

3. Démarche de recherche 14

3.1. Modèles existants et conséquences pour l’outil créé 14

3.1.1. Modèles pour l’étude de la durabilité des résistances spécifiques 14

3.1.2. Modèles pour le contrôle d’une maladie 15

3.1.3. Modèles qui prennent explicitement en compte les pratiques culturales 17

3.1.4. Modèles spécifiques au phoma et au colza 17

3.1.5. Conséquences pour le modèle créé 4

3.2. Structure de SIPPOM 18

4. Plan de la thèse 20

Chapitre 2. Acquisition de connaissances pour compléter les formalismes de SIPPOM, formalisme des forces évolutives 21

1. Acquisition de connaissances : expérimentation et analyses de données existantes 22

1.1. Récurrence de l’épidémie : lien entre la sévérité de la maladie et la quantité potentielle d’inoculum primaire l’année suivante 22

1.1.1. Le phoma (Leptosphaeria maculans/L. biglobosa) du colza (Brassica napus): la note G2 est-elle un indicateur représentatif de la répartition des effectifs observés dans les classes de nécrose ?

Phoma stem canker (Leptosphaeria maculans/L. biglobosa) of oilseed rape (Brassica napus): is the G2 Disease Index a good indicator of the distribution of the observed canker severities? 22

1.1.1.1. Résumé / Abstract 22

1.1.1.2. Key words 23

1.1.1.3. Introduction 23

1.1.1.4. Materials and methods 25

1.1.1.5. Results 21

1.1.1.6. Discussion 21

1.1.1.7. Acknowledgments 27

1.1.2. Relation entre la sévérité de la nécrose au collet (causé par le complexe d’espèces Leptosphaeria maculans/L. biglobosa) et la production d’inoculum sur les résidus de culture de colza.

Relationship between the severity of blackleg (Leptosphaeria maculans/L. biglobosa species complex) and subsequent primary inoculum production on oilseed rape stubble 28

1.1.2.1. Résumé / Abstract 28

1.1.2.2. Key words 29

1.1.2.3. Introduction 29

1.1.2.4. Materials and methods 30

1.1.2.5. Results 34

1.1.2.6. Discussion 21

1.1.2.7. Acknowledgments 39

1.1.3. Discussion 21



1.2. Estimation du rendement accessible de la culture de colza sans maladie 41

1.2.1. Introduction 41

1.2.2. Matériels et méthodes 42

1.2.3. Résultats 44

1.2.4. Bilan et discussion 45

1.3. Effet de la résistance quantitative sur l’indice de sévérité de la maladie 46

1.3.1. Introduction 46

1.3.2. Matériels et méthodes 46

1.3.3. Résultats 21

1.3.4. Discussion 48

2. Forces évolutives 21

2.1. Migration 49

2.1.1. Définition 49

2.1.2. Expérimentation : étude de la dispersion des ascospores de L. maculans 49

2.1.2.1. Introduction 49

2.1.2.2. Matériels et méthodes 50

2.1.2.3. Résultats et discussion 51

2.1.3. Formalisation dans SIPPOM 21

2.1.4. Discussion 52

2.2. Sélection 53

2.2.1. Définition 53

2.2.2. Formalisation dans SIPPOM 54

2.2.3. Discussion 55

2.3. Recombinaison 21

2.3.1. Définition 56

2.3.2. Formalisation dans SIPPOM 56

2.3.3. Discussion 57

2.4. Effet Allee 57

2.4.1. Définition 57

2.4.2. Formalisation dans SIPPOM 21

2.4.3. Discussion 58

2.5. Autres forces évolutives 59

2.5.1. Mutation 59

2.5.2. Coût de virulence 60

2.5.3. Dérive génétique 60

3. Conclusion 61



Chapitre 3. Structure et analyse de sensibilité de SIPPOM 21

1. SIPPOM-WOSR : un simulateur pour la gestion intégrée des populations pathogènes pour l’étude du Phoma du colza 62

SIPPOM-WOSR: a Simulator for Integrated Pathogen POpulation Management adapted to study blackleg on Winter OilSeed Rape 62

1.1. Résumé / Abstract 62

1.2. Key words 63

1.3. Introduction 64

1.4. General description of SIPPOM-WOSR 67

1.4.1. General frame 67

1.4.2. Input variables 68

1.4.3. Output variables 68

1.5. Structure of SIPPOM-WOSR 69

1.5.1. Primary inoculum production sub-model 69

1.5.2. Dispersion sub-model 71

1.5.3. Dynamic crop growth sub-model 71

1.5.4. Genetic sub-model 72

1.5.5. Infection and relative yield loss sub-model 74

1.6. Examples of simulation with SIPPOM-WOSR 76

1.7. Discussion 62

1.7.1. Examples of simulation: consequences for the structure of SIPPOM-WOSR 78

1.7.2. Simplifications and limits 79

1.7.3. Evaluation and sensitivity analysis 80

1.8. Conclusion and perspectives 81

1.9. Acknowledgments 81

2. Analyse de sensibilité de SIPPOM-WOSR, simulateur pour la gestion intégrée des populations pathogènes adapté au Phoma du colza 82

Sensitivity analysis of SIPPOM-WOSR, a Simulator for Integrated Pathogen POpulation Management adapted to phoma stem canker on Winter OilSeed Rape 82

2.1. Résumé / Abstract 82

2.2. Key words 83

2.3. Introduction 83

2.4. Materials and methods 62

2.4.1. Description of the SIPPOM-WOSR model 86

2.4.2. Sensitivity analysis method 87

2.4.2.1. Input variables 87

2.4.2.2. Choice of parameters 89

2.4.2.3. State and output variables tested 91

2.4.2.4. Statistical analysis 62

2.5. Results 62

2.5.1. Crop growth sub-model 93

2.5.2. Primary inoculum production sub-model 62

2.5.3. Infection sub-model 62

2.5.4. Dispersion sub-model 62

2.5.5. Genetic sub-model 100

2.6. Discussion 62

2.7. Acknowledgments 104

3. Autres exemples de simulation avec SIPPOM-WOSR 62

3.1. Matériels et méthodes 105

3.2. Résultats et discussion 62



Chapitre 4. Discussion générale 112

1. Rappel de l’objectif et bilan des tâches réalisées 112

2. Evolutions possibles de SIPPOM 114

2.1. Amélioration du module peuplement 114

2.2. Amélioration du module infection 115

2.3. Prise en compte des effets de bord 116

2.4. Prise en compte d’éléments omis dans la version actuelle de SIPPOM 117

2.5. Amélioration des temps de calcul 119

3. Représentation de l’évolution de la structure des populations pathogènes 119

4. Stratégies de gestion durable des résistances spécifiques 121

Conclusion et perspectives 122

Références 125

Sigles et abréviations 125

Annexes 136

1. Annexe 1. Classes de sévérité de nécrose évaluées à l’échelle de la plante, calcul de l’indice de sévérité à l’échelle de la parcelle, biologie et observation des pseudothèces 137

2. Annexe 2. Calculs de la sortie économique et des sorties environnementales dans SIPPOM 139

3. Annexe 3. Module déplacement vertical des résidus (d’après Schneider, 2005) 142

4. Annexe 4. Module Dispersion 143

5. Annexe 5. Définition de critères statistiques d’évaluation de la qualité prédictive d’un modèle 145

6. Annexe 6. Liste des publications scientifiques et communications associées au travail de thèse 146

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