Stress, strain and tolerance of Septoria tritici blotch
The preceding section described the yield components, the associated major fluxes along with the monocarpic senescence, and finally proposed a definition of the potential yield regarding the source availability. It was stated that, because of environment or genotype potential, the yield observed in healthy crops often remains below its potential considering the associated HAD. In addition, the pressure caused by a disease or an abiotic stress lowers the attainable yield. This pressure can be decomposed into the stress and the strain: the stress leading to a specific strain exerted on the crop (Levitt, 1972). The stress is defined as any environmental factor (e.g. a drought, nutrient deficiency, airborne spores concentration) with the capacity to elicit from the plants a harmful chemical or physical change. The change is the strain (e.g. reduction of cell turgor, reduction of nitrogen content, disease symptoms).
The distinction between stress and strain circumscribes tolerance to a specific area of the plant protection strategies. In the domain of crop protection and working at the level of the genotype, in order to avoid crop losses, the most direct strategy is to avoid the stress by avoidance or escape strategies. This can be achieved, for instance, by modifying the architecture of the plant or imposing modification of the growth and development rate. If the stress can not be avoided, then two options can be developed. The first is to reduce the strain, the symptoms in the case of biotic stress, this is the resistance strategy. The second solution is to tolerate the strain which is precisely the definition of tolerance studied within the project: the ability of a plant to maintain performance in the presence of expressed disease (Ney et al., 2013).
The PhD project focuses on the tolerance of wheat to the STB, as a biotic stress. Therefore, in this section the essential information about STB is provided. Then, the quantification methods of tolerance of STB are proposed. Finally, previous studies pro-viding evidence and hypotheses for the identification of genotype tolerance traits.
The Septoria tritici blotch: a biotic stress of wheat crops
The Septoria tritici blotch (STB) is a leaf disease of wheat (Eyal et al., 1987), charac-terised by large necrotic area dotted by black pycnidia emerging from the leaf stomatae (further description of the symptoms, Fig. 1.4). It is caused by the Ascomycetes Zymosep-toria tritici (Desm.), also formerly known as Mycosphaerella graminicola (Fückel).
The prevalence of STB was relatively low prior to the 1960s-70s (Eyal et al., 1987) in Western Europe (Torriani et al., 2015). Since then, the breeding strategies increased the potential yield of wheat varieties and also increasing STB incidence appeared. The introduction of early maturing and semi-dwarf varieties (Eyal et al., 1987) and priority given to yield increase (Torriani et al., 2015) was inadvertently associated with higher STB yield loss. This was illustrated in Syngenta trials reported by Torriani et al. (2015) as a strong negative association of varietal resistance with yield potential that may explain the increase in yield susceptibility to STB.
The STB is today certainly the most important disease of wheat crops in the European Union (Burke and Dunne, 2006; Cools and Fraaije, 2013; Fones and Gurr, 2015). In a disease management guide, the AHDB (2016) described STB as the most damaging foliar disease in the UK where yield loss ranging 30-50% has been reported (the average yield loss in untreated trials being 20%). According to Fones and Gurr (2015), the STB loss is worth in the order of billions euros for each of the three main wheat growing countries in Europe (France, Germany, UK). Its economic damage can be illustrated by 70% of the wheat EU fungicide market that STB represents worth in 2014 (about 1bn euros) (Torriani et al., 2015).
Quantify the tolerance of Septoria tritici blotch
Formerly, Schafer (1971) considered tolerance as a relative property, evident by com-parison of crops exposed to comparable severity disease (Bingham et al., 2009). Several attempts to quantify tolerance have been developed. Kramer et al. (1980) or Inglese and Paul (2006) considered the tolerance based on estimation of the disease severity (re-spectively leaf rust, Puccinia hordei on barley and alien rust fungi on groundsel, Senecio vulgaris). They used the relationship between the Area Under the Disease Progress Curve (AUDPC) and the yield to quantify the tolerance. However, the disease information (AU-DPC) is relative and does not take into account the absolute size of the grain source, likely to change between sites, seasons and genotypes. Instead of the AUDPC, the present study considers the e ect of STB on the healthy area duration (HAD, the area under the green leaf lamina area progress curve). The HAD summarises the variability of the canopy size across genotypes and sites and seasons (Parker et al., 2004). For instance Bryson et al. (1997) evidenced that HAD conversion into grain yield depended also on the level of radiation that characterise the environment. The STB symptoms on the upper leaves reduce the HAD, and therefore the grain yield loss per unit of HAD reduction was used as a quantitative trait of the wheat crops for STB intolerance in tolerance studies (Parker et al., 2004; Foulkes et al., 2006; Castro and Simón, 2016).
Field experiment 2015-16
The experiment relied on a post-anthesis growth analysis of six UK-grown winter wheat cultivars in a field randomized block experiment at Sutton Bonington (University of Nottingham farm, Leicestershire, UK). The cultivars were sown after a winter oilseed rape on 2 October 2015, at a rate of 325 seeds m 2. Two disease control managements were applied to obtain either a full control of STB, or no control of STB while maintaining the non-targeted disease symptoms to low levels. The aim was to provide yield loss data with source sink quantification for validation of the highlighted potential tolerance traits and environmental e ect. The cultivars were: Sacramento, Dickens, Evolution, Zulu, Cougar, Cashel. Sacramento is a very early flowering genotype, a two week variation for heading date amongst the six cultivars was observed in previous experiments. Cougar is characterised by a good resistance to STB.
Selection of genotypes or cultivars
In the data mining study, nine cultivars studied were selected within historical data. The main target was to obtain a balanced data set with comparable cultivar profiles between the 9 sites seasons experiments. This allowed the study of genotype envi-ronment interaction. The nine cultivars expressed a wide range of heading stage (GS55) date.
In the glasshouse experiment and the field experiment 2014-15, the studied genotypes were part of a large panel derived from two doubled-haploid populations and previously screened for STB tolerance and yield potential. The first population was derived from a cross between UK spring wheat Cadenza and UK winter wheat Lynx (C L), the second between UK winter wheat Rialto and the Mexican CIMMYT spring wheat LSP2 of large ear-phenotype (LSP2 R); the LSP2 R lines were included in order to obtain a wider range of source/sink phenotypes to study STB tolerance in high yielding genotypes. The previous tolerance estimation results are briefly described in Chapter 4. Four and six genotypes amongst these panels were chosen in the present experiments to represent a wide range of tolerance of STB.
Finally, the last field experiment (2015-16) examined commercial cultivars contrast-ing for heading date stage. The cultivars were Cashel (KWS UK, first season on Recom-mended List (RL) 2014-15, bread-making market), Cougar (RAGT seeds UK, RL 2013-14, feed market), Dickens (Secobra France, RL 2013-14, feed market), Evolution (Sejet Denmark, RL 2014-2015, feed market), Zulu (Limagrain UK, RL 2014-15, feed/biscuit market), Sacramento (RAGT, registration 2014, bread-making market) (HGCA, 2015; RAGT, 2016; AHDB, 2016). They were characterised by a 15 day range for heading date (GS55, Zadoks et al., 1974). Sacramento is not amongst the UK recommendation list but is a proposed cultivar in France.
In the three experiments, growth analysis was performed from heading stage to ma-turity. It relied on samples of fertile (ear-bearing) shoots:
• Glasshouse 2014-15: 4 – 5 sampling dates; sample size: 5 ear-bearing shoots per sample.
• Field 2014-15: 4 sampling dates; sample size: 5 ear-bearing shoots per sample.
• Field 2015-16: 2 sampling dates; sample size: 10 ear-bearing shoots per sample.
1. Ear-bearing shoot selection: between heading and anthesis, ear-bearing shoot were selected and tagged to provide homogeneous material to sample over time. Di erent methods were applied regarding requirements of each experiment. In the field 2014- 15 experiment, average ear length was determined and used 5% to select the shoots. In the glasshouse 2014-15 experiment, the distance between the first and second ligules of the main stem on all plants was measured. Regarding this measure, lots of 15 plants equivalent in mean and standard deviation were identified. Finally, in the field 2015-16 experiment for each genotype, ear-bearing shoots reaching GS55 on the same day were tagged.
2. Shoot dissection: sampled shoots were then assessed as follows. The three upper leaf laminas (flag leaf to leaf 3) were detached at the ligule. Ears were separated at the ear collar below the lowest spikelet. The stem was cut above the node 3. The two upper stem internodes and leaf sheaths and the peduncle were referred as the stem. Lower internodes and associated organs comprised the base. Post-anthesis ear sam-ples were manually or mechanically threshed, the grains counted (Grain Number per ear-bearing shoot, GNe) and weighed (grain yield per ear-bearing shoot, Ye).
Every sample was oven dried for 48 h at 80 C and dry weighed to obtain the dry matter weight (DM). All grain yields and biomass weights in the result chapters are at 100% DM.
Noticeable experiment specificity:
• glasshouse experiment: the leaf sheath was detached from the true stem internodes and considered independently along with the other dissected organs.
• degraining treatment (Field 2014-15), the removed top half of the ear was also sampled and dry weighted to estimate the cha and grain biomass removed when degraining.
Nitrogen concentration per organ was measured using the Dumas combustion method in the glasshouse experiment and for two genotypes (LSP2 R 16 and LSP2 R 127) at three sampling dates in the field 2014-15 experiment. The nitrogen amount was calculated as DM N c. Variation in DM or N is expected to be under the influence of i) the treatment applied (e.g. spikelet removal, nitrogen nutrition), ii) the sampling date and iii) random variation in the shoot dimensions between di erent samples (but exposed to the same treatment e ects). Unlike i) and ii) corresponding to physiological responses, iii) must be limited (if not avoided). This is first achieved by the constitution of the samples themselves, as described hereafter. The data analysis can also reduce the random variations between samples exposed to the same treatment combination. Within each treatment combination replicate (each plot in the field experiments), and because the analysis was based mainly on the post-anthesis phase it was assumed that the random shoot size variation between sampling dates was accountable for Grain Number per ear-bearing shoot (GNe) variation. Then, it was considered that the average of Grain Number per ear-bearing shoot (GNe)(t) measured at various sampling date (t) was a good estimator of the GNe of every treatment combination replicate. Further averaging GNe(t) across the replicates of a treatment combination yielded a standard grain number (sGNe) that was used to scale the dry matter or nitrogen amount actually measured at the di erent sampling dates. Doing so, the comparison of DM or N and fluxes was done on a comparable standard ear size across the replicates and sampling date, minimising random variation due to the shoot variability within samples. For instance, the above-ground dry matter (DMa(t)), associated with a grain count GNe(t) for one replicate at one sampling date t was scaled to the standard sGNe to obtain the standarized sDMa: sGN e sDM a = DM a(t).
Table of contents :
1 Introduction and Literature Review
1.2 Literature Review
1.2.1 Potential yield
126.96.36.199 Overview of the wheat physiology: growth and development
The wheat development until anthesis
The grain filling phase
The source/sink manipulations and grain yield limitation .
188.8.131.52 Fate of Nitrogen and Carbon fluxes during the senescence
The end of nitrogen uptake
Senescence and N remobilisation
Senescence and reduction of carbon assimilation
184.108.40.206 An equation for potential yield
1.2.2 Stress, strain and tolerance of Septoria tritici blotch
220.127.116.11 The Septoria tritici blotch: a biotic stress of wheat crops
Life cycle of Zymoseptori tritici
Plant pathogen interactions
18.104.22.168 Methods for crop control of STB
Breeding for resistance
Avoidance and sanitary measures
22.214.171.124 Quantify the tolerance of Septoria tritici blotch
1.2.3 Identification of STB tolerance traits
1.3 Rationale, objectives and hypotheses
2 Materials and methods
2.2 Overview of the study methods
2.2.1 Data mining
2.2.2 Field experiment 2014-15
2.2.3 Glasshouse experiment 2014-15
2.2.4 Field experiment 2015-16
2.3 Selection of genotypes or cultivars
2.4 Growth analysis
2.5 The Healthy Area Duration (HAD)
2.5.1 Leaf lamina area and green leaf lamina area
2.5.2 Post-heading green leaf lamina area kinetics
2.5.3 Healthy Area Duration
2.6 Source / sink ratio and tolerance
3 Genotype and environment eect on senescence and grain weight
3.2 Materials and methods
3.2.1 Dataset, response and explanatory variables
126.96.36.199 E, G and GE
188.8.131.52 The response variables: the senescence timing and the Thousand Grain Weight (TGW)
184.108.40.206 Explanatory variables
3.2.2 Statistical analysis
220.127.116.11 Classification of explanatory variables by Random Forest models
18.104.22.168 Linear model selection
22.214.171.124 Random eects: E, G and GE
3.2.3 External validation
3.3.1 Identifying the main explanatory variables using Random Forest modeling
126.96.36.199 Fitting the Random Forest models
188.8.131.52 Ranking of explanatory variables according to their contribution
3.3.2 Multiple regression models
3.3.3 Identifying the origin of G, E or GE
184.108.40.206 Variance component analysis
220.127.116.11 Partial regressions
3.3.4 External validation
18.104.22.168 Validation of the Random Forest models
22.214.171.124 Validation of the linear models
3.4.2 Senescence timings
3.4.3 The TGW
3.4.4 Hypotheses to improve tolerance of STB
4 Field experiment at Hereford, 2014-15
4.2 Materials and methods
4.2.1 Genotypes screened for tolerance
4.2.2 Experimental design and treatments
4.2.3 Crop measurements
4.2.4 Data analysis
4.3.1 The tolerance grade of the genotypes
4.3.2 Variability of grain filling source traits
4.3.3 Variability of grain sink traits
4.3.4 Source-sink balance
4.3.5 Tolerance prediction using source and sink traits
4.4.1 Tolerance and yield
4.4.2 TGW was co-limited by source and sink
4.4.3 Low source limitation is a genotype tolerance trait
4.4.4 The grain source?
4.4.5 Tolerance estimation in healthy crops
5 Glasshouse experiment at Grignon, 2014-15
5.2 Materials and methods
5.2.1 Experimental design
126.96.36.199 Genotype materials
188.8.131.52 Obtaining a field-like crop in the glasshouse
184.108.40.206 Experimental treatments
220.127.116.11 Growth analysis
5.2.2 Data analysis
18.104.22.168 Population settings
22.214.171.124 The grain yield
126.96.36.199 The Healthy Area Duration (HAD)
188.8.131.52 Dry matter weight and Nitrogen amounts
184.108.40.206 Tolerance estimation
5.3.1 From source:sink characterisation to tolerance
220.127.116.11 Development rate
18.104.22.168 The source traits for grain filling
22.214.171.124 The grain sink traits
126.96.36.199 Relation between the grain yield and HAD
5.3.2 Analysis of dry-matter and nitrogen balance behaviour of cultivars
188.8.131.52 Dry-matter fluxes
184.108.40.206 Nitrogen fluxes
5.4.1 Obtaining a field-like canopy in the glasshouse
5.4.2 Traits associated with the putative tolerance of the genotypes.
5.4.3 GNe tolerance of the genotypes
5.4.4 Nitrogen and tolerance of the genotypes
5.A.1 Composition of nutrient solution
6 Field experiment at Sutton Bonington, 2015-16
6.2 Materials and methods
6.2.1 Experimental design and treatment
6.2.3 Data analysis
220.127.116.11 Senescence kinetics and healthy area duration
6.3.1 Development rate
6.3.2 The sources for grain filling
18.104.22.168 Leaf area characterisation
22.214.171.124 The senescence parameters
126.96.36.199 The healthy area duration
188.8.131.52 Alternative sources: the dry matter remobilisation
6.3.3 The grain sink
184.108.40.206 Yield and yield losses
220.127.116.11 Yield and grain number
18.104.22.168 Grain weight
6.3.4 Source-sink relationship, HAD and yield
22.214.171.124 Cultivar tolerance estimation
126.96.36.199 Tolerance and healthy crop traits
6.4.1 Range of potential tolerance traits
6.4.2 Insights on the estimation of tolerance
6.4.3 Main results on tolerance and tolerance traits
6.A.1 Healthy trait correlation with tolerance estimates
7.1 Highlights of the results
7.2 The range of source/sink balance generated
7.3 The quantification of tolerance and the scale
7.3.1 Limits of the single slope-based estimation of tolerance
7.3.2 Improve the tolerance estimation
7.3.3 The grain scale relevance
7.4 The STB-tolerance traits
7.4.1 Comparison of the experimental results
7.4.2 Tolerance traits
7.4.3 Grain-source availability
7.4.4 Interaction between tolerance traits
7.5 Environment eect
7.6 Tolerance and trade-o
8 Synthèse du manuscrit en français
8.2 Les études réalisées
8.2.1 Présentation générale du matériel et des méthodes
8.2.2 Datamining : eets du génotype et de l’environnement sur la sénescence et le poids des grains
8.2.3 Champ 2014-15 (C2015)
8.2.4 Serre 2014-15 (S2015)
8.2.5 Champ 2014-16 (C2016)