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Research Centre of Clermont-Fd - Theix - Lyon

International Workshop on Modelling Quality Traits and their Genetic

Variability for Wheat

INRA • Clermont-Ferrand • France • 18-21 July 2004

A satellite meeting of the VIII ESA congress KVL • Copenhagen • Denmark • 11–15 July 2004

European Society for Agronomy 2004 http://www.esacopenhagen2004.kvl.dk/

A meeting of the GCTE Focus 3, Wheat Network http://www.nmw.ac.uk/gctefocus3/networks/wheat.htm

Organising and National Committee Pierre Martre (Clermont-Ferrand, France) Eugène Triboï (Clermont-Ferrand, France) International Committee Gérard Branlard (Clermont-Ferrand, France) Frances M. DuPont (Albany, CA, USA) Tony (LA) Hunt (Guelph, Canada) Peter D. Jamieson (Christchurch, New Zealand) John R. Porter (Copenhagen, Denmark) Peter R. Shewry (Harpenden, UK) Thomas R. Sinclair . (Gainesville, FL, USA) Hubert (JHJ) Spiertz (Wageningen, The Netherlands) Erik J. van Oosterom (Brisbane, Australia) Jeffrey W. White (Phoenix, AZ, USA)

International Workshop 1 Modelling quality traits and their genetic variability for Wheat 18 - 21 July 2004, Clermont-Ferrand, France

The organising committee of the workshop gratefully acknowledges the support by

Département Environnement et Agronomie

Département de Génétique et d’Amélioration des Plantes


Welcome to Clermont-Ferrand The organizing committee warmly welcomes you to the International Workshop on Modelling Quality Traits and their Genetic Variability for Wheat in Clermont-Ferrand. This workshop is a satellite meeting of the 8th Congress of the European Society for Agronomy in Copenhagen, Denmark. This meeting is also part of an informal series of workshops of the Global Change & Terrestrial Ecosystems (GCTE) core project on Agroecology and Production Systems. This series started in 1992 Saskatoon, Canada, where the Wheat Network was launched and chaired by John R Porter. The following workshops (1993 Lunteren, Netherlands; 1995 Reading, UK; 1998, Potdsam, Poland) undertook a detailed model sensitivity analysis and modelling approach comparison using Network datasets, in particular to predict grain yield, given information on weather, soils and some management parameters. The former meeting of the Wheat Network, in conjunction with the International Consortium for Agricultural Systems Applications (ICASA) was held in 2000 Kellogg, MI, USA. The highlights of this meeting was (1) the incorporation of more robust soil and pest management routines into wheat crop models, (2) a discussion on how to model crop quality in cereals chaired by Eugène Triboï and Pete Jamieson. At the end of the Kellogg meeting Crop Quality was pointed out as an important “Future Directions” for the Wheat Network. One of the most significant properties of wheat is the extremely wide variety of uses it serves. While other grain crops also have important secondary and industrial functions, neither matches wheat for sheer versatility, and this is largely because its grain contains proteins with unique chemical and physical properties. Only few studies have considered the dynamic of the genotype by environment interactions responsible for the observed variations of grain protein composition in wheat. Although some wheat simulation models predict grain protein percentage, grain quality (e.g., protein composition, protein state of polymerisation and aggregation) has barely been addressed by modellers. In order to define the relevant quality traits to be modelled, and facilitate transdisciplinary discussions and work, it was important to open this meeting to wheat physiologists, genetists, molecular biologists, and nutritionists with interest in quality traits. The aim of this workshop is to have an informal meeting to (1) present and discuss recent achievements and present up-to-date results on the genetical and environmental determination of grain composition (especially grain protein percentage and composition); (2) develop strategies to integrate these results into simulation models at different scales (crop, whole plant, and grain), with the objective to analyse the mechanisms of genetic - environmental interactions for quality traits. The use of ecophysiological simulation models to manage grain quality and to help molecular biologists and geneticists to better define their research targets, will be the heart of the discussions. The hope is that such effort will enhance our understanding of grain quality traits responses to environmental and genetical variations, and thus help breeders to improve wheat quality and its stability. On behalf of the organising committee, welcome to Clermont!


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Sunday July 18 2004 18.00 – 20.00 Welcome cocktail at the Coubertin Hotel 20.00 – 23.00 Dinner at the Coubertin Hotel

Monday July 19 2004 19.00 – 20.30 Walk on the top of the Puy de Dome volcano 20.30 – 23.00 Gala dinner at the Mont Fraternité restaurant

Tuesday July 20 2004 20.00 – 23.00 Dinner at the Coubertin Hotel


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Sunday July 18 2004 18.00 – 20.00 Registration at the Coubertin Hotel

Monday July 19 2004 8.00 – 8.30 Registration at the congress venue and poster set up 8.30 – 8.40 Opening (C. Touraille, President of Clermont-Theix-Lyon INRA Centre) 8.40 – 8.50 Presentation of the meeting (P.D. Jamieson, P. Martre, J.R. Porter, E.

Triboï) Session 1 - Genetic, Molecular and Ecophysiological Determinants of Grain Quality

Traits. (Chairpersons: G. Branlard and P.R. Shewry). 8.50 – 9.15 S1.1 Branlard G. (Clermont-Ferrand, France)

Genetic determination of protein quality in wheat grain 9.15 – 9.40 S1.2 Habash D., Bernard S., Quarrie S., Schondelmaier J., Weyen J.

(Harpenden, UK) The genetic determinants of nitrogen use in wheat

9.40 – 10.05 S1.3 Shewry P.R. (Harpenden, UK) Molecular approaches to understanding and manipulating wheat grain quality

10.05 – 10.30 S1.4 DuPont F.M., Altenbach S.B., Hurkman W.J., Vensel W. (Albany, CA, USA) Environmental effects on proteins and starch accumulation in wheat grains

10.30 – 11.00 Coffee break

11.00 – 11.25 S1.5 Samson M.-F., Bonicel J., Abecassis J., Morel M.-H. (Montpellier, France) Grain protein content and composition of durum wheat: changes during grain filling and relation with crop quality

11.25 – 11.50 S1.6 Triboï E., Martre P. (Clermont-Ferrand, France) Ecophysiological determinants of grain yield and protein concentration for wheat

11.50 – 12.15 S1.7 Kobiljski B., Dencic S., Mladenov N., Hristov N., Djuric V., Vapa L. (Novi Sad, Serbia and Montenegro) Evaluation of copious database for bread making quality related traits in Novi Sad, Serbia, wheat breeding program conducted over last 50 years

12.15 – 12.45 General Discussion 12.45 – 14.00 Lunch and poster viewing


Session 2 - Modelling nitrogen uptake, redistribution and grain protein composition. (Chairpersons: P.D. Jamieson and T.R. Sinclair). 14.00 – 14.25 S2.1 Sinclair T.R., Weiss A. (Gainesville, FL, USA)

Modeling protein concentration as a result of independent accumulation of carbon and nitrogen by grain

14.25 – 14.50 S2.2 Weiss A., Moreno-Sotomayer A. (Lincoln, NE, USA) A cultivar sensitive approach to simulating grain protein content

14.50 – 15.15 S2.3 Yin X., Schapendonk A.H.C.M., Spiertz H. (Wageningen, The Netherlands) Modelling source-sink interactions and assessing quality traits for wheat genotypes

15.15 – 15.40 S2.4 Asseng S. (Perth, Australia) Nitrogen uptake and grain protein concentration - model and testing


16.10 – 16.35 S2.5 Jamieson P.D., Zyskowski R.F., Semenov M.A., Martre P. (Christchurch, New Zealand) The State of the art in modelling N dynamics and protein composition in wheat

16.35 – 17.00 S2.6 Martre P., Triboï E., Jamieson P.D., Porter J.R. (Clermont-Ferrand, France) Modelling the responses of grain protein content and composition to environmental variations

17.00 – 17.25 S2.7 Bailleau X. (Paris, France) Cropvision.com: An internet agricultural decision support system based on a simulation model of crop growth and development using satellite images

17.25 – 17.55 General Discussion

Tuesday July 20 2004 Session 3 - Towards modelling non-protein grain components: accumulation and

compartmentation as related with grain size and shape. (Chairpersons: J.R. Porter and L.A. Hunt). 9.00 – 9.25 S3.1 Hunt L.A., McMaster G.S. (Guelph, Canada)

Kernel weight: science and simulation 9.25 – 9.50 S3.2 Mabille F., Mueangdee N., Abecassis J. (Montpellier, France)

Modelling the morphology of wheat grain and applications 9.50 – 10.15 S3.3 Leenhardt F., Mijalovsky A., Lyan B., Chanliaud E., Rémésy C.

(Clermont-Ferrand, France) Carotenoïds content of wheat: importance of selection and impact of breadmaking



10.45 – 11.10 S3.4 Batifoulier F., Verny M.-A., Chanliaud E., Demigné C. , Rémésy C. (Clermont-Ferrand, France) Factors responsible for variability of vitamins B contents in wheat grain, milling fractions and bread products

11.10 – 11.35 S3.5 Charmet G., Oury F.-X. (Clermont-Ferrand, France) Genetic and environmental variations of micronutrients contents in wheat grain

11.35 – 12.00 General Discussion 12.00 – 13.30 Lunch and poster viewing Session 4 - From gene to crop scale. Modelling genetic variability of agronomic and

quality traits. (Chairpersons: J.W. White and E.J. van Oosterom). 13.30 – 13.55 S4.1 Branlard G., Bancel E., Majoul T., Martre P., Triboï E. (Clermont-

Ferrand, France) Temperature induced variations of the proteome of developing and mature wheat grain

13.55 – 14.20 S4.2 Génard M., Quilot B., Lescourret F. (Avignon, France) A virtual fruit to analyse the genetic variability of quality traits

14.20 – 14.45 S4.3 White J.W. (Phoenix, AZ, USA) From genome to wheat: emerging opportunities for modelling wheat growth and development

14.45 – 15.10 S4.4 van Oosterom E.J., Hammer G.L., Borrell A.K., Chapman S.C., Broad I.J. (Brisbane, Australia) Functional dynamics of the nitrogen balance of sorghum: Narrowing the divide between QTL's and phenotypic expression of stay-green.

15.10 – 15.40 Coffee break 15.40 – 16.05 S4.5 Barbottin A., Jeuffroy M.-H. (Grignon, France)

The use of a crop model simulating the grain protein content of winter wheat to define breeding targets

16.05 – 16.30 S4.6 Martre P., Triboï E., Samoil V., Charmet G., Branlard G. (Clermont-Ferrand, France) Modelling genetic variations of wheat grain protein composition

16.30 – 17.00 General Discussion

Wednesday July 21 2004 Round table-workshops 8.00 – 8.30 Introduction 8.30 – 10.30 Work in breakout sessions


Three themes will be considered: 1- How can we break the productivity/composition antagonism in the prospect of low input

agricultural systems? (Chairpersons: E. Triboï, T.R. Sinclair) • What are the processes beyond the productivity/N concentration antagonism? • Does Low input agricultural systems are compatible with the improvement of crops

productivity and N content? • How can we increase the N content of wheat crops?

2- Modelling G × E interactions for quality traits. (Chairpersons: E.J. van Oosterom, J.W.

White) • Gene networks versus trait dissection versus reparameterising current models. • Level of detail required (e.g., do we need to model hormones?). • How to connect phenotypic information to QTL maps and gene functions? • Explore performance of a new combination of traits/genes in the target

environment. If different physiological mechanisms can lead to a similar phenotype, should we target certain mechanisms to specific target environments?

3- How can we close the gap between genotype and phenotype in grain quality traits?

(Chairpersons: H. Spiertz, F.M. DuPont) • can we improve the stability of grain quality traits and reduce the effects of weather

extremes. • How can breeders, crop physiologists and geneticists interact in improving quality

traits.

10.30 – 11.00 Coffee break 11.00 – 12.00 Report back to the meeting and discussion 12.00 – 12.30 Meeting summary Special meeting of the GCTE Wheat Network (Organized by Tony Hunt, Guelph, Canada) 12.30 – 14.00 Lunch (registration before July 12) For this Modelling Exercise Afternoon three experiments in different agro-ecological zones are considered:

• Kansas, US (winter wheat, ksas8101) • Hertfordshire, England (winter wheat, roro7401) • Saskatchewan, Canada (spring wheat, swsw7501)

Data have been distributed (clermont.zip) in the original DSSAT original format. Codes are given in the CDE files which are taken from those currently used by Cropsim. The exercise is to simulate all treatments for all three experiments (adjust anthesis and maturity dates to match observed) and to organize the results into measured-simulated plots for presentation. Graphs needed are:


• Above ground dry mass (Mg ha-1) • Grain yield (Mg ha-1) • Grain number per square meter • Grain dry mass (Mg ha-1) • Total above-ground nitrogen (kg N ha-1) • Total grain nitrogen content (kg N ha-1) • Grain nitrogen concentration (kg N kg (DM)-1)

After presentation of the outputs we will have some discussion of results, of reasons for model differences, and of steps necessary to improve the simulations.


LLiisstt ooff PPoosstteerrss P1 Obreht D., Kobiljski B., Dencic S., Vapa L. (Novi Sad, Serbia and Montenegro)

Molecular evaluation of bread making quality parameters in Novi Sad, Serbia, wheat breeding program

P2 Gyori Z., Sipos P. (Debrecen, Hungary) Changes of quality parameters of winter wheat at maturing

P3 Dupont F.M., Altenbach S.B., Hurkman W.J., Chan R., Kothari K., Tanaka C., Vensel W. (Albany, CA, USA)

Effects of environment on wheat flour protein composition

P4 Rharrabti Y., Royo C., Martos V., Isidro J., Garcia del Moral L.F. (Granada, Spain) Amino acid content in durum wheat genotypes as affected by water regime in Southern Spain

P5 Simionescu V., Bulica I. (Constanta, Romania) Winter wheat crops quality under fertilisation and irrigation in a long-term experiment in Dobrogea, Romania

P6 Burstin J., Duc G., Lecomte C., Munier-Jolain N., Salon C., Thompson R. (Dijon, France)

Genetical and ecophysiological determinants of legume seed production. An integrated project developed by the URLEG - INRA Dijon

P7 Lacaze X., Roumet P. (Montpellier, France) Environmental characterization to interpret the variations of genotype performances. An example with the durum wheat grain protein content

P8 Koppel R., Ingver A. (Jõgeva MK, Estonia) Investigation of components of baking quality of wheat in Estonia

P9 Prokisch J., Kovács B., Győri Z. (Debrecen, Hungary) Metrological problems and solutions in the measurement of chromium content of winter wheat

P10 Charmet G., Robert N., Bérard P., Linossier L., Martre P., Triboï E. Genetic analysis of nitrogen accumulation and protein composition in wheat grain

P11 Jeuffroy M.-H., Girard M.-L. (Grignon, France) A model simulating the grain nitrogen content of winter wheat

P12 Barbottin A., Jeuffroy M.-H. (Grignon, France) Varietal adaptation of a model simulating the grain protein content of winter wheat

P13 Colomb B., Desclaux D., Debaeke P., Justes E., Burger P. (Toulouse, France) Ability of new durum wheat pure lines to meet yield stability and quality requirements in low input and organic systems

P14 Bindi M., Triossi A., Ferrise R., Moribondo M. and Flagella Z. (Florence, Italy) A model oriented field experiment on the accumulation of N and proteins in durum wheat grains: Preliminary results

P15 Ritcher G. M., (Harpenden, UK) New paradigm needed for modelling root activity?

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Genetic, Molecular and Ecophysiological Determinants of Grain Quality Traits

International Workshop 12Modelling quality traits and their genetic variability for Wheat 18 - 21 July 2004, Clermont-Ferrand, France

Session 1 - Genetic, Molecular and Ecophysiological Determinants of Grain Quality Traits

S1.1. Genetic determination of protein quality in wheat grain Gérard Branlard INRA-UBP ASP, 234 avenue du Brezet, F-63039 Clermont Ferrand Cedex 02, France (E mail : [email protected]) Wheat endosperm storage proteins, namely gliadins and glutenins, are the major components of gluten. Because they play an important role in dough properties and in bread making quality in various wheat varieties they received a great attention in the two last decades. Using a reducing agent, the glutenins were divided into two groups: high molecular weigh (HMW-GS, 80-120 kDa) and low molecular weight (LMW-GS, 30-50kDa) glutenin subunits The HMW-GS, inherited at Glu-A1, Glu-B1 and Glu-A1 loci on long arm of chromosomes 1, were found the major protein components whose polymorphism is highly responsible of the wheat quality variations. The allelic diversity of the HMW-GS found in bread wheat has been described by many scientists who routinely use these proteins as genetic markers for quality improvement in breeding programs. The main LMW-GS (named B-subunits) are controlled by genes called Glu-A3, Glu-B3 and Glu-D3, located on the short arms of group 1 chromosomes. The LMW-GS were also reported for their additive effects on some rheological properties of dough such as visco-elasticity and extensibility. Most gliadins are controlled by six main Gli loci located in the homoeologous chromosomes of group 1 (Gli-A1, Gli-B1 and Gli-D1) and 6 (Gli-A2, Gli-B2 and Gli-D2). Several additional loci encoding a few minor gliadin bands have been identified. Marked multiple allelism has been described in bread wheat at each of these loci (Metakovsky, 1991). The genetic polymorphism of the gliadins and glutenins has been used to analyse genetic diversity within several germplasms. Few α, β, γ, and ω-gliadin alleles were also significantly associated to dough quality parameters. The quantity of total gliadins influences the extensibility of dough. Quantitative variations reported for these storage proteins were associated to kernel protein content. Genetic and environmental factors have been partly associated to the quantitative variation of gliadins and glutenins. Most of the loci each encoding either gliadins or LMW-GS are formed of clusters of identical or highly similar genes. For the HMW-GS the Glu-B1x7 gene was found duplicated in some cultivars and the over-expression of the HMW-GS 7OE was associated to a 43bp insertion in promoter sequence (Juhász et al., 2003). Proteomic analyses of monosomic and nullisomic lines of cultivar Courtot have evidenced that quantitative expression of glutenins and gliadins are regulated by gene interactions between homologous and heterologous chromosomes (Dumur et al., 2004). Environmental factors such as nitrogen supply and heat stress strongly influence the quantity of storage proteins. Further molecular studies using both transcriptomic and proteomic approaches are needed to better understand the quantitative expression of the storage proteins. Both gliadins and glutenins are accumulated in protein bodies (vacuolar organite), in a largely unknown process, where HMW-GS, LMW-GS and some gliadins are polymerised. The polymerisation is strongly accelerated when kernel dehydration occurs. At maturity the disrupted protein bodies are composed of monomers (most of the gliadins) and polymerised proteins leading to a complex gluten network whose visco-elastic properties will depend on the respective quantities of gliadins and glutenins and also on the number of S-S bonds engaged between the different proteins.



Key-words: gliadin, glutenin, polymorphism, quantitative expression, dough properties. References Dumur J, et al. (2004) Proteomic analysis of aneuploid lines in the homoeologous group 1 of the hexaploid wheat cultivar Courtot. Proteomics, in press. Juhász A, Gárdonyi M, Tamás L, Bedõ Z (2003) Characterisation of the promoter region of Glu-1Bx7 gene from overexpressing lines of an old Hungarian wheat variety. In Proceedings of the Xth International Wheat Genetics Symposium, 1-6 September 2003, Paestum, Italy, 1348-1350. Metakovsky EV (1991) Gliadin allele identification in common wheat. II Catalogue of gliadin alleles in common wheat. Journal of Genetics & Breeding 45: 325-344.



S1.2. The genetic Determinants of Nitrogen Use in Wheat1 Dimah Habash2,*, Stephanie Bernard2, Steve Quarrie3, Jörg Schondelmaier4 and Jens Weyen4 2Crop Performance and Improvement Division, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK (*corresponding author, E-mail: [email protected]) 3Faculty of Agriculture, University of Belgrade, Nemanjina 6, 11080 Belgrade-Zemun, Yugoslavia 4Saaten-Union Resistenzlabor GmbH, Hovedisser Str. 92,D-33818 Leopoldshoehe, Germany

Wheat plants differ in the efficiency in which they use nitrogen. These differences may be manifested in terms of plant architecture, root:shoot ratio, inorganic nitrogen uptake processes, nitrogen assimilation, nitrogen remobilisation and assimilate partitioning. Whilst current elite wheat cultivars are invariably similar in their nitrogen harvest index when tested under high nitrogen inputs, variation exists when examined at lowered nitrogen input. To understand the genetic elements underlying such differences we have employed quantitative genetics to dissect the polygenic trait of nitrogen use efficiency (NUE) in hexaploid wheat. A doubled haploid mapping population of spring wheat (CSxSQ1) has been studied which shows variations in growth, nitrogen uptake and nitrogen content in the grain (Quarrie et al.). We have examined nitrogen use, specifically during grain filling, and have determined plant development, leaf size, leaf soluble protein, leaf senescence, leaf glutamine synthetase (GS, EC 6.3.1.2) activity, grain and ear weight, number and nitrogen content, peduncle nitrogen content, tiller nitrogen content at anthesis and at the end of grain filling. Further aspects relating to grain quality parameters are currently under study. Several quantitative trait loci (QTL) have been identified which correlate with the traits studied some of which were also identified in field experiments. Further work is necessary to validate those QTL regions towards the goal of identifying molecular markers that can be used by plant breeders to select for wheat varieties of high NUE. This represents a first step in the process of identifying major genetic factors underlying NUE in wheat. References Quarrie S, et al. (2004) A genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring x SQ1 and its use to compare QTLs for grain yield across a range of environments- accepted with corrections in Theoretical and Applied Genetics

1 This work is funded by an EU-FV project: Developing wheat with enhanced nitrogen use efficiency towards a sustainable system of production 'SUSTAIN'. http://www.iacr.ac.uk/cpi/sustain/sustain.htm Rothamsted Research is grant aided by the Biotechnology and Biological Sciences Research Council of the UK.



S1.3. Molecular Approaches to Understanding and Manipulating Wheat Grain Quality

Peter R. Shewry Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK (E-mail: [email protected]) The end use quality of wheat is largely determined by the amounts, properties and interactions of the major components of the starchy endosperm cells, proteins, starch and cell walls, and how they are affected by genetic and environmental factors. We are using a combination of approaches to explore the molecular basis for these effects. Firstly we are using transgensis to determine the functional properties of individual proteins, focusing on the control of grain texture and gluten strength, and how these relate to the pathways and mechanisms of protein trafficking and deposition in the developing grain. Secondly, we are using a combination of transcriptome analysis, biochemical analyses, biomechanical measurements and functionality analyses to determine how the compositions and interactions of the major grain components are affected by environmental factors during grain development and how these effects impact on the end use properties of the mature grain.



S1.4. Environmental Effects on Protein and Starch Accumulation in Wheat Grains Frances M. Dupont*, Susan B. Altenbach, William J. Hurkman and William Vensel USDA Agricultural Research Service, Western Regional Research Center, 800 Buchanan Center, Albany, CA 94710 (*corresponding author, E-mail: [email protected]) Genomic and proteomic studies are being used to resolve complex patterns of gene expression during grain development. Eventually it will be possible to pinpoint key regulatory processes that are influenced by the environment, and develop a better understanding of the molecular basis for environmental effects on flour composition and quality (DuPont and Altenbach, 2003). Availability of over 500,000 wheat ESTs makes this attempt feasible. Developmental profiles of transcript and protein accumulation in endosperm of a U.S. hard red spring wheat provide key data for understanding the timing of biological processes during endosperm development and provide suggestions for genes and proteins that can be used as markers for specific stages of development. Accumulation profiles of transcripts were established in developing endosperm for a selection of genes involved in metabolism, signal transduction, carbohydrate and storage protein synthesis or defense. In addition, more than 250 endosperm proteins were identified using 2-D gel electrophoresis and mass spectroscopy. Protein accumulation profiles during endosperm development were generated using computer-based image analysis. Effects of environment were superimposed on the intrinsic temporal patterns of gene expression and the studies highlight the compression of developmental processes that occurred under high temperature conditions. In addition to effects on temporal patterns, environmental variables are likely to alter regulatory interactions and fluxes along metabolic pathways, and high temperatures may interrupt some processes. Our studies indicated that temperature and fertilizer influenced the rate and duration of protein accumulation and starch deposition in unique ways, and by different mechanisms (Altenbach et al., 2003). Studies of grain development under controlled environmental conditions are essential for determining the underlying molecular mechanisms by which wheat grain yield and flour quality are influenced by the effects of environment during grain fill.

Key-words: development, temperature, fertilizer, proteomics, genomics. References DuPont FM, Altenbach SB (2003) Molecular and biochemical impacts of environmental factors on wheat grain development and protein synthesis. Journal of Cereal Science 38: 133-146. Altenbach SB, et al. (2003) Temperature, water and fertilizer influence the timing of key events during grain development in a U.S. spring wheat. Journal of Cereal Science 37: 9-20.



S1.5. Grain protein content and composition of durum wheat: changes during grain filling and relation with crop quality

Marie-Françoise Samson, Joëlle Bonicel, Joël Abécassis and Marie-Hélène Morel* Technology of Cereals and Agropolymers. UMR IATE (INRA, ENSA.M, UM II, CIRAD), 2 place Viala, F-34060, Montpellier, France (*corresponding author, E-mail: [email protected]) Depending on harvests, durum wheat kernel texture varies from fully vitreous to piebald and mealy. Piebald grains are characterized by the presence of adjacent mealy and vitreous areas. Poor nitrogen availability is considered to be the most critical factor in the prevalence of yellow berry. In order to clarify the biochemical basis of the occurrence of piebald grain, crops from 4 durum wheat cultivars grown with increasing nitrogen levels (40 to 380 kg/ha) during 2 years and at several locations were considered. Analysis of harvests showed that prevalence of yellow berry was related to the crop overall protein content. Grains picked at random from the cropped grains were also analysed. We found that fully vitreous grains always presented a higher protein content than fully mealy grains. Hence, among an analysed population of 270 grains (148 vitreous and 122 mealy), more than 95% of the mealy ones contained less than 9.73% protein. From these two results we conclude that piebald grain results from the juxtaposition of high and low protein areas within the endosperm. Occurrence of mealy endosperm parts in piebald grain would arise from local group of endosperm cells much starved of nitrogen than the surroundings. Protein composition of grains was analysed by SE-HPLC. A linear and positive relationship between the grain protein content and the gliadin/glutenin ratio was observed. Thus, high nitrogen availability would favour a preferential accumulation of gliadin. In order to get a better understanding of protein accumulation mechanism, we analysed the protein composition of immature grains taken from flowering to harvest. Large changes in SE-HPLC profiles were observed during grain filling. Strikingly, the gliadin peak of immature grains showed an extra shoulder that disappeared about 30 days after flowering. A similar shoulder was previously observed upon stepwise reduction of gluten storage protein by thiol reducing agent. The shoulder vanishing coincided to an abrupt increased of glutenin polymers content. At the same time we noticed a drop of the endosperm thiol reducing groups. Thus, we may assume that the shoulder revealed the presence of unreduced low-molecular weight glutenin subunits, which polymerize when the endosperm sulfhydryl content dropped. In consequence, the estimation of gliadin and glutenin contents from SE-HPLC areas would be accurate only at the last stages of grain filling. Simulation of gliadin and glutenin accumulation during grain filling stages, suggests that gliadin accumulation might be slower than that of glutenin. In addition, analysis of immature grains from Lloyd cultivar, showed that when gliadin and glutenin accumulations were plotted against total protein accumulation, similar changes were observed for plants grown with 0 or 60 kg/ha of nitrogen supply. Consequently, increase in gliadin to glutenin ratio with grain protein content, might arise from differential rates of protein and starch formation during grain filling, the latter being less dependant on nitrogen supply.



S1.6. Ecophysiological determinants of grain yield and protein concentration for wheat Eugène Triboï* and Pierre Martre INRA, UR874 Agronomie, 234 Avenue du Brezet, F-63039 Clermont-Ferrand Cedex, France (*corresponding author, E-mail: [email protected]) During the last five decades, genetic and technical innovations have increased the average wheat yield in France more than 0.1 Mg ha-1 y-1. This continuous yield increase was associated with a decrease in grain protein concentration, the most important quality criteria for wheat (Feil, 1997). To understand the mechanisms of the negative relationship between grain yield and protein concentration we studied the relations between C and N accumulation at different source to sink ratios. This ratio was modified by environmental (N, temperature, and water), in semi-controlled conditions and in the field, or by genotypic factors or by artificially manipulating grain number. From the results of these experiments, we elaborated a general concept which integrates the relationships between productivity and grain composition (Triboï and Triboï-Blondel, 2002). For a given environment, the genetic potential of grain yield is essentially determined by total biomass production and its harvest index. Unlike C production, grain N accumulation is essentially source limited (Martre et al., 2003), and most of grain N comes from N stored in vegetative tissues before anthesis. Hence, grain N accumulation is less dependent on the duration of grain filling than grain C accumulation. As a consequence, environmental variations of grain C are higher than that of grain N. The effects of environmental factors depend on the developmental stage of the crop. During the reproductive phase, variations of grain number induce compensatory mechanisms, which increase the quantity of available C and N per grain and thus grain dry mass, but decrease total grain yield (i.e., grain number times grain dry mass). Crop biomass production, source to sink ratio, and grain number, and hence yield potential, are essentially determined by soil N supply determines. During grain filling, because in thermal time the duration of grain filling is relatively constant, temperature is the main factor determining post-anthesis C assimilation. Hence, any increase of temperature causes a decrease of the source to sink ratio, grain dry mass and total grain yield. In contrast, there is a compensatory mechanism between grain filling duration and rate of grain N accumulation when expressed in days, leading to a good translocation of vegetative N to the grain (nitrogen harvest index close to 80%). The effect of drought depends on yield potential, as determined by temperature. Yield potential is inversely related to temperature and drought effects are more important under low temperature conditions. Moreover, under conditions of limiting sink capacity (i.e., high source to sink ratio), the duration of grain filling can be shorten and yield decreases despite an increase of individual grain dry mass. In these conditions, the source to sink ratio increases more for N than for C, and N concentration can be higher. Other factors like CO2, radiation, or disease resistance have a positive effect on C assimilation, and lead to an increase of yield but to a decrease of grain N concentration. Possibilities to increase yield potential and protein content, such as increased grain filling duration and grain N sources (remobilisable N pools at anthesis and post-anthesis root N uptake) are discussed. References Feil B (1997) The inverse yield-protein relationship in cereals: possibilities and limitations for genetically improving the grain protein yield. Trends in Agronomy 1: 103-119. Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modelling grain nitrogen accumulation and protein composition to understand the sink/source regulation of nitrogen remobilization for wheat. Plant Physiology 133: 1959-1967. Triboï E, Triboï-Blondel AM (2002) Productivity and grain or seed composition: a new approach to an old problem - invited paper. European Journal of Agronomy 16: 163-186.



S1.7. Evaluation of a copious database for bread making quality in Novi Sad, Serbia, Wheat breeding program conducted over last 36 years

B. Kobiljski1,*, S. Dencic1, N. Mladenov1, N. Hristov1, V. Djuric1 and Lj. Vapa2 1Institute of Field and Vegetable Crops, 21000 Novi Sad, Serbia and Montenegro (*corresponding author, E-mail: [email protected]) 2Faculty of Natural Sciences, Dept. of Biology and Ecology, 21000 Novi Sad, Serbia and Montenegro Wheat is the most important crop globally, in terms of both total acreage planted and human consumption. Since wheat bread is a major commodity in the world, it is logical the major objective of each wheat breeding program is to increase yield and improve quality. Development of new wheat cultivars that contain the desirable end-use quality is a long and costly process. To obtain reliable and useful information regarding such a complex trait as wheat quality, it is necessary to establish a huge database which will allow breeders to rapidly and accurately assess the quality potential of breeding lines, facilitate their utilization and accelerate decision making. Having this in mind, in last 50 years, wheat breeders from Novi Sad devoted much attention to large-scale evaluation of quality parameters. This work had started in 1955, with the release of first domestic wheat varieties (Denčić, 2001). Unfortunately, documentation exists only from 1968. During these 36 years, 227,599 samples were evaluated, the number of lines tested annually ranging from 1,553 (1979) to 13,776 (1986). Since the importance of environmental effects on bread quality of a given wheat variety is well recognized, many samples (genotypes) were evaluated in 2 to 15 years, so the actual number of analyzed genotypes is lower as compared with the total number of samples. Some tests (Berliner test, Zeleny test, etc.) were done only in earlier periods, and some have been introduced more recently: amylograph (1988); falling number (1999); alveograph (2001). On average, 5925 of the so-called “small samples” were evaluated per year (Berliner test - before 1979; sedimentation value and protein content - after 1979). Furthermore, on average, 566 of the so-called “big samples” were evaluated per year (for milling and baking properties), for as much as 32 parameters. Such approach to quality testing helped hard winter wheat breeders in Novi Sad to release wheats with good or excellent milling/baking quality. The problems and benefits in collecting and using this copious wheat quality database, together with an overview of changes in breeding objectives related to quality improvement over last 36 years are discussed. Key-words: bread-making quality, evaluation, wheat. References Denčić S (2001) Yugoslav wheat pool. In: The World Wheat Book. A history of wheat breeding (Eds. AP Bonjean, JA Angus), Lavoisier Publishing, France, pp. 377-402.

Modelling nitrogen uptake, redistribution and grain protein composition


Session 2 - Modelling nitrogen uptake, redistribution and grain protein composition S2.1. Modeling Protein Concentration as a Result of Independent Accumulation of

Carbon and Nitrogen by Grain Thomas R. Sinclair1,* and Albert Weiss2 1Agricultural Research Service, USDA, Agronomy Physiology Laboratory, P.O. Box 110965, University of Florida, Gainesville, FL 32611-0965, USA. (*corresponding author, E-mail: [email protected]) 2Department of Agricultural Meteorology, University of Nebraska, P.O. Box 830728, Lincoln, NE 68583-0728, USA A relatively simple model has been previously developed to simulate the growth and yield of wheat under various environmental conditions including soil fertility (Sinclair and Amir, 1992). In this model, development is calculated mainly as a function of temperature and growth mainly as a function of intercepted solar radiation. Nitrogen during vegetative development is recovered from the soil when available to fully meet defined N concentrations of leaves and stems. Inadequate N results in reduced N concentration of the stems and then a loss in leaf area. The growth of the grain is calculated by assuming a constant, linear increase in harvest index during seed fill. This previous model was explored as a basis for predicting grain N concentration. The hypothesis studied was that N deposited in the grain resulted from transfer of N from vegetative tissue. The amount to be transferred from the leaves and stems was calculated based on the N in these tissues at the beginning of seed fill minus the N concentration expected of senesced tissue (0.4 g N m-2 for leaves and 3 mg N g-1 for stems). Potential rate of N transfer was defined by the total amount of transferable N divided by the thermal unit duration of seed fill (550 TU). Potential N transfer each day was calculated based on the thermal units for that day. Minimum and maximum N concentration for the grain was defined for each cultivar, and the actual N transfer was adjusted if required to maintain N concentration within these limits. Other than these two constraints, the N concentration was dependent only on the amount and rate of N transfer grain. This model was tested using data collected in western Nebraska. The experiment from which the data were collected included several wheat cultivars and soil N fertility treatments. Daily weather was obtained throughout the growing season as the input data for the model. In general the model proved robust for these conditions in simulating both yield and grain N concentration. There were instances, however, where poor agreement was obtained between simulations and observations. Research is continuing to understand the basis for the cases were agreement was not found. Key-words: grain yield, grain N concentration, soil N fertility. Sinclair TR, Amir J (1992) A model to assess nitrogen limitations on the growth and yield of spring wheat. Field Crops Research 30: 63-78.



S2.2. A Cultivar Sensitive Approach to Simulating Grain Protein Content Albert Weiss* and Alex Moreno-Sotomayer School of Natural Resources, University of Nebraska, P. O. Box 830728, 245 L. W. Chase Hall, Lincoln, NE, 68583-0728, USA (*corresponding author, E-mail: [email protected])

There is an evolution in wheat simulation modeling to go beyond simulating yield components and yield to simulate indicators of grain end-use quality, as evidenced by this workshop. One indicator of grain end-use quality is percent kernel nitrogen or grain protein content. Different cultivars of wheat have different inherent protein contents and are used for different applications, e.g., bread making, Asian noodles, flat breads, cakes, and cookies. These inherent protein contents can be modified by environmental extremes characterized by very warm air temperatures for varying durations, which may make the effected wheat less desirable for its intended purpose. The objective of this research effort was to develop and evaluate an algorithm to simulate grain protein content applicable to a wide range of cultivars. The hypothesis is that nitrogen is translocated from sources (leaves and stems) to the sink (grain) as a temperature dependent relationship unique for each cultivar. Assumptions associated with this algorithm are: initial kernel nitrogen concentration is equal to the simulated value of nitrogen concentration of the shoot at the beginning of the linear grain fill period and nitrogen from senescent leaves is added to the labile pool for translocation to the grain, This algorithm is a component of a modified version CERES-Wheat (V3.0, CWM). CWM also contains modified algorithms to simulate kernel number and kernel weight. Data for developing this algorithm came from three years of field experiments in eastern Nebraska, which is characterized by a sub-humid climate. Simulated protein content from CWM was compared with independent field data collected from nitrogen application experiments at several locations in western Nebraska (a semi arid climate) over four years. Detailed results from these simulations will be presented at the workshop.

Key-words: CERES-Wheat, grain protein, nitrogen translocation.



S2.3. Modeling Source-Sink Interactions and Assessing Quality Traits for Wheat genotypes Xinyou Yin*, Ad Schapendonk and Huub Spiertz Crop and Weed Ecology Group, Wageningen University, The Netherlands (*corresponding author, E-mail: [email protected]) Modelling seed nitrogen accumulation and its related quality traits has increasingly received attention from crop physiologists (Martre et al., 2003). Improvement of grain yield and quality requires an optimization of dynamic interactions of both storage and photosynthetic processes. Heat affects grain yield and quality of wheat through sink development and source capacity. Narrowing the gap between genetic potential and phenotypic expression requires knowledge about the physiological mechanism of tolerance to high ambient temperatures during the vegetative and reproductive stages (Spiertz and Schapendonk 2003). We aim at analyzing the effects of heat on sink and source processes and consequently on grain yield and quality. Wheat genotypes expressed a differential response to chronic heat as well as a heat shock (Yang et al., 2002). A lower ambient temperature showed the highest biomass and grain yield; raising the temperature from 18/13 to 25/20°C reduced the grain yield of a heat sensitive genotype more than of heat tolerant genotypes. The latter genotypes showed also a full recovery of the reduction of the rate of photosynthesis after a heat shock treatment, while the susceptible genotype showed only a partial recovery. Differences in heat tolerance between genotypes were clearly reflected in individual grain weights and grain yields per culm rather than in rate of leaf photosynthesis. A heat shock reduced grain weight and yield, especially, when plants were grown at a low ambient temperature. Responses in quality traits of different genotypes to heat were associated with differences found in individual grain weight. The latter value may therefore be used as a proxy parameter to assess differences in tolerance to heat shocks between genotypes. Mechanistic crop modeling can be an effective tool to understand crop phenotypes in response to environmental variables and genotypic characteristics (Yin et al., 2003). A new model GECROS – Genotype-by-Environment CROp Simulator - will be used to integrate some experimental results. This model uses robust yet simple algorithms to summarise the current knowledge of individual physiological processes. It models each process with a consistent level of detail and deals with interactive aspects and feedback mechanisms of crop growth. This applies to photosynthesis-transpiration-coupling via stomatal conductance, carbon-nitrogen interaction on leaf area index, functional balance between shoot and root activities, and interplay between source supply and sink demand on reserve formation and remobilization. The analysis using GECROS, aiming at a time-resolved simulation of sink-source interactions during (heat) stress periods at different stages of development, will be presented. References Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand sink/source regulations of nitrogen remobilization for wheat. Plant Physiology 133: 1959-1967. Spiertz JHJ, Schapendonk AHCM (2002) Opportunities for wheat improvement: the role of crop physiology revisited. In: Proceedings ‘Warren E. Kronstad Symposium’, (Eds. J Reeves, A McNab, S Rajaram). CIMMYT, Cd. Obregon, 15-17 March 2001, pp. 42-47. Yang J, Sears RG, Gill BS, Paulsen GM (2002) Genotypic differences in utilization of assimilate sources during maturation of wheat under chronic heat and heat shock stresses. Euphytica 125: 179-188. Yin X, Stam P, Kropff MJ, Schapendonk AHCM (2003) Crop modeling, QTL mapping, and their complementary role in plant breeding. Agronomy Journal 95: 90-98.



S2.4. Nitrogen uptake and grain protein concentration - model and testing Senthold Asseng CSIRO Plant Industry, Private Bag 5, PO Wembley WA 6913, Australia ([email protected]) Abstract Nitrogen uptake in wheat models is often linked to biomass via N concentration. In these models, a maximum N concentration in the crop biomass changes with phonological stages. At any given time, N concentration and biomass then allow a calculation of an absolute N demand as the difference between maximum and current N content. This approach has been proven a simple and robust method in simulating the N dynamics in wheat crops. A large number of observed N uptake dynamics were reproduced with APSIM-Nwheat, a wheat model which employs such an N demand function. During the comparison of the model with observed data, larger discrepancies between observed and simulated leaf areas were apparent. Nevertheless, crop biomass was consistently well simulated with the model. Although biomass growth is related to leaf area and intercepted radiation, biomass growth is less sensitive to variation in leaf area due to little effect of increases in leaf area on intercepted radiation with leaf area index > 3. Other limitations to biomass growth rather than light interception like low temperature, water or N deficit reduce the sensitivity of biomass to leaf area. As a consequence, perfect leaf area simulations are not critical in simulating crop growth. Leaf area simulations are also not critical for the simulation of crop N dynamics and transpiration as long as N and transpiration are linked to biomass. A good simulation of crop N uptake is one of the pre-requests to simulate grain protein. Grain protein is the ratio of grain N and grain weight multiplied by 5.7. Grain N and grain weight accumulate as two independent temperature functions but are constraint at their extremes (Asseng et al., 2002). An upper boundary of daily protein transfer to the grain is set to 23%. A lower boundary of grain protein is set to 7%. Testing the grain protein routine as part of APSIM-Nwheat gave RMSD’s of below 2%. Additional comparisons of the model outputs with observed grain protein response curves to temperature, nitrogen and water supply and protein versus grain yield relationships confirmed the consistent good performance of the model under a wide range of growing conditions. Key-words: APSIM-Nwheat, grain yield, grain N content, crop-soil system, Triticum aestivum L.

Reference Asseng S, et al. (2002) Simulation of grain protein content in wheat. European Journal of Agronomy. 16: 25-42.



S2.5. The State of the art in modelling N dynamics and protein composition in wheat Peter D. Jamieson1, *, Robert F. Zyskowski1, Michael A. Semenov2 and Pierre Martre3 1New Zealand Institute For Crop and Food Research, Private Bag 4704, Christchurch, New Zealand (*Corresponding author, E-mail: Jamiesonp@crop.,cri.nz) 2Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK 3INRA, UR874 Agronomie - APAC, 234 Avenue du Brezet, F-63039 Clermont-Ferrand Cedex, France The most widely used models for calculating N demand and distribution in wheat crops assume minimum, critical and maximum N concentrations within whole shoots that change with ontogeny. Such models are definitely not state-of-the-art. The state-of-the-art is represented by a much simpler but more mechanistic approach that was published in 2000 (Jamieson and Semenov, 2000) and is incorporated into the Sirius wheat model (Jamieson et al., 1998). Nitrogen is assigned to three main pools during vegetative growth.

• Structural N is not recoverable to be used elsewhere in the plant. Vegetative structures are assumed to be complete at anthesis and no more N is assigned there from that time. All N in the roots is assumed to be structural.

• “Green” N, associated with chlorophyll, is assigned at a constant specific concentration (1.5 g m-2 of green tissue).

• Labile N is stored in non-green tissue – i.e., that portion of the plant not involved in photosynthesis. The capacity to store labile N is assumed to be 1.2% of the non-green biomass. Actual concentrations can vary from zero up to that maximum.

With this model it is possible to derive the above minimum, critical and maximum shoot N concentrations and how they change with time. The model provides a very good structure for predicting grain protein accumulation. During grain growth all of the non-structural N is available to be translocated to the grain and this takes place at a constant rate in thermal time. Add to this some partitioning rules about which protein fractions are the destination for the N, and a model for protein composition is developed. Because recent research has shown that grain protein concentration is mostly source regulated (Martre et al., 2003), allowing variation in the size of the vegetative N pools provides a basis for describing variation in grain N concentration with cultivar. Reference Jamieson PD, Semenov MA (2000) Modelling nitrogen uptake and redistribution in wheat. Field Crops Research 68: 21-29. Jamieson PD, Semenov MA, Brooking IR, Francis GS (1998) Sirius: a mechanistic model of wheat response to environmental variation. European Journal Agronomy 8: 161-179. Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiology 133: 1959-1967.



S2.6. Modelling the responses of grain protein content and composition to environmental variations

Pierre Martre1, *, Eugène Triboï1, Peter D. Jamieson2 and John R. Porter3 1INRA, UR874 Agronomie, 234 Avenue du Brezet, F-63039 Clermont-Ferrand Cedex 2, France (*corresponding author: E-mail: [email protected]) 2New Zealand Institute For Crop and Food Research, Private Bag 4704, Christchurch, New Zealand 3Department of Agricultural Sciences, Royal Veterinary and Agricultural University, 2630 Taastrup, Denmark Protein composition is a key component of the end-use value for wheat grain. Although the qualitative composition of the grain is genetically determined, the quantitative composition is significantly modified by the growing conditions, and there are significant management x genotype × environment interactions (Zhu and Khan, 2001). We recently reported a model of grain N accumulation and partitioning for wheat grain (Martre et al., 2003). The main hypotheses of this model are: (1) the accumulation of structural/metabolic N is sink-driven and is a function of temperature; (2) the accumulation of storage N is supply limited; (4) the allocation of structural/metabolic N between albumin-globulin and amphiphilic protein fractions and the allocation of storage N between gliadin and glutenin fractions during grain growth is constant. This grain model has been coupled with the crop simulation model Sirius (Jamieson and Semenov, 2000) allowing us to analyse the interaction between the vegetative sources and the reproductive sinks for N at the crop level. The aims of the coupled model are (1) to analyze the mechanisms of the responses of grain protein level and composition to environmental variations; (2) and to analyze the genetic control of grain protein level and composition. This model was evaluated against several datasets that covered a broad range of N and water supplies and temperatures. The model gave good simulations of the timing and rates of accumulation of the different protein fractions. At maturity, simulated and observed quantities of albumins-globulins were poorly correlated. The amphiphilic proteins represented only 4% to 8% of the total grain protein and there was a good agreement between observed and simulated quantity of this fraction at maturity. The quantity of storage proteins varied more than 3-fold, and there was a good agreement between simulated and observed quantities of gliadins and glutenins. The close simulations of total N and storage proteins accumulation provided by the model confirm that accumulation of grain N is source- rather than sink-regulated (Martre et al., 2003). The model gives a simple mechanistic framework that explains environmental effects on grain protein content and composition. An important point for the use of this model to simulate the genetic control of grain protein composition is the low number of parameters needed (only seven) to model the accumulation of the different protein fractions. Key-words: gliadins, glutenins, protein composition, simulation model, wheat quality. References Jamieson PD, Semenov MA (2000) Modelling nitrogen uptake and redistribution in wheat. Field Crops Research 68: 21-29. Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiology 133: 1959-1967. Zhu, J, Khan K (2001) Effects of genotype and environment on glutenin polymers and breadmaking quality. Cereal Chemistry 78: 125-130.



S2.7. Cropvision.com: An internet agricultural decision support system based on a simulation model of crop growth and development using satellite images

Xavier Bailleau Quantix Agro, 34 Rue de Liège, F-75 008 Paris, France, http://www.quantix.fr (E-mail: [email protected]) Introduction— Farmers live in Europe the New reform of the European Common Agricultural Policy. Subsidies will be linked to the respect of constraints regarding environment. The reduction of production costs is necessary to maintain profitability because of the increase of price volatility. Cropvision is an online integrated crop management decision making tool dedicated to precision farming. Cropvision is designed for preserving environment, optimisation of competitiveness and profitability by applying only the right product at the right dose, at the right place, and at the right moment.

Base concepts— Cropvision is based on the combination of several crop growth and development models, geo-referenced remote sensing data from satellite images, weather datas and differents parameters for the crop and the soil.

Cropvision enables:

• Agronomical and economic diagnosis. • Forecast of growth stages, potential yield and input requirements (nitrogen, irrigation...). • Weather and disease risks. • Decision support for input management.

Agronomic models— Wheat growth stages models are based on the final number of leaves calculation as a function of genetic behavior of the variety, weather and daylength during vernalisation and the phyllochron is a function of daylength and sun radiation changes during the emergence phase. Others growth stages are calculated as a function of temperature. The yield is calculated as a function of intercepted radiation by the leaf area index of the crop and the conversion into biomass according the soil water and nitrogen balance. So the leaf area index calculation is decisive into the predicted biomass and in the final yield through an harvest index. Remote sensing— In Cropvision is included a GIS for field digitization. The satellite images are incorporated into the models after treatment: the satellite records a canopy reflectance in several wavelengths, and through the inversion of a canopy and leaf radiative transfer model (Prosail), the leaf area index is calculated and then used as an input into the crop physiology model with an at in field level. Future work and conclusion— The next comings up models in Cropvision are dedicated to the wheat nitrogen nutrition, with the aim to calculate the nitrogen nutrition index, which is already done on a wheat database in Beauce, France, at the field level. Through a new kind of radiative transfert inversion theory (Neural network and genetic algorithms), we hope to be able to calculate the chlorophyll content at the field level and protein content.

Towards modelling non-protein grain components: accumulation and compartmentation as related with grain size and shape


Session 3 - Towards modelling non-protein grain components: accumulation and compartmentation as related with grain size and shape

S3.1. Kernel Weight: Science and Simulation L.A. Hunt1, * and G.S. McMaster2 1Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada (*corresponding author, E-mail: [email protected]) 2USDA-ARS, Great Plains Systems Research, 301 S Howes St, Fort Collins, CO 80521 USA Kernel size and shape, and the variability in these aspects, are important quality parameters for the milling industry. Cereal crop simulation models, to be of value in an economic setting, should thus be able to deal with these aspects. Kernel size varies among cultivars, but with any given mono-cultivar crop, there is also considerable variation depending both on the overall size of the inflorescence and on the position of the kernel within the spike or panicle. Further, even with good moisture and nitrogen supply, kernel size depends on the temperature conditions experienced during both pre- and post-anthesis phases. With inadequate moisture and/or nitrogen supplies, kernel size may be considerably reduced from its potential, with the degree of reduction depending partly on the amount of reserve carbohydrate accumulated in the plant. Most simulation models do not account for kernel variability stemming from differences between plants in a crop, from differences in shoot position within any given plant, and from differences within individual inflorescences. With the Spikegro model however, an attempt has been made to account for such variability, and in Cropsim potential kernel size is related to the number of kernels set per plant. Many models relate kernel growth, kernel filling duration and kernel size to temperature conditions during the grain filling period. In the Afrc and Ceres models, kernel growth rate is assumed to increase almost linearly with temperatures from zero to at least 35oC; in Swheat curvilinearly from a base of 8oC to a maximum at 30oC, and in Cropsim linearly from zero but with a decrease at higher temperatures. For kernel development rate, in the Afrc model there is an increase from 0 at 8oC to a maximum at 26oC and then a decrease to 0 at 37oC; in Ceres a linear increase from 0 at 0oC to a maximum rate at 26oC, with this rate being maintained at higher temperatures; and in Swheat there is a linear increase from a small value at 0oC to values comparable with those used by Ceres at temperatures around 30oC. The net effect of these changes is that under good moisture and nitrogen conditions, simulated kernel weight decreases markedly to a temperature of 26oC in Afrc; decreases slightly in Ceres to 26oC, but then increases; and shows an optimum response pattern in Swheat. Under poor moisture and nitrogen conditions, simulated kernel weight depends on how each model treats resource capture, resource accumulation, and senescence; and thus response patterns cannot be summarized readily. However, the situation under poor conditions, with considerable variability apparent among models in algorithms and parameters, matches that apparent among simulation models for some aspects relevant to good conditions. Until such differences are examined and resolved, it will not be possible to use predictions of kernel mass with a high degree of confidence.



S3.2. Modelling the morphology of wheat grain and applications F. Mabille*, N. Mueangdee and J. Abecassis INRA, UMR-IATE, Agropolymers Engineering and Emerging Technologies, 2 place Viala, 34060 Montpellier, France (*corresponding author, E-mail: [email protected]) The milling quality of the wheat grain is highly connected to the shape and to the size of the grain. However, the morphological specificities, like length, width, and thickness, do not have the same importance for the grain volume and for the coats surface. A thorough comparison of a mathematical model describing the grain shape with cross-, side- and longitudinal-sections of French wheat varieties led to a proposal of geometrical systems capable of describing the grain shape. This morphological characterisation provides a new and more accurate method for estimating the milling yield through a volume/surface ratio calculation. It also enables the determination of a high number of geometrical characteristics, like bran proportion inside the crease and facilitates the setting up of a grid that can be used to allot characteristics to each element such as component content or mechanical properties in a finite elements type calculation. Another innovation of the model is to propose a new visual support to represent and compare the topology of wheat components inside grains. Key-words: wheat, grain morphology, modelling, milling.



S3.3. Carotenoids Content of Wheat: Importance of Selection and Impact of Breadmaking

F. Leenhardt1, *, A. Mijalovsky1, B. Lyan1, E. Chanliaud2 and C. Rémésy1

1INRA, U3M, Theix, F-63122 Saint Genes Champanelle, France (*corresponding author, E-mail: [email protected]) 2ULICE, ZAC des portes de Rion 63200 Riom, France There is consistent evidence that diets rich in whole grains are associated with moderately lower overall mortality rates and lower death rates from cardiovascular disease and some types of cancer. The “antioxidant hypothesis” proposes that vitamin E, carotenoids and other antioxidant nutrients afford protection against chronic diseases by decreasing oxidative damage. Lutein and its stereo isomer zeaxanthin are the major carotenoids of yellow-colored kernels, included in the group of xantophylls. In plants, lutein and zeaxanthin function as antioxidants and protect from photo-induced free radical damage (Demmig-Adams et al., 1996). They are hypothesized to play a similar role in humans as in plants (Krinsky, 2002). Known mostly for its importance for eye health, consumption and serum levels of lutein have been shown to be inversely related to the risk for ocular diseases, including age-related macular degeneration (AMD; Mares-Perlman et al., 2001), and cataracts (Chasan-Taber et al., 1999; Gale et al., 2001). Recent studies suggest that xanthophylls, particularly lutein and zeaxanthin, may also help maintain heart health by reducing the risk of atherosclerosis (Dwyer et al., 2001; Mares-Perlman, et al., 2002). Moreover, xanthophyll content in flours was found to be correlated to the aging of wheat seeds (Pinzino et al., 1999). The present work is part of a large project, which explore and try to improve the nutritional value of wheat and wheat based products by a multidisciplinary approach involving plant selection, agricultural strategies and transformation processes. Carotenoids are one of our objectives. Lutein and zeaxanthin were determined by means of HPLC analysis, which enable discrete separation of carotenoids. Ten lines of common wheat (Triticum aestivum L.) in selection were screened to quantify the variation of carotenoids content. The seed samples for each genotype were harvested from plants grown in the same place (Auvergne, France) the same year (2003). Our results provided evidence that wide differences existed among kernel samples for carotenoid components examined: lutein and zeaxanthin, ranged respectively from 1.26 (Recital) to 3.99 (Toronit) and from 0.13 (Apache) to 0.24 mg kg−1 (Charger). Total carotenoid level of one cultivar of Triticum monococcum (2n = 2x = 14, AA), an ancient diploid wheat, was about 3-fold above the average of hexaploid common wheat (2n = 6x = 42, AABBDD). Carotenoids occurred in all anatomical parts of the wheat kernel, however, their content in germ was about 5-fold higher than in the white flour, reaching 5.2 mg kg-1, thus pointing to a higher concentration of this compound in the endosperm. The consumption of wheat is mainly from bread. It is therefore important to insure that the antioxidant potential at the wheat level is maintained in the final products. Wheat grain contains several enzymes that are known to be involved in the destruction of carotenoid pigments during dough mixing. As lipoxygenase, polyphenol oxidase and peroxidase are highly concentrated in bran layers (Rani et al., 2001), we have compared carotenoids losses during dough-making and bread-making of whole durum wheat flour and semolina. Dough mixing resulted in the loss of 49% total carotenoids present in whole durum wheat flour, whereas only 15% of carotenoids were lost when the endosperm of durum wheat grain (semolina) was used only. With whole einkorn flour, only 22% of carotenoids were lost



during dough mixing, suggesting varietal differences for oxidases level. Further losses have also been observed during the baking stage, with an overall reduction in carotenoid content being about 70% in whole wheat bread, 47% in einkorn bread, and 33 to 50% in refined wheat bread compared with the fresh ingredients. If the genetic variability observed may be improved and exploited by extending the screening at exotic genotypes for the carotenoid content, oxidases activity ratio, modification in the milling process that retain the germ fraction or supplementation in germ fraction could be a complementary solution to further increase the micronutrient recovery yield. Key-words: carotenoids, wheat genotypes, milling fractions, oxidases, bread-making. References Demmig-Adams B, Gilmore AM, Adams WW (1996) 3rd. Carotenoids 3: in vivo function of carotenoids in higher plants. Faseb Journal 10: 403-412. Krinsky NI (2002) Possible biologic mechanisms for a protective role of xanthophylls. Journal of Nutrition 132: 540S-542S. Mares-Perlman JA, et al. (2001) Lutein and zeaxanthin in the diet and serum and their relation to age-related maculopathy in the third national health and nutrition examination survey. American Journal of Epidemiology 153: 424-432. Chasan-Taber L, Willett WC, Seddon JM, Stampfer MJ, Rosner B, (1999) A prospective study of vitamin supplement intake and cataract extraction among U.S. women. Epidemiology 10: 679-684. Gale CR, Hall NF, Phillips DI, Martyn CN (2001) Plasma antioxidant vitamins and carotenoids and age-related cataract. Ophthalmology 108: 1992-1998. Dwyer JH, et al. (2001) Oxygenated carotenoid lutein and progression of early atherosclerosis: the Los Angeles atherosclerosis study. Circulation 103: 2922-2927. Mares-Perlman JA, Millen AE, Ficek TL, Hankinson SE (2002) The body of evidence to support a protective role for lutein and zeaxanthin in delaying chronic disease. Overview. Journal of Nutrition 132: 518S-524S. Pinzino C, et al. (1999) Aging, free radicals, and antioxidants in wheat seeds. Journal of Agricultural and Food Chemistry 47: 1333-1339. Rani KU, Prasada Rao UJS, Leelavathi K, Haridas Rao P (2001) Distribution of Enzymes in Wheat Flour Mill Streams. Journal of Cereal Science 34: 233-242.



S3.4. Factors responsible for variability of vitamins B contents in wheat grain, milling fractions and bread products

F. Batifoulier1, *, M.-A. Verny1, E. Chanliaud2, C. Demigné1 and C. Rémésy1 1INRA, U3M, Theix, F-63 122 Saint-Genès Champannelle, France (*corresponding author, E-mail: [email protected]) 2ULICE, ZAC des portes de Riom, F-63200 Riom, France Whole bread represents an important source of dietary fibber (9 to 13%) and micronutrients such as minerals and vitamins (B1, B2, B6). A recent study carried out during 2 years showed that a significant proportion of the French population could be at risk of deficiency: 56.5%, 39.5% and 60.3% of the population were between 2/3 of the ANC in thiamine, riboflavin and pyridoxine. Furthermore, bread consumption has dramatically decreased from 500 to 100 g over the last century. In addition, cereals are consumed as more refined products, which lower the daily B vitamins intake (Borgies et al., 2001). The importance of bread in the diets of most people makes loss of vitamins due to milling and bread making a source of concern all the more since their bioavailability in cereals products is relatively high (Ranhotra et al., 1985). The selection of wheat grain variety, the use of less refined flours and the consumption of whole bread with high nutritional density could contribute to maintain good status for B vitamins. The extraction and analysis of B vitamins using HPLC apparatus equipped with a fluorescence detector has been carried out to discriminate 50 French cultivars of wheat grain (Ndaw et al., 2000). Wheat grains show a high variability (factor 2 to 3) of B vitamins: values ranged (in µg per 100 g of dry matter) from 260 to 613 µg for thiamine, 48 to 106 µg for riboflavin and 145 to 316 µg for pyridoxine. Variability remained unchanging during milling and bread making. Ten French of 50 cultivars were selected and corresponding flours and breads were analysed. After milling, only 43% of thiamine and 20% of pyridoxine were recovered in white flour whilst 80 and 95 % were found in whole flour respectively. The results suggests that drastic milling conditions of refined flour are implicated in the reduction of B vitamins contents when compared to unrefined flour and illustrates the importance of bran and middling fractions for vitamins B provision. Stability of B vitamins during bread making was carried out on both white and whole flours from ten cultivars. Classical French bread making affects totals both pyridoxine and thiamine contents. Temperature and time of heating were important factors leading to losses of thiamine and pyridoxine. The impoverishment in vitamins levels was less pronounced in whole bread. 31% of thiamine and 37% of pyridoxine were lost in whole bread and 37% and 62% in white bread respectively. However, riboflavin levels increase significantly in whole and white bread during bread making suggesting a potential synthesis or a gain from ferments. In order to limit B vitamins losses and optimise their levels in bread, the effect of baking method as well as the effect of fermentation times and type of ferments were examined. A classically used French variety (Soissons) was selected for its interesting B vitamins concentration. Long fermentation with yeast in whole bread making maximized thiamine, riboflavin and pyridoxine concentration in wheat bread. It appeared that loss in B vitamins during bread making was avoided by long fermentation times (6 hours). Type of ferments is an important factor to be kept in mind but it depends on vitamins B. In the case of thiamine and pyridoxine, sourdough or yeast bread making was effective to restore vitamin levels in whole bread compared to whole flour. For riboflavin, the use of yeast raised



riboflavin concentration, as a result of provision by yeast as well as de novo synthesis. The use of mixed ferments had no synergic impact on vitamins B levels. Keys-words: wheat cultivars, thiamine, riboflavin, pyridoxine, baking, milling. References Borgies B, et al. (2001) Enquête sur le rôle et la place du pain dans l'équilibre alimentaire des étudiants de la métropole lilloise. Medecine et Nutrition 37: 71-81. Ranhotra G, Gelroth J, Novak F, Bohannon F (1985) Bioavailability for rats of thiamin in whole wheat and thiamin-restored white bread. Journal of Nutrition 115: 601-606. Ndaw S, Bergaentzlé M, Aoudé-Werner D, Hasselmann C (2000) Extraction procedures for liquid chromatographic determination of thiamin, riboflavin and vitamin b6 in foodstuffs. Food chemistry 71: 129-138.



S3.5. Genetic variation for grain mineral content in bread wheat F.-X. Oury1,*, F. Balfourier1, F. Leenhardt2, C. Rémésy2, E. Chanliaud3, B. Duperrier4 and G. Charmet1 1INRA, UMR ASP, 234 av du Brézet, F-63039 Clermont-Ferrand Cedex, France (*corresponding author, E-mail: [email protected]) 2INRA, U3M, Theix, F-63122 Saint Genes Champanelle, France 3ULICE, ZAC des portes de Riom F-63200 Riom Cedex, France 4Mais Angevin-Nickerson SA, Domaine de Mons, BP 115, F-63203 Riom, France Wheat flour, particularly whole flour made from entire kernels including aleurone layer, can provide a significant part of human daily requirements in minerals, namely magnesium, iron and zinc. A diet enriched in cereal products is thus encouraged by nutritionist. Therefore we wished to know whether grain mineral composition was genetically controlled, in order to set up, if necessary, a breeding programme aimed at improving wheat mineral content. Three experimental design have been implemented for this objective: 1. A trial with 51 elite breeding lines from INRA breeding programme grown in three

locations in France in 2002. This material can be considered as representative of modern germplasm adapted to North-West Europe.

2. A trial with six highly contrasted lines: two widely grown cultivars, Soissons and Shango, two strong improving cultivars: Qualital and ULI3, and two old landraces, Blé des Dômes (local) and BGW76 (China). These six lines were grown at four locations in 2002.

3. A collection of 180 accessions from INRA genetic resources collections which covered the range of geographic origins of wheat. This collection was cultivated at Clermont Ferrand in 2002 in a row nursery (three rows per accessions), while the other trials were conducted in replicated plots at usual sowing density.

The main conclusions from this preliminary study are: • For magnesium content, there are high genetic effects and moderate G × E interactions.

Consequently, rank correlations of genotypes among locations are high. The range of variation is about 600 to 1400 ppm in modern material, and can reach 2000 ppm in some exotic lines. Although these variations can partly be caused by a dilution effect, the negative correlation with yield is moderate in modern material (trial 1). Thus a breeding programme can be reasonably envisaged for this trait.

• For zinc content, G × E interactions are more significant, which can make direct selection more difficult. However, as zinc and magnesium contents are positively correlated (r² = 0.64 in trail 1), zinc content would likely respond positively to selection for high magnesium.

• Iron content is much more influenced by G × E interactions and poorly correlated to other minerals. Therefore improving it by means of selection seems illusory.

Key-words: genetic resources, G × E interactions, magnesium, iron, zinc.

From gene to crop scale. Modelling genetic variability of agronomic and quality traits


Session 4 - From gene to crop scale. Modelling genetic variability of agronomic and quality traits

S4.1. Temperature induced variations of the proteome of developing and mature wheat

grain Gérard Branlard1, *, Emmanuelle Bancel1, Thouraya Majoul1, Pierre Martre2, and Eugène Triboï2 1UMR INRA-UBP ASP, 234 avenue du Brezet, F-63039 Clermont Ferrand Cedex 02, France (*corresponding author: [email protected]) 2INRA, UR874 Agronomie, 234 Avenue du Brezet, F-63039 Clermont-Ferrand Cedex 2, France Elevated temperatures during grain filling have been identified as a major source of variation in wheat quality characteristics. Reduced grain weight and modification of the rheological properties are commonly reported as the consequences of heat stress on wheat. In order to better understand the molecular basis of the heat stress response on the wheat grain proteome two experiments have been carried out in outdoor, sunlit controlled-environment chambers. For both experiments proteomic analysis (using 2D gel electrophoresis IPG x SDS PAGE, Image analysis, MALDI-Tof and tandem MS/MS mass spectrometry, database interrogation) was performed on total protein extract and also on soluble proteins namely albumins and globulins.

The first study was carried out on the winter wheat cultivar Thésée, which was submitted to normal (18°C day, 10°C night) or stressing (34°C day, 10°C night) air temperature for duration of the grain filling period, till complete maturity. Three samples were harvested during the post-anthesis period based on thermal time (i.e., cumulative daily average air temperature, base 0°C; degree-day, °Cd): 319 °Cd, 484 °Cd, 697 °Cd and 313 °Cd, 488 °Cd, and 763 °Cd, for control and treated plants respectively. Several spots which were almost absent in the control grains were heat induced (group 1), over spots were significantly over expressed (group 2) and several spots were significantly reduced (group 3). The group 1 was composed of heat shock proteins (HSPs), which are known chaperonines able to protect the protein structure and to reduce the polymerisation process. The group 2 was mainly composed of some gliadins spots, which were from 3 to 27 fold increased in stressed grains (Majoul et al., 2003). The quantities of high molecular weight glutenin subunit spots (HMW-GS), although slightly lower in the stressed grains, were not significantly different in the unstressed grains. Quantitative variations of gliadins and glutenins could explain the higher dough extensibility often reported on heat stress wheat samples. The group 3 was composed of 19 spots specifically revealed in the albumin and globulin families (Majoul et al., 2004). Many of these spots were enzymes associated to starch synthesis, such as glucose-1-phosphate adenyltransferase (G1P-ATase) and Granule Binding Starch Synthase. The decrease of the amount of the G1P-ATase could be concomitant with a decrease of the duration of photosynthesis often noticed in heat stressed wheat. The reduced grain weight, generally observed for heat stressed wheats, would results of these two phenomenons.

A second study was carried out on the winter wheat cultivar Récital, which was submitted to a 4-days stress between 300 and 400 °Cd after anthesis. The normal temperature, applied through out the grain filling period, was 18°C day 10°C night for the control samples. The stressed samples had the same temperature conditions as the control excepted they were warmed during four days at 38°C for 4 hours (during the light period) and 20°C for 20 hours. Four samples were harvested between anthesis and maturity in order to study the heat stress



response during development. Proteomic tools allowed the characterization of heat-responsive proteins at each stage of development. Among the protein spots which had changed in amount (down- and up-regulation) in response to stress, many were identified. Some storage proteins and many enzymes such as the protein disulfide isomerase and heat shock protein were found to be heat-increased. At grain maturity stage (i.e., more than 20 days after the stress) several proteins were still changed in the stressed samples. The function of each heat changed protein as well as its expression during grain development will be discussed. Key-words: gliadins, glutenins, albumins, globulins, heat stress. References Majoul T, Bancel E, Triboï E, Ben Hamida J, Branlard G (2003) Proteomic analysis of the effect of heat stress on hexaploid wheat grain: Characterization of heat-responsive proteins from total endosperm. Proteomics 3: 175-183. Majoul T, Bancel E, Triboï E, Ben Hamida J, Branlard G (2004) Proteomic analysis of the effect of heat stress on hexaploid wheat grain: Characterization of heat-responsive proteins from non-prolamins fraction. Proteomics 4: 505-513.



S4.2. A virtual fruit to analyse the genetic variability of quality traits

Michel Génard*, Bénédicte Quilot and Françoise Lescourret

INRA, UR Plantes et systèmes de culture horticoles, Domaine St Paul, Site Agroparc, F-84914 Avignon Cedex, France (*corresponding author, E-mail: [email protected])

We evaluated the possibility of using ecophysiological models to describe genetic variations observed in a population, identify QTLs (Quantitative Trait Loci) for the model parameters and combine QTLs and ecophysiological models. This approach was applied to peach quality observed in an advanced backcross between Prunus persica (L. Batsch) and Prunus davidiana. The ecophysiological model predicts fruit and stone dry and fresh masses and total sugar concentration in relation to environmental conditions. The model made it possible to account for genotypic variations in fruit quality and for genotype x environment interactions. A preliminary study was conducted to select relevant parameters among the 39 ones of the model. In a first step these parameters were estimated experimentally for nearly 130 individuals of the population and independent data were acquired to test the predictive quality of the model. In a second step, model analysis allowed us to identify the key genotypic parameters. In a third step, QTL analysis resulted in the description of a model of genetic control of these parameters which predicts the parameters values for any genotype according to the alleles present at each locus of interest. To combine ecophysiological and genetic models, we replaced in the ecophysiological model the measured values of the parameters by the values predicted by the genetic model. The combined model was satisfactory for fruit and stone size and has to be improved for dry matter content and sugar concentration. Ten genotypic parameters, involved in identified ecophysiological processes, appeared essential. Tight links appeared between some of the parameters. QTLs for the ten parameters were detected and co-locations between QTLs for quality traits and QTLs for parameters were observed. The biological meaning of the parameters should make it possible to interpret the role of the corresponding genes.



S4.3. From genome to wheat: emerging opportunities for modelling wheat growth and development

Jeffrey W. White* U.S. Water Conservation Laboratory, USDA-ARS, 4331 E. Broadway Rd., Phoenix, AZ 85040-8834, USA (*corresponding author, E-mail: [email protected]) Crop simulation models integrate knowledge from agronomic disciplines ranging from plant breeding to soil physics. Arguably, representations of the physiological processes in models have evolved little over the past 20 years. However, applications of models increasingly require accurate predictions of crop response to such factors as water deficits, elevated [CO2] and soil nitrogen. Recent advances in plant genomics suggest various options for moving toward more detailed representations of processes, especially in modeling cultivar performance across environments (G x E). Gene-based models can serve as a bridge between more qualitative information acquired through genomics and the quantitative information required to model crop growth and development. This paper reviews progress in gene-based modeling and outlines two related approaches for collaborative work on gene-based wheat modeling. The GeneGro model (White and Hoogenboom, 1996) simulated effects of seven genes on physiological processes in common bean (Phaseolus vulgaris L.), using the BEANGRO model as a starting point. After calibrating a set of cultivars with known genotypes, genetic effects were incorporated by estimating the cultivar-specific parameters used in BEANGRO (e.g., for photoperiod sensitivity or characteristic specific leaf area) through simple linear models. A cultivar that was homozygous dominant for a given gene was assigned a value of 1, and if recessive, a value of 0. Both for the calibration data and an extensive validation data set, GeneGro performed as well as BEANGRO, but GeneGro offered the advantage of specifying cultivars differences with only seven binary coefficients, the genotypes, that could be determined without field calibration data. Wheat is amenable to a similar gene-based modeling approach. In wheat, the genetic control of photoperiod, vernalization, earliness per se and stem height are sufficiently well understood to permit modeling their effects. Genes for frost tolerance, grain size and osmoregulation also merit consideration. A logical first step is to characterize genotypes of cultivars from widely used sets of field data, such as those in the GCTE Focus 3 Wheat Network's listings. Currently, genotype data largely has to be gleaned from publications and databases such as GrainGenes and KOMUGI, but over the next two years, genotyping with molecular tools should be routinely available for many loci. With information on genotypes in hand, efforts can focus on estimating cultivar coefficients from a diverse set of trials that will ensure that genetic differences are strongly expressed. Estimating equations can either be coded into a specific model (as done in GeneGro) or implemented through an external, stand-alone coefficient estimator. The latter approach seems especially suited to the GCTE community since it permits modelers to implement a gene-based approach without having to modify model source code. In theory, one estimator should serve several models. Parallel to the above work, modelers should seek to directly apply process-level information from genomics research. For example, cloning of the Vrn genes (Yan et al., 2004) suggests approaches for quantifying degree of vernalization as well as localizing “cold sensing” tissues more precisely. Similarly, since the Rht genes are homologous to the GAI gene in Arabidopsis thaliana, pleoiotropic effects of the Rht genes can be analyzed through comparison with effects of GAI. Gene-based approaches to wheat modeling can greatly enhance our ability to predict how global change will impact agricultural production, including aspects relating to grain quality.



Modelers and physiologists, however, must be proactive in accessing and applying information emerging from the plant genomics community. References White JW, Hoogenboom G (1996) Simulating effects of genes for physiological traits in a process-oriented crop model. Agronomy Journal 88: 416-422. Yan L, et al. (2004) The wheat VRN2 gene is a flowering repressor down-regulated by vernalization. Science 303:1640-1644.



S4.4. Functional dynamics of the nitrogen balance of sorghum. Narrowing the divide between QTL’s and phenotypic expression of stay-green E.J. van Oosterom1,*, G.L. Hammer1,2, A.K. Borrell3, S.C. Chapman4 and I.J. Broad2

1Agricultural Production Systems Research Unit/University of Queensland, School of Land and Food Sciences, Brisbane, Qld 4072, Australia (*corresponding author, E-mail: [email protected]) 2Agricultural Production Systems Research Unit/ Queensland Department of Primary Industries, 203 Tor Street, Toowoomba, Qld 4350, Australia 3Agricultural Production Systems Research Unit/Queensland Department of Primary Industries, Hermitage Research Station, MS 508, Warwick, Qld 4370, Australia 4CSIRO Plant Industry, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, Qld 4067, Australia The large amount of information produced by genomic research provides opportunities for improved efficiency of crop improvement programs, if such information can be physiologically linked to phenotypic consequences. Crop growth simulation models that have the physiological functionality to capture feedbacks between different physiological processes provide a powerful tool to establish such linkages (Hammer et al., 2004). This functionality can be achieved by dissecting complex traits, with large genotype x environment (GxE) interactions, into simpler, underlying traits with smaller GxE interactions. In such a framework, the expression of the complex trait becomes a consequence of the dynamic interactions between the underlying traits. Here, we present a trait dissection of stay-green in sorghum through the development of a physiologically functional framework for the nitrogen supply-demand balance. The framework was developed from an experiment in which three hybrids with contrasting crop height were grown under well-watered conditions under three levels of N-supply. During the pre-anthesis period, leaf-N demand was determined by a critical SLN, which was independent of development stage, provided the heterogeneous distribution of N throughout the canopy was considered. Stem-N demand of the short hybrids was met from the leaf sheath, the demand of which was proportional to the leaf-N demand on a dry mass basis. For the tall hybrid, by contrast, additional N was allocated to the stem during elongation, thus reducing the N-allocation to the leaves. During grain filling. grain-N demand was sink determined, although the maximum demand could be restricted by the grain growth rate. Translocation of N from the vegetative parts occurred preferentially from the stem, with leaf-N translocation occurring if the grain-N demand could not be met otherwise. The potential rates of leaf- and stem-N translocation were source determined. Structural stem N% and SLN were slightly higher under high-N conditions than under N-stress. The above framework can explain G and GxE differences in the expression of stay-green as an emergent consequence of differences in physiological processes, caused by differences in underlying traits like leaf size, dry matter partitioning, and possibly transpiration or transpiration efficiency. Work is currently underway to incorporate the effects of drought stress on stay-green and to link QTL’s for stay-green, that have been identified in sorghum, to the expression of these underlying traits. Knowledge of the functionality and effects of each of these QTL’s will allow plant breeders to select for QTL’s associated with mechanisms most likely to have a positive effect on the expression of stay-green in the target population of environments. Key-words: specific leaf nitrogen, supply-demand balance; trait dissection. Reference Hammer GL, Sinclair TR, Chapman SC, van Oosterom EJ (2004) On systems thinking, systems biology and the in silico plant. Plant Physiology 134: 909-911.



S4.5. The use of a crop model simulating the grain protein content of winter wheat to define breeding targets

Aude Barbottin and Marie-Hélène Jeuffroy* UMR d’Agronomie INRA INA P-G, BP01, 78850 Thiverval-Grignon (*corresponding author, E-mail: [email protected]) Grain protein content (GPC) is one of the main criteria used to judge the quality of wheat grains in commercial trades, and partially determines the price of the harvest, for farmers and for industrial users. As GPC is highly variable among varieties, it has been since a few years a major breeding target. However, a given variety does not reach the same GPC in various croing conditions: genotype × environment interactions exist on this variable. As the main phenotypic determinants of GPC variability were identified, using a crop simulation model (Barbottin and Jeuffroy, this workshop), we aimed at using the model adapted to cultivars to (i) choose the varieties that will give the best GPC and yield in various pedo-climatic conditions, and (ii) identify the best combinations of phenotypic characteristics to reach a stable and high grain protein content, that could represent breeding targets, as proposed by Asseng et al. (2002). We used the crop model Azodyn, simulating GPC. In a previous work, its cultivar parameters were identified and estimated for 14 genotypes (Barbottin and Jeuffroy, 2004). Using sensitivity analysis of this model to variations of its cultivar parameters (taken individually or in combination), for various pedo-climatic conditions in France, we identified the characteristics of the varieties reaching the best yields and/or GPC in the different growth conditions. These results were validated, by comparing the results from the simulations with observed results on an experimental network conducted in 6 locations in France during 2 years. These characteristics can then be used by farmers to choose the varieties to grow in their fields, according to their targeted yield and GPC. These results can also be used by breeders to define their research targets, that arre the combinations of phenotypic characteristics that are necessary to reach high GPC and high yields. Key-words: wheat, grain protein content, model, breeding, cultivar parameters. References Barbottin A, Jeuffroy MH (2004) Varietal adaptation of a model simulating the grain protein content of winter wheat. This volume, p. 100. Asseng S, Turner NC, Ray JD, Keating BA (2002) A simulation analysis that predicts the influence of physiological traits on the potential yield of wheat. European Journal of Agronomy 17: 123-141.



S4.6. Modelling genetic variations of wheat grain protein composition Pierre Martre1, *, Eugène Triboï1, Vitalie Samoil2, Gilles Charmet3 and Gérard Branlard3 1INRA UR874 Agronomie, 234 Avenue du Brezet, F-63039 Clermont-Ferrand Cedex 2, France (*corresponding author: [email protected]) 2 Institute of Plant Physiology, Center of Advanced Technologies, str Padurii 26/2, MD 2001 Chisinau, Republic of Moldova 3INRA-UBP ASP, 234 avenue du Brezet, F-63039 Clermont Ferrand Cedex 02, France Protein composition is a key component of the end-use value for wheat grain. Although the qualitative composition of the grain is genetically determined, the quantitative composition is significantly modified by the growing conditions, and there are significant genotype × environment interactions (Zhu and Khan, 2001). To analyze these interactions, we recently proposed a model of grain N accumulation and partitioning for wheat grain (Martre et al., 2003). The main hypotheses of this model are: (1) the accumulation of structural/metabolic proteins is sink-driven and is a function of temperature; (2) the accumulation of storage proteins is supply limited; (4) the allocation of structural/metabolic proteins between albumin-globulin and amphiphilic protein fractions and the allocation of storage proteins between gliadin (Gli) and glutenin (Glu) fractions during grain growth is constant. Here we present results from several experiments design to assess the environmental and genetic variability of the parameters of this model. The model parameters were evaluated for four contrasted winter wheat cultivars (Arche, Récital, Renan, and Tamaro) grown in Clermont-Ferrand, France, over a significant range of N fertilisation and post-anthesis temperature and water supply. The results showed that the coefficients of protein allocation were not significantly different for the four cultivars and the eight experiment treatments. When using the parameters estimated for the winter wheat cultivar Thésée the model gave close simulations of the accumulation of the grain storage protein fractions for the four cultivars. These results suggest that variations of grain protein composition which were previously reported as resulting from genotype × environment interactions are essentially due to variations of the total quantity of N per grain (GNG). This hypothesis was tested on a broader genetic background including several genotypes of T. durum, T. monococcum, Aegilops speltoides, A. searsii, and T. tauschii. Unique relationships were found for the different genotypes of T. aestivum and T. durum between the quantity of the protein fractions and QNG. Interestingly, the diploid species were significantly outside these relationships. In particular, diploid species allocated more N to the Gli fraction compared with the tetraploid and hexaploid species. Unique relationships were also found for the different genotypes between the quantity of the Gli sub-units and the total quantity of Gli and between the quantities of high and low molecular weight Glu sub-units and the total quantity of Glu. However, in contrast with the protein fractions, for the Glu and Glu sub-units the same relationships held for the diploid, tetraploid, and hexaploid species. The genetic bases of the variations of grain protein composition was further analysed using a population of 194 recombinant inbred lines (RIL) from the cross between the cultivars Récital and Renan. The population was tested at two locations in France. Sodium dodecyl sulphate (SDS) extraction and size exclusion HPLC were used to separate proteins according to their size. Results from this study show that tight relationships exist between the quantity of the albumin-globulin, Gli, and SDS soluble and insoluble protein polymers and QNG, determined by the Kjeldahl method. A genetic map of 254 markers was used for quantitative traits loci (QTLs) analysis. Two major QTLs, that explained more than 80% of the variance, were found for the scaling coefficients of the power relations between the quantity of N of



pick 3 (mainly Gli and low molecular weight Glu sub-units) and peak 4 (mainly Glu) of the soluble protein fraction and QNG. The model used here gives a simple mechanistic framework that explains environmental effects on grain protein content and composition. Overall, the allocation of N between the different protein fractions or Gli and Glu subunits appeared to be tightly regulated by the total quantity of N per grain. Key-words: gliadins, glutenins, protein composition, simulation model, wheat quality. References Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiology 133:1959-1967. Zhu J, Khan K (2001) Effects of genotype and environment on glutenin polymers and breadmaking quality. Cereal Chemistry 78:125-130.

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Poster Presentations


P1. Molecular Evaluation of Bread-Making Quality Parameters in Wheat D. Obreht1, B. Kobiljski2, *, S. Dencic2 and Lj. Vapa1 1Faculty of Natural Sciences, Dept. of Biology and Ecology, 21000 Novi Sad, Serbia and Montenegro (*corresponding author, E-mail: [email protected]) 2Institute of Field and Vegetable Crops, 21000 Novi Sad, Serbia and Montenegro Bread wheat breeding programs developed in Novi Sad breeding center have produced a wide range of quality cultivars with strong flours and hard grain texture. For 20 years in the process of bread-making quality prediction, composition of HMW glutenin subunits was analyzed beside the standard parameters, protein content, falling number, sedimentation test, wet gluten, and rheological test. To further improve our breeding strategies, a new generation of PCR-based BMQ related markers was included in selection programs. High dough strength is used as a predictor of good quality bread wheat and it has been attributed largely to the type of allele present at the Glu-D1 locus, where the Glu-D1d allele is most favorable. It has been observed recently that cultivars with overexpressed subunit Bx7OE at the Glu-B1 have enhanced dough strength (Butow et al., 2003). The present work illustrates the use of a PCR marker for Glu-B1 allelic discrimination in the set of genotypes most frequently used in the Novi Sad wheat breeding program. It was discovered that the marker originally developed for discrimination of Bx7 (Bx7oe) and Bx17 allelic variants was also able to differentiate other Glu-B1 alleles. The obtained results confirm the extended use of the marker and create an opportunity to improve the method of polymorphism scoring in pre-breeding material. Since grain texture is also important for milling and bread making properties, allelic variability of puroindoline loci is also included in molecular evaluation of our breeding germplasm. Full-length puroindoline a and puroindoline b SNP allelic variant (Giroux and Morris, 1997) were amplified and two alleles were scored both at the Pina-D1 and the Pinb-D1. Such limited diversity was expected since analyzed genotypes were mainly hard winter wheat cultivars. Another aim of our program was to survey for potential diagnostic SSR markers for loaf volume and/or falling number gene(s) on 3A chromosome, based on observations found by Cauvaini et al. (2002). Allelic variability screening of 3 (4) SSRs loci (GWM 369, 32, 674, and 720) revealed different level of polymorphism in the analyzed cultivars. Comparison of SSRs allelic distribution and data for loaf volume and/or falling number showed that certain allelic forms are related to better loaf volume. Further validation of this observation is needed before drawing final conclusions and possibly making marker designation. Key-words: bread-making quality, marker-assisted selection, wheat. References Butow BJ, et al. (2003) Molecular discrimination of Bx7 alleles demonstrates that a highly expressed high-molecular-weight glutenin allele has a major impact on wheat flour dough strength. Theoretical and Applied Genetics 107: 1524-1532. Cauvaini SP, Law C, handari D, Salmon S, Worland AJ (2002) HGCA Project report No. 276. Giroux MJ, Morris CF (1997) A glycine to serine change in puroindoline b is associated with wheat grain hardness and low levels of starch-surface friabilin. Theoretical and Applied Genetics 95: 857-864.



P2. Changes of quality parameters of winter wheat at maturing Zoltán Győri* and Péter Sipos Department of Food Science and Quality Assurance, University of Debrecen, Centre of Agricultural Sciences, Faculty of Agronomy, 138. Böszörményi Street, H-4032 Debrecen, Hungary (*corresponding author, E-mail: [email protected]) We have examined the nitrogen and sulphur content and Farinographic parameters at different ear parts of winter wheat. Our aim was to determinate the changing of these parameters with maturing and find out how can we represent the maturing processes with the average sample. Samples were collected from calcareous chernozem soil at Latokep Experimental Station of University of Debrecen, Centre of Agricultural Sciences. The analysed samples are from an area left out from experiment and sowed with Mv Magdaléna winter wheat variety. At maturing it was high temperature (20-27°C daily average) and dry (in the three-week-length sampling period there was only 3.1 mm precipitation). The plant protection interventions were made properly but the plot was not fertilized. The ear samples were collected in two repeats between 10th June, 2003 and 1st July, 2003 in every 2nd – 3rd day. The tests were made in the laboratory of Department of Food Science and Quality Assurance at University of Debrecen, Centre of Agricultural Sciences. The ear samples were cut into 3 equal length parts alongside. The dry matter content of ear parts was determined in drying oven at 60°C. Thrashing of plant parts was made by hand. Nitrogen and sulphur content of flour was determined by CNS. Milling was made by FQC-2000 micro scale labmill (Metefém, Budapest). Rheological properties of flour was determined by FQA-2000 Micro Z-arm Mixer (Metefém, Budapest) using 4 g of test flour per test. The Z-arm mixer mimics the conventional wheat dough test of Valorigraf and Farinograph. Each test was carried out for 15 minutes. The analysis of curves was performed by an experimental software of Péter Sipos. We experienced that the nitrogen content was highest in the bottom part and lowest in upper part of the ear. The formation of nitrogen content is well described with quadratic equation both in different ear parts and the whole ear (R2 = 0.914, 0.711 and 0.260 in the lower, middle and upper part of ear and 0,845 in the whole ear). The sulphur content is well describable with quadratic equation too (R2 = 0.803, 0.659 and 0.407 in the lower, middle and upper part of ear and 0.757 in the whole ear) but the order of different parts is changeful. We measured the highest sulphur content six days before than the highest nitrogen content so the sulphur accumulation is enacted at an earlier date. The N/S ratio is increased from 13.5 to 15 when the nitrogen content reached its maximum and stayed at 15. Usually this ratio was higher in the lowest part because of the higher nitrogen content. The water absorption and the development time of curve of flour samples decreased until the nitrogen content increased but then the trend changed back. These parameters shows strong quadratic connection to the sampling time (in case of water absorption the R2 is 0.758, 0.892 and 0.825 in the lower, middle and upper part of ear and 0.868 in the whole ear and in case of development time it is 0.914, 0.711 and 0.260 in the lower, middle and upper part of ear and 0.845 in the whole ear). In most cases the development time was higher in the upper part of ear. The stability of curves were low with high standard deviation. The degree of softening and Hungarian baking value changed crosswise. The baking value decreased at maturing and we experienced the highest dough softening and the lowest baking value 6 days before harvest. The part of grains in ear has not have effect on the baking value. Key-words: winter wheat, maturing, nitrogen and sulphur content, rheology.



P3. Effects of Environment on Wheat Flour Protein Composition F.M. Dupont*, W.J. Altenbach, R. Chan, K. Kothari, W.J. Hurkman, C. Tanaka, and W. Vensel USDA Agricultural Research Service, Western Regional Research Center, 800 Buchanan Center, Albany, CA 94710 (*corresponding author, E-mail: [email protected]) A hard red spring wheat typical of those produced in the U.S. was grown in controlled environments from anthesis to maturity. Three temperature regimens (24/17oC, 37/17oC, and 37/28oC day/night temperatures), three fertilizer levels and two watering regimens were evaluated. The 37/28oC regimen decreased the duration of grain fill from 44 to 26 days, reduced single kernel weight approx. 60% and produced flour with reduced mixing tolerance compared to the 24/17oC regimen. However, there were remarkably few changes in protein composition, which was determined by quantitative, selective extraction followed by RP-HPLC and SDS-PAGE, or by 2D-PAGE. Amounts of all major flour protein types increased as flour protein content increased and the relative proportions of most gliadins and glutenins were remarkably similar for all treatments. Highly conserved proportions of high molecular weight glutenins Ax2* : Bx7 : By9 : Dx5 : Dy10 were 0.14 : 0.30 : 0.13 : 0.21 : 0.24, based on HPLC peak areas and corrected for molecular weight. The ratio of high molecular weight to low molecular weight glutenins was also conserved. Effects of fertilizer regimen on the proportions of ω-gliadins and albumins were observed. Proportions of ω-gliadins increased two-fold or more under the highest post-anthesis fertilizer level, and proportions of some albumins decreased. Steady-state transcript levels for the major gliadin and glutenin gene families were not affected by the treatments, except that transcript levels for the ω-gliadins declined in the absence of post-anthesis fertilizer. Minor changes in protein composition are being investigated by 2-D PAGE or RP-HPLC and 1-D PAGE, followed by mass spectrometry and Edman sequence determination to identify and characterize individual proteins of interest. Key-words: protein, glutenin, gliadin, albumin, temperature.



P4. Amino Acid Content in Durum Wheat Genotypes as Affected by Water Regime in Southern Spain

Y. Rharrabti1,*, C. Royo2, V. Martos1, J. Isidro1 and L.F. Garcia del Moral1

1Dpto. Fisiologia Vegetal, Facultad de Ciencias, Universidad de Granada, 18071–Granada 2Centre UdL-IRTA, Area de Conreus Extensius, Rovira Roure, 191, 25198–Lleida. (*corresponding author, E-Mail: [email protected]) Amino acid composition is an important characteristic that determines nutritional value of wheat grain for human and animal diets. Environmental conditions are known to influence protein quantity as well as grain production, and, in turn, amino acid composition. Many evidences underline that lysine content in durum wheat decreased as total protein of wheat increased, particularly under water stress conditions (Rharrabti et al., 2001). Under rainfed conditions, the contents of other essential amino acids also decrease, except for phenylalanine, but there are large increases in the amounts of the non-essential glutamic acid and proline. The objectives of this work were to study the changes in amino acid content of ten durum wheat genotypes grown under different water regimes in southern Spain, and to investigate the relationships between amino acid composition and protein content. During the 1998 growing-season, three field trials were carried out in southern Spain, a Mediterranean-type environment, one with supplement irrigation and the rest under rainfed conditions. Ten durum wheat (Triticum turgidum L. var durum) genotypes were studied including four Spanish commercial varieties and six advanced lines from the durum wheat selection program of CIMMYT/ICARDA. Protein content was determined using the standard Kjeldhal method. Grain protein percentage was calculated after multiplying Kjeldhal nitrogen by 5.7 and is expressed on dry basis. Amino acids were quantitatively analysed with high performance liquid chromatography (HPLC) using the Waters Pico-Tag Method (Cohen et al., 1989) with previous peroxidation and acid hydrolysis. Amino acid composition was expressed as percentage of protein content (i.e. in g per 100g of protein). The analysis of variance showed that water regime was the main factor that influenced amino acid composition, explaining more than 50% of total variations. Genotypic effects and genotype × environment interactions were relatively of minor magnitude and non significant in the majority of cases. On the other hand, correlations between total amino acid content and environmental variables (residual soil N, N fertilization rate, and water input) demonstrated that water availability during the growing season is the factor that most determine modifications in amino acid composition. With the exception of glutamic acid and proline, the amount of amino acids in the grain was negatively correlated with the percentage of protein, which makes difficult the selection for both an increased protein content and improved amino acid composition under Mediterranean conditions similar to those of our study. Key-words: durum wheat; amino acid content; protein content; water regime; Mediterranean

conditions. References Rharrabti Y, Elhani S, Martos-Nuñez V, Garcia del Moral LF (2001) Protein and lysine content, grain yield, and other technological traits in durum wheat under Mediterranean conditions. Journal of Agricultural and Food Chemistry 49: 3802-3807. Cohen SA, Meys M, Tarvin TL (1989) The Pico-Tag Method. A Method of Advanced Techniques for Amino-Acids Analysis. Millipore Corporation, Bedfort, MA.



P5. Winter Wheat Crops Quality Under Feritilization and irrigation in a long-term experiment in Dobrogea, Romania

Violeta Simionescu1, 2, * and Ion Bulica2 1Ovidius”University, Faculty of Natural Sciences and Agronomy Sciences, Str. Mamaia, No. 124, Constanta, Romania, Cod 8700 (*corresponding author, E-mail: [email protected]) 2Researche Station for Irrigated Crops, Dobrogea”, Valu lui Traian, Constanta, Romania, Cod 8720. Dobrogea is a well defined, but complex, physic-geographic unit in South-Eastern Romania. The climate is warm, dry-subhumid. According to the Koppen classification the climate is BSax, and De Martonne aridity index as an average 18.8. The annual average temperature is over 11 Celsius grade; annual precipitation amount to 360-500 mm. The experiment was established in 1970, within a network conducted by the Research Institute for Cereals and Technical Plants. Combination of different applications of nitrogen (0, 50, 100,150, or 200 kg ha-1), phosphorus (0, 50, 100, 150, or 200 kg ha-1) and organic fertilizers (0, 20, 40, or 60 kg ha-1) were used in split-plot experimental design. During the 1994-1998, in a long-term experiment, was tested the influence of fertilizers and of proceeding plant on the protein content, the protein yield per hectare and the some technological indices of flour at wheat. The winter wheat reacts favorably upon the mineral and organic fertilization, registering important yield increase, the improving of grains quality raw protein content, raw protein yield, of the technological indices of wheat flour and of bread quality.

Key-words: nitrogen, phosphorus, fertility, quality, winter wheat, protein, flour, gluten. References Hera Cr (1980) Some theoretical problems of the crops fertilization. Theoretical and Applied Agrophytotechny Problems, Vol. II., no. 4, pp. 309-312. Hera Cr (1980) The influence of the fertilizers and water ensuring interaction upon the wield quality. Theoretical and Applied Agrophytotechny Problems, Vol. III., no. 4, pp. 325-340. Popescu S, Hera Cr, Idriceanu A, Vines I, Corbean S (1981) The influence of the technological factors upon the protein content and quality. Theoretical and Applied Agrophytotechny Problems, Vol. III., no. 1, pp. 1-20.



P6. Genetic and ecophysiological determinants of legume seed production. An integrated project developed by the URLEG - INRA Dijon

Management committee of the project (2000-2003): Judith Burstin, Gérard Duc, Nathalie Munier-Jolain, Christophe Salon (Poster prepared by Christophe Lecomte for the URLEG, direction Richard Thompson) UR Génétique et Ecophysiologie des Légumineuses à Graines, INRA, Domaine d'Époisses, F-21 110 Bretenières, France (*corresponding authors, E-mail: [email protected], [email protected], [email protected], [email protected]) Context: Deficits of the pea crop. • Yield and grain protein content too low • Instability of yield and grain protein content • Insufficient digestibility of the constituents Long-term objectives. • Increase and stabilise protein content and grain digestibility • Increase the crop area: by promoting new crop practices by creating cultivars more adapted to environmental constraints General objectives of the URLEG unit. • Analyse the genetic variability and the Genotype × Environment Interaction of the

mechanisms involved in the grain composition of legumes • Identify the environmental factors responsible for the variations and model the phenotypic

response to them • Identify the main genes associated with these mechanisms • Guide the introgression of these genes in high-performance agronomic backgrounds • Develop biological resources for the target species Pea and for the model species Medicago

truncatula Organisation of the work: 3 STUDY MODULES OF THE "ELEMENTARY

MECHANISMS".

1- Efficiency of the pathways of nitrogen nutrition. • Model the complementarity between the assimilation and symbiotic sources of N in relation to: - Phenology - C flux toward and within roots - Soil nitrate availability

2- Remobilization during the grain filling.

• Understand the remobilization processes • Understand their modulation (by the environment and the genotype)

• Understand nitrogen remobilization effects on C flux



3- Grain morphogenesis and accumulation of the reserves. • Model the cotyledon cell number to analyse the establishment of grain growth potential in

relation to: - The environmental modulation (trophic, physical) and the role of the genotype

- C and N flux into the grains • Model the C and N reserve accumulation

For each of these modules: Environment → Which environmental factors play a role and which are the

phenotypic responses? Genotypes → What is the genetic variability and which genes control the

variation? 1 INTEGRATING MODULE AT THE GENOTYPE AND CROP LEVELS.

4- Integrating the mechanisms and guiding the building of ideotypes. • Model the functioning of the two grain legumes crops Pea and Medicago truncatula taking

into account the environmental factors and the genetic variability responsible for the agronomic variations:

- estimate the relative contribution of the different physiological processes in determining the agronomic characters (cf. 3 analytical modules)

- integrate these processes to simulate plant functioning taking into account the environmental and genotypic factors.



P7. Environmental Characterization to Interpret the Variations of Genotype Performances. An Example with the Durum wheat Grain Protein Content Xavier Lacaze* and Pierre Roumet UMR 1097 Diversité et Génome des Plantes Cultivées, Institut de la Recherche Agronomique INRA-SGAP, Domaine de Melgueil, F-34130 Mauguio, France (*corresponding authors, E-mail: [email protected]) Performances of genotypes usually vary non-homogeneously between different environments. This differential behavior is classically appreciated by statistical analysis in terms of genotype by environment interactions, which get bigger as differences between genotypic behavior increases. However, if this interaction term allows detecting differences of behavior, it does not tell why special genotypes have better performances in certain environments (adaptation). To interpret variability of genotypic performances in terms of adaptation it is necessary to characterize environments by limiting factors. Therefore, the search of genotypic determinants of adaptation to these limiting factors will lead to adapt genotypic responses to environmental stresses. The approach described here is to set a methodology to characterize environments in terms of limiting factors, to quantify the impact of these limiting factors on the expression of the target character and to search for genetical bases of adaptation to these limiting factors. The target character is the grain protein content of durum wheat; it is a major criterion for semolina and pasta transformations and it is submitted to genotype by environment interactions. Two approaches were carried out: (1) setting up a methodology to characterize different environments in terms of limiting factors by phenological phases (Lacaze, 2003). (2) Building a genetic map and searching for QTLs implicated in the response to special environments and limiting factors. From 1998, 288 recombinants inbred lines consisted of 6 populations resulting from crosses between 4 varieties (half diallel) are being evaluated under field conditions for grain protein content. As a whole these populations will be evaluated in 9 different environments. An agronomic diagnosis based on the behavior of the 4 parental lines for each environment will be elaborated based on crop simulation model (STICS, Brisson et al., 1998). Each cropping cycle will be characterized by plant satisfaction rate for water, nitrogen, and temperature, along the different phenological phases of the plant cycle. STICS is being evaluated through the data collected in 5 experiments from 2001. The quality of model prediction is discussed. The search for QTLs in each environment of our experimental network will lead to identify which are detected in every environment and which are only detected in certain environments. A statistical analysis based on application of factorial regression is currently processed to check the correlation between the presence of certain limiting factors and QTLs (Van Eeuwijk et al., 2001). In the same time the genetic map is being developed with AFLP and microsatellite markers. Key-words: local adaptation, QTL × Environment interactions, crop simulation model, grain protein

content. References Brisson N, et al. (1998) STICS: a generic model for simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18: 311-346. Lacaze X, Roumet P (2003) How to characterize the environmental conditions to improve the understanding of genotype stability: example of grain protein content in durum wheat. In Proceedings of the Xth International Wheat Genetics Symposium, 1-6 September 2003, Paestum, Italy, pp. 148-151. Van Eeuwijk FA, Crossa J, Vargas M, Ribaut JM (2001) Variants of factorial regression for analysing QTL by environment interaction In: Eucarpia, Quantitative genetics and breeding methods: the way ahead (Eds. A Gallais, C Dillmann and I Goldringer), INRA Editions, Versailles, Les colloques series n° 96 pp. 107-116.



P8. Investigation of Components of Baking Quality of Wheat in Estonia Reine Koppel* and Anne Ingver Jõgeva Plant Breeding Institute, Aamisepa 1, Jõgeva alevik, 48 309, Jõgeva MK, Estonia (*corresponding authors, E-mails: [email protected]; [email protected]) Wheat in Estonia should satisfy the needs of baking industries. Therefore good baking quality has been one of the priorities in wheat breeding at Jõgeva Plant Breeding Institute (PBI). The study was carried out with wheat samples (perspective breeding material and varieties at the Estonian Variety List) from the trials of the Jõgeva PBI during 1999-2003. Influence of different amounts of nitrogen fertilizer to quality has been tested for several years. Breeding for baking quality is hampered by its complexity. For predicting baking quality we assessed protein content, gluten content, gluten strength (gluten index), Zeleny value, falling number, baking test parameters (including farinogram and extensiogram parameters) and combination of HMW glutenin subunits. Quality requirements for our breeding program are: protein content>12%; gluten content>25%; gluten index 50…90; high Zeleny value, falling number > 250 sec, stability of dough at least 6 min, composition of HMW subunits with high quality score by Payne. If we compare two types of wheat, spring wheat had better baking quality than winter wheat during the testing period. Spring wheat had higher protein and gluten content and stronger gluten. Protein content had a greater influence on overall processing quality than any other single factor. Spring wheat had higher farinograph absorption and slower dough softening. Winter wheat had shorter dough development time and stronger resistance to stretching. Spring wheat produced higher valorimeter value, dough elasticity and bigger loaf volume. Differences in crumb structure and elasticity between different wheat types were not noticeable. According to Spearman Rank Correlations protein and gluten content influence both – the farinogram and extensiogram parameters. Extensiogram parameters were more depending on gluten index values. Correlations between different quality parameters were found. Biochemical fingerprints by electrophoresis of the most of the Estonian Variety List varieties have Glu-1 subunits 2*, 7+9 (winter), 7+8 (spring) and 5+10. The best lines and varieties are included in our breeding program; the lines and varieties without an agronomical value but with some unique quality parameters were passed over to genebank at Jõgeva PBI. From the Listed varieties the best baking quality had ´Sani´, ´Ramiro´, ´Tarso´ (winter wheat) and ´Manu´, ´Vinjett´, ´Helle´(spring wheat).

Key-words: wheat, variety, baking quality.



P9. Metrological problems and solutions in the measurement of chromium content of winter wheat

József Prokisch*, Bela Kovács and Zoltán Győri

Department of Food Science and Quality Assurance, Debrecen University, Centre for Agricultural Sciences, Debrecen 4015 P.O.BOX 36 Hungary (*corresponding author, E-mail: [email protected]) Chromium is an essential micronutrient for mammals. In Hungary, one of the main sources of chromium in the human diet is bread. Chromium concentration of winter wheat grains is about 120 µg kg-1, and fertilization has significant increasing effect on it. Comparing the daily requirement of chromium for adults (50 to 200 µg) and the quantity of bread in the daily diet in Hungary (300 g), bread can provide 18 to 72% of the total daily chromium requirement. The measurement itself has numerous metrological problems. Sampling, cleaning, grinding and preparation must be optimised for accurate and precise measurements. The Achilles heel of the measurement of chromium from the wheat kernels is the appropriate sample cleaning in sample preparation. Without washing, the measured chromium values are two to three times higher and the deviation is much higher. The reason is that soil dust particles can be adsorbed very strongly in the crease and on the hairy part of wheat grain. The fine fraction of soil dust contains three to five times more chromium than the soil itself (i.e., 100 mg kg-1) while chromium concentration in the grain is 100 to150 µg kg-1. If a metal grinder is used, the grinder can cause chromium contamination as well because of high chromium content of stainless steel. There is another problem with the grinding. The particle size distribution of grains has two peaks if a laboratory or industrial grinder is used, making homogeneous sampling difficult. Only cryo grinding can results in appropriate particle size distribution, but this technique is too expensive for everyday practice. Grains can be prepared without grinding, but we have to determine the minimum sample mass. A new method and algorithm were developed for the calculation. The smallest theoretically achievable uncertainty value for chromium concentration determination is limited by the particle size distribution and the concentration distribution of chromium in the different particle size fractions of the sample. The mass of 2000 grains was determined and were classified in 13 classes mass and their microelement content was determined. From these data the sample mass - theoretically minimal deviation of measurement can be calculated.



P10. Genetic analysis of nitrogen accumulation and protein composition in wheat grain Gilles Charmet1,*, Nathalie Robert2,4, Pierre Bérard1, Laurent Linossier2, Pierre Martre3 and Eugène Triboï3 1INRA, Unité d’amélioration des plantes, 234 avenue du Brézet, F-63039 Clermont-Ferrand cedex 2, France (*corresponding author, E-mail: [email protected]) 2ULICE, ZAC des portes de Riom, F-63200 Riom, France 3INRA, Unité d’Agronomie, 234 avenue du Brézet, F-63039 Clermont-Ferrand cedex 2, France 4present address: ISAB, BP 30313, F-60026 Beauvais Cedex, France Grain filling parameters, namely rate and duration for both nitrogen and dry matter accumulation have been evaluated in a progeny of 194 recombinant inbred lines (RILs) from the cross between Récital and Renan, two French cultivars previously identified as being contrasted for their accumulation kinetics. Final protein composition was analysed on the same progeny by both capillary electrophoresis, which enabled to quantify the different storage protein fractions (α+β+γ-gliadins, ω-gliadins, LMW glutenins, HMW glutenins, and even each subunit for these last ones), and SE-HPLC, whose 6 peaks are indicative of the size of protein polymers and aggregates. Correlation analyses reveal that the kinetics of dry matter and nitrogen are closely related, and that protein composition is mostly influenced by accumulation rates. The most concerned components are LMW-glutenins and α+β+γ−gliadins, the most abundant protein fractions. A genetic map of 254 molecular markers, covering nearly 80% of the wheat genome, was used for quantitative trait locus (QTL) analysis. Five chromosome regions were found to have significant effect on kinetics parameters, out of which four also influence protein composition. On the other hand, six QTLs affect only the relative content of protein fractions. Among them, two major QTLs, explaining more than 70% of the trait variance, were found for protein aggregation (e.i., peak 3 vs. 4) and for the content of subunit Glu1B-x. Some causal hypotheses are discussed. Key-words: grain growth, grain filling, Triticum aestivum L., gliadins, glutenins.



P11. A model simulating the grain nitrogen content of winter wheat Marie-Hélène Jeuffroy1,* and Marie-Laure Girard2 1UMR d’Agronomie INRA INA P-G, BP01, 78850 Thiverval-Grignon (*corresponding author, E-mail: [email protected]) 2present address: Hydro Agri, Hanninghof 35, D- 48249 Duelmen, Germany Grain protein content (GPC) is one of the main criteria used to judge the quality of wheat grains in commercial trades, firstly because it is a good indicator of the technological quality of the grains, and secondly because it is easy to measure. In the field, GPC is highly variable, among varieties, among years and among fields. In order to manage the nitrogen fertilisation, such that a targeted GPC required by the market can be reached, or to forecast GPC before harvest (Le Bail and Makowski, 2004), a crop model simulating GPC can be used. Several models exist in the literature, but their use on commercial farms for crop management is rarely possible, because there are too complex or because they require inputs difficult to measure in the fields, or because their formalism is not adapted to the objective. This paper aims at describing a model of grain filling in winter wheat, simulating GPC. The daily calculations begin at flowering stage and end at grain maturity. The model has three main modules simulating : (1) grain requirement for nitrogen and biomass, (2) nitrogen availability in the plant for grains, and (3) biomass supply to the grains. Each day, the model compares the grain requirement to the plant supply, for nitrogen and biomass, and the grain accumulation is simulated as the minimum of these two variables. The grain requirement was simulated mainly for biomass, as nitrogen accumulation in grains has been shown to depend much more on nitrogen availability in the plant than on grain requirement. The time-course change of grain maximum biomass accumulation was quantified, taking into account the effects of high temperature on the grain filling rate. The nitrogen available for grain filling has two main origins: the remobilisation of nitrogen stored in vegetative parts before anthesis, and the crop nitrogen uptake after this stage, considered as available for the grains. These two variables were simulated independently. The biomass accumulated in the grains is mainly represented by crop growth between anthesis and maturity (Bidinger et al., 1977). This growth was simulated according to intercepted radiation and conversion of this radiation into biomass. The effect of plant senescence on these two processes is simulated in the model, from the decrease of nitrogen in the vegetative parts, as proposed by Sinclair and de Wit (1975). The evaluation of the model was realised on one cultivar, in various situations, giving a good predictive quality. Its adaptation to other cultivars and its use for the management of N fertilisation or for the quality management at the scale of a collector are in progress. Key-words: wheat, grain protein content, model, nitrogen, biomass. References Bidinger F, Musgrave RB, Fischer RA (1977) Contribution of stored pre-anthesis assimilate to grain yield in wheat and barley. Nature 270: 431-433. Le Bail M, Makowski D (2004) A model-based approach for optimizing segregation of soft wheat in country elevators. European Journal of Agronomy, in press. Sinclair TR, de Wit CT (1975) Photosynthate and nitrogen requirements for seed production by various crops. Science 189: 565-567.



P12. Varietal adaptation of a model simulating the grain protein content of winter wheat Aude Barbottin and Marie-Hélène Jeuffroy* UMR d’Agronomie INRA INA P-G, BP01, 78850 Thiverval-Grignon (*corresponding author, E-mail: [email protected]) Grain protein content (GPC) is highly variable, among fields of a same variety, but also among varieties. Many factors, such as the climate, the soil characteristics and the nitrogen fertilisation applied on the crop, influence grain protein content. In order to understand the main determinants (that is to say the varietal characteristics responsible) of the GPC variability among varieties, in various pedo-climatic conditions, we aimed at adapting to several cultivars a model simulating the GPC and to evaluate the interest of such an adaptation on the predictive quality of the model. The model used for this work was Azodyn (Jeuffroy and Recous, 1999; Jeuffroy and Girard, this workshop). Firstly, we identified the parameters of the model that had an influence on the model outputs (mainly GPC, but also grain yield and its components) and that change among varieties. Secondly, we estimated these parameters for a group of 14 genotypes, on experimental trials conducted two years in one location. Then, we analysed the interest to include varietal phenotypic parameters in the model, on its predictive quality. The evaluation was realised on an experimental network conducted two years, in 6 locations, and with various crop managements. The phenotypic parameters concerned the developmental stages, the maximum yield and maximum mean weight per grain and the capacity of the variety to product grains. We showed that no varietal parameter was necessary to include for the simulation of nitrogen remobilisation and crop nitrogen uptake. Taking into account the phenotypic parameters improved the model predictive quality, for all outputs. The use of the adapted model to choose the best varieties adapted to various regions, according to their pedo-climatic characteristics was analysed (Barbottin, 2004). This model could also be used to help breeders to define the best combinations of phenotypic characteristics to reach a stable or a high grain protein content. Key-words: wheat, grain protein content, model, cultivar, parameter. References Barbottin A (2004) Utilisation d’un modèle de culture pour évaluer le comportement des génotypes : pertinence de l’utilisation d’Azodyn pour analyser la variabilité du rendement et de la teneur en protéies du blé tendre, PhD Thesis, Institut National Agronomique Paris-Grignon. Jeuffroy MH, Recous S (1999) Azodyn: a simple model simulating the date of nitrogen deficiency for decision support in wheat fertilisation. European Journal of Agronomy 10: 129-144.



P13. Ability of new durum wheat pure lines to meet yield stability and quality requirements in low input and organic systems

Bruno Colomb1,*, Dominique Desclaux2, Philippe Debaeke1, Eric Justes1 and Philippe Burger1 1INRA Toulouse, UMR ARCHE, B.P. 27, Auzeville – F-31326 Castanet Tolosan, France (*corresponding author, E-mail: [email protected]) 2INRA Montpellier, UMR DGPC, Domaine de Melgueil – F-34130 Mauguio, France Low-input production schemes adopted in stockless organic or conventional farms require crop varieties that combine efficient nutrient uptake traits and high yield stability under different environmental conditions. Four newly created durum wheat pure lines (T. turgidum ssp durum) were evaluated according to these traits and compared to a highly productive variety currently cultivated in southern France. These lines were selected according to their grain protein content (GPC) found in the F7 generation field experiments conducted near Montpellier in 2001-2002. All cultivars were grown organically with 0, 120 or 210 kg N ha-1 (hydrolysed feather meal), and in a conventional way with 180 kg N ha-1 (ammonitrate) or without any fertilizer input. Pre-anthesis and post-anthesis drought stress led to a low N uptake during the vegetative period and limited carbohydrate incorporation during the grain filling period. Three of the new lines showed a higher GPC with equal or higher yields than the reference variety in organic treatments. Without N input GPC remained less than 13%. An organic input of 120 kg N ha-1 was enough to allow all lines except the reference one to meet this commercial protein threshold. Variation in N uptake after anthesis contributed more than variation in N use efficiency to the improved GPC of the new the lines.



P14. A model oriented field experiment on the accumulation of N and proteins in durum wheat grains: Preliminary results

M. Bindi1, A. Triossi1*, R. Ferrise1, M. Moribondo2* and Z. Flagella3

1DISAT-University of Florence, P.le delle Cascine 18, 50144 Florence, Italy (*corresponding authors, E-mail: [email protected], [email protected]) 2Applied Meteorological Foundation, Florence, Italy 3DiSACD-University of Foggia, Italy Proteins are the most important components of bread wheat (Triticum aestivum L.) and durum wheat (T. durum L.) grains determining the final use quality. In wheat, grain proteins have been classified on the basis of their solubility; and the content of soluble and insoluble proteins and their ratios are useful criteria to evaluate the technological properties of the yield. The ratio between soluble and insoluble proteins is a function of the protein composition that is mainly genetically controlled. However, environmental factors such as temperature, water and nitrogen nutrition have also influence on this ratio (Triboï et al., 2000; Daniel and Triboï, 2002; Triboï et al., 2003). Despite of the large number of studies concerning the effects of environment on the variation of the polymer composition and distribution, the data available on the effects of environmental factors on the protein accumulation in durum wheat varieties were very limited. These aspects (rate and duration of the accumulation) are particularly important in modeling approach where the study of dynamics of protein accumulation is fundamental to determine the final quality of the grains. The aim of this work was to analyze the accumulation of N and proteins in durum wheat grains grown under different environmental conditions (fertilisation and sowing date), and to provide a solid dataset for including, in existing wheat growth simulation models, quality aspects of yields (soluble and insoluble protein components). For this purpose a field experiment on durum wheat cv. Creso was carried out including 2 sowing dates (11 December 2002 and 27 January 2003) and 4 N treatment levels (0, 60, 120 and 180 kg ha-1). Phenological monitoring was followed at 2-3 days intervals and 8 destructive plant samples were made during the grain ripening period (from anthesis to harvest time) at week internal. In each sampling date, the dry weight and N content of each aboveground biomass component were determined. Moreover, a part of the grains were frozen, lyophilized and then used to extract soluble and insoluble protein fractions. The patterns of dry mass and total N accumulation in grains showed, as expected, that their rates of accumulation were affected by N treatment levels and sowing dates; whilst the preliminary analyses of the soluble and insoluble protein patterns (only on 3 sampling dates and for the first sowing dates) showed that these were not markedly modified by the N treatments. Key-words: soluble and insoluble proteins, sowing dates, N fertilization, grain yield and N. References Triboï E, et al. (2000) Environmental effects on the quality of two wheat genotypes: 1. quantitative and qualitative variation of storage proteins. Europrean Journal of Agronomy 13: 47-64. Daniel C, Triboï E, (2002) Changes in wheat protein aggregation during grain development: effects of temperatures and water. Europrean Journal of Agronomy 16: 1-12. Triboï E, Martre P, Triboï–Blondel AM (2003) Environmentally-induced changes in protein composition in developing grains of wheat are related to changes in total protein. Journal of Experimental Botany 388: 1731-1742.



P15. New paradigm needed for modelling root activity? Goetz M. Richter Agriculture and Environment Div., Rothamsted Research, Harpenden, AL5 2JQ, UK, (E-mail: [email protected]) Root growth, their distribution and function in the soil-plant system are often simplified in crop modelling. The processes are complex in nature, and measurements in situ and in the soil environment are difficult, tedious and most time consuming and expensive. The questions arise how to define and measure root activity, how to model it, and finally, whether models can assist in the process of variety selection. What do we know about the genome with respect to relevant indicators, are our indicators appropriate, and finally, can we model their dynamics? Little of the current work in plant genetics refers to root traits, and it is mainly concerned with root architecture, number of roots and their physical characteristics. However, we know that cereal varieties greatly vary with respect to nutrient uptake rates and that root protein nitrogen content (RPN) may be a useful indicator for this activity. Varieties also vary with respect to root hair production but the response may be different in different environments. Root porosity may also vary among varieties and their response to environmental stress (e.g., anoxia). A closer look to the root shows different kind of pores, uncharged aquaporins and channels charged with transporters. Proteins on/in the plasmalemma may have very different functions depending on their location. Therefore, RPN may be too simple, and more specific indicators needs to be considered. In addition, there seems to be a huge gap between phenotypic and genotypic criteria, especially with respect to roots. How can we improve the root model and how can models assist? We need to distinguish but link water and nutrient uptake: Water uptake is passive (convective and diffusive) in the apoplast and through aquaporins in the plasmalemma – and so is the uptake or discharge of uncharged molecules (e.g., urea or CO2). Nutrient uptake (e.g., nitrate/ammonium) is actively controlled by transporters in the membrane and could be described using a Michaelis-Menten type equation. In modelling the coupled root water (soil solution) and nutrient transport we need to parameterise the partitioning of passive and active uptake, apoplastic and symplastic transport. With respect to low input and sustainability of stress exposed systems we pursue the following questions using widely different varieties which are mapped for other traits: What is the impact of water limitation on apoplastic and symplastic transport? How change conductance and reflection coefficients in the composite approach of water and nutrient transport and what are simple, measurable parameters? And, if root activity (RA) and Root Protein Nitrogen (RPN) relate to Michaelis-Menten coefficients, how do these parameters change with age and under physical stress?

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List of Participants


Joël Abecassis UMR Ingénierie des Agropolymères et Technologie Emergente INRA 2 place Viala F-34 060 Montpellier France [email protected] Senthold Asseng Plant Industry Division CSIRO Private Bag 5 PO Wembley WA 6913 Australia [email protected] Xavier Bailleau Quantix Pro 34 Rue de Liège F-75 008 Paris France [email protected] Christine Bar Laboratoire Qualité des Céréales ARVALIS - Institut du végétal 16 rue Nicolas Fortin F-75 013 Paris France [email protected] Frédérique Batifoullier Unité Maladies Métaboliques et Micronutriments INRA F-63 122 Saint-Genès-Champanelle France [email protected] Boris Boicean Selectia Research Institute Beltsy, Calea Esilor, 28 Republic of Moldova [email protected] Gérard Branlard UMR Amélioration et Santé des Plantes INRA 234 avenue du Brézet F-63 039 Clermont-Ferrand France [email protected] Elisabeth Chanliaud Limagrain Agro-Industrie, site ULICE Zac les portes de Riom BP173 F- 63204 Riom cedex France [email protected] Gilles Charmet UMR Amélioration et Santé des Plantes INRA 234 avenue du Brézet F-63 039 Clermont-Ferrand France [email protected]

Bruno Colomb UMR ARCHE INRA B.P. 27 F-31 326 Castanet Tolosan France [email protected] Philippe Dufour Laboratoire de Génotypage Limagrain Agro-Industrie, site ULICE Zac les portes de Riom BP173 F- 63204 Riom cedex France [email protected] Frances DuPont Agricultural Research Service Western Regional Research Center USDA 800 Buchanan St CA-94710, Albany USA [email protected] Anthony Fardet Unité Maladies Métaboliques et Micronutriments INRA F-63 122 Saint-Genès-Champanelle France [email protected] Christian Fournier UMR Environnement et grandes cultures INRA F-78 850 Thiverval-Grignon France [email protected] Luis F. García del Moral Dpto. Fisiologia Vegetal Facultad de Ciencias Universidad de Granada 18071 Granada Spain [email protected] Michel Génard UR Plantes et systèmes de culture horticoles INRA Domaine St Paul, Site Agroparc F-84 914 Avignon France [email protected] Christine Girousse UR874 Agronomie - APAC INRA 234 Avenue du Brézet F-63 039 Clermont-Ferrand France [email protected]

Zoltán Gyõri Department of Food Science and Quality Assurance University of Debrecen Centre of Agricultural Sciences Faculty of Agronomy 138. Böszörményi Street H-4032 Debrecen Hungary [email protected] Dimah Habash Crop Performance and Improvement Division Rothamsted Research Harpenden Hertfordshire AL5 2JQ UK [email protected] Tony Hunt Crop Science Department University of Guelph NIG 2W1 Ontario Canada [email protected] Strenghetto Ilaria Agronomia ambientale e produzioni vegetali Facoltà di Agraria Università degli studi di Padova viale dell Università, 16 35020 Padova Italy [email protected] Anne Ingver Jogeva Plant Breeding Institute Aamisepa 1, Jõgeva alevik 48 309 Jõgeva MK Estonia [email protected] Gheorghe Ittu Agricultural Research & Development Institute 1, N. Titulescu str. 915200 Fundulea Romania [email protected] Peter Jamieson Crop & Food Research Institute Private Bag 4705 Christchurch New Zealand [email protected] Marie-Hélène Jeuffroy UMR Agronomie INRA BP01 F-78 850 Thiverval-Grignon France [email protected] Borislav Kobiljski Small Grains Department Institute of Field and Vegetable Crops Maksima Gorkog 30 21000 Novi Sad Serbia and Montenegro [email protected]



Reine Koppel Jogeva Plant Breeding Institute Aamisepa 1, Jõgeva alevik 48 309 Jõgeva MK Estonia [email protected] Xavier Lacaze UMR Diversité et Génome des Plantes Cultivées INRA Domaine de Melgueil F-34 130 Mauguio France [email protected] Cátálin Lazár Plant Physiology Department Agricultural Research & Development Institute 1, N. Titulescu str. 915200 Fundulea Romania [email protected] Christophe Lecomte UR Génétique et Ecophysiologie des Légumineuses à Graines INRA Domaine d'Époisses F-21 110 Bretenières France [email protected] Volker Lein Saaten Union Recherche 163 avenue de Flandre F-60 190 Estrées-Saint-Denis France [email protected] Jacques Le Gouis UR Génétique et Amélioration des Plantes INRA BP 136 F-80 203 Péronne Cedex France [email protected] Fanny Lehnardt Unité Maladies Métaboliques et Micronutriments INRA F-63 122 Saint-Genès-Champanelle France [email protected] Céline Loussert Unité de Recherche sur les Protéines Végétales et leurs Interactions INRA rue de la geraudiere BP 71627 F-44316 Nantes France [email protected] Frédéric Mabille UMR Ingénierie des Agropolymères et Technologie Emergente INRA 2 place Viala F-34 060 Montpellier France [email protected]

Pierre Martre UR874 Agronomie - APAC INRA 234 Avenue du Brézet F-63 039 Clermont-Ferrand France [email protected] Benoît Méléard Laboratoire Qualité des Céréales ARVALIS - Institut du végétal 16 rue Nicolas Fortin F-75 013 Paris France [email protected] Saeed Moghiseh College of Agriculture University of Tehran Iran [email protected] Marie-Hélène Morel UMR Ingénierie des Agropolymères et Technologie Emergente INRA 2 place Viala F-34 060 Montpellier France [email protected] Marco Moriondo Applied Meteorological Foundation, Florence via A. Eintstein 35 50013 Florence Italy [email protected] François-Xavier Oury UMR Amélioration et Santé des Plantes INRA 234 avenue du Brézet F-63 039 Clermont-Ferrand France [email protected] John Porter Department of Agricultural Sciences Royal Veterinary and Agricultural University Hoebakkegaard Alle 2 2630 Taastrup Denmark [email protected] Jozsef Prokisch Department of Food Science and Quality Assurance University of Debrecen, Centre for Agricultural Sciences P.O.BOX 36 4015 Debrecen Hungary [email protected] Mariann Rakszegi Martonvashar Research Institute Brunszvik VT 2 2462 Martonvasar Hungary [email protected]

Christian Rémésy Unité Maladies Métaboliques et Micronutriments INRA F-63 122 Saint-Genès-Champanelle France [email protected] Yahia Rharrabti Dpto. Fisiologia Vegetal Facultad de Ciencias Universidad de Granada 18071 Granada Spain [email protected] Goetz Richter Rothamsted Research Harpenden Hertfordshire AL5 2JQ UK [email protected] Pierre Roumet UMR Diversité et Génome des Plantes Cultivées INRA Domaine de Melgueil F-34 130 Mauguio France [email protected] Michel Rousset UMR Génétique Végétale INRA Ferme du Moulon F-91 190 Gif-sur-Yvette France [email protected] Vitalie Samoil Institute of Plant Physiology Center of Advanced Technologies str Padurii 26/2 MD 2001 Chisinau Republic of Moldova [email protected] Nick Saulescu Agricultural Research & Development Institute 1, N. Titulescu str. 8264 Fundulea Romania [email protected] Michael Semenov Rothamsted Research Harpenden Hertfordshire AL5 2JQ UK [email protected] Peter Shewry Rothamsted Research Harpenden Hertfordshire AL5 2JQ UK [email protected]



Violeta Simionescu Faculty of Natural Sciences and Agricultural Sciences Ovidius University Str. Mamaia, No. 124 8700 Constanta Romania [email protected] Thomas Sinclair Agricultural Research Service Agronomy Physiology Laboratory USDA University of Florida P.O. Box 110965 FL 32611-0965, Gainesville USA [email protected] Hubert Spiertz Plant Sciences - Group Crop and Weed Ecology Wageningen University and Research Center P.O. Box 430 6700 AK, Wageningen The Netherlands [email protected] Paul Alexis Thiebot UR Génétique et Amélioration des Plantes INRA Mons BP 136 F-80 203 Péronne Cedex France [email protected] Anne-Marie Triboï UR874 Agronomie - APAC INRA 234 Avenue du Brézet F-63 039 Clermont-Ferrand France [email protected] Eugène Triboï UR874 Agronomie - APAC INRA 234 Avenue du Brézet F-63 039 Clermont-Ferrand France [email protected] Andrea Triossi DISAT University of Florence P.le delle Cascine 18 50144 Florence Italy [email protected] Erik van Oosterom Agricultural Production Systems Research Unit School of Land and Food Sciences The University of Queensland Qld 4072, Brisbane Australia [email protected]

Anne-Catherine Villain Danone Vitapole RD 128 F-91767 Palaiseau France [email protected] Albert Weiss School of Natural Resources University of Nebraska P. O. Box 830728 245 LW Chase Hall NE 68583-0728, Lincoln USA [email protected] Jeffrey White Agricultural Research Service Water Conservation Laboratory USDA 4331 E Broadway Rd AZ 85040-8834, Phoenix USA [email protected] Xinyou Yin Crop and Weed Ecology Group Wageningen University P.O. Box 430 6700 AK, Wageningen The Netherlands [email protected] Robert Zyskowski Crop & Food Research Institute Private Bag 4705 Christchurch New Zealand [email protected]

Programme Overview

Sunday 18 July 2004 1800 – 2000 Registration and Welcome Cocktail at the Coubertin Hotel 2000 – 2300 Dinner at the Coubertin Hotel

Monday 19 July 2004 0800 – 0830 Registration at the meeting venue

0830 – 0850 Opening 0850 – 1030 Session 1 1030 – 1100 Coffee break

1100 – 1245 Session 1 – follow-up 1245 – 1400 Lunch & Poster viewing

1400 – 1540 Session 2 1540 – 1610 Coffee break 1610 – 1755 Session 2 – follow-up 1900 – 2030 Walk on the top of the Puy de Dome volcano

2030 – 2300 Meeting dinner at the Mont Fraternité Restaurant

Tuesday 20 July 2004 0900 – 1015 Session 3 1015 – 1045 Coffee break 1045 – 1200 Session 3 – follow-up 1200 – 1330 Lunch & Poster viewing 1330 – 1510 Session 4 1510 – 1540 Coffee break 1540 – 1700 Session 4 – follow-up 2000 – 2300 Dinner at the Coubertin Hotel

Wednesday 20 July 2004 0800 – 0830 Introduction to the Round Table-Workshops 0830 – 1030 Round-Table Workshops 1030 – 1100 Coffee break 1100 – 1200 Report of the Round-Table Workshops 1200 – 1230 Meeting Summary 1230 – 1400 Lunch 1400 – 1800 Special meeting of the GCTE Wheat Network

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