defra wheat genetic improvement network – the core ... · resources from rice to orphan crops:...

Cover picture: Richard Gutteridge (non WGIN funded) sowing the AE Watkins collection regenerated by JIC on the Rothamsted farm in October 2007

Defra Wheat Genetic Improvement Network – The Core Research Project Background The UK government is committed to a more sustainable agriculture. Wheat is grown on a larger area and is more valuable than any other arable crop in the UK. The overall aim of this project is to generate pre-breeding material carrying novel traits to the UK breeding companies and to deliver accessible technologies thereby ensuring the means are available to produce new, improved varieties. An integrated scientific 'core' which combines underpinning

molecular markers, genetic and genomic research, together with novel trait identification, are being pursued to achieve this goal. The programme is managed by a team including representatives of the key UK research groups and breeders. They ensure the programme and its outputs are communicated to the wider scientific and end user communities, via a web site, a stakeholder forum, focused meetings and peer reviewed publications. The WGIN will ensure collaborations with equivalent operations overseas to ensure the programme is internationally competitive.

Contents: Introduction to WGIN Page 1 Impact on other research Page 6 Assessing allelic diversity in the

AE Watkins Collection Page 8 WGIN Nitrogen use efficiency trials Page 11 Second Wheat Syndrome trial resultsPage 12 WGIN publications Forthcoming events

This project The Core Project started in 2003 provides genetic and molecular resources for research in the Satellite defra Projects and for a wide range of wheat research projects in the UK. These resources include wheat genetic stocks, mapping populations, molecular markers and marker technologies, trait identification and evaluation, genomics and bioinformatics. The Research Platform will promote the integration of the funded work.

IMPACT ON OTHER RESEARCH WGIN is now into its fifth year. To assess its impact on other research, members of the UK science and wheat breeding community were approached over the last couple of months to give feedback on their use of WGIN data. The tables on the following pages contain information we have received so far on existing projects using WGIN data and on new projects

that have used WGIN data in their grant proposal applications. WGIN scientists have also published several articles in peer reviewed international journals since the start of the project. WGIN publications are listed on the last page of this newsletter.

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ewsletter Special focus: Impacts on other research projects May 2008

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Table 1: Already funded projects now using WGIN data and resources Principal Investigator

Co-PIs PI location General Research

Topic

Sponsor Project type

Project Title Duration: from

to

Funding amount (total) in £

M Hawkesford Rothamsted Research

Nutrient Utilisation in

Wheat BBSRC CSG*

An integrative transcriptome and metabolic profiling study of resource mobilization in wheat 2005-2008 318,425.--

A Greenland (many) NIAB Grain Quality defra Link*

Development and evaluation of low-phytate wheat to reduce diffuse phosphate pollution from pig and poultry production units 2006-2009

No data

J Foulkes (many)

University of Nottingham

Nitrogen Use Efficiency BBSRC/INRA

Special initiative

Traits and markers to reduce the N requirement and improve the grain protein % of winter wheat 2006-2010

BBSRC funding = 713,000.--

I King

I Armstead, I Donnison, J King IGER

Grass Genomics

BBSRC-INRA

BBSRC-CSI*

The establishment and application of a forward genetic resource for the development of efficient breeding strategies in grass and cereals 2007-2008 866,609.--

J Pickett R Gordon- Weeks

Rothamsted Research

Disease resistance in

wheat BBSRC CSI*

The potential to control insects and other organisms antagonistic to wheat by the up regulation of Hydroxamic acids 2007-2010 450,000.--

J Walsh

K Kanyuka, G Barker (WHRI), K Hammond-Kosack

Warwick HRI

Plant Pathology BBSRC CSI*

Exploiting eIF4E-based and associated broad-spectrum recessive resistance to potyviruses in dicots and monocots 2007-2010 610,000.--

Total to date 2,958,034.--

WGIN n


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Table 2: Projects using WGIN data in grant proposal applications Principal Investigator


Topic



to

Funding amount (total) in £

K Hammond-Kosack PR Shewry RRes

Plant Pathology

Rothamsted International Fellowship

Exploiting T. monococcum as a source of novel traits genes and variant alleles 2003-2004 30,000.00

K Hammond-Kosack

AL Phillips, P Hedden, A Karp RRes

Tools and Resource

Development BBSRC REI*

High throughput screening of sequence variation in crop and model plants - TILLING platform 2004-2005 130,092.00

J Bakker

K Hammond-Kosack, K Kanyuka, A Huttly, H Jones

Wageningen Netherlands

Plant Pathology EU FP 6

Integrated Project

BIOEXPLOIT – Exploitation of natural plant biodiversity for the pesticide-free production of food. 2005-2008 315,640.00

PR Shewry (many in EU) RRes Grain

Improvement EU FP 6 Integrated project

HEALTHGRAIN - Nutritional enhancement of wheat 2005-2010 355,095.00

AL Phillips (many) RRes Grain Quality

BBSRC/ defra/ HGCA Link

An integrated approach to stabilising HFN in wheat: screens, genes and understanding 2006-2010 537,638.00

AL Phillips K Hammond-Kosack RRes

Tools and Resource

Development BBSRC

Tools and Resources Initiative

Development of the lightscanner as a generic platform for novel allele discovery 2007-2008 99,900.00

WGIN n


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Table 2 continued Principal Investigator


Topic



to

Funding amount (total)

in £

R Gutteridge none RRes Plant

Pathology HGCA Link *

Use of PreDicta B in the UK to detect Take-all inoculum in soil use in the UK. 2007-2008 20,000.00

S Griffiths none JIC

Molecular Marker

Development BBSRC Tools and Resources

From rice to orphan crops: robust, high throughput genetic markers for all the grasses. 2007-2008 69,019.00

AM Smith K Denyer JIC Starch

Composition BBSRC CSI* The Smart Carbohydrate Centre 2007-2010 872,161.00

PR Shewry A Huttly RRes Grain

Architecture BBSRC CSI* Control of grain shape and size in wheat 2007-2010 393,000.00

J Snape J Dicks JIC

Marker development,

Mapping BBSRC CSI*

The establishment and application of a forward genetic resource for the development of efficient breeding strategies in grass and the cereals 2007-2010 61,504.00

A Schulman

K Hammond-Kosack, AL Philips, K Kanyuka Finland

Plant Pathology EU FP 6

COST* action

TritiGen Triticeae genomics for the advancement of essential European crops 2007-2011 2,000.00

WGIN n

http://oasis.bbsrc.ac.uk/netans-bin/gate.exe?f=doc&state=225fgt.1.1




WGIN newsletter Special focus: Impacts on other research projects May 2008

Table 2 continued Principal Investigator


Topic



to

Funding amount (total)

in £

J Snape JH Doonan, PJ Shaw JIC Grain Quality BBSRC CSI*

Optimising grain shape for improved processing quality 2007-2011 333,230.00

AL Phillips M Boulton RRes Stature &

Stress BBSRC CSI*

Enhancing wheat field performance and response to abiotic stress with novel growth-regulatory alleles 2007-2012 1,058,632.00

CE Durel, I Denholm

K Hammond-Kosack INRA, RRes

Plant Pathology EU-FP6

Integrated Project

Selection pressure exerted by combinations of resistance genes and QTLs on Mycosphaerella graminicola populations 2008-2009 35,509.00

Total to date 4,313,420.00 * Footnotes: CSG: Core Strategic Grant CSI: Crop Science Initiative COST: European Cooperation in the field of Scientific and Technical Research Link: project part sponsored by industry REI: Research Equipment Initiative

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UPDATE ON PROJECT OBJECTIVES

Assessing allelic diversity in the AE Watkins Collection (Objective 2) The John Innes Centre contributes a number of resources to the WGIN including: • Paragon mutant populations (EMS and

Gamma). • The Avalon X Cadenza double haploid

population with genotypes and phenotypes.

• Maintenance of precise genetic stocks. • The development of gene based

markers. • The maintenance and study of

germplasm collections including Gediflux (major European wheat from the last fifty years), and the AE Watkins collection.

In this report we focus on the AE Watkins Collection and what this unique resource has to offer the wheat community. What is the AE Watkins Collection? AE Watkins was based at Cambridge University in the 1930s and used his connections with the London Board of Trade to assemble a collection of diverse wheat genotypes. Seed was sent from around the world creating a unique snapshot of world wheat germplasm available at that time. We are interested in

studying this material because it may contain alleles (variant gene types) that do not exist in modern wheat varieties. This work has focused on an 800 line subset of the AE Watkins Collection. The lines originate from the 32 countries shown in Figure 1.

WGIN is responsible for increasing the accessibility of the AE Watkins Collection to the scientific community The value of the collection as a source of new, interesting, and potentially useful genetic variation is unquestionable. However, to really use this resource scientists and plant breeders need an idea of the characteristics and genetic structure of the material. To this end, AE Watkins field trials were conducted by Simon Orford of the John Innes Centre. The material was assessed for homogeneity within each sample. Originally seed was simply collected from local markets so there is potential for any one accession to contain a mixture of lines. To test for this, four lines from each accession were grown to check their heading dates, height and growth habit. Other interesting phenotypes were noted such as extreme disease susceptibility. Using the data from the trial it was assessed that 72% of the lines were visually homogenous with the other 28% requiring a sub accession line now to guarantee purity of stock. Valuable genetic material is locked up in this collection some of which has been unexploited in today’s modern varieties Growth Habit as a test case for untapped genetic variation in the AE Watkins Collection In the work described above the AE

Watkins collection was categorised according to growth habit, some lines need a long period of cold in order to flower (winter wheat) and some do not (spring wheat). Figure 2 overleaf shows a plot of a winter Watkins line surrounded by plots of spring Watkins lines. Due to the work of Jorge Dubcovsky’s group at University of California Davis, we know all the common alleles that give

rise to spring habit. Leodie Alibert of the John Innes Centre used this information to see whether spring wheat lines in the AE Watkins collection lacked these alleles. If they did this would give a concrete demonstration of the value of the collection in the search for new genetic variation.

Figure 1. Countries of origin for Watkins wheat lines


Figure 3 shows the results of this work. For each country the pie charts show spring and winter wheat lines, and if they are spring whether the allele is already known. The orange section of the pie chart

represents spring wheat lines that do not have known spring alleles. This work clearly demonstrates

that as important

genes are identified in modern wheat the AE

Watkins collection will provide a great resource for the

identification of new alleles.

More background and data from the Watkins field trial can accessed via the WGIN website www.wgin.org where the full extent of the diversity of the collection can be explored.

Figure 2: Demonstration of different growth habits in AE Watkins lines

Figure 3. Selected cases of growth habit designations for AE Watkins lines. Green represents lines with known spring alleles and orange represents the proportion of spring lines that do not have known spring habit alleles of the genes Vrn-A1, Vrn-B1, or Vrn-D1.

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http://www.wgin.org/


WGIN Nitrogen use efficiency trials (Objective 5.7 and 5.9) Need for the trials Nitrogen is essential for the production, growth and functioning of all plant tissues throughout development and is a major determinant of yield in wheat. However, N-fertilisers are costly and their production has a large carbon footprint. Excessive applications can have negative environmental impacts. Highly nitrogenous run-off water from arable land causes major ecological problems including eutrophication of inland lakes, rivers and coastal waters. In addition, the denitrification of fertilizer in wet soil releases damaging greenhouse gases. Improving the efficient use of applied fertiliser is essential for meeting yield

demand whilst minimising environmental impacts. Many definitions may be used to quantify efficient use of nitrogen. Within the WGIN project the definitions we have adopted are defined in Figure 4. NUE, the overall nitrogen use efficiency, is the product of the two component traits, NUpE and NUtE, both of which themselves are complex plant traits and the products of multiple pathways, enzymes and genes. At the initiation of the project, it was felt that there was a paucity of basic information relating to nitrogen use parameters for modern wheat varieties, particularly at lower nitrogen inputs. Objective 5.7 was designed to fill this gap and provide a background data set as a springboard for genetic approaches. Recently, grain prices have become a major issue; this adds a new imperative to the study of efficient nitrogen use, that of generating the maximum yield for a given input.

Summary of trials undertaken (2004-2008) Large scale, fully replicated N x variety field trials have been performed at Rothamsted in all 5 years of the project. Yield and nitrogen content measurements (grain and straw) have been made as part of the WGIN-funded project, and the trials have been available for study to any interested researchers (there were none). They have been a popular point of interest and a good advertisement for defra-WGIN for national and international visitors during this time. The design has evolved with experience and there have been changes in varieties each year, but a basic subset of 14 varieties has been grown in all years. In summary, in 2004, 32 varieties were grown at 4 nitrogen inputs (0, 50, 200 and 350 kg/ha N); in 2005, 20

varieties were grown at 0, 100 and 200 kg/ha; in 2006, 2007 and 2008, the same 24 varieties have been grown at 0, 100, 200 and 350 kg N/ha. An aerial photograph which includes both the N x variety trial and the Avalon x Cadenza doubled haploid (DH) trials (see below), taken in June 2007, is shown in Figure 5. Soil nitrogen has been

NUE has two independent components: uptake efficiency and utilisation efficiency

N-uptake efficiency (NUpE) is total crop N uptake divided by N available from soil and fertilizer (uptake/supply)

N-utilisation efficiency (NUtE) is grain yield (100%DM) divided by total N taken up (yield/uptake)

Overall N-use efficiency, NUE = NUpE x NUtE (=yield/N available)

Figure 4. Definition of nitrogen use efficiency parameters used in this study

Figure 5. Aerial view of N-diversity and Avalon x Cadenza trial in 2007

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determined routinely in order to evaluate nitrogen uptake efficiency, and patchiness with fields confirms the necessity for these measurements (Figure 6).

Emerging datasets Taking as an example data from the 2007 trial, the clear impact of increasing N inputs on yield is apparent, at least up to 200 kg N/ha (Figure 7). Also evident is the

variation in yield responses between the varieties. In Figure 7, the varieties are plotted in the same order at each of the N inputs: the discrepancies in the rankings at

N inputs other than 200 kg/ha indicate that ranking of variety performance is different at the other N inputs. Variation in overall nitrogen use efficiency (NUE) and the two component traits, nitrogen uptake efficiency (NUpE) and nitrogen utilisation efficiency (NUtE), for the 2004 dataset, are shown in Figure 8. It is immediately clear that all parameters are dependent upon nitrogen inputs, with greatest efficiencies obtained at lower inputs; this coincides with major impacts on yield. NUtE reflects yield (yield/N-taken up), and hence NUtE at 200 and 350 kg/ha are coincident; however the greater N-uptake is reflected in a higher grain N-content at 350 kg/ha, a benefit for bread-making quality. Variation exists in all parameters at all N-

levels evaluated (note that rankings are independent for the 4 N levels and therefore the specific varieties cannot be plotted on the abscissa). Comparing variation across the varieties for the two

N (kg/ha)N (kg/ha)

Figure 6. Residual soil nitrogen in diversity trial field prior to spring N application (top 30 cm).

2007 Yields WGIN Diversity Trial

0

2

4

6

8

10

12

0 100 200 350

N level (kg/ha)

yiel

d (t

/ha

85%

DM

)

Figure 7.Grain yields from the WGIN nitrogen – variety diversity trial in 2007

LSD (5%) = 0.197 LSD (5%) = 5.6 (10.7)LSD (5%) = 6.33

uptake use efficiency overall

Ranked NUpE

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 4 7 10 13 16 19 22 25 28 31Variety

Nup

E(k

g-N

/kg-

N)

N0N1N2N3

Ranked NUtE

20

30

40

50

60

70

80

1 4 7 10 13 16 19 22 25 28 31Variety

Nut

E(k

g-D

M/k

g-N

)

N0N1N2N3

Ranked NUE

0

10

20

30

40

50

60

1 4 7 10 13 16 19 22 25 28 31Variety

NU

E (k

g-D

M/k

g-N

)

N0N1N2N3

LSD (5%) = 0.197 LSD (5%) = 5.6 (10.7)LSD (5%) = 6.33

uptake use efficiency overall

Ranked NUpE

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 4 7 10 13 16 19 22 25 28 31Variety

Nup

E(k

g-N

/kg-

N)

N0N1N2N3

Ranked NUtE

20

30

40

50

60

70

80

1 4 7 10 13 16 19 22 25 28 31Variety

Nut

E(k

g-D

M/k

g-N

)

N0N1N2N3

Ranked NUE

0

10

20

30

40

50

60

1 4 7 10 13 16 19 22 25 28 31Variety

NU

E (k

g-D

M/k

g-N

)

N0N1N2N3

Figure 8. Evidence for genetic diversity in NUE and component traits amongst the wheat varieties studied

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components, NUpE and NUtE, indicates that the variation is completely independent, as would be expected from traits involved in root uptake and seed production, respectively, which involve quite independent processes. The variation suggests that both traits could be independently improved and that combination of the best lines would produce varieties with substantially improved NUE. Year to year integration Data is available for the first 4 years of the project and integration of the data sets gives added confidence in rank performance and allows an evaluation of the stability of the measured traits on a

year-to-year basis (Figure 9). Overall it is clear that the determined rank order of performance is remarkably stable over the 4 years of trial data. However, some varieties show considerable NUtE variation which is coupled with year to year yield instability. The Avalon x Cadenza mapping population (part of objective 5.9) The population of doubled-haploid (DH) individuals, derived from F1 progeny of a cross between cvs. Avalon and Cadenza, was developed by Clare Ellerbrook, Lesley Fish and the late Tony Worland (John Innes Centre), as part of a defra funded project led by ADAS. The

parents were originally chosen, to contrast for canopy architecture traits, by Steve Parker (CSL), Tony Worland and Darren Lovell (Rothamsted Research). This population which segregates for height (Rht-D1) and flowering time (Figures 5 and 10) is also being evaluated for NUE (at 200 kg/ha in 2007 and at 100 kg/ha at two sites in 2008). Data from 2007 indicates significant variation in yield, nitrogen uptake, post-anthesis remobilisation to the grain and nitrogen utilisation efficiency between the lines. The extent of variation indicates considerable transgressive segregation compared to the N-use phenotypes of the parents. These parameters along with flowering time and

height have been placed on a genetic map and both co-localising and unique QTLs have been identified. These will be verified with the 2008 data.

WGIN Diversity trial set (200 kg/ha) NUtE 2004-2007

0

10

20

30

40

50

60

variety

NU

tE(k

g/kg

)

means2004200520062007

Figure 9. Variation in performance of nitrogen utilisation efficiency in the diversity trial over 4 years, with 200 kg/ha N. Varieties are ranked by mean performance which is plotted as a continuous line, and individual year performances are also shown.

Figure 10. Sampling the Avalon x Cadenza DH plots.

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Gene identification The WGIN trial has provided data to facilitate the choice of varieties with high and low NUE parameters for further molecular studies at Rothamsted, funded by the BBSRC. In this work, transcriptome profiles have been determined in leaf tissues following anthesis. Subsets of genes have been identified whose expression correlates with a range of measured agronomic parameters including NUE and its components. These subsets of genes, particularly the transcription factors which are hypothesised to be key control elements, represent new potential targets for breeding for improved NUE. Summary of the second wheat syndrome project (Objective 5.7) A study was conducted to compare the performance of six UK winter wheat cultivars in first and second wheat situations, primarily to test for cultivar by rotational position interaction and to investigate possible causes. Cultivars were selected on their yield performance when grown as a second wheat, based on the data provided by the annual Recommended List trials; three cultivars were considered to have performed well and three had performed poorly. The relative performance of varieties in contrasting rotational positions was characterised in the field with the use of artificial inoculum and by ‘phasing-in’ previous cropping (2005-06 only), using highly replicated trial designs. The experiments removed the confounding of rotational position and site which interferes with the interpretation of second wheat

performance in RL trials. Experiments were carried out over a two year cropping period (2004-05 and 2005-06) at two sites which differed in soil type and disease pressure. Treatments included natural infection in a first and second wheat situation and artificial inoculation with Gaeumannomyces graminis var. tritici (Ggt) within a first wheat situation. Development of the take-all epidemic was monitored by means of sequential sampling in discard plots of susceptible cv. Equinox. Take-all was assessed in all plots during grain filling or post-harvest. All plots were taken to yield and samples were retained for grain quality determination. The relative performance of varieties was broadly consistent across the two contrasting experimental sites, despite differences in disease pressure, and significant cultivar by rotational position interactions for yield were found at both sites. Cordiale showed lower levels of take-all than the other varieties, consistently across experiments, although the differences were usually not significant. Differences in yield loss between varieties within each experiment were greater than differences in disease severity, suggesting that second wheat performance differences are due more to tolerance/intolerance (yield loss per unit disease severity) than resistance. At any given level of take-all severity, yield losses were greater in phased second wheat plots than in Ggt inoculated first wheat plots. The last of these findings was not explained by differences in cereal cyst

Figure 11: left: Effects of Take All in the field, right: A wheat root infected by Take All

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nematode burden or eyespot severity, both of which were at negligible levels in all trials. Possible mechanisms to explain the effects seen include differences in (i) crop nutrition, (ii) absolute healthy root number, affecting below-ground resource capture, or iii) the temporal or spatial distribution of the inoculated and natural (2nd wheat) take-all epidemics. Fertile shoot/ear numbers (which are sensitive to nitrogen uptake), total root counts and the disease progress curves did not differ sufficiently between treatments to support nutrition, healthy root number or epidemic timing differences as plausible explanations. A more likely hypothesis for the greater yield loss per unit disease in second wheat plots is that primary infection lesions arising from artificial inoculation, and the secondary infections arising from them, were largely confined to the sampled upper roots. Whereas lesions arising from natural inoculum in the second wheats may also have resulted in lesions on roots below those sampled. The complete report from the 2 year study will soon be available on the WGIN website.

WGIN Peer reviewed publications: Hayden M J, Stephenson P, Logojan A M, Khatkar D, Rogers C, Elsden J, Koebner R M D, Snape J W and Sharp P J . Development and genetic mapping of sequence tagged microsatellites (STMs) in bread wheat (Triticum aestivum L). Theoretical and Applied Genetics 113 (2006), pp. 1271-1281 Hai-Chun Jing, Dimitry Kornyukhin, Kostya Kanyuka, Simon Orford, Anastasiya Zlatska, Olga P. Mitrofanova, Robert Koebner, Kim Hammond-Kosack. Identification of variation in adaptively important traits and genome-wide analysis of trait-marker associations in Triticum monococcum. Journal of Experimental Botany 58 (2007), pp. 3749-3764. Nadia Al-Kaff, Emilie Knight, Isabelle Bertin, Tracie Foote, Nicola Hart, Simon Griffiths and Graham Moore. Detailed dissection of the chromosomal region containing the Ph1 locus in wheat Triticum aestivum: with deletion mutants and expression profiling, Annals of Botany 2008 101(6):863-872 In Press: Hai-Chun Jing, Darren Lovell, Richard Gutteridge, Daniel Jenk, Dmitry Kornyukhin, Olga Mitrovanova, Gert Kema, Kim Hammond-Kosack. Phenotypic and genetic analysis of the Triticum monococcum - Mycosphaerella graminicola interaction. New Phytologist.

For further information on the WGIN project please see www.wgin.org.uk or contact us at [email protected]

The contributors to this newsletter were: At Rothamsted Research Malcolm Hawkesford, Kim Hammond-

Kosack and Elke Anzinger and at the John Innes Centre Simon Griffiths Neil Paveley at ADAS and Rosemary Bayles at NIAB contributed the section on the second wheat

syndrome project.

Next WGIN stakeholder meeting: 14th November 2008 at Rothamsted Research

Next WGIN management meeting:

20 June 2008 at Rothamsted Research This management meeting will include a visit to the WGIN field trials and the

Rothamsted Research classical experiments

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mailto:[email protected]


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defra wheat genetic improvement network – the core ... · resources from rice to orphan crops:...

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