Theoretical and Applied Genetics

, Volume 128, Issue 6, pp 999–1017 | Cite as

MAGIC populations in crops: current status and future prospects

  • B. Emma HuangEmail author
  • Klara L. Verbyla
  • Arunas P. Verbyla
  • Chitra Raghavan
  • Vikas K. Singh
  • Pooran Gaur
  • Hei Leung
  • Rajeev K. Varshney
  • Colin R. Cavanagh


Key message

MAGIC populations present novel challenges and opportunities in crops due to their complex pedigree structure. They offer great potential both for dissecting genomic structure and for improving breeding populations.


The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery.


Quantitative Trait Locus Quantitative Trait Locus Analysis Quantitative Trait Locus Mapping Collaborative Cross Marker Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Many thanks to three anonymous reviewers for their helpful suggestions. Dr. Huang is the recipient of an Australian Research Council Discovery Early Career Researcher Award (Project Number DE120101127).

Conflict of interest

No authors have any conflicts of interest.


  1. Ahfock D, Wood I, Stephen S, Cavanagh CR, Huang BE (2014) Characterizing uncertainty in high-density maps from multiparental populations. Genetics 198:117–128CrossRefPubMedGoogle Scholar
  2. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61CrossRefPubMedGoogle Scholar
  3. Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA et al (2011) Genetic analysis of complex traits in the emerging collaborative cross. Genome Res 21:1213–1222CrossRefPubMedCentralPubMedGoogle Scholar
  4. Bailey DW (1971) Recombinant-inbred strains: an aid to finding identity, linkage, and function of histocompatibility and other genes. Transplantation 11:325–327CrossRefPubMedGoogle Scholar
  5. Bandillo N, Raghavan C, Muyco PA, Sevilla MAL, Lobina IT et al (2013) Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding. Rice 6:11CrossRefPubMedGoogle Scholar
  6. Bardol N, Ventelon M, Mangin B, Jasson S, Loywick V et al (2013) Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theor Appl Genet 126:2717–2736CrossRefPubMedGoogle Scholar
  7. Bink MCAM, Boer MP, Ter Braak CJF, Jansen J, Voorrips RE et al (2008) Bayesian analysis of complex traits in pedigreed plant populations. Euphytica 161:85–96CrossRefGoogle Scholar
  8. Blakeslee AF, Belling J, Farnham ME, Bergner AD (1922) A haploid mutant in the Jimson weed, “Datura Stramonium”. Science 55:646–647CrossRefPubMedGoogle Scholar
  9. Bottomly D, Ferris MT, Aicher LD, Rosenzweig E, Whitmore A et al (2012) Expression quantitative trait loci for extreme host response to influenza A in pre-Collaborative Cross mice. G3 2:213–221CrossRefPubMedCentralPubMedGoogle Scholar
  10. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y et al (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635CrossRefPubMedGoogle Scholar
  11. Brim CA (1966) A modified pedigree method of selection in soybeans. Crop Sci 6:220CrossRefGoogle Scholar
  12. Broman K (2005) The genomes of recombinant inbred lines. Genetics 169:1133–1146CrossRefPubMedCentralPubMedGoogle Scholar
  13. Broman KW (2012) Genotype probabilities at intermediate generations in the construction of recombinant inbred lines. Genetics 190:403–412CrossRefPubMedCentralPubMedGoogle Scholar
  14. Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890CrossRefPubMedGoogle Scholar
  15. Buet C, Dubreuil P, Tixier M-H, Durantin K, Praud S et al (2013) The molecular characterization of a MAGIC population reveals high potential for gene discovery. MaizeGDB proceedingsGoogle Scholar
  16. Butler D (2009) asreml: asreml() fits the linear mixed model. R package version 3.0.
  17. Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Op Plant Biol 11:215–221CrossRefGoogle Scholar
  18. Cavanagh C, Chao S, Wang S, Huang BE, Stephen S et al (2013) Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc Natl Acad Sci 110:8057–8062CrossRefPubMedCentralPubMedGoogle Scholar
  19. Collaborative Cross Consortium (2012) The genome architecture of the collaborative cross mouse genetic reference population. Genetics 190:389–401CrossRefPubMedCentralGoogle Scholar
  20. Complex Trait Consortium (2004) The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet 36:1133–1137CrossRefGoogle Scholar
  21. Corbett-Detig RB, Zhou J, Clark AG, Hartl DL, Ayroles JF (2013) Genetic incompatibilities are widespread within species. Nature 504:135–137CrossRefPubMedGoogle Scholar
  22. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393CrossRefGoogle Scholar
  23. Darvasi A, Soller M (1995) Advanced intercross lines, an experimental population for fine genetic mapping. Genetics 141:1199–1207PubMedCentralPubMedGoogle Scholar
  24. Das S, Zijdenbos AP, Harlap J, Vins D, Evans AC (2011) LORIS: a web-based data management system for multi-center studies. Front Neuroinform 5:37PubMedCentralPubMedGoogle Scholar
  25. Demarest K, Koyner J, McCaughran J Jr, Cipp L, Hitzemann R (2001) Further characterization and high- resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behav Genet 31:79–91CrossRefPubMedGoogle Scholar
  26. Durrant C, Mott R (2010) Bayesian quantitative trait locus mapping using inferred haplotypes. Genetics 184:839–852CrossRefPubMedCentralPubMedGoogle Scholar
  27. Durrant C, Swertz MA, Alberts R, Arends D, Moller S et al (2012) Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs. Brief Bioinform 13:135–142CrossRefPubMedCentralPubMedGoogle Scholar
  28. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K et al (2011) A robust, simple Genotyping-by-Sequencing (GBS) approach for high diversity species. PLoS One 6(5):e19379CrossRefPubMedCentralPubMedGoogle Scholar
  29. Esch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES (2007) Using crossover breakpoints in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics 177:1851–1858CrossRefPubMedCentralPubMedGoogle Scholar
  30. Forster BP, Bors-Heberle E, Kasha KJ, Touraev A (2007) The resurgence of haploids in higher plants. Trends Plant Sci 12:368–375CrossRefPubMedGoogle Scholar
  31. Furbank RT, Tester M (2011) Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644CrossRefPubMedGoogle Scholar
  32. Gan X, Stegle O, Behr J, Steffen JG, Drewe P et al (2011) Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature 477:419–423CrossRefPubMedGoogle Scholar
  33. Gaur PM, Jukanti AK, Varshney RK (2012) Impact of genomic technologies on chickpea breeding strategies. Agronomy 2:199–221CrossRefGoogle Scholar
  34. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V et al (2014) Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the Flint and Dent heterotic groups for maize. Genetics 198:1717–1734CrossRefPubMedGoogle Scholar
  35. Goulden CH (1939) Problems in plant selection. In: Proceedings of the Seventh International Genetics Congress. Cambridge University Press, pp 132–133Google Scholar
  36. Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–732CrossRefGoogle Scholar
  37. Gyenesei A, Moody J, Semple CAM, Haley CS, Wei W-H (2012) High throughput analysis of epistasis in genome-wide association studies with BiForce. Bioinformatics 28:1957–1964CrossRefPubMedCentralPubMedGoogle Scholar
  38. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324CrossRefPubMedGoogle Scholar
  39. Harushima Y, Yano M, Shomura A, Sato M, Shimano T et al (1998) A high-density rice genetic linkage map with 2275 markers using a single F2 population. Genetics 148:479–494PubMedCentralPubMedGoogle Scholar
  40. Hemani G, Theocharidis A, Wei W, Haley C (2011) EpiGPU: exhaustive pairwise epistasis scans parallelized on consumer level graphics cards. Bioinformatics 27:1462–1465CrossRefPubMedGoogle Scholar
  41. Hickey JM, Gorjanc G, Hearne S, Huang BE (2014) AlphaMPSim: flexible simulation of multi-parent crosses. Bioinformatics 30:2686–2688CrossRefPubMedGoogle Scholar
  42. Howe D, Costanzo M, Fey P, Gojobori T, Hannick L et al (2008) Big data: the future of biocuration. Nature 455:47–50CrossRefPubMedCentralPubMedGoogle Scholar
  43. Huang BE, George AW (2011) R/mpMap: a computational platform for the genetic analysis of multi-parent recombinant inbred lines. Bioinformatics 27:727–729CrossRefPubMedGoogle Scholar
  44. Huang X, Feng Q, Qian Q, Zhao Q, Wang L et al (2009) High-throughput genotyping by whole-genome resequencing. Genome Res 19:1068–1076CrossRefPubMedCentralPubMedGoogle Scholar
  45. Huang X, Paulo M-J, Boer M, Effgen S, Keizer P et al (2011) Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. PNAS 108(11):4488–4493CrossRefPubMedCentralPubMedGoogle Scholar
  46. Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ et al (2012) A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J 10:826–839CrossRefPubMedGoogle Scholar
  47. Huang BE, Clifford D, Cavanagh C (2013) Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity. Theor Appl Genet 126:379–388CrossRefGoogle Scholar
  48. Huang BE, Raghavan C, Mauleon R, Broman KW, Leung H (2014) Imputation of low-coverage genotyping-by-sequencing in multi-parental crosses. Genetics 197:401–404CrossRefPubMedCentralPubMedGoogle Scholar
  49. Jansen RC (1994) Controlling the type I and type II errors in mapping quantitative trait loci. Genetics 138:871–881PubMedCentralPubMedGoogle Scholar
  50. Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216PubMedCentralPubMedGoogle Scholar
  51. Kass RE (1993) Bayes factors in practice. Statistician 42:551–560CrossRefGoogle Scholar
  52. Kass RE, Raftery AE (1995) Bayes factors. JASA 90:773–795CrossRefGoogle Scholar
  53. King EG, Macdonald SJ, Long AD (2012a) Properties and power of the Drosophila Synthetic Population Resource for the routine dissection of complex traits. Genetics 191:935–949CrossRefPubMedCentralPubMedGoogle Scholar
  54. King EG, Merkes CM, McNeil CL, Hoofer SR, Sen S et al (2012b) Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res 22:1558–1566CrossRefPubMedCentralPubMedGoogle Scholar
  55. King EG, Sanderson BJ, McNeil CL, Long AD, Macdonald SJ (2014) Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet 10(5):e1004322CrossRefPubMedCentralPubMedGoogle Scholar
  56. Klasen JR, Piepho H-P, Stich B (2012) QTL detection power of multi-parental RIL populations in Arabidopsis thaliana. Heredity 108:626–632CrossRefPubMedCentralPubMedGoogle Scholar
  57. Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM et al (2009) A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet 5(7):e1000551CrossRefPubMedCentralPubMedGoogle Scholar
  58. Lai K, Lorenc MT, Edwards D (2012) Genomic databases for crop improvement. Agronomy 2:62–73CrossRefGoogle Scholar
  59. Leroux D, Rahmani A, Jasson S, Ventelon M, Louis F et al (2014) Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theor Appl Genet 127:921–933CrossRefPubMedCentralPubMedGoogle Scholar
  60. Mace ES, Hunt CH, Jordan DR (2013) Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time. Theor Appl Genet 126:1377–1395CrossRefPubMedGoogle Scholar
  61. Mackay IJ, Bansept-Basler P, Barber T, Bentley AR, Cockram J et al (2014) An eight-parent Multiparent Advanced Generation Inter-Cross population for winter-sown wheat: creation, properties and validation. G3 4:1603–1610CrossRefPubMedCentralPubMedGoogle Scholar
  62. Malosetti M, van Eeuwijk DA, Boer MP, Casas AM, Elia M et al (2011) Gene and QTL detection in a three-way barley cross under selection by a mixed model with kinship information using SNPs. Theor Appl Genet 122:1605–1616CrossRefPubMedCentralPubMedGoogle Scholar
  63. Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modelling genotype-by-environment interaction and its genetic basis. Front Physiol 4:44CrossRefPubMedCentralPubMedGoogle Scholar
  64. Maluszynski M, Kasha KJ, Szareiko I (2003) Published doubled haploid protocols in plant species. In: Doubled haploid production in crop plants, a manual. Kluwer Academic Publishers, Dordecht, pp 309–335Google Scholar
  65. McClearn GE, Wilson JR, Meredith W (1970) The use of isogenic and heterogenic mouse stocks in behavioral research. In: Lindzey G, Thiessen D (eds) Contributions to behavior-genetic analysis: the mouse as a prototype. Appleton Century Crofts, New York, pp 3–22Google Scholar
  66. McMullen MD, Kresovich S, Villeda HS, Bradbury PJ, Li H et al (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740CrossRefPubMedGoogle Scholar
  67. Meuwissen TH, Goddard ME (2001) Prediction of identity by descent probabilities from marker-haplotypes. Genet Sel Evol 33:605CrossRefPubMedCentralPubMedGoogle Scholar
  68. Mohring J, Piepho H-P (2009) Comparison of weighting in two-stage analyses of series of experiments. Crop Sci 39:1977–1988CrossRefGoogle Scholar
  69. Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436CrossRefPubMedGoogle Scholar
  70. Mott R, Talbot CJ, Turri MG, Collins AC, Flint J (2000) A new method for fine-mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci USA 97:12649–12654CrossRefPubMedCentralPubMedGoogle Scholar
  71. Pascual L, Desplat N, Huang BE, Desgroux A, Bruguier L et al (2015) Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era. Plant Biotechnol J (in press)Google Scholar
  72. Pea G, Dell’Acqua M, Hlaing ALL, Pe ME (2013) From mice to maize: a multiparental population for fine mapping in Zea mays. MAGIC Populations Workshop.
  73. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedCentralPubMedGoogle Scholar
  74. Ram R, Mehta M, Balmer L, Gatti DM, Morahan G (2014) Rapid identification of major effect genes using the Collaborative Cross. Genetics 198:75–86CrossRefPubMedCentralPubMedGoogle Scholar
  75. Rebetzke GJ, Verbyla AP, Verbyla KL, Morell MK, Cavanagh CR (2014) Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth. Plant Biotechnol J 12:219–230CrossRefPubMedGoogle Scholar
  76. Sannemann W, Huang BE, Mathew B, Léon J (2015) Multi-parent advanced generation inter-cross in barley: high-resolution quantitative trait locus mapping for flowering time as a proof of concept. Mol Breeding 35:86CrossRefGoogle Scholar
  77. Schmitt CP, Burchinal M (2011) Data management practices for collaborative research. Front Psychiatry 2:47CrossRefPubMedCentralPubMedGoogle Scholar
  78. Schnaithmann F, Kopahnke D, Pillen K (2014) A first step toward the development of a barley NAM population and its utilization to detect QTLs conferring leaf rust seedling resistance. Theor Appl Genet 127:1513–1525CrossRefPubMedGoogle Scholar
  79. Scutari M, Howell P, Balding DJ, Mackay IJ (2014) Multiple quantitative trait analysis using Bayesian networks. Genetics 198:129–137CrossRefPubMedGoogle Scholar
  80. Smith AB, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci Camb 144:393–409CrossRefGoogle Scholar
  81. Smith AB, Thompson R, Butler DC, Cullis BR (2011) The design and analysis of variety trials using mixtures of composite and individual plot samples. J Royal Stat Soc C 60:437–455CrossRefGoogle Scholar
  82. Smith AB, Butler DG, Cavanagh CR, Cullis BR (2015) Multi-phase variety trials using both composite and individual replicate samples: a model-based design approach. J Agric Sci Camb (in press)Google Scholar
  83. Stich B (2009) Comparison of mating designs for establishing Nested Association Mapping populations in maize and Arabidopsis thaliana. Genetics 183:1525–1534CrossRefPubMedCentralPubMedGoogle Scholar
  84. Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R et al (2012) High-resolution genetic mapping using the Mouse Diversity outbred population. Genetics 190:437–447CrossRefPubMedCentralPubMedGoogle Scholar
  85. Thépot S, Restoux G, Goldringer I, Hospital F, Gouache D, Mackay I, Enjalbert J (2015) Efficiently tracking selection in a multiparental population: the case of earliness in wheat. Genetics 199:609–623CrossRefPubMedGoogle Scholar
  86. Valdar W, Flint J, Mott R (2006) Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics 172:1783–1797CrossRefPubMedCentralPubMedGoogle Scholar
  87. Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182:1263–1277CrossRefPubMedCentralPubMedGoogle Scholar
  88. van Eeuwijk FA, Bink MC, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13:193–205CrossRefPubMedGoogle Scholar
  89. Verbyla AP, Cullis BR (2012) Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments. Theor Appl Genet 125:933–953CrossRefPubMedGoogle Scholar
  90. Verbyla AP, Cullis BR, Thompson R (2007) The analysis of QTL by simultaneous use of the full linkage map. Theor Appl Genet 116:95–111CrossRefPubMedGoogle Scholar
  91. Verbyla AP, George AW, Cavanagh CR, Verbyla KL (2014a) Whole genome QTL analysis for MAGIC. Theor Appl Genet 127:1753–1770CrossRefPubMedGoogle Scholar
  92. Verbyla AP, Cavanagh CR, Verbyla KL (2014b) Whole genome analysis of multi-environment or multi-trait QTL in MAGIC G3(4):1569–1584Google Scholar
  93. Wang J, de Villena FP, Lawson HA, Cheverud JM, Churchill GA et al (2012) Imputation of single-nucleotide polymorphisms in inbred mice using local phylogeny. Genetics 190:449–458CrossRefPubMedCentralPubMedGoogle Scholar
  94. Wang S, Wong D, Forrest K, Allen A, Chao S et al (2014) Characterization of polyploidy wheat genomic diversity using the high-density 90,000 SNP array. Plant Biotechnol J 12:787–796CrossRefPubMedCentralPubMedGoogle Scholar
  95. Xu S (1996) Mapping quantitative trait loci using four-way crosses. Genet Res 68:175–181CrossRefGoogle Scholar
  96. Yalcin B, Flint J, Mott R (2005) Using progenitor strain information to identify quantitative trait nucleotides in outbred mice. Genetics 171:673–681CrossRefPubMedCentralPubMedGoogle Scholar
  97. Yamamoto E, Iwata H, Tanabata T, Mizobuchi R, Yonemaru J et al (2014) Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice. BMC Genet 15:50CrossRefPubMedCentralPubMedGoogle Scholar
  98. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551CrossRefPubMedCentralPubMedGoogle Scholar
  99. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedCentralPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • B. Emma Huang
    • 1
    Email author
  • Klara L. Verbyla
    • 2
  • Arunas P. Verbyla
    • 3
  • Chitra Raghavan
    • 4
  • Vikas K. Singh
    • 5
  • Pooran Gaur
    • 5
  • Hei Leung
    • 4
  • Rajeev K. Varshney
    • 5
    • 6
  • Colin R. Cavanagh
    • 7
  1. 1.Digital Productivity and Agriculture FlagshipsCSIRODutton ParkAustralia
  2. 2.Digital Productivity and Agriculture FlagshipsCSIROCanberraAustralia
  3. 3.Digital Productivity and Agriculture FlagshipsCSIROAthertonAustralia
  4. 4.Plant Breeding, Genetics and Biotechnology DivisionInternational Rice Research InstituteManilaPhilippines
  5. 5.International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)PatancheruIndia
  6. 6.School of Plant Biology and Institute of AgricultureThe University of Western AustraliaCrawleyAustralia
  7. 7.Agriculture FlagshipCSIROCanberraAustralia

Personalised recommendations