Skip to main content

Genotype by Environment Interaction and Adaptation

  • Living reference work entry
  • First Online:

Glossary

Adaptation (specific and wide):

A genotype is considered to have wide adaptation if its yield performance is better than that of a reference genotype. When this superiority covers the full range of potential growing conditions, the target population of environments (TPE), we call the genotype generally, widely, or broadly adapted. When it concerns a specific part of the growing conditions the genotype is called specifically or narrowly adapted. Wide adaptation invariably means a high mean yield, and therefore widely adapted genotypes have, statistically speaking, a large genotypic main effect. Narrowly adapted genotypes have relatively high yield under specific conditions and typically don’t have a high genotypic main effect.

CGM:

A common way to understand a crop growth modelis as a set of coupled mathematical equations that together predict a target phenotype (commonly grain yield) and a number of related intermediate phenotypes (yield components, like biomass and grain...

This is a preview of subscription content, log in via an institution.

Bibliography

  1. Adee E, Roozeboom K, Balboa GR, Schlegel A, Ciampitti IA (2016) Drought-tolerant corn hybrids yield more in drought-stressed environments with no penalty in non-stressed environments. Front Plant Sci 7:1–9. http://journal.frontiersin.org/article/10.3389/fpls.2016.01534/full

    Google Scholar 

  2. Alimi NA (2016) Statistical methods for QTL mapping and genomic prediction of multiple traits and environments: case studies in pepper. http://edepot.wur.nl/390205

  3. Allen AM, Winfield MO, Burridge AJ, Downie RC, Benbow HR, Barker GLA, Wilkinson PA, Coghill J, Waterfall C, Davassi A, Scopes G, Pirani A, Webster T, Brew F, Bloor C, Griffiths S, Bentley AR, Alda M, Jack P, Phillips AL, Edwards KJ (2017) Characterization of a Wheat Breeders’ Array suitable for high-throughput SNP genotyping of global accessions of hexaploid bread wheat (Triticum aestivum). Plant Biotech J 15:390–401

    Article  CAS  Google Scholar 

  4. Alqudah AM, Sharma R, Pasam RK, Graner A, Kilian B, Schnurbusch T (2014) Genetic dissection of photoperiod response based on GWAS of pre-anthesis phase duration in spring barley. PLoS One 9(11):e113120

    Article  CAS  Google Scholar 

  5. Alqudah AM, Koppolu R, Wolde GM, Graner A, Schnurbusch T (2016) The genetic architecture of barley plant stature. Front Genet 7:117

    Article  CAS  Google Scholar 

  6. Alvarez MA, Tranquilli G, Lewis S, Kippes N, Dubcovsky J (2016) Genetic and physical mapping of the earliness per se locus Eps-A m 1 in Triticum monococcum identifies EARLY FLOWERING 3 (ELF3) as a candidate gene. Funct Integr Genomics 16:365–382

    Article  CAS  Google Scholar 

  7. Álvaro F, Isidro J, Villegas D, García del Moral LF, Royo C (2008) Breeding effects on grain filling, biomass partitioning, and remobilization in Mediterranean durum wheat. Agron J 100:361–370

    Article  Google Scholar 

  8. Annicchiarico P (2002) Genotype × environment interactions: challenges and opportunities for plant breeding and cultivar recommendations. FAO plant production and protection paper no. 174. FAO, Rome

    Google Scholar 

  9. Annicchiarico P (2009) Coping with and exploiting genotype-by-environment interactions. In: Ceccarelli S, Guimaraës EP, Weltzien E (eds) Participatory plant breeding. FAO, Rome, pp 519–564

    Google Scholar 

  10. Appendino ML, Slafer GA (2003) Earliness per se and its dependence upon temperature in diploid wheat lines differing in the major gene Eps-A m 1 alleles. J Agric Sci 141:149–154

    Article  CAS  Google Scholar 

  11. Appleyard M, Kirby EJM, Fellowes G (1982) Relationships between the duration of phases in the pre-anthesis life cycle of spring barley. Aust J Agric Res 33:917–925

    Article  Google Scholar 

  12. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. http://www.sciencedirect.com/science/article/pii/S1360138513001994

    Article  CAS  Google Scholar 

  13. Araus JL, Bort J, Steduto P, Villegas D, Royo C (2003) Breeding cereals for Mediterranean conditions: ecophysiological clues for biotechnology application. Ann Appl Biol 142:129–141

    Article  Google Scholar 

  14. Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27:377–412

    Article  Google Scholar 

  15. Atlin GN, Baker RJ, McRae KB, Lu X (2000) Selection response in subdivided target regions. Crop Sci 40(1):7–13. https://www.crops.org/publications/cs/abstracts/40/1/7

    Article  Google Scholar 

  16. Atlin GN, Kleinknecht K, Singh GP, Piepho HP (2011) Managing genotype x environment interaction in plant breeding programs: a selection theory approach. J Indian Soc Agric Stat 65(2):237–247

    Google Scholar 

  17. Auinger H-J, Schönleben M, Lehermeier C, Schmidt M, Korzun V, Geiger HH, Piepho H-P, Gordillo A, Wilde P, Bauer E, Schön C-C (2016) Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.) Theor Appl Genet 129(11):2043–2053. https://doi.org/10.1007/s00122-016-2756-5

    Article  CAS  Google Scholar 

  18. Baum M, Grando S, Backes G, Jahoor A, Sabbagh A, Ceccarelli S (2003) QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross ‘Arta’ × H. spontaneum 41-1. Theor Appl Genet 107:1215–1225

    Article  CAS  Google Scholar 

  19. Bayer MM, Rapazote-Flores P, Ganal M, Hedley PE, Macaulay M, Plieske J, Ramsay L, Russell J, Shaw PD, Thomas W, Waugh R (2017) Development and evaluation of a barley 50 k iSelect SNP array. Front Plant Sci 8:1792

    Article  Google Scholar 

  20. Beales J, Turner A, Griffiths S, Snape JW, Laurie DA (2007) A pseudo-response regulator is misexpressed in the photoperiod insensitive Ppd-D1a mutant of wheat (Triticum aestivum L.) Theor Appl Genet 115:721–733

    Article  CAS  Google Scholar 

  21. Bernardo R (2014) Genomewide selection when major genes are known. Crop Sci 54(1):68–75. https://www.crops.org/publications/cs/abstracts/54/1/68

    Article  Google Scholar 

  22. Bezant J, Laurie D, Pratchett N, Chojecki J, Kearsey M (1996) Marker regression mapping of QTL controlling flowering time and plant height in a spring barley (Hordeum vulgare L.) cross. Heredity 77:64–73

    Article  CAS  Google Scholar 

  23. Biscarini F, Nazzicari N, Bink M, Arús P, Aranzana MJ, Verde I, Micali S, Pascal T, Quilot-Turion B, Lambert P, da Silva Linge C, Pacheco I, Bassi D, Stella A, Rossini L (2017) Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies. BMC Genomics 18(1):432. https://doi.org/10.1186/s12864-017-3781-8

    Article  Google Scholar 

  24. Blum A (2005) Drought resistance, water-use efficiency, and yield potential – are they compatible, dissonant, or mutually exclusive? Aust J Agric Res 56:1159–1168. www.publish.csiro.au/journals/ajar

    Article  Google Scholar 

  25. Boer MP, Wright D, Feng L, Podlich D, Luo L, Cooper M, van Eeuwijk FA (2007) A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize. Genetics 177(3):1801–1813. http://www.genetics.org/content/177/3/1801.abstract

    Article  Google Scholar 

  26. Bonnin I, Rousset M, Madur D, Sourdille P, Dupuits C, Brunel D, Goldringer I (2008) FT genome A and D polymorphisms are associated with the variation of earliness components in hexaploid wheat. Theor Appl Genet 116:383–394

    Article  CAS  Google Scholar 

  27. Börner A, Buck-Sorlin GH, Hayes PM, Malyshev S, Korzun V (2002) Molecular mapping of major genes and quantitative trait loci determining flowering time in response to photoperiod in barley. Plant Breed 121:129–132

    Article  Google Scholar 

  28. Borràs G, Romagosa I, van Eeuwijk F, Slafer GA (2009) Genetic variability in the duration of pre-heading phases and relationships with leaf appearance and tillering dynamics in a barley population. Field Crop Res 113:95–104

    Article  Google Scholar 

  29. Borràs-Gelonch G, Slafer GA, Casas A, van Eeuwijk F, Romagosa I (2010) Genetic control of pre-heading phases and other traits related to development in a double haploid barley population (Hordeum vulgare L.). Field Crop Res 119:36–47

    Google Scholar 

  30. Borràs-Gelonch G, Rebetzke G, Richards R, Romagosa I (2011) Genetic control of duration of pre-anthesis phases in wheat (Triticum aestivum L.) and relationships to leaf appearance, tillering and dry matter accumulation. J Exp Bot 63:69. https://doi.org/10.1093/jxb/err230

    Article  CAS  Google Scholar 

  31. Borràs-Gelonch G, Denti M, Thomas WTB, Romagosa I (2012) Genetic control of pre-heading phases in the Steptoe × Morex barley population under different conditions of photoperiod and temperature. Euphytica 183:303–321. https://doi.org/10.1007/s10681-011-0526-7

    Article  Google Scholar 

  32. Bradshaw AD, Caspari EW, Thoday JM (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155

    Google Scholar 

  33. Braun H-J, Rajaram S, van Ginkel M (1996) CIMMYT’s approach to breeding for wide adaptation. Euphytica 92(1–2):175–183. https://doi.org/10.1007/BF00022843

    Article  Google Scholar 

  34. Buckler ES, Holland JB, Bradbury PJ et al (2009) The genetic architecture of maize flowering time. Science 325:714–718

    Article  CAS  Google Scholar 

  35. Bullrich L, Appendino ML, Tranquilli G, Lewis S, Dubcovsky J (2002) Mapping of a thermo-sensitive earliness per se gene on Triticum monococcum chromosome 1Am. Theor Appl Genet 105:585–593

    Article  CAS  Google Scholar 

  36. Bustos-Korts D, Malosetti M, Chapman S, Biddulph B, van Eeuwijk F (2016) Improvement of predictive ability by uniform coverage of the target genetic space. G3 Genes Genom Genet 6(11):3733–3747. http://www.g3journal.org/content/early/2016/09/22/g3.116.035410.abstract

    Google Scholar 

  37. Bustos-Korts D, Malosetti M, Chapman S, van Eeuwijk FA (2016) Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics. In: Yin X, Struik PC (eds) Crop systems biology – narrowing the gaps between crop modelling and genetics. Springer, pp 55–82

    Google Scholar 

  38. Bustos-Korts D, Malosetti M, Chapman SC, Chenu K, Boer M, van Eeuwijk FA (2017) A protocol combining statistical and crop growth modelling to evaluate phenotyping strategies useful for selection under different drought patterns. Modelling of genotype by environment interaction and prediction of complex traits across multiple environments as a synthesis of crop growth modelling, genetics and statistics. PhD thesis

    Google Scholar 

  39. Calderini DF, Slafer GA (1999) Has yield stability changed with genetic improvement of wheat yield? Euphytica 107(1):51–59. https://doi.org/10.1023/A:1003579715714

    Article  Google Scholar 

  40. Calixto CPG, Waugh R, Brown JWS (2015) Evolutionary relationships among barley and Arabidopsis core circadian clock and clock-associated genes. J Mol Evol 80:108–119

    Article  CAS  Google Scholar 

  41. Campoli C, Pankin A, Drosse B, Casao CM, Davis SJ, von Korff M (2013) HvLUX1 is a candidate gene underlying the early maturity 10 locus in barley: phylogeny, diversity, and interactions with the circadian clock and photoperiodic pathways. New Phytol 199:1045–1059

    Article  CAS  Google Scholar 

  42. Casao MC, Igartua E, Karsai I, Bhat PR, Cuadrado N, Gracia MP, Lasa JM, Casas AM (2011) Introgression of an intermediate VRNH1 allele in barley (Hordeum vulgare L.) leads to reduced vernalization requirement without affecting freezing tolerance. Mol Breed. https://doi.org/10.1007/s11032-010-9497

  43. Cavanagh C, Morell M, Mackay I, Powell P (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11:215–221

    Article  CAS  Google Scholar 

  44. Ceccarelli S (1989) Wide adaptation: how wide? Euphytica 40(3):197–205. https://doi.org/10.1007/BF00024512

    Article  Google Scholar 

  45. Ceccarelli S (1996) Adaptation to low/high input cultivation. Euphytica 92(1–2):203–214. https://doi.org/10.1007/BF00022846

    Article  Google Scholar 

  46. Ceccarelli S, Grando S, Impiglia A (1998) Choice of selection strategy in breeding barley for stress environments. Euphytica 103(3):307–318. https://doi.org/10.1023/A%3A1018647001429

    Article  Google Scholar 

  47. Chapman SC, Cooper M, Hammer GL, Butler DG (2000) Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Aust J Agric Res 51(2):209–222. http://www.publish.csiro.au/paper/AR99021

    Article  Google Scholar 

  48. Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95(1):99–113. https://dl.sciencesocieties.org/publications/aj/abstracts/95/1/99

    Article  Google Scholar 

  49. Chapman SC, Merz T, Chan A, Jackway P, Hrabar S, Dreccer MF, Holland E, Zheng B, Ling TJ, Jimenez-Berni J (2014) Pheno-copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4(2):279–301

    Article  Google Scholar 

  50. Chapuis R, Delluc C, Debeuf R, Tardieu F, Welcker C (2012) Resiliences to water deficit in a phenotyping platform and in the field: how related are they in maize? Eur J Agron 42:59–67

    Article  Google Scholar 

  51. Chen A, Baumann U, Fincher GB, Collins NC (2009) Flt-2L, a locus in barley controlling flowering time, spike density, and plant height. Funct Integr Genomics 9:243–254

    Article  CAS  Google Scholar 

  52. Chen A, Li C, Hu W, Lau MY, Lin H, Rockwell NC, Martin SS, Jernstedt JA, Lagarias JC, Dubcovsky J (2014) PHYTOCHROME C plays a major role in the acceleration of wheat flowering under long-day photoperiod. Proc Natl Acad Sci 111:10037–10044

    Article  CAS  Google Scholar 

  53. Chenu K, Chapman SC, Hammer GL, McLean G, Salah HBH, Tardieu F (2008) Short-term responses of leaf growth rate to water deficit scale up to whole-plant and crop levels: an integrated modelling approach in maize. Plant Cell Environ 31(3):378–391. https://doi.org/10.1111/j.1365-3040.2007.01772.x

    Article  Google Scholar 

  54. Chenu K, Chapman SC, Tardieu F, McLean G, Welcker C, Hammer GL (2009) Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a “gene-to-phenotype” modeling approach. Genetics 183(4):1507–1523. http://www.genetics.org/content/183/4/1507.abstract

    Article  Google Scholar 

  55. Chenu K, Cooper M, Hammer GL, Mathews KL, Dreccer MF, Chapman SC (2011) Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia. J Exp Bot 62(6):1743–1755. http://jxb.oxfordjournals.org/content/62/6/1743.abstract

    Article  CAS  Google Scholar 

  56. Chenu K, Deihimfard R, Chapman SC (2013) Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt – spatial and temporal trends. New Phytol 198(3):801–820. https://doi.org/10.1111/nph.12192

    Article  Google Scholar 

  57. Chenu K, Porter JR, Martre P, Basso B, Chapman SC, Ewert F, Bindi M, Asseng S (2017) Contribution of crop models to adaptation in wheat. Trends Plant Sci 22(6):472–490. https://doi.org/10.1016/j.tplants.2017.02.003

    Article  CAS  Google Scholar 

  58. Cockram J, Jones H, Leigh FJ, O’Sullivan D, Powell W, Laurie DA, Greenland A (2007) Control of flowering time in temperate cereals, genes, domestication, and sustainable productivity. J Exp Bot 58:1231–1244

    Article  CAS  Google Scholar 

  59. Cockram J, Thiel T, Steuernagel B, Stein N, Taudien S, Bailey PC, O’Sullivan DM (2012) Genome dynamics explain the evolution of flowering time CCT domain gene families in the Poaceae. PLoS One 7(9):e45307

    Article  CAS  Google Scholar 

  60. Comadran J, Thomas WTB, van Eeuwijk FA, Ceccarelli S, Grando S, Stanca AM, Pecchioni N, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Bort J, Romagosa I, Hacket CA, Russell JR (2009) Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association mapping population for the Mediterranean basin. Theor Appl Genet 119:175–187

    Article  CAS  Google Scholar 

  61. Comadran J, Kilian B, Russell J, Ramsay L, Stein N, Ganal M, Shaw P, Bayer M, Thomas W, Marshall D, Hedley P, Tondelli A, Pecchioni N, Francia E, Korzun V, Walther A, Waugh R (2012) Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley. Nat Genet 44:1388–1392

    Article  CAS  Google Scholar 

  62. Comstock R (1977) Quantitative genetics and the design of breeding programme. In: Proceedings of the international conference on quantitative genetics. Iowa State University Press, Ames, pp 705–718

    Google Scholar 

  63. Comstock RE, Moll RH (1963) Genotype-environment interactions. In: Hanson WD, Robinson HF (eds) Statistical genetics and plant breeding: a symposium and workshop. National Academy of Sciences-National Research Council, Washington, DC, pp 164–196

    Google Scholar 

  64. Cooper M, Fox PN (1996) Environmental characterization based on probe and reference genotypes. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 529–547

    Google Scholar 

  65. Cooper M, Hammer GL (eds) (1996) Plant adaptation and crop improvement. CAB International, Wallingford

    Google Scholar 

  66. Cooper M, Woodruff DR, Eisemann RL, Brennan PS, DeLacy IH (1995) A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor Appl Genet 90(3–4):492–502. https://doi.org/10.1007/BF00221995

    Article  CAS  Google Scholar 

  67. Cooper M, van Eeuwijk FA, Hammer GL, Podlich DW, Messina C (2009) Modeling QTL for complex traits: detection and context for plant breeding. Curr Opin Plant Biol 12(2):231–240

    Article  CAS  Google Scholar 

  68. Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014) Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J Exp Bot. http://jxb.oxfordjournals.org/content/early/2014/03/03/jxb.eru064.abstract

  69. Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65(4):311–336. http://www.publish.csiro.au/paper/CP14007

    Article  CAS  Google Scholar 

  70. Cooper M, Technow F, Messina C, Gho C, Radu Totir L (2016) Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial. Crop Sci 56(5):2141–2156

    Article  Google Scholar 

  71. Cornelius PL (1993) Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Sci 33:1186–1193

    Article  Google Scholar 

  72. Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Ceron-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K, Burgueño J (2013) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity (Edinb) 112(1):48–60. https://doi.org/10.1038/hdy.2013.16

    Article  Google Scholar 

  73. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975

    Article  CAS  Google Scholar 

  74. Cuesta-Marcos A, Casas AM, Hayes PM, Gracia MP, Lasa JM, Ciudad F, Codesal P, Molina-Cano JL, Igartua E (2009) Yield QTL affected by heading date in Mediterranean grown barley. Plant Breed 128:46–53

    Article  CAS  Google Scholar 

  75. Cullis BR, Gleeson AC (1991) Spatial analysis of field experiments-an extension to two dimensions. Biometrics 47(4):1449–1460. http://www.jstor.org/stable/2532398

    Article  Google Scholar 

  76. Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Env Stat 11(4):381–393. https://doi.org/10.1198/108571106X154443

    Article  Google Scholar 

  77. Cullis BR, Smith AB, Beeck CP, Cowling WA (2010) Analysis of yield and oil from a series of canola breeding trials. Part II. Exploring variety by environment interaction using factor analysis. Genome 53(11):1002–1016. http://www.ingentaconnect.com/content/nrc/gen/2010/00000053/00000011/art00019

    Article  CAS  Google Scholar 

  78. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182(1):375–385. http://www.genetics.org/content/182/1/375.abstract

    Article  CAS  Google Scholar 

  79. Dekkers JCM (2007) Prediction of response to marker-assisted and genomic selection using selection index theory. J Anim Breed Genet 124(6):331–341. https://doi.org/10.1111/j.1439-0388.2007.00701.x

    Article  CAS  Google Scholar 

  80. Deng W, Casao MC, Wang P, Sato K, Hayes PM, Finnegan EJ, Trevaskis B (2015) Direct links between the vernalization response and other key traits of cereal crops. Nat Commun 6:5882

    Article  Google Scholar 

  81. Denis JB (1988) Two-way analysis using covariates. Statistics 19:123–132

    Article  Google Scholar 

  82. Denis JB, Gower JC (1996) Asymptotic confidence regions for biadditive models: interpreting genotype-environment interactions. Appl Stat 45:479–492

    Article  Google Scholar 

  83. Denison RF (2009) Darwinian agriculture: real, imaginary and complex trade-offs as constraints and opportunities. In: Sadras VO, Calderini D (eds) Crop physiology. Applications for genetic improvement and agronomy. Academic, Burlington, pp 215–234

    Google Scholar 

  84. DeWitt TJ, Scheiner SM (2004) Phenotypic plasticity: functional and conceptual approaches. Oxford University Press, Oxford

    Google Scholar 

  85. Digel B, Tavakol E, Verderio G, Tondelli A, Xu X, Cattivelli L, Rossini L, von Korff M (2016) Photoperiod-H1 (Ppd-H1) controls leaf size. Plant Physiol 172:405–415

    Article  CAS  Google Scholar 

  86. Distelfeld A, Tranquilli G, Chengxia L, Yan L, Dubcovsky J (2009) Genetic and molecular characterization of the Vrn2 loci in tetraploid wheat. Plant Physiol 149:245–257

    Article  CAS  Google Scholar 

  87. Dixit S, Singh A, Sandhu N, Bhandari A, Vikram P, Kumar A (2017) Combining drought and submergence tolerance in rice: marker-assisted breeding and QTL combination effects. Mol Breed 37(12)

    Google Scholar 

  88. Dobzhansky T, Spassky B (1963) Genetics of natural populations. Xxxiv. Adaptive norm, genetic load and genetic elite in Drosophila pseudoobscura. Genetics 48(11):1467–1485. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1210433/

    CAS  Google Scholar 

  89. Eagles HA, Cane K, Vallance N (2009) The flow of alleles of important photoperiod basically and vernalisation genes through Australian wheat. Crop Pasture Sci 60:646–657

    Article  CAS  Google Scholar 

  90. Evans LT, Fischer RA (1999) Yield potential: its definition, measurement, and significance. Crop Sci 39(6):1544–1551. https://www.crops.org/publications/cs/abstracts/39/6/1544

    Article  Google Scholar 

  91. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, Essex

    Google Scholar 

  92. Faure S, Higgins J, Turner A, Laurie DA (2007) The flowering locus T-like gene family in barley (Hordeum vulgare). Genetics 176:599–609

    Article  CAS  Google Scholar 

  93. Faure S, Turner AS, Gruszka D, Christodoulou V, Davis SJ, von Korff M, Laurie DA (2012) Mutation at the circadian clock gene EARLY MATURITY 8 adapts domesticated barley (Hordeum vulgare) to short growing seasons. Proc Natl Acad Sci 109:8328–8333

    Article  CAS  Google Scholar 

  94. Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant breeding programme. Aust J Agric Res 14:742–754

    Article  Google Scholar 

  95. Fischer RA (2007) Understanding the physiological basis of yield potential in wheat. J Agric Sci 145:99–113

    Article  Google Scholar 

  96. Flintham JE, Börner A, Worland AJ, Gale MD (1997) Optimizing wheat grain yield: effects of Rht (gibberellin-insensitive) dwarfing genes. J Agr Sci 128(1):11–25. https://doi.org/10.1017/S0021859696003942

    Article  Google Scholar 

  97. Foulkes J, Slafer GA, Davies WJ, Berry P, Sylvester-Bradley R, Martre P, Calderini DF, Griffiths S, Reynolds M (2011) Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. J Exp Bot 62:469–486

    Article  CAS  Google Scholar 

  98. Fox PN, Crossa J, Romagosa I (1997) Multi-environment testing and genotype by environment interaction. In: Kempton RA, Fox PN (eds) Statistical methods for plant variety evaluation. Chapman and Hall, London, pp 117–137

    Chapter  Google Scholar 

  99. Franckowiak JD (1997) Revised linkage maps for morphological markers in barley, Hordeum vulgare. Barley Genet Newsl 26:9–21

    Google Scholar 

  100. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9:432–441

    Article  Google Scholar 

  101. Fu D, Szűcs P, Liuling Y, Helguera M, Skinner JS, Zitzewitz J, Hayes PM, Dubcovsky J (2005) Large deletions within the first intron in Vrn-1 are associated with spring growth habit in barley and wheat. Mol Gen Genomics 273:54–65

    Article  CAS  Google Scholar 

  102. Gabriel KR (1978) Least squares approximation of matrices by additive and multiplicative models. J R Stat Soc Ser B 40:186–196

    Google Scholar 

  103. Gabriel KR (1998) Generalised bilinear regression. Biometrika 85:689–700

    Article  Google Scholar 

  104. Gaffney J, Schussler J, Löffler C, Cai W, Paszkiewicz S, Messina C, Groeteke J, Keaschall J, Cooper M (2015) Industry-scale evaluation of maize hybrids selected for increased yield in drought-stress conditions of the US corn belt. Crop Sci 55(4):1608–1618

    Article  Google Scholar 

  105. Garin V, Wimmer V, Mezmouk S, Malosetti M, van Eeuwijk F (2017) How do the type of QTL effect and the form of the residual term influence QTL detection in multi-parent populations? A case study in the maize EU-NAM population. Theor. Appl. Genet 130(8):1–12

    Google Scholar 

  106. Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44:705–715

    Article  Google Scholar 

  107. Gauch HG (1992) Statistical analysis of regional yield trials. Elsevier, Amsterdam

    Google Scholar 

  108. Gawroński P, Ariyadasa R, Himmelbach A, Poursarebani N, Kilian B, Stein N, Steuernagel B, Hensel G, Kumlehn J, Sehgal SK, Gill BS, Gould P, Hall A, Schnurbusch T (2014) A distorted circadian clock causes early flowering and temperature-dependent variation in spike development in the Eps-3Am mutant of einkorn wheat. Genetics 196:1253–1261

    Article  CAS  Google Scholar 

  109. Goldringer I, Prouin C, Rousset M, Galic N, Bonnin I (2006) Rapid differentiation of experimental populations of wheat for heading time in response to local climatic conditions. Ann Bot 98:805–817

    Article  Google Scholar 

  110. Gollob HF (1968) A statistical model which combines features of factor analysis and analysis of variance techniques. Psychometrika 33:73–115

    Article  CAS  Google Scholar 

  111. González FG, Slafer GA, Miralles DJ (2002) Vernalization and photoperiod responses in wheat pre-flowering reproductive phases. Field Crop Res 74:183–195

    Article  Google Scholar 

  112. González FG, Slafer GA, Miralles DJ (2005) Pre-anthesis development and number of fertile florets in wheat as affected by photoperiod sensitivity genes Ppd-D1 and Ppd-B1. Euphytica 146:253–269

    Article  Google Scholar 

  113. Grando S, Ceccarelli S (2009) Breeding for quantitative variables. Part 3: breeding for resistance to abiotic stress. In: Ceccarelli S, Guimaraës EP, Weltzien E (eds) Participatory plant breeding. FAO, Rome, pp 391–417

    Google Scholar 

  114. Griffiths S, Simmonds J, Leverington M, Wang Y, Fish L, Sayers L, Alibert L, Orford S, Wingen L, Herry L, Faure S, Laurie D, Bilham L, Snape J (2009) Meta-QTL analysis of the genetic control of ear emergence in elite European winter wheat germplasm. Theor Appl Genet 119:383–395

    Article  CAS  Google Scholar 

  115. Guo Z, Tucker D, Basten C, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127(3):749–762. https://doi.org/10.1007/s00122-013-2255-x

    Article  Google Scholar 

  116. Guo Z, Chen D, Alqudah AM, Röder MS, Ganal MW, Schnurbusch T (2017) Genome-wide association analyses of 54 traits identified multiple loci for the determination of floret fertility in wheat. New Phytol 214:257–270

    Article  CAS  Google Scholar 

  117. Halliwell J, Borrill P, Gordon A, Kowalczyk R, Pagano ML, Saccomanno B, Bentley AR, Uauy C, Cockram J (2016) Systematic investigation of FLOWERING LOCUS T-like Poaceae gene families identifies the short-day expressed flowering pathway gene, TaFT3 in wheat (Triticum aestivum L.) Front Plant Sci 7:857

    Article  Google Scholar 

  118. Halloran GM, Pennell AL (1982) Duration and rate of development phases in wheat in two environments. Ann Bot 49:115–121

    Article  Google Scholar 

  119. Hammer G, Cooper M, Tardieu F, Welch S, Walsh B, van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11(12):587–593. http://www.sciencedirect.com/science/article/pii/S1360138506002810

    Article  CAS  Google Scholar 

  120. Hammer G, Messina C, van Oosterom E, Chapman S, Singh V, Borrell A, Jordan D, Cooper M (2016) Molecular breeding for complex adaptive traits: how integrating crop ecophysiology and modelling can enhance efficiency BT. In: Yin X, Struik PC (eds) Crop systems biology: narrowing the gaps between crop modelling and genetics. Springer International Publishing, Cham, pp 147–162

    Google Scholar 

  121. Hansen JW, Potgieter A, Tippett MK (2004) Using a general circulation model to forecast regional wheat yields in Northeast Australia. Agric For Meteorol 127(1–2):77–92. http://www.sciencedirect.com/science/article/pii/S0168192304001844

    Article  Google Scholar 

  122. Hayes PM, Liu BH, Knapp SJ, Chen F, Jones B, Blake T, Franckowiak J, Rasmusson D, Sorrells M, Ullrich SE, Wesenberg D, Kleinhofs A (1993) Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theor Appl Genet 87:392–401

    Article  CAS  Google Scholar 

  123. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31(2):423–447. http://europepmc.org/abstract/MED/1174616

    Article  CAS  Google Scholar 

  124. Heslot N, Akdemir D, Sorrells M, Jannink J-L (2013) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480. https://doi.org/10.1007/s00122-013-2231-5

    Article  Google Scholar 

  125. Heslot N, Jannink J-L, Sorrells ME (2015) Perspectives for genomic selection applications and research in plants. Crop Sci 55(1):1. https://dl.sciencesocieties.org/publications/cs/abstracts/55/1/1

    Article  Google Scholar 

  126. Hoogendoorn J (1985) A reciprocal F1 monosomic analysis of the genetic control of time of ear emergence, number of leaves and number of spikelets in wheat (Triticum aestivum L.) Euphytica 34:545–558

    Article  Google Scholar 

  127. Huang X, Paulo MJ, Boer M, Effgen S, Keizer P, Koornneef M, van Eeuwijk FA (2011) Analysis of natural allelic variation in Arabidopsis using a multiparent recombinant inbred line population. Proc Natl Acad Sci 108:4488–4493

    Article  CAS  Google Scholar 

  128. International Wheat Genome Sequencing Consortium (IWGSC) (2014) A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345(6194):1251788

    Article  CAS  Google Scholar 

  129. Jackson PA, Byth DE, Fischer KS, Johnston RP (1994) Genotype × environment interactions in progeny from a barley cross: II. Variation in grain yield, yield components and dry matter production among lines with similar times to anthesis. Field Crop Res 37:11–23

    Article  Google Scholar 

  130. Jarquín D, Crossa J, Lacaze X, Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgueño J, Campos G (2013) A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet (3):1–13. https://doi.org/10.1007/s00122-013-2243-1

    Article  Google Scholar 

  131. Jia Y, Jannink J-L (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192(4):1513–1522. http://www.genetics.org/content/192/4/1513.abstract

    Article  Google Scholar 

  132. Jones H, Leigh FJ, Mackay I, Bower MA, Smith LMJ, Charles MP, Jones G, Jones MJ, Brown TA, Powell W (2008) Population-based resequencing reveals that the flowering time adaptation of cultivated barley originated east of the Fertile Crescent. Mol Biol Evol 25:2211–2219

    Article  CAS  Google Scholar 

  133. Junker A, Muraya MM, Weigelt-Fischer K, Arana-Ceballos F, Klukas C, Melchinger AE, Meyer RC, Riewe D, Altmann T (2015) Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems. Front Plant Sci 5:770. http://journal.frontiersin.org/article/10.3389/fpls.2014.00770/abstract

    Article  Google Scholar 

  134. Kang MS (1998) Using genotype-by-environment interaction for crop cultivar development. Adv Agron 62:199–252

    Article  Google Scholar 

  135. Kang MS, Gauch HG (1996) Genotype by environment interaction: new perspectives. CRC Press, Boca Raton

    Google Scholar 

  136. Karsai I, Igartua E, Casas AM, Kiss T, Soós V, Balla K, Bedo Z, Veisz O (2013) Developmental patterns of a large set of barley (Hordeum vulgare) cultivars in response to ambient temperature. Ann Appl Biol 162(3):309–323

    Article  Google Scholar 

  137. Kato K, Miura H, Sawada S (2002) Characterization of QEet.ocs-5A.1, a quantitative trait locus for ear emergence time on wheat chromosome 5AL. Plant Breed 121:389–393

    Article  CAS  Google Scholar 

  138. Kempton RA (1984) The use of biplots in interpreting variety by environment interactions. J Agric Sci 103:123–135

    Article  Google Scholar 

  139. Kempton RA, Fox PN (1997) Statistical methods for plant variety evaluation. Chapman and Hall, London

    Google Scholar 

  140. Kernich GC, Halloran GM, Flood RG (1995) Variation in development patterns of wild barley (Hordeum spontaneum L) and cultivated barley (H vulgare L). Euphytica 82:105–115

    Article  Google Scholar 

  141. Kernich GC, Halloran GM, Flood RG (1997) Variation in duration of pre-anthesis phases of development in barley (Hordeum vulgare). Aust J Agric Res 48:59–66

    Article  Google Scholar 

  142. Kippes N, Debernardi JM, Vasquez-Gross HA, Akpinar BA, Budak H, Kato K, Chao S, Akhunov E, Dubcovsky J (2015) Identification of the VERNALIZATION 4 gene reveals the origin of spring growth habit in ancient wheats from South Asia. Proc Natl Acad Sci 112:E5401–E5410

    Article  CAS  Google Scholar 

  143. Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M, Hund A, Walter A (2017) The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol 44(1):154–168

    Article  Google Scholar 

  144. Kiss T, Balla K, Veisz O, Láng L, Bedó Z, Griffiths S, Isaac P, Karsai I (2014) Allele frequencies in the VRN-A1, VRN-B1 and VRN-D1 vernalization response and PPD-B1 and PPD-D1 photoperiod sensitivity genes, and their effects on heading in a diverse set of wheat cultivars (Triticum aestivum L.) Mol Breeding 34:297–310

    Article  CAS  Google Scholar 

  145. Kiss T, Dixon LE, Soltész A, Bányai J, Mayer M, Balla K, Allard V, Galiba G, Slafer GA, Griffiths S, Veisz O, Karsai I (2017) Effects of ambient temperature in association with photoperiod on phenology and on the expressions of major plant developmental genes in wheat (Triticum aestivum L.) Plant Cell Environ 40(8):1629–1642

    Article  CAS  Google Scholar 

  146. Kitchen BM, Rasmusson DC (1983) Duration and inheritance of leaf initiation, spike initiation and spike growth in barley. Crop Sci 23:939–943

    Article  Google Scholar 

  147. Krasileva KV, Vasquez-Gross HA, Howell T, Bailey P, Paraiso F, Clissold L, Simmonds J, Ramirez-Gonzalez RH, Wang X, Borrill P, Fosker C, Ayling S, Phillips AL, Uauy C, Dubcovsky J (2017) Uncovering hidden variation in polyploid wheat. Proc Natl Acad Sci 114(6):E913–E921. http://www.pnas.org/lookup/doi/10.1073/pnas.1619268114

    Article  CAS  Google Scholar 

  148. Kuchel H, Hollamby G, Langridge P, Williams K, Jefferies SP (2006) Identification of genetic loci associated with ear-emergence in bread wheat. Theor Appl Genet 113:1103–1112

    Article  CAS  Google Scholar 

  149. Lado B, Barrios PG, Quincke M, Silva P, Gutiérrez L (2016) Modeling genotype × environment interaction for genomic selection with unbalanced data from a wheat breeding program. Crop Sci. https://doi.org/10.2135/cropsci2015.04.0207

  150. Laidig F, Piepho H-P, Drobek T, Meyer U (2014) Genetic and non-genetic long-term trends of 12 different crops in German official variety performance trials and on-farm yield trends. Theor Appl Genet 127(12):2599–2617. https://doi.org/10.1007/s00122-014-2402-z

    Article  Google Scholar 

  151. Lasa JM, Igartua E, Ciudad FJ, Codesal P, Garcia EV, Gracia MP, Medina B, Romagosa I, Molina-Cano JL, Montoya JL (2001) Morphological and agronomical diversity patterns in the Spanish barley core collection. Hereditas 135:217–225

    Article  CAS  Google Scholar 

  152. Laurie DA, Pratchett N, Bezant JH, Snape JW (1995) RFLP mapping of five major genes and eight quantitative trait loci controlling flowering time in a winter × spring barley (Hordeum vulgare L.) cross. Genome 38:575–585

    Article  CAS  Google Scholar 

  153. Law CN, Worland AJ (1997) Genetic analysis of some flowering time and adaptative traits in wheat. New Phytol 137:19–28

    Article  Google Scholar 

  154. Lewis S, Faricelli ME, Appendino ML, Valárik M, Dubcovsky J (2008) The chromosome region including the earliness per se locus Eps-A m 1 affects the duration of early developmental phases and spikelet number in diploid wheat. J Exp Bot 59:3595–3607

    Article  CAS  Google Scholar 

  155. Li JZ, Huang XQ, Heinrichs F, Ganal MW, Röder MS (2006) Analysis of QTLs for yield components, agronomic traits and disease resistance in an advanced backcross population of spring barley. Genome 49:454–466

    Article  CAS  Google Scholar 

  156. Limin A, Corey A, Hayes PM, Fowler DB (2007) Low-temperature acclimation of barley cultivars used as parents in mapping populations: response to photoperiod, vernalization and phenological development. Planta 226:139–146

    Article  CAS  Google Scholar 

  157. Loss SP, Siddique KHM (1994) Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv Agron 52:229–276

    Article  CAS  Google Scholar 

  158. Lundqvist U, Franckowiak JD, Konishi T (1997) New and revised descriptions of barley genes. Barley Genet Newsl 26:22–516

    Google Scholar 

  159. Lynch M, Walsh JB (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland

    Google Scholar 

  160. Malosetti M, Voltas J, Romagosa I, Ullrich SE, van Eeuwijk FA (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137(1):139–145. https://doi.org/10.1023/B:EUPH.0000040511.46388.ef

    Article  CAS  Google Scholar 

  161. Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front. Physiol 4:1–17. http://www.frontiersin.org/Journal/Abstract.aspx?s=907&name=plant_physiology&ART_DOI=10.3389/fphys.2013.00044

    Google Scholar 

  162. Malosetti M, Bustos-Korts D, Boer MP, van Eeuwijk FA (2016) Predicting responses in multiple environments: issues in relation to genotype × environment interactions. Crop Sci 56:2210–2222. https://doi.org/10.2135/cropsci2015.05.0311

    Article  Google Scholar 

  163. Mandel J (1969) The partitioning of interaction in analysis of variance. J Res NBS 73B:309–328

    Google Scholar 

  164. Mascher M, Richmond TA, Gerhardt DJ, Himmelbach A, Clissold L, Sampath D, Ayling S, Steuernagel B, Pfeifer M, D’Ascenzo M, Akhunov ED, Hedley PE, Gonzales AM, Morrell PL, Kilian B, Blattner FR, Scholz U, Mayer KFX, Flavell AJ, Muehlbauer GJ, Waugh R, Jeddeloh JA, Stein N (2013) Barley whole exome capture: a tool for genomic research in the genus Hordeum and beyond. Plant J 76(3):494–505. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4241023/

    Article  CAS  Google Scholar 

  165. Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J, Bayer M, Ramsay L, Liu H, Haberer G, Zhang XQ, Zhang Q, Barrero RA, Li L, Taudien S, Groth M, Felder M, Hastie A, Šimková H, Staňková H, Vrána J, Chan S, Muñoz-Amatriaín M, Ounit R, Wanamaker S, Bolser D, Colmsee C, Schmutzer T, Aliyeva-Schnorr L, Grasso S, Tanskanen J, Chailyan A, Sampath D, Heavens D, Clissold L, Cao S, Chapman B, Dai F, Han Y, Li H, Li X, Lin C, McCooke JK, Tan C, Wang P, Wang S, Yin S, Zhou G, Poland JA, Bellgard MI, Borisjuk L, Houben A, Doležel J, Ayling S, Lonardi S, Kersey P, Langridge P, Muehlbauer GJ, Clark MD, Caccamo M, Schulman AH, Mayer KFX, Platzer M, Close TJ, Scholz U, Hansson M, Zhang G, Braumann I, Spannagl M, Li C, Waugh R, Stein N (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544:427–433

    Article  CAS  Google Scholar 

  166. Maurer A, Draba V, Jiang Y, Schnaithmann F, Sharma R, Schumann E, Kilian B, Reif JC, Pillen K (2015) Modelling the genetic architecture of flowering time control in barley through nested association mapping. BMC Genomics 16:290

    Article  CAS  Google Scholar 

  167. Messina CD, Sinclair TR, Hammer GL, Curan D, Thompson J, Oler Z, Gho C, Cooper M (2015) Limited-transpiration trait may increase maize drought tolerance in the US corn belt. Agron J 107(6):1978–1986

    Article  CAS  Google Scholar 

  168. Messina CD, Technow F, Tang T, Totir RL, Gho C, Cooper M (2018) Leveraging biological insight and environmental variation to improve phenotypic prediction: integrating crop growth models (CGM) with whole genome prediction (WGP). Eur J Agr. https://doi.org/10.1016/j.eja.2018.01.007

  169. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829. http://www.genetics.org/content/157/4/1819.abstract

    CAS  Google Scholar 

  170. Millet E, Welcker C, Kruijer W, Negro S, Nicolas S, Praud S, Ranc N, Presterl T, Tuberosa R, Bedo Z, Draye X, Usadel B, Charcosset A, van Eeuwijk F, Tardieu F, Coupel-Ledru A, Bauland C (2016) Genome-wide analysis of yield in Europe: allelic effects as functions of drought and heat scenarios. Plant Physiol 172:00621.2016. http://www.plantphysiol.org/lookup/doi/10.1104/pp.16.00621

    Google Scholar 

  171. Miralles DJ, Richards RA (2000) Responses of leaf and tiller emergence and primordium initiation in wheat and barley to interchanged photoperiod. Ann Bot 85:655–663

    Article  Google Scholar 

  172. Miralles DJ, Slafer GA (1995) Yield, biomass and yield components in dwarf, semidwarf and tall isogenic lines of spring wheat under recommended and late sowing dates. Plant Breed 114:392–396. https://doi.org/10.1111/j.1439-0523.1995.tb00818.x

    Article  Google Scholar 

  173. Miralles DJ, Slafer GA (2007) Sink limitations to yield in wheat, how could it be reduced? J Agric Sci 145:139–149

    Article  Google Scholar 

  174. Miralles DF, Katz SD, Colloca A, Slafer GA (1998) Floret development in near isogenic wheat lines differing in plant height. Field Crop Res 59:21–30

    Article  Google Scholar 

  175. Molina-Cano JL, García del Moral LF, Ramos JM, García del Moral MB, Romagosa I, Roca de Togores F (1990) Quantitative phenotypical expression of three mutant genes in barley and the basis for defining an ideotype for Mediterranean environments. Theor Appl Genet 80:762–768

    Article  CAS  Google Scholar 

  176. Müller D, Technow F, Melchinger AE (2015) Shrinkage estimation of the genomic relationship matrix can improve genomic estimated breeding values in the training set. Theor Appl Genet 128(4):693–703. https://doi.org/10.1007/s00122-015-2464-6

    Article  Google Scholar 

  177. Nadolska-Orczyk A, Rajchel IK, Orczyk W, Gasparis S (2017) Major genes determining yield-related traits in wheat and barley. Theor Appl Genet 130:1081–1098

    Article  CAS  Google Scholar 

  178. Neumann K, Klukas C, Friedel S, Rischbeck P, Chen D, Entzian A, Stein N, Graner A, Kilian B (2015) Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis. Plant Cell Environ 38(10):1980–1996

    Article  CAS  Google Scholar 

  179. Nishida H, Ishihara D, Ishii M, Kaneko T, Kawahigashi H, Akashi Y, Saisho D, Tanaka K, Handa H, Takeda K, Kato K (2013) Phytochrome C is a key factor controlling long-day flowering in barley. Plant Physiol 163:804–814

    Article  CAS  Google Scholar 

  180. Ortiz-Monasterio I, Sayre KD, Rajaram S, McMahon M (1997) Genetic progress in wheat yield and nitrogen use efficiency under four nitrogen rates. Crop Sci 37(3):898–904. https://www.agronomy.org/publications/cs/abstracts/37/3/898

    Article  Google Scholar 

  181. Pankin A, Campoli C, Dong X, Kilian B, Sharma R, Himmelbach A, Saini R, Davis SJ, Stein N, Schneeberger K, von Korff M (2014) Mapping-by-sequencing identifies HvPHYTOCHROME C as a candidate gene for the early maturity 5 locus modulating the circadian clock and photoperiodic flowering in barley. Genetics 198:383–396

    Article  Google Scholar 

  182. Passioura JB (2002) Environmental biology and crop improvement. Funct Plant Biol 29:537–546

    Article  Google Scholar 

  183. Paterson AH (1998) Molecular dissection of complex traits. CRC Press, Boca Raton

    Google Scholar 

  184. Payne RW, Murray DA, Harding SA, Baird DB, Soutar DM (2010) GenStat for windows (13th edition) introduction. VSN International, Hemel Hempstead

    Google Scholar 

  185. Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49:1165–1176. https://dl.sciencesocieties.org/publications/cs/abstracts/49/4/1165

    Article  Google Scholar 

  186. Piepho HP, Möhring J (2005) Best linear unbiased prediction of cultivar effects for subdivided target regions. Crop Sci 45(3):1151–1159. https://dl.sciencesocieties.org/publications/cs/abstracts/45/3/1151

    Article  Google Scholar 

  187. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161(1–2):209–228. https://doi.org/10.1007/s10681-007-9449-8

    Article  Google Scholar 

  188. Quintero A, Molero G, Reynolds MP, Calderini DF (2018) Trade-off between grain weight and grain number in wheat depends on GxE interaction: a case study of an elite CIMMYT panel (CIMCOG). Eur J Agron 92:17–29. https://doi.org/10.1016/j.eja.2017.09.007

    Article  Google Scholar 

  189. Rasmusson DC (1996) Germplasm is paramount. In: Reynolds MP, Rajaram S, McNab A (eds) Increasing yield potential in wheat: breaking the barriers. CIMMYT, Mexico, pp 28–37

    Google Scholar 

  190. Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard EG, Mayer JE (2012) A multisite managed environment facility for targeted trait and germplasm phenotyping. Funct Plant Biol 40(1):1–13. http://www.publish.csiro.au/paper/FP12180

    Article  Google Scholar 

  191. Reymond M, Muller B, Leonardi A, Charcosset A, Tardieu F (2003) Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol 131(2):664–675. http://www.plantphysiol.org/content/131/2/664.abstract

    Article  CAS  Google Scholar 

  192. Reynolds MP, Borlaug NE (2006) Impacts of breeding on international collaborative wheat improvement. J Agric Sci 144:3–17. Cambridge

    Article  Google Scholar 

  193. Reynolds M, Langridge P (2016) Physiological breeding. Curr Opin Plant Biol 31:162–171. https://doi.org/10.1016/j.pbi.2016.04.005

    Article  Google Scholar 

  194. Reynolds MP, Foulkes J, Slafer GA, Berry P, Parry MJ, Snape JW, Angus WJ (2009) Raising yield potential in wheat. J Exp Bot 60:1899–1918

    Article  CAS  Google Scholar 

  195. Rhoné B, Vitalis R, Goldringer I, Bonnin I (2010) Evolution of flowering time in experimental wheat populations: a comprehensive approach to detect genetic signatures of natural selection. Evolution 64:2110–2125

    Google Scholar 

  196. Richards RA (1991) Crop improvement for temperate Australia, future opportunities. Field Crop Res 26:141–169

    Article  Google Scholar 

  197. Richards RA (1996) Increasing yield potential in wheat – source and sink limitations. In: Reynolds MP, Rajaram S, McNab A (eds) Increasing yield potential in wheat: breaking the barriers. CIMMYT, Mexico, pp 134–149

    Google Scholar 

  198. Rimbert H, Darrier B, Navarro J, Kitt J, Choulet F, Leveugle M et al (2018) High throughput SNP discovery and genotyping in hexaploid wheat. PLoS One 13(1):e0186329

    Article  CAS  Google Scholar 

  199. Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodríguez VM, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen C-C, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.) Genetics 192(2):715–728. http://www.genetics.org/content/192/2/715.abstract

    Article  CAS  Google Scholar 

  200. Rincent R, Kuhn E, Monod H, Oury F-X, Rousset M, Allard V, Le Gouis J (2017) Optimization of multi-environment trials for genomic selection based on crop models. Theor Appl Genet:1–18. https://doi.org/10.1007/s00122-017-2922-4

  201. Rodríguez-Alvarez MX, Lee D, Kneib T, Durbán M, Eilers P (2015) Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm. Statistics and Computing, 25(5):941–957

    Article  Google Scholar 

  202. Rodríguez-Álvarez MX, Boer MP, van Eeuwijk FA, Eilers PHC (2017) Correcting for spatial heterogeneity in plant breeding experiments with P-splines. Spat Stat. http://linkinghub.elsevier.com/retrieve/pii/S2211675317301070

  203. Romagosa I, Fox PN (1993) Genotype-environment interaction and adaptation. In: Hayward MD, Bosemark NO, Romagosa I (eds) Plant breeding, principles and prospects. Chapman and Hall, London, pp 373–390

    Chapter  Google Scholar 

  204. Romagosa I, Fox PN, del Moral G, Ramos JM, García del Moral B, Roca de Togores F, Molina-Cano JL (1993) Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines. Theor Appl Genet 86:822–826

    Article  CAS  Google Scholar 

  205. Romagosa I, Ullrich SE, Han F, Hayes PM (1996) Use of the AMMI model in QTL mapping for adaptation in barley. Theor Appl Genet 93:30–37

    Article  CAS  Google Scholar 

  206. Romagosa I, van Eeuwijk FA, Thomas WTB (2009) Statistical analyses of genotype by environment data. In: Carena M (ed) Handbook of plant breeding, vol 3. Cereals. Springer, New York, pp 291–331

    Google Scholar 

  207. Royo C, Rodríguez A, Romagosa I (1993) Differential adaptation of complete and substituted triticale to acid soils. Plant Breed 111:113–119

    Article  Google Scholar 

  208. Russell J, Mascher M, Dawson IK, Kyriakidis S, Calixto C, Freund F, Bayer M, Milne I, Marshall-Griffiths T, Heinen S, Hofstad A, Sharma R, Himmelbach A, Knauft M, van Zonneveld M, Brown JWS, Schmid K, Kilian B, Muehlbauer GJ, Stein N, Waugh R (2016) Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat Genet 48(9):1024–1030. https://doi.org/10.1038/ng.3612

    Article  CAS  Google Scholar 

  209. Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Efficient use of historical data for genomic selection: a case study of stem rust resistance in wheat. Plant Genome 8. https://doi.org/10.3835/plantgenome2014.09.0046

  210. Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, Reynolds M, Singh R (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3 Genes Genom Genet 6(9):2799–2808. http://g3journal.org/lookup/doi/10.1534/g3.116.032888

    Google Scholar 

  211. Sadras VO, Calderini D (2015) Crop physiology. Applications for genetic improvement and agronomy, 2nd edn. Academic, Burlington

    Google Scholar 

  212. Scarth R, Law CN (1983) The location of the photoperiodic gene, Ppd2, and an additional factor for ear-emergence time on chromosome 2B of wheat. Heredity 51:607–619

    Article  Google Scholar 

  213. Shamsudin NAA, Swamy BPM, Ratnam W, Cruz MTS, Raman A, Kumar A (2016) Marker assisted pyramiding of drought yield QTLs into a popular Malaysian rice cultivar, MR219. BMC Genet 17(1):1–14. https://doi.org/10.1186/s12863-016-0334-0

    Article  CAS  Google Scholar 

  214. Shindo C, Tsujimoto H, Sasakuma T (2003) Segregation analysis of heading traits in hexaploid wheat utilizing recombinant inbred lines. Heredity 90:56–63

    Article  CAS  Google Scholar 

  215. Siddique KHM, Belford RK, Perry MW, Tennant D (1989) Growth, development and light interception of old and modern wheat cultivars in a Mediterranean environment. Aust J Agric Res 40:473–487

    Google Scholar 

  216. Slafer GA (1996) Differences in phasic development rate amongst wheat cultivars independent of responses to photoperiod and vernalization. A viewpoint of the intrinsic earliness hypothesis. J Agric Sci 126:403–419

    Article  Google Scholar 

  217. Slafer GA (2003) Genetic basis of yield as viewed from a crop physiologist’s perspective. Ann Appl Biol 142:117–128

    Article  Google Scholar 

  218. Slafer GA, Andrade FH (1993) Physiological attributes related to the generation of grain yield in bread wheat cultivars released at different eras. Field Crop Res 31:351–367

    Article  Google Scholar 

  219. Slafer GA, Araus JL (2007) Physiological traits for improving wheat yield under a wide range of conditions. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations. Springer, Dordrecht, pp 147–156

    Chapter  Google Scholar 

  220. Slafer GA, Rawson HM (1994) Sensitivity of wheat phasic development to major environmental factors: a re-examination of some assumptions made by physiologists and modellers. Aust J Plant Physiol 21:393–426

    Article  Google Scholar 

  221. Slafer GA, Rawson HM (1995) Base and optimum temperatures vary with genotype and stage of development in wheat. Plant Cell Environ 18:671–679

    Article  Google Scholar 

  222. Slafer GA, Abeledo LG, Miralles DJ, González FG, Whitechurch EM (2001) Photoperiod sensitivity during stem elongation as an avenue to raise potential yield in wheat. Euphytica 119:191–197

    Article  Google Scholar 

  223. Slafer GA, Araus JL, Royo C, García del Moral LF (2005) Promising eco-physiological traits for genetic improvement of cereal yields in Mediterranean environments. Ann Appl Biol 146:61–70

    Article  Google Scholar 

  224. Smith A, Cullis B, Gilmour A (2001) Applications: the analysis of crop variety evaluation data in Australia. Aust N Z J Stat 43(2):129–145. https://doi.org/10.1111/1467-842X.00163

    Article  Google Scholar 

  225. Smith AB, Cullis BR, Thomson R, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agr Sci 143(6):449–462. https://doi.org/10.1017/S0021859605005587

    Article  Google Scholar 

  226. Smith AB, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agr Sci 144(5):393–409. https://doi.org/10.1017/S0021859606006319

    Article  Google Scholar 

  227. Sourdille P, Snape JW, Charmet G, Nakata N, Bernard S, Bernard M (2000) Detection of QTLs for heading time and photoperiod response in wheat using a doubled-haploid population. Genome 43:487–494

    Article  CAS  Google Scholar 

  228. Speed D, Balding DJ (2014) MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24(9):1550–1557

    Article  CAS  Google Scholar 

  229. Steinfort U, Trevaskis B, Fukai S, Bell KL, Dreccer MF (2017) Vernalisation and photoperiod sensitivity in wheat: impact on canopy development and yield components. Field Crops Res 201:108–121

    Article  Google Scholar 

  230. Steinfort U, Fukai S, Trevaskis B, Glassop D, Chan A, Dreccer MF (2017) Vernalisation and photoperiod sensitivity in wheat: the response of floret fertility and grain number is affected by vernalisation status. Field Crops Res 203:243–255

    Article  Google Scholar 

  231. Stracke S, Börner A (1998) Molecular mapping of the photoperiod response gene ea7 in barley. Theor Appl Genet 97:797–800

    Article  CAS  Google Scholar 

  232. Sun J, Rutkoski JE, Poland JA, Crossa J, Jannink J-L, Sorrells ME (2017) Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. Plant Genome. https://doi.org/10.3835/plantgenome2016.11.0111

  233. Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8(1):9–14. http://www.sciencedirect.com/science/article/pii/S1360138502000080

    Article  CAS  Google Scholar 

  234. Tardieu F, Parent B (2017) Predictable “meta-mechanisms” emerge from feedbacks between transpiration and plant growth and cannot be simply deduced from short-term mechanisms. Plant Cell Environ 40(6):846–857

    Article  CAS  Google Scholar 

  235. Tardieu F, Reymond M, Hamard P, Granier C, Muller B (2000) Spatial distributions of expansion rate, cell division rate and cell size in maize leaves: a synthesis of the effects of soil water status, evaporative demand and temperature. J Exp Bot 51(350):1505–1514. http://jxb.oxfordjournals.org/content/51/350/1505.abstract

    Article  CAS  Google Scholar 

  236. Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017) Plant phenomics, from sensors to knowledge. Curr Biol 27(15):R770–R783. http://linkinghub.elsevier.com/retrieve/pii/S0960982217306218

    Article  CAS  Google Scholar 

  237. Technow F, Messina CD, Totir LR, Cooper M (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS One 10(6):1–20

    Article  CAS  Google Scholar 

  238. Tinker NA, Mather DE, Blake TK, Briggs KG, Choo TM, Dahleen L, Dofing SM, Falk DE, Ferguson T, Franckowiak JD, Graf R, Hayes PM, Hoffman D, Irvine RB, Kleinhofs A, Legge W, Rossnagel BG, Saghai Maroof MA, Scoles GJ, Shugar LP, Steffenson B, Ullrich S, Kasha KJ (1996) Regions of the genome that affect agronomic performance in two-row barley. Crop Sci 36:1053–1062

    Article  Google Scholar 

  239. Trevaskis B, Bagnall DJ, Ellis MH, Peacock WJ, Dennis ES (2003) MADS box genes control vernalization-induced flowering in cereals. Proc Natl Acad Sci 100(22):13099–13104

    Article  CAS  Google Scholar 

  240. Turner A, Beales J, Faure S, Dunford RP, Laurie DA (2005) The pseudo-response regulator Ppd-H1 provides adaptation to photoperiod in barley. Science 310:1031–1034

    Article  CAS  Google Scholar 

  241. van Eeuwijk FA (1995) Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica 84:1–7

    Article  Google Scholar 

  242. van Eeuwijk FA (1995) Multiplicative interaction in generalized linear models. Biometrics 51:1017–1032

    Article  Google Scholar 

  243. van Eeuwijk FA (2006) Genotype by environment interaction: basics and beyond. In: Lamkey K, Lee M (eds) Plant breeding: the Arnell Hallauer international symposium. Blackwell, Oxford, pp 155–170

    Google Scholar 

  244. van Eeuwijk FA, Denis JB, Kang MS (1996) Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 15–50

    Chapter  Google Scholar 

  245. van Eeuwijk FA, Crossa J, Vargas M, Ribaut JM (2001) Variants of factorial regression for analysing QTL by environment interaction. In: Gallais A, Dillmann C, Goldringer I (eds) Eucarpia, quantitative genetics and breeding methods: the way ahead, vol 96. INRA Editions Versailles Les Colloques series. INRA, Paris, pp 107–116

    Google Scholar 

  246. van Eeuwijk FA, Crossa J, Vargas M, Ribaut JM (2002) Analysing QTL by environment interaction by factorial regression, with an application to the CIMMYT drought and low nitrogen stress programme in maize. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CAB International, Wallingford, pp 245–256

    Google Scholar 

  247. van Eeuwijk FA, Malosetti M, Yin X, Struik PC, Stam P (2005) Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust J Agric Res 56:883–894

    Article  Google Scholar 

  248. van Eeuwijk FA, Bink MCAM, Chenu K, Chapman SC (2010) Detection and use of QTL for complex traits in multiple environments. Curr Opin Plant Biol 13:193–205

    Article  CAS  Google Scholar 

  249. van Eeuwijk FA, Bustos-Korts DV, Malosetti M (2016) What should students in plant breeding know about the statistical aspects of genotype × environment interactions? Crop Sci 56:2119–2140. https://doi.org/10.2135/cropsci2015.06.0375

    Article  Google Scholar 

  250. van Eeuwijk FA, Bustos-Korts D, Millet EJ, BoerM, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis K, Yu K, Tardieu F, Chapman S (2018) Assessing the efficiency of phenotyping strategies. Accepted in Plant Science (PSL_2017_1008)

    Google Scholar 

  251. van Oosterom EJ, Acevedo E (1992) Adaptation of barley (Hordeum vulgare L.) to harsh Mediterranean environments. Euphytica 62:15–27

    Article  Google Scholar 

  252. Van Oosterom EJ, Kleijn DM, Ceccarelli S, Nachit MM (1993) Genotype-by-environment interactions of barley in the Mediterranean region. Crop Sci 33:669–674

    Article  Google Scholar 

  253. Vargas M, Crossa J, van Eeuwijk FA, Ramírez ME, Sayre K (1999) Using AMMI, factorial regression, and partial least squares regression models for interpreting genotype × environment interaction. Crop Sci 39:955–967

    Article  Google Scholar 

  254. Velazco JG, Rodríguez-Álvarez MX, Boer MP, Jordan DR, Eilers PHC, Malosetti M, van Eeuwijk FA (2017) Modelling spatial trends in sorghum breeding field trials using a two-dimensional P-spline mixed model. Theor Appl Genet 130(7):1375–1392

    Article  CAS  Google Scholar 

  255. Voltas J, Romagosa I, Lafarga A, Armesto AP, Sombrero A, Araus JL (1999) Genotype by environment interaction for grain yield and carbon isotope discrimination of barley in Mediterranean Spain. Aust J Agric Res 50(7):1263–1271. http://www.publish.csiro.au/paper/AR98137

    Article  Google Scholar 

  256. Voltas J, van Eeuwijk FA, Sombrero A, Lafarga A, Igartua E, Romagosa I (1999) Integrating statistical and ecophysiological analysis of genotype by environment interaction for grain filling of barley in Mediterranean areas I. Individual grain weight. Field Crop Res 62:63–74

    Article  Google Scholar 

  257. Voltas J, van Eeuwijk FA, Araus JL, Romagosa I (1999) Integrating statistical and ecophysiological analysis of genotype by environment interaction for grain filling of barley in Mediterranean areas II. Grain growth. Field Crop Res 62:75–84

    Article  Google Scholar 

  258. Voltas J, van Eeuwijk FA, Igartua E, Garcia del Moral LF, Molina-Cano JL, Romagosa I (2002) Genotype by environment interaction and adaptation in barley breeding: basic concepts and methods of analysis. In: Slafer GA, Molina-Cano JL, Savin R, Araus JL, Romagosa I (eds) Barley science: recent advances from molecular biology to agronomy of yield and quality. Haworth Press, Binghamton, pp 205–241

    Google Scholar 

  259. Voss-Fels KP, Robinson H, Mudge SR, Richard C, Newman S, Wittkop B, Stahl A, Friedt W, Frisch M, Gabur I, Miller-Cooper A, Campbell BC, Kelly A, Fox G, Christopher J, Christopher M, Chenu K, Franckowiak J, Mace ES, Borrell AK, Eagles H, Jordan DR, Botella JR, Hammer G, Godwin ID, Trevaskis B, Snowdon RJ, Hickey LT (2017) VERNALIZATION1 modulates root system architecture in wheat and barley. Mol Plant 11:226–229

    Article  CAS  Google Scholar 

  260. Walsh B, Lynch M. Evolution and selection of quantitative traits: II. Advanced topics in breeding and evolution. http://nitro.biosci.arizona.edu/zbook/NewVolume_2/newvol2.html#2B

  261. Wang H, van Eeuwijk FA (2014) A new method to infer causal phenotype networks using qtl and phenotypic information. PLoS One 9(8):e103997. https://doi.org/10.1371/journal.pone.0103997

    Article  Google Scholar 

  262. Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, IWGSC LM, Mather D, Appels R, Dolferus R, Brown-Guedira G, Korol A, Akhunova AR, Feuillet C, Salse J, Morgante M, Pozniak C, Luo MC, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards KJ, Hayden M, Akhunov E (2014) Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. Plant Biotech J 12:787–796

    Article  CAS  Google Scholar 

  263. Wang H, Paulo J, Kruijer W, Boer M, Jansen H, Tikunov Y, Usadel B, van Heusden S, Bovy A, van Eeuwijk F (2015) Genotype-phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes. Mol BioSyst 11(11):3101–3110. https://doi.org/10.1039/C5MB00477B

    Article  CAS  Google Scholar 

  264. Wang J, Wen W, Hanif M, Xia X, Wang H, Liu S, Liu J, Yang L, Cao S, He Z (2016) TaELF3-1DL, a homolog of ELF3, is associated with heading date in bread wheat. Mol Breed 36:161

    Article  CAS  Google Scholar 

  265. Weber VS, Melchinger AE, Magorokosho C, Makumbi D, Bänziger M, Atlin GN (2012) Efficiency of managed-stress screening of elite maize hybrids under drought and low nitrogen for yield under rainfed conditions in Southern Africa. Crop Sci 52(3):1011–1020

    Article  Google Scholar 

  266. Wei J, Xu S (2016) A random-model approach to QTL mapping in multiparent advanced generation intercross (MAGIC) populations. Genetics 202(2):471–486

    Article  CAS  Google Scholar 

  267. Whitechurch EM, Slafer GA, Miralles DJ (2007) Variability in the duration of stem elongation in wheat and barley genotypes. J Agron Crop Sci 193:138–145

    Article  Google Scholar 

  268. Wilhelm EP, Turner AS, Laurie DA (2009) Photoperiod insensitive Ppd-A1a mutations in tetraploid wheat (Triticum durum Desf.) Theor Appl Genet 118:285–294

    Article  CAS  Google Scholar 

  269. Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink J-L, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 Genes Genom Genet 2(11):1427–1436. http://www.g3journal.org/content/2/11/1427.abstract

    Google Scholar 

  270. Windhausen VS, Wagener S, Magorokosho C, Makumbi D, Vivek B, Piepho H-P, Melchinger AE, Atlin GN (2012) Strategies to subdivide a target population of environments: results from the CIMMYT-led maize hybrid testing programs in Africa. Crop Sci 52(5):2143–2152. https://dl.sciencesocieties.org/publications/cs/abstracts/52/5/2143

    Article  Google Scholar 

  271. Winfield MO, Allen AM, Burridge AJ, Barker GLA, Benbow HR, Wilkinson PA, Coghill J, Waterfall C, Davassi A, Scopes G, Pirani A, Webster T, Brew F, Bloor C, King J, West C, Griffiths S, King I, Bentley AR, Edwards KJ (2016) High-density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool. Plant Biotech J 14:1195–1206

    Article  CAS  Google Scholar 

  272. Woltereck R (1909) Weitere experimentelle Untersuchungen über Artveränderung, speziel über das Wesen quantitativer Artunterschiede bei Daphnien. Verhandlungen der Dtsch Zool Gesellschaft 19:110–173

    Google Scholar 

  273. Worland AJ (1996) The influence of flowering time genes on environmental adaptability in European wheats. Euphytica 89:49–57

    Article  Google Scholar 

  274. Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton

    Google Scholar 

  275. Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605

    Article  Google Scholar 

  276. Yan L, Loukoianov A, Tranquilli G, Helguera M, Fahima T, Dubcovsky J (2003) Positional cloning of the wheat vernalization gene Vrn1. Proc Natl Acad Sci 100:6263–6268

    Article  CAS  Google Scholar 

  277. Yan L, Loukoianov A, Blechl A, Tranquilli G, Ramakrishna W, San Miguel P, Bennetzen JL, Echenique V, Dubcovsky J (2004) The wheat Vrn2 gene, a flowering repressor down-regulated by vernalization. Science 303:1640–1644

    Article  CAS  Google Scholar 

  278. Yan L, Fu D, Li C, Blechl A, Tranquilli G, Bonafede M, Sanchez A, Valarik M, Yasuda S, Dubcovsky J (2006) The wheat and barley vernalization gene Vrn3 is an orthologue of FT. Proc Natl Acad Sci 103:19581–19586

    Article  CAS  Google Scholar 

  279. Yates F, Cochran WG (1938) The analysis of groups of experiments. J Agric Sci 28:556–580

    Article  Google Scholar 

  280. Yin X, Chasalow SD, Dourleijn CJ, Stam P, Kropff MJ (2000) Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley. Heredity (Edinb) 85(6):539–549. https://doi.org/10.1046/j.1365-2540.2000.00790.x

    Article  CAS  Google Scholar 

  281. Yin X, Stam P, Kropff MJ, Schapendonk AHCM (2003) Crop modeling, QTL mapping, and their complementary role in plant breeding. Agron J 95(1):90–98. https://www.crops.org/publications/aj/abstracts/95/1/90

    Article  CAS  Google Scholar 

  282. Yoshida T, Nishida H, Zhu J, Nitcher R, Distelfed A, Akashi Y, Kato K, Dubcovsky J (2010) Vrn-D4 is a vernalization gene located on the centromeric region of chromosome 5D in hexaploid wheat. Theor Appl Genet 120:543–552

    Article  CAS  Google Scholar 

  283. Young KJ, Elliott GA (1994) An evaluation of barley accessions for adaptation to the cereal growing regions of western Australia, based on time to ear emergence. Aust J Agric Res 45:75–92

    Article  Google Scholar 

  284. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  Google Scholar 

  285. Zakhrabekova S, Gough SP, Braumann I, Müller AH, Lundqvist J, Ahmann K, Dockter C, Matyszczak I, Kurowska M, Druka A, Waugh R, Graner A, Stein N, Steuernagel B, Lundqvist U, Hansson M (2012) Induced mutations in circadian clock regulator Mat-a facilitated short-season adaptation and range extension in cultivated barley. Proc Natl Acad Sci 109:4326–4331

    Article  CAS  Google Scholar 

  286. Zanke C, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Beier S, Ganal MW, Röder MS (2014) Genetic architecture of main effect QTL for heading date in European winter wheat. Front Plant Sci 5:217

    Article  Google Scholar 

  287. Zheng B, Chenu K, Fernanda Dreccer M, Chapman SC (2012) Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Glob Chang Biol 18(9):2899–2914. https://doi.org/10.1111/j.1365-2486.2012.02724.x

    Article  Google Scholar 

  288. Zheng B, Biddulph B, Li D, Kuchel H, Chapman S (2013) Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments. J Exp Bot 64(12):3747–3761. http://jxb.oxfordjournals.org/content/64/12/3747.abstract

    Article  CAS  Google Scholar 

  289. Zheng C, Boer MP, van Eeuwijk FA (2015) Reconstruction of genome ancestry blocks in multi-parental populations. Genetics 200(4):1073–1087

    Article  Google Scholar 

  290. Zhou Y, Li W, Wu W, Chen Q, Mao D, Worland AJ (2001) Genetic dissection of heading time and its components in rice. Theor Appl Genet 102:1236–1242

    Article  CAS  Google Scholar 

  291. Zikhali M, Wingen LU, Leverington-Waite M, Specel S, Griffiths S (2017) The identification of new candidate genes Triticum aestivum FLOWERING LOCUS T3-B1 (TaFT3-B1) and TARGET OF EAT1 (TaTOE1-B1) controlling the short-day photoperiod response in bread wheat. Plant Cell Environ 40:2678–2690

    Article  CAS  Google Scholar 

Download references

Acknowledgments

DBK and FvE contributed to this chapter thanks to the funding of European Community’s Seventh Framework Programme (FP7/ 2007-2013) under the grant agreement n°FP7- 613556, Whealbi. The Spanish Ministry of Economy, Industry and Competitiveness (project AGL2015-69435-C3) supported IR’s contribution.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ignacio Romagosa .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bustos-Korts, D., Romagosa, I., Borràs-Gelonch, G., Casas, A.M., Slafer, G.A., van Eeuwijk, F. (2018). Genotype by Environment Interaction and Adaptation. In: Meyers, R. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2493-6_199-3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2493-6_199-3

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2493-6

  • Online ISBN: 978-1-4939-2493-6

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

Publish with us

Policies and ethics