Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
FARRER REVIEW

Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction

Mark Cooper A D , Carlos D. Messina A , Dean Podlich B , L. Radu Totir B , Andrew Baumgarten B , Neil J. Hausmann B , Deanne Wright B and Geoffrey Graham C
+ Author Affiliations
- Author Affiliations

A DuPont-Pioneer, 7250 NW 62nd Avenue, PO Box 552, Johnston, IA 50131, USA.

B DuPont-Pioneer, 8305 NW 62nd Avenue, PO Box 7060, Johnston, IA 50131, USA.

C DuPont-Pioneer, 7300 NW 62nd Avenue, PO Box 1004, Johnston, IA 50131, USA.

D Corresponding author. Email: mark.cooper@pioneer.com

Crop and Pasture Science 65(4) 311-336 https://doi.org/10.1071/CP14007
Submitted: 4 January 2014  Accepted: 27 February 2014   Published: 23 April 2014

Abstract

For the foreseeable future, plant breeding methodology will continue to unfold as a practical application of the scaling of quantitative biology. These efforts to increase the effective scale of breeding programs will focus on the immediate and long-term needs of society. The foundations of the quantitative dimension will be integration of quantitative genetics, statistics, gene-to-phenotype knowledge of traits embedded within crop growth and development models. The integration will be enabled by advances in quantitative genetics methodology and computer simulation. The foundations of the biology dimension will be integrated experimental and functional gene-to-phenotype modelling approaches that advance our understanding of functional germplasm diversity, and gene-to-phenotype trait relationships for the native and transgenic variation utilised in agricultural crops. The trait genetic knowledge created will span scales of biology, extending from molecular genetics to multi-trait phenotypes embedded within evolving genotype–environment systems. The outcomes sought and successes achieved by plant breeding will be measured in terms of sustainable improvements in agricultural production of food, feed, fibre, biofuels and other desirable plant products that meet the needs of society. In this review, examples will be drawn primarily from our experience gained through commercial maize breeding. Implications for other crops, in both the private and public sectors, will be discussed.

Additional keywords: envirotyping, genetics, genotyping, modeling, phenotyping, physiology, prediction, selection.


References

Allard RW (1960) ‘Principles of plant breeding.’ (John Wiley and Sons, Inc.: New York)

Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science 19, 52–61.
Field high-throughput phenotyping: the new crop breeding frontier.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhs1CrtbjL&md5=0fa17f25347429636c978178a971de1cCAS | 24139902PubMed |

Bänziger M, Setimela PS, Hodson D, Vivek B (2006) Breeding for improved abiotic stress tolerance in maize adapted to southern Africa. Agricultural Water Management 80, 212–224.
Breeding for improved abiotic stress tolerance in maize adapted to southern Africa.Crossref | GoogleScholarGoogle Scholar |

Barker T, Campos H, Cooper M, Dolan D, Edmeades G, Habben J, Schussler J, Wright D, Zinselmeier C (2005) Improving drought tolerance in maize. Plant Breeding Reviews 25, 173–253.

Basford KE, Williams ER, Cullis BR, Gilmour A (1996) Experimental design and analysis for variety trials. In ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer) pp. 125–138. (CAB International: Wallingford, UK)

Bink MCAM, Totir LR, ter Braak CJF, Winkler CR, Boer MP, Smith OS (2012) QTL linkage analysis of connected populations using ancestral marker and pedigree information. Theoretical and Applied Genetics 124, 1097–1113.
QTL linkage analysis of connected populations using ancestral marker and pedigree information.Crossref | GoogleScholarGoogle Scholar |

Boer MP, Wright D, Feng L, Podlich DW, 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, 1801–1813.
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.Crossref | GoogleScholarGoogle Scholar | 17947443PubMed |

Borlaug NE, Dowswell CR (2005) Feeding a world of ten billion people: A 21st century challenge. In ‘In the wake of the double helix from the Green Revolution to the Gene Revolution. Proceedings of International Congress’. 27–31 May 2003, University of Bologna, Italy. (Eds R Tuberosa, RL Phillips, M Gale) pp. 3–23. (Avenue Media: Bologna, Italy)

Boyer JS, Byrne P, Cassman KG, Cooper M, Delmer D, Greene T, Gruis F, Habben J, Hausmann N, Kenny N, Lafitte R, Paszkiewicz S, Porter D, Schlegel A, Schussler J, Setter T, Shanahan J, Sharp RE, Vyn TJ, Warner D, Gaffney J (2013) The US drought of 2012 in perspective: A call to action. Global Food Security 2, 139–143.
The US drought of 2012 in perspective: A call to action.Crossref | GoogleScholarGoogle Scholar |

Campos H, Cooper M, Habben JE, Edmeades GO, Schussler JR (2004) Improving drought tolerance in maize: a view from industry. Field Crops Research 90, 19–34.
Improving drought tolerance in maize: a view from industry.Crossref | GoogleScholarGoogle Scholar |

Campos H, Cooper M, Edmeades GO, Löffler C, Schussler JR, Ibañez M (2006) Changes in drought tolerance in maize associated with fifty years of breeding for yield in the U.S. corn belt. Maydica 51, 369–381.

Castiglioni P, Warner D, Bensen RJ, Anstrom DC, Harrison J, Stoecker M, Abad M, Kumar G, Salvador S, D’Ordine R, Navarro S, Back S, Fernandes M, Targolli J, Dasgupta S, Bonin C, Luethy MH, Heard JE (2008) Bacterial RNA chaperones confer abiotic stress tolerance in plants and improved grain yield in maize under water-limited conditions. Plant Physiology 147, 446–455.
Bacterial RNA chaperones confer abiotic stress tolerance in plants and improved grain yield in maize under water-limited conditions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXnsVyhsb4%3D&md5=1534167dce0024f598fc24f10d2757ceCAS | 18524876PubMed |

Chapman SC, Hammer GL, Butler DG, Cooper M (2000) Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Australian Journal of Agricultural Research 51, 223–233.
Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments.Crossref | GoogleScholarGoogle Scholar |

Chapman S, Cooper M, Podlich D, Hammer G (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agronomy Journal 95, 99–113.
Evaluating plant breeding strategies by simulating gene action and dryland environment effects.Crossref | GoogleScholarGoogle Scholar |

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 modeling water-deficit patterns in north-eastern Australia. Journal of Experimental Botany 62, 1743–1755.
Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modeling water-deficit patterns in north-eastern Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXjsFyis7w%3D&md5=8282c863a9541a9c8d5865e5ced1e870CAS | 21421705PubMed |

Comstock RE (1977) Quantitative genetics and the design of breeding programs. In ‘Proceedings of the International Conference on Quantitative Genetics’. 16–21 August 1976. (Eds E Pollack, O Kempthorne, TB Bailey Jr) pp. 705–718. (Iowa State University Press: Ames, IA, USA)

Comstock RE (1996) ‘Quantitative genetics with special reference to plant and animal breeding.’ (Iowa State University Press: Ames, IA, USA)

Cooper M, Hammer GL (Eds) (1996) ‘Plant adaptation and crop improvement.’ (CAB International: Wallingford, UK)

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. Theoretical and Applied Genetics 90, 492–502.
A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c7htlWkug%3D%3D&md5=4a3098061fbf4918c25e8932ad55f642CAS | 24173943PubMed |

Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Science 37, 1168–1176.
Wheat breeding nurseries, target environments, and indirect selection for grain yield.Crossref | GoogleScholarGoogle Scholar |

Cooper M, Chapman SC, Podlich DW, Hammer GL (2002) The GP problem: Quantifying gene-to-phenotype relationships. In Silico Biology 2, 151–164.

Cooper M, Smith OS, Graham G, Arthur L, Feng L, Podlich DW (2004) Genomics, genetics, and plant breeding: A private sector perspective. Crop Science 44, 1907–1913.
Genomics, genetics, and plant breeding: A private sector perspective.Crossref | GoogleScholarGoogle Scholar |

Cooper M, Podlich DW, Smith OS (2005) Gene to phenotype models and complex trait genetics. Australian Journal of Agricultural Research 56, 895–918.
Gene to phenotype models and complex trait genetics.Crossref | GoogleScholarGoogle Scholar |

Cooper M, Smith OS, Merrill RE, Arthur L, Podlich DW, Löffler CM (2006) Integrating breeding tools to generate information for efficient breeding: Past, present, and future. In ‘Plant Breeding: The Arnel R. Hallauer International Symposium’. (Eds KR Lamkey, M Lee) pp. 141–154. (Blackwell Publishing Ltd: Oxford, UK)

Cooper M, van Eeuwijk FA, Hammer GL, Podlich DW, Messina C (2009) Modeling QTL for complex traits: detection and context for plant breeding. Current Opinion in Plant Biology 12, 231–240.
Modeling QTL for complex traits: detection and context for plant breeding.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsVOguro%3D&md5=525fdc7554e28e23400f0b09dbc24d71CAS | 19282235PubMed |

Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014) Breeding drought tolerant maize hybrids for the US corn-belt: Discovery to product. Journal of Experimental Botany
Breeding drought tolerant maize hybrids for the US corn-belt: Discovery to product.Crossref | GoogleScholarGoogle Scholar | 24596174PubMed | in press.

Crossa J, Perez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112, 48–60.
Genomic prediction in CIMMYT maize and wheat breeding programs.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3srjsl2qsg%3D%3D&md5=db9e78d8d18ac60efef3e62c7fce5e60CAS | 23572121PubMed |

Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological & Environmental Statistics 11, 381–393.
On the design of early generation variety trials with correlated data.Crossref | GoogleScholarGoogle Scholar |

Dekkers JC, Chakraborty R (2001) Potential gain for optimizing multigeneration selection on an identified quantitative trait locus. Journal of Animal Science 79, 2975–2990.

Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nature Reviews. Genetics 3, 22–32.
The use of molecular genetics in the improvement of agricultural populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XhsV2gsbw%3D&md5=6345e0c3dbb4b8be81a64fdda57c08dbCAS |

DeLacy IH, Basford KE, Cooper M, Bull JK, McLaren CG (1996) Analysis of multi-environment trials—An historical perspective. In ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer) pp. 39–124. (CAB International: Wallingford, UK)

Dong Z, Danilevskaya O, Abadie T, Messina C, Coles N, Cooper M (2012) A gene regulatory network model for floral transition of the shoot apex in maize and its dynamic modeling. PLoS ONE 7, e43450
A gene regulatory network model for floral transition of the shoot apex in maize and its dynamic modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xht1CnsbzL&md5=708b79b9b2151caee74ba74b1a29bf1fCAS | 22912876PubMed |

Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. In ‘Plant Breeding Reviews 24: Long term selection: Crops, animals, and bacteria’. Vol. 24, Part 2. (Ed. J Janick), pp. 109–151. (John Wiley & Sons: New York)

Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Science 47, S154–S163.
Molecular markers in a commercial breeding program.Crossref | GoogleScholarGoogle Scholar |

Edgerton MD (2009) Increasing crop productivity to meet global needs for feed, food, and fuel. Plant Physiology 149, 7–13.
Increasing crop productivity to meet global needs for feed, food, and fuel.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjt1Wqs7g%3D&md5=8a6f1d964c86a1381f0680bb2dad6b27CAS | 19126690PubMed |

Edmeades GO, Bolaños J, Hernandez M, Bello S (1993) Causes for silk delay in lowland tropical maize population. Crop Science 33, 1029–1035.
Causes for silk delay in lowland tropical maize population.Crossref | GoogleScholarGoogle Scholar |

Evans LT (1996) ‘Crop evolution, adaptation and yield.’ (Cambridge University Press: Cambridge, UK)

Evans LT (1998) ‘Feeding the ten billion: plants and population growth.’ (Cambridge University Press: Cambridge, UK)

Federer WT, Nair RC, Raghavarao D (1975) Some augmented row-column designs. Biometrics 31, 361–374.
Some augmented row-column designs.Crossref | GoogleScholarGoogle Scholar |

Federer WT, Reynolds M, Crossa J (2001) Combining results from augmented designs over sites. Agronomy Journal 93, 389–395.
Combining results from augmented designs over sites.Crossref | GoogleScholarGoogle Scholar |

Fehr WR (Ed.) (1984) ‘Genetic contributions to yield gains of five major crop plants.’ CSSA Special Publication No. 7. (American Society of Agronomy and Crop Science Society of America: Madison, WI, USA)

Fehr WR (1991) ‘Principles of cultivar development. Vol. 1, Theory and technique.’ (Macmillan Publishing Company: London)

Feng L, Sebastian S, Smith S, Cooper M (2006) Temporal trends in SSR allele frequencies associated with long-term selection for yield of maize. Maydica 51, 293–300.

Fernando RL, Grossman M (1989) Marker assisted selection using best linear unbiased prediction. Genetics, Selection, Evolution. 21, 467–477.
Marker assisted selection using best linear unbiased prediction.Crossref | GoogleScholarGoogle Scholar |

Fischer KS, Edmeades GO, Johnson EC (1989) Selection for the improvement of maize yield under moisture-deficits. Field Crops Research 22, 227–243.
Selection for the improvement of maize yield under moisture-deficits.Crossref | GoogleScholarGoogle Scholar |

Furbank RT, Tester M (2011) Phenomics technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
Phenomics technologies to relieve the phenotyping bottleneck.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=7502fe5daf66f7e17f225d5ce9ad745aCAS | 22074787PubMed |

Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173, 1761–1776.
Genomic-assisted prediction of genetic value with semiparametric procedures.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XptVeiu74%3D&md5=3b1a705db79f975f579ed25070503cc2CAS | 16648593PubMed |

Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological & Environmental Statistics 2, 269–293.
Accounting for natural and extraneous variation in the analysis of field experiments.Crossref | GoogleScholarGoogle Scholar |

Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ‘ASReml user guide release 3.0.’ (VSN International Ltd: Hemel Hempstead, UK) Available at: www.vsni.co.uk

Grassini P, Eskridge KM, Cassman KG (2013) Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications 4, 2918
Distinguishing between yield advances and yield plateaus in historical crop production trends.Crossref | GoogleScholarGoogle Scholar | 24346131PubMed |

Guo M, Rupe MA, Wei J, Winkler C, Goncalves-Butruille M, Weers B, Cerwick S, Dieter JA, Duncan KE, Howard RJ, Hou Z, Löffler CM, Cooper M, Simmons CR (2013) Maize ARGOS1 (ZAR1) transgenic alleles increase hybrid maize yeild. Journal of Experimental Botany
Maize ARGOS1 (ZAR1) transgenic alleles increase hybrid maize yeild.Crossref | GoogleScholarGoogle Scholar | 24218327PubMed |

Habben JE, Bao X, Bate NJ, DeBruin J, Dolan D, Hasegawa D, Helentjaris TG, Lafitte HR, Lovan N, Mo H, Reimann K, Schussler JR (2014) Transgenic alteration of ethylene biosynthesis increases grain yield in maize under field drought-stress conditions. Plant Biotechnology Journal
Transgenic alteration of ethylene biosynthesis increases grain yield in maize under field drought-stress conditions.Crossref | GoogleScholarGoogle Scholar | 24618117PubMed | in press.

Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186–198.
Extension of the Bayesian alphabet for genomic selection.Crossref | GoogleScholarGoogle Scholar | 21605355PubMed |

Hallauer AR, Miranda Filho JB (1988) ‘Quantitative genetics in maize breeding.’ 2nd edn. (Iowa State University Press: Ames, IA)

Hammer GL, Chapman S, van Oosterom E, Podlich DW (2005) Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Australian Journal of Agricultural Research 56, 947–960.
Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems.Crossref | GoogleScholarGoogle Scholar |

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 in Plant Science 11, 587–593.
Models for navigating biological complexity in breeding improved crop plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1eqtbzJ&md5=122672c6efb119c2840a8645be9ffde0CAS | 17092764PubMed |

Hammer GL, Dong Z, McLean G, Doherty A, Messina C, Schussler J, Zinselmeier C, Paszkiewicz S, Cooper M (2009) Can changes in canopy and/or root systems architecture explain historical maize yield trends in the U.S. corn belt? Crop Science 49, 299–312.
Can changes in canopy and/or root systems architecture explain historical maize yield trends in the U.S. corn belt?Crossref | GoogleScholarGoogle Scholar |

Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Science 49, 1–12.
Genomic selection for crop improvement.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjsF2it78%3D&md5=aecf6a77c7c8c592fde59ef52e072856CAS |

Henderson CR (1984) ‘Applications of linear models in animal breeding.’ (University of Guelph: Guelph, ON, Canada)

Heslot N, Akdemir D, Sorrells ME, Jannink JL (2013) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theoretical and Applied Genetics
Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.Crossref | GoogleScholarGoogle Scholar | 24264761PubMed |

Hill WG (2014) Applications of population genetics to animal breeding, from Wright, Fisher and Lush to genomic prediction. Genetics 196, 1–16.
Applications of population genetics to animal breeding, from Wright, Fisher and Lush to genomic prediction.Crossref | GoogleScholarGoogle Scholar | 24395822PubMed |

Kauffman SA (1993) ‘The origins of order: self-organization and selection in evolution.’ (Oxford University Press: New York)

Kirigwi FM, van Ginkel M, Trethowan R, Sears RG, Rajaram S, Paulsen GM (2004) Evaluation of selection strategies for wheat adaptation across water regimes. Euphytica 135, 361–371.
Evaluation of selection strategies for wheat adaptation across water regimes.Crossref | GoogleScholarGoogle Scholar |

Kush GS (2005) Green revolution: challenges ahead. In ‘In the wake of the bouble helix from the Green Revolution to the Gene Revolution. Proceedings of an International Congress’. 27–31 May 2003, University of Bologna, Italy. (Eds R Tuberosa, RL Phillips, M Gale) pp. 37–51. (Avenue Media: Bologna, Italy)

Lamkey KR, Lee M (Eds) (2006) ‘Plant breeding: The Arnel R. Hallauer International Symposium.’ (Blackwell Publishing Ltd: Oxford, UK)

Lande R, Thompson R (1990) Efficiency of marker assisted selection in the improvement of quantitative traits. Genetics 124, 743–756.

Löffler CM, Wei J, Fast T, Gogerty J, Langton S, Bergman M, Merrill B, Cooper M (2005) Classification of maize environments using crop simulation and geographic information systems. Crop Science 45, 1708–1716.
Classification of maize environments using crop simulation and geographic information systems.Crossref | GoogleScholarGoogle Scholar |

Lynch M, Walsh B (1998) ‘Genetics and analysis of quantitative traits.’ (Sinauer Associates, Inc.: Sunderland, MA, USA)

Mansfield BD, Mumm RH (2014) Survey of plant density tolerance in U.S. maize germplasm. Crop Science 54, 157–173.
Survey of plant density tolerance in U.S. maize germplasm.Crossref | GoogleScholarGoogle Scholar |

Marjoram P, Zubair A, Nuzhdin SV (2014) Post-GWAS: where next? More samples, more SNPs or more biology. Heredity 112, 79–88.
Post-GWAS: where next? More samples, more SNPs or more biology.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhvFCqs7rL&md5=a2f4af49cad4a9e554a60b9244bce872CAS | 23759726PubMed |

Messina CD, Hammer GL, Dong Z, Podlich D, Cooper M (2009) Modelling crop improvement in a GxExM framework via gene-trait-phenotype relationships. In ‘Crop physiology: interfacing with genetic improvement and agronomy’. (Eds V Sadras, D Calderini) pp. 235–265. (Elsevier: Amsterdam)

Messina CD, Podlich D, Dong Z, Samples M, Cooper M (2011) Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance. Journal of Experimental Botany 62, 855–868.
Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtVekurs%3D&md5=d56efdbbb118c508c2d6196cc27dc97dCAS | 21041371PubMed |

Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.

Muchow RC, Cooper M, Hammer GL (1996) Characterizing environmental challenges using models. In ‘Plant adaptation and crop improvement’. (Eds M Cooper, GL Hammer) pp. 349–364. (CAB International: Wallingford, UK)

Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010) New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. Journal of Experimental Botany 61, 3499–3507.
New phenotyping methods for screening wheat and barley for beneficial responses to water deficit.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtVert7jJ&md5=52df62a3d7c02be06e1fec9ca091326aCAS | 20605897PubMed |

Nelson DE, Repetti PP, Adams TR, Creelman RA, Wu J, Warner DC, Anstrom DC, Bensen RJ, Castiglioni PP, Donnarummo MG, Hinchey BS, Kumimoto RW, Maszle DR, Canales RD, Krolikowski KA, Dotson SB, Gutterson N, Ratcliffe OJ, Heard JE (2007) Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved yields on water-limited acres. Proceedings of the National Academy of Sciences of the United States of America 104, 16450–16455.
Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved yields on water-limited acres.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXht1Whs7%2FK&md5=d30c145cec1aa275f1a18049dd7ca934CAS | 17923671PubMed |

Passioura JB (1977) Grain yield, harvest index, and water use of wheat. Journal of the Australian Institute of Agricultural Science 43, 117–120.

Passioura JB (2006) The perils of pot experiments. Functional Plant Biology 33, 1075–1079.
The perils of pot experiments.Crossref | GoogleScholarGoogle Scholar |

Passioura JB (2012) Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Functional Plant Biology 39, 851–859.
Phenotyping for drought tolerance in grain crops: when is it useful to breeders?Crossref | GoogleScholarGoogle Scholar |

Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Science 49, 1165–1176.
Ridge regression and extensions for genomewide selection in maize.Crossref | GoogleScholarGoogle Scholar |

Piepho HP, Williams ER (2006) A comparison of experimental designs for selection in breeding trials with nested treatment structure. Theoretical and Applied Genetics 113, 1505–1513.
A comparison of experimental designs for selection in breeding trials with nested treatment structure.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD28nlt1KisQ%3D%3D&md5=8efb2bc03de4d708a11133d8fc04fd06CAS | 17028902PubMed |

Podlich DW, Cooper M (1999) Modelling plant breeding programs as search strategies on a complex response surface. Lecture Notes in Computer Science 1585, 171–178.
Modelling plant breeding programs as search strategies on a complex response surface.Crossref | GoogleScholarGoogle Scholar |

Podlich DW, Cooper M, Basford KE (1999) Computer simulation of a selection strategy to accommodate genotype–environment interactions in a wheat recurrent selection programme. Plant Breeding 118, 17–28.
Computer simulation of a selection strategy to accommodate genotype–environment interactions in a wheat recurrent selection programme.Crossref | GoogleScholarGoogle Scholar |

Podlich DW, Winkler CR, Cooper M (2004) Mapping as you go: An effective approach for marker-assisted selection of complex traits. Crop Science 44, 1560–1571.
Mapping as you go: An effective approach for marker-assisted selection of complex traits.Crossref | GoogleScholarGoogle Scholar |

Qiao CG, Basford KE, Delacy IH, Cooper M (2000) Evaluation of experimental designs and spatial analyses in wheat breeding trials. Theoretical and Applied Genetics 100, 9–16.
Evaluation of experimental designs and spatial analyses in wheat breeding trials.Crossref | GoogleScholarGoogle Scholar |

Qiao CG, Basford KE, Delacy IH, Cooper M (2004) Advantage of single-trial models for response to selection in wheat breeding multi-environment trials. Theoretical and Applied Genetics 108, 1256–1264.
Advantage of single-trial models for response to selection in wheat breeding multi-environment trials.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2c3gvVaitA%3D%3D&md5=053fd0d6860fbf464904e33ffa0c9729CAS | 14689186PubMed |

Rebetzke GJ, Chenu K, Biddulph B, Moeller C, Deery DM, Rattey AR, Bennett D, Barrett-Lennard EG, Mayer JE (2013) A multisite managed environment facility for targeted trait and germplasm phenotyping. Functional Plant Biology 40, 1–13.
A multisite managed environment facility for targeted trait and germplasm phenotyping.Crossref | GoogleScholarGoogle Scholar |

Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, Meeley R, Ananiev EV, Svitashev S, Bruggemann E, Li B, Hainey CF, Radovic S, Zaina G, Rafalski JA, Tingey SV, Miao GH, Phillips RL, Tuberosa R (2007) Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proceedings of the National Academy of Sciences of the United States of America 104, 11376–11381.
Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXnvFOmsrc%3D&md5=73ceba4981548722531c6a5468cab688CAS | 17595297PubMed |

Sebastian SA, Streit LG, Stephens PA, Thompson JA, Hedges BR, Fabrizius MA, Soper JF, Schmidt DH, Kallem RL, Hinds MA, Feng L, Hoeck JA (2010) Context-specific marker-assisted selection for improved grain yield in elite soybean populations. Crop Science 50, 1196–1206.
Context-specific marker-assisted selection for improved grain yield in elite soybean populations.Crossref | GoogleScholarGoogle Scholar |

Sinclair TR (2011) Challenges in breeding for yield increase for drought. Trends in Plant Science 16, 289–293.
Challenges in breeding for yield increase for drought.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnsVyrs74%3D&md5=55a3758d80767b21fac1311f1f0c0f90CAS | 21419688PubMed |

Smith A, Cullis B, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57, 1138–1147.
Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD38%2Fjs12jsw%3D%3D&md5=9bdddf9e3204620ada215d0846da9981CAS | 11764254PubMed |

Smith A, Cullis B, Thompson R (2002) Exploring variety-environment data using random effects AMMI models with adjustments for spatial field trend: Part 1: Theory. In ‘Quantitative genetics, genomics, and plant breeding’. (Ed. MS Kang) pp. 323–335. (CAB International: Wallingford, UK)

Smith JSC, Duvick DN, Smith OS, Cooper M, Feng L (2004) Chages in pedigree backgrounds of Pioneer brand maize hybrids widely grown from 1930 to 1999. Crop Science 44, 1935–1946.
Chages in pedigree backgrounds of Pioneer brand maize hybrids widely grown from 1930 to 1999.Crossref | GoogleScholarGoogle Scholar |

Smith JSC, Smith OS, Lamkey KR (2005a) Maize breeding. Maydica 50, 185–192.

Smith AB, Cullis BR, Thompson R (2005b) Centenary Review: The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. The Journal of Agricultural Science 143, 449–462.
Centenary Review: The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches.Crossref | GoogleScholarGoogle Scholar |

Smith S, Löffler C, Cooper M (2006) Genetic diversity among maize hybrids widely grown in contrasting regional environments in the United States during the 1990s. Maydica 51, 233–242.

Sorensen D, Gianola D (2002) ‘Likelihood, Bayesian, and MCMC methods in quantitative genetics.’ (Springer-Verlag: Berlin, Heidelberg)

Sprague GF, Dudley JW (Eds) (1988) ‘Corn and corn improvement.’ 3rd edn (American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., Publishers: Madison, WI, USA)

ter Braak CJF, Boer MP, Totir LR, Winkler CR, Smith OS, Bink MCAM (2010) Identity-by-descent matrix decomposition using latent ancestral allele models. Genetics 185, 1045–1057.
Identity-by-descent matrix decomposition using latent ancestral allele models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFOnu7jP&md5=bcf7a4b74568480554ccdee2d3f1472eCAS |

Trethowan RM, Reynolds MW, Sayre K, Ortiz-Monasterio I (2005) Adapting wheat cultivars to resource conserving farming practices and human nutritional needs. Annals of Applied Biology 146, 405–413.
Adapting wheat cultivars to resource conserving farming practices and human nutritional needs.Crossref | GoogleScholarGoogle Scholar |

Tuberosa R, Phillips RL, Gale M (Eds) (2005) ‘In the wake of the double helix from the Green Revolution to the Gene Revolution. Proceedings of an International Congress.’ 27–31 May 2003 University of Bologna, Italy. (Avenue Media: Bologna, Italy)

van Eeuwijk FA, Cooper M, DeLacy IH, Ceccarelli S, Grando S (2001) Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials. Euphytica 122, 477–490.
Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials.Crossref | GoogleScholarGoogle Scholar |

van Eeuwijk FA, Boer M, Totir LR, Bink M, Wright D, Winkler CR, Podlich D, Boldman K, Baumgarten A, Smalley M, Arbelbide M, ter Braak CJF, Cooper M (2010) Mixed model approaches for the identification of QTLs within a maize hybrid breeding program. Theoretical and Applied Genetics 120, 429–440.
Mixed model approaches for the identification of QTLs within a maize hybrid breeding program.Crossref | GoogleScholarGoogle Scholar | 19921142PubMed |

Vega CRC, Andrade FH, Sadras VO (2001) Reproductive partitioning and seed set efficiency in soybean, sunflower and maize. Field Crops Research 72, 163–175.
Reproductive partitioning and seed set efficiency in soybean, sunflower and maize.Crossref | GoogleScholarGoogle Scholar |

Walsh B (2005) The struggle to exploit non-additive variation. Australian Journal of Agricultural Research 56, 873–881.
The struggle to exploit non-additive variation.Crossref | GoogleScholarGoogle Scholar |

Walsh B (2014) Special issues on advances in quantitative genetics: introduction. Heredity 112, 1–3.
Special issues on advances in quantitative genetics: introduction.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c3lsVGgsQ%3D%3D&md5=b12cff1fda2ec0a8985d136883f3f8e5CAS | 24326299PubMed |

Wang J, van Ginkel M, Podlich D, Ye G, Trethowan R, Pfeiffer W, DeLacy IH, Cooper M, Rajaram S (2003) Comparison of two breeding strategies by computer simulation. Crop Science 43, 1764–1773.
Comparison of two breeding strategies by computer simulation.Crossref | GoogleScholarGoogle Scholar |

Weber VS, Melchinger AE, Magorokosho C, Makumbi D, Bänzinger 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 Science 52, 1011–1020.
Efficiency of managed-stress screening of elite maize hybrids under drought and low nitrogen for yield under rainfed conditions in Southern Africa.Crossref | GoogleScholarGoogle Scholar |

Williams ER, Matheson AC, Harwood CE (2002) ‘Experimental design and analysis for tree improvement.’ 2nd edn (CSIRO: Melbourne)

Williams ER, John JA, Whitaker D (2006) Construction of Resolvable Spatial Row-Column Designs. Biometrics 62, 103–108.
Construction of Resolvable Spatial Row-Column Designs.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD287ltlWmtQ%3D%3D&md5=09698ef8d536091d4db00d20fd05afe8CAS | 16542235PubMed |

Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. In ‘Proceedings of the 6th International Congress of Genetics’. Ithaca, New York. pp. 356–366. (Brooklyn Botanic Garden: Menasha, WI, USA)