Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

  • Graeme HammerEmail author
  • Charlie Messina
  • Erik van Oosterom
  • Scott Chapman
  • Vijaya Singh
  • Andrew Borrell
  • David Jordan
  • Mark Cooper


Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.


Genotype-to-phenotype Phenotypic prediction Trait physiology QTL Functional genomics Crop improvement 



This paper summarises the research of a team of people, which would not have been possible without their enthusiasm and dedication nor without the financial support of a number of funders including Australian Research Council, Grains Research and Development Corporation, DuPont Pioneer, European Union FP7, and Australian Centre for International Agricultural Research. The authors acknowledge Crop and Pasture Science (CSIRO Publishing) and Journal of Experimental Botany (Oxford Journals) for permissions to re-use figures.


  1. Alam MM, Hammer GL, van Oosterom EJ, Cruickshank A, Hunt C, Jordan DR (2014a) A physiological framework to explain genetic and environmental regulation of tillering in sorghum. New Phytol 203:155–167PubMedCrossRefGoogle Scholar
  2. Alam MM, Mace ES, van Oosterom EJ, Cruickshank A, Hunt CH, Hammer GL, Jordan DR (2014b) QTL analysis in multiple sorghum populations facilitates the dissection of the genetic and physiological control of tillering. Theor Appl Genet 127:2253–2266PubMedCrossRefGoogle Scholar
  3. Benfey PN, Mitchell-Olds T (2008) From genotype to phenotype: systems biology meets natural variation. Science 320:495–497PubMedPubMedCentralCrossRefGoogle Scholar
  4. Borrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR (2014a) Drought adaptation of stay-green in sorghum associated with canopy development, leaf anatomy, root growth and water uptake. J Exp Bot 65:6251–6263PubMedPubMedCentralCrossRefGoogle Scholar
  5. Borrell AK, van Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE, Hammer GL (2014b) Stay-green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol 203:817–830PubMedCrossRefGoogle Scholar
  6. Chapman SC, Cooper M, Podlich D, Hammer GL (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95:99–113CrossRefGoogle Scholar
  7. Chenu K, Chapman SC, Hammer GL, McLean G, Ben Haj Salah H, 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 31:378–391PubMedCrossRefGoogle Scholar
  8. 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 183:1507–1523PubMedPubMedCentralCrossRefGoogle Scholar
  9. Choudhary S, Sinclair TR (2014) Hydraulic conductance differences among sorghum genotypes to explain variation in restricted transpiration rates. Funct Plant Biol 41:270–275CrossRefGoogle Scholar
  10. Choudhary S, Sinclair TR, Messina CD, Cooper M (2014) Hydraulic conductance of maize hybrids differing in transpiration response to vapor pressure deficit. Crop Sci 54:1147–1152CrossRefGoogle Scholar
  11. Cooper M, Hammer GL (1996) Synthesis of strategies for crop improvement. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, ICRISAT & IRRI, Wallingford, pp 591–623Google Scholar
  12. Cooper M, Chapman SC, Podlich DW, Hammer GL (2002) The GP problem: quantifying gene-to-phenotype relationships. In Silico Biol 2:151–164.
  13. Cooper M, Podlich DP, Smith OS (2005) Gene-to-phenotype models and complex trait genetics. Aust J Agr Res 56:895–918CrossRefGoogle Scholar
  14. 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 12:231–240PubMedCrossRefGoogle Scholar
  15. Cooper M, Gho C, Leafgren R, Tang T, Messina C (2014a) Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J Exp Bot 65:6191–6204PubMedCrossRefGoogle Scholar
  16. Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014b) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65:311–336CrossRefGoogle Scholar
  17. de Pury DGG, Farquhar GD (1997) Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ 20:537–557CrossRefGoogle Scholar
  18. 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:e43450PubMedPubMedCentralCrossRefGoogle Scholar
  19. Fletcher AL, Sinclair TR, Allen LH Jr (2007) Transpiration responses to vapor pressure deficit in well watered and ‘slow wilting’ and commercial soybean. Environ Exp Bot 61:145–151CrossRefGoogle Scholar
  20. Gholipoor M, Prasad PVV, Mutava RN, Sinclair TR (2010) Genetic variability of transpiration response to vapor pressure deficit among sorghum genotypes. Field Crops Res 119:85–90Google Scholar
  21. Gholipoor M, Choudhary S, Sinclair TR, Messina CD, Cooper M (2013) Transpiration response of maize hybrids to atmospheric vapour pressure deficit. J Agron Crop Sci 199:155–160CrossRefGoogle Scholar
  22. Gu J, Yin X, Stomph T-J, Struik PC (2014) Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis. Plant Cell Environ 37:22–34PubMedCrossRefGoogle Scholar
  23. Hammer G (2006) Pathways to prosperity: breaking the yield barrier in sorghum. Agric Sci 19:16–22Google Scholar
  24. Hammer GL, Jordan DR (2007) An integrated systems approach to crop improvement. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations, vol 21, Wageningen UR – Frontis series. Springer, The Netherlands, pp 45–61Google Scholar
  25. Hammer GL, Wright GC (1994) A theoretical analysis of nitrogen and radiation effects on radiation use efficiency in peanut. Aust J Agr Res 45:575–589CrossRefGoogle Scholar
  26. Hammer GL, Carberry PS, Muchow RC (1993) Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. I. Whole plant level. Field Crops Res 33:293–310CrossRefGoogle Scholar
  27. Hammer GL, Kropff MJ, Sinclair TR, Porter JR (2002) Future contributions of crop modelling – from heuristics and supporting decision-making to understanding genetic regulation and aiding crop improvement. Eur J Agron 18:15–31CrossRefGoogle Scholar
  28. Hammer G, Chapman S, van Oosterom E, Podlich D (2005) Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Aust J Agr Res 56:947–960CrossRefGoogle Scholar
  29. 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 11:587–593PubMedCrossRefGoogle Scholar
  30. Hammer GL, van Oosterom E, McLean G, Chapman SC, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61:2185–2202PubMedCrossRefGoogle Scholar
  31. Hammer GL, McLean G, Chapman S, Zheng B, Doherty A, Harrison MT, van Oosterom E, Jordan D (2014) Crop design for specific adaptation in variable dryland production environments. Crop Pasture Sci 65:614–626. Google Scholar
  32. Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  33. Jyostna Devi M, Sinclair TR, Vadez V (2010) Genotypic variation in peanut for transpiration response to vapor pressure deficit. Crop Sci 50:191–196CrossRefGoogle Scholar
  34. Kholova J, Hash CT, Kumar PL, Yadav RS, Kočová M, Vadez V (2010) Terminal drought-tolerant pearl millet [Pennisetum glaucum (L.) R. Br.] have high leaf ABA and limit transpiration at high vapour pressure deficit. J Exp Bot 61:1431–1440PubMedPubMedCentralCrossRefGoogle Scholar
  35. Kim HK, Luquet D, van Oosterom E, Dingkuhn M, Hammer G (2010) Regulation of tillering in sorghum: genotypic effects. Ann Bot 106:69–78PubMedPubMedCentralCrossRefGoogle Scholar
  36. Kirkegaard JA, Lilley JM, Howe GN, Graham JM (2007) Impact of subsoil water use on wheat yield. Aust J Agr Res 58:303–315CrossRefGoogle Scholar
  37. Lyon DJ, Hammer GL, McLean GB, Blumenthal JM (2003) Simulation supplements field studies to determine no-till dryland corn population recommendations for semiarid western Nebraska. Agron J 95:884–891CrossRefGoogle Scholar
  38. Mace ES, Singh V, van Oosterom EJ, Hammer GL, Hunt CH, Jordan DR (2012) QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co-locate with QTL for traits associated with drought adaptation. Theor Appl Genet 124:97–109PubMedCrossRefGoogle Scholar
  39. Mace ES, Hunt CH, Jordan DR (2013) Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time. Theor Appl Genet 126:1377–1395PubMedCrossRefGoogle Scholar
  40. Manschadi AM, Christopher J, deVoil P, Hammer GL (2006) The role of root architectural traits in adaptation of wheat to water-limited environments. Funct Plant Biol 33:823–837CrossRefGoogle Scholar
  41. Messina C, Hammer G, Dong Z, Podlich D, Cooper M (2009) Modelling crop improvement in a G*E*M framework via gene-trait-phenotype relationships. In: Sadras VO, Calderini D (eds) Crop physiology: applications for genetic improvement and agronomy. Academic/Elsevier, The Netherlands, pp 235–265CrossRefGoogle Scholar
  42. 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. J Exp Bot 62:855–868PubMedCrossRefGoogle Scholar
  43. Morrell PL, Buckler ES, Ross-Ibarra J (2012) Crop genomics: advances and applications. Nat Rev Genet 13:85–96Google Scholar
  44. Morris GP, Ramu P, Deshpande SP et al (2012) Population genomic and genome-wide association studies of agroclimatic traits in sorghum. Proc Natl Acad Sci U S A 110:453–458PubMedPubMedCentralCrossRefGoogle Scholar
  45. Ravi Kumar S, Hammer GL, Broad I, Harland P, McLean G (2009) Modelling environmental effects on phenology and canopy development of diverse sorghum genotypes. Field Crops Res 111:157–165CrossRefGoogle Scholar
  46. Ray JD, Samson BK, Sinclair TR (1997) Vegetative growth and soil water extraction of two maize hybrids during water deficits. Field Crops Res 52:135–142Google Scholar
  47. 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 131:664–675PubMedPubMedCentralCrossRefGoogle Scholar
  48. Sadok W, Sinclair TR (2009a) Genetic variability of transpiration response to vapor pressure deficit among soybean (Glycine max [L.] Merr.) genotypes selected from a recombinant inbred line population. Field Crops Res 113:156–160Google Scholar
  49. Sadok W, Sinclair TR (2009b) Genetic variability of transpiration response to vapor pressure deficit among soybean cultivars. Crop Sci 49:955–960CrossRefGoogle Scholar
  50. Salazar JD, Saithong T, Brown PE et al (2009) Prediction of photoperiodic regulators from quantitative gene circuit models. Cell 139:1170–1179PubMedCrossRefGoogle Scholar
  51. Sinclair TR, Muchow RC (1998) Radiation use efficiency. Adv Agron 65:215–265CrossRefGoogle Scholar
  52. Sinclair T, Purcell L, Sneller CH (2004) Crop transformation and the challenge to increase yield potential. Trends Plant Sci 9:70–75PubMedCrossRefGoogle Scholar
  53. Sinclair TR, Hammer GL, van Oosterom EJ (2005) Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate. Funct Plant Biol 32:945–952CrossRefGoogle Scholar
  54. Sinclair TR, Messina CD, Beatty A, Samples M (2010) Assessment across the United States of the benefits of altered soybean drought traits. Agron J 102:475–482CrossRefGoogle Scholar
  55. Singh V, van Oosterom EJ, Jordan DR, Hammer GL (2010) Morphological and architectural development of root systems in sorghum and maize. Plant and Soil 333:287–299CrossRefGoogle Scholar
  56. Singh V, van Oosterom EJ, Jordan DR, Hammer GL (2011) Genetic variability and control of root architecture in sorghum. Crop Sci 51:2011–2020CrossRefGoogle Scholar
  57. Singh V, van Oosterom EJ, Jordan DR, Hammer GL (2012) Genetic control of nodal root angle in sorghum and its implications on water extraction. Eur J Agron 42:3–10CrossRefGoogle Scholar
  58. Tardieu F (2003) Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends Plant Sci 8:9–14PubMedCrossRefGoogle Scholar
  59. Tardieu F, Reymond M, Muller B, Simonneau T, Sadok W, Welcker C (2005) Linking physiological and genetic analyses of the control of leaf growth under changing environmental conditions. Aust J Agr Res 56:937–946CrossRefGoogle Scholar
  60. van Oosterom EJ, Hammer GL, Chapman SC, Doherty A, Mace E, Jordan DR (2006) Predicting flowering time in sorghum using a simple gene network: functional physiology or fictional functionality? In: Borrell AK et al. (eds) Proceedings of the 5th Australian sorghum conference, Gold Coast, 30 Jan–2 Feb 2006Google Scholar
  61. van Oosterom EJ, Borrell AK, Deifel K, Hammer GL (2011) Does increased leaf appearance rate enhance adaptation to post-anthesis drought stress in sorghum? Crop Sci 51:2728–2740CrossRefGoogle Scholar
  62. Welcker CB, Boussuge CB, Ribaut JM, Tardieu F (2007) Are source and sink strengths genetically linked in maize plants subjected to water deficit? A QTL study of the responses of leaf growth and of anthesis-silking interval to water deficit. J Exp Bot 58:339–349PubMedCrossRefGoogle Scholar
  63. Whish J, Butler G, Castor M, Cawthray S, Broad I, Carberry P, Hammer G, McLean G, Routley R, Yeates S (2005) Modelling the effects of row configuration on sorghum yield in north-eastern Australia. Aust J Agr Res 56:11–23CrossRefGoogle Scholar
  64. Wu AC, Morell MK, Gilbert RG (2013) A parameterized model of amylopectin synthesis provides key insights into the synthesis of granular starch. PLoS One 8(6):e65768PubMedPubMedCentralCrossRefGoogle Scholar
  65. Yang Z, Sinclair TR, Zhu M, Messina CD, Cooper M, Hammer GL (2012) Temperature effect on transpiration response of maize plants to vapour pressure deficit. Environ Exp Bot 78:157–162CrossRefGoogle Scholar
  66. Yin X, Struik PC (2008) Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. New Phytol 179:629–642PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Graeme Hammer
    • 1
    Email author
  • Charlie Messina
    • 2
  • Erik van Oosterom
    • 1
  • Scott Chapman
    • 3
  • Vijaya Singh
    • 1
  • Andrew Borrell
    • 4
  • David Jordan
    • 4
  • Mark Cooper
    • 2
  1. 1.Centre for Plant Science, Queensland Alliance for Agriculture and Food InnovationThe University of QueenslandBrisbaneAustralia
  2. 2.DuPont-PioneerJohnstonUSA
  3. 3.CSIRO Plant Industry and Climate Adaptation FlagshipQueensland Bioscience PrecinctSt LuciaAustralia
  4. 4.Centre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, Hermitage Research FacilityThe University of QueenslandWarwickAustralia

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