Skip to main content
View expanded cover

Crop Systems Biology pp 219–227Cite as

Crop Systems Biology: Where Are We and Where to Go?

  • Chapter

Abstract

The preceding chapters outline approaches in systems biology, genetic mapping and crop modelling, and have shown whether and how these approaches could potentially be integrated to form an effective ‘crop systems biology’ approach in support of crop improvement. To fulfil the great expectations from the integrated modelling, crop models should be improved based on understandings at lower organizational levels, in the meanwhile ensuring that model-input parameters can be easily phenotyped. The ‘crop systems biology’ approach is believed ultimately to realize the expected roles of modelling in narrowing genotype-phenotype gaps and predicting the phenotype from genomic data. Such an approach could be an important tool to solve some imminent food-, feed-, and energy-related, ‘real-world’ problems.

Keywords

  • System Biology
  • Crop System
  • High Aggregation Level
  • Crop Simulation Model
  • Preceding Chapter

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-20562-5_10
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-20562-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)
Learn about institutional subscriptions

References

  • Boote KJ, Jones JW, White JW, Asseng S, Lisaso JI (2013) Putting mechanisms into crop production models. Plant Cell Environ 36:1658–1672

    PubMed  CAS  CrossRef  Google Scholar 

  • Brown HE, Jamieson PD, Brooking IR, Moot DJ, Huth NI (2013) Integration of molecular and physiological models to explain time of anthesis in wheat. Ann Bot 112:1683–1703

    PubMed  CAS  PubMed Central  CrossRef  Google Scholar 

  • 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 183:1507–1523

    PubMed  PubMed Central  CrossRef  Google Scholar 

  • Chew YH, Wenden B, Flis A, Mengin V, Taylor J, Davey CL, Tindal C, Thomas H, Ougham HJ, de Reffye P, Stitt M, Williams M, Muetzelfeldt R, Halliday KJ, Millar AJ (2014) Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proc Natl Acad Sci USA 111(39):E4127–E4136

    Google Scholar 

  • Cooper M, Hammer GL (2005) Complex traits and plant breeding – can we understand the complexities of gene-to-phenotype relationships and use such knowledge to enhance plant breeding outcomes? Aust J Agric Res 56:869–872

    CrossRef  Google Scholar 

  • de Wit CT (1959) Potential photosynthesis of crop surfaces. Neth J Agric Sci 7:141–149

    Google Scholar 

  • 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

    PubMed  CAS  PubMed Central  CrossRef  Google Scholar 

  • Dwivedi SL, Crouch JH, Mackill DJ, Xu Y, Blair MW, Ragot M, Upadhyaya HD, Ortiz R (2007) The molecularization of public sector crop breeding: progress, problems and prospects. Adv Agron 95:163–318

    CAS  CrossRef  Google Scholar 

  • Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78–90

    PubMed  CAS  CrossRef  Google Scholar 

  • Giersch C (2000) Mathematical modelling of metabolism. Curr Opin Plant Biol 3:249–253

    PubMed  CAS  CrossRef  Google Scholar 

  • Gu J, Yin X, Stomph TJ, Struik PC (2014) Can exploiting natural genetic variation in leaf photosynthesis contribute to increasing rice productivity? A simulation analysis. Plant Cell Environ 37:22–34

    PubMed  CAS  CrossRef  Google Scholar 

  • Hammer G, Cooper M, Tardieu F, Welch S, Walch B, van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593

    PubMed  CAS  CrossRef  Google Scholar 

  • Jackson P, Robertson M, Cooper M, Hammer G (1996) The role of physiological understanding in plant breeding, from a breeding perspective. Field Crops Res 49:11–37

    CrossRef  Google Scholar 

  • Lawlor DW (2002) Carbon and nitrogen assimilation in relation to yield: mechanisms are the key to understanding production systems. J Exp Bot 53:773–787

    PubMed  CAS  CrossRef  Google Scholar 

  • Loomis RS, Rabbinge R, Ng E (1979) Explanatory models in crop physiology. Annu Rev Plant Physiol 30:339–367

    CrossRef  Google Scholar 

  • Martin L, Cook C, Matasci N, Williams J, Bastow R (2015) Data mining with iPlant: a meeting report from the 2013 GARNet workshop, Data mining with iPlant. J Exp Bot 66:1–6

    PubMed  CAS  CrossRef  Google Scholar 

  • Martre P, Porter JR, Jamieson PD, Triboï E (2003) Modeling grain nitrogen accumulation and protein composition to understand the sink/source regulations of nitrogen remobilization for wheat. Plant Physiol 133:1959–1967

    PubMed  CAS  PubMed Central  CrossRef  Google Scholar 

  • Parent B, Tardieu F (2014) Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature? J Exp Bot 65:6179–6189

    PubMed  CAS  CrossRef  Google Scholar 

  • Penning de Vries FWT, Brunsting AHM, van Laar HH (1974) Products, requirements and efficiency of biosynthesis: a quantitative approach. J Theor Biol 45:339–377

    PubMed  CAS  CrossRef  Google Scholar 

  • Perez-Martin A, Michelazzo C, Torres-Ruiz JM, Flexas J, Fernandez JE, Sebastiani L, Diaz-Espejo A (2014) Regulation of photosynthesis and stomatal and mesophyll conductance under water stress and recovery in olive trees: correlation with gene expression of carbonic anhydrase and aquaporins. J Exp Bot 65:3143–3156

    PubMed  PubMed Central  CrossRef  Google Scholar 

  • Sinclair TR, Purcell LC, Sneller CH (2004) Crop transformation and the challenge to increase yield potential. Trends Plant Sci 9:70–75

    PubMed  CAS  CrossRef  Google Scholar 

  • Spiertz JHJ, Struik PC, van Laar HH (2007) Scale and complexity in plant systems research: gene-plant-crop relations, vol 21, Wageningen UR frontis series. Springer, Dordrecht, 329 pp

    CrossRef  Google Scholar 

  • Stitt M, Fernie AR (2003) From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon–nitrogen interactions. Curr Opin Biotechnol 14:136–144

    PubMed  CAS  CrossRef  Google Scholar 

  • Struik PC, Cassman KG, Koornneef M (2007) A dialogue on interdisciplinary collaboration to bridge the gap between plant genomics and crop sciences. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations. Springer, Dordrecht, pp 319–328

    CrossRef  Google Scholar 

  • Sun Y, Gu L, Dickinson RE, Norby RJ, Pallardy SG, Hoffman FM (2014) Impact of mesophyll diffusion on estimated global land CO2 fertilization. Proc Natl Acad Sci U S A 111:15774–15779

    PubMed  CAS  PubMed Central  CrossRef  Google Scholar 

  • von Caemmerer S (2013) Steady-state models of photosynthesis. Plant Cell Environ 36:1617–1630

    CrossRef  Google Scholar 

  • Weiss A (2003) Introduction. Agron J 95:1–3

    CrossRef  Google Scholar 

  • Welch SM, Roe JL, Dong Z (2003) Genetic neural network model of flowering time control in Arabidopsis thaliana. Agron J 95:71–81

    CrossRef  Google Scholar 

  • White JW, Hoogenboom G (1996) Simulating effects of genes for physiological traits in a process-oriented crop model. Agron J 88:416–422

    CrossRef  Google Scholar 

  • White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenk MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang G (2012) Field-based phenomics for plant genetics research. Field Crop Res 133:101–112

    CrossRef  Google Scholar 

  • Wilczek AM, Roe JL, Knapp MC, Cooper MD, Lopez-Gallego C, Martin LJ, Muir CD, Sim S, Walker A, Anderson J, Egan JF, Moyers BT, Petipas R, Giakountis A, Charbi E, Coupland G, Welch SM, Schmitt J (2009) Effects of genetic perturbation on seasonal life history plasticity. Science 323:930–934

    PubMed  CAS  CrossRef  Google Scholar 

  • Yin X (2013) Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity. Ann Bot 112:465–475

    PubMed  CAS  PubMed Central  CrossRef  Google Scholar 

  • Yin X, Struik PC (2007) Crop systems biology: an approach to connect functional genomics with crop modelling. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations. Springer, Dordrecht, pp 61–71

    Google Scholar 

  • 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 179:629–642

    PubMed  CAS  CrossRef  Google Scholar 

  • Yin X, Struik PC (2009) C3 and C4 photosynthesis models: an overview from the perspective of crop modelling. NJAS Wagening J Life Sci 57:27–38

    CrossRef  Google Scholar 

  • Yin X, Struik PC (2010) Modelling the crop: from system dynamics to systems biology. J Exp Bot 61:2171–2183

    PubMed  CAS  CrossRef  Google Scholar 

  • Zhu XG, Zhang GL, Tholen D, Wang Y, Xin CP, Song QF (2011) The next generation models for crops and agro-ecosystems. Sci China Inf Sci 54:589–597

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Plant Sciences, Centre for Crop Systems Analysis, Wageningen University, 430, 6700 AK, Wageningen, The Netherlands

    Xinyou Yin & Paul C. Struik

Authors
  1. Xinyou Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul C. Struik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyou Yin .

Editor information

Editors and Affiliations

  1. Department of Plant Sciences Centre for Crop Systems Analysis, Wageningen University, Wageningen, The Netherlands

    Xinyou Yin

  2. Department of Plant Sciences Centre for Crop Systems Analysis, Wageningen University, Wageningen, The Netherlands

    Paul C. Struik

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Yin, X., Struik, P.C. (2016). Crop Systems Biology: Where Are We and Where to Go?. In: Yin, X., Struik, P. (eds) Crop Systems Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-20562-5_10

Download citation