Skip to main content

An Integrated Systems Approach to Crop Improvement

  • Conference paper
Book cover Scale and Complexity in Plant Systems Research

Part of the book series: Wageningen UR Frontis Series ((WURF,volume 21))

Abstract

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) given the resources available to search among possible combinations in the target population of environments (E). Crop improvement can be viewed as a search strategy on a complex G×M×E adaptation or fitness landscape. Here we consider design of an integrated systems approach to crop improvement that incorporates advanced technologies in molecular markers, statistics, bio-informatics, and crop physiology and modelling. We suggest that such an approach can enhance the efficiency of crop improvement relative to conventional phenotypic selection by changing the focus from the paradigm of identifying superior varieties to a focus on identifying superior combinations of genetic regions and management systems . A comprehensive information system to support decisions on identifying target combinations is the critical core of the approach. We discuss the role of ecophysiology and modelling in this integrated systems approach by reviewing (i) applications in environmental characterization to underpin weighted selection; (ii) complex-trait physiology and genetics to enhance the stability of QTL models by linking the vector of coefficients defining the dynamic model to the genetic regions generating variability; and (iii) phenotypic prediction in the target population of environments to assess the value of putative combinations of traits and management systems and enhance the utility of QTL models in selection. We examine in silico evidence of the value of ecophysiology and modelling to crop improvement for complex traits and note that, while there is no definitive position, it seems clear that there is sufficient promise to warrant continued effort. We discuss criteria determining the nature of models required and argue that a greater degree of biological robustness is required for modelling the physiology and genetics of complex traits. We conclude that, while an integrated systems approach to crop improvement is in its infancy, we expect that the potential benefits and further technology developments will likely enhance its rate of development.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Aggarwal, P.K., Kropff, M.J., Cassman, K.G., et al., 1997. Simulating genotypic strategies for increasing rice yield potential in irrigated, tropical environments. Field Crops Research, 51 (1/2), 5-17.

    Article  Google Scholar 

  • Asseng, S. and Van Herwaarden, A.F., 2003. Analysis of the benefits to wheat yield from assimilates stored prior to grain filling in a range of environments. Plant and Soil, 256 (1), 217-229.

    Article  CAS  Google Scholar 

  • Blazquez, M.A., 2000. Flower development pathways. Journal of Cell Science, 113 (20), 3547-3548.

    PubMed  CAS  Google Scholar 

  • Boote, K.J., Kropff, M.J. and Bindraban, P.S., 2001. Physiology and modelling of traits in crop plants: implications for genetic improvement. Agricultural Systems, 70 (2/3), 395-420.

    Article  Google Scholar 

  • Borrell, A.K., Hammer, G.L. and Henzell, R.G., 2000. Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield. Crop Science, 40 (4), 1037-1048.

    Article  Google Scholar 

  • Borrell, A., Hammer, G. and Van Oosterom, E., 2001. Stay-green: a consequence of the balance between supply and demand for nitrogen during grain filling? Annals of Applied Biology, 138 (1), 91-95.

    Article  Google Scholar 

  • Chapman, S.C., Hammer, G.L. and Meinke, H., 1993. A sunflower simulation model. I. Model development. Agronomy Journal, 85 (3), 725-735.

    Article  Google Scholar 

  • Chapman, S.C., Cooper, M., Hammer, G.L., et al., 2000a. Genotype by environment interactions affecting grain sorghum. II. Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Australian Journal of Agricultural Research, 51 (2), 209-221.

    Article  Google Scholar 

  • Chapman, S.C., Hammer, G.L., Butler, D.G., et al., 2000b. 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 (2), 223-233.

    Article  Google Scholar 

  • Chapman, S.C., Hammer, G.L., Podlich, D.W., et al., 2002. Linking bio-physical and genetic models to integrate physiology, molecular biology and plant breeding. In: Kang, M.S. ed. Quantitative genetics, genomics, and plant breeding . CAB International, Wallingford, 167-187.

    Google Scholar 

  • Chapman, S.C., Cooper, M., Podlich, D., et al., 2003. Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agronomy Journal, 95 (1), 99-113.

    Article  Google Scholar 

  • Cooper, M. and Hammer, G.L., 1996. Synthesis of strategies for crop improvement. In: Cooper, M. and Hammer, G.L. eds. Plant adaptation and crop improvement . CAB International, Wallingford, 591­623.

    Google Scholar 

  • Cooper, M. and Hammer, G.L., 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? Australian Journal of Agricultural Research, 56 (9), 869-872.

    Article  Google Scholar 

  • Cooper, M., Chapman, S.C., Podlich, D.W., et al., 2002. The GP problem: quantifying gene-to-phenotype relationships. In Silico Biology, 2 (2), 151-164.

    PubMed  CAS  Google Scholar 

  • Cooper, M., Podlich, D.W. and Smith, O.S., 2005. Gene-to-phenotype models and complex trait genetics. Australian Journal of Agricultural Research, 56 (9), 895-918.

    Article  Google Scholar 

  • Dingkuhn, M., Luquet, D., Quilot, B., et al., 2005. Environmental and genetic control of morphogenesis in crops: towards models simulating phenotypic plasticity. Australian Journal of Agricultural Research, 56 (11), 1289-1302.

    Article  Google Scholar 

  • Dong, Z., 2003. Incorporation of genomic information into the simulation of flowering time in Arabidopsis thaliana . PhD Thesis, Kansas State University, Manhattan. Duvick, D.N., Smith, J.S.C. and Cooper, M., 2004. Long-term selection in a commercial hybrid maize breeding program. Plant Breeding Reviews, 24 (2), 109-152.

    Google Scholar 

  • Hammer, G.L. and Muchow, R.C., 1994. Assessing climatic risk to sorghum production in water-limited subtropical environments. I. Development and testing of a simulation model. Field Crops Research, 36 (3), 221-234.

    Article  Google Scholar 

  • Hammer, G.L. and Vanderlip, R.L., 1989. Genotype-by-environment interaction in grain sorghum. III. Modeling the impact in field environments. Crop Science, 29 (2), 385-391.

    Article  Google Scholar 

  • Hammer, G.L., Butler, D.G., Muchow, R.C., et al., 1996. Integrating physiological understanding and plant breeding via crop modelling and optimization. In: Cooper, M. and Hammer, G.L. eds. Plant adaptation and crop improvement . CAB International, Wallingford, 419-441.

    Google Scholar 

  • Hammer, G.L., Kropff, M.J., Sinclair, T.R., et al., 2002. Future contributions of crop modelling: from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. European Journal of Agronomy, 18 (1/2), 15-31.

    Article  Google Scholar 

  • Hammer, G.L., Sinclair, T.R., Chapman, S.C., et al., 2004. On systems thinking, systems biology and the in silico plant. Plant Physiology, 134 (3), 909-911. [http://www.plantphysiol.org/cgi/reprint/134/3/909.pdf]

    Google Scholar 

  • Hammer, G.L., Chapman, S., Van Oosterom, E., et al., 2005. Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Australian Journal of Agricultural Research, 56 (9), 947-960.

    Article  Google Scholar 

  • Henzell, R.G. and Jordan, D.R., in press. History of grain sorghum breeding in Australia, including the development of resistances to midge, drought and ergot. In: 5th Australian Sorghum Conference, 30 Jan-2 Feb 2006, Gold Coast, Australia . Australian Institute of Agricultural Science,Melbourne.

    Google Scholar 

  • Jaccoud, D., Peng, K., Feinstein, D., et al., 2001. Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Research, 29 (4), e25. [http://nar.oxfordjournals.org/cgi/content/full/29/4/e25]

    Google Scholar 

  • Jordan, D.R., Hammer, G.L. and Henzell, R.G., in press. Breeding for yield in the DPI&F breeding program. In: 5th Australian Sorghum Conference, 30 Jan-2 Feb 2006, Gold Coast, Australia . Australian Institute of Agricultural Science, Melbourne.

    Google Scholar 

  • Jordan, D.R., Tao, Y.Z., Godwin, I.D., et al., 2004. Comparison of identity by descent and identity by state for detecting genetic regions under selection in a sorghum pedigree breeding program. Molecular Breeding, 14 (4), 441-454.

    Article  CAS  Google Scholar 

  • Kim, H.K., Van Oosterom, E.J., Luquet, D., et al., in press. Physiology and genetics of tillering. In: 5th Australian Sorghum Conference, 30 Jan-2 Feb 2006, Gold Coast, Australia . Australian Institute of Agricultural Science, Melbourne.

    Google Scholar 

  • Kitano, H., 2004. Biological robustness. Nature Reviews Genetics, 5 (11), 826-837. [http://www.symbio.jst.go.jp/symbio2/papers/NRGRobustnessKitano2004.pdf]

    Google Scholar 

  • Koornneef, M., Alonso-Blanco, C., Peeters, A.J.M., et al., 1998. Genetic control of flowering time in Arabidopsis . Annual Review of Plant Physiology and Plant Molecular Biology, 49, 345-370.

    Article  PubMed  CAS  Google Scholar 

  • Leon, A.J., Lee, M. and Andrade, F.H., 2001. Quantitative trait loci for growing degree days to flowering and photoperiod response in sunflower (Helianthus annuus L.). Theoretical and Applied Genetics, 102 (4), 497-503.

    Article  Google Scholar 

  • Löffler, C.M., Wei, J., Fast, T., et al., 2005. Classification of maize environments using crop simulation and geographic information systems. Crop Science, 45 (5), 1708-1716.

    Article  Google Scholar 

  • Luquet, D., Dingkuhn, M., Kim, H.K., et al., 2006. EcoMeristem, a model of morphogenesis and competition among sinks in rice. 1. Concept, validation and sensitivity analysis. Functional Plant Biology, 33 (4), 309-323.

    Article  Google Scholar 

  • Lynch, M. and Walsh, B., 1997. Genetics and analysis of quantitative traits . Sinauer Associates Inc., Sunderland.

    Google Scholar 

  • Messina, C.D., Jones, J.W., Boote, K.J., et al., 2006. A gene-based model to simulate soybean development and yield responses to environment. Crop Science, 46 (1), 456-466.

    Article  CAS  Google Scholar 

  • Morgan, P.W. and Finlayson, S.A., 2000. Physiology and genetics of maturity and height. In: Smith, C.W. and Frederiksen, R.A. eds. Sorghum: origin, history, technology and production . John Wiley & Sons, New York, 227-259.

    Google Scholar 

  • Morgan, P.W., Finlayson, S.A., Childs, K.L., et al., 2002. Opportunities to improve adaptability and yield in grasses: lessons from Sorghum . Crop Science, 42 (6), 1791-1799.

    Article  Google Scholar 

  • Muchow, R.C., Hammer, G.L. and Carberry, P.S., 1991. Optimising crop and cultivar selection in response to climatic risk. In: Muchow, R.C. and Bellamy, J.A. eds. Climatic risk in crop production: models and management for the semiarid tropics and subtropics . CAB International, Wallingford, 235-262.

    Google Scholar 

  • Muchow, R.C., Cooper, M. and Hammer, G.L., 1996. Characterizing environmental challenges using models. In: Cooper, M. and Hammer, G.L. eds. Plant adaptation and crop improvement . CAB International, Wallingford, 349-364.

    Google Scholar 

  • Podlich, D.W. and Cooper, M., 1998. QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics, 14 (7), 632-653.

    Article  PubMed  CAS  Google Scholar 

  • Podlich, D.W., Cooper, M. and Basford, K.E., 1999. Computer simulation of a selection strategy to accommodate genotype-environment interactions in a wheat recurrent selection programme. Plant Breeding, 118 (1), 17-28.

    Article  Google Scholar 

  • Podlich, D.W., Winkler, C.R. and Cooper, M., 2004. Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Science, 44 (5), 1560-1571.

    Article  Google Scholar 

  • Reymond, M., Muller, B., Leonardi, A., et al., 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 Physiology, 131 (2), 664-675.

    Article  PubMed  CAS  Google Scholar 

  • Reynolds, M.P., Singh, R.P., Ibrahim, A., et al., 1998. Evaluating physiological traits to complement empirical selection for wheat in warm environments. Euphytica, 100 (1/3), 85-94.

    Article  Google Scholar 

  • Richards, R.A., Rebetzke, G.J., Condon, A.G., et al., 2002. Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereals. Crop Science, 42 (1), 111-121.

    Article  PubMed  Google Scholar 

  • Sinclair, T.R. and Muchow, R.C., 2001. System analysis of plant traits to increase grain yield on limited water supplies. Agronomy Journal, 93 (2), 263-270.

    Article  Google Scholar 

  • Sinclair, T.R., Hammer, G.L. and Van Oosterom, E.J., 2005. Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate. Functional Plant Biology, 32 (10), 945-952.

    Article  Google Scholar 

  • Somerville, C. and Dangl, J., 2000. Plant biology in 2010. Science, 290 (5499), 2077-2078.

    Article  PubMed  CAS  Google Scholar 

  • Spitters, C.J.T. and Schapendonk, A.H.C.M., 1990. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Plant and Soil, 123 (2), 193-203.

    Article  Google Scholar 

  • Tao, Y.Z., Henzell, R.G., Jordan, D.R., et al., 2000. Identification of genomic regions associated with stay green in sorghum by testing RILs in multiple environments. Theoretical and Applied Genetics, 100 (8), 1225-1232.

    Article  CAS  Google Scholar 

  • Tardieu, F., 2003. Virtual plants: modelling as a tool for the genomics of tolerance to water deficit. Trends in Plant Science, 8 (1), 9-14.

    Article  PubMed  CAS  Google Scholar 

  • Tardieu, F., Granier, C. and Muller, B., 1999. Modelling leaf expansion in a fluctuating environment: are changes in specific leaf area a consequence of changes in expansion rate? New Phytologist, 143 (1), 33-43.

    Article  Google Scholar 

  • Van Eeuwijk, F.A., Malosetti, M., Yin, X., et al., 2005. Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Australian Journal of Agricultural Research, 56 (9), 883-894.

    Article  Google Scholar 

  • Van Oosterom, E.J., Bidinger, F.R. and Weltzien, E.R., 2003. A yield architecture framework to explain adaptation of pearl millet to environmental stress. Field Crops Research, 80 (1), 33-56.

    Article  Google Scholar 

  • Van Oosterom, E.J., Hammer, G.L., Chapman, S.C., et al., in press. A simple gene network model of photoperiod sensitivity of transition to flowering in sorghum can generate genotype-by-environment interaction in grain yield at the crop level. In: Proceedings of the 13th Australasian Plant Breeding Conference, Christchurch, New Zealand. April 2006 .

    Google Scholar 

  • Verbyla, A.P., Eckermann, P.J., Thompson, R., et al., 2003. The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model. Australian Journal of Agricultural Research, 54 (11/12), 1395-1408.

    Article  CAS  Google Scholar 

  • Wade, L.J., Douglas, A.C.L. and Bell, K.L., 1993. Variation among sorghum hybrids in the plant density required to maximise grain yield over environments. Australian Journal of Experimental Agriculture, 33 (2), 185-191.

    Article  Google Scholar 

  • Wang, E., Robertson, M.J., Hammer, G.L., et al., 2002. Development of a generic crop model template in the cropping system model APSIM. European Journal of Agronomy, 18 (1/2), 121-140.

    Article  Google Scholar 

  • Welch, S.M., Roe, J.L. and Dong, Z.S., 2003. A genetic neural network model of flowering time control in Arabidopsis thaliana . Agronomy Journal, 95 (1), 71-81.

    Article  Google Scholar 

  • Welch, S.M., Dong, Z.S., Roe, J.L., et al., 2005. Flowering time control: gene network modelling and the link to quantitative genetics. Australian Journal of Agricultural Research, 56 (9), 919-936.

    Article  Google Scholar 

  • Whish, J., Butler, G., Castor, M., et al., 2005. Modelling the effects of row configuration on sorghum yield reliability in north-eastern Australia. Australian Journal of Agricultural Research, 56 (1), 11­23.

    Article  Google Scholar 

  • White, J.W. and Hoogenboom, G., 1996. Simulating effects of genes for physiological traits in a process-oriented crop model. Agronomy Journal, 88 (3), 416-422.

    Article  Google Scholar 

  • Yin, X., Kropff, M.J. and Stam, P., 1999. The role of ecophysiological models in QTL analysis: the example of specific leaf area in barley. Heredity, 82 (4), 415-421.

    Article  PubMed  Google Scholar 

  • Yin, X., Struik, P.C. and Kropff, M.J., 2004. Role of crop physiology in predicting gene-to-phenotype relationships. Trends in Plant Science, 9 (9), 426-432.

    Article  PubMed  CAS  Google Scholar 

  • Yin, X., Struik, P.C., Van Eeuwijk, F.A., et al., 2005. QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley. Journal of Experimental Botany, 56 (413), 967-976.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this paper

Cite this paper

Hammer, G., Jordan, D. (2007). An Integrated Systems Approach to Crop Improvement. In: Spiertz, J., Struik, P., Laar, H.V. (eds) Scale and Complexity in Plant Systems Research. Wageningen UR Frontis Series, vol 21. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5906-X_5

Download citation

Publish with us

Policies and ethics