Abstract
Crop simulation models of plant processes capture the biological interactions between the sensing of signals at an organ level (e.g. drought affecting roots), the response of the plant at a biochemical level (e.g. change in development rate) and the result at the organ (or crop) level (e.g. reduced growth). In dissecting the complex control of phenotypes like yield, simulation models have several roles. Models have been used to generate an index of the climatic environment (e.g. of drought stress) for breeding programme trials. In wheat and sorghum grown in northern Australia, this has shown that mid-season drought generates large genotype by environment interaction. By defining gene action to calculate the value of input trait parameters to crop models, simulated multi-environment trials estimate the yield of ‘synthetic’ sorghum cultivars grown in historical or artificial climates with current or potential management regimes. In this way, the biological interactions among traits constrain the crop yields to only those that are biologically possible in the given set of environments. This allows the construction of datasets that are more ‘realistic’ representations of gene by trait by environment interaction than is possible using only the statistical attributes (e.g. means, variances and correlations) of real-world trait datasets. This approach has an additional advantage in that ‘biological and experimental noise’ can be manipulated separately. These ‘testbeds’ for statistical techniques can be extended to the interpretation of a crossing and selection programme where the processes of chromosomal recombination are simulated using a quantitative genetics model and applied to the trait parameters. Statisticians are challenged to develop improved methods for the resulting simulated phenotype datasets, with the objective of revealing the (known) underlying genetic and environment structure that was input to the simulations. These improved methods can then be applied to existing plant breeding programmes.
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References
Bartels D, Furini A, Ingram J, Salamini F, Belhassen E (1996) Response of plants to dehydration stress: a molecular analysis. First INTERDROUGHT international conference 1995 Drought tolerance in higher plants: genetical, physiological and molecular biological analysis. CABI, Montpellier, pp 111–118
Bidinger FR, Hammer GL, Muchow RC (1996) The physiological basis of genotype by environment interaction in crop adaptation. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 329–348
Campos H, Cooper A, Habben JE, Edmeades GO, Schussler JR (2004) Improving drought tolerance in maize: a view from industry. Field Crops Res 90:19–34
Chapman SC, Crossa J, Edmeades GO (1997) Genotype by environment effects and selection for drought tolerance in tropical maize. 1. Two mode pattern analysis of yield. Euphytica 95:1–9
Chapman SC, Cooper M, Butler DG, Henzell RG (2000a) Genotype by environment interactions affecting grain sorghum. I. Characteristics that confound interpretation of hybrid yield. Aust J Agric Res 51:197–207
Chapman SC, Cooper M, Hammer GL, Butler DG (2000b) 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 51:209–221
Chapman SC, Hammer GL, Butler DG, Cooper M (2000c) Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Aust J Agric Res 51:223–233
Chapman SC, Mathews KL, Cooper M, Jensen N, Wang E, Butler D, Sheppard J, Sahama T (2001) Using environment characterization to interpret wheat yield in water-limited environments. In: Proceedings of the 10th wheat breeding assembly, Mildura, 16–21 September, 2001, pp 136–139
Chapman SC, Cooper M, Hammer GL (2002a) Using crop simulation to generate genotype by environment interaction effects for sorghum in water-limited environments. Aust J Agric Res 53:379–389
Chapman SC, Hammer GL, Podlich DW, Cooper M (2002b) Linking biophysical and genetic models to integrate physiology, molecular biology and plant breeding. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI, Wallingford, pp 167–187
Chapman SC, Cooper M, Podlich D, Hammer GL (2003) Evaluating plant breeding strategies by simulating gene action and dryland environment effects. Agron J 95:99–113
Chenn K, Chapman SC, Hammer GL, McLean G, Tardien 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 and Environment (in press)
Comstock RE (1977) Quantitative genetics and the design of breeding programs. In: Proceedings of the international conference on quantitative genetics. Iowa State University Press, Ames, USA, August 16–21, 1976, pp 705–718
Condon AG, Richards RA, Rebetzke GJ, Farquhar GD (2004) Breeding for high water-use efficiency. J Exp Bot 55:2447–2460
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 90:492–502
Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci 37:1168–1176
Cooper M, Podlich DW, Micallef KP, Smith OS, Jensen NM, Kruger NL (2002) Complexity, quantitative traits and plant breeding: A role for simulation modelling in the genetic improvement of crops. In: Kang MS (ed) Quantitative genetics, genomics, and plant breeding. CABI, Wallingford, pp 143–166
Cooper M, Podlich DW, Löffler CM, Van Eeuwijk F, Chapman SC (2006) Genotype-by-environment interactions under water-limited conditions. In: Ribaut J-M (ed) Drought adaptation in cereals. Food Products Press, New York, pp 51–96
Cullis BR, Thomson FM, Fisher JA, Gilmour AR, Thompson R (1996) The analysis of the NSW wheat variety database. II. Variance component estimation. Theor Appl Genet 92:28–39
DeLacy IH, Basford KE, Cooper M, Bull JK, McLaren CG (1996) Analysis of multi-environment trials—an historical perspective. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 39–124
Edmeades GO, Bolaños J, Chapman SC, Lafitte HR, Banziger M (1999) Selection improves drought tolerance in tropical maize populations: I. Gains in biomass, grain yield, and harvest index. Crop Sci 39:1306–1315
Edmeades GO, McMaster GS, White JW, Campos H (2004) Genomics and the physiologist: bridging the gap between genes and crop response. Field Crops Res 90:5–18
Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293
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. Springer, The Netherlands, pp 45–61
Hammer GL, Butler DG, Muchow RC, Meinke H (1996a) Integrating physiological understanding and plant breeding via crop modelling and optimization. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 419–441
Hammer GL, Chapman SC, Muchow RC (1996b) Modelling sorghum in Australia: the state of the science and its role in the pursuit of improved practices. In: Proceedings of the third Australian sorghum conference. AIAS Occasional Publication, Tamworth, 20–22 February 1996, pp 43–61
Hammer GL, van Oosterom E, Chapman SC, Mclean G (2001) Supply and demand economics applied to crop growth. In: Borrell AK, Henzell RG (eds) Proceedings of the fourth Australian sorghum conference. CD-rom format. Range Media Pty Ltd. ISBN: 0–7242–2163–8, Kooralbyn, 5–8 February 2001
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. Aust J Agric Res 56:947–960
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 11:587–593
Hsiao TC (1973) Plant responses to water stress. Ann Rev Plant Physiol 24:519–570
Loffler 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 Sci 45:1708–1716
Ludlow MM, Muchow RC (1990) A critical evaluation of traits for improved crop yields in water-limited environments. Adv Agron 43:107–153
Malosetti M, Visser RGF, Celis-Gamboa C, van Eeuwijk FA (2006) QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato. Theor Appl Genet 113:288–300
Mathews KL, Chapman SC, Butler D, Cooper M, DeLacy IH, Sheppard J, Sahama T (2002) Inter-annual changes in genotypic and genotype by environment variance components for different stages of the Northern Wheat Improvement Program. In: McComb JA (ed) Plant breeding for the 11th millennium proceedings 12th Australasian plant breeding conference. Perth, 15–20 September 2002, pp 650–654
Podlich D, Cooper M (1998) QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics 14:632–653
Qiao CG, Basford KE, DeLacy IH, Cooper M (2000) Evaluation of experimental designs and spatial analyses in wheat breeding trials. Theor Appl Genet 100:9–16
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 131:664–675
Rizza F, Badeck FW, Cattivelli L, Lidestri O, Di Fonzo N, Stanca AM (2004) Use of a water stress index to identify barley genotypes adapted to rainfed and irrigated conditions. Crop Sci 44:2127–2137
Saulescu NN, Kronstad WE (1995) Growth simulation outputs for detection of differential cultivar response to environmental factors. Crop Sci 35:773–778
Thornley JHM, France J (2004) Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences, 2nd edn. CABI Pub, Wallingford
Voltas J, Van Eeuwijk FA, Araus JL, Romagosa I (1999) Integrating statistical and ecophysiological analyses of genotype by environment interaction for grain filling of barley II. Grain growth. Field Crops Res 62:75–84
Wang E, Robertson MJ, Hammer GL, Carberry PS, Holzworth D, Meinke H, Chapman SC, Hargreaves JNG, Huth NI, McLean G (2002) Development of a generic crop model template in the cropping system model APSIM. Eur J Agronomy. Elsevier Science B.V., Amsterdam Netherlands, pp 121–140
Acknowledgements
The work described here has been completed over more than 10 years with the major collaborators being Mark Cooper, Graeme Hammer, Ky Mathews, Greg McLean, Erik van Oosterom and Dean Podlich. In addition to CSIRO, the research has been supported by The University of Queensland, the Australian Grains Research and Development Corporation (GRDC) and the Generation Challenge Programme, and the author also thanks the organising committee of the Eucarpia conference in Croatia for support to present at this meeting.
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Chapman, S.C. Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials. Euphytica 161, 195–208 (2008). https://doi.org/10.1007/s10681-007-9623-z
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DOI: https://doi.org/10.1007/s10681-007-9623-z