Modelling Crop Improvement in a G×E×M Framework via Gene–Trait–Phenotype Relationships

Messina, Carlos D.; Hammer, Graeme; Dong, Zhanshan; Podlich, Dean; Cooper, Mark

doi:10.1016/b978-0-12-374431-9.00010-4

Cited by 98 publications

(103 citation statements)

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“…However, the absence of any trait validation is a major blow to credibility. This wheat case, using modern computer power to tackle huge simulation tasks, does not differ from those of other crops above, illustrated by Messina et al (2009) who modelled the effects of variation in five hypothetical traits (in all combinations) with APSIM-Maize. In all cases there is a serious lack of validation of the consequences for yield of hypothesised trait changes, and of the linking physiology; there is also daunting complexity.…”

Section: Simulation Modellingmentioning

confidence: 99%

“…Indeed this is seen by many as a key way forward for crop breeding, including the genetic engineering of yield. A courageous start has been made with genes in models of other crops like beans, soybeans, and barley, and with wheat phenology mentioned earlier (White et al 2008), while APSIM is the vehicle by which the task is being approached in sorghum (Hammer et al 2005), maize (Messina et al 2009), and very recently, wheat (Chenu et al 2010). But the initial efforts on traits and yield in wheat have been less ambitious.…”

Section: Simulation Modellingmentioning

confidence: 99%

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Wheat physiology: a review of recent developments

Fischer¹

2011

Crop Pasture Sci.

345

251

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Abstract. This review focuses on recent advances in some key areas of wheat physiology, namely phasic development, determination of potential yield and water-limited potential yield, tolerance to some other abiotic stresses (aluminium, salt, heat shock), and simulation modelling. Applications of the new knowledge to breeding and crop agronomy are emphasized. The linking of relatively simple traits like time to flowering, and aluminium and salt tolerance, in each case to a small number of genes, is being greatly facilitated by the development of molecular gene markers, and there is some progress on the functional basis of these links, and likely application in breeding. However with more complex crop features like potential yield, progress at the gene level is negligible, and even that at the level of the physiology of seemingly important component traits (e.g., grain number, grain weight, soil water extraction, sensitivity to water shortage at meiosis) is patchy and generally slow although a few more heritable traits (e.g. carbon isotope discrimination, coleoptile length) are seeing application. This is despite the advent of smart tools for molecular analysis and for phenotyping, and the move to study genetic variation in soundly-constituted populations. Exploring the functional genomics of traits has a poor record of application; while trait validation in breeding appears underinvested. Simulation modeling is helping to unravel G Â E interaction for yield, and is beginning to explore genetic variation in traits in this context, but adequate validation is often lacking. Simulation modelling to project agronomic options over time is, however, more successful, and has become an essential tool, probably because less uncertainty surrounds the influence of variable water and climate on the performance of a given cultivar. It is the everincreasing complexity we are seeing with genetic variation which remains the greatest challenge for modelling, molecular biology, and indeed physiology, as they all seek to progress yield at a rate greater than empirical breeding is achieving.

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Section: Simulation Modellingmentioning

confidence: 99%

Section: Simulation Modellingmentioning

confidence: 99%

Wheat physiology: a review of recent developments

Fischer¹

2011

Crop Pasture Sci.

345

251

View full text Add to dashboard Cite

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“…Ideotypes should not claim to have universal validity but be aimed at specific breeding objectives (Messina et al 2009), and any modelling should recognize the population of environments at which the breeding is targeted (Cooper et al 2014). By evaluating timothy performance on the five selected sites across Norway, we effectively designed a Norwayscale ideotype.…”

Section: An Ideotype For Timothy In Norwaymentioning

confidence: 99%

“…Messina et al (2009) andCooper et al (2014) sketch a future of plant breeding in which unravelling the genetic basis of modelled plant properties will support high-throughput phenotyping, which they consider essential for the scaling up of breeding programmes to larger numbers of genotypes. For most plant parameters, however, this will require much progress in elucidating the links between the different levels of organisation that separate genes, cells, plant organs, plant individuals and crops (Tardieu 2003;Messina et al 2009;Sinclair 2011).…”

Section: From Ideotype To Selection Criteriamentioning

confidence: 99%

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Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design

Oijen

Höglind

2015

Euphytica

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Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway.Keywords BASGRA Á Cold tolerance Á Genotype-environment interaction Á Plant breeding Á Process-based modelling Á Yield stability Terminology This paper aims to bridge two different plant disciplines, i.e. process-based modelling and breeding, so terms need to be defined. In modelling, we distinguish between plant parameters, which are genotype-specific constants, and output variables, which are plant characteristics that are predicted by the model and that vary between environments. Process-based models (PBMs) are dynamic models that represent physiological and morphological processes in plants, and their interaction with the environment. Simulation is the process of specifying environment and parameter values followed by running the PBM to calculate the output variables. In plant breeding, traits are measurable properties of plants that arise from the interaction between the genotype and its environment (G 9 E). The influence of the environment may be large for some traits (e.g. yield) or small (e.g. flower size). A quantitative trait is a property measured on a Electronic supplementary material The online version of this article

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