A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes

Xu, Linhao; Henke, M.; Zhu, Jun; Kurth, Winfried; Buck-Sorlin, Gerhard

doi:10.1093/aob/mcq264

Cited by 74 publications

(49 citation statements)

References 58 publications

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“…The same results were obtained with regard to the biomass of Lactuca sativa L. under the effects of NPK fertilizer and water content (Xu et al, 2011). In this study the effects of different concentrations of NPK, different water depths and soil types on the growth patterns of S. grossus and important weed in rice fields and waterways, was investigated.…”

Section: Introductionsupporting

confidence: 58%

Aerial Ramet Dynamics and General Clonal Growth Patterns of Scirpus grossus in Grown at Different Water Depths and Fertilizer Regimes in Paddy Soils

Majrashi¹,

Khandaker²,

Bakar³

et al. 2015

J. of Agronomy

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A B S T R A C TScirpus grossus L. is a principal rhizomatous weed in the rice fields, drainage and irrigation canals, river banks, abandoned rice fields and wasteland in Malaysia. This study provides evidences that plants of S. grossus have a remarkable ability to alter their development in response water depth regimes. This phenotypic plasticity allows them to continually adapt to their local environment, a necessity for plants as sessile organisms. The results of this current study showed that devoid of fertilizer application (F0) and not inundated, S. grossus plants with mean monthly population counts up to 16 weeks, taken at 4-weekly intervals were as follows: 61. 33 2 ), denoting increased mortality of ramets with increased fertilizer applications. Ramet mortality of S. grossus increased with the greater depths of the water level and with increasing NPK concentrations. It was also observed that population number of ramets decreased with increased depths of inundation, irrespective of fertilizer regimes. In relation to plant height, recorded results showed that plant height was highest in the 10 cm water depth treatment followed by 20, 5 and 0 cm water depth treatments, respectively. It can be concluded that depth of inundation and fertilizer regimes have significant effects on aerial growth and ramets mortality of Scirpus grossus.

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Section: Introductionsupporting

confidence: 58%

Aerial Ramet Dynamics and General Clonal Growth Patterns of Scirpus grossus in Grown at Different Water Depths and Fertilizer Regimes in Paddy Soils

Majrashi¹,

Khandaker²,

Bakar³

et al. 2015

J. of Agronomy

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“…Generally GxE interaction will be more complex (Tardieu 2003;Cooper et al 2014), hampering the calculation of parameter values as functions of QTLs. The effort to explicitly link model parameters to the genome remains an active area of research (Chenu et al 2009;Hammer et al 2006;Tardieu 2003;Xu et al 2011;Zheng et al 2013).…”

Section: Cultivar-specific Parameter Estimationmentioning

confidence: 99%

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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“…One of the earliest efforts was a gene-based crop simulation model of the dry bean Phaseolus vulgaris, which used genotype-specific parameter values to represent different cultivars (White and Hoogenboom, 1996). Some studies have also attempted to link information from quantitative trait loci analyses to model parameters to incorporate genotype-phenotype relationships into crop models (Yin et al, 2000a;Letort et al, 2008;Xu et al, 2011). By incorporating such links, statistics from the quantitative trait loci analysis can partly remove the random errors associated with model parameters, thus improving model prediction of yield differences among different inbred lines (Yin et al, 2000b(Yin et al, , 2003.…”

Section: Applying Contemporary Crop Modeling Approaches To a Thalianamentioning

confidence: 99%

Mathematical Models Light Up Plant Signaling

et al. 2014

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A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes

Cited by 74 publications

References 58 publications

Aerial Ramet Dynamics and General Clonal Growth Patterns of Scirpus grossus in Grown at Different Water Depths and Fertilizer Regimes in Paddy Soils

Aerial Ramet Dynamics and General Clonal Growth Patterns of Scirpus grossus in Grown at Different Water Depths and Fertilizer Regimes in Paddy Soils

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

Mathematical Models Light Up Plant Signaling

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