Quantifying physiological determinants of genetic variation for yield potential in sunflower. SUNFLO: a model-based analysis

Lecœur

et al. 2014

Ecological Modelling

Self Cite

Please cite this article in press as: Kang, F., et al., SUNLAB: A functional-structural model for genotypic and phenotypic characterization of the sunflower crop. Ecol. Model. (2014), http://dx. a b s t r a c tA new functional-structural model SUNLAB for the crop sunflower (Helianthus annuus L.) was developed. It is dedicated to simulate the sunflower organogenesis, morphogenesis, biomass accumulation and biomass partitioning to organs. It is adapted to model phenotypic responses of different genotypic variants to diverse environmental factors including temperature stress and water deficit. A sensitivity analysis was conducted to quantify the relative parameter influences on the main trait of interest, the grain yield. The model was calibrated for four genotypes on two experimental datasets collected on plants grown under standard non-limiting conditions and moderate water stress. Its predictive ability was then tested on an additional dataset. The four considered genotypes -"Albena", "Melody", "Heliasol" and "Prodisol" -are the products of more than 30 years of breeding effort. Comparing the values found for the four parameter sets associated to each variant, allows to identify genotype-specific parameters. The model also provides a novel way of investigating genotype performances under different environmental conditions. These promising results are a first step toward the potential use of the model as a support tool to design sunflower ideotypes adapted to the current worldwide ecological and economical challenges and to assist the breeding procedure.

Section: Modeling: Sunlab Modulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SUNLAB: A functional–structural model for genotypic and phenotypic characterization of the sunflower crop

Kang

Lecœur

et al. 2014

Ecological Modelling

Self Cite

“…The platform is currently developed and used in the laboratory of Applied Mathematics and Systems at Ecole Centrale Paris, but is also tested by a few other labs. Provided some basic knowledge in C++, it is very easy to implement models and to use the proposed methods: approximately 20 models of plant growth in interaction with the environment and variants are currently implemented in the platform: mostly in the GreenLab family [21] and variants of the STICS [6], CERES [32], SUNFLO [40] models, for different species. As such, it provides an interesting tool in the process of model comparison and benchmarking in the plant growth community as illustrated in [2].…”

Section: Discussionmentioning

confidence: 99%

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

Math. Model. Nat. Phenom.

Chen

et al. 2013

Abstract. Mathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics.Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application.Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques.

“…Likewise, the use of genotype specific parameters in models can only be of interest if we are able to determine significantly different parameter values for different genotypes [2]. However, most parameters cannot be measured directly or experimental protocols are heavy to implement [3] which indicate delicate parametrization. Consequently, some parameters have to be estimated from experimental data by model inversion.…”

Section: Introductionmentioning

confidence: 99%

Assessment of parameter uncertainty in plant growth model identification

Chen

2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications

2012

Abstract-For the parametric identification of plant growth models, we generally face limited or uneven experimental data, and complex nonlinear dynamics. Both aspects make model parametrization and uncertainty analysis a difficult task. The Generalized Least Squares (GLS) estimator is often used since it can provide estimations rather rapidly with an appropriate goodness-of-fit. However, the confidence intervals are generally calculated based on linear approximations which make the uncertainty evaluation unreliable in the case of strong nonlinearity. A Bayesian approach, the Convolution Particle Filtering (CPF), can thus be applied to estimate the unknown parameters along with the hidden states. In this case, the posterior distribution obtained can be used to evaluate the uncertainty of the estimates. In order to improve its performance especially with stochastic models and in the case of rare or irregular experimental data, a conditional iterative version of the Convolution Particle Filtering (ICPF) is proposed. When applied to the Log Normal Allocation and Senescence model (LNAS) with sugar beet data, the two CPF related approaches showed better performance compared to the GLS method. The ICPF approach provided the most reliable estimations. Meanwhile, two sources of the estimation uncertainty were identified: the variance generated by the stochastic nature of the algorithm (relatively small for the ICPF approach) and the residual variance partly due to the noise models.