Challenges in Integrating Genetic Control in Plant and Crop Models

Baldazzi, Valentina; Bertin, Nadia; Génard, Michel; Gautier, Hélène; Desnoues, Elsa; Quilot-Turion, Bènèdicte

doi:10.1007/978-3-319-20562-5_1

Cited by 6 publications

(6 citation statements)

References 129 publications

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“…The integration of quantitative genetic models with WCP models has long been proposed as a solution to the challenge of genotype × environment × management (G × E × M) prediction (Baldazzi et al, 2016;Chapman, Cooper, Podlich, & Hammer, 2003;Cooper & Hammer, 1996;Cooper & Podlich, 2002;Cooper et al, 2014Cooper et al, , 2016Hammer et al, 2006Hammer et al, , 2016Hoogenboom et al, 1997;Messina, Jones, Boote, & Vallejos, 2006;Parent & Tardieu, 2014;Reymond, Muller, Leonardi, Charcosset, & Tardieu, 2003;Technow et al, 2015;Wang et al, 2003;White & Hoogenboom, 1996;Yin, Struik, van Eeuwijk, Stam, & Tang, 2005). Various strategies for how this might be accomplished have been proposed, but in general, none of the strategies have been fully explored, implemented, and tested.…”

Section: Integrating Physiological Models With Genomic Predictionmentioning

confidence: 99%

Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models

2020

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Maize (Zea mays L.) has been a focus of scientific research and breeding for over a century. It is also one of the most economically important crops in the world, with a value of approximately US$50 billion per year in the United States alone. Additionally, maize has long been the model species of choice for the study and exploitation of hybrid vigor, and it continues to be one of the world's most efficient converters of photosynthetic energy into starch. This review discusses the history and future of maize predictive breeding in the context of both genotype centric methods, and those focusing on genotype × environment × management interactions. Current prediction challenges are highlighted, as well as important advances in technology, methods, datasets, interdisciplinary collaborations, and scientific culture that will enable accelerated progress in predictive maize (and other crop species) breeding for years to come.

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Section: Integrating Physiological Models With Genomic Predictionmentioning

confidence: 99%

Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models

2020

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“…A methodology involving hierarchical mixed effects models and testing of variance components is proposed in Baey et al, (in preparation) and should be used in order to clearly identify which parameters can be considered constant or varying among the population of genotypes. Finally, being able to identify plant model parameters varying among genotypes from high-throughput phenotyping data is the first step toward the integration of genetic control into plant models (Baldazzi et al, 2016; Xu and Buck-Sorlin, 2016) via QTL or association mapping (Myles et al, 2009). …”

Section: Discussionmentioning

confidence: 99%

“…As described by Yin and Struik (2010) or Baldazzi et al (2016), the tendency is to complicate the mechanistic description of biophysical processes, by linking ecophysiology to omics sciences as an attempt to fully comprehend the regulatory networks from which plant robustness and plasticity is supposed to emerge (Hirai et al, 2004) whilst the related robustness appears to be difficult to achieve at the cell or tissue level. The modeling of plant growth and development lends itself to such an integrative approach.…”

Section: Introductionmentioning

confidence: 99%

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

Viaud¹,

Cournède²

2017

Front. Plant Sci.

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A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of Arabidopsis thaliana over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for A. thaliana and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.

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“…Nevertheless, only a few efforts have been conducted to use such potential for improving the grain quality simulation modules (Quilot et al, 2005). To date, the greatest challenge facing the development of gene-based models for complex traits is the lack of information on responsible genes and their interactions (Baldazzi et al, 2016).…”

Section: Shortcomings Of Current Crop Models and Future Perspectivesmentioning

confidence: 99%

The potential of crop models in simulation of barley quality traits under changing climates: A review

Rezaei

Rojas

Zhu

et al. 2022

Field Crops Research

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Challenges in Integrating Genetic Control in Plant and Crop Models

Cited by 6 publications

References 129 publications

Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models

Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models

Leaf Segmentation and Tracking in Arabidopsis thaliana Combined to an Organ-Scale Plant Model for Genotypic Differentiation

The potential of crop models in simulation of barley quality traits under changing climates: A review

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