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Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

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  • Abhishes Lamsal
  • Stephen M Welch
  • Jeffrey W White
  • Kelly R Thorp
  • Nora M Bello

Abstract

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p

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  • Abhishes Lamsal & Stephen M Welch & Jeffrey W White & Kelly R Thorp & Nora M Bello, 2018. "Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    • Handle: RePEc:plo:pone00:0195841
    DOI: 10.1371/journal.pone.0195841
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    References listed on IDEAS

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    3. Lamsal, Abhishes & Welch, S.M. & Jones, J.W. & Boote, K.J. & Asebedo, Antonio & Crain, Jared & Wang, Xu & Boyer, Will & Giri, Anju & Frink, Elizabeth & Xu, Xuan & Gundy, Garrison & Ou, Junjun & Arachc, 2017. "Efficient crop model parameter estimation and site characterization using large breeding trial data sets," Agricultural Systems, Elsevier, vol. 157(C), pages 170-184.
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    1. Viswanathan, Michelle & Scheidegger, Andreas & Streck, Thilo & Gayler, Sebastian & Weber, Tobias K.D., 2022. "Bayesian multi-level calibration of a process-based maize phenology model," Ecological Modelling, Elsevier, vol. 474(C).

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