Abstract
Introduction: Dynamic crop growth models are an important tool to predict complex traits, like crop yield, for modern and future genotypes in their current and evolving environments, as those occurring under climate change. Phenotypic traits are the result of interactions between genetic, environmental, and management factors, and dynamic models are designed to generate the interactions producing phenotypic changes over the growing season. Crop phenotype data are becoming increasingly available at various levels of granularity, both spatially (landscape) and temporally (longitudinal, time-series) from proximal and remote sensing technologies. Methods: Here we propose four phenomenological process models of limited complexity based on differential equations for a coarse description of focal crop traits and environmental conditions during the growing season. Each of these models defines interactions between environmental drivers and crop growth (logistic growth, with implicit growth restriction, or explicit restriction by irradiance, temperature, or water availability) as a minimal set of constraints without resorting to strongly mechanistic interpretations of the parameters. Differences between individual genotypes are conceptualized as differences in crop growth parameter values. Results: biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years. While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation. Discussion: A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation.
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Claims 4
Claim · model-written summary
Each of the four models fits well to particular combinations of genotype and trial.
Verbatim
e0“While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials”
e1“because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation”
e2“Results: biomass development of 199 genotypes and data of environmental variables over the course of the growing season at four Australian locations over 31 years”
Claim · model-written summary
No one of the four models provides the best fit across the full set of genotypes by trials.
Verbatim
e0“While each of the four models fits well to particular combinations of genotype and trial, none of them provides the best fit across the full set of genotypes by trials”
e1“because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation”
Claim · model-written summary
Different environmental drivers limit crop growth in different trials and genotypes.
Verbatim
e1“because different environmental drivers will limit crop growth in different trials and genotypes in any specific trial will not necessarily experience the same environmental limitation”
Claim · model-written summary
The authors propose low-complexity phenomenological models as a possible forecasting tool for crop growth.
Verbatim
e3“A combination of low-complexity phenomenological models covering a small set of major limiting environmental factors may be a useful forecasting tool for crop growth under genotypic and environmental variation”