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Improving process-based crop models to better capture genotype×environment×management interactions.

Enli Wang1, Hamish E Brown2, Greg J Rebetzke1

  • 1CSIRO Agriculture & Food, Canberra, ACT, Australia.

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|March 29, 2019
PubMed
Summary
This summary is machine-generated.

Improving crop models to predict wheat performance requires better simulation of genotype by environment by management interactions. Integrating genetic and molecular data enhances phenotype prediction for breeding selections.

Keywords:
APSIMcrop modellingearly vigourgene-based modellinggenotype–phenotype predictiontrait evaluation

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Area of Science:

  • Agricultural Science
  • Plant Physiology
  • Computational Biology

Background:

  • Process-based crop models face challenges in accurately predicting phenotypes from genotypes, hindering breeding selections.
  • Genotype (G) by Environment (E) by Management (M) interactions are crucial for crop improvement but difficult to model.
  • Wheat (Triticum aestivum) is used as a model crop to analyze current crop modeling limitations.

Purpose of the Study:

  • To analyze the status of process-based crop models for simulating G×E×M interactions.
  • To identify needs for improving eco-physiological process simulation and genetic linkage in crop models.
  • To explore integrating genetic and molecular approaches for advanced crop modeling in challenging environments.

Main Methods:

  • Analysis of the APSIM farming systems model for wheat.
  • Examination of opportunities to capture physiological traits and integrate genetic data.
  • Outline of a three-stage approach: improving crop physiology modeling, linking models to genotypes/genes, and connecting to gene pathways.

Main Results:

  • Current crop models require enhanced simulation of key physiological processes and genetic controls.
  • Integrating genetic information can improve genotype effect estimation and phenotype prediction.
  • A structured approach is proposed for linking crop models with genetic and molecular data.

Conclusions:

  • Model improvement necessitates reducing uncertainty in physiological simulations and capturing genetic control of trait responses.
  • A three-stage integration strategy facilitates combining modeling, phenotyping, and gene detection for G×E×M studies.
  • Gene-based modeling, when applicable, can significantly advance the accuracy of crop performance predictions.