Bringing genetics and biochemistry to crop modelling, and vice versa

Xinyou Yin*, Gerard van der Linden, Paul C. Struik

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)


Genetics, biochemistry, and crop modelling are independently evolving disciplines; however, they complement each other in addressing some of the important challenges that crop science faces. One of these challenges is to improve our understanding of crop genotype-to-phenotype relationships in order to assist the development of high-yielding and resource-use efficient genotypes that can adapt to particular (future) target environments. Crop models are successful in predicting the impact of environmental changes on crop productivity. However, when critically tested against real experimental data, crop models have been shown to be less successful in predicting the impact of genotypic variation and genotype-by-environment interactions exhibited in genetic populations. In order to better model gene-trait-crop performance relationships in support of breeding and genetic engineering programmes, crop models need to be improved in terms of both model parameters and model structure. We argue that integration of quantitative genetics and photosynthesis biochemistry with modelling is a first step towards a new generation of improved crop models. With genetic information and biochemical understanding incorporated, crop modelling also generates new insights and concepts that can in turn be used to improve genetic analysis and biochemical modelling of complex traits. This modelling-genetics-biochemistry framework (the MGB triangle framework) stresses the synergy among the three disciplines, and may best serve as a step to achieve the ultimate goal of the more broadly framed "Crop Systems Biology" approach to improve efficiency of both classical breeding and genetic engineering programmes.
Original languageEnglish
Pages (from-to)132-140
JournalEuropean Journal of Agronomy
Early online date3 Mar 2018
Publication statusPublished - Oct 2018


  • Complex phenotype
  • Crop improvement
  • G×E
  • Interdisciplinary approach
  • Systems modelling


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