The Genotype-by-Environment interaction on CROp growth Simulator model

Code: 9781835456392
Publication date: 06-02-2026
Extent: 36 pages

Contributions by: Xinyou Yin, Wageningen University & Research, The Netherlands

Chapter synopsis:

The Genotype-by-Environment interaction on CROp growth Simulator (GECROS) is a generic crop model that specifically addresses carbon-nitrogen, root-shoot, and source-sink interactions to predict crop responses to weather variables and soil water and nitrogen supply. GECROS uses relatively few, easily measurable parameters across many genotypes, making it useful for linking crop physiology with genetic mapping to predict genotype-by-environment interactions. Owing to its coupled modelling of carbon and nitrogen assimilates, the model predicts not only biomass yield, but also the protein content of harvestable products (grains, seeds, tubers, fruits). The chapter discusses the development and components of the GECROS model as well as its wide range of potential applications in improving decision making to make agriculture more resilient and sustainable. The chapter also highlights major uncertainties e.g. in simulating sink size and root nitrogen (N) uptake that currently prevent the model from accurately predicting subtle genotype-phenotype relations in a genetic population.



DOI: 10.19103/AS.2025.0155.14
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Table of contents
  • 1 Introduction
  • 2 Improving crop models: algorithms, structure, andinput parameters
  • 3 The Genotype-by-Environment interaction on CROpgrowth Simulator model
  • 4 Model algorithms: photosynthesis, transpiration,respiration, and senescence
  • 5 Model algorithms: nitrogen uptake and assimilatepartitioning
  • 6 Model algorithms: phenological development
  • 7 Recent model improvements
  • 8 Model applications: genotype-by-environment(G E) interactions
  • 9 Model applications: improving photosynthesis
  • 10 Model applications: assessing agroecosystemfunctioning in response to climate change
  • 11 Model applications: evaluating agrivoltaic systemsto optimize land use for energy production
  • 12 Model applications: supporting smart nitrogenmanagement
  • 13 Conclusion and future trends
  • 14 Acknowledgements
  • 15 References

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