Crop modelling to aid crop improvement


Substantial genotype x environment interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. Here we outline approaches based on modelling to bridge this predictability gap and increase the efficiency of crop improvement.

  1. Crop models were used to quantify the timing and intensity of abiotic stresses affecting crops during their life cycle. Such environment characterisation helps interpretation of genotype x environment interactions in multi-environment trials and is used to assist breeder and pre-breeder decisions.
  2. Crop models incorporating physiological understanding of traits were used to scale-up known effects of traits from organ to crop level. The value of these traits was assessed for the target population of environments, and exploited to focus phenotying on the most promising traits.
  3. Modeling was used to dissect yield into traits with genetic controls more consistently expressed across environments. The scaling-up with crop modelling of the impact of such genetic controls can be linked to breeding-system modelling to improve the efficiency of breeding programs.
  4. Crop models were used to anticipate whether allelic diversity within breeding germplasm pools is sufficient to allow adaption to future projected climates.

Case studies will be presented to illustrate these different approaches and their value for crop improvement.

The 9th International Wheat Conferenc
Bangyou Zheng
Bangyou Zheng
Data Scientist / Digital Agronomist

a research scientist of digital agriculture at the CSIRO.