Improving models and plant phenotyping pipelines for a smart agriculture under abiotic stress combination and elevated CO2

Climate change accelerates the need for a smarter, more efficient, more secure agriculture. Because climate change is predicted to increase spatial and temporal variability, crop models able to predict the best local allele/phene combinations within a species, in addition to the best management systems (such as, for instance, species choice, rotations, sowing dates…) will be of great value for farmers and breeders worldwide. However, current crop models have large uncertainties in particular under drought and high temperatures that often occur in combination and while their occurrences are likely to increase in several regions of the world.

Accounting for the impact of elevated atmospheric CO2 in the picture will add another level of difficulty with possible positive or negative influences depending on complex interactions, we thus raise the double hypothesis that important reasons for crop model uncertainties are:

  1. Lack of accurate dataset under combined stresses hampering proper parameterisation;
  2. Inappropriate modelling hypotheses

Because CO2 control in experimental facilities is the exception rather than the rule, our project will aim at delivering to simple, low cost, principles and solutions for manipulating combined stresses, including elevated CO2, in experimental set-ups.