Yield impact was assessed for the thermal time required to reach floral initiation ( tt_end_of_juvenile), the water extractability by roots ( ll_modifier), the radiation use efficiency ( y_rue), and biomass allocation to grains ( potential_grain_filling_rate). The impact on crop yield allowed to screen component traits for influential traits (n = 42) in the target population of environments while a study on trait × environment interactions was used to explore their variability across environments. The impact of the 90 component traits were considered for 8 output variables (“integrated traits”, which result from the complexity of the dynamic modeling of development and growth). Simulations for those genotypes were performed with APSIM-Wheat (Version 7.5). Each of the 90 component traits was assumed to vary in a ± 20% range around the value for the reference cultivar Hartog and the Morris method was used to sample the total parameter space (90 traits, 6 levels, 100 reps i.e. In summary, 90 independent APSIM-Wheat parameters considered as “component traits”, associated with the main physiological processes that are modeled, were selected to reflect a potential genetic variability. This workflow presents how the “genetic diversity” was considered, sampled and screened in silico. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Ī global sensitivity analysis was applied on the APSIM-Wheat crop model to identify potential candidate traits for yield improvement in a large population of environments. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. A crop can be viewed as a complex system with outputs (e.g.
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