Abstract
BACKGROUND AND AIMS: Germination and heterotrophic growth are crucial steps for stand establishment. Numerical experiments based on the modelling of these early stages in relation to major environmental factors at sowing were used as a powerful tool to browse the effects of the genetic diversity of Medicago truncatula, one of the model legume species, under a range of agronomic scenarios, and to highlight the most important plant parameters for emergence. To this end, the emergence of several genotypes of M. truncatula was simulated under a range of sowing conditions with a germination and emergence simulation model. METHODS: After testing the predictive quality of the model by comparing simulations to field observations of several genotypes of M. truncatula, numerical experiments were performed under a wide range of environmental conditions (sowing dates × years × seedbed structure). Germination and emergence was simulated for a set of five genotypes previously parameterized and for two virtual genotypes engineered to maximize the potential effects of genetic diversity. KEY RESULTS: The simulation results gave an average value of 5-10 % difference in final emergence between genotypes, which was low, but the analysis underlined considerable inter-annual variation. The effects of parameters describing germination and emergence processes were quantified and ranked according to their contribution to the variation in emergence. Seedling non-emergence was mainly related to mechanical obstacles (40-50 %). More generally, plant parameters that accelerated the emergence time course significantly contributed to limiting the risk of soil surface crusting occurring before seedling emergence. CONCLUSIONS: The model-assisted analysis of the effects of genetic diversity demonstrated its usefulness in helping to identify the parameters which have most influence that could be improved by breeding programmes. These results should also enable a deeper analysis of the genetic determinism of the main plant parameters influencing emergence, using the genomic tools available for this model plant.