Integrating genetic information into dynamical models is key to understand variations among genotypes and improve crop performances face to current agronomic and ecological constraints. A first and necessary step in building gene-to-phenotype models is the calibration of a large number of genotypes. We compared here two different strategies for the calibration of an Ordinary Differential Equations (ODE) kinetic model simulating the accumulation of different sugars during peach fruit development. First, the model was calibrated for each genotype independently using a Genotype-Based (GB) strategy. Two formulations of the problem have been tested, either as a Single-Objective Optimization (GBS) problem or as a Multi-Objective Optimization (GBM) problem. Second, the model was calibrated for all genotypes simultaneously using a Population-Based (PB) strategy. The two strategies were first applied to a set of simulated data and then to a real dataset derived from an interspecific population of 106 peach genotypes. Results showed that the GB strategy allowed for a high goodness of fit for most genotypes, especially in the GBS formulation. However, the estimated parameters suffered from a lack of practical identifiability as independent repetitions of the estimation algorithm did not always converge to the same value for most genotypes. The PB calibration strategy overcame this issue showing a good identifiability of the population parameter values, a goodness of fit comparable to the one obtained with the GB strategy and a good characterisation of parameter variations within the progeny, which is a key to assess the inter-individual genetic variability. These results are an important step towards the development of reliable gene-to-phenotype models.