Strawberry is an economically and socially important crop in several regions worldwide. Thus, studies that provide information on topics in strawberry growth are important and must be constantly updated. The aims of this study were to fit a logistic growth model to describe strawberry fruit production and to estimate the partial derivatives of the fitted model in order to estimate and interpret the critical points, in addition to using multivariate analyses. To do this, data on 16 treatments [combinations of two cultivars (Albion and Camarosa), two origins (national and imported), and four mixed organic substrates (70% crushed sugar cane residue + 30% organic compost, 70% crushed sugar cane residue + 30% commercial substrate, 70% burnt rice husk + 30% organic compost, and 70% burnt rice husk + 30% commercial substrate)] conducted in a randomized complete block design (RCBD) with four replicates were used. A logistic model was fitted to the accumulated fruit production stratified by treatment and replication. Partial derivatives related to the accumulated thermal sum were estimated in order to quantify the critical points of the model. Subsequently, a principal component analysis was performed. The results show that the use of growth models substantially increases the inferences that can be made about crop growth, and the multivariate analysis summarizes this information, simplifying its interpretation. Approaches such as those carried out in this study are still rarely used, but, compared to simpler models, they increase the amount of inferences that can be made and provide greater elucidation of the results.