Nonlinear regression models as a tool to describe strawberry vegetative growth

Abstract

The strawberry plant is a species whose fruits are appreciated worldwide, and the way in which these plants grow and develop has an impact on their productivity and quality. Knowledge about the growth of the plant allows technicians, researchers, and especially producers to master the management of the crop over time, so it is necessary that these growth dynamics be elucidated in scientific studies. The objective of this work was to study the increment of leaf dry mass of two strawberry cultivars during a cultivation cycle, using the classic logistic and Gompertz models, and the Chanter model. The experiment was carried out in a greenhouse, using the Albion and San Andreas cultivars, grown in gutters with substrate, and recirculation of the drained nutrient solution. Seven collections were carried out from March 2022 to March 2023, respectively at 30, 60, 120, 180, 240, 300 and 360 days after planting. Estimates of the model parameters were obtained, two of which had the following practical interpretations: 1 – moment of growth stabilization; 2 - moment of slowdown in growth. Three statistical measures were used to evaluate the goodness of fit of the models: Akaike information criterion (AIC), Bayes information criterion (BIC), and residual standard deviation (SRD). It is concluded that, for the Albion cultivar, the Chanter model presented lower values for the three evaluators, however, for the San Andreas cultivar, the AIC and BIC evaluators indicated the logistic model with the best adjustment, and the DPR indicated the Chanter model.