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Nonlinear regression for description of strawberry (Fragaria x ananassa) production

Diel, Maria Ines, Sari, Bruno Giacomini, Krysczun, Dionatan Letzer, Olivoto, Tiago, Pinheiro, Marcos Vinicius Marques, Meira, Daniela, Schmidt, Denise, Lucio, Alessandro Dal'Col
Journal of horticultural science & biotechnology 2019 v.94 no.2 pp. 259-273
Fragaria ananassa, algorithms, composts, cultivars, fruits, growth models, logit analysis, nonlinear models, rice hulls, strawberries, sugarcane
The aim of this study was to select growth models to describe strawberry (Fragaria xananassa)fruit production. To do this, data on 16 treatments (combination of 2 cultivars[Albion and Camarosa], 2 origins [National and Imported], and 4 organic substrates mixed [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 4 replicates were used. Different parameterizations of the Logistic, Gompertz,and von Bertalanffy models were adjusted for number and weight of fruits per plant (g)accumulated in multiple harvests. The model adjustment and parameter estimation were obtained by ordinary least squares, using a Gauss Newton algorithm. The selection of the best model was chosen by intrinsic and parametric non linearity. Among the adjusted nonlinear models, the best adjustment for both variables was achieved by the parameterization 2 of the Logistic model and parameterization 1 of the Gompertz model, because they had lower results with less parametric and intrinsic non linearity. However, care should be taken when using the Gompertz model because it tends to overestimate the production estimate and may cause misunderstandings in interpretation.