%0 Journal Article
%9 Article
%W National Agricultural Library
%~ PubAg
%B Scientia horticulturae
%T Describing tomato plant production using growth models
%A Sari, Bruno Giacomini
%A Lúcio, Alessandro Dal’Col
%A Santana, Cinthya Souza
%A Savian, Taciana Villela
%V 2019 v.246
%K confidence interval
%K crops
%K early development
%K field experimentation
%K fruits
%K genotype
%K growth models
%K logit analysis
%K salads
%K seedlings
%K tomatoes
%M 6284517
%X The aim of this study is to describe the productive behavior of salad tomato genotypes using adjusted growth models. Data were obtained from field experiments performed in 2015/2016 using the Cordillera, Ellen and Santa Clara genotypes, and in 2016/2017 using the Cordillera and Gaucho genotypes. Five and nine harvests were carried out in 2015/2016 and 2016/2017 respectively, and the variables measured were number and weight of fruit per plant. The Brody, Gompertz, logistic and von Bertalanffy models were adjusted using the accumulated values per plant for each harvest, with the dependent variables being those measured and the independent variable being the number of days after the seedlings had been transplanted. Using the model that best fit the data, the confidence interval of the inflection point was estimated and the similarity of the parameters between the genotypes was measured. The logistic model fit best for both variables. Using the estimates for the biologically interpreted parameters and the inflection point, it was possible to compare the final production of the genotypes and increase the inferences that could be made regarding production over time, differentiating these in terms of productive precocity. In the first experiment, the Cordillera and Ellen genotypes were more premature, while in the second the most premature was the Gaucho genotype. Therefore, the use of growth models can increase the inferences that can be made in terms of the productive behavior of other crops in multiple harvests.
%D 2019
%= 2019-06-24
%G
%8 2019-02-27
%V v. 246
%P pp. 146-154
%R 10.1016/j.scienta.2018.10.044