Abstract
Brazil has stood out worldwide as one of the main producers and consumers of beans, which makes their cultivation important for the economic and social development of the country. As the bean plant has a short growth cycle, its modeling is essential for optimizing management plans for this crop. This modeling can be performed by linear and non-linear models, but the latter have stood out for providing more information to the researcher, mainly due to the practical interpretation of their parameters. In this sense, in the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations. As criteria to assess the quality of the fit, the adjusted coefficient of determination, the corrected Akaike information criterion and the residual standard deviation were used. The logistic model best fitted the data.
Highlights
Common bean (Phaseolus vulgaris L.) has stood out as one of the main crops of Brazilian agribusiness, ranking the country as the third largest producer of beans in the world, only behind India and Myanmar (FAOSTAT, 2019)
In the R statistical software, the third-degree linear polynomial model and the Logistic and Gompertz non-linear models were adjusted to height data, in centimeters, in relation to time, in days after emergence, totaling 11 observations
All tests were non-significant (p-value > 0.01) for the polynomial and logistic models, which indicates that the residuals are independent and identically distributed following a normal
Summary
Common bean (Phaseolus vulgaris L.) has stood out as one of the main crops of Brazilian agribusiness, ranking the country as the third largest producer of beans in the world, only behind India and Myanmar (FAOSTAT, 2019). Among the countries that make up Mercosur, Brazil stands out as the main producer and consumer of this legume, producing around 3.1 million tons per year (CONAB, 2019) and, stimulating family farming and the local economy, as it is a crop explored from the small to the large producer (MALTA et al, 2017). For the study of growth curves, linear and non-linear models can be used, nonlinear models stand out for their parsimony and practical interpretations of the parameters, which helps the researcher to find practical applications of their characteristics in addition to summarizing various information in a few parameters (OLIVEIRA et al, 2013; ARCHONTOULIS, MIGUEZ, 2015; FERNANDES et al, 2017; LIMA et al, 2017; RIBEIRO et al, 2018)
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