Modelos de crescimento na cultura de Crotalária juncea

Abstract

The objective of this research was to adjust non-linear models, Gompertz and Logistic, in the description of morphological and productive traits of sunn hemp in two sowing season, October the 22th 2014 and December the 3th 2014. Two uniformity were performed. The seeds were between rows 0.5m with rows and density of 20 plants per row meter of floor area of 52m x 50m (2.600m2). The evaluations began on October the 29th 2014 and December the 16th 2014, totaling 94 and 76 evaluation days for seasons 1 and 2, respectively. After the emergence of the seeds of sunn hemp, for season 1 from 7 days after sowing, and from 2 to 13 days after sowing, on each day, they were collected randomly four plants. The traits: plant height (PH), number of leaves (NL), stem diameter (SD), root length (RL), fresh matter leaf (FML), fresh matter stem (FMS), fresh matter root (FMR), fresh matter shoot (FMSH), the fresh matter total (FMT), mass of dry matter leaf (DML), dry matter stem (DMS), dry matter root (DMR), dry matter shoot (DMSH), and total dry matter (DMT). The residual assumptions of the characters studied were verified by test Shapiro-Wilk, test Breusch-Pagan and test Durbin-Watson. To model was used Gompertz equation: and the Logistic model was used the equation: . The inflection point (pi), maximum acceleration point (pam), maximum deceleration point (pdm) and asymptotic deceleration point (pda) for the Gompertz and Logistic models were calculated. For both models the confidence interval (CI) was calculated. The quality setting of the Gompertz and Logistic models was verified by the determination coefficient (R²), the Akaike information criteria(AIC), residual standard deviation (RSD), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean prediction error (MPE). The calculations were performed with the help of SOLVER of Microsoft Office Excel® application and software statistic R. The Gompertz model when compared between the sowing dates by means of the CI of the parameters, for the morphological and 9 productive characters differ. Same result was found for the Logistic model. The Gompertz and Logistic models are suitable for adjusting the morphological and productive traits of the sunn hemp.