Critical points on growth curves in autoregressive and mixed models
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Data
2014-01-01
Autores
de Pinho, Sheila Zambello [UNESP]
de Carvalho, Lídia Raquel [UNESP]
Mischan, Martha Maria [UNESP]
Passos, José Raimundo de Souza [UNESP]
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Adjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.
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Scientia Agricola, v. 71, n. 1, p. 30-37, 2014.