Best-fitting growth curves of the von Bertalanffy-Pütter type. (20th March 2019)

• Record Type: Journal Article
• Authors: Kühleitner, Manfred; Brunner, Norbert; Nowak, Werner-Georg; Renner-Martin, Katharina; Scheicher, Klaus
• Is Part Of: Poultry science. Volume 98:Number 9(2019:Sep.)
• Page Start: 3587
• Page End: 3592
• Publication Date: 2019-03-20
• Subjects: Athens Canadian Random Bred chicken -- growth model -- von Bertalanffy-Pütter differential equation -- simulated annealing; Poultry -- Periodicals; Poultry -- Periodicals; Poultry Diseases -- Periodicals; Electronic journals; 636.5005
• DOI: 10.3382/ps/pez122
• Languages: English
• ISSNs: 0032-5791

Abstract

ABSTRACT: Introduction: A large body of literature aims at identifying growth models that fit best to given mass-at-age data. The von Bertalanffy-Pütter differential equation is a unifying framework for the study of growth models. Problem: The most common growth models used in poultry science literature fit into this framework, as these models correspond to different exponent-pairs (e.g., Brody, Gompertz, logistic, Richards, and von Bertalanffy models). Here, we search for the optimal exponent-pairs ( a and b ) amongst all possible exponent-pairs and expect a significantly better fit of the growth curve to concrete mass-at-age data. Method: Data fitting becomes more difficult, as there is a large region of nearly optimal exponent-pairs. We therefore develop a fully automated optimization method, with computation time of about 1 to 2 wk per data-set. For the proof of principle, we applied it to literature data about 217 male meat-type chickens, Athens Canadian Random Bred, that were reared under controlled conditions and weighed 28 times during a time span of 170 D. Results: We compared 2 methods of data fitting, least squares using the sum of squared errors ( SSE ), which is common in literature, and a variant using the sum of squared log-errors SSElog . For these data, the optimal exponent-pairs were (0.43, 4.06) for SSE = 2, 208.6 (31% improvement over literature values for the residual standard deviation) and (0.89, 0.93) for SSElog = 0.04599. Both optimal exponents were clearly distinct from the exponent-pairs of the common models in literature. This finding was reinforced by considering the region of nearly optimal exponents. Discussion: We explain, why we recommend using SSElog for data fitting and we discuss prognosis, where data from the first 8 wk of growth would not be enough.