Abstract The study evaluated the use of nano copper in semi-purified diets for laying quails and its effect on performance, metabolic state, and bioavailability. A total of 160 (180-days-old) quails were distributed in a completely randomized design, in a 3x3+1 factorial. The copper sources used were copper sulfate, copper oxide, and nano copper oxide, at levels of 200, 400, and 800 ppm each, totaling nine treatments plus a negative control (with no copper inclusion). The following variables were determined: weight gain, feed intake, egg production, egg weight, hemoglobin, hematocrit, Cu in the tissues and Cu bioavailability. Data were subjected to analysis of variance at 5% probability. The effect of sources and levels, as well as the interaction between the factors were evaluated. When interaction was observed, the effect of sources was evaluated separately by the Tukey’s test and the effect of levels by regression, both at 5% probability. Copper nano oxide can be used at up to 800 ppm in the diet of laying quails without altering the productive performance, and with higher bioavailability than conventional copper oxide. Hemoglobin increases with the inclusion of 200 and 400 ppm of nano copper oxide and the hematocrit with 400 ppm. semipurified semi purified performance state 16 180daysold daysold 180 days old (180-days-old design 3x31 x 3x3 1 3x3+ factorial sulfate 80 each inclusion. . inclusion) determined gain intake production hemoglobin 5 probability observed Tukeys Tukey s regression 20 40 18 3x 8 2 4

RESUMO O crescimento em comprimento em função do tempo tem uma relação não linear; por isso, funções não lineares são recomendáveis para descrever essa relação. Seis modelos não lineares foram usados para calcular o ganho em comprimento de truta-arco-íris (Oncorhynchus mykiss) durante 98 dias, na fase final da engorda, submetidas a três dietas diferentes em grupos triplicados. Foram ajustadas as equações de von Bertalanffy, Gompertz, logístico, Brody, função potencial e exponencial a dados individuais de comprimento-idade de 900 peixes. O ajuste foi feito pelo método dos mínimos quadrados, usando-se o algoritmo iterativo de Marquardt. A precisão do ajuste foi avaliada pelo uso de critérios combinados de ajuste: quadrado médio do resíduo (QMR), erro médio absoluto (EMA) e o critério de informação de Akaike corrigido para tamanhos amostrais pequenos (AICc). Todos os modelos atingiram a convergência para cada caso avaliado. Os critérios de avaliação do modelo logístico indicaram o melhor ajuste geral (0,67 vez dos critérios combinados MSR, MAE e AICc) para cada grupo de peixe avaliado, seguido pelo modelo exponencial (0,185) e os modelos von Bertalanffy e Brody, com 0,074, respectivamente. Similarmente, os resultados de ΔAICc identificaram-se ao modelo logístico e ao de Gompertz, com grande suporte das informações em 100% dos casos. Por fim, o modelo logístico pode ser sugerido na predição do crescimento em comprimento de truta-arco-íris cultivada.

ABSTRACT Length growth as a function of time has a non-linear relationship, so nonlinear equations are recommended to represent this kind of curve. We used six nonlinear models to calculate the length gain of rainbow trout (Oncorhynchus mykiss) during the final grow-out phase of 98 days under three different feed types in triplicate groups. We fitted the von Bertalanffy, Gompertz, Logistic, Brody, Power Function, and Exponential equations to individual length-at-age data of 900 fish. Equations were fitted to the data based on the least square method using the Marquardt iterative algorithm. Accuracy of the fitted models was evaluated using a model performance metrics combining mean squared residuals (MSR), mean absolute error (MAE) and Akaike's Information Criterion corrected for small sample sizes (AICc). All models converged in all cases tested. Evaluation criteria for the Logistic model indicated the best overall fit (0.67 of combined metric MSR, MAE and AICc) under all different feeding types, followed by the Exponential model (0.185), and the von Bertalanffy and Brody model (0.074, respectively). Additionally, ∆AICc results identify the Logistic and Gompertz models as being substantially supported by the data in 100% of cases. The logistic model can be suggested for length growth prediction in aquaculture of rainbow trout.