Response surface of dietary energy and protein in Japanese quail from 7 to 14 days of age

Abstract

An experiment was conducted to determine dietary energy (ME) and CP requirements of quail chicks using response surface methodology. A total of 40 floor pens of 20 birds each were assigned to 9 diets of central composite design (CCD) containing 5 levels of ME (2,809 to 3,091 kcal/kg) and CP (19 to 24.8% of diet) from 7 to 14 d of age. The experimental results of CCD were fitted with quadratic response models, and ridge analysis was used to compute the optimal response for BW gain (BWG) and feed conversion ratio (FCR). Regression analysis showed that the linear effect of independent variables was significant on bird responses. The quadratic and cross-product effects did not have significant effects on performance. Dietary levels of CP linearly affected BWG and FCR, but the effect of dietary ME was not significant. The ridge maximum analysis on BWG and minimum analysis on FCR models revealed that the maximum BWG may be achieved with 2,950 kcal of ME/kg and 25% CP; and minimum FCR may be obtained with 2,878 kcal of ME/kg and 24.4% CP. The results of this study showed that response surface analysis with the CCD platform was successfully used to optimize dietary requirements of Japanese quail and this methodology could be used for other nutrients.

Estimation of lysine requirements for growing Japanese quails

“…In this study, we used several regression models, five types of broken line models and the quadratic polynomial, to estimate the lysine requirement of growing quails. Different models and design of experiments (DOE) have been used for estimation of nutritional requirements in birds and domestic animals (Baker, Batal, Parr, Augspurger, & Parsons, 2002;De Leon, Kidd, & Corzo, 2010;Ghazaghi, Mehri, Yousef-Elahi, & Rokouei, 2012;Mehri, 2012Mehri, , 2014Mehri, Davarpanah, & Mirzaei, 2012;Mehri & Ghazaghi, 2014;Mehri, Nassiri Moghaddam, Kermanshahi, & Danesh-Mesgaran, 2013;Mercer, 1992) but as Pesti et al (2009) suggested, each model may have some advantages and disadvantages. For instance, although second-order polynomials are easy to fit to data, the inclusion of input data below or above those points that are required for maximum or minimum responses, resulting in remarkable changes in requirement estimates ).…”

Optimization of response surface and neural network models in conjugation with desirability function for estimation of nutritional needs of methionine, lysine, and threonine in broiler chickens

“…Because the nutritional experiments involve more than one response, determination of optimum conditions on the independent variables requires simultaneous consideration of all the responses. However, the most studies report single evaluation of each response (e.g., BW gain or feed conversion ratio, FCR) in the optimization process at a time Baker, 1993, 1994a;Baker and Han, 1994;Baker et al, 2002;Mehri et al, 2010aMehri et al, ,b, 2012Zaboli et al, 2011;Ghazaghi et al, 2012;Mehri, 2012), and complex relationships among multiple responses have been overlooked. In addition, the bird responses are usually correlated functionally (e.g., FCR is a function of the feed consumed); therefore, analyzing performance data using a ABSTRACT The optimization algorithm of a model may have significant effects on the final optimal values of nutrient requirements in poultry enterprises.…”

“…In such cases, Shearer (2000) pointed out 2 important steps for modeling growth data: first, choosing an appropriate design of experiment (DOE) for gathering data, and second, implementation of a robust model to analyze data. Regarding DOE, application of fractional factorial designs such as Box-Behnken design (BBD) and central composite design (CCD) in poultry research has been suggested (De Leon et al, 2010;Ghazaghi et al, 2012;Mehri et al, 2012). These DOE allow the researcher to investigate the main effects of independent variables to improve the responses and to include interactions effects of nutrients in the model.…”

“…The growth data obtained from DOE may be analyzed by response surface methodology (Ghazaghi et al, 2012;Mehri et al, 2012), soft-computing artificial neural networks (ANN), or both (Ahmadi and Golian, 2011;Mehri, 2012). Because the nutritional experiments involve more than one response, determination of optimum conditions on the independent variables requires simultaneous consideration of all the responses.…”

A hybrid model of uniform design and artificial neural network for the optimization of dietary metabolizable energy, digestible lysine, and methionine in quail chicks

“…Some reports were recently published on the metabolizable energy:crude protein (ME:CP) ratio in growing (Ghazaghi et al, 2012;Siyadati et al, 2011) and adult quails (Tarasewicz et al, 2006). The ME content of diet may control feed intake and, therefore, the digestible intake of each amino acid (NRC, 1994;Richards and Proszkowiec-Weglarz, 2007).…”