A robust model that estimates the ME intake over broiler breeder lifetime is essential for formulating diets with optimum nutrient levels. The objectives of the current study were to 1) evaluate inclusion of random terms associated with individual MEm, ADG, and age in a ME partitioning model on residual dependency, model fitting and predictive performance; 2) evaluate how including random terms associated with individual maintenance ME, ADG, and age could bias the ME partitioning model; 3) evaluate the effect of chunking BW, ADG, and egg production data into different chunk sizes (daily, 4-d, weekly, 2-wk, or 3-wk) on fitting and predictive performance of ME partitioning model; and 4) determine the effect of an increased (10%) prepubertal BW gain and earlier pubertal phase growth on energy efficiency of broiler breeders.
The experiment was conducted as a randomized controlled trial with 40 Ross 708 broiler breeder pullets reared on 1 of 10 target growth trajectories, which were designed with 2 levels of cumulative BW gain in prepubertal growth phase and 5 levels of timing of growth around puberty. The BW trajectories were implemented for each individual bird using a PF system. This study investigated the effect of growth pattern on energy efficiency of birds and tested the effects of dividing data into daily, 4-d, weekly, 2-wk, and 3-wk periods and the inclusion of random terms associated with individual maintenance ME and ADG requirements, and age on ME partitioning model fit and predictive performance.
Model [I] was: MEId = a × BWb + c × ADGp + d × ADGn + e × EM + E, where MEId was daily ME intake (kcal/d); BW in kg; ADGp was positive ADG; ADGn was negative ADG (g/d); EM was egg mass
(g/d); E was the model residual. Models [II to IV] were nonlinear mixed models based on the model [I] with inclusion of a random term for individual maintenance requirement, age, and ADG, respectively. Model [II] – 3 wk was chosen as the most parsimonious based on lower autocorrelation bias, closer fit of the estimates to the actual data (lower model MSE and closer R2 to 1), and greater predictive performance among the models. Estimated ME partitioned
to maintenance in model [II] – 3 wk was 100.47 ± 7.43 kcal/kg0.56, and the ME requirement for ADGp, ADGn, and EM were 3.49 ± 0.37; 3.16 ± 3.91; and 2.96 ± 0.13 kcal/g, respectively. Standard treatment had lower residual heat production (RHP; -0.68 kcal/kg BW0.56) than high early growth treatment (0.79 kcal/kg BW0.56), indicating greater efficiency in utilizing the ME consumed. Including random term associated with individual maintenance ME in a 3-wk chunk size provided a robust, biologically sound life-time energy partitioning model for breeders.
To increase robustness of broiler breeder energy partitioning models, a novel chunking procedure was applied on precision feeding system data. Increasing chunk size of data provided closer fit of the models estimated coefficients to the actual data by accounting for more variation in the residuals. A mixed effect ME partitioning model containing a random term associated with individual maintenance requirement in a 3-wk chunked data (model [II] – 3wk) increased inferential efficiency. The model can be used as a tool to estimate ME requirements and to facilitate choosing a precise energy level in feed formulation practices. Furthermore, applying Ross 708 guideline data in the model suggested a revision on the breeder-recommended target BW. The current study results indicated that an earlier pubertal growth strategy could reduce energy efficiency in broiler breeders.
A robust model that estimates the ME intake over broiler breeder lifetime is essential for formulating diets with optimum nutrient levels. The experiment was conducted as a randomized controlled trial with 40 Ross 708 broiler breeder pullets reared on 1 of 10 target growth trajectories, which were designed with 2 levels of cumulative BW gain in prepubertal growth phase and 5 levels of timing of growth around puberty. This study investigated the effect of growth pattern on energy efficiency of birds and tested the effects of dividing data into daily, 4-d, weekly, 2-wk, and 3-wk periods and the inclusion of random terms associated with individual maintenance ME and ADG requirements, and age on ME partitioning model fit and predictive performance. Model [I] was: MEId = a × BWb + c × ADGp + d × ADGn + e × EM + ε, where MEId was daily ME intake (kcal/d); BW in kg; ADGp was positive ADG; ADGn was negative ADG (g/d); EM was egg mass (g/d); ε was the model residual. Models [II to IV] were nonlinear mixed models based on the model [I] with inclusion of a random term for individual maintenance requirement, age, and ADG, respectively. Model [II] – 3 wk was chosen as the most parsimonious based on lower autocorrelation bias, closer fit of the estimates to the actual data (lower model MSE and closer R2 to 1), and greater predictive performance among the models. Estimated ME partitioned to maintenance in model [II] – 3 wk was 100.47 ± 7.43 kcal/kg0.56, and the ME requirement for ADGp, ADGn, and EM were 3.49 ± 0.37; 3.16 ± 3.91; and 2.96 ± 0.13 kcal/g, respectively. Standard treatment had lower residual heat production (RHP; -0.68 kcal/kg BW0.56) than high early growth treatment (0.79 kcal/kg BW0.56), indicating greater efficiency in utilizing the ME consumed. Including random term associated with individual maintenance ME in a 3-wk chunk size provided a robust, biologically sound life-time energy partitioning model for breeders.
