A growth model to predict body weight and body composition of broilers

Models predicting the nutrient partitioning and animal performance have been developed for decades. Nowadays, growth models are used in practical animal nutrition, and they have particular importance in precision livestock farming. The aim of the present study was to introduce a broiler model and to provide examples on model application. The model predicts protein and fat deposition as well as the body weight of an individual broiler chicken from digestible nutrient intake over time. Feed intake (FI) and the digestible nutrient content of the feed are inputs as well as some animal factors like: initial BW, feed intake at 1 and 2 kg of BW, precocity and mean protein deposition. The protein and energy metabolism is represented as in the classical nutrient partitioning models. The protein deposition (PD) is driven by digestible amino acid supply and is under “genetic control”, the so-called potential PD limits the actual PD if protein is oversupplied. The authors discuss how the model can be used to simulate the animal response upon different scenarios. Examples are given to show that the diet might be limiting if some animal trait is changed. Applicability of the model has shown through running the model by using different feed strategies (three- vs five-phase-feeding) and variations with animal factors. In conclusion, growth models are useful tools to support decision making for defining the most suitable feeds used in a broiler farm. The model presented in this paper shows a high sensibility and flexibility to test different scenarios. By challenging the model with different inputs, the animal response in terms of changes in body weight and feed conversion can be understood more by studying the shift in deposition of chemical constituents. The examples provided in the present paper shows the benefit of using mathematical models and their applicability in precision nutrition. It can be concluded that the growth model helps to apply “from desired feed to desired food” concept

Metabolizable energy requirement for maintenance estimated by regression analysis of body weight gain or metabolizable energy intake in growing pigs

Objective: Feed energy required for pigs is first prioritized to meet maintenance costs. Additional energy intake in excess of the energy requirement for maintenance is retained as protein and fat in the body, leading to weight gain. The objective of this study was to estimate the metabolizable energy requirements for maintenance (MEm) by regressing body weight (BW) gain against metabolizable energy intake (MEI) in growing pigs.Methods: Thirty-six growing pigs (26.3 +/- 1.7 kg) were allotted to 1 of 6 treatments with 6 replicates per treatment in a randomized complete block design. Treatments were 6 feeding levels which were calculated as 50%, 60%, 70%, 80%, 90%, or 100% of the estimated ad libitum MEI (2,400 kJ/kg BW0.60 d). All pigs were individually housed in metabolism crates for 30 d and weighed every 5 d. Moreover, each pig from each treatment was placed in the open-circuit respiration chambers to measure heat production (HP) and energy retained as protein (REp) and fat (REf) every 5 d. Serum biochemical parameters of pigs were analyzed at the end of the experiment.Results: The average daily gain (ADG) and HP as well as the REp and REf linearly increased with increasing feed intake (p< 0.010). beta-hydroxybutyrate concentration of serum tended to increase with increasing feed intake (p = 0.080). The regression equations of MEI on ADG were MEI, kJ/kg BW0.60 d = 1.88xADG, g/d+782 (R-2 = 0.86) and MEm was estimated at 782 kJ/kg BW0.60 d. Protein retention of growing pigs would be positive while REf would be negative at this feeding level via regression equations of REp and REf on MEI.Conclusion: The MEm was estimated at 782 kJ/kg BW0.60 d in current experiment. Furthermore, growing pigs will deposit protein and oxidize fat if provided feed at the estimated maintenance level

Application d’un programme d’alimentation de précision chez le porc en croissance alimenté à volonté : effet sur les performances et l’utilisation des nutriments

Within the Horizon 2020 EU program Feed-a-Gene, a decision support system (DSS) was developed to implement precision feeding (PF) in commercial pig farms and to help improve feed efficiency. This study aimed to perform PF with the DSS in practical conditions with growing pigs fed ad libitum and to assess consequences on performance and nutrient use. Sixty-four pigs were reared from 77 to 161 days of age (33.5 to 108.8. kg body weight, BW) in a single pen equipped with an automatic weighing-sorting system and eight automatic feeders that register feed intake and deliver a tailored blend of two diets (A and B, respectively 1.0 and 0.4 g SID Lysine(Lys)/MJ net energy (NE), and 9.7 MJ NE/kg) to individual pigs. The control group received a blend providing 0.9 g Lys/MJ NE until the group weighed 65 kg on average (growing phase) and 0.7 g Lys/MJ NE thereafter (finishing phase). For the PF group, the Lys requirement was assessed individually and on a daily basis, based on up to 20 previous records of BW and feed intake, and diets A and B were blended accordingly. Daily feed intake, average daily gain, and feed conversion ratio did not differ between treatments. During the growing period, Lys and nitrogen (N) intake and N excretion were 11%, 9%, and 14% lower in the PF group than those in the control group, respectively (P 0.66). These results could be explained by the slightly higher feed intake in the PF group (+100 g/d, P = 0.24) and the lower Lys content used during the finishing period of the 2-phase strategy compared to standard diets

Review. Divergent selection for residual feed intake in the growing pig

To view supplementary material for this article, please visit https:/doi.org/10.1017/S175173111600286XThis review summarizes the results from the INRA (Institut National de la Recherche Agronomique) divergent selection experiment on residual feed intake (RFI) in growing Large White pigs during nine generations of selection. It discusses the remaining challenges and perspectives for the improvement of feed efficiency in growing pigs. The impacts on growing pigs raised under standard conditions and in alternative situations such as heat stress, inflammatory challenges or lactation have been studied. After nine generations of selection, the divergent selection for RFI led to highly significant ( P<0.001) line differences for RFI (−165 g/day in the low RFI (LRFI) line compared with high RFI line) and daily feed intake (−270 g/day). Low responses wereobserved on growth rate (−12.8 g/day, P <0.05) and body composition (+0.9mm backfat thickness, P = 0.57; −2.64% lean meat content, P<0.001) with a marked response on feed conversion ratio (−0.32 kg feed/kg gain, P<0.001). Reduced ultimate pH and increased lightness of the meat ( P<0.001) were observed in LRFI pigs with minor impact on the sensory quality of the meat. These changes in meat quality were associated with changes of the muscular energy metabolism. Reduced maintenance energy requirements (−10% after five generations of selection) and activity (−21% of time standing after six generations of selection) of LRFI pigs greatly contributed to the gain in energy efficiency. However, the impact of selection for RFI on the protein metabolism of the pig remains unclear. Digestibility of energy and nutrients was not affected by selection, neither for pigs fed conventional diets nor for pigs fed high-fibre diets. A significant improvement of digestive efficiency could likely be achieved by selecting pigs on fibre diets. No convincing genetic or blood biomarker has been identified for explaining the differences in RFI, suggesting that pigs have various ways to achieve an efficient use of feed. No deleterious impact of the selection on the sow reproduction performance was observed. The resource allocation theory states that low RFI may reduce the ability to cope with stressors,via the reduction of a buffer compartment dedicated to responses to stress. None of the experiments focussed on the response of pigs to stress or challenges could confirm this theory. Understanding the relationships between RFI and responses to stress and energy demanding processes, as such immunity and lactation, remains a major challenge for a better understanding of the underlying biological mechanisms of the trait and to reconcile the experimental results with the resource allocation theory

Estimating the Continuous-Time Dynamics of Energy and Fat Metabolism in Mice

The mouse has become the most popular organism for investigating molecular mechanisms of body weight regulation. But understanding the physiological context by which a molecule exerts its effect on body weight requires knowledge of energy intake, energy expenditure, and fuel selection. Furthermore, measurements of these variables made at an isolated time point cannot explain why body weight has its present value since body weight is determined by the past history of energy and macronutrient imbalance. While food intake and body weight changes can be frequently measured over several weeks (the relevant time scale for mice), correspondingly frequent measurements of energy expenditure and fuel selection are not currently feasible. To address this issue, we developed a mathematical method based on the law of energy conservation that uses the measured time course of body weight and food intake to estimate the underlying continuous-time dynamics of energy output and net fat oxidation. We applied our methodology to male C57BL/6 mice consuming various ad libitum diets during weight gain and loss over several weeks and present the first continuous-time estimates of energy output and net fat oxidation rates underlying the observed body composition changes. We show that transient energy and fat imbalances in the first several days following a diet switch can account for a significant fraction of the total body weight change. We also discovered a time-invariant curve relating body fat and fat-free masses in male C57BL/6 mice, and the shape of this curve determines how diet, fuel selection, and body composition are interrelated

Estimation of Activity Related Energy Expenditure and Resting Metabolic Rate in Freely Moving Mice from Indirect Calorimetry Data

Physical activity (PA) is a main determinant of total energy expenditure (TEE) and has been suggested to play a key role in body weight regulation. However, thus far it has been challenging to determine what part of the expended energy is due to activity in freely moving subjects. We developed a computational method to estimate activity related energy expenditure (AEE) and resting metabolic rate (RMR) in mice from activity and indirect calorimetry data. The method is based on penalised spline regression and takes the time dependency of the RMR into account. In addition, estimates of AEE and RMR are corrected for the regression dilution bias that results from inaccurate PA measurements. We evaluated the performance of our method based on 500 simulated metabolic chamber datasets and compared it to that of conventional methods. It was found that for a sample time of 10 minutes the penalised spline model estimated the time-dependent RMR with 1.7 times higher accuracy than the Kalman filter and with 2.7 times higher accuracy than linear regression. We assessed the applicability of our method on experimental data in a case study involving high fat diet fed male and female C57Bl/6J mice. We found that TEE in male mice was higher due to a difference in RMR while AEE levels were similar in both groups, even though female mice were more active. Interestingly, the higher activity did not result in a difference in AEE because female mice had a lower caloric cost of activity, which was likely due to their lower body weight. In conclusion, TEE decomposition by means of penalised spline regression provides robust estimates of the time-dependent AEE and RMR and can be applied to data generated with generic metabolic chamber and indirect calorimetry set-ups