RESUMEN
Current feed formulation and evaluation practices rely on static values for the nutritional value of feed ingredients and assume additivity. Hereby, the complex interplay among nutrients in the diet and the highly dynamic digestive processes are ignored. Nutrient digestion kinetics and diet × animal interactions should be acknowledged to improve future predictions of the nutritional value of complex diets. Therefore, an in silico nutrient-based mechanistic digestion model for growing pigs was developed: "SNAPIG" (Simulating Nutrient digestion and Absorption kinetics in PIGs). Aiming to predict the rate and extent of nutrient absorption from diets varying in ingredient composition and physicochemical properties, the model represents digestion kinetics of ingested protein, starch, fat, and non-starch polysaccharides, through passage, hydrolysis, absorption, and endogenous secretions of nutrients along the stomach, proximal small intestine, distal small intestine, and caecum + colon. Input variables are nutrient intake and the physicochemical properties (i.e. solubility, and rate and extent of degradability). Data on the rate and extent of starch and protein hydrolysis of different ingredients per digestive segment were derived from in vitro assays. Passage of digesta from the stomach was modelled as a function of feed intake level, dietary nutrient solubility and diet viscosity. Model evaluation included testing against independent data from in vivo studies on nutrient appearance in (portal) blood of growing pigs. When simulating diets varying in physicochemical properties and nutrient source, SNAPIG can explain variation in glucose absorption kinetics (postprandial time of peak, TOP: 20-100 min observed vs 25-98 min predicted), and predict variation in the extent of ileal protein and fat digestion (root mean square prediction errors (RMSPE) = 12 and 16%, disturbance error = 12 and 86%, and concordance correlation coefficient = 0.34 and 0.27). For amino acid absorption, the observed variation in postprandial TOP (61 ± 11 min) was poorly predicted despite accurate mean predictions (58 ± 34 min). Recalibrating protein digestion and amino acid absorption kinetics require data on net-portal nutrient appearance, combined with observations on digestion kinetics, in pigs fed diets varying in ingredient composition. Currently, SNAPIG can be used to forecast the time and extent of nutrient digestion and absorption when simulating diets varying in ingredient and nutrient composition. It enhances our quantitative understanding of nutrient digestion kinetics and identifies knowledge gaps in this field of research. Already useful as research tool, SNAPIG can be coupled with a postabsorptive metabolism model to predict the effects of dietary and feeding-strategies on the pig's growth response.
Asunto(s)
Alimentación Animal , Digestión , Animales , Digestión/fisiología , Alimentación Animal/análisis , Dieta/veterinaria , Almidón/metabolismo , Íleon/metabolismo , Nutrientes , Aminoácidos , Fenómenos Fisiológicos Nutricionales de los AnimalesRESUMEN
Resilience, when defined as the capacity of an animal to respond to short-term environmental challenges and to return to the prechallenge status, is a dynamic and complex trait. Resilient animals can reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. The ability of modern technologies to simultaneously record multiple performance measures of individual animals over time is a huge step forward to evaluate the resilience of farm animals. However, resilience is not directly measurable and requires mathematical models with biologically meaningful parameters to obtain quantitative resilience indicators. Furthermore, interpretive models may also be needed to determine the periods of perturbation as perceived by the animal. These applications do not require explicit knowledge of the origin of the perturbations and are developed based on real-time information obtained in the data during and outside the perturbation period. The main objective of this paper was to review and illustrate with examples, different modelling approaches applied to this new generation of data (i.e., with high-frequency recording) to detect and quantify animal responses to perturbations. Case studies were developed to illustrate alternative approaches to real-time and post-treatment of data. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented. Quantification of resilience at the individual level makes possible the inclusion of this trait into future breeding programmes. This would allow improvement of the capacity of animals to adapt to a changing environment, and therefore potentially reduce the impact of disease and other environmental stressors on animal welfare. Moreover, such quantification allows the farmer to tailor the management strategy to help individual animals to cope with the perturbation, hence reducing the use of pharmaceuticals, and decreasing the level of pain of the animal.
Asunto(s)
Animales Domésticos , Drogas Veterinarias , Animales , Humanos , Bienestar del Animal , Agricultores , Dolor/veterinariaRESUMEN
Quantifying robustness of farm animals is essential before it can be implemented in breeding and management strategies. A generic modelling and data analysis procedure was developed to quantify the feed intake response of growing pigs to perturbations in terms of resistance and resilience. The objective of this study was to apply this procedure to quantify these traits in 155 pigs from an experiment where they received diets with or without cereals contaminated with the mycotoxin deoxynivalenol (DON). The experimental pigs were divided equally in a control group and three DON-challenged groups. Pigs in each of the challenged groups received a diet contaminated with DON for 7 days early on (from 113 to 119 days of age), later on (from 134 to 140 days of age) or in both periods of the experiment. Results showed that the target feed intake trajectory of each pig could be estimated independently of the challenge. The procedure also estimated relatively accurately the times when DON was given to each challenged group. Results of the quantification of the feed intake response indicated that age and previous exposure to DON have an effect on the resilience capacity of the animals. The correlation between resistance and resilience traits was modest, indicating that these are different elements of robustness. The feed intake analysis procedure proved its capacity to detect and quantify the response of animals to perturbations, and the resulting response traits can potentially be used in breeding strategies.
Asunto(s)
Alimentación Animal , Contaminación de Alimentos , Micotoxinas , Tricotecenos , Alimentación Animal/análisis , Animales , Dieta/veterinaria , Grano Comestible/química , Porcinos/crecimiento & desarrollo , Tricotecenos/análisis , Tricotecenos/toxicidadRESUMEN
Improving robustness of farm animals is one of the goals in breeding programmes. However, robustness is a complex trait and not measurable directly. The objective of this study was to quantify and characterize (elements of) robustness in growing pigs. Robustness can be analysed by examining the animal's response to perturbations. Although the origin of perturbations may not be known, their effect on animal performance can be observed, for example, through changes in voluntary feed intake. A generic model and data analysis procedure was developed (1) to estimate the target trajectory of feed intake, which is the amount of feed that a pig desires to eat when it is not facing any perturbations; (2) to detect potential perturbations, which are deviations of feed intake from the estimated target trajectory; and (3) to characterize and quantify the response of the growing pigs to the perturbations using voluntary feed intake as response criterion. The response of a pig to a perturbation is characterized by four parameters. The start and end times of the perturbation are 'imposed' by the perturbing factor, while two other parameters describe the resistance and resilience potential of the pig. One of these describes the immediate reduction in daily feed intake at the start of the perturbation (i.e., a 'resistance' trait) while another parameter describes the capacity of the pig to adapt to the perturbation through compensatory feed intake to rejoin the target trajectory of feed intake (i.e., a 'resilience' trait). The procedure has been employed successfully to identify the target trajectory of feed intake in growing pigs and to quantify the pig's response to a perturbation.