The relationship between meal composition and long-term diet choice

Technical note: Evaluation of bimodal distribution models to determine meal criterion in heifers fed a high-grain diet

Meals are clusters of feedbunk visit (BV) events that are differentiated from the next meal by a nonfeeding interval that is longer compared with the nonfeeding intervals within a meal. The longest nonfeeding interval considered to be part of a meal is defined as the meal criterion. The objective of this study was to determine which combination of 2 probability density functions [(PDF): Gaussian normal (G), Weibull (W), Log-Normal, Gamma, and Gumbel] used in a bimodal distribution model had the best fit of nonfeeding interval data collected in beef heifers. Feeding behavior traits (572,627 total BV events) were measured in 119 heifers fed a high-grain diet (3.08 Mcal ME/kg DM), using a GrowSafe system for 66 d. The frequency and duration of BV events averaged 75 ± 15 events/d and 73.0 ± 22.3 min/d, respectively. The bimodal PDF combinations were fitted to the log(10)-transformed interval lengths between BV events for each animal, using R mixdist package (2.13). The Akaike Information Criterion (AIC) was used to assess goodness of fit of the 25 bimodal PDF combinations. The PDF model with the least AIC value was selected as the best fit for each individual. A χ(2) analysis of the selected best PDF distribution across individuals revealed that 78.2% of the heifers best fit were G-W or W-W PDF models. The likelihood probability estimates were calculated from the average AIC deviation of each model from the standard G-G model. The G-W likelihood probability estimate was greater (P = 0.001) than the W-W combination (0.997 vs. 0.727). Our analysis indicated the G-W model had a statistically better fit and is most likely the best approach to define meal criterion in beef heifers fed high-grain diets.

The temporal structure of feeding behavior

Meals have long been considered relevant units of feeding behavior. Large data sets of feeding behavior of cattle, pigs, chickens, ducks, turkeys, dolphins, and rats were analyzed with the aims of 1) describing the temporal structure of feeding behavior and 2) developing appropriate methods for estimating meal criteria. Longer (between-meal) intervals were never distributed as the negative exponential assumed by traditional methods, such as log-survivorship analysis, but as a skewed Gaussian, which can be (almost) normalized by log-transformation of interval lengths. Log-transformation can also normalize frequency distributions of within-meal intervals. Meal criteria, i.e., the longest interval considered to occur within meals, can be estimated after fitting models consisting of Gaussian functions alone or of one Weibull and one or more Gaussian functions to the distribution of log-transformed interval lengths. Nonuniform data sets may require disaggregation before this can be achieved. Observations from all species were in conflict with assumptions of random behavior that underlie traditional methods for criteria estimation. Instead, the observed structure of feeding behavior is consistent with 1) a decrease in satiety associated with an increase in the probability of animals starting a meal with time since the last meal and 2) an increase in satiation associated with an increase in the probability of animals ending a meal with the amount of food already consumed. The novel methodology proposed here will avoid biased conclusions from analyses of feeding behavior associated with previous methods and, as demonstrated, can be applied across a range of species to address questions relevant to the control of food intake.

A personal view of how ruminant animals control their intake and choice of food: minimal total discomfort

Voluntary food intake and the selection between foods are important subjects especially in ruminants in view of the economic importance of this class of animal and the complex digestive system with its attendant metabolic peculiarities. There is evidence that intake is limited by the capacity of the rumen as well as by metabolic factors; some theories assume that intake is controlled by the first limiting factor but this is not satisfying on physiological grounds and there is evidence that signals from feedback factors are integrated in an additive manner. It is now well established from research in which animals are given the chance to learn the metabolic consequences of eating food with a particular sensory profile, including a choice of foods, that animals including ruminants can adjust their diet, both quantitatively and qualitatively, to their nutrient requirements. It is proposed that they do this in order to minimise the total of the discomfort generated by the several signals from various body systems. The learning process is aided by the considerable day-to-day variation often seen in the intake of individual animals. An optimisation model is proposed and presented in a simple form, involving the addition of discomforts (calculated as the square of the deviation of the supply of metabolisable energy, crude protein and neutral-detergent fibre) and iterative elucidation of the intake at which total discomfort is minimal. With parameters appropriate for growing lambs the model provides reasonable agreement with observations, both in terms of daily intake and selection between foods of different protein contents. Manipulation of food composition and of nutrient requirements produces predictions broadly in agreement with reality except that protein deficiency has less severe consequences for the model than for real animals; it is proposed that protein deficiency be given more weighting than protein excess, and this may be true for other resources as well. This model is proposed as a philosophy and a starting point for further development and is not purveyed as a complete, working model. It nevertheless provides support for the concept of total minimal discomfort as a suitable base from which to view the control of intake and selection in all animals.