Symposium review: Fueling appetite: Nutrient metabolism and the control of feed intake

Conceptual models developed over the past century describe 2 key constraints to feed intake (FI) of healthy animals: gut capacity and metabolic demand. Evidence that greater energy demands (e.g., greater milk production) drive a corresponding increase in caloric intake led to the dominant concept that animals "eat to energy requirements." Although this model provides reasonable initial estimates of FI, it lacks a proposed physiological basis for the control system, does not consider nutrient constraints beyond energy, and fails to explain differential energy intake responses to different fuels. To address these gaps, research has focused on mechanisms for sensing nutrient availability and providing feedback to hypothalamic centers that integrate signals to control feeding behavior. The elimination of FI response to certain nutrients by vagotomy suggests that peripheral tissues play a role in nutrient sensing. These findings and the central role of the liver in metabolic flux led to the development of the hepatic oxidation theory (HOT). According to the HOT, liver energy charge is the regulated variable that induces dietary intake changes and consequently affects whole-body energy balance. Evidence in support of HOT includes associations between hepatic energy charge and meal patterns, increased FI in response to phosphate trapping, and reduced FI in response to phosphate loading. In accordance with the HOT, infusion studies in dairy cattle have consistently demonstrated that providing fuels that either oxidize or stimulate oxidation in the liver decreases FI and energy intake to a greater extent than fuels that bypass the liver. Importantly, this holds true for glucose, which is readily oxidized by nerve cells, but is rarely taken up by the bovine liver. Although the brain integrates multiple signals including those related to gastric distention and illness, the HOT provides a physiological framework for understanding the dominant role the liver likely plays in sensing short-term energy status. Understanding this model provides insights into how to use or bypass the regulatory system to manage FI of animals.

Predicting feed intake using modelling based on feeding behaviour in finishing beef steers

Current techniques for measuring feed intake in housed cattle are both expensive and time-consuming making them unsuitable for use on commercial farms. Estimates of individual animal intake are required for assessing production efficiency. The aim of this study was to predict individual animal intake using parameters that can be easily obtained on commercial farms including feeding behaviour, liveweight and age. In total, 80 steers were used, and each steer was allocated to one of two diets (40 per diet) which consisted of (g/kg; DM) forage to concentrate ratios of either 494:506 (MIXED) or 80:920 (CONC). Individual daily fresh weight intakes (FWI; kg/day) were recorded for each animal using 32 electronic feeders over a 56-day period, and individual DM intakes (DMI; kg/day) subsequently calculated. Individual feeding behaviour variables were calculated for each day of the measurement period from the electronic feeders and included: total number of visits to the feeder, total time spent at the feeder (TOTFEEDTIME), total time where feed was consumed (TIMEWITHFEED) and average length of time during each visit to the feeder. These feeding behaviour variables were chosen due to ease of obtaining from accelerometers. Four modelling techniques to predict individual animal intake were examined, based on (i) individual animal TOTFEEDTIME relative expressed as a proportion of the dietary group (GRP) and total GRP intake, (ii) multiple linear regression (REG) (iii) random forests (RF) and (iv) support vector regressor (SVR). Each model was used to predict CONC and MIXED diets separately, giving eight prediction models, (i) GRP_CONC, (ii) GRP_MIXED, (iii) REG_CONC, (iv) REG_MIXED, (v) RF_CONC, (vi) RF_MIXED, (vii) SVR_CONC and (viii) SVR_MIXED. Each model was tested on FWI and DMI. Model performance was assessed using repeated measures correlations (R²_RM) to capture the repeated nature of daily intakes compared with standard R², RMSE and mean absolute error (MAE). REG, RF and SVR models predicted FWI with R²_RM = 0.1–0.36, RMSE = 1.51–2.96 kg and MAE = 1.19–2.49 kg, and DMI with R²_RM = 0.13–0.19, RMSE = 1.15–1.61 kg and MAE = 0.9–1.28 kg. The GRP models predicted FWI with R²_RM = 0.42–0.49, RMSE = 2.76–3.88 kg and MAE = 2.46–3.47 kg, and DMI with R²_RM = 0.32–0.44, RMSE = 0.32–0.44 kg, MAE = 1.55–2.22 kg. Whilst more simplistic GRP models showed higher R²_RM than regression and machine learning techniques, these models had larger errors, likely due to individual feeding patterns not being captured. Although regression and machine learning techniques produced lower errors associated with individual intakes, overall precision of prediction was too low for practical use.

Characterization of feeding behavior traits in steers with divergent residual feed intake consuming a high-concentrate diet

The objective of this study was to examine the differences in feeding behavior patterns of steers with divergent phenotypes for residual feed intake (RFI). Three trials were conducted with 508 Angus-based composite crossbred steers (body weight [BW] = 309 ± 57 kg) fed a high-concentrate diet in pens equipped with electronic feed bunks (GrowSafe System). Initial and final carcass ultrasound measurements (intra-muscular fat, backfat depth, and rib-eye area) were collected on days 0 and 70, and BW measured at 14-d intervals. Individual dry matter intake (DMI) and feeding behavior traits were collected for 70 d, and RFI calculated as the residual from the regression of DMI on average daily gain (ADG) and mid-test BW0.75. Steers were ranked by RFI and assigned to low-, medium-, and high-RFI classes based on ± 0.5 SD from the mean RFI within the trial. The feeding behavior traits evaluated in this study included frequency and duration of bunk visit (BV) and meal events, head-down (HD) duration, mean meal length, time-to-bunk interval, the maximum nonfeeding interval, and the day-to-day variation of these traits, defined as the root mean squared error (RMSE) from linear regression of each trait on the day of trial. Additionally, three ratio traits were evaluated: BV events per meal, HD duration per BV event, and HD duration per meal event. Low-RFI (feed-efficient) steers consumed 16% less (P < 0.01) DMI, while BW and ADG were not different compared with high-RFI steers. Low-RFI steers had 18% fewer and 21% shorter (P < 0.01) BV events, and 11% fewer and 13% shorter (P < 0.01) meal events per day compared with high-RFI steers. Furthermore, low-RFI steers exhibited less (P < 0.05) day-to-day variance in DMI, as well as in frequency and duration of BV and meal events and HD duration compared with high-RFI steers. Differences in feeding behavior traits due to RFI were minimally affected by covariate adjustment for DMI, indicating that steers with divergent RFI have distinct feeding behavior patterns that are largely independent of differences in DMI. These results suggest that feeding behavior traits may be useful biomarkers for the prediction of feed efficiency in beef cattle.

A comparison of two rotational stocking strategies on the foraging behaviour and herbage intake by grazing sheep

An understanding of the processes involved in grazing behaviour is a prerequisite for the design of efficient grassland management systems. The purpose of managing the grazing process is to identify sward structures that can maximize animal forage daily intake and optimize grazing time. Our aim was to evaluate the effect of different grazing management strategies on foraging behaviour and herbage intake by sheep grazing Italian ryegrass under rotational stocking. The experiment was carried out in 2015 in southern Brazil. The experimental design was a randomized complete block with two grazing management strategies and four replicates. The grazing management treatments were a traditional rotational stocking (RT), with pre- and post-grazing sward heights of 25 and 5 cm, respectively, and a 'Rotatinuous' stocking (RN) with pre- and post-grazing sward heights of 18 and 11 cm, respectively. Male sheep with an average live weight of 32 ± 2.3 kg were used. As intended, the pre- and post-grazing sward heights were according to the treatments. The pre-grazing leaf/stem ratio of the Italian ryegrass pasture did not differ between treatments (P > 0.05) (~2.87), but the post-grazing leaf/stem ratio was greater (P < 0.001) in the RN than in the RT treatment (1.59 and 0.76, respectively). The percentage of the non-grazed area was greater (P < 0.01) in post-grazing for RN compared with RT treatment, with an average of 29.7% and 3.49%, respectively. Herbage nutritive value was greater for the RN than for the RT treatment, with greater CP and lower ADF and NDF contents. The total time spent grazing, ruminating and resting did not differ between treatments (P > 0.05), with averages of 439, 167 and 85 min, respectively. The bite rate, feeding stations per min and steps per min by sheep were greater (P < 0.05) in the RN than in the RT treatment. The grazing time per hour and the bite rate were greater (P < 0.05) in the afternoon than in the morning in both treatments. The daily herbage intake by sheep grazing Italian ryegrass was greater (P < 0.05) in the RN than in the RT treatment (843.7 and 707.8 g organic matter/sheep, respectively). Our study supports the idea that even though the grazing time was not affected by the grazing management strategies when the animal behaviour responses drive management targets, such as in 'Rotatinuous' stocking, the sheep herbage intake is maximized, and the grazing time is optimized.

Animal responses to environmental variation: physiological mechanisms in ecological models of performance in deer (Cervidae)

Context Proper assessment of the consequences of environmental variation on animals depends on our ability to predict how they will perform under different circumstances. This requires two kinds of information. We need to know which environmental factors influence animal performance and their mode of action, i.e. whether a given factor acts alone or through interaction with other factors, directly or indirectly, instantaneously or after a delay and so on. This essentially correlative process falls within the domain of ecology. We also need to know what determines the direction, amplitude and limits of animal responses to environmental variation and change. This essentially experimental process falls within the domain of physiology. Physiological mechanisms are frequently poorly integrated within the correlative framework of ecological models. This is evident where programmed responses are attributed to environmental forcing and where the effect of environmental factors is evaluated without reference to the physiological state and regulatory capacity of the animal on which they act. Aims Here we examine ways in which the impacts of external (environmental) stimuli and constraints on performance are moderated by the animals (deer) on which they impinge. Key results The analysis shows (1) how trade-offs in foraging behaviour, illustrated by the timing of activity under the threat of predation, are modulated by integration of short-term metabolic feedback and animal emotions that influence the motivation to feed, (2) how the influence of thermal and nutritional challenges on performance, illustrated by the effect of weather conditions during gestation on the body mass of reindeer (Rangifer tarandus) calves at weaning, depends on the metabolic state of the female at the time the challenge occurs and (3) how annual cycles of growth, appetite and reproduction in seasonal species of deer are governed by innate circannual timers, such that their responses to seasonal changes in food supply are anticipatory and governed by rheostatic systems that adjust homeostatic set- points, rather than being purely reactive. Conclusions Concepts like ‘maintenance’ and ‘energy balance’, which were originally derived from non-seasonal domestic ruminants, are unable to account for annual cycles in metabolic and nutritional status in seasonal deer. Contrasting seasonal phenotypes (fat and anoestrous in summer, lean and oestrous in winter) represent adaptive solutions to the predictable challenges presented by contrasting seasonal environments, not failure of homeostasis in one season and its success in another. Implications The analysis and interpretation of responses to environment in terms of interaction between the external stimuli and the internal systems that govern them offer a more comprehensive, multifaceted understanding of the influence of environmental variation on performance in deer and open lines of ecological enquiry defined by non-intuitive aspects of animal function.

The assessment of supplementation requirements of grazing ruminants using nutrition models

This paper was aimed to summarize known concepts needed to comprehend the intricate interface between the ruminant animal and the pasture when predicting animal performance, acknowledge current efforts in the mathematical modeling domain of grazing ruminants, and highlight current thinking and technologies that can guide the development of advanced mathematical modeling tools for grazing ruminants. The scientific knowledge of factors that affect intake of ruminants is broad and rich, and decision-support tools (DST) for modeling energy expenditure and feed intake of grazing animals abound in the literature but the adequate predictability of forage intake is still lacking, remaining a major challenge that has been deceiving at times. Despite the mathematical advancements in translating experimental research of grazing ruminants into DST, numerous shortages have been identified in current models designed to predict intake of forages by grazing ruminants. Many of which are mechanistic models that rely heavily on preceding mathematical constructions that were developed to predict energy and nutrient requirements and feed intake of confined animals. The data collection of grazing (forage selection, grazing behavior, pasture growth/regrowth, pasture quality) and animal (nutrient digestion and absorption, volatile fatty acids production and profile, energy requirement) components remains a critical bottleneck for adequate modeling of forage intake by ruminants. An unresolved question that has impeded DST is how to assess the quantity and quality, ideally simultaneously, of pasture forages given that ruminant animals can be selective. The inadequate assessment of quantity and quality has been a hindrance in assessing energy expenditure of grazing animals for physical activities such as walking, grazing, and forage selection of grazing animals. The advancement of sensors might provide some insights that will likely enhance our understanding and assist in determining key variables that control forage intake and animal activity. Sensors might provide additional insights to improve the quantification of individual animal variation as the sensor data are collected on each subject over time. As a group of scientists, however, despite many obstacles in animal and forage science research, we have thrived, and progress has been made. The scientific community may need to change the angle of which the problem has been attacked, and focus more on holistic approaches.

Grazing management: setting the table, designing the menu and influencing the diner

Pastoral livestock-production systems are under increasing environmental, social and consumer pressures to reduce environmental impacts and to enhance biodiversity and animal welfare. At the same time, farmers face the challenge of managing grazing, which is intimately linked with profitability. Recent advances in understanding grazing patterns and nutritional ecology may help alleviate such pressures. For instance, by managing grazing to (1) manipulate links between ingestive–digestive decisions and temporal patterns of nutrient excretion, (2) provide phytochemically diverse diets at appropriate temporal (the menu) and spatial (the table) scales and (3) influence the behaviour of animals (the diners) on the basis of their specific ‘personalities’ and needs, to overcome or enhance animal differences, thereby enhancing their and farm productivity and welfare, as well as our health. Under pastoral systems, synergies between animals’ and farmers’ grazing decisions have the potential to offer greater benefits to the animal, the environment and the farm than does simple and parsimonious grazing management based on a single component of the system. In the present review, we look at grazing and its management through an alternate lens, drawing ideas and hypotheses to stimulate thinking, dialogue and discussions that we anticipate will evolve into innovative research programs and grazing strategies. To do so, we combined experimental and observational studies from a wide range of disciplines with simulation-modelling exercises. We envisage a more holistic approach to manage grazing based on recent advances in the understanding of the nutritional ecology of grazing animals, and propose management practices that may enable pastoral livestock-production systems to evolve continually as complex creative systems.

Behavioral Timing without Clockwork: Photoperiod-Dependent Trade-Off between Predation Hazard and Energy Balance in an Arctic Ungulate

Occurrence of 24-h rhythms in species apparently lacking functional molecular clockwork indicates that strong circadian mechanisms are not essential prerequisites of robust timing, and that rhythmical patterns may arise instead as passive responses to periodically changing environmental stimuli. Thus, in a new synthesis of grazing in a ruminant (MINDY), crepuscular peaks of activity emerge from interactions between internal and external stimuli that influence motivation to feed, and the influence of the light/dark cycle is mediated through the effect of low nocturnal levels of food intake on gastric function. Drawing on risk allocation theory, we hypothesized that the timing of behavior in ruminants is influenced by the independent effects of light on motivation to feed and perceived risk of predation. We predicted that the antithetical relationship between these 2 drivers would vary with photoperiod, resulting in a systematic shift in the phase of activity relative to the solar cycle across the year. This prediction was formalized in a model in which phase of activity emerges from a photoperiod-dependent trade-off between food and safety. We tested this model using data on the temporal pattern of activity in reindeer/caribou Rangifer tarandus free-living at natural mountain pasture in sub-Arctic Norway. The resulting nonlinear relationship between the phasing of crepuscular activity and photoperiod, consistent with the model, suggests a mechanism for behavioral timing that is independent of the core circadian system. We anticipate that such timing depends on integration of metabolic feedback from the digestive system and the activity of the glucocorticoid axis which modulates the behavioral responses of the animal to environmental hazard. The hypothalamus is the obvious neural substrate to achieve this integration.

Effect of time of maize silage supplementation on herbage intake, milk production, and nitrogen excretion of grazing dairy cows

The objective of this study was to evaluate the effect of feeding maize silage at different times before a short grazing bout on dry matter (DM) intake, milk production, and N excretion of dairy cows. Thirty-six Friesian × Jersey crossbred lactating dairy cows were blocked in 9groups of 4 cows by milk solids (sum of protein and fat) production (1.26±0.25kg/d), body weight (466±65kg), body condition score (4±0.48), and days in milk (197±15). Groups were then randomly assigned to 1 of 3 replicates of 3 treatments: control; herbage only, supplemented with 3kg of DM/cow of maize silage after morning milking approximately 9h before pasture allocation (9BH); and supplemented with 3kg of DM/cow of maize silage before afternoon milking approximately 2h before pasture allocation (2BH). Herbage allowance (above the ground level) was 22kg of DM/cow per day for all groups of cows. Cows were allocated to pasture from 1530 to 2030 h. Maize silage DM intake did not differ between treatments, averaging 3kg of DM/cow per day. Herbage DM intake was greater for control than 2BH and 9BH, and greater for 9BH than 2BH (11.1, 10.1, and 10.9kg of DM/cow per day for control, 2BH, and 9BH, respectively). The substitution rate (kilograms of herbage DM per kilograms of maize silage DM) was greater for 2BH (0.47) than 9BH (0.19). Milk solids production was similar between treatments (overall mean 1.2kg/cow per day). Body weight loss tended to be less for supplemented than control cows (-0.95, -0.44, and -0.58kg/cow per day for control, 2BH, and 9BH, respectively). Nitrogen concentration in urine was not affected by supplementation or time of supplementation, but estimated urinary N excretion tended to be greater for control than supplemented cows when urinary N excretion estimated using plasma or milk urea N. At the time of herbage meal, nonesterified fatty acid concentration was greater for control than supplemented cows and greater for 9BH than 2BH (0.58, 0.14, and 0.26mmol/L for control, 2BH, and 9BH, respectively). Timing of maize silage supplementation relative to a short and intensive herbage meal can reduce the substitution rate and increase herbage DM intake of grazing dairy cows.

Managing the herbage utilisation and intake by cattle grazing rangelands

Rangelands throughout the world provide clean water, fix solar energy in plants, sequester carbon, and offer recreational opportunities, with other ecosystem goods and services, including food from wild and domestic herbivores. Grazing rangelands with cattle requires constant management to balance the economic sustainability of the farm with other ecological services that rangelands provide. The challenges in management arise from the diversity of the rangeland forage resources at extremely large spatial and temporal scales. To be able to predict the performance of cattle grazing in extensive rangeland environments, estimating herbage intake is paramount because it quantifies energy intake and performance. Nutrient demand is the major driver of herbage intake, and characteristics of the sward and terrain of the landscape dictate how this demand is met. System models that integrate changes in weather patterns and herbage over long periods of time will allow farmers and scientist to monitor changes in herbage mass and utilisation. Dynamic models that include herbage growth components sensitive to weather patterns and animal demands are needed to predict how long-term changes in beef herd management will affect performance and range condition. Vegetation indexes captured across biomes with satellites can accurately quantify the dynamics of aboveground net primary production and changes in nutritional value with confidence. The computer software, PCRANCH, is a program for simulating cow–calf herd dynamics over long periods of time. The models within the PCRANCH software can simulate herbage growth and animal utilisation at large spatial and temporal scales needed for rangeland management and allow ranchers to evaluate the impacts of management on other ecological services. Knowing the long-term impact of management changes on swards enable ranchers to anticipate the ecological and economic benefits of improvements or demonstrate a protection of current ecological services.

Continuous bite monitoring: a method to assess the foraging dynamics of herbivores in natural grazing conditions

Accurate estimates of bite mass and variations in the short-term intake rate of grazing herbivores has been historically considered as a fundamental methodological difficulty, a difficulty that increases with the complexity of the feeding environment. Improving these methodologies will help understand foraging behaviours in natural grazing conditions, where habitat structure and interactions among different forages influence feeding decisions and patterns. During the past 30 years, we have been developing the ‘continuous bite-monitoring’ method, an observational method that allows continuous assessment of foraging behaviours, including bite mass, instantaneous intake rate and food selection, in simple to complex feeding environments. The centrepiece of the method is a ‘bite-coding grid’ where bites are categorised by structural attributes of the forage to reflect differences in bite masses. Over the years, we have been using this method with goats, sheep, llamas and cattle across a range of different habitats. After reviewing the development of the method, we detail its planning and execution in the field. We illustrate the method with a study from southern Brazilian native Pampa grassland, showing how changes in the forages consumed by heifers strongly affect short-term intake rate during meals. Finally, we emphasise the importance of studying animals grazing in their natural environments to first identify the relevant processes that can later be tested in controlled experiments.

Synthesis: foraging decisions link plants, herbivores and human beings

Herbivores make decisions about where to forage and what combinations and sequences of foods to eat, integrating influences that span generations, with choices manifest daily within a lifetime. These influences begin in utero and early in life; they emerge daily from interactions among internal needs and contexts unique to biophysical and social environments; and they link the cells of plants with the palates of herbivores and humans. This synthesis summarises papers in the special issue of Animal Production Science that explore emerging understanding of these dynamics, and suggests implications for future research that can help people manage livestock for the benefit of landscapes and people by addressing (1) how primary and secondary compounds in plants interact physiologically with cells and organs in animals to influence food selection, (2) temporal and spatial patterns of foraging behaviours that emerge from these interactions in the form of meal dynamics across landscapes, (3) ways humans can manage foraging behaviours and the dynamics of meals for ecological, economic and social benefits, and (4) models of foraging behaviour that integrate the aforementioned influences.