A mechanistic, dynamic model was developed to calculate body composition in growing lambs by calculating heat production (HP) internally from energy transactions within the body. The model has a fat pool (f) and three protein pools: visceral (v), nonvisceral (m), and wool (w). Heat production is calculated as the sum of fasting heat production, heat of product formation (HrE), and heat associated with feeding (HAF). Fasting heat production is represented as a function of visceral and nonvisceral protein mass. Heat associated with feeding (HAF) is calculated as ((1 - km) x MEI), where km is partial efficiency of ME use for maintenance, and MEI = metabolizable energy intake) applies at all levels above and below maintenance. The value of km derived from data where lambs were fed above maintenance was 0.7. Protein change (dp/dt) is the sum of change in the m, v, and w pools, and change in fat is equal to net energy available for gain minus dp/dt. Heat associated with a change in body composition (HrE) is calculated from the change in protein and fat with estimated partial efficiencies of energy use of 0.4 and 0.7 for protein and fat, respectively. The model allows for individuals to gain protein while losing fat or vice versa.When evaluated with independent data, the model performed better than the current Australian feeding standards () for predicting protein gain in the empty body but did not perform as well as for gain of fat and fleece-free empty body weight. Models performed similarly for predicting clean wool growth. By explicit representation of the major energy using processes in the body, and through simplification of the way body composition is computed in growing animals, the model is more transparent than current feeding systems while achieving similar performance. An advantage of this approach is that the model has the potential for wider applicability across different growth trajectories and can explicitly account for the effects of systematic changes on energy transactions, such as the effects of selective breeding, growth manipulation, or environmental changes. This paper presents a revised dynamic, mechanistic model of heat production and body composition in sheep where heat production is calculated internally as the sum of fasting heat production, heat associated with feeding, and heat from changes in bodily protein and fat pools. The resulting model reflects the variation in heat production that arises both from feed, the animal, and the intersection of these two sources, and is a simpler and more flexible way to predict energy requirements and heat production in growing ruminants than traditional feeding systems. Based on prior work by , a revised dynamic, mechanistic model was developed to improve the prediction of the composition of protein and fat in the body of growing ruminants. The revised model calculates heat production (HP) internally as a function of fasting HP, heat associated with feeding, and HP from changes in fat and protein within the body. Heat associated with product formation is calculated from changes in body protein and fat, with separate efficiencies for each, while heat associated with feeding is a constant proportion of metabolizable energy intake and applies at all levels of feeding above and below maintenance. When evaluated against novel data, the revised model performed similarly to current Australian feeding standards () Unlike the Freer model, the revised model captures variation in HP arising from feed as well as gain of protein and fat. The revised model explicitly represents protein in the body as two pools with markedly different rates of energy expenditure, improving representation of the underlying biology compared to current feeding systems. This provides a more flexible way to predict energy requirements and body composition in growing animals while achieving similar performance to current feeding systems.

Variation in nutrition is a key determinant of growth, body composition, and the ability of animals to perform to their genetic potential. Depending on the quality of feed available, animals may be able to overcome negative effects of prior nutritional restriction, increasing intake and rates of tissue gain, but full compensation may not occur. A 2 × 3 × 4 factorial serial slaughter study was conducted to examine the effects of prior nutritional restriction, dietary energy density, and supplemental rumen undegradable protein (RUP) on intake, growth, and body composition of lambs. After an initial slaughter (n = 8), 124 4-mo-old Merino cross wethers (28.4 ± 1.8 kg) were assigned to either restricted (LO, 500 g/d) or unrestricted (HI, 1500 g/d) intake of lucerne and oat pellets. After 8 wk, eight lambs/group were slaughtered and tissue weights and chemical composition were measured. Remaining lambs were randomly assigned to a factorial combination of dietary energy density (7.8, 9.2, and 10.7 MJ/kg DM) and supplemental RUP (0, 30, 60, and 90 g/d) and fed ad libitum for a 12- to 13-wk experimental period before slaughter and analysis. By week 3 of the experimental period, lambs fed the same level of energy had similar DMI (g/d) and MEI (MJ/d) (P > 0.05), regardless of prior level of nutrition. Restricted-refed (LO) lambs had higher rates of fat and protein gain than HI lambs (P < 0.05) but had similar visceral masses (P > 0.05). However, LO lambs were lighter and leaner at slaughter, with proportionally larger rumens and livers (P < 0.05). Tissue masses increased with increasing dietary energy density, as did DMI, energy and nitrogen (N) retention (% intake), and rates of protein and fat gain (P < 0.05). The liver increased proportionally with increasing dietary energy density and RUP (P < 0.05), but rumen size decreased relative to the empty body as dietary energy density increased (P < 0.05) and did not respond to RUP (P > 0.05). Fat deposition was greatest in lambs fed 60 g/d supplemental RUP (P < 0.05). However, lambs fed 90 g/d were as lean as lambs that did not receive supplement (P0, P > 0.05), with poorer nitrogen retention and proportionally heavier livers than P0 lambs (P < 0.05). In general, visceral protein was the first tissue to respond to increased intake during refeeding, followed by non-visceral protein and fat, highlighting the influence of differences in tissue response over time on animal performance and body composition.

Target protein mass at maturity is a common “attractor” used in animal models to derive components of animal growth. This target muscle protein at maturity, M*, is used as a driver of a model of animal growth and body composition with pools representing muscle and visceral protein; where viscera is heart, lungs, liver, kidneys, reticulorumen and gastrointestinal tract; and muscle is non-visceral protein. This M* term then drives changes in protein mass and heat production, based on literature data stating that heat production scales linearly with protein mass but not liveweight. This led us to adopt a modelling approach where energy utilization is directly related to protein content of the animal, and energy not lost as heat or deposited as protein is fat. To maintain continuity with existing feeding systems we estimate M* from Standard Reference Weight (SRW) as follows: M* (kJ) = SRW * SHRINK * (1-FMAT) * (MUSC) * (CPM)* 23800. Where SRW is standard reference weight (kg), SHRINK is the ratio of empty body to live weight (0.86), FMAT is proportion of fat in the empty body at maturity (0.30), MUSC is the proportion of empty body protein that is in muscle (0.85), CPM is the crude protein content of fat-free muscle at maturity (0.21), and 23800 is the energetic content (kJ) of a kilogram of crude protein. Values for SHRINK, FMAT, MUSC and CPM were derived from a synthesis of our own experimental data and the literature. For sheep, these values show M* to be: M* (kJ) = SRW * 0.86* (1-0.3) * 0.85 * 0.21 *23800 = SRW * 2557. This method allows for use of existing knowledge regarding standard reference weight and other parameters in estimating target muscle mass at maturity, as part of a model of body composition and performance in ruminants.

The nutritional effects of variation in feed supply and subsequent compensatory gain can play a significant role in cattle and beef production, due to their effects on carcass quality and feed costs. A system that predicts changes in fat and protein content of muscle and viscera in animals of different life stages and nutritional histories could therefore assist management to optimize performance and reduce costs of feed. A method has been developed to simultaneously estimate body composition and nutrient requirements of ruminants. This method estimates body composition from the difference in energy balance derived from ME intake, and heat production from ME intake and protein content of muscle and viscera, but requires information on protein content of fat free mass in viscera and non-viscera “muscle” tissue. Data from the literature was combined with unpublished data from a study conducted in growing lambs. This experiment tested the effects of ad libitum intake of diets of varying energy density and added RUP on performance and carcass composition of lambs that had been previously restricted or unrestricted prior to a 12–13 week finishing phase, and both carcass and viscera components were chemically analyzed on an individual basis. On a fat-free basis, muscle crude protein averaged 20.8%; this value is in agreement with literature values, which lie between the range of 20–24% and which do not appear to vary substantially with age in postweaning sheep. In the same sheep, crude protein content of viscera averaged 15.6% on a fat-free basis; while data on visceral composition in the literature is limited, these values are within the range of available data. This data is use to parameterize functions describing growth of viscera and changes in heat production over time. This contributes to the method we have developed to estimate nutritional effects on body composition.

Long-term effects of dietary supplements on productivity, economics, and greenhouse gas (GHG) emissions of 2 beef enterprises were simulated, using AusBeef integrated with AusFarm (R), across 30 years: Enterprise 1. Angus steers (1.5 head/ha) in New South Wales, Australia, grazing for 238 days/year, and Enterprise 2. British x Charolais steers (1.0 head/ha) in California, USA, grazing for 148 days/year. Simulation effects of 3 supplements with potential to reduce enteric methane (CH4) emissions were evaluated: (1) nitrate (NO3-), (2) lipid, and (3) NO3- + lipid. All supplementation effects were evaluated against a baseline simulation (i.e., no supplement). Results on beef production, rumen products, GHG emissions, and enterprise gross margins are reported. Simulations indicated that supplementing steers with lipid alone relative to the baseline in Enterprises 1 and 2: increased final live weight (LW) by 68 and 25 kg, decreased emissions intensity (EI) by 69 and 49 g CH4/kg live weight gain (LWG), and decreased total GHG by 0.08 and 0.04 t CO2-e/ha/year, respectively. Supplementing steers with NO3- + lipid relative to the baseline: increased final LW by 70 and 30 kg, decreased EI by 89 and 77 g CH4/kg LWG, and decreased total GHG by 0.27 and 0.12 t CO2-e/ha/year for Enterprises 1 and 2 respectively. The most profitable mitigation strategy, across all years, for Enterprise 1 was the lipid supplement with a median gross margin of $AUD753/ha and for Enterprise 2 was the NO3-+ lipid supplement with a median gross margin of $AUD224/ha. The NO3- supplement alone was the least preferred option across both enterprises, consistently delivering lower returns than other options across the entire probability range. The results indicate the potential economic benefit of lipid supplementation, either alone or in combination with NO3-, as GHG mitigation strategies that increase profitability and inhibit methanogenesis for beef production across diverse environments.

The present study evaluated the behaviour of the AusBeef model for beef production as part of a 2 x 2 study simulating performance on forage-based and concentrate-based diets from Oceania and North America for four methane (CH4)-relevant outputs of interest. Three sensitivity analysis methods, one local and two global, were conducted. Different patterns of sensitivity were observed between forage-based and concentrate-based diets, but patterns were consistent within diet types. For the local analysis, 36, 196, 47 and 8 out of 305 model parameters had normalized sensitivities of 0, >0, >0.01 and >0.1 across all diets and outputs, respectively. No parameters had a normalized local sensitivity >1 across all diets and outputs. However, daily CH4 production had the greatest number of parameters with normalized local sensitivities >1 for each individual diet. Parameters that were highly sensitive for global and local analyses across the range of diets and outputs examined included terms involved in microbial growth, volatile fatty acid (VFA) yields, maximum absorption rates and their inhibition due to pH effects and particle exit rates. Global sensitivity analysis I showed the high sensitivity of forage-based diets to lipid entering the rumen, which may be a result of the use of a feedlot-optimized model to represent high-forage diets and warrants further investigation. Global sensitivity analysis II showed that when all parameter values were simultaneously varied within +/- 10% of initial value, >96% of output values were within +/- 20% of the baseline, which decreased to >50% when parameter value boundaries were expanded to +/- 25% of their original values, giving a range for robustness of model outputs with regards to potential different 'true' parameter values. There were output-specific differences in sensitivity, where outputs that had greater maximum local sensitivities displayed greater degrees of non-linear interaction in global sensitivity analysis I and less variance in output values for global sensitivity analysis II. For outputs with less interaction, such as the acetate : propionate ratio and microbial protein production, the single most sensitive term in global sensitivity analysis I contributed more to the overall total-order sensitivity than for outputs with more interaction, with an average of 49, 33, 15 and 14% of total-order sensitivity for microbial protein production, acetate : propionate ratio, CH4 production and energy from absorbed VFAs, respectively. Future studies should include data collection for highly sensitive parameters reported in the present study to improve overall model accuracy.

SUMMARYAs demand for animal products, such as meat and milk, increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. The objectives of the present study were to describe the representation of processes for growth and enteric methane (CH4) production in AusBeef, a whole-animal, dynamic, mechanistic model for beef production; evaluate AusBeef for its ability to predict daily methane production (DMP, g/day), gross energy intake (GEI, MJ/day) and methane yield (MJ CH4/MJ GEI) using an independent data set; and to compare AusBeef estimates to those from the empirical equations featured in the current National Academies of Sciences, Engineering and Medicine (NASEM, 2016) beef cattle requirements for growth and the Ruminant Nutrition System (RNS), a dynamic, mechanistic model of Tedeschi & Fox, 2016. AusBeef incorporates a unique fermentation stoichiometry that represents four microbial groups: protozoa, amylolytic bacteria, cellulolytic bacteria and lactate-utilizing bacteria. AusBeef also accounts for the effects of ruminal pH on microbial degradation of feed particles. Methane emissions are calculated from net ruminal hydrogen balance, which is defined as the difference between inputs from fermentation and outputs due to microbial use and biohydrogenation. AusBeef performed similarly to the NASEM empirical model in terms of prediction accuracy and error decomposition, and with less root mean square predicted error (RMSPE) than the RNS mechanistic model when expressed as a percentage of the observed mean (RMSPE, %), and the majority of error was non-systematic. For DMP, RMSPE for AusBeef, NASEM and RNS were 24·0, 19·8 and 50·0 g/day for the full data set (n = 35); 25·6, 18·2 and 56·2 g/day for forage diets (n = 19); and 21·8, 21·5 and 41·5 g/day for mixed diets (n = 16), respectively. Concordance correlation coefficients (CCC) were highest for GEI, with all models having CCC > 0·66, and higher CCC for forage diets than mixed, while CCC were lowest for MY, particularly forage diets. Systematic error increased for all models on forage diets, largely due to an increase in error due to mean bias, and while all models performed well for mixed diets, further refinements are required to improve the prediction of CH4 on forage diets.

Role-based access control (RBAC) has gained popularity for the expression of access constraints in information systems but existing RBAC models do not provide sufficient expressive power to support the kinds of fine-grained access control often required. In this paper, we define a new formal model for RBAC which supports controlled role appointment and transitions, object attributes analogous to subject roles and a transitive role/attribute derivation relationship.

In this paper, we describe a formal specification language (RASP) for expressing fine-grained access control constraints in information systems. The design of the language is motivated by two HIS case studies which demonstrate the complexity of the access constraints which arise if minimal (need-to-know) access is to be strictly enforced. RASP supports modularity, parameterization, role acquisition, constraint expressions and a symmetrical approach to role transitions and attribute transitions. No existing access control specification language supports all of these complex, realistic requirements.

In this paper we describe the formal specification language RASP for expressing fine-grained access control constraints in information systems. The design of the language is motivated by a number of IS case studies which demonstrate the complexity of the access constraints which arise if minimal (need-to-know) access is to be strictly enforced. RASP supports modularity, parameterization, role acquisition, constraint expressions and a symmetrical approach to role transitions and attribute transitions. No existing access control specification language supports all of these complex, realistic requirements.