A. Bannink

Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations.

Equations to describe gas production profiles, obtained using manual or automated systems for in vitro fermentation of ruminant feeds, were derived from first principles by considering a simple three-pool scheme. The pools represented were the potentially degradable and undegradable feed fractions, and accumulated gases. The equations derived and investigated mathematically were the generalized Mitscherlich, generalized Michaelis-Menten, Gompertz, and logistic. They were obtained by allowing the fractional rate of degradation to vary with time. The equations permit the extent of ruminal degradation (hence the supply of microbial protein to the duodenum) to be evaluated, thus linking the gas production technique to animal production.

Aspects of rumen microbiology central to mechanistic modelling of methane production in cattle

Methane, in addition to being a significant source of energy loss to the animal that can range from 0·02 to 0·12 of gross energy intake, is one of the major greenhouse gases being targeted for reduction by the Kyoto protocol. Thus, one of the focuses of recent research in animal science has been to develop or improve existing methane prediction models in order to increase overall understanding of the system and to evaluate mitigation strategies for methane reduction. Several dynamic mechanistic models of rumen function have been developed which contain hydrogen gas balance sub-models from which methane production can be predicted. These models predict methane production with varying levels of success and in many cases could benefit from further development. Central to methane prediction is accurate volatile fatty acid prediction, representation of the competition for substrate usage within the rumen, as well as descriptions of protozoal dynamics and pH. Most methane models could also largely benefit from an expanded description of lipid metabolism and hindgut fermentation. The purpose of the current review is to identify key aspects of rumen microbiology that could be incorporated into, or have improved representation within, a model of ruminant digestion and environmental emissions.

Algorithms Determining Ammonia Emission from Buildings Housing Cattle and Pigs and from Manure Stores

Livestock excreta and manure stored in housing, in manure stores, in beef feedlots, or cattle hardstandings are the most important sources of ammonia (NH3) in the atmosphere. There is a need to quantify the emission, to assess the effect of emission on NH3 and ammonium (NH4+) deposition to ecosystems and on the health risks posed by NH4+-based particles in the air. To obtain a reliable estimate of the emission from these sources, the processes involved in the transfer of NH3 from the manure to the free atmosphere have to be described precisely. A detailed knowledge of the processes of NH3 transfer from the manure and transport to the free atmosphere will contribute to development of techniques and housing designs that will contribute to the reduction of NH3 emission to the atmosphere. For this reason, this review presents the processes and algorithms involved in NH3 emission from livestock manure in livestock buildings and manure stores for pigs and cattle. Emission from poultry buildings and following land application of manure, although significant sources of NH3, have been reported in earlier reviews and are not included here. A clear description of the features that contribute to the total NH3 emission from buildings will include information on stock class, diet and excreta composition, the distribution of emitting surfaces and knowledge of their mass transfer characteristics in relation to the building as a whole, as well as environmental variables. Other relevant information includes the quantity and composition of excreta produced by different classes of livestock and the influence of feeding regime; the influence of environmental variables on the production of NH3 from excreta; how excreta is distributed and managed in livestock buildings; and factors that affect mass transfer of NH3 in the building to the atmosphere outside. A major factor is the pH of the manure. There is a great need for algorithms that can predict pH as affected by feeding and management. This chapter brings together published estimates of NH3 emissions and abatement techniques, and relates these to the factors listed above (excreta, NH3 production, building, and mass transfer).

Genetic parameters for predicted methane production and potential for reducing enteric emissions through genomic selection.

Mitigation of enteric methane (CH₄) emission in ruminants has become an important area of research because accumulation of CH₄ is linked to global warming. Nutritional and microbial opportunities to reduce CH₄ emissions have been extensively researched, but little is known about using natural variation to breed animals with lower CH₄ yield. Measuring CH₄ emission rates directly from animals is difficult and hinders direct selection on reduced CH₄ emission. However, improvements can be made through selection on associated traits (e.g., residual feed intake, RFI) or through selection on CH₄ predicted from feed intake and diet composition. The objective was to establish phenotypic and genetic variation in predicted CH₄ output, and to determine the potential of genetics to reduce methane emissions in dairy cattle. Experimental data were used and records on daily feed intake, weekly body weights, and weekly milk production were available from 548 heifers. Residual feed intake (MJ/d) is the difference between net energy intake and calculated net energy requirements for maintenance as a function of body weight and for fat- and protein-corrected milk production. Predicted methane emission (PME; g/d) is 6% of gross energy intake (Intergovernmental Panel on Climate Change methodology) corrected for energy content of methane (55.65 kJ/g). The estimated heritabilities for PME and RFI were 0.35 and 0.40, respectively. The positive genetic correlation between RFI and PME indicated that cows with lower RFI have lower PME (estimates ranging from 0.18 to 0.84). Hence, it is possible to decrease the methane production of a cow by selecting more-efficient cows, and the genetic variation suggests that reductions in the order of 11 to 26% in 10 yr are theoretically possible, and could be even higher in a genomic selection program. However, several uncertainties are discussed; for example, the lack of true methane measurements (and the key assumption that methane produced per unit feed is not affected by RFI level), as well as the limitations of predicting the biological consequences of selection. To overcome these limitations, an international effort is required to bring together data on feed intake and methane emissions of dairy cows.

Diet effects on urine composition of cattle and N 2 O emissions

Ruminant production contributes to emissions of nitrogen (N) to the environment, principally ammonia (NH3), nitrous oxide (N2O) and di-nitrogen (N2) to air, nitrate (NO3 -) to groundwater and particulate N to surface waters. Variation in dietary N intake will particularly affect excretion of urinary N, which is much more vulnerable to losses than is faecal N. Our objective is to review dietary effects on the level and form of N excreted in cattle urine, as well as its consequences for emissions of N2O. The quantity of N excreted in urine varies widely. Urinary N excretion, in particular that of urea N, is decreased upon reduction of dietary N intake or an increase in the supply of energy to the rumen microorganisms and to the host animal itself. Most of the N in urine (from 50% to well over 90%) is present in the form of urea. Other nitrogenous components include purine derivatives (PD), hippuric acid, creatine and creatinine. Excretion of PD is related to rumen microbial protein synthesis, and that of hippuric acid to dietary concentration of degradable phenolic acids. The N concentration of cattle urine ranges from 3 to 20 g/l. High-dietary mineral levels increase urine volume and lead to reduced urinary N concentration as well as reduced urea concentration in plasma and milk. In lactating dairy cattle, variation in urine volume affects the relationship between milk urea and urinary N excretion, which hampers the use of milk urea as an accurate indicator of urinary N excretion. Following its deposition in pastures or in animal houses, ubiquitous microorganisms in soil and waters transform urinary N components into ammonium (NH4 +), and thereafter into NO3 - and ultimately in N2 accompanied with the release of N2O. Urinary hippuric acid, creatine and creatinine decompose more slowly than urea. Hippuric acid may act as a natural inhibitor of N2O emissions, but inhibition conditions have not been defined properly yet. Environmental and soil conditions at the site of urine deposition or manure application strongly influence N2O release. Major dietary strategies to mitigating N2O emission from cattle operations include reducing dietary N content or increasing energy content, and increasing dietary mineral content to increase urine volume. For further reduction of N2O emission, an integrated animal nutrition and excreta management approach is required.

Update of the Dutch protein evaluation system for ruminants: the DVE/OEB2010 system

In the current Dutch protein evaluation system (the DVE/OEB 1991 system), two characteristics are calculated for each feed: true protein digested in the intestine (DVE) and the rumen degradable protein balance (OEB). Of these, DVE represents the protein value of a feed, while OEB is the difference between the potential microbial protein synthesis (MPS) on the basis of available rumen degradable protein and that on the basis of available rumen degradable energy. DVE can be separated into three components: (i) feed crude protein undegraded in the rumen but digested in the small intestine, (ii) microbial true protein synthesized in the rumen and digested in the small intestine, and (iii) endogenous protein lost in the digestive processes. Based on new research findings, the DVE/OEB 1991 system has recently been updated to the DVE/OEB 2010 system. More detail and differentiation is included concerning the representation of chemical components in feed, the rumen degradation characteristics of these components, the efficiency of MPS and the fractional passage rates. For each chemical component, the soluble, washout, potentially degradable and truly non-degradable fractions are defined with separate fractional degradation rates. Similarly, fractional passage rates for each of these fractions were identified and partly expressed as a function of fractional degradation rate. Efficiency of MPS is related to the various fractions of the chemical components and their associated fractional passage rates. Only minor changes were made with respect to the amount of DVE required for maintenance and production purposes of the animal. Differences from other current protein evaluation systems, viz. the Cornell Net Carbohydrate and Protein system and the Feed into Milk system, are discussed.

Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact.

Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution.The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data.Empirical models lack the biological basis necessary to evaluate mitigation strategies to reduce excretion of waste, including nitrogen, phosphorus and methane. Such models may have little predictive value when comparing various feeding strategies. Examples include the Intergovernmental Panel on Climate Change (IPCC) Tier II models to quantify methane emissions and current protein evaluation systems to evaluate low protein diets to reduce nitrogen losses to the environment. Nutrient based mechanistic models can address such issues. Since environmental issues generally attract more funding from governmental offices, further development of nutrient based models may well take place within an environmental framework.

Gaseous nitrogen emissions from livestock farming systems

Publisher Summary This chapter discusses the origin and controlling factors of gaseous nitrogen emissions, the uncertainty in the estimates, and possible measures that may be taken to decrease these emissions. The uncertainty in the estimated contributions of livestock farming systems to the total emissions of NH 3 , NO and N 2 O into the atmosphere stems in part from the paucity in measurement data of gaseous N losses from animal housing systems and manure storage systems, especially also for livestock farming systems in the developing countries. The number of farm animals is larger in developing countries than in developed countries, while the number of measurements of the emissions of NH 3, NO, N 2 O and N 2 from livestock farming systems is much larger in developed countries than in developing countries. Further, there are more data about N losses associated with the application of slurry and manure to agricultural land than about N losses from animal housing systems and manure storage systems, while N losses from housing systems, manure storage systems and from slurry application to land may be equally large.

Nutrient digestion and utilization in farm animals : modelling approaches

Introduction: History and Future Focus, J France, University of Guelph, Canada The Nordic Dairy Cow Model Karoline - Development of Volatile Fatty Acid Sub-Model, J Sveinbjornsson, Icelandic Agricultural Research Institute (RALA), Iceland, P Huhtanen, MTT Afrifood Research Institute, Finland and P Uden, Swedish University of Agricultural Science, Sweden A Three-Compartment Model of Transmembrane Fluxes of Valine across the Tissues of the Hindquarters of Growing Lambs Infected with Trichostrongylus colubriformis N C Roy. AgResearch Ltd, Palmerston North, New Zealand, E N Bermingham and W C McNabb Using Rumen Degradation Model to Evaluate Microbial Protein Yield and Intestinal Digestion of Grains in Cattle P Paengkoum, Suranaree University of Technology, Thailand Simulation of Rumen Particle Dynamics using a Non-Steady State Model of Rumen Digestion and Nutrient Availability in Dairy Cows Fed Sugarcane, E A Collao-Saenz, A Bannink, Wageningen University, Netherlands, E Kebreab, University of Guelph, Canada, J France and J Dijkstra, Wageningen University Netherlands Modelling Fluxes of Volatile Fatty Acids from Rumen to Portal Blood, P Noziere, INRA, France and T Hoch The Role of Rumen Fill in Terminating Grazing Bouts of Dairy Cows under Continuous Stocking, H Z Toweel, Wageningen University, Netherlands, B M Tas, S Tamminga and J Dijkstra Functions for Microbial Growth, S Lopez, Universidad de Leon, Spain, M Prieto, J Dijkstra, E Kebreab, M S Dhanoa, Institute of Grassland & Environmental Research (IGER), Wales, UK and J France Obtaining Information on Gastric Emptying Patterns in Horses from Appearance of an Oral Acetaminophen Dose in Blood Plasma, J P Cant, University of Guelph, Canada, V N Walsh and R J Geor A Model to Evaluate Beef Cow Efficiency, L O Tedeschi, Cornell University, USA, D G Fox, M J Baker and K L Long Prediction of Energy Requirement for Growing Sheep with the Cornell Net Carbohydrate and Protein System, A Cannas, Universita of Sassari, Italy, L O Tedeschi, A S Atzori and D G Fox Prediction of Body Weight and Composition from Body Dimension Measurements in Lactating Dairy Cows, T Yan, Agricultural Research Institute, Hillsborough, Ireland, R E Agnew, C S Mayne and D C Patterson Relationships between Body Composition and Ultrasonic Measurements in Lactating Dairy Cows, R E Agnew, T Yan, D C Patterson and C S Mayne Empirical Model of Dairy Cow Body Composition, O Martin, INRA, UMR Physiology de la Nutrition et Alimentation, France and D Sauvant, Institut National Agronomique Paris-Grignon, France Simulating Chemical and Tissue Composition of the Growing Beef Cattle. From the Model to the Tool, T Hoch, Ph Pradel, P Champclaux and J Agabriel, INRA, France Representation of Fat and Protein Gain at Low Levels of Growth and Improved Prediction of Variable Maintenance Requirement In a Ruminant Growth and Composition Model, J W Oltjen, R D Sainz, University of California, USA, A B Pleasants, T K Soboleva and V H Oddy, Meat and Livestock Australia, Australia. Growth Patterns of Nellore vs. British Beef Cattle Breeds Assessed using a Dynamic, Mechanistic Model of Cattle Growth and Composition, R D Sainz, L G Barioni, Embrapa Cerrados, Brazil, P V Paulino, S C Valadares Filho and J W Oltjen A Kinetic Model of Phosphorus Metabolism in Growing Sheep, R Souza Dias, Center for Nuclear Energy in Agriculture, Brazil, A P Roque, V F Nascimento Filho, D M S S Vitti and I C S Bueno Dynamic Simulation of Phosphorus Utilization in Salmonid Fish, K Hua, University of Guelph, Canada, J P Cant and D P Bureau Development of a Dynamic Model of Ca and P Flows in Layers, J Dijkstra, E Kebreab, R P Kwakkel and J France Estimating the Risk of Hypomagnesaemic Tetany in Dairy Herds, S T Bell, A E McKinnon, Lincoln University, New Zealand and A R Sykes Modelling the Effects of Environmental Stressors on The Performance of Growing Pigs: From Individuals to Populations, I J Wellock, Scottish Agricultural College, UK, G C Emmans and I Kyriazakis Empirical Modelling Through Meta Analysis Vs Mechanistic Modelling, D Sauvant and O Martin Iterative Development, Evaluation and Optimal Parameter Estimation of a Dynamic Simulation Model: A Case Study, L G Barioni, J W Oltjen and R D Sainz Segmented, Constrained, Nonlinear, Multi-objective, Dynamic Optimization Methodology Applied to the Dairy Cow Ration Formulation Problem, R C Boston, University of Pennsylvania, USA and M D Hanigan, Land O Lakes, Missouri, USA A Model to Simulate the Effects of Different Dietary Strategies on the Sustainability of a Dairy Farm System, A del Prado, Institute of Grassland and Environmental Research (IGER), UK, D Scholefield and L Brown Advantages of a Dynamical Approach to Rumen Function to Help Resolve Environmental Issues, A Bannink, J Dijkstra, E Kebreab and J France Evaluation of Models to Predict Methane Emissions from Enteric Fermentation in North American Dairy Cattle, E Kebreab, J France, B W McBride, N Odongo, A Bannink, J A N Mills and J Dijkstra Investigating Daily Changes in Food Intake by Ruminants, G McL Dryden An Ingredient-Based Input Scheme for Molly, M D Hanigan, H G Bateman, J G Fadel, University of California, USA, J P McNamara and N E Smith Metabolic Control: Improvement of a Dynamic Model of Lactational Metabolism in Early Lactation, J McNamara Rostock Feed Evaluation System - An Example of the Transformation of Energy and Nutrient Utilization Models to Practical Application, A Chudy, FBN / Degussa, Germany The Nordic Dairy Cow Model Karoline - Model Description, A Danfaer, P Huhtanen, P Uden, J Sveinbjornsson and H Volden, Agricultural University of Norway, Norway The Nordic Dairy Cow Model KAROLINE - Model Evaluation, A Danfaer, P Huhtanen, P Uden, J Sveinbjornsson and H Volden A composite model of growth, pregnancy and lactation, K Vetharaniam, AgResearch Limited, New Zealand.

Comparison and evaluation of mechanistic rumen models

Mechanistic rumen models of Baldwin (1995), Danfaer (1990) and Dijkstra et al. (1992) were compared on identical inputs that were derived from trials with lactating dairy cows fed on grass herbage. Consistent differences were detected between models and between predicted and observed outputs. None of the models seemed to predict all nutrient flows best. The models particularly differed in the representation of microbial metabolism: degradation of insoluble substrate, fermentation of substrate into volatile fatty acids, and incorporation of substrate into microbial matter. Differences amongst models in the prediction of these processes compensated for each other and consequently all models predicted the duodenal flow of non-NH3 N, microbial N and organic matter reasonably well. Large differences remained in the prediction of individual nutrient flows, however, and it was stressed that in order to enhance prediction of the profile of nutrient flows, the mechanisms of microbial metabolism need to be tested on their ability to describe the intraruminal transactions. However, this requires more-detailed information on individual nutrient flows and on the microbial or non-microbial origin of duodenal contents. Parameter inputs for physical and chemical feed properties were identified that are improperly defined in extant models or susceptible to error. The description of these feed characteristics needs to be developed further and become identifiable for a wide range of dietary conditions.