Danny G. Fox

Development and evaluation of a tropical feed library for the Cornell Net Carbohydrate and Rrotein System model

ABSTRACT: The Cornell Net Carbohydrate and Protein System (CNCPS) model has been increasinglyused in tropical regions for dairy and beef production. However, the lack of appropriate characterizationof the feeds has restricted its application. The objective of this study was to develop and evaluate afeed library containing feeds commonly used in tropical regions with characteristics needed as inputsfor the CNCPS. Feed composition data collected from laboratory databases and from experimentspublished in scientific journals were used to develop this tropical feed library. The total digestiblenutrients (TDN) predicted at 1x intake of maintenance requirement with the CNCPS model agreed withthose predicted by the Weiss et al. (1992) equation (r 2 of 92.7%, MSE of 13, and bias of 0.8%) over allfeeds. However, the regression r 2 of the tabular TDN values and the TDN predicted by the CNCPSmodel or with the Weiss equation were much lower (58.1 and 67.5%, respectively). A thoroughcomparison between observed and predicted TDN was not possible because of insufficient data tocharacterize the feeds as required by our models. When we used the mean chemical compositionvalues from the literature data, the TDN predicted by our models did not agree with the measuredvalues. We conclude using the TDN values calculated using the Weiss equation and the CNCPS modelthat are based on the actual chemical composition of the feeds result in energy values that moreaccurately represent the feeds being used in specific production situations than do the tabular values.Few papers published in Latin America journals that were used in this study reported information needby models such as the CNCPS.Key words: CNCPS, evaluation, feed library, tropical feeds

Mathematical models in ruminant nutrition

Mathematical models can be used to improve performance, reduce cost of production, and reduce nutrient excretion by accounting for more of the variation in predicting requirements and feed utilization in each unique production situation. Mathematical models can be classified into five or more categories based on their nature and behavior. Determining the appropriate level of aggregation of equations is a major problem in formulating models. The most critical step is to describe the purpose of the model and then to determine the appropriate mix of empirical and mechanistic representations of physiological functions, given development and evaluation dataset availability, inputs typically available and the benefits versus the risks of use associated with increased sensitivity. We discussed five major feeding systems used around the world. They share common concepts of energy and nutrient requirement and supply by feeds, but differ in structure and application of the concepts. Animal models are used for a variety of purposes, including the simple description of observations, prediction of responses to management, and explanation of biological mechanisms. Depending upon the objectives, a number of different approaches may be used, including classical algebraic equations, predictive empirical relationships, and dynamic, mechanistic models. The latter offer the best opportunity to make full use of the growing body of knowledge regarding animal biology. Continuing development of these types of models and computer technology and software for their implementation holds great promise for improvements in the effectiveness with which fundamental knowledge of animal function can be applied to improve animal agriculture and reduce its impact on the environment.

Development of a mechanistic model to represent the dynamics of particle flow out of the rumen and to predict rate of passage of forage particles in dairy cattle.

A mechanistic and dynamic model was developed to represent physiological aspects of particle dynamics in the reticulo-rumen (RR) and to predict rate of passage out of the RR (Kp) of forage particles quantitatively. The model consists of 2 conceptual pools with 3 spatial compartments of particles; the compartment the particle enters is based on functional specific gravity (FSG). The model assumes 2 major pressure gradient-driven flows of particles out of the RR through the reticulo-omasal orifice between 2 consecutive primary reticular contractions. One is associated with the second phase of primary reticular contraction and involves propulsion of particles in the vicinity of the honeycomb structure of the reticulum from the RR. The second flow involves movement of particles in the reticulum without selection by size. Particle outflow rate was assumed to be proportional to liquid outflow rate. The passage coefficient, defined as the ratio of particle to liquid outflow rate, was estimated for each particle group by an equation derived from the probability of passage based on FSG and particle size. Particles retained on a 1.18-mm screen were defined as large particles. When the model was evaluated with 41 observations in an independent database, it explained 66% of the variation in observed Kp of forage particles with a root mean square prediction error of 0.009. With 16 observations that also included measurements of liquid passage rate, the model explained 81 and 86% of the variation in observed Kp liquid and Kp forage, respectively. An analysis of model predictions using a database with 455 observations indicated that the assumptions underlying the model seemed to be appropriate to describe the dynamics of forage particle flow out of the RR. Sensitivity analysis showed that probability of a particle being in the pool likely to escape is most critical in the passage of large forage particles, whereas the probability of being in the reticulum as well as in the likely to escape pool is important in the passage of small forage and concentrate particles. The FSG of a particle is more important in determining the fate of a particle than its size although they are correlated, especially for forage particles. We conclude that this model can be used to understand the factors that affect the dynamics of particle flow out of the RR and predict Kp of particles out of the RR in dairy cattle.

A nutrition mathematical model to account for dietary supply and requirements of energy and other nutrients for domesticated small ruminants: The development and evaluation of the Small Ruminant Nutrition System

A mechanistic model that predicts nutrient requirements and biological values of feeds for sheep (Cornell Net Carbohydrate and Protein System; CNCPS-S) was expanded to include goats and the name was changed to the Small Ruminant Nutrition System (SRNS). The SRNS uses animal and environmental factors to predict metabolizable energy (ME) and protein, and Ca and P requirements. Requirements for goats in the SRNS are predicted based on the equations developed for CNCPS-S, modified to account for specific requirements of goats, including maintenance, lactation, and pregnancy requirements, and body reserves. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal fermentation rates, forage, concentrate and liquid passage rates, and microbial growth. The evaluation of the SRNS for sheep using published papers (19 treatment means) indicated no mean bias (MB; 1.1 g/100 g) and low root mean square prediction error (RMSPE; 3.6 g/100g) when predicting dietary organic matter digestibility for diets not deficient in ruminal nitrogen. The SRNS accurately predicted gains and losses of shrunk body weight (SBW) of adult sheep (15 treatment means; MB = 5.8 g/d and RMSPE = 30 g/d) when diets were not deficient in ruminal nitrogen. The SRNS for sheep had MB varying from -34 to 1 g/d and RSME varying from 37 to 56 g/d when predicting average daily gain (ADG) of growing lambs (42 treatment means). The evaluation of the SRNS for goats based on literature data showed accurate predictions for ADG of kids (31 treatment means; RMSEP = 32.5 g/d; r2= 0.85; concordance correlation coefficient, CCC, = 0.91), daily ME intake (21 treatment means; RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99), and energy balance (21 treatment means; RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats. In conclusion, the SRNS for sheep can accurately predict dietary organic matter digestibility, ADG of growing lambs and changes in SBW of mature sheep. The SRNS for goats is suitable for predicting ME intake and the energy balance of lactating and non-lactating adult goats and the ADG of kids of dairy, meat, and indigenous breeds. The SRNS model is available at http://nutritionmodels.tamu.edu.

Improved Feed Protein Fractionation Schemes for Formulating Rations with the Cornell Net Carbohydrate and Protein System

Adequate predictions of rumen-degradable protein (RDP) and rumen-undegradable protein (RUP) supplies are necessary to optimize performance while minimizing losses of excess nitrogen (N). The objectives of this study were to evaluate the original Cornell Net Carbohydrate Protein System (CNCPS) protein fractionation scheme and to develop and evaluate alternatives designed to improve its adequacy in predicting RDP and RUP. The CNCPS version 5 fractionates CP into 5 fractions based on solubility in protein precipitant agents, buffers, and detergent solutions: A represents the soluble nonprotein N, B1 is the soluble true protein, B2 represents protein with intermediate rates of degradation, B3 is the CP insoluble in neutral detergent solution but soluble in acid detergent solution, and C is the unavailable N. Model predictions were evaluated with studies that measured N flow data at the omasum. The N fractionation scheme in version 5 of the CNCPS explained 78% of the variation in RDP with a root mean square prediction error (RMSPE) of 275 g/d, and 51% of the RUP variation with RMSPE of 248 g/d. Neutral detergent insoluble CP flows were overpredicted with a mean bias of 128 g/d (40% of the observed mean). The greatest improvements in the accuracy of RDP and RUP predictions were obtained with the following 2 alternative schemes. Alternative 1 used the inhibitory in vitro system to measure the fractional rate of degradation for the insoluble protein fraction in which A = nonprotein N, B1 = true soluble protein, B2 = insoluble protein, C = unavailable protein (RDP: R(2) = 0.84 and RMSPE = 167 g/d; RUP: R(2) = 0.61 and RMSPE = 209 g/d), whereas alternative 2 redefined A and B1 fractions as the non-amino-N and amino-N in the soluble fraction respectively (RDP: R(2) = 0.79 with RMSPE = 195 g/d and RUP: R(2) = 0.54 with RMSPE = 225 g/d). We concluded that implementing alternative 1 or 2 will improve the accuracy of predicting RDP and RUP within the CNCPS framework.

Use of chromium mordanted neutral detergent residue as a predictor of fecal output to estimate intake in grazing high producing Holstein cows

Abstract Two experiments were conducted to evaluate use of chromium mordanted neutral detergent residue (Cr-NDr) and cobalt EDTA (Co-EDTA) as predictors of dry matter intake (DMI) in high producing grazing dairy cows. The first experiment was conducted with 10 lactating Holstein cows individually fed a total mixed ration (TMR) in confinement, and dosed with Cr-NDr and Co-EDTA twice daily at milking times for 12-days to validate the markers used for the second experiment. The Cr-NDr accounted for 96% of the variation ( r 2 ) in DMI, while Co-EDTA underpredicted DMI by 43% ( r 2 =0.65). The second experiment was conducted on a pasture-based dairy farm, to evaluate the use of Cr-NDr to predict DMI of grazing dairy cows. 15 and 14 high producing dairy cows in trial 1 and 2, respectively, were dosed twice a day at milking times with Cr-NDr for 12-days. Mean total DMI estimated from marker recoveries were unrealistically high (5.95 and 5.52% of body weight for trials 1 and 2, respectively). It was concluded that either diurnal variation in fecal excretion of the marker or a failure in the technique of collecting pasture samples that reflected the cows’ true grazing selection in order to determine pasture composition occurred.

The role of ruminant animals in sustainable livestock intensification programs

Food supply has improved considerably since the eighteenth century industrial revolution, but inadequate attention has been given to protecting the environment in the process. Feeding a growing world population while reducing the impact on the environment requires immediate and effective solutions. Sustainability is difficult to define because it embodies multifaceted concepts and the combination of variables that make a production system sustainable can be unique to each production situation. Sustainability represents the state of a complex system that is always evolving. It is an intrinsic characteristic of the system that needs to be shaped and managed. A sustainable system has the ability to coexist with other systems at a different output level after a period of perturbation. Resilience is the ability of a system to recover and reestablish a dynamic equilibrium after it has been perturbed. Sustainable intensification (SI) produces more output(s) through the more efficient use of resources while reducing negative impact on the environment; it provides opportunities for increasing animal and crop production per area while employing sustainable production alternatives that fully consider the three pillars of sustainability (planet, people, and profit). Identifying the most efficient animals and feeding systems is the prerequisite to successful applications of sustainable livestock intensification programs. Animal scientists must develop strategies that forecast the rate and magnitude of global changes as well as their possible influences on the food production chain. System modeling is a powerful tool because it accounts for many variables and their interactions involved in identifying sustainable systems in each situation.

A dynamic model to predict fat and protein fluxes and dry matter intake associated with body reserve changes in cattle

The objective of this paper was to develop the structure and concepts of a dynamic model to simulate dry matter intake (DMI) pattern and the fluxes of fat and protein in the body reserves of cattle associated with changes in body condition score (BCS) for application within the structure of applied nutrition models. This model was developed to add the capability of evaluating the effects of factors affecting pre- and postcalving DMI, daily energy and protein balances, and changes in BCS over a reproductive cycle. Input variables are average DMI, diet metabolizable energy, and animal information (body weight, BCS, milk production, and calf birth body weight) from each diet fed over the reproductive cycle. Because the depletion and repletion of body reserves in cattle is a complex system of coordinated metabolic processes that reflect hormonal and physiological changes caused by negative or positive energy balances, the system dynamics modeling methodology was used to develop this model. The model was used to evaluate the effect of the dynamic interactions between dietary supply and animal requirements for energy and protein on the fluxes of body fat and body protein of dairy cows over the reproductive cycle and Monte Carlo simulations were used to assess the sensitivity of the parameters. The main long-term factor affecting DMI pattern was the growth of the gravid uterus causing an increase in the volume of abdominal organs and a compression of the rumen, consequentially reducing feed intake. Changes in body reserves (fat and protein) were computed based on metabolizable energy balance, assuming different efficiency of utilization coefficients for fat and protein during repletion and mobilization. The model was evaluated with data from 37 dairy cows individually fed 3 different diets over the lactation and dry periods. The model was successful in simulating the observed pattern of DMI (mean square error was 3.59, 3.97, and 3.66 for diets A, B, and C, respectively), but it tended to underpredict DMI during late lactation [around 200 to 285 d in milk (DIM)] for all diets, suggesting changes in the model structure might be needed. The predicted BCS pattern had a trend similar to the observed values. Assuming that observed BCS represents actual body fat, the model tended to overpredict observed BCS during early lactation (0.125 BCS for 0 to 120 DIM) and underpredict it during late lactation (0.06 BCS for 180 to 270 DIM). A long-term simulation (5 lactations and 4 dry periods) with diet A indicated that the cows on this diet would have a net loss of body fat if all conditions were constant.

Desempenho e composição corporal de novilhas alimentadas com dois níveis de concentrado e bagaço de cana submetidos a diferentes processos de hidrólise

The objective of this work was to test two forms of hydrolysis of sugarcane bagasse (with [17 kgf/cm2] or without [4 kgf/cm2] fast decompression post-hydrolysis) and two levels of concentrate (25 and 45%. DM) on the performance and final body composition of growing heifers in a 112 day feedlot period. Twenty-four Nellore and 12 Canchim heifers were allotted to a randomized block design in a 2x2 factorial arrangement. The results demonstrated that the omission of decompression post hydrolysis and the use of higher levels of concentrate increased daily gain and feed intake. The results for weight gain and dry matter intake, for treatment without vs with decompression and with 45 vs 25% of concentrate, were, respectively, .76 vs .67 and .76 vs .66 kg/d and 6.7 vs 5.8 and 6.8 vs 5.7 kg/d. However, there was no difference among treatments for feed:gain ratio. No differences were observed among treatments for body composition determined by deuterium dilution at the end of the feedlot period, although the estimated rate of lipid deposition was higher for the treatment without fast decompression (216 vs 175 g/d) and the treatment with 45% concentrate (225 vs 166 g/d). No advantage was observed in the fast decompression post-hydrolysis at high pressure on the efficiency of feed:gain ratio of the feeds. The results demonstrated that animals fed hydrolyzed bagasse at high levels of intake (2.9% LW) and lower proportions of concentrate presented an efficiency of feed utilization similar to the diets with higher levels of concentrate.

Models of protein and amino acid requirements for cattle

Protein supply and requirements by ruminants have been studied for more than a century. These studies led to the accumulation of lots of scientific information about digestion and metabolism of protein by ruminants as well as the characterization of the dietary protein in order to maximize animal performance. During the 1980s and 1990s, when computers became more accessible and powerful, scientists began to conceptualize and develop mathematical nutrition models, and to program them into computers to assist with ration balancing and formulation for domesticated ruminants, specifically dairy and beef cattle. The most commonly known nutrition models developed during this period were the National Research Council (NRC) in the United States, Agricultural Research Council (ARC) in the United Kingdom, Institut National de la Recherche Agronomique (INRA) in France, and the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. Others were derivative works from these models with different degrees of modifications in the supply or requirement calculations, and the modeling nature (e.g., static or dynamic, mechanistic, or deterministic). Circa 1990s, most models adopted the metabolizable protein (MP) system over the crude protein (CP) and digestible CP systems to estimate supply of MP and the factorial system to calculate MP required by the animal. The MP system included two portions of protein (i.e., the rumen-undegraded dietary CP - RUP - and the contributions of microbial CP - MCP) as the main sources of MP for the animal. Some models would explicitly account for the impact of dry matter intake (DMI) on the MP required for maintenance (MPm; e.g., Cornell Net Carbohydrate and Protein System - CNCPS, the Dutch system - DVE/OEB), while others would simply account for scurf, urinary, metabolic fecal, and endogenous contributions independently of DMI. All models included milk yield and its components in estimating MP required for lactation (MPl) and calf birth weight and some form of an empirical, exponential equation to compute MP for pregnancy (MPp). The MP required for growth (MPg) varied tremendously among the original models and their derivative works mainly due to the differences in computing growth pattern and the composition of the gain. The calculation of MCP differs among models; some rely on the total digestible nutrient (TDN; e.g., NRC, CNCPS level 1) intake to estimate MCP, while others use fermentable organic matter (FOM; e.g., INRA, DVE/OEB), fermentable carbohydrate (e.g., CNCPS level 2, NorFor), or metabolizable energy (ME; e.g., ARC, CSIRO, Rostock). Most models acknowledged the importance of ruminal recycled N, but not all accounted for it. Our Monte Carlo simulation indicated the prediction of most models for required MPl overlapped, confirming uniformity among models when predicting requirements for lactating animals, but a large variation in required MPg for growing animals exists.