Predicting microbial protein synthesis in beef cattle: relationship to intakes of total digestible nutrients and crude protein

Protein supplementation during mid-gestation affects maternal voluntary feed intake, performance, digestibility, and uterine blood flow of beef cows

This study aimed to assess the impact of protein supplementation and its interaction with calf sex (CS) on the performance, metabolism and physiology of pregnant beef cows. Fifty-two multiparous Zebu beef cows carrying female (n = 22) and male (n = 30) fetuses were used. Cows were individually housed from day 100 to 200 of gestation and randomly assigned to restricted (RES, n = 26) or supplemented (SUP, n = 26) groups. The RES cows were ad libitum fed a basal diet (corn silage + sugarcane bagasse + mineral mixture), achieving 5.5% crude protein (CP), while SUP cows received the same basal diet plus a protein supplement (40% CP, at 3.5 g/kg of body weight). All cows were fed the same diet during late gestation. Differences were declared at p < 0.05. No significant interaction between maternal nutrition and calf sex was found for maternal outcomes (p ≥ 0.34). The SUP treatment increased the total dry matter (DM) intake (p ≤ 0.01) by 32% and 19% at mid- and late-gestation respectively. The total tract digestibility of all diet components was improved by SUP treatment at day 200 of gestation (p ≤ 0.02), as well as the ruminal microbial CP production (p ≤ 0.01). The SUP treatment increased (p ≤ 0.03) the cows' body score condition, ribeye area, the average daily gain (ADG) of pregnant components (PREG; i.e., weight accretion of cows caused by pregnancy) and the ADG of maternal tissues (i.e., weight accretion discounting the gain related to gestation) in the mid-gestation. The SUP cows exhibited a lower maternal ADG (p < 0.01) compared to RES cows in late pregnancy. There was a 24% additional gain (p < 0.01) in the PREG components for SUP cows during late gestation, which in turn improved the calf birthweight (p = 0.05). The uterine arterial resistance and pulsatility indexes (p ≤ 0.01) at mid-gestation were greater for RES cows. In conclusion, protein supplementation during mid-gestation is an effective practice for improving maternal performance, growth of the gravid uterus and the offspring's birth weight.

Effect of coconut processing by-product graded feeding on carcass traits and meat quality of lambs

The inclusion of by-product coconut mesocarp skins (CMS) in diets was evaluated in feedlot lambs. The objective of this study was to evaluate CMS levels effects on carcass traits and meat quality of lambs. Thirty-five male lambs with an initial body weight of 16.9 ± 2.93 kg were distributed in a completely randomized design with five CMS levels in total dry matter (0; 4.8; 9.6; 14.4 and 19.2%) and fed during 71 d until slaughter. High levels of CMS decreased the intake of dry matter and negatively affected the performance of lambs. Fat and protein contents of Longissimus lumborum muscle (P < 0.05) and the saturated fatty acid (FA) decreased (P < 0.001) whereas polyunsaturated FA increased (P < 0.01) with the inclusion of CMS. The ratio t10/t11-18:1 increased with the inclusion of CMS (P < 0.001). The instrumental color descriptors were unaffected by CMS levels. According to the effects on the investigated meat quality traits we recommend up to 4.8% CMS in diets of confined lambs.

Estimating Microbial Protein Synthesis in the Rumen-Can 'Omics' Methods Provide New Insights into a Long-Standing Question?

Rumen microbial protein synthesis (MPS) provides at least half of the amino acids for the synthesis of milk and meat protein in ruminants. As such, it is fundamental to global food protein security. Estimating microbial protein is central to diet formulation, maximising nitrogen (N)-use efficiency and reducing N losses to the environment. Whilst factors influencing MPS are well established in vitro, techniques for in vivo estimates, including older techniques with cannulated animals and the more recent technique based on urinary purine derivative (UPD) excretion, are subject to large experimental errors. Consequently, models of MPS used in protein rationing are imprecise, resulting in wasted feed protein and unnecessary N losses to the environment. Newer 'omics' techniques are used to characterise microbial communities, their genes and resultant proteins and metabolites. An analysis of microbial communities and genes has recently been used successfully to model complex rumen-related traits, including feed conversion efficiency and methane emissions. Since microbial proteins are more directly related to microbial genes, we expect a strong relationship between rumen metataxonomics/metagenomics and MPS. The main aims of this review are to gauge the understanding of factors affecting MPS, including the use of the UPD technique, and explore whether omics-focused studies could improve the predictability of MPS, with a focus on beef cattle.

Predicting microbial crude protein synthesis in cattle from intakes of dietary energy and crude protein

Accurate predictions of microbial crude protein (MCP) synthesis are needed to predict metabolizable protein supply in ruminants. Since 1996, the National Academies of Sciences, Engineering, and Medicine series on beef cattle nutrient requirements has used the intake of total digestible nutrients (TDN) to predict ruminal MCP synthesis. Because various tabular energy values for feeds are highly correlated, our objective was to determine whether intakes of digestible energy (DE), metabolizable energy (ME), and net energy for maintenance (NEm) could be used as predictors of MCP synthesis in beef cattle. A published database of 285 treatment means from experiments that evaluated MCP synthesis was updated with 50 additional treatment mean observations. When intakes of TDN, fat-free TDN, DE, ME, NEm, dry matter, organic matter, crude protein (CP), ether extract, neutral detergent fiber, and starch were used in a stepwise regression analysis to predict MCP, only intakes of DE and CP met the P < 0.10 criterion for entry into the model. Mixed-model regression analyses were used to adjust for random intercept and slope effects of citations to evaluate intake of DE alone or in combination with CP intake as predictors of MCP synthesis, and the intakes of TDN, ME, and NEm as alternatives to DE intake. Similar precisions in predicting MCP synthesis were obtained with all measures of energy intake (CV = root mean square error [RMSE] as a percentage of the overall mean MCP varied from 9% to 9.67%), and adding CP intake to statistical models increased precision (CV ranged from 8.43% to 9.39%). Resampling analyses were used to evaluate observed vs. predicted values for the various energy intake models with or without CP intake, as well as the TDN-based equation used in the current beef cattle nutrient requirements calculations. The coefficient of determination, concordance correlation coefficient, and RMSE of prediction as a percentage of the mean averaged 0.595%, 0.730%, and 28.6% for the four measures of energy intake, with average values of 0.630%, 0.757%, and 27.4%, respectively, for equations that included CP intake. The TDN equation adopted by the 2016 beef cattle nutrient requirements system yielded similar results to newly developed equations but had a slightly greater mean bias. We concluded that any of the measures of energy intake we evaluated can be used to predict MCP synthesis by beef cattle and that adding CP intake improves model precision.

Evaluating differences between formulated dietary net energy values and net energy values determined from growth performance in finishing beef steers

Based on principles of the California Net Energy System, the dry matter intake (DMI) by feedlot cattle can be subdivided into DMI required for maintenance and DMI required for gain. Thus, if DMI along with body weight at a compositional endpoint and shrunk weight gain are known, dietary concentrations of net energy for maintenance and gain (NEm and NEg, respectively) can be calculated from growth performance data. Close agreement between growth performance-predicted and tabular NEm and NEg values implies the system can be used to accurately predict growth performance and be used to evaluate marketing and management decisions. We used 747 pen means from 21 research studies conducted at Texas Tech University and South Dakota State University to assess the agreement between growth performance-predicted NEm and NEg values and those calculated from tabular energy values for feeds reported by the 2016 National Academies of Science, Engineering, and Medicine publication on beef cattle nutrient requirements. Regression of growth performance-predicted values on tabular values with adjustment for random effects of study indicated that the intercepts of the two regressions did not differ from zero, and the slopes did not differ from one. Residuals (tabular minus growth performance-predicted values) for NEm and NEg averaged -0.003 and -0.005, respectively. Nonetheless, the precision of growth performance-predicted values was low, with approximately 40.3% of performance-predicted NEm values and 30.9% of NEg values falling within 2.5% of the corresponding tabular value. Residuals for NEm were divided into quintiles to evaluate dietary, growth performance, carcass, and energetics variables that might help explain lack of precision in growth performance-predicted values. Among the variables considered, gain:feed ratio was the most discriminating, with differences (P < 0.05) among each of the quintiles. Despite these differences, however, gain:feed ratio did not explain important percentages of variation in components of growth performance-predicted NEm values like maintenance energy requirements (r2 = 0.112) and retained energy (r2 = 0.003). Further research with large datasets that include dietary composition, growth performance and carcass data, and environmental variables, along with fundamental research on maintenance requirements and energy retention, will be required to identify ways to improve the precision of growth performance-predicted NE values.

Short Communication: Prediction of Methane per Unit of Dry Matter Intake in Growing and Finishing Cattle from the Ratio of Dietary Concentrations of Starch to Neutral Detergent Fiber Alone or in Combination with Dietary Concentration of Ether Extract

Previous research demonstrated that a fixed value of 0.2433 (SE = 0.0134) Mcal of CH₄/kg of dry matter intake (DMI) could be used to predict CH₄ production with accuracy and precision on par with similar equations in the literature. Slope bias was substantially less for the fixed-coefficient equation than noted for the other DMI- or gross energy intake (GEI)-based equations, but mean bias was substantially greater, presumably reflecting the failure of the fixed-coefficient approach to account for dietary factors that affect CH₄ production. In this article, we report on the use of the dietary ratio of concentrations of starch to neutral detergent fiber (NDF) and dietary ether extract (EE) concentration to improve the accuracy and precision of the fixed-coefficient equation. The same development data set used to create the fixed-coefficient equation was used in the present study, which included 134 treatment means from 34 respiration calorimetry studies. Based on stepwise regression with dietary NDF, starch, crude protein, EE, and the starch:NDF ratio as possible dependent variables, the starch:NDF ratio and EE were the only dietary variables selected (P ≤ 0.15). The study-adjusted relationship with the starch:NDF ratio (r² = 0.673; root mean square error [RMSE] = 0.0327) was: Mcal of CH₄/kg of DMI = 0.2883 − 0.03474 × starch:NDF; whereas the relationship with a model that included both starch:NDF ratio and dietary EE (r² = 0.738; RMSE = 0.0315) was: Mcal of CH₄/kg of DMI = 0.3227 − 0.0334 × starch:NDF − 0.00868 × % EE. A previously published independent data set with 129 treatment means from 30 respiration calorimetry studies was used to evaluate these two equations, along with two additional equations in which g/d of CH₄ was predicted directly from DMI, starch:NDF ratio, and/or dietary EE. The two Mcal of CH₄/kg of DMI equations had superior fit statistics to the previously published 0.2433 Mcal of CH₄/kg of DMI equation, with a substantial decrease in mean bias and improved concordance correlation coefficients. Moreover, the Mcal of CH₄/kg of DMI equations resulted in improved fit relative to direct prediction of g/d of CH₄ from DMI, the starch:NDF ratio, and % EE. Based on these results, further evaluation of the dietary ratio of starch-to-NDF concentrations and EE concentration to predict methane production per unit DMI in beef cattle is warranted.

Predicting Metabolizable Energy from Digestible Energy for Growing and Finishing Beef Cattle and Relationships to Prediction of Methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r² = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH₄, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r² = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

Metabolizable Protein: 1. Predicting Equations to Estimate Microbial Crude Protein Synthesis in Small Ruminants

Microbial crude protein (MCP) produced in rumen could be estimated by a variety of protocols of experimental sampling and analysis. However, a model to estimate this value is necessary when protein requirements are calculated for small ruminants. This model could be useful to calculate rumen degradable protein (RDP) requirements from metabolizable protein (MP). Then, our objective was to investigate if there is a difference in MCP efficiency between sheep and goats, and to fit equations to predict ruminal MCP production from dietary energy intake. The database consisted of 19 studies with goats (n = 176) and sheep (n = 316), and the variables MCP synthesis (g/day), total digestible nutrients (TDN), and organic matter (OM) intakes (g/day), and OM digestibility (g/kg DM) were registered for both species. The database was used for two different purposes, where 70% of the values were sorted to fit equations, and 30% for validation. A meta-analytical procedure was carried out using the MIXED procedure of SAS, specie was considered as the fixed dummy effect, and the intercept and slope nested in the study were considered random effects. No effect of specie was observed for the estimation of MCP from TDN, digestible Organic Matter (dOM), or metabolizable energy (ME) intakes (P > 0.05), considering an equation with or without an intercept. Therefore, single models including both species at the same fitting were validated. The following equations MCP (g/day) = 12.7311 + 59.2956 × TDN intake (AIC = 3,004.6); MCP (g/day) = 15.7764 + 62.2612 × dOM intake (AIC = 2,755.1); and MCP (g/day) = 12.7311 + 15.3000 × ME intake (AIC = 3,007.3) presented lower values for the mean square error of prediction (MSEP) and its decomposition, and similar values for the concordance correlation coefficient (CCC) and for the residual mean square error (RMSE) when compared with equations fitted without an intercept. The intercept and slope pooled test was significant for equations without an intercept (P < 0.05), indicating that observed and predicted data differed. In contrast, predicted and observed data for complete equations were similar (P > 0.05).

Developing Equations for Converting Digestible Energy to Metabolizable Energy for Korean Hanwoo Beef Cattle

Simple Summary

The available energy in feedstuff represents the largest proportion of the total cost for intensive beef production. Therefore, the energy content of feeds must be known before diet formulation. The determination of digestible energy (DE) and metabolizable energy (ME) values by animal experiments is both time-consuming and costly. Predictive equations to estimate the ME from DE can be useful for feed ingredient evaluations and diet formulations. A range of regression equations were developed in the present study, taking into consideration the gender and body weights of the animals, as well as the feed nutrients, to predict the relationship between the DE and ME. An evaluation of these equations suggested predicting the ME value based on ME = 0.9215 × DE − 0.1434 (R² = 0.999). The generation of these predictive equations represents a step towards updating the ME:DE default conversion factor value of 0.82 adopted from the National Research Council to meet the ME requirements of beef cattle in Korea. The new recommended predictive equation enables the adjustment of the nutrient requirements, thus enhancing animal productivity and maximising the economic return for beef farmers.

Abstract

This study was performed to update and generate prediction equations for converting digestible energy (DE) to metabolizable energy (ME) for Korean Hanwoo beef cattle, taking into consideration the gender (male and female) and body weights (BW above and below 350 kg) of the animals. The data consisted of 141 measurements from respiratory chambers with a wide range of diets and energy intake levels. A simple linear regression of the overall unadjusted data suggested a strong relationship between the DE and ME (Mcal/kg DM): ME = 0.8722 × DE + 0.0016 (coefficient of determination (R²) = 0.946, root mean square error (RMSE) = 0.107, p < 0.001 for intercept and slope). Mixed-model regression analyses to adjust for the effects of the experiment from which the data were obtained similarly showed a strong linear relationship between the DE and ME (Mcal/kg of DM): ME = 0.9215 × DE − 0.1434 (R² = 0.999, RMSE = 0.004, p < 0.001 for the intercept and slope). The DE was strongly related to the ME for both genders: ME = 0.8621 × DE + 0.0808 (R² = 0.9600, RMSE = 0.083, p < 0.001 for the intercept and slope) and ME = 0.7785 × DE + 0.1546 (R² = 0.971, RMSE = 0.070, p < 0.001 for the intercept and slope) for male and female Hanwoo cattle, respectively. By BW, the simple linear regression similarly showed a strong relationship between the DE and ME for Hanwoo above and below 350 kg BW: ME = 0.9833 × DE − 0.2760 (R² = 0.991, RMSE = 0.055, p < 0.001 for the intercept and slope) and ME = 0.72975 × DE + 0.38744 (R² = 0.913, RMSE = 0.100, p < 0.001 for the intercept and slope), respectively. A multiple regression using the DE and dietary factors as independent variables did not improve the accuracy of the ME prediction (ME = 1.149 × DE − 0.045 × crude protein + 0.011 × neutral detergent fibre − 0.027 × acid detergent fibre + 0.683).

Predicting ruminally undegraded and microbial protein flows from the rumen

The objectives of the present work were (1) to identify the cause of the linear bias in predictions of rumen-undegradable protein (RUP) content of feeds, and devise methods to remove the bias from prediction equations, and (2) to further explore the impact of rumen-degradable protein (RDP) on microbial N (MiN) outflow from the rumen. The kinetic model used by NRC (2001), which is based on protein fractionation and rates of degradation (Kd) and passage (Kp), displays considerable slope bias (-0.30 kg/kg), indicating parameter or structural problems. Regressing Kp by feed class and a static adjustment factor for the in situ-derived Kd on observed RUP flows completely resolved the slope bias problem, and the model performed significantly better than models using unadjusted Kd and marker-based Kp. The Kd adjustment was 3.82%/h, which represents approximately a 50% increase in rates of degradation over the in situ values, indicating that in situ analyses severely underestimate true rates of protein degradation. The Kp for concentrate-derived protein was 5.83%/h, which was slightly less than the marker-predicted rate of 6.69%/h. However, the derived forage protein rate was 0.49%/h, which was considerably less than the marker-based rate of 5.07%/h. Compartmental analysis of data from a single study corroborated the regression analysis, indicating that a 25% reduction in the overall passage rate and an 87% increase in the rate of degradation were required to align ruminal N pool sizes and the extent of protein degradation with the observed data. Therefore, one must conclude that both the in situ-derived degradation rates and the marker-based particle passage rates are biased relative to protein passage and cannot be used directly to predict RUP outflow from the rumen. The effects of RDP supply on microbial nitrogen (MiN) flow were apparent when intakes of individual nutrients were offered but not when DM intake and individual nutrient concentrations were offered, due to collinearity problems. Microbial N flow from the rumen was found to be linearly related to ruminally degraded starch, ruminally degraded neutral detergent fiber (NDF), RDP, and forage NDF intakes; and quadratically related to residual OM intake. More complicated models containing 2- and 3-way interactions among nutrients were also supported by the data. Independent MiN responses to RDP, ruminally degraded starch, and ruminally degraded NDF aligned with the expected responses to each of those nutrients. Nonlinear representations of MiN were found to be inferior to the linear models. Despite using unbiased predictions of RUP and MiN as drivers of AA flows, predictions of Arg, His, Ile, and Lys flow exhibited linear slope bias relative to the observed data, indicating that representations of the AA composition of the proteins may be biased or the observed data are biased. This is an improvement over the NRC (2001) predictions, where bias adjustments were required for all of the essential AA. Despite the bias for 4 AA flows, the revised prediction system was a substantial improvement over the prior work.

An improved algorithm for solving profit-maximizing cattle diet problems

Feeding cattle with on-pasture supplementation or feedlot diets can increase animal efficiency and system profitability while minimizing environmental impacts. However, cattle system profit margins are relatively small and nutrient supply accounts for most of the costs. This paper introduces a nonlinear profit-maximizing diet formulation problem for beef cattle based on well-established predictive equations. Nonlinearity in predictive equations for nutrient requirements poses methodological challenges in the application of optimization techniques. In contrast to other widely used diet formulation methods, we develop a mathematical model that guarantees an exact solution for maximum profit diet formulations. Our method can efficiently solve an often-impractical nonlinear problem by solving a finite number of linear problems, that is, linear time complexity is achieved through parametric linear programming. Results show the impacts of choosing different objective functions (minimizing cost, maximizing profit and maximizing profit per daily weight gain) and how this may lead to different optimal solutions. In targeting improved ration formulation on feedlot systems, this paper demonstrates how profitability and nutritional constraints can be met as an important part of a sustainable intensification production strategy.

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2

This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate real-life situations into mathematical formulations to describe existing patterns or forecast future behaviors in real-life situations. The appropriateness of the virtual representation of real-life situations through MM depends on the modeler's ability to synthesize essential concepts and associate their interrelationships with measured data. The development of MM paralleled the evolution of digital computing. The scientific community has only slightly accepted and used MM, in part because scientists are trained in experimental research and not systems thinking. The scientific advancements in ruminant production have been tangible but incipient because we are still learning how to connect experimental research data and concepts through MM, a process that is still obscure to many scientists. Our inability to ask the right questions and to define the boundaries of our problem when developing models might have limited the breadth and depth of MM in agriculture. Artificial intelligence (AI) has been developed in tandem with the need to analyze big data using high-performance computing. However, the emergence of AI, a computational technology that is data-intensive and requires less systems thinking of how things are interrelated, may further reduce the interest in mechanistic, conceptual MM. Artificial intelligence might provide, however, a paradigm shift in MM, including nutrition modeling, by creating novel opportunities to understand the underlying mechanisms when integrating large amounts of quantifiable data. Associating AI with mechanistic models may eventually lead to the development of hybrid mechanistic machine-learning modeling. Modelers must learn how to integrate powerful data-driven tools and knowledge-driven approaches into functional models that are sustainable and resilient. The successful future of MM might rely on the development of redesigned models that can integrate existing technological advancements in data analytics to take advantage of accumulated scientific knowledge. However, the next evolution may require the creation of novel technologies for data gathering and analyses and the rethinking of innovative MM concepts rather than spending resources in collecting futile data or amending old technologies.

Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques

Nitrogen is a component of essential nutrients critical for the productivity of ruminants. If excreted in excess, N is also an important environmental pollutant contributing to acid deposition, eutrophication, human respiratory problems, and climate change. The complex microbial metabolic activity in the rumen and the effect on subsequent processes in the intestines and body tissues make the study of N metabolism in ruminants challenging compared with nonruminants. Therefore, using accurate and precise measurement techniques is imperative for obtaining reliable experimental results on N utilization by ruminants and evaluating the environmental impacts of N emission mitigation techniques. Changeover design experiments are as suitable as continuous ones for studying protein metabolism in ruminant animals, except when changes in body weight or carryover effects due to treatment are expected. Adaptation following a dietary change should be allowed for at least 2 (preferably 3) wk, and extended adaptation periods may be required if body pools can temporarily supply the nutrients studied. Dietary protein degradability in the rumen and intestines are feed characteristics determining the primary AA available to the host animal. They can be estimated using in situ, in vitro, or in vivo techniques with each having inherent advantages and disadvantages. Accurate, precise, and inexpensive laboratory assays for feed protein availability are still needed. Techniques used for direct determination of rumen microbial protein synthesis are laborious and expensive, and data variability can be unacceptably large; indirect approaches have not shown the level of accuracy required for widespread adoption. Techniques for studying postruminal digestion and absorption of nitrogenous compounds, urea recycling, and mammary AA metabolism are also laborious, expensive (especially the methods that use isotopes), and results can be variable, especially the methods based on measurements of digesta or blood flow. Volatile loss of N from feces and particularly urine can be substantial during collection, processing, and analysis of excreta, compromising the accuracy of measurements of total-tract N digestion and body N balance. In studying ruminant N metabolism, nutritionists should consider the longer term fate of manure N as well. Various techniques used to determine the effects of animal nutrition on total N, ammonia- or nitrous oxide-emitting potentials, as well as plant fertilizer value, of manure are available. Overall, methods to study ruminant N metabolism have been developed over 150 yr of animal nutrition research, but many of them are laborious and impractical for application on a large number of animals. The increasing environmental concerns associated with livestock production systems necessitate more accurate and reliable methods to determine manure N emissions in the context of feed composition and ruminant N metabolism.

Effect of dietary crude protein degradability and corn processing on lactation performance and milk protein composition and stability

The present study aimed to evaluate the effect of crude protein degradability and corn processing on lactation performance, milk protein composition, milk ethanol stability (MES), heat coagulation time (HCT) at 140°C, and the efficiency of N utilization for dairy cows. Twenty Holstein cows with an average of 162 ± 70 d in milk, 666 ± 7 kg of body weight, and 36 ± 7.8 kg/d of milk yield (MY) were distributed in a Latin square design with 5 contemporaneous balanced squares, 4 periods of 21 d, and 4 treatments (factorial arrangement 2 × 2). Treatment factor 1 was corn processing [ground (GC) or steam-flaked corn (SFC)] and factor 2 was crude protein (CP) degradability (high = 10.7% rumen-degradable protein and 5.1% rumen-undegradable protein; low = 9.5% rumen-degradable protein and 6.3% rumen-undegradable protein; dry matter basis). A significant interaction was observed between CP degradability and corn processing on dry matter intake (DMI). When cows were fed GC with low CP degradability, DMI increased by 1.24 kg/d compared with cows fed GC with high CP degradability; however, CP degradability did not change DMI when cows were fed SFC. Similar interactions were observed for MY, HCT, and lactose content. When cows were fed GC diets, high CP degradability reduced MY by 2.3 kg/d, as well as HCT and lactose content, compared with low CP degradability. However, no effect of CP degradability was observed on those variables when cows were fed SFC diets. The SFC diets increased dry matter and starch total-tract digestibility and reduced β-casein (CN) content (% total milk protein) compared with GC diets. Cows fed low-CP degradability diets had higher glycosylated κ-CN content (% total κ-CN) and MES, as well as milk protein content, 3.5% fat-corrected milk, and efficiency of N for milk production, than cows fed high-CP degradability diets. Therefore, GC and high-CP degradability diets reduced milk production and protein stability. Overall, low CP degradability increased the efficiency of dietary N utilization and MES, probably due to changes in casein micelle composition, as CP degradability or corn processing did not change the milk concentration of ionic calcium. The GC diets increased β-CN content, which could contribute to reducing HTC when cows were fed GC and high-CP degradability diets.

Factors affecting urinary creatinine in heifers and cows

A series of total urine collections were conducted to evaluate the effects of age, diet, gestation, and body condition score (BCS) on urinary creatinine (UC) and purine derivative (PD) excretion in heifers and cows. For each collection, urine was collected over a 5-d period and composited by animal within day. Daily samples were analyzed for UC and PD concentration and averaged over the 5-d period. All animals were fed in individual stanchions at 2.0% of body weight (BW). To evaluate the relationship between age and UC excretion, 21 animals ranging from 5 to 80 months of age were fed a forage-based diet supplemented with dried distillers grains (DDG). Creatinine excretion (mg/kg BW) was not correlated with age (P = 0.37). To determine if diet alters UC, 11 heifers were sampled for two urine collection periods. In period 1, heifers were fed a forage-based diet supplemented with DDG. In period 2, heifers were fed a finishing diet (90% concentrate, 10% forage). Creatinine excretion (mg/kg BW) and PD:creatinine (PD:C) was greater (P = 0.01) for heifers when fed the forage-based diet than when fed the concentrate-based diet. Eleven cows fed a forage-based diet supplemented with DDG were sampled to determine the effect of gestation on urinary metabolites. Gestation did not affect UC (P = 0.42) or PD:C (P = 0.30). To evaluate the relationship between 12th rib fat thickness and metabolite excretion, 40 heifers were fed a common finishing diet. There was no relationship between UC (mg/kg BW; P = 0.28) or PD:UC (P = 0.47) and 12th rib fat thickness. To evaluate the relationship between BCS and UC, 11 cows were fed a forage diet supplemented with DDG. There was no relationship between BCS and UC (mg/kg BW; P = 0.99) or PD:C (P = 0.84). To evaluate daily and diurnal variation in UC, nine heifers were fed a forage diet supplemented with DDG. Seven of the heifers were fed a finishing diet (90% concentrate, 10% forage) in a second period. Urine was collected every 2 h from 0600 to 1800 hours. When expressed as mg/kg BW, UC excretion was not different across animals fed the forage-based (P = 0.40) or concentrate-based diet (P = 0.18). Stepwise regression indicated that at least 3 d of collection were required to estimate UC. Time within day and day within period effects were observed (P < 0.01) for UC from 2-h interval samples. The UC varies with type of diet and diurnal variation is present. Variation among animals is relatively small.

Nitrogen recycling and feed efficiency of cattle fed protein-restricted diets

The ability of cattle to grow and reproduce when ingesting low-protein diets is a crucial attribute for productive beef cattle systems in the seasonally dry tropics and subtropics. Nitrogen (N) recycling to the rumen is an important and known physiological mechanism allowing ruminants to efficiently grow in low-protein diets, but is usually disregarded in the nutritional models. This review discusses the role and magnitude of N recycling to provide additional N as microbial substrate in the rumen and in determining the efficiency of ruminants ingesting low-protein diets, to better understand the major factors regulating N recycling to the rumen. In addition to a review of the literature, study-adjusted regressions were used to evaluate various aspects of crude protein (CP) intake and availability, N recycling and excretion. There is large variation in N excretion and N-use efficiency among diets and among individuals, illustrating the opportunity for improvement in overall efficiency of cattle production. These data indicated that N recycling to the entire gastrointestinal tract supplies from half to twice as much N available for microbial growth as does the diet. Addition of rumen-degradable protein can increase rumen efficiency in using the available energy, as, conversely, the addition of fermentable energy can increase rumen efficiency in using the available CP. The present review has demonstrated that both are possible because of greater N recycling. Also, the importance of preserving the available N for determining individual variation in feed efficiency and the implications for selection are discussed. Nitrogen recycling can be controlled at both the epithelial wall of compartments of the gastrointestinal tract and at the liver, where ureagenesis occurs. Addition of fermentable energy can increase N recycling to the rumen and to post-ruminal tract by acting at both sites, and the mechanisms for this are discussed in the text. Although the effect of altering CP concentration in the diet has been substantially investigated, other factors potentially modulating N recycling, such as total fermentable energy, sources of protein and energy, hormonal modulation, and genetic variance, remain poorly understood. The selection of more efficient animals and development of diets with a lower environmental impact inescapably means further elucidation of the N-recycling mechanism.

Impact of microbial efficiency to predict MP supply when estimating protein requirements of growing beef cattle from performance

Data from 16 trials were compiled to calculate microbial CP (MCP) production and MP requirements of growing cattle on high-forage diets. All cattle were individually fed diets with 28% to 72% corn cobs in addition to either alfalfa, corn silage, or sorghum silage at 18% to 60% of the diet (DM basis). The remainder of the diet consisted of protein supplement. Source of protein within the supplement varied and included urea, blood meal, corn gluten meal, dry distillers grains, feather meal, meat and bone meal, poultry by-product meal, soybean meal, and wet distillers grains. All trials included a urea-only treatment. Intake of all cattle within an experiment was held constant, as a percentage of BW, established by the urea-supplemented group. In each trial the base diet (forage and urea supplement) was MP deficient. Treatments consisted of increasing amounts of test protein replacing the urea supplement. As protein in the diet increased, ADG plateaued. Among experiments, ADG ranged from 0.11 to 0.73 kg. Three methods of calculating microbial efficiency were used to determine MP supply. Gain was then regressed against calculated MP supply to determine MP requirement for maintenance and gain. Method 1 (based on a constant 13% microbial efficiency as used by the beef NRC model) predicted an MP maintenance requirement of 3.8 g/kg BW and 385 g MP/kg gain. Method 2 calculated microbial efficiency using low-quality forage diets and predicted MP requirements of 3.2 g/kg BW for maintenance and 448 g/kg for gain. Method 3 (based on an equation predicting MCP yield from TDN intake, proposed by the Beef Cattle Nutrient Requirements Model [BCNRM]) predicted MP requirements of 3.1 g/kg BW for maintenance and 342 g/kg for gain. The factorial method of calculating MP maintenance requirements accounts for scurf, endogenous urinary, and metabolic fecal protein losses and averaged 4.2 g/kg BW. Cattle performance data demonstrate formulating diets to meet the beef NRC model recommended MP maintenance requirement (3.8 g/kg S) works well when using 13% microbial efficiency. Therefore, a change in how microbial efficiency is calculated necessitates a change in the proposed MP maintenance requirement to not oversupply or undersupply RUP. Using the 2016 BCNRM to predict MCP production and formulate diets to meet MP requirements also requires changing the MP maintenance requirement to 3.1 g/kg BW.

BOARD-INVITED REVIEW: Efficiency of converting digestible energy to metabolizable energy and reevaluation of the California Net Energy System maintenance requirements and equations for predicting dietary net energy values for beef cattle

For the past several decades, nutrient requirement systems for beef cattle in North America have recommended that dietary ME can be calculated as dietary DE × 0.82, but considerable published data suggest a variable relationship between DE and ME. We reviewed the literature and tabulated the results of 23 respiration calorimetry studies (87 treatment mean data points), in which measurements of fecal, urinary, and gaseous energy were determined with beef cattle (bulls, steers, and heifers) and growing dairy cattle. Mixed-model regression analyses to adjust for the effects of the citation from which the data were obtained suggested a strong linear relationship between ME and DE (Mcal/kg of DM; ME = 0.9611 × DE - 0.2999; = 0.986, root mean square error [RMSE] = 0.048, < 0.001 for intercept, slope ≠ 0). Analysis of residuals from this simple linear regression equation indicated high correlations of residuals with other dietary components, and a slight increase in precision was obtained when dietary CP, ether extract, and starch (% of DM) concentrations were included in a multiple linear regression equation (citation-adjusted = 0.992, RMSE = 0.039). Using the simple linear relationship, we reevaluated the original data used to develop the California Net Energy System (CNES) for beef cattle by recalculating ME intake and heat production and regressing the logarithm of heat production on ME intake (both per BW, kg daily). The resulting intercept and slope of the recalculated data did not differ ( ≥ 0.34) from those reported for the original analyses of the CNES data, suggesting that use of the linear equation for calculating ME concentration was consistent with NEm and NEg values as derived in the CNES. Nonetheless, because the cubic equations recommended by the NRC to calculate dietary NEm and NEg from ME were based on conversion of DE to ME using 0.82, these equations were mathematically recalculated to account for the linear relationship between DE and ME. Overall, our review and analyses suggested that there is a strong linear relationship between DE and ME, which seems to be consistent across a wide range of dietary conditions, cattle types, and levels of intake. Applying this linear relationship to predict ME concentrations agreed with the original CNES calculations for NE requirements, thereby allowing the development of new equations for predicting dietary NEm and NEg values from ME.

Recent advances in estimating protein and energy requirements of ruminants

Considerable efforts have been made in gathering scientific data and developing feeding systems for ruminant animals in the past 50 years. Future endeavours should target the assessment, interpretation and integration of the accumulated knowledge to develop nutrition models in a holistic and pragmatic manner. We highlight some of the areas that need improvement. A fixed metabolisable-to-digestible energy ratio is an oversimplification and does not represent the diversity of existing feedstock, but, at the same time, we must ensure the internal consistency and dependency of the energy system in models. For grazing animals, although data exist to compute energy expenditure associated with walking in different terrains, nutrition models must incorporate the main factors that initiate and control grazing. New equations have been developed to predict microbial crude protein (MCP) production, but efforts must be made to account for the diversity of the rumen microbiome. There is large and unexplained variation in the efficiency of MCP synthesis (9.81–16.3 g MCP/100 g of fermentable organic matter). Given the uncertainties in the determination of MCP, current estimates of metabolisable protein required for maintenance are biased. The use of empirical equations to predict MCP, which, in turn, is used to estimate metabolisable protein intake, is risky because it establishes a dependency between these estimates and creates a specificity that is not appropriate for mechanistic systems. Despite the existence of data and knowledge about the partitioning of retained energy into fat and protein, the prediction of retained protein remains unsatisfactory, and is even less accurate when reported data on the efficiency of use of amino acids are employed in the predictive equations. The integrative approach to develop empirical mechanistic nutrition models has introduced interconnected submodels, which can destabilise the predictability of the model if changed independently.

Meta-analysis of postruminal microbial nitrogen flows in dairy cattle. I. Derivation of equations

The objective was to summarize the literature and derive equations that relate the chemical composition of diet and rumen characteristics to the intestinal supply of microbial nitrogen (MicN), efficiency of microbial protein synthesis (EMPS), and flow of nonammonia nonmicrobial N (NANMN). In this study, 619 treatment means from 183 trials were assembled for dairy cattle sampled from the duodenum or omasum. Backward elimination multiple regression was used to derive equations to estimate flow of nitrogenous components over a large range of dietary conditions. An intercept shift for sample location revealed that omasal sampling estimated greater MicN flow relative to duodenal sampling, but sample location did not interact with any other variables tested. The ruminal outflow of MicN was positively associated with dry matter intake (DMI) and with dietary starch percentage at a decreasing rate (quadratic response). Also, MicN was associated with DMI and rumen-degraded starch and neutral detergent fiber (NDF). When rumen measurements were included, ruminal pH and ammonia-N were negatively related to MicN flow along with a strong positive association with ruminal isovalerate molar proportion. When evaluating these variables with EMPS, isovalerate interacted with ammonia such that the slope for EMPS with increasing isovalerate increased as ammonia-N concentration decreased. A similar equation with isobutyrate confirms the importance of branched-chain volatile fatty acids to increase growth rate and therefore assimilation of ammonia-N into microbial protein. The ruminal outflow of NANMN could be predicted by dietary NDF and crude protein percentages, which also interacted. This result is probably associated with neutral detergent insoluble N contamination of NDF in certain rumen-undegradable protein sources. Because NANMN is calculated by subtracting MicN, sample location was inversely related compared with the MicN equation, and omasal sampling underestimated NANMN relative to duodenal sampling. As in the MicN equation, sampling location did not interact with any other variables tested for NANMN. Equations derived from dietary nutrient composition are robust across dietary conditions and could be used for prediction in protein supply-requirement models. These empirical equations were supported by more mechanistic equations based on the ruminal carbohydrate degradation and ruminal variables related to dietary rumen degradable protein.