Investigation of the effect of pegbovigrastim on some periparturient immune disorders and performance in Mexican dairy herds.

This study analyzed the efficacy of pegbovigrastim (Imrestor, Elanco Animal Health, Greenfield, IN) on some health and production parameters in lactating dairy cows. Primiparous and multiparous Holsteins from 17 Mexican dairy herds (n = 10,238) were included in this study, which was conducted in 2015. Treated cows (n = 5,025) received Imrestor approximately 7 d before expected calving and again within 24 h after calving; control cows (n = 5,213) did not receive a placebo. Clinical mastitis (MAS; 0 to 30 DIM), retained placenta (RP), and clinical metritis (MET; 0 to 21 DIM) occurrences were recorded, as well as the number of medication days, medical treatments needed, and the number of days that milk was discarded due to medication administered for disease. Milk yield was measured from calving until 120 d in milk. Imrestor reduced the incidence of MAS by 25%, and the odds ratio of having mastitis in the first 30 d in milk was 35% greater for control cows than for Imrestor cows. Imrestor treatment reduced the number of medical treatments required for MAS by 6%, and less milk was discarded due to medication for MAS as a result of the Imrestor treatment. The incidence of RP was reduced by 4.15% with Imrestor and the odds of cows having RP were 4.6% greater for control than Imrestor treatments, but they did not differ. The MET incidence was increased by 17.1% with Imrestor. The use of Imrestor around parturition increased by 5.8% the odds of inseminating cows during the first 100 d after calving. Imrestor-treated multiparous cows with MAS produced 2.1 kg/d more milk than control cows with MAS during the first 30 d of their lactation. Imrestor-treated multiparous cows with MET produced 2.3 kg/d more milk than MET control cows during the first 120 d of their lactation. We conclude that Imrestor can help the dairy cow cope with immune periparturient disorders and can increase the milk yield of dairy cows due to a healthier transition, despite a reported increase in the incidence of MET.

Effect of replacing alfalfa with panicled-tick clover or sericea lespedeza in corn-alfalfa-based substrates on in vitro ruminal methane production.

Methane emissions from ruminant livestock contribute to total anthropogenic greenhouse gas emissions and reduce metabolizable energy intake by the animal. Condensed tannins (CT) are polyphenolic plant secondary compounds commonly produced by some perennial forage legumes that characteristically bind to protein, carbohydrates, and minerals. The degree to which CT may affect ruminant nutrition depends upon the concentration, structural composition, and biological activity of the CT. The objective of our experiment was to determine the effect of replacing alfalfa in a corn-alfalfa-based substrate with a legume containing CT on in vitro CH4 production and the dynamics of fermentation using an in vitro gas production technique. All fermented substrates contained 50% ground corn as the energy concentrate portion, whereas the forage portion (50%) of each diet was comprised of alfalfa (control) or some combination of alfalfa and sericea lespedeza (SL) or panicled-tick clover (PTC). Our treatments consisted of PTC or SL 15, 30, and 45, which corresponded with 15, 30, or 45% replacement of the diet (alfalfa component) with either PTC or SL. Substrates containing 45% PTC or SL reduced in vitro CH4 production. Treatments did not affect total gas production as compared with that of the control. Replacement of alfalfa with SL or PTC increased fermentable organic matter (FOM). The PTC treatment increased FOM by as much as 1.8% at the 45% replacement level, whereas FOM of SL 45 was increased by less than 1%. The replacement of alfalfa with PTC increased substrate nutritive value greater than replacement with SL. There were no correlations between any physicochemical constituent of the substrates and CH4 production. A combination of factors associated with the inclusion of PTC and SL contributed to the in vitro CH4 production, and CT in these forages was likely a major contributing factor. Further confirmation of these results on in situ or in vivo animal systems is required. If proven effective in an in vivo production scenario, replacement of commonly fed non-CT-containing legumes, such as alfalfa, with legumes containing CT might be a viable method to decrease the effect of animal agriculture on greenhouse gas production.

Using the Small Ruminant Nutrition System to develop and evaluate an alternative approach to estimating the dry matter intake of goats when accounting for ruminal fiber stratification.

The first objective of this research was to assess the ability of the Small Ruminant Nutrition System (SRNS) mechanistic model to predict metabolizable energy intake (MEI) and milk yield (MY) when using a heterogeneous fiber pool scenario (GnG1), compared with a traditional, homogeneous scenario (G1). The second objective was to evaluate an alternative approach to estimating the dry matter intake (DMI) of goats to be used in the SRNS model. The GnG1 scenario considers an age-dependent fractional transference rate for fiber particles from the first ruminal fiber pool (raft) to an escapable pool (λr), and that this second ruminal fiber pool (i.e., escapable pool) follows an age-independent fractional escape rate for fiber particles (ke). Scenario G1 adopted only a single fractional passage rate (kp). All parameters were estimated individually by using equations published in the literature, except for 2 passage rate equations in the G1 scenario: 1 developed with sheep data (G1-S) and another developed with goat data (G1-G). The alternative approach to estimating DMI was based on an optimization process using a series of dietary constraints. The DMI, MEI, and MY estimated for the GnG1 and G1 scenarios were compared with the results of an independent dataset (n=327) that contained information regarding DMI, MEI, MY, and milk and dietary compositions. The evaluation of the scenarios was performed using the coefficient of determination (R(2)) between the observed and predicted values, mean bias (MB), bias correction factor (Cb), and concordance correlation coefficient. The MEI estimated by the GnG1 scenario yielded precise and accurate values (R(2) = 082; MB = 0.21 Mcal/d; Cb = 0.98) similar to those of the G1-S (R(2) = 0.85; MB = 0.10 Mcal/d; Cb=0.99) and G1-G (R(2) = 0.84; MB = 0.18 Mcal/d; Cb = 0.98) scenarios. The results were also similar for the MY, but a substantial MB was found as follows: GnG1 (R(2) = 0.74; MB = 0.70 kg/d; Cb = 0.79), G1-S (R(2) = 0.71; MB = 0.58 kg/d(1); Cb = 0.85) and G1-G (R(2) = 0.71; MB = 0.65 kg/d; Cb = 0.82). The alternative approach for DMI prediction provided better results with the G1-G scenario (R(2)=0.88; MB = -71.67 g/d; Cb = 0.98). We concluded that the GnG1 scenario is valid within mechanistic models such as the SRNS and that the alternative approach for estimating DMI is reasonable and can be used in diet formulations for goats.

A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability.

The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21 farms had dRI<-1 SD, 32 farms were between -1 SD and 0, 67 farms were between 0 and +1 SD, and 15 farms had dRI>+1 SD. The top 10% of the farms had a dRI greater than 0.170 €/L, whereas the bottom 10% farms had a dRI lower than 0.116 €/L. This stochastic approach allowed us to understand the relationships among the inputs of the studied dairy farms and to develop a ranking index for comparison purposes. The developed methodology may be improved by using more inputs at the dairy farm level and considering the actual cost to measure profitability.

Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production.

Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.

Review: Harnessing extant energy and protein requirement modeling for sustainable beef production.

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day's ADG to compute the current day's kg). We believe that generalized models developed by merging different models' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.

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.

The ability to accurately and precisely predict microbial protein synthesis is crucial in predicting the supply of metabolizable protein in beef cattle. We updated a database previously used to predict microbial crude protein (CP) supply in beef cattle. Then we evaluated the utility of using total digestible nutrients (TDN) and different measures of energy intake to predict ruminal microbial protein synthesis. Using regression analyses, we found that intakes of digestible and metabolizable energy and intake of net energy for maintenance were as effective as the intake of TDN as independent variables to predict microbial protein synthesis. Adding the intake of CP to the models improved the precision of predictions, and all models performed as well or better than the regression model adopted by the 2016 nutrient requirement system for beef cattle.

Using dynamic modelling to enhance the assessment of the beef water footprint.

Current water footprint assessment methods make a meaningful assessment of livestock water consumption difficult as they are mainly static, thus poorly adaptable to understanding future water consumption and requirements. They lack the integration of fundamental ruminant nutrition and growth equations within a dynamic context that accounts for short- and long-term behaviour and time delays associated with economically significant beef-producing areas. The current study utilised the System Dynamics methodology to conceptualise a water footprint for beef cattle within a dynamic and mechanistic modelling framework. The problem of assessing the water footprint of beef cattle was articulated, and a dynamic hypothesis was formed to represent the Texas livestock water use system as the initial step in developing the Dynamic Beef Water Footprint model (DWFB). The dynamic hypothesis development resulted in three causal loop diagrams (CLD): cattle population, growth and nutrition, and the livestock water footprint, that captured the daily water footprint of beef (WFB). Simulations and sensitivity analysis from the hypothesised CLD structures indicated that the framework was able to capture the dynamic behaviour of the WFB system. These behaviours included key reinforcing and balancing feedback processes that drive the WFB. It is extremely difficult to identify policy interventions (i.e., management strategies) for complex systems, like the U.S. beef cattle system, because there are many actors (i.e., cow-calf, stocker, feedlot) and interrelated variables that have delayed effects within and across the supply chain. Identification and understanding of feedback processes driving water use over time will help to overcome policy resistance for more sustainable beef production. Thus, the causal loops identified in the current study provide a system-level insight for the drivers of the WFB within and across each major segment of the beef supply chain to address freshwater concerns more adequately. Further, the nutrient scenarios and sensitivity analysis revealed that the high versus low nutrient composition of pasture, hay, and concentrates resulted in a significant difference in the WFB (2 669 L/kg boneless beef, P < 0.05). The WFB was sensitive to changes in nutrient composition and specific water demand (m3/t) for each production phase, not only phases with high levels of concentrate feed use. As models evolve, there is potential for the DWFB to integrate precision livestock data, further improving quantification of the WFB, precision water-efficient strategies, and selection of water-efficient livestock.

A system dynamics approach to model heat stress accumulation in dairy cows during a heatwave event.

Climate change is expected to increase the number of heat wave events, leading to prolonged exposures to severe heat stress (HS) and the corresponding adverse effects on dairy cattle productivity. Modelling dairy cattle productivity under HS conditions is complicated because it requires comprehending the complexity, non-linearity, dynamicity, and delays in animal response. In this paper, we applied the System Dynamics methodology to understand the dynamics of animal response and system delays of observed milk yield (MY) in dairy cows under HS. Data on MY and temperature-humidity index were collected from a dairy cattle farm. Model development involved: (i) articulation of the problem, identification of the feedback mechanisms, and development of the dynamic hypothesis through a causal loop diagram; (ii) formulation of the quantitative model through a stock-and-flow structure; (iii) calibration of the model parameters; and (iv) analysis of results for individual cows. The model was successively evaluated with 20 cows in the case study farm, and the relevant parameters of their HS response were quantified with calibration. According to the evaluation of the results, the proposed model structure was able to capture the effect of HS for 11 cows with high accuracy with mean absolute percent error <5%, concordance correlation coefficient >0.6, and R2 > 0.6, except for two cows (ID #13 and #20) with R2 less than 0.6, implying that the rest of the nine animals do not exhibit heat-sensitive behaviour for the defined parameter space. The presented HS model considered non-linear feedback mechanisms as an attempt to help farmers and decision makers quantify the animal response to HS, predict MY under HS conditions, and distinguish the heat-sensitive cows from heat-tolerant cows at the farm level.

Forages and pastures symposium: forage biodegradation: advances in ruminal microbial ecology.

The rumen microbial ecosystem provides ruminants a selective advantage, the ability to utilize forages, allowing them to flourish worldwide in various environments. For many years, our understanding of the ruminal microbial ecosystem was limited to understanding the microbes (usually only laboratory-amenable bacteria) grown in pure culture, meaning that much of our understanding of ruminal function remained a "black box." However, the ruminal degradation of plant cell walls is performed by a consortium of bacteria, archaea, protozoa, and fungi that produces a wide variety of carbohydrate-active enzymes (CAZymes) that are responsible for the catabolism of cellulose, hemicellulose, and pectin. The past 15 years have seen the development and implementation of numerous next-generation sequencing (NGS) approaches (e.g., pyrosequencing, Illumina, and shotgun sequencing), which have contributed significantly to a greater level of insight regarding the microbial ecology of ruminants fed a variety of forages. There has also been an increase in the utilization of liquid chromatography and mass spectrometry that revolutionized transcriptomic approaches, and further improvements in the measurement of fermentation intermediates and end products have advanced with metabolomics. These advanced NGS techniques along with other analytic approaches, such as metaproteomics, have been utilized to elucidate the specific role of microbial CAZymes in forage degradation. Other methods have provided new insights into dynamic changes in the ruminal microbial population fed different diets and how these changes impact the assortment of products presented to the host animal. As more omics-based data has accumulated on forage-fed ruminants, the sequence of events that occur during fiber colonization by the microbial consortium has become more apparent, with fungal populations and fibrolytic bacterial populations working in conjunction, as well as expanding understanding of the individual microbial contributions to degradation of plant cell walls and polysaccharide components. In the future, the ability to predict microbial population and enzymatic activity and end products will be able to support the development of dynamic predictive models of rumen forage degradation and fermentation. Consequently, it is imperative to understand the rumen's microbial population better to improve fiber degradation in ruminants and, thus, stimulate more sustainable production systems.

Forage degradation in the rumen is critical to producing ruminant animals. For many years, scientists were limited to biochemical techniques to understand how ruminal microbes degraded forage, impairing our understanding of which microbes were involved with degrading which forage components. However, we have understood that as the ruminant opened up plant cells to microbial activity, a succession of microbes was involved in colonizing and breaking fiber into increasingly smaller pieces. The recent development of sequencing techniques has allowed a more detailed understanding of changes in the microbial population of the rumen during forage degradation and the types of degradative enzymes produced by this complex microbial ecosystem. We described the enzymes involved in the degradation of specific forage components, how their end products impact the microbial population through cross-feeding interactions, and how fermentation products can impact food animal production.

An unprotected conjugated linoleic acid supplement decreases milk production and secretion of milk components in grazing dairy ewes.

Feeding conjugated linoleic acid (CLA) in a rumen-inert form to dairy ewes has been shown to increase milk production, alter milk composition, and increase the milk fat CLA content. However, few studies have tested ruminally unprotected CLA sources. The objective of this study was to evaluate the effects of an unprotected CLA supplement (29.8% of cis-9,trans-11 and 29.9% of trans-10,cis-12 isomers as methyl esters) on milk yield and composition of dairy ewes. Twenty-four lactating Lacaune ewes were used in a crossover design and received 2 dietary treatments: (1) control: basal diet containing no supplemental lipid and (2) basal diet plus CLA (30 g/d). The CLA supplement was mixed into the concentrate and fed in 2 equal meals after morning and afternoon milkings. Each experimental period consisted of 21 d: 7 d for adaptation and 14 d for data collection. The CLA supplement decreased milk fat content and yield by 31.3 and 38.0%, respectively. Milk yield and secretion of milk lactose and protein were decreased by 8.0, 9.8, and 5.6%, respectively. On the other hand, milk protein content and linear SCC score were 1.8 and 17.7% higher in ewes fed the CLA supplement. The concentration of milk fatty acids originating from de novo synthesis (C16) was increased by 22.6% in ewes fed the CLA supplement. The CLA supplement decreased C14:1/C14:0, C16:1/C16:0, and C18:1/C18:0 desaturase indexes by 25, 18.7, and 0.1%, respectively, but increased the cis-9,trans-11 CLA/trans-11 C18:1 ratio by 8.6%. The concentrations of trans-10,cis-12 CLA and cis-9,trans-11 CLA in milk fat was 309 and 33.4% higher in ewes fed CLA. Pronounced milk fat depression coupled with the deleterious effects on milk yield, milk SCC, and secretion of all milk solids observed in ewes fed an unprotected CLA supplement is likely to be associated with high doses of trans-10,cis-12 CLA reaching the mammary gland, corroborating previous results obtained with dairy cows.

The effects of metabolizable energy intake on body fat depots of adult Pelibuey ewes fed roughage diets under tropical conditions.

The objective of this work was to evaluate the effect of metabolizable energy intake (MEI) on changes in fat depots of adult Pelibuey ewes fed roughage diets under tropical conditions. Eighteen 3-year-old Pelibuey ewes with similar body weight (BW) of 37.6 ± 4.0 kg and body condition score (BCS) of 2.5 ± 0.20 were randomly assigned to three groups of six ewes each in a completely randomized design. Ewes were housed in metabolic crates and fed three levels of MEI: low (L), medium (M), and high (H) for 65 days to achieve different BW and BCS. At the end of the experiment, the ewes were slaughtered. Data recorded at slaughter were: weights of viscera and carcass. Internal fat (IF, internal adipose tissue) was dissected, weighed, and grouped as pelvic (around kidneys and pelvic region), omental, and mesenteric regions. Carcass was split at the dorsal midline in two equal halves, weighed, and chilled at 6°C during 24 h. After refrigeration, the left half of the carcass was completely dissected into subcutaneous and intermuscular fat (carcass fat). Dissected carcass fat (CF) of the left carcass was adjusted as whole carcass. At low levels of MEI, proportion of IF and CF was approximately 50%; however, as the MEI was increased, the proportion of IF was increased up to 57% and 60% for M and H, respectively. Omental and pelvic fat depots were those which increased in a larger proportion with respect to the mesenteric fat depot. Regression equations between the weight of each body fat depot and BW had a coefficient of determination (r (2)) that ranged between 0.37 for mesenteric fat and 0.87 for CF. The regression with BCS had a r (2) that ranged between 0.57 for mesenteric and 0.71 for TBF. BW was the best predictor for TBF, CF, omental fat, and pelvic fat; whereas, BCS was better than BW in predicting IF and mesenteric fat. Inclusion of both BW and BCS in multiple regressions improved the prediction for all fat depots, except for pelvic fat, which was best estimated by BCS alone. The greater slope of the regression for the pelvic fat depot equation, relative to TBF (1.40), EBW (4.02), and BCS (2.36), suggested that pelvic fat has a greater capacity to accumulate and mobilize fat. These results indicated that adult Pelibuey ewes seem to store a considerable proportion of absorbed energy in the IF depots rather than in the carcass.

Ruminal pH and its relationship with dry matter intake, growth rate, and feed conversion ratio in commercial Australian feedlot cattle fed for 148 days.

OBJECTIVES: Measurement of ruminal pH throughout a 148-day feeding period in cattle fed commercial diets and to relate this to feed intake, growth rate and feed conversion ratio. Factors contributing to variation in rumen pH, including meal frequency, duration and weight, and, total daily intake, were also evaluated. METHODS: Forty-eight cattle were randomly allocated to two pens and 12 randomly selected from each pen had rumen pH monitoring boli inserted. Ruminal pH was measured every 10 min and feed intake was measured continually. The cattle were fed a commercial feedlot diet for 148 days and weighed into and out of the feedlot to measure growth rate and to calculate feed conversion ratio. Cattle from both pens were registered to collect individual feed intake data using the GrowSafe® feed management system. RESULTS: Mean ruminal pH decreased with days on feed. Mean daily dry matter intake was the major contributor to greater average daily gain and lower ruminal pH. Lower mean ruminal pH was associated with greater average daily gain and lower feed conversion ratio, where it remained above the threshold of 5.6. There was no association between ruminal pH and average daily gain or feed conversion ratio for mean ruminal pH below 5.6. CONCLUSIONS: Ruminal acidosis can occur at any time during the feeding period, and the risk could be greater as days on feed increase. Feedlot production outcomes are not improved by ruminal pH depression below the threshold of 5.6.

Evaluation of in vitro gas production and rumen bacterial populations fermenting corn milling (co)products.

The objective of this study was to evaluate the fermentation dynamics of 2 commonly fed corn (co)products in their intact and defatted forms, using the in vitro gas production (IVGP) technique, and to investigate the shifts of the predominant rumen bacterial populations using the 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP) technique. The bTEFAP technique was used to determine the bacterial profile of each fermentation time at 24 and 48 h. Bacterial populations were identified at the species level. Species were grouped by substrate affinities (guilds) for cellulose, hemicellulose, pectin, starch, sugars, protein, lipids, and lactate. The 2 (co)products were a dried distillers grain (DDG) plus solubles produced from a low-heat drying process (BPX) and a high-protein DDG without solubles (HP). Chemical analysis revealed that BPX contained about 11.4% ether extract, whereas HP contained only 3.88%. Previous studies have indicated that processing methods, as well as fat content, of corn (co)products directly affect fermentation rate and substrate availability, but little information is available regarding changes in rumen bacterial populations. Fermentation profiles of intact and defatted BPX and HP were compared with alfalfa hay as a standard profile. Defatting before incubation had no effect on total gas production in BPX or HP, but reduced lag time and the fractional rate of fermentation of BPX by at least half, whereas there was no effect for HP. The HP feed supported a greater percentage of fibrolytic and proteolytic bacteria than did BPX. Defatting both DDG increased the fibrolytic (26.8 to 38.7%) and proteolytic (26.1 to 37.2%) bacterial guild populations and decreased the lactate-utilizing bacterial guild (3.06 to 1.44%). Information regarding the fermentation kinetics and bacterial population shifts when feeding corn (co)products may lead to more innovative processing methods that improve feed quality (e.g., deoiling) and consequently allow greater inclusion rates in dairy cow rations.

Evaluation of protein fractionation and ruminal and intestinal digestibility of corn milling co-products.

Novel corn milling co-products developed from technological advancements in ethanol production vary widely in chemical composition and nutrient availability. The objectives of this study were to characterize feed protein fractions and evaluate differences in rumen-undegradable protein (RUP) and its digestible fraction (dRUP), amino acid concentration, and in vitro gas production of 7 corn milling co-products. The crude protein (CP; % of dry matter) of co-products was 12.7 for germ, 26.9 for dried distillers grains plus solubles that had no heat exposure before fermentation (DDGS1), 45.4 for high-protein dried distillers grains (HPDDG), 12.7 for bran, 30.2 for wet distillers grains plus solubles (WDGS), 23.1 for wet corn gluten feed (WCGF), and 26.0 for dried distillers grains plus solubles that had heat exposure before fermentation (DDGS2). Two ruminally and duodenally fistulated Holstein steers weighing 663+/-24 kg were used to determine RUP and dRUP with the in situ and mobile bag techniques. Samples of each feed were ruminally incubated for 16 h, and mobile bags were exposed to simulated abomasal digestion before insertion into the duodenum and subsequent collection in the feces. Protein fractions A, B(1), B(2), B(3), and C were characterized as follows (% CP): germ=30.0, 15.0, 38.1, 13.5, 3.4; DDGS1=17.0, 7.0, 67.0, 4.8, 4.2; HPDDG=7.4, 0.6, 82.4, 8.8, 0.8; bran=33.5, 4.0, 54.3, 6.0, 2.2; WDGS=18.6, 2.4, 53.1, 11.0, 14.9; WCGF=36.6, 15.9, 33.2, 10.1, 4.1; and DDGS2=17.9, 2.1, 41.1, 11.1, 27.9. The proportions of RUP and dRUP were different and are reported as follows (% CP): DDGS2=56.3, 91.9; HPDDG=55.2, 97.7; WDGS=44.7, 93.1; DDGS1=33.2, 92.1; bran=20.7, 65.8; germ=16.5, 66.8; and WCGF=11.5, 51.1. The concentrations of Lys and Met in the RUP were different and are listed as follows (% CP): germ=2.9, 2.0; DDGS1=1.9, 2.0; HPDDG=2.0, 3.2; bran=3.2, 1.5; WDGS=1.9, 2.3; WCGF=3.5, 1.6; and DDGS2=1.9, 2.4. In vitro gas production (mL/48h) was highest for germ (52.1) followed by bran (50.1), WDGS (40.7), DDGS2 (40.1), WCGF (39.0), DDGS1 (38.6), and HPDDG (37.5). Comparison of co-products defined differences in chemical composition, protein fractionation, ruminal availability, and microbial fermentation.

A multi-inverse approach for a holistic understanding of applied animal science systems.

Technological and mathematical advances have provided opportunities to investigate new approaches for the holistic quantification of complex biological systems. One objective of these approaches, including the multi-inverse deterministic approach proposed in this paper, is to deepen the understanding of biological systems through the structural development of a useful, best-fitted inverse mechanistic model. The objective of the present work was to evaluate the capacity of a deterministic approach, that is, the multi-inverse approach (MIA), to yield meaningful quantitative nutritional information. To this end, a case study addressing the effect of diet composition on sheep weight was performed using data from a previous experiment on saccharina (a sugarcane byproduct), and an inverse deterministic model (named Paracoa) was developed. The MIA successfully revealed an increase in the final weight of sheep with an increase in the percentage of corn in the diet. Although the soluble fraction also increased with increasing corn percentage, the effective nonsoluble degradation increased fourfold, indicating that the increased weight gain resulted from the nonsoluble substrate. A profile likelihood analysis showed that the potential best-fitted model had identifiable parameters, and that the parameter relationships were affected by the type of data, number of parameters and model structure. It is necessary to apply the MIA to larger and/or more complex datasets to obtain a clearer understanding of its potential.

Effect of quebracho condensed tannin extract on fecal gas flux in steers.

Naturally occurring gaseous by-products of ruminant production-carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O)-can negatively affect the environment. Along with enteric fermentation, manure on pasture is among the most significant contributors to non-CO2 emissions. Condensed tannins, a group of naturally occurring phenolic compounds, can alter the route of nutrient excretion and interact with microbes, suggesting they are a plausible feed additive for reducing excreta gas emissions. We evaluated how quebracho (Schinopsis balansae) tannin extract fed at 0, 15, 30, and 45 g kg-1 of dry matter (DM) within a roughage-based diet affected fecal gas emissions at multiple locations (College Station and Stephenville, TX) during two periods corresponding to winter and spring. During both periods, CO2 , CH4 , and N2 O fluxes were determined using the vented-static chamber methodology over 39 d, and cumulative emissions were calculated. A random coefficients model with animal nested within dietary treatment and period as the random factor was analyzed by location due to the presence of collinearity with soil parameters within periods. Daily CO2 flux was influenced by soil moisture and temperature (r = .34; P < .01), whereas CH4 and N2 O were associated with soil moisture. Cumulative gas production confirmed a dietary effect for CO2 and gross CO2 equivalent at the College Station site (P ≤ .001), demonstrating a linear reduction as quebracho inclusion increased. Variance partitioning indicated that dietary treatment and seasonal period likely influenced animal digestive and metabolic parameters. Within specific environments, quebracho supplementation may assist in reducing fecal gas emissions.

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.

Effects of chemical composition variation on the dynamics of ruminal fermentation and biological value of corn milling (co)products.

The objectives of this study were to evaluate the dynamics of gas production of several corn (co)products, to develop equations to predict the rate of ruminal fiber digestion, to estimate total digestible nutrients (TDN) and net energy for lactation (NE(L)), and to assess the stochasticity of chemical composition and nutritive value variability. Four corn milling (co)products were evaluated in this study: high protein dried distillers grains (HP-DDG), corn bran (BRAN) and dehydrated germ (GERM), and a dried distillers grains plus soluble produced with a low-heat drying process (BPX). Alfalfa hay was used as an internal standard feed in the in vitro fermentation dynamics analysis. Standard chemical analyses, in vitro digestibility, and in vitro gas production techniques were used to obtain the necessary physicochemical characterization of feeds. The in vitro dry matter digestibility at 24 and 48 h of incubation decreased exponentially as acid detergent insoluble nitrogen increased. However, the degree of in vitro dry matter digestibility reduction was more accentuated at 24 than at 48 h of incubation. The difference among these feeds regarding the dynamics of the anaerobic fermentation within different substrates (intact feed, and fiber and defatted residues) was evaluated. Results suggested that the proportion of fiber digested in the rumen was affected by the degree of sample processing and fat removal. Fractional fermentation rate (kf) of neutral detergent residue (without sodium sulfite) and defatted fiber residue for BRAN, GERM, HP-DDG, and BPX was estimated to be 0.0635 and 0.0852 h(-1), 0.0803 and 0.0914 h(-1), 0.118 and 0.117 h(-1), and 0.0695 and 0.0844 h(-1), respectively. The most influential variables affecting kf(NDR) of HP-DDG and BPX also affected the predicted TDN, suggesting that fiber quality is essential to ensure higher TDN values for these feeds. Our study indicated that it is possible to routinely quantify the rate of fiber digestion and this approach may be based on common analytical procedures namely estimates of neutral detergent fiber, acid detergent fiber, acid detergent insoluble nitrogen, ether extract, and acid detergent lignin. Our simulations of TDN values demonstrated that differences in fermentability and chemical composition of these corn (co)products might considerably affect the supply of energy to lactating dairy cow. The analytical methods developed in this study may serve as a valuable tool to assess nutrient quality and uniformity when samples differ in chemical composition.

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.