D.P. Casper

Prediction of enteric methane emissions from cattle

Agriculture has a key role in food production worldwide and it is a major component of the gross domestic product of several countries. Livestock production is essential for the generation of high quality protein foods and the delivery of foods in regions where animal products are the main food source. Environmental impacts of livestock production have been examined for decades, but recently emission of methane from enteric fermentation has been targeted as a substantial greenhouse gas source. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. The predictive ability of current methane emission models remains poor. Moreover, the availability of information on livestock production systems has increased substantially over the years enabling the development of more detailed methane prediction models. In this study, we have developed and evaluated prediction models based on a large database of enteric methane emissions from North American dairy and beef cattle. Most probable models of various complexity levels were identified using a Bayesian model selection procedure and were fitted under a hierarchical setting. Energy intake, dietary fiber and lipid proportions, animal body weight and milk fat proportion were identified as key explanatory variables for predicting emissions. Models here developed substantially outperformed models currently used in national greenhouse gas inventories. Additionally, estimates of repeatability of methane emissions were lower than the ones from the literature and multicollinearity diagnostics suggested that prediction models are stable. In this context, we propose various enteric methane prediction models which require different levels of information availability and can be readily implemented in national greenhouse gas inventories of different complexity levels. The utilization of such models may reduce errors associated with prediction of methane and allow a better examination and representation of policies regulating emissions from cattle.

Quantifying body water kinetics and fecal and urinary water output from lactating Holstein dairy cows

Reliable estimates of fresh manure water output from dairy cows help to improve storage design, enhance efficiency of land application, quantify the water footprint, and predict nutrient transformations during manure storage. The objective of the study was to construct a mechanistic, dynamic, and deterministic mathematical model to quantify urinary and fecal water outputs (kg/d) from individual lactating dairy cows. The model contained 4 body water pools: reticulorumen (QRR), post-reticulorumen (QPR), extracellular (QEC), and intracellular (QIC). Dry matter (DM) intake, dietary forage, DM, crude protein, acid detergent fiber and ash contents, milk yield, and milk fat and protein contents, days in milk, and body weight were input variables to the model. A set of linear equations was constructed to determine drinking, feed, and saliva water inputs to QRR and fractional water passage from QRR to QPR. Water transfer via the rumen wall was subjected to changes in QEC and total water input to QRR. Post-reticulorumen water passage was adjusted for DM intake. Metabolic water production and respiratory cutaneous water losses were estimated with functions of heat production in the model. Water loss in urine was driven by absorbed N left after being removed via milk. Model parameters were estimated simultaneously using observed fecal and urinary water output data from lactating Holstein cows (n=670). The model was evaluated with data that were not used for model development and optimization (n=377). The observations in both data sets were related to thermoneutral conditions. The model predicted drinking water intake, fecal, urinary, and total fresh manure water output with root mean square prediction errors as a percentage of average values of 18.1, 15.6, 30.6, and 14.6%, respectively. In all cases, >97% of the prediction error was due to random variability of data. The model can also be used to determine saliva production, heat and metabolic water production, respiratory cutaneous water losses, and size of major body water pools in lactating Holstein cows under thermoneutral conditions.

Effect of microbial inoculants on the quality and aerobic stability of bermudagrass round-bale haylage

The objective of this study was to compare the efficacy of using 4 commercially available microbial inoculants to improve the fermentation and aerobic stability of bermudagrass haylage. We hypothesized that the microbial inoculants would increase the fermentation and aerobic stability of the haylages. Bermudagrass (4-wk regrowth) was harvested and treated with (1) deionized water (control); (2) Buchneri 500 (B500; Lallemand Animal Nutrition, Milwaukee, WI) containing 1×10(5) of Pediococcus pentosaceus and 4×10(5) of Lactobacillus buchneri 40788; (3) Biotal Plus II (BPII; Lallemand Animal Nutrition) containing 1.2×10(5) of P. pentosaceus and Propionibacteria freudenreichii; (4) Silage Inoculant II (SI; AgriKing Inc., Fulton, IL) containing 1×10(5) of Lactobacillus plantarum and P. pentosaceus; and (5) Silo King (SK; AgriKing Inc.), containing 1×10(5) of L. plantarum, Enterococcus faecium, and P. pentosaceus, respectively. Forty round bales (8 per treatment; 441±26kg; 1.2×1.2 m diameter) were made and each was wrapped with 7 layers of plastic. Twenty bales were stored for 112 d and the remaining 20 were stored for 30 d and sampled by coring after intermediary storage periods of 0, 3, 7, and 30 d. The pH of control and inoculated haylages sampled on d 3 did not differ. However, B500 and BPII had lower pH (5.77±0.04 vs. 6.16±0.04; 5.06±0.13 vs. 5.52±0.13) than other treatments by d 7 and 30, respectively. At final bale opening on d 112, all treatments had lower pH than the control haylage (4.77±0.07 vs. 5.37±0.07). The B500, BPII, and SI haylages had greater lactic acid and lactic-to-acetic acid ratios than SK and control haylages. No differences were detected in neutral detergent fiber digestibility, dry matter losses, dry matter, lactic and acetic acid concentrations, and yeast and coliform counts. The SK haylage had lower clostridia counts compared with the control (1.19±0.23 vs. 1.99±0.23 cfu/g). Treatments B500, BPII, SI, and SK tended to reduce mold counts and they improved aerobic stability by 236, 197, 188, and 95%, respectively, compared with the control (276±22 vs. 99±22h).

Multivariate and univariate analysis of energy balance data from lactating dairy cows.

The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in maintenance requirements in modern milk production systems is consistent with the literature that describes increased fasting heat production in cows of higher genetic merit. The increase in utilization of dietary energy for milk production and tissue gain was partially attributed to the changes in dietary composition, in particular to the increase in dietary ether extract to levels closer to currently observed in modern milk production systems. Therefore, the estimated energetic parameters from this study can be used to update maintenance requirements and partial efficiencies of energy utilization in North American feeding systems for lactating cows.

Predicting Nitrogen Excretion from Cattle

Manure nitrogen (N) from cattle production facilities can lead to negative environmental effects, such as contribution to greenhouse gas emissions, leaching and runoff to aqueous ecosystems leading to eutrophication, and acid rain. To mitigate these effects and to improve the efficiency of N use, accurate prediction of N excretion and secretions are required. A genetic algorithm was implemented to select models to predict fecal, urinary, and total manure N excretions, and milk N secretions from 3 classes of animals: lactating dairy cows, heifers and dry cows, and steers. Two tiers of model classes were developed for each category of animals based on model input requirements. A total of 6 models for heifers and dry cows and steers and an additional 2 models for lactating dairy cattle were developed. Evaluation of the models using K-fold cross validation based on all data and using the most recent 6 yr of data showed better prediction for total manure N and fecal N compared with urinary N excretion, which was the most variable response in the database. Compared with extant models from the literature, the models developed in this study resulted in a significant improvement in prediction error for fecal and urinary N excretions from lactating cows. For total manure production by lactating cows, extant and new models were comparable in their prediction ability. Both proposed and extant models performed better than the prediction methods used by the US Environmental Protection Agency for the national inventory of greenhouse gases. Therefore, the proposed models are recommended for use in estimation of manure N from various classes of animals.

Modeling the trade-off between diet costs and methane emissions: A goal programming approach

Enteric methane emission is a major greenhouse gas from livestock production systems worldwide. Dietary manipulation may be an effective emission-reduction tool; however, the associated costs may preclude its use as a mitigation strategy. Several studies have identified dietary manipulation strategies for the mitigation of emissions, but studies examining the costs of reducing methane by manipulating diets are scarce. Furthermore, the trade-off between increase in dietary costs and reduction in methane emissions has only been determined for a limited number of production scenarios. The objective of this study was to develop an optimization framework for the joint minimization of dietary costs and methane emissions based on the identification of a set of feasible solutions for various levels of trade-off between emissions and costs. Such a set of solutions was created by the specification of a systematic grid of goal programming weights, enabling the decision maker to choose the solution that achieves the desired trade-off level. Moreover, the model enables the calculation of emission-mitigation costs imputing a trading value for methane emissions. Emission imputed costs can be used in emission-unit trading schemes, such as cap-and-trade policy designs. An application of the model using data from lactating cows from dairies in the California Central Valley is presented to illustrate the use of model-generated results in the identification of optimal diets when reducing emissions. The optimization framework is flexible and can be adapted to jointly minimize diet costs and other potential environmental impacts (e.g., nitrogen excretion). It is also flexible so that dietary costs, feed nutrient composition, and animal nutrient requirements can be altered to accommodate various production systems.

Development of mathematical models to predict volume and nutrient composition of fresh manure from lactating Holstein cows

Organic compounds in dairy manure undergo a series of reactions producing pollutants such as ammonia and methane. Because various organic compounds have different reaction rates, the emissions could be accurately determined if amounts and concentrations of individual nutrients in manure are known. A set of empirical models were developed for predicting faecal and urinary water, carbon (C), nitrogen (N), acid detergent fibre and neutral detergent fibre output (kg/day) from lactating Holstein cows. Dietary nutrient contents, milk yield and composition, bodyweight, age and days in milk were used with or without dry matter intake (DMI) as potential predictor variables. Multi-collinearity, goodness of fit, model complexity, and random study and animal effects were taken into account during model development, which used 742 measured faecal or urinary nutrient output observations (kg/day). The models were evaluated with an independent dataset (n = 364). When DMI was used as a predictor variable, the models predicted faecal and urinary nutrient outputs successfully with root mean square prediction error as a percentage of average observed values (RMSPE%) ranging from 9.1% to 20.7%. All the predictions except urine output had RMSPE% ranging from 18.3% to 24.6% when DMI was not used. The nutrient output predictions were in reasonable agreement with observed values throughout the data range (systematic bias <14% of total bias). Fresh manure C : N ratio predictions were acceptable (RMSPE% = 14.3–15.2%) although the systematic bias were notable (17.1–20.7% of total bias). The models could be integrated successfully with process-based manure or soil models to assess nutrient transformation in dairy production systems.

Prediction of enteric methane production, yield and intensity in dairy cattle using an intercontinental database

Abstract Enteric methane (CH 4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Single-dose infusion of sodium butyrate, but not lactose, increases plasma β-hydroxybutyrate and insulin in lactating dairy cows

Several studies have identified beneficial effects of butyrate on rumen development and intestinal health in preruminants. These encouraging findings led to further investigations related to butyrate supplementation in the mature ruminant. However, the effects of elevated butyrate concentrations on rumen metabolism have not been investigated, and consequently the maximum tolerable dosage rate of butyrate has not been established. Therefore, the first objective of this work was to evaluate the effect of a short-term increase in rumen butyrate concentration on key metabolic indicators. The second objective was to evaluate the source of butyrate, either directly dosed in the rumen or indirectly supplied via lactose fermentation in the rumen. Jugular catheters were inserted into 4 ruminally fistulated Holstein cows in a 4×4 Latin square with 3-d periods. On d 1 of each period, 1h after feeding, cows were ruminally dosed with 1 of 4 treatments: (1) 2L of water (CON), (2) 3.5g/kg of body weight (BW) of lactose (LAC), (3) 1g/kg of BW of butyrate (1GB), or (4) 2g/kg of BW of butyrate (2GB). Sodium butyrate was the source of butyrate, and NaCl was added to CON (1.34g/kg of BW), LAC (1.34g/kg of BW), and 1GB (0.67g/kg of BW) to provide equal amounts of sodium as the 2GB treatment. Serial plasma and rumen fluid samples were collected during d 1 of each period. Rumen fluid pH was greater in cows given the 1GB and 2GB treatments compared with the cows given the LAC treatment. Cows administered the 1GB and 2GB treatments had greater rumen butyrate concentrations compared with LAC. Those cows also had greater plasma butyrate concentrations compared with cows given the LAC treatment. Plasma β-hydroxybutyrate was greater and insulin tended to be greater for butyrate treatments compared with LAC. No difference in insulin was found between the 1GB and 2GB treatments. Based on plasma and rumen metabolites, singly infusing 3.5g/kg of BW of lactose into the rumen is not as effective at providing a source of butyrate as compared with singly infusing 1 or 2g/kg of BW of butyrate into the rumen. Additionally, rumen pH, rumen butyrate, plasma β-hydroxybutyrate, glucose, and plasma butyrate were less affected in cows administered the 1GB treatment than in cows given the 2GB treatment. This finding suggests that singly dosing 1g/kg of BW of butyrate could serve as the maximum tolerable concentration for future research.

Estimating the energetic cost of feeding excess dietary nitrogen to dairy cows

Feeding N in excess of requirement could require the use of additional energy to metabolize excess protein, and to synthesize and excrete urea; however, the amount and fate of this energy is unknown. Little progress has been made on this topic in recent decades, so an extension of work published in 1970 was conducted to quantify the effect of excess N on ruminant energetics. In part 1 of this study, the results of previous work were replicated using a simple linear regression to estimate the effect of excess N on energy balance. In part 2, mixed model methodology and a larger data set were used to improve upon the previously reported linear regression methods. In part 3, heat production, retained energy, and milk energy replaced the composite energy balance variable previously proposed as the dependent variable to narrow the effect of excess N. In addition, rumen degradable and undegradable protein intakes were estimated using table values and included as covariates in part 3. Excess N had opposite and approximately equal effects on heat production (+4.1 to +7.6 kcal/g of excess N) and retained energy (-4.2 to -6.6 kcal/g of excess N) but had a larger negative effect on milk gross energy (-52 to -68 kcal/g of excess N). The results suggest that feeding excess N increases heat production, but more investigation is required to determine why excess N has such a large effect on milk gross energy production.