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.
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.
A compilation of N balance data (n = 1801) was partitioned into four groups to define the mean excretion of manure and N and to develop empirical equations to estimate these excretions from Holstein herds. Mean excretion of manure for cows that averaged 29 kg/d of milk production was 3 kg/d per 1000 kg of body weight (BW) more than the value for dairy cows reported by the American Society of Agricultural Engineers; N excretion was 0.09 kg/d per 1000 kg of BW higher than the value reported by the American Society of Agricultural Engineers. Mean excretion of manure and N for cows that averaged 14 kg/d of milk production and that for nonlactating cows were substantially lower than the values reported by the American Society of Agricultural Engineers. Growing and replacement cattle excreted 10 kg/d per 1000 kg of BW more manure and 0.11 kg/d per 1000 kg of BW more N than was reported by the American Society for Agricultural Engineers for beef cattle. Estimation of manure and N excretion was more accurate than mean values when using regression equations that included variables for milk production, concentration of crude protein and neutral detergent fiber in the diet, BW, days in milk, and days of pregnancy. Equations that contained intake variables did not significantly affect predictions of manure and N excretion, and the use of such equations is discouraged unless dry matter intake is measured and not estimated. Accurate estimates of excreta output could improve the planning of storage and handling systems for manure and the calculation of nutrient balances on dairy farms.
Four ruminally cannulated Holstein cows in midlactation were randomly assigned to a 4 x 4 Latin square design with a 2 x 2 factorial arrangement of treatments to evaluate two nonstructural carbohydrate sources (corn or barley) with two sources of ruminally undegradable protein (soybean meal or extruded soybean meal) on milk production, ruminal fermentation, and digesta passage rates. Milk production (25.1, 27.5, 23.8, and 23.5 kg/d for the corn and soybean meal, corn and extruded soybean meal, barley and soybean meal, and barley and extruded soybean meal, respectively) and dry matter intake per unit of body weight (3.9, 4.1, 3.7, and 3.7%) were greater for cows fed corn than for cows fed barley and were similar for cows fed soybean meal or extruded soybean meal. Concentrations of ruminal NH3-N were greater for cows fed the corn and soybean meal diet than for cows fed other diets (15.0, 10.4, 9.0, and 11.3 mg/dl). Rumen volatile fatty acid concentrations were greater for cows fed corn than barley (133, 139, 121, and 118 mumol/ml). Fractional passage rates of solids from the rumen were greater for cows fed the barley and soybean meal diet than cows fed the corn and soybean meal diet (3.4, 3.9, 4.2, and 3.8%/h), and ruminal liquid dilution rates were similar for cows fed all diets (11.2, 11.0, 11.1, and 11.9%/h). The attempt to synchronize ruminal nonstructural carbohydrate and crude protein degradability produced minimal benefits for midlactation dairy cows.
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