Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total 2manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake

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

Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive and impractical at large scales; therefore, models are commonly used to predict CH4 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 CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/d 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 CH4 prediction accuracy. The intercontinental database covered Europe (EU), the US (US), Chile (CL), Australia (AU), and New Zealand (NZ). 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, 14.4, and 19.8% for intercontinental, EU, and US regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 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 CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary NDF concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: a meta-analysis

publishedVersio

Symposium review: uncertainties in enteric methane inventories,measurement techniques, and prediction models

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes

Performance and nutrient utilisation of dairy cows offered silages produced from three successive harvests of either a red clover–perennial ryegrass sward or a perennial ryegrass sward

The need to reduce reliance on imported protein feeds within the UK and Ireland has stimulated interest in locally grown forage legume crops, including red clover (Trifolium pratense L.). This 13-wk study examined the performance of 28 dairy cows offered silages produced from three successive harvests (H) of either a pure grass sward (GS) receiving 315 kg N/ha per annum or a red clover–perennial ryegrass sward (RCGS) receiving 22 kg N/ha per annum. The crops of H1, H2 and H3 were wilted for 48, 72 and 72 h, respectively. Silages from H1, H2 and H3 were offered for 5, 5 and 3 wk, respectively, with cows supplemented with 8.0 kg concentrate/d throughout the experiment. Digestibility of DM and the effectively degradable protein content were lower, while protein degradability was higher, for RCGS than for GS. Silage DM intakes (DMIs) were higher for RCGS than for GS at H1 and H2, with no differences at H3. Milk yield was higher with RCGS than with GS at H3, with no differences at H1 and H2. Milk fat and milk protein contents were lower with RCGS than with GS at H3 but did not differ at H1 and H2. Faecal N/N intake was higher in the RCGS group than in the GS group at H1, with no differences at H2 and H3. Gross energy digestibility was lower for RCGS than for GS at H2. Although cow performance was higher with RCGS treatment, the responses were variable between harvests, largely reflecting the changing proportion of RC in the swards as the season progresse

Quantitative macroscopic anatomy of the giraffe (giraffa camelopardalis) digestive tract

Quantitative data on digestive anatomy of the world’s largest ruminant, the giraffe, are scarce. Data were collected from a total of 25 wild-caught and 13 zoo-housed giraffes. Anatomical measures were quantified by dimension, area or weight and analysed by allometric regression. The majority of measures scaled positively and isometrically to body mass. Giraffes had lower tissue weight of all stomach compartments and longer large intestinal length than cattle. When compared to other ruminants, the giraffe digestive tract showed many of the convergent morphological adaptations attributed to browsing ruminants, for example lower reticular crests, thinner ruminal pillars and smaller surface area of the omasal laminae. Salivary gland weight of the giraffe, however, resembled that of grazing ruminants. This matches a previous finding of similarly small salivary glands in the other extant giraffid, the okapi (Okapia johnstoni), suggesting that not all convergent characteristics need be expressed in all species and that morphological variation between species is a combination of phylogenetic and adaptational signals

Problems involved in the use of the mobile bag method for prediction of intestinal digestibility of protein

organized by: The Kielanowski Institute of Animal Physiology and Nutrition, Polish Academy of Sciences, Jablonna and National Research Institute of Animal Nutrition, Krakowvo