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

Animal Feed Science and Technology 96 1/2 1 13 Amsterdam, Netherlands: Elsevier Science B.V..

A three-step in vitro procedure was developed by Calsamiglia and Stern [J. Anim. Sci. 73 (1995) 1459] to estimate intestinal protein digestion after 16 h incubation in the rumen. The objective of our work was to modify this procedure to give an accurate estimation of N digestibility without the use of surgically modified animals by replacing the rumen incubation step by an in vitro protease enzyme incubation, and the use of small nylon bags for samples instead of the time-consuming filtration step. The results were compared with those from mobile nylon bag studies or ADIN determinations. In addition, the effect of heat treatment of grains and soyabean on protein digestibility was investigated. Briefly, samples were weighed into small nylon bags, sealed, and a maximum of 30 bags were placed in a 2.4 1 bottle containing borate-phosphate buffer at 39 degrees C. After 1 h, protease solution (protease type xiv, Streptomyces griseus) was added. After 4 h, the bags were removed and rinsed thoroughly. Half of the bags were dried at 50-55 degrees C for 48 h and the dry weight was recorded. The rest of the bags were incubated for 1 h in pepsin solution, then NaOH and pancreatin solutions were added and the bags were incubated for 24 h at 39 degrees C. Bags were rinsed thoroughly and dried at 50-55 degrees C for 48 h. The dry weight of the bags was recorded and the bag residue and the original feeds were analysed for N. Samples were barley, oats, wheat and soybeans, heat-treated for various times, and were obtained from other trials for which mobile bag digestibility values were determined with or without 16 h rumen incubation. Mobile bag digestibility values without rumen incubation ranged from 77 to 97%. Regression analysis of the relationship of N digestibility (mobile bag) and N digestibility (in vitro) for all grain samples resulted in a highly significant relationship (r2=0.95, P<0.0001). When soyabean samples were included in the analysis, the r2 was only 0.02. Residual trypsin inhibitor activity of some of the samples interfered with mobile bag digestibility (no rumen incubation), while there was no interference of trypsin inhibitor with the in vitro digestibility determinations, demonstrating a benefit of the proposed method. There was also no interference for mobile bag digestibility when the samples were first incubated in the rumen for 16 h. This in vitro procedure closely simulated the physiological conditions in the animal allowing a prediction of intestinal N digestibility without the use of surgically modified animals. The method was sensitive to heat damage of the samples and could be used to rapidly evaluate the nutritional value of grain and soyabean feedstuffs for ruminants.

Predicting feed intake and feed efficiency in lactating dairy cows using digesta marker techniques

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