1407 Automation of statistical procedures to screen raw data and construct feed composition databases

Tran, H.; Caprez, Adam; Kononoff, P.J.; Miller, Phillip S.; Weiss, W.P.

doi:10.2527/jam2016-1407

Cited by 1 publication

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“…If the nutrient composition of the constituent ingredients was reported, those values were used; otherwise, tabular values from an integrated feed library were used in place of the missing values. The feed library was generated from the NRC (2001) feed library, a library derived from commercial laboratory data (collected by the National Animal Nutrition Program; www .animalnutrition .org; Tran et al, 2016), and the Cornell Net Carbohydrate and Protein System (CNCPS) feed library. Order of use to construct the integrated library gave preference to the commercial library, followed by the NRC ( 2001) library, and the CNCPS library.…”

Section: Model Descriptionmentioning

confidence: 99%

A revised representation of urea and ammonia nitrogen recycling and use in the Molly cow model

Titgemeyer

Hanigan

2019

Journal of Dairy Science

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Accurately predicting nitrogen (N) digestion, absorption, and metabolism will allow formulation of diets that more closely match true animal needs from a broad range of feeds, thereby allowing efficiency of N utilization and profit to be maximized. The objectives of this study were to advance representations of N recycling between blood and the gut and urinary N excretion in the Molly cow model. The current work includes enhancements (1) representing ammonia passage to the small intestine; (2) deriving parameters defining urea synthesis and ruminal urea entry rates; (3) adding representations of intestinal urea entry, microbial protein synthesis in the hindgut, and fecal urea-N excretion; and (4) altering existing urinary N excretion equations to scale with body weight and adding purine derivatives as a component of urinary N excretion. After the modifications, prediction errors for ruminal outflows of total N, microbial N, and nonammonia, nonmicrobial N were 29.8, 32.3, and 26.2% of the respective observed mean values. Prediction errors of each were approximately 7 percentage units lower than the corresponding values before model modifications and fitting due primarily to decreased slope bias. The revised model predicted ruminal ammonia and blood urea concentrations with substantially decreased overall error and reductions in slope and mean bias. Prediction errors for gut urea-N entry were decreased from 70.5 to 26.7%, which was also a substantial improvement. Adding purine derivatives to urinary N predictions improved the accuracy of predictions of urinary N output. However, urinary urea-N excretion remains poorly predicted with 69.0% prediction errors, due mostly to overestimated urea-N entry rates. Adding representations of undigested microbial nucleic acids, microbial protein synthesized in the hindgut, and urea-N excretion in feces decreased prediction errors for fecal N excretion from 21.1 to 17.1%. The revised model predicts that urea-N entry into blood accounts for approximately 64% of dietary N intake, of which 64% is recycled to the gut lumen. Between 48 and 67% of the urea recycled to the gut flows into the rumen largely depending on diet, which accounts for 29 to 54% of total ruminal ammonia production, and 65 to 76% of this ammonia-N is captured in microbial protein, which represents 17% of N intake. Based on model simulations, feeding a diet with moderately low crude protein and high rumen-undegradable protein could increase apparent ruminal N efficiency by 20%.

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Section: Model Descriptionmentioning

confidence: 99%