Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

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

J. A. D. Ranga Niroshan Appuhamy A E , L. E. Moraes A , C. Wagner-Riddle B , D. P. Casper C , J. France D and E. Kebreab A
+ Author Affiliations
- Author Affiliations

A Department of Animal Science, University of California, Davis, CA 95616, USA.

B School of Environmental Sciences, University of Guelph, Guelph, ON, N1G 2W1, Canada.

C Department of Dairy Science, South Dakota State University, Brookings, SD 57007, USA.

D Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, ON, N1G 2W1, Canada.

E Corresponding author. Email: jaappuhamy@ucdavis.edu

Animal Production Science 54(12) 1927-1938 https://doi.org/10.1071/AN14533
Submitted: 1 May 2014  Accepted: 20 June 2014   Published: 2 September 2014

Abstract

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.

Additional keywords: faeces, dairy cows, nutrient excretion, prediction models, urine.


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