Development of a dynamic model for prediction of energy in milk protein, lactose, fat and enteric methane emissions in goats based on energy balance and indirect calorimetry studies
Abstract
Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein and fat is transferred to milk (protein, lactose and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%) and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals…
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