Prediction of phosphorus output in manure and milk by lactating dairy cows

Mathematical models for predicting P excretions play a key role in evaluating P use efficiency and monitoring the environmental impact of dairy cows. However, the majority of extant models require feed intake as predictor variable, which is not routinely available at farm level. The objectives of the study were to (1) explore factors explaining heterogeneity in P output; (2) develop a set of empirical models for predicting P output in feces (P-f), manure (P-Ma), and milk (P-m, all in g/cow per day) with and without dry matter intake (DMI) using literature data; and (3) evaluate new and extant P models using an independent data set. Random effect meta-regression analyses were conducted using 190 Pf, 97 P-Ma, and 118 P-m or milk P concentration (P-MilkC) treatment means from 38 studies. Dietary nutrient composition, milk yield and composition, and days in milk were used as potential covariates to the models with and without DMI. Dietary phosphorus intake (P1) was the major determinant of P-f and P-Ma. Milk yield negatively affected P-i partitioning to P-f or P-Ma. In the absence of DMI, milk yield, body weight, and dietary P content became the major determinants of P-f and P-Ma. Milk P concentration (P-MilkC) was heterogeneous across the treatment groups, with a mean of 0.92 g/kg of milk. Milk yield, days in milk, and dietary. Ca-to-ash ratio were negatively correlated with P-MilkC and explained 42% of the heterogeneity. The new models predicted P-f and P-Ma with root mean square prediction error as a percentage of observed mean (RMSPE%) of 18.3 and 19.2%, respectively, using DMI when evaluated with an independent data set. Some of the extant models also predicted P-f and P-Ma well (RMSPE% = 19.3 to 20.0%) using DMI. The new models without DMI as a variable predicted P-f and P-Ma with RMSPE% of 22.3 and 19.6%, respectively, which can be used in monitoring P excretions at farm level. When evaluated with an independent data set, the new model and extant models based on milk protein content predicted P-MilkC with RMSPE% of 12.7 to 19.6%. Although models using P intake information gave better predictions, P output from lactating dairy cows can also be predicted well without intake using milk yield, milk protein content, body weight, and dietary P, Ca, and total ash contents.