# Urinary and fecal potassium excretion prediction in dairy cattle: A meta-analytic approach

## TL;DR

This study develops models to predict potassium excretion in dairy cattle, helping assess and reduce the environmental impact of dairy farming.

## Contribution

The paper introduces empirical predictive models for urinary and fecal potassium excretion in dairy cattle using a meta-analytic approach.

## Key findings

- Daily potassium excretion in dairy cattle is predominantly through urine rather than feces.
- Models using potassium intake showed the best predictive performance with minimal systematic bias.
- Reducing potassium intake can lower excretion, but adequate levels are needed for cow health and productivity.

## Abstract

Summary: Understanding potassium (K) excretion in dairy cattle is vital for assessing the environmental impact of producing milk. To enhance the environmental monitoring of dairy cattle, there is a need for a simple, costeffective, and less labor-intensive method to quantify K excretion on dairy farms. Therefore, developing empirical predictive models for K excretion in dairy cattle would provide a more efficient and cost-effective way to quantify K excretion. Models to predict urinary (KUr, g/day) and fecal K (KFa, g/day) excretion were constructed using published literature from various studies. Urinary and fecal K excretion regression models showed better predictive performance with minimal systematic biases.

Summary: Understanding potassium (K) excretion in dairy cattle is vital for assessing the environmental impact of producing milk. To enhance the environmental monitoring of dairy cattle, there is a need for a simple, costeffective, and less labor-intensive method to quantify K excretion on dairy farms. Therefore, developing empirical predictive models for K excretion in dairy cattle would provide a more efficient and cost-effective way to quantify K excretion. Models to predict urinary (KUr, g/day) and fecal K (KFa, g/day) excretion were constructed using published literature from various studies. Urinary and fecal K excretion regression models showed better predictive performance with minimal systematic biases.

•Overall, the daily K excreted via urine was greater than through feces in dairy cattle.•This study developed simple linear mixed models for the prediction of KUr and KFa excretion in dairy cattle.•Among all the proposed models, KUr and KFa were best predicted with K intake with minimal systematic biases.

Overall, the daily K excreted via urine was greater than through feces in dairy cattle.

This study developed simple linear mixed models for the prediction of KUr and KFa excretion in dairy cattle.

Among all the proposed models, KUr and KFa were best predicted with K intake with minimal systematic biases.

Quantification of potassium (K) excretion in dairy cattle is important to understand the environmental impact of dairy farming. To improve and monitor the environmental impact of dairy cows, there is a need for a simple, inexpensive, and less laborious method to quantify K excretion on dairy farms. The adoption of empirical mathematical models has been shown to be a promising tool to address this issue. Thus, the current study aimed to develop empirical predictive models for K excretion in dairy cattle from urine and feces that can help evaluate efficiency and monitor the environmental impact of milk production. To develop urine K (KUr, g/d) and fecal K (KFa, g/d) excretion prediction models, published literature that involved 45 and 54 treatment means from 10 and 14 studies, respectively, were used. Some studies reported either urinary or fecal K excretion or both, but in total, treatment means used to develop the models were from 17 studies. The linear mixed models were fitted with the fixed effect of K intake, DMI, dietary K content, urine volume, milk yield, and water intake, and the random effect of study weighted according to the number of observations. Leave-one-study out cross-validation was used to evaluate the performance of the proposed models and the best model was based on the lowest root mean square prediction error as a percentage of the observed mean values (RMSPE%) and highest concordance correlation coefficient (CCC). As expected, most daily K excretion was through urine (202.5 ± 92.1 g/d) than through feces (43.5 ± 21.0 g/d), and among the proposed models, the model including dietary K concentration showed poor predictive ability for both KUr and KFa with the lowest CCC values (−0.15 and −0.02, respectively) and systematic bias. The model developed using DMI to predict KFa excretion showed reasonable accuracy, as indicated by RMSPE, CCC, and R2marginal of 46.6%, 0.42, and 48%, respectively. Among the proposed models for KUr and KFa, the model with K intake demonstrated better predictive performance, showing minimal systematic bias and random errors due to data variability of >92%. While these proposed models suggested that reducing K intake can lead to a decrease in K excretion, it is important to ensure that dairy cows receive adequate amounts of this nutrient to maintain optimal health and productivity, especially during periods of heat stress.