Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows
Dairy cows’ urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine t...
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MDPI AG
2023-02-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/13/4/620 |
| _version_ | 1797622824845705216 |
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| author | Matthew Beck Cameron Marshall Konagh Garrett Terra Campbell Andrew Foote Ronaldo Vibart David Pacheco Pablo Gregorini |
| author_facet | Matthew Beck Cameron Marshall Konagh Garrett Terra Campbell Andrew Foote Ronaldo Vibart David Pacheco Pablo Gregorini |
| author_sort | Matthew Beck |
| collection | DOAJ |
| description | Dairy cows’ urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin’s concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (<i>p</i> > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems. |
| first_indexed | 2024-03-11T09:15:44Z |
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| language | English |
| last_indexed | 2024-03-11T09:15:44Z |
| publishDate | 2023-02-01 |
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| series | Animals |
| spelling | doaj.art-22a629a6674345c48cb6730baed6306a2023-11-16T18:39:11ZengMDPI AGAnimals2076-26152023-02-0113462010.3390/ani13040620Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy CowsMatthew Beck0Cameron Marshall1Konagh Garrett2Terra Campbell3Andrew Foote4Ronaldo Vibart5David Pacheco6Pablo Gregorini7Livestock Nutrient Management Research Unit, The Agricultural Research Service, The United States Department of Agriculture (USDA-ARS), Bushland, TX 79012, USAFaculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New ZealandFaculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New ZealandLivestock Nutrient Management Research Unit, The Agricultural Research Service, The United States Department of Agriculture (USDA-ARS), Bushland, TX 79012, USADepartment of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74048, USAGrasslands Research Centre, AgResearch Ltd., Palmerston North 4442, New ZealandGrasslands Research Centre, AgResearch Ltd., Palmerston North 4442, New ZealandFaculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New ZealandDairy cows’ urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin’s concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (<i>p</i> > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems.https://www.mdpi.com/2076-2615/13/4/620environmental modelingenvironmental impactsdairy cowsurine nitrogen excretion |
| spellingShingle | Matthew Beck Cameron Marshall Konagh Garrett Terra Campbell Andrew Foote Ronaldo Vibart David Pacheco Pablo Gregorini Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows Animals environmental modeling environmental impacts dairy cows urine nitrogen excretion |
| title | Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows |
| title_full | Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows |
| title_fullStr | Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows |
| title_full_unstemmed | Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows |
| title_short | Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows |
| title_sort | meta regression to develop predictive equations for urinary nitrogen excretion of lactating dairy cows |
| topic | environmental modeling environmental impacts dairy cows urine nitrogen excretion |
| url | https://www.mdpi.com/2076-2615/13/4/620 |
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