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Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows
A new evaluation scheme to assess the nutritional status of dairy cows on the basis of milk constituents was derived from 7.37 million German records of milk testing. The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measu...
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| Published in: | Journal of dairy science 2020-11, Vol.103 (11), p.10175-10194 |
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| creator | Glatz-Hoppe, J. Boldt, A. Spiekers, H. Mohr, E. Losand, B. |
| description | A new evaluation scheme to assess the nutritional status of dairy cows on the basis of milk constituents was derived from 7.37 million German records of milk testing. The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measured energy and nutrient intake and metabolic characteristics (data set B) were analyzed. Data set A included 32 commercial dairy farms in northeast Germany, with 72,982 records of 43,863 German Holstein cows; data set B included 12 German experimental farms, with 49,275 records of 1,650 German Holstein, Simmental, and Brown Swiss cows. Milk traits were linked to health disorders and metabolic and feeding characteristics. Frequently used limits of milk constituents were compared with ranges of the new “Dummerstorf feeding evaluation.” To distinguish an optimal from a deficient energy supply, a milk protein content ≥3.20% (previously used) and a milk fat:protein ratio (FPR) ≤1.4 (new scheme) were chosen and compared with feed energy intake in relation to demand. Energy status was more often correctly assigned by FPR than by milk protein content (80.7 and 68.7%, respectively). Over all data, the new optimum range of milk urea between 150 and 250 mg/L was better suited to dietary crude protein intake in relation to demand than the previously used range of 150 to 300 mg/L (42.4 and 38.0%, respectively). Ketosis or blood values associated with ketosis such as β-hydroxybutyrate >1.2 mmol/L or nonesterified fatty acids >1,000 µmol/L, as well as strong mobilization of body weight ≥1.5 kg/d, loss of back fat thickness ≥10 mm, and loss of body condition score ≥1 unit in first 60 days in milk were compared with different milk trait thresholds. For the updated scheme FPR >1.4 was used in combination with either milk protein content below the individual statistical lower limit of milk protein content, or milk fat content greater than the individual statistical upper limit of milk fat content; FPR >1.5 was taken as a frequently used threshold. For these ketosis indicators, the new scheme had higher sensitivities. Energy oversupply or the risk of overconditioning could not be identified by milk constituents alone. Urinary acid-base content was not related to milk content. Similarly, milk testing data did not allow a clear distinction to be made between the diagnoses of acidosis and, for example, ketosis. Essential requirements for good herd management are the continuous observa |
| doi_str_mv | 10.3168/jds.2019-17981 |
| format | article |
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The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measured energy and nutrient intake and metabolic characteristics (data set B) were analyzed. Data set A included 32 commercial dairy farms in northeast Germany, with 72,982 records of 43,863 German Holstein cows; data set B included 12 German experimental farms, with 49,275 records of 1,650 German Holstein, Simmental, and Brown Swiss cows. Milk traits were linked to health disorders and metabolic and feeding characteristics. Frequently used limits of milk constituents were compared with ranges of the new “Dummerstorf feeding evaluation.” To distinguish an optimal from a deficient energy supply, a milk protein content ≥3.20% (previously used) and a milk fat:protein ratio (FPR) ≤1.4 (new scheme) were chosen and compared with feed energy intake in relation to demand. Energy status was more often correctly assigned by FPR than by milk protein content (80.7 and 68.7%, respectively). Over all data, the new optimum range of milk urea between 150 and 250 mg/L was better suited to dietary crude protein intake in relation to demand than the previously used range of 150 to 300 mg/L (42.4 and 38.0%, respectively). Ketosis or blood values associated with ketosis such as β-hydroxybutyrate >1.2 mmol/L or nonesterified fatty acids >1,000 µmol/L, as well as strong mobilization of body weight ≥1.5 kg/d, loss of back fat thickness ≥10 mm, and loss of body condition score ≥1 unit in first 60 days in milk were compared with different milk trait thresholds. For the updated scheme FPR >1.4 was used in combination with either milk protein content below the individual statistical lower limit of milk protein content, or milk fat content greater than the individual statistical upper limit of milk fat content; FPR >1.5 was taken as a frequently used threshold. For these ketosis indicators, the new scheme had higher sensitivities. Energy oversupply or the risk of overconditioning could not be identified by milk constituents alone. Urinary acid-base content was not related to milk content. Similarly, milk testing data did not allow a clear distinction to be made between the diagnoses of acidosis and, for example, ketosis. Essential requirements for good herd management are the continuous observation of milk testing data in combination with other established instruments of feeding and animal monitoring.</description><identifier>ISSN: 0022-0302</identifier><identifier>EISSN: 1525-3198</identifier><identifier>DOI: 10.3168/jds.2019-17981</identifier><identifier>PMID: 32861490</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>3-Hydroxybutyric Acid - blood ; Animals ; Body Weight ; Cattle - metabolism ; Cattle Diseases - metabolism ; Cultured Milk Products ; Dairying - methods ; Diet - veterinary ; energy balance ; Energy Intake ; fat:protein ratio ; Fats - analysis ; Fatty Acids, Nonesterified - blood ; Female ; Germany ; Ketosis - blood ; Ketosis - veterinary ; Lactation ; Milk - chemistry ; Milk Proteins - analysis ; milk urea ; Nutritional Status - physiology ; optiKuh project ; protein balance ; Urea - analysis</subject><ispartof>Journal of dairy science, 2020-11, Vol.103 (11), p.10175-10194</ispartof><rights>2020 American Dairy Science Association</rights><rights>The Authors. 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The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measured energy and nutrient intake and metabolic characteristics (data set B) were analyzed. Data set A included 32 commercial dairy farms in northeast Germany, with 72,982 records of 43,863 German Holstein cows; data set B included 12 German experimental farms, with 49,275 records of 1,650 German Holstein, Simmental, and Brown Swiss cows. Milk traits were linked to health disorders and metabolic and feeding characteristics. Frequently used limits of milk constituents were compared with ranges of the new “Dummerstorf feeding evaluation.” To distinguish an optimal from a deficient energy supply, a milk protein content ≥3.20% (previously used) and a milk fat:protein ratio (FPR) ≤1.4 (new scheme) were chosen and compared with feed energy intake in relation to demand. Energy status was more often correctly assigned by FPR than by milk protein content (80.7 and 68.7%, respectively). Over all data, the new optimum range of milk urea between 150 and 250 mg/L was better suited to dietary crude protein intake in relation to demand than the previously used range of 150 to 300 mg/L (42.4 and 38.0%, respectively). Ketosis or blood values associated with ketosis such as β-hydroxybutyrate >1.2 mmol/L or nonesterified fatty acids >1,000 µmol/L, as well as strong mobilization of body weight ≥1.5 kg/d, loss of back fat thickness ≥10 mm, and loss of body condition score ≥1 unit in first 60 days in milk were compared with different milk trait thresholds. For the updated scheme FPR >1.4 was used in combination with either milk protein content below the individual statistical lower limit of milk protein content, or milk fat content greater than the individual statistical upper limit of milk fat content; FPR >1.5 was taken as a frequently used threshold. For these ketosis indicators, the new scheme had higher sensitivities. Energy oversupply or the risk of overconditioning could not be identified by milk constituents alone. Urinary acid-base content was not related to milk content. Similarly, milk testing data did not allow a clear distinction to be made between the diagnoses of acidosis and, for example, ketosis. Essential requirements for good herd management are the continuous observation of milk testing data in combination with other established instruments of feeding and animal monitoring.</description><subject>3-Hydroxybutyric Acid - blood</subject><subject>Animals</subject><subject>Body Weight</subject><subject>Cattle - metabolism</subject><subject>Cattle Diseases - metabolism</subject><subject>Cultured Milk Products</subject><subject>Dairying - methods</subject><subject>Diet - veterinary</subject><subject>energy balance</subject><subject>Energy Intake</subject><subject>fat:protein ratio</subject><subject>Fats - analysis</subject><subject>Fatty Acids, Nonesterified - blood</subject><subject>Female</subject><subject>Germany</subject><subject>Ketosis - blood</subject><subject>Ketosis - veterinary</subject><subject>Lactation</subject><subject>Milk - chemistry</subject><subject>Milk Proteins - analysis</subject><subject>milk urea</subject><subject>Nutritional Status - physiology</subject><subject>optiKuh project</subject><subject>protein balance</subject><subject>Urea - analysis</subject><issn>0022-0302</issn><issn>1525-3198</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kMtKAzEUhoMotla3LiUP0Km5zCWzlOINCoLoOmSSE5s6l5Kklr69aavuXB3y8Z-fnA-ha0pmnJbidmXCjBFaZ7SqBT1BY1qwIuO0FqdoTAhjGeGEjdBFCKv0pIwU52jEmShpXpMx6l-hVdENfVi6NW4gbgF63Ln2E-sEo4sb6GPA1g_dEUdItP_Aqjd4CaqNyym2ACax6QF2EFUztE5jo6LCg03T-V3q24ZLdGZVG-DqZ07Q-8P92_wpW7w8Ps_vFpnmIo9ZQQvLAHRjCsYrUuclQK4roqqSU6ghN6oslLI0J0orwwwXZSWEbSzLQZQNn6DZsVf7IQQPVq6965TfSUrkXpxM4uRenDyISws3x4X1punA_MV_TaWAOAYgffvLgZdBO-h1OtyDjtIM7r_ub3IEfnk</recordid><startdate>202011</startdate><enddate>202011</enddate><creator>Glatz-Hoppe, J.</creator><creator>Boldt, A.</creator><creator>Spiekers, H.</creator><creator>Mohr, E.</creator><creator>Losand, B.</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-4452-5645</orcidid><orcidid>https://orcid.org/0000-0003-1454-0123</orcidid><orcidid>https://orcid.org/0000-0001-6048-4359</orcidid><orcidid>https://orcid.org/0000-0002-8666-7839</orcidid><orcidid>https://orcid.org/0000-0003-3464-8465</orcidid></search><sort><creationdate>202011</creationdate><title>Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows</title><author>Glatz-Hoppe, J. ; Boldt, A. ; Spiekers, H. ; Mohr, E. ; Losand, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-515f2eecbd52370946ee4c70a7631e9e4da65aaf140acad2d386788fbf24e86b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>3-Hydroxybutyric Acid - blood</topic><topic>Animals</topic><topic>Body Weight</topic><topic>Cattle - metabolism</topic><topic>Cattle Diseases - metabolism</topic><topic>Cultured Milk Products</topic><topic>Dairying - methods</topic><topic>Diet - veterinary</topic><topic>energy balance</topic><topic>Energy Intake</topic><topic>fat:protein ratio</topic><topic>Fats - analysis</topic><topic>Fatty Acids, Nonesterified - blood</topic><topic>Female</topic><topic>Germany</topic><topic>Ketosis - blood</topic><topic>Ketosis - veterinary</topic><topic>Lactation</topic><topic>Milk - chemistry</topic><topic>Milk Proteins - analysis</topic><topic>milk urea</topic><topic>Nutritional Status - physiology</topic><topic>optiKuh project</topic><topic>protein balance</topic><topic>Urea - analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Glatz-Hoppe, J.</creatorcontrib><creatorcontrib>Boldt, A.</creatorcontrib><creatorcontrib>Spiekers, H.</creatorcontrib><creatorcontrib>Mohr, E.</creatorcontrib><creatorcontrib>Losand, B.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Journal of dairy science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Glatz-Hoppe, J.</au><au>Boldt, A.</au><au>Spiekers, H.</au><au>Mohr, E.</au><au>Losand, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows</atitle><jtitle>Journal of dairy science</jtitle><addtitle>J Dairy Sci</addtitle><date>2020-11</date><risdate>2020</risdate><volume>103</volume><issue>11</issue><spage>10175</spage><epage>10194</epage><pages>10175-10194</pages><issn>0022-0302</issn><eissn>1525-3198</eissn><abstract>A new evaluation scheme to assess the nutritional status of dairy cows on the basis of milk constituents was derived from 7.37 million German records of milk testing. The aim of this work was to validate this new scheme. Two data sets with fertility and health information (data set A) and with measured energy and nutrient intake and metabolic characteristics (data set B) were analyzed. Data set A included 32 commercial dairy farms in northeast Germany, with 72,982 records of 43,863 German Holstein cows; data set B included 12 German experimental farms, with 49,275 records of 1,650 German Holstein, Simmental, and Brown Swiss cows. Milk traits were linked to health disorders and metabolic and feeding characteristics. Frequently used limits of milk constituents were compared with ranges of the new “Dummerstorf feeding evaluation.” To distinguish an optimal from a deficient energy supply, a milk protein content ≥3.20% (previously used) and a milk fat:protein ratio (FPR) ≤1.4 (new scheme) were chosen and compared with feed energy intake in relation to demand. Energy status was more often correctly assigned by FPR than by milk protein content (80.7 and 68.7%, respectively). Over all data, the new optimum range of milk urea between 150 and 250 mg/L was better suited to dietary crude protein intake in relation to demand than the previously used range of 150 to 300 mg/L (42.4 and 38.0%, respectively). Ketosis or blood values associated with ketosis such as β-hydroxybutyrate >1.2 mmol/L or nonesterified fatty acids >1,000 µmol/L, as well as strong mobilization of body weight ≥1.5 kg/d, loss of back fat thickness ≥10 mm, and loss of body condition score ≥1 unit in first 60 days in milk were compared with different milk trait thresholds. For the updated scheme FPR >1.4 was used in combination with either milk protein content below the individual statistical lower limit of milk protein content, or milk fat content greater than the individual statistical upper limit of milk fat content; FPR >1.5 was taken as a frequently used threshold. For these ketosis indicators, the new scheme had higher sensitivities. Energy oversupply or the risk of overconditioning could not be identified by milk constituents alone. Urinary acid-base content was not related to milk content. Similarly, milk testing data did not allow a clear distinction to be made between the diagnoses of acidosis and, for example, ketosis. Essential requirements for good herd management are the continuous observation of milk testing data in combination with other established instruments of feeding and animal monitoring.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>32861490</pmid><doi>10.3168/jds.2019-17981</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-4452-5645</orcidid><orcidid>https://orcid.org/0000-0003-1454-0123</orcidid><orcidid>https://orcid.org/0000-0001-6048-4359</orcidid><orcidid>https://orcid.org/0000-0002-8666-7839</orcidid><orcidid>https://orcid.org/0000-0003-3464-8465</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 3-Hydroxybutyric Acid - blood Animals Body Weight Cattle - metabolism Cattle Diseases - metabolism Cultured Milk Products Dairying - methods Diet - veterinary energy balance Energy Intake fat:protein ratio Fats - analysis Fatty Acids, Nonesterified - blood Female Germany Ketosis - blood Ketosis - veterinary Lactation Milk - chemistry Milk Proteins - analysis milk urea Nutritional Status - physiology optiKuh project protein balance Urea - analysis |
| title | Relationship between milk constituents from milk testing and health, feeding, and metabolic data of dairy cows |
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