Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model
Genetic selection programs have driven development of most lactation models, to estimate the magnitude of animals’ productive capacity from sampled milk production data. There has been less attention to management and research applications, where it may also be important to quantify the shape of lac...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2013-03-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/54.pdf |
| _version_ | 1797425567684886528 |
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| author | James L. Ehrlich |
| author_facet | James L. Ehrlich |
| author_sort | James L. Ehrlich |
| collection | DOAJ |
| description | Genetic selection programs have driven development of most lactation models, to estimate the magnitude of animals’ productive capacity from sampled milk production data. There has been less attention to management and research applications, where it may also be important to quantify the shape of lactation curves, and predict future daily milk production for incomplete lactations since residuals between predicted and actual daily production can be used to quantify the response to an intervention. A model may decrease the confounding effects of lactation stage, parity, breed, and possibly other factors depending on how the model is constructed and used, thus increasing the power of statistical analyses. Models with a mechanistic derivation may allow direct inference about biology from fitted production data. The MilkBot® lactation model is derived from abstract suppositions about growth of udder capacity. This permits inference about shape of the lactation curve directly from parameter values, but not direct conclusions about physiology. Individual parameters relate to the overall scale of the lactation, the ramp, or rate of growth around parturition, decay describing the senescence of productive capacity (inversely related to persistence), and the relatively insignificant time offset between calving and the physiological start of milk secretion. A proprietary algorithm was used to fit monthly test data from two parity groups in 21 randomly selected herds, and results displayed in box-and-whisker charts and Z-test tables. Fitted curves are constrained by the MilkBot® equation to a single peak that blends into an exponential decline in late lactation. This is seen as an abstraction of productive capacity, with actual daily production higher or lower due to random error plus short-term environmental effects. The four MilkBot® parameters, and metrics calculated directly from them including fitting error, peak milk and cumulative production, can be used to describe and compare individual lactations or groups of lactations. There is considerable intra-herd and inter-herd variability in scale, ramp, decay, RMSE, peak milk, and cumulative production, suggesting that management and environment have significant influence on both shape and magnitude of normal lactation curves. |
| first_indexed | 2024-03-09T08:17:58Z |
| format | Article |
| id | doaj.art-c660ea55f66142e3bce447bb08f09f9c |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T08:17:58Z |
| publishDate | 2013-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-c660ea55f66142e3bce447bb08f09f9c2023-12-02T21:59:42ZengPeerJ Inc.PeerJ2167-83592013-03-011e5410.7717/peerj.5454Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation modelJames L. Ehrlich0Dairy Veterinarians Group, Argyle, NY, USAGenetic selection programs have driven development of most lactation models, to estimate the magnitude of animals’ productive capacity from sampled milk production data. There has been less attention to management and research applications, where it may also be important to quantify the shape of lactation curves, and predict future daily milk production for incomplete lactations since residuals between predicted and actual daily production can be used to quantify the response to an intervention. A model may decrease the confounding effects of lactation stage, parity, breed, and possibly other factors depending on how the model is constructed and used, thus increasing the power of statistical analyses. Models with a mechanistic derivation may allow direct inference about biology from fitted production data. The MilkBot® lactation model is derived from abstract suppositions about growth of udder capacity. This permits inference about shape of the lactation curve directly from parameter values, but not direct conclusions about physiology. Individual parameters relate to the overall scale of the lactation, the ramp, or rate of growth around parturition, decay describing the senescence of productive capacity (inversely related to persistence), and the relatively insignificant time offset between calving and the physiological start of milk secretion. A proprietary algorithm was used to fit monthly test data from two parity groups in 21 randomly selected herds, and results displayed in box-and-whisker charts and Z-test tables. Fitted curves are constrained by the MilkBot® equation to a single peak that blends into an exponential decline in late lactation. This is seen as an abstraction of productive capacity, with actual daily production higher or lower due to random error plus short-term environmental effects. The four MilkBot® parameters, and metrics calculated directly from them including fitting error, peak milk and cumulative production, can be used to describe and compare individual lactations or groups of lactations. There is considerable intra-herd and inter-herd variability in scale, ramp, decay, RMSE, peak milk, and cumulative production, suggesting that management and environment have significant influence on both shape and magnitude of normal lactation curves.https://peerj.com/articles/54.pdfLactation curvePersistencyMilkBotDairy managementLactation |
| spellingShingle | James L. Ehrlich Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model PeerJ Lactation curve Persistency MilkBot Dairy management Lactation |
| title | Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model |
| title_full | Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model |
| title_fullStr | Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model |
| title_full_unstemmed | Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model |
| title_short | Quantifying inter-group variability in lactation curve shape and magnitude with the MilkBot® lactation model |
| title_sort | quantifying inter group variability in lactation curve shape and magnitude with the milkbot r lactation model |
| topic | Lactation curve Persistency MilkBot Dairy management Lactation |
| url | https://peerj.com/articles/54.pdf |
| work_keys_str_mv | AT jameslehrlich quantifyingintergroupvariabilityinlactationcurveshapeandmagnitudewiththemilkbotlactationmodel |