Dynamic forecasting of individual cow milk yield in automatic milking systems

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

LanguageEnglish
Pages10428-10439
JournalJournal of Dairy Science
Volume101
Issue number11
Early online date30 Aug 2018
DOIs
Publication statusPublished - Nov 2018

Fingerprint

milking
dynamic models
milk yield
Linear Models
Milk
linear models
milk
somatic cell count
farms
Mastitis
Cell Count
robots
lactation stage
Lactation
mastitis
Cell Communication
dairies
dairy cows
planning
farmers

Keywords

  • dairy cow
  • dynamic linear model
  • milk yield
  • somatic cell count

Cite this

@article{76a1601a653045a4ab653dffcf79d20d,
title = "Dynamic forecasting of individual cow milk yield in automatic milking systems",
abstract = "Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.",
keywords = "dairy cow, dynamic linear model, milk yield, somatic cell count",
author = "Jensen, {Dan B.} and {van der Voort}, Mariska and Henk Hogeveen",
year = "2018",
month = "11",
doi = "10.3168/jds.2017-14134",
language = "English",
volume = "101",
pages = "10428--10439",
journal = "Journal of Dairy Science",
issn = "0022-0302",
publisher = "American Dairy Science Association",
number = "11",

}

Dynamic forecasting of individual cow milk yield in automatic milking systems. / Jensen, Dan B.; van der Voort, Mariska; Hogeveen, Henk.

In: Journal of Dairy Science, Vol. 101, No. 11, 11.2018, p. 10428-10439.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Dynamic forecasting of individual cow milk yield in automatic milking systems

AU - Jensen, Dan B.

AU - van der Voort, Mariska

AU - Hogeveen, Henk

PY - 2018/11

Y1 - 2018/11

N2 - Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

AB - Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per milking, as they are milked in milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given milking. Data from 169,774 milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in milking robots, and that this model has potential to be used as part of a mastitis detection system.

KW - dairy cow

KW - dynamic linear model

KW - milk yield

KW - somatic cell count

U2 - 10.3168/jds.2017-14134

DO - 10.3168/jds.2017-14134

M3 - Article

VL - 101

SP - 10428

EP - 10439

JO - Journal of Dairy Science

T2 - Journal of Dairy Science

JF - Journal of Dairy Science

SN - 0022-0302

IS - 11

ER -