Use of a stochastic simulation model to assess effects of diagnostic specificity of systems for detecting ovulating cows on herd reproductive performance in year-round calving dairy herds

Hockey, C.D. and Morton, J.M. (2010) Use of a stochastic simulation model to assess effects of diagnostic specificity of systems for detecting ovulating cows on herd reproductive performance in year-round calving dairy herds. Animal Reproduction Science, 122 3-4: 189-199. doi:10.1016/j.anireprosci.2010.08.009


Author Hockey, C.D.
Morton, J.M.
Title Use of a stochastic simulation model to assess effects of diagnostic specificity of systems for detecting ovulating cows on herd reproductive performance in year-round calving dairy herds
Journal name Animal Reproduction Science   Check publisher's open access policy
ISSN 0378-4320
1873-2232
Publication date 2010-12
Sub-type Article (original research)
DOI 10.1016/j.anireprosci.2010.08.009
Volume 122
Issue 3-4
Start page 189
End page 199
Total pages 11
Place of publication Amsterdam, Netherlands.
Publisher Elsevier BV
Collection year 2011
Language eng
Abstract Many automated systems for detecting ovulating cows in dairy herds require decisions when designing algorithms and selecting cutpoints that require a compromise between diagnostic sensitivity (probability of classifying an ovulating cow as ovulating) and diagnostic specificity [daily probability of not classifying a non-ovulating cow (whether open or pregnant but not yet diagnosed as pregnant) as ovulating]. Because sensitivity must be moderately high, this compromise often results in specificity below 100%. However, little is understood about the effects of reduced specificity on herd reproductive performance. A stochastic model was developed that simulates the reproductive process in a year-round calving dairy herd to assess effects of changes in specificity at various combinations of sensitivity and conception rate (proportion of inseminations resulting in pregnancy) on herd reproductive measures of economic importance. The model included effects of inseminations in pregnant cows on probability of conceptus loss, and variation in the interval from conceptus loss to next ovulation (i.e. the next opportunity to reconceive). Using moderate assumptions of the probability of conceptus loss following insemination in pregnant cows, reductions in specificity from 99.9 to 99.5, 99, 98 and 97%, resulted in decreases in mean 100 day in-calf rate (100DICR; the proportion of cows with a positive pregnancy diagnosis to an insemination on or before 100 days since calving) of 1.2, 3.3, 6.8 and 9.7 percentage points, respectively. These same reductions in Sp resulted in increases in mean 200 day not in-calf rate (200DNICR; the proportion of cows with negative pregnancy diagnosis results to all inseminations on or before 200 days since calving) of 0.5, 1.6, 3.6 and 6 percentage points, and increases in mean number of inseminations per calving (Insems/Calving; the total number of inseminations in the herd divided by the number of cows that recalved) by factors of 1.2, 1.5, 2.1 and 2.8, respectively. The relationship between specificity for detecting ovulating cows and the 100DICR, 200DNICR and Insems/Calving was sensitive to changes in the probability of conceptus loss following inseminations in pregnant cows. However, even with conservative assumptions, specificity still had important effects on 100DICR and 200DNICR. Varying parameters for the interval from conceptus loss to next ovulation had little effect on the relationships between specificity and these measures. These results demonstrate that specificity is an important consideration when designing algorithms and selecting cutpoints in automated systems for detecting ovulating cows. Low specificity not only increases Insems/Calving but also prolongs intervals from calving to the establishment of a sustained pregnancy resulting in substantial reductions in 100DICR and increases in 200DNICR. This model could assist when determining economically optimal combinations of ovulation detection sensitivity and specificity when developing automated systems for selecting ovulating cows in commercial herds. © 2010 Elsevier B.V.
Keyword Computer model
Reproduction
Oestrus
Ovulation
Dairy
Cow
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Veterinary Science Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Sun, 20 Feb 2011, 00:07:05 EST