Reproduction in Domestic Animals

120 NC Friggens, M Bjerring, C Ridder, S Højsgaard and T Larsenby a classical ELISA method. It may be expected thatsuch a method is more precise than the types ofmeasurement resulting from in situ biosensors. Conversely,milk samples used in the present study werecollected from cows milked robotically, resulting invariable inter milking intervals, and thus variable milkfat content (Friggens and Rasmussen 2001). Thisintroduces a greater variability in the milk progesteronemeasurements (Waldmann et al. 1999) than wouldbe expected from milk samples collected more conventionally.On balance, the results presented in thisstudy probably reflect what can be expected undercommercial conditions, but this remains to be quantified.ConclusionsThe type of model needed to condense and interpretprogesterone profile data in real-time on-farm has beentested and found to perform substantially better in termsof sensitivity of oestrus detection than other existingdetection systems. The model also was shown to providevaluable information about other aspects of reproductivestatus such as pregnancy determination and commencementof luteal activity.AcknowledgementsWe gratefully acknowledge the valuable contribution of the farm staffat KFC, Jens Clausen, Carsten Berthelsen, Peter Løvendahl, JesNielsen, and Connie Middelhede for their efforts in securing thismassive number of samples. This study, which was part of the Biosensproject, was funded by the Danish Ministry of Food, Agriculture andFisheries and the Danish Cattle Association.AppendixDays to next sampling functions for the luteal phase and duringpregnancyThe original days to next sampling (DNS) function in the lutealphase (Friggens and Chagunda 2005) decreased sampling frequencyas duration of oestrus cycle (cyclen) increased towards 21 but thenstayed low in prolonged luteal phases. The function was changed intwo ways. The default luteal DNS function (DNS1LdefR) waschanged from a declining sigmoid to a declining sigmoid plus asubsequent climbing sigmoid (i.e. it rises again after expectedcyclen). The slope modifier was adjusted to use the absolutedifference between the maximum progesterone concentration sincelast oestrus (CycMax) and current progesterone concentration ratherthan the slope:DayFromNextOest = abs((Date - DayOest) - Cyclen)MaxStep ¼ Cyclen DNSLPropDNS1Ldef ¼ MaxStep expð expðDNSLRatðDayFromNextOest DNSLlagÞÞÞSlopeMod ¼ expðexpðSModRat*((CycMax - Level)(CycMax/SModT))))DNS ¼ DNS1Ldef SlopeModwhere date is the current day, DayOest is the day of the precedingoestrus, and level is the smoothed progesterone concentration (ng ⁄ ml).DNSLProp, DNSLRat, DNSLlag, SModRat, and SModT are constantswith the following values: 0.25, )0.4, 5, 0.75 and 4, respectively.The DNS function during pregnancy was modified in a similar way toconcentrate sampling frequency around the time approximately22 days post-insemination and reduce it thereafter:PregStepT = Cyclen + PregStepLagMaxStepPreg ¼ðTopPregStep BotPregStepÞ expð expðPregStepRat ððRunTime AITimeÞPregStepTÞÞÞ þ BotPregStepDNS2def ¼ MaxStepPreg expð expðDNSLRatðDayFromNextOest DNSLlagÞÞÞTimeFromAIFun ¼ expðexpðTfAIRat*((RunTimeAITime) - TfAIT)))offsetLevTime ¼ expð expðPLevRat ðLevel ðPLevTþ DNS2LevToffsetÞTimeFromAIFunRÞÞÞDNS2LevTime ¼ð1 ð1 TimeFromAIFunÞÞ offsetLevTime þð1 TimeFromAIFunÞDNSR ¼ DNS2def DNS2LevTimewhere RunTime is the current time and AITime is the time ofinsemination. PregStepLag, TopPregStep, BotPregStep, PregStepRat,TfAIRat, TfAIT, PLevRat, PLevT and DNS2LevToffset are constantswith the following values: 6, 10, 5, )0.4, )0.3, 12, )0.5, 12 and 6,respectively.ReferencesBulman DC, Lamming GE, 1978: Milk progesterone levelsin relation to conception, repeat breeding and factorsinfluencing acyclicity in dairy cows. J Reprod Fertil 54,447–458.Cavalieri J, Eagles VE, Ryan M, Macmillan KL, 2003:Comparison of four methods for detection of oestrus indairy cows with resynchronised oestrous cycles. Aust Vet J81, 422–425.de Mol RM, Keen A, Kroeze GH, Achten JMFH, 1999:Description of a detection model for oestrus and diseases indairy cattle based on time series analysis combined with aKalman filter. Comput Electron Agr 22, 171–185.de Mol RM, Ouweltjes W, Kroeze GH, Hendriks MMWB,2001: Detection of estrus and mastitis: field performance ofa model. Appl Eng Agr 17, 399–407.Delwiche MJ, Tang X, BonDurant RH, Munro CJ, 2001:Estrus detection with a progesterone biosensor. TransASAE 44, 2003–2008.Faustini M, Battocchio M, Vigo D, Prandi A, Veronesi MC,Comin A, Cairoli F, 2007: Pregnancy diagnosis in dairycows by whey progesterone analysis: an ROC approach.Theriogenology 67, 1386–1392.Firk R, Stamer E, Junge W, Krieter J, 2002: Automation ofoestrus detection in dairy cows: a review. Livest Prod Sci 75,219–232.Firk R, Stamer E, Junge W, Krieter J, 2003: Improving oestrusdetection by combination of activity measurements withinformation about previous oestrus cases. Livest Prod Sci82, 97–103.Friggens NC, Chagunda MGG, 2005: Prediction of thereproductive status of cattle on the basis of milk progesteronemeasures: model description. Theriogenology 64,155–190.Ó 2008 The Authors. Journal compilation Ó 2008 Blackwell Verlag