novel approaches to expression and detection of oestrus in dairy cows

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which is more difficult in a commercial situation (Friggens and Chagunda, 2005) to produce usable data for predicting potential eligibility for AI. A more sustainable method of hormone analysis for oestrous detection would be to use an automated in-line approach (Delwiche et al., 2001a). The progesterone biosensor has been reported to correctly identify all 19 ovulatory events occurring in one particular study, although it had a 26% error rate arising from variability and percentage fat in individual milk samples (Delwiche et al., 2001a). A commercially available method of inline milk progesterone monitoring is HerdNavigator with elements to detect health, fertility and metabolic status (Lovendahl and Friggens, 2008). The method, as described by Friggens and Chagunda (2005), has reported 99.2% detection using the model of confirmed oestruses (where insemination resulted in a confirmed pregnancy, n=121) (Friggens et al., 2008). Furthermore this method detected oestrus in a number of cases (n=16) where progesterone concentration did not decrease below 4ng/ml, the set threshold (Friggens et al., 2008), which is advantageous in situations where cows may not display behavioural oestrus due to high progesterone concentration. Furthermore this model also had sufficiently high detection rate when using ratified oestrus (where the shape of the progesterone profile matches that of the average progesterone profile at confirmed oestrus) as a measure of detection confirmation; 93.7% (Friggens et al., 2008). However despite this method performing as well as other detection methods, progesterone monitoring is still not precise at detecting the correct time for AI (Lovendahl and Friggens, 2008), unlike increases in oestradiol concentration which correlate more precisely with time of ovulation (Lopez et al., 2002). It has been reported that fixed time AI on the basis of progesterone concentration actually decreased pregnancy rate (Eddy and Clark, 1987). Progesterone measurements can however aid detection by determining errors in oestrous detection methods (Nebel, 1988). By accurately predicting physiological status and any irregularities in oestrous cycles (Lovendahl and Friggens, 2008) and through detection of silent oestrus where cows fail to display any overt behaviour (Ranasinghe et al., 2010) as described in the current study where 2 synchronised cows with low progesterone did not demonstrate oestrous behaviour, progesterone monitoring is advantageous. Due to the disadvantages of milk progesterone monitoring Lovendahl and Friggens (2008) decided to combine activity monitoring with milk progesterone measurements (where possible) to overcome the negative aspects. Activity 146
monitoring could also monitor non-lactating heifers, yet in lactating cows aid progesterone measurements to provide a more accurate estimate of time of ovulation and optimal time to AI, as activity measurements are given every 1 or 2 hours (Lovendahl and Friggens, 2008). Combining methods of detection has been reported to increase detection rate (Peralta et al., 2005) where this work is advantageous because future generations of UWB have potential to incorporate activity monitoring as well as mounting and standing to be mounted. It is well known that activity increases at the time of oestrus as first reported by Farris (1954) and later confirmed by many others. Activity has been reported to increase from 2 to 4 fold at the time of oestrus, and varies widely per cow (Kiddy, 1977). Activity measurements are valuable for detecting oestrus. Detection rates for monitoring oestrus with pedometers and activity monitors average 80%-90% in most investigations (Lehrer et al., 1992;Firk et al., 2002), which is sufficiently high for detection of oestrus, and there have also been reports of 100% detection rate (Schofield et al., 1991;Arney et al., 1994). Although it has been suggested by Van Vliet and Van Eerdenburg (1996) that activity monitoring must be combined with observation of standing heat in order to detect all cows in oestrus. This is because the increases in activity recorded by pedometers in their study were only related to specific types of oestrous behaviour (Van Vliet and Van Eerdenburg, 1996). However in contrast to the high detection rate, error rate is also high, reported between 17-55%, attracting a large number of false positive responses (Firk et al., 2002) which is one of the main problems associated with automated technologies. Peralta et al., (2005) compared the efficiency of detection systems and concluded that detection efficiency was only 37.2% by comparing detected periods of oestrus with total number of oestrus periods, although this study was carried out under heat stressed conditions. However, efficiency is largely affected by the threshold used to define the increase in activity as it differs between cows (Table 5.8; Roelofs et al., 2005) and can be largely affected by routine management tasks (as seen in this study; Figure 5.9 to 5.11) such as introducing new cows to an established group where new dominance order is reestablished increasing activity, indicating false oestrus events. Therefore this method may still require use of cow calendars to achieve high detection rates. However, increase in activity cannot always accurately predict time of ovulation as it has been reported that there is a reduced relationship between standing to be mounted by 147
Page 1 and 2:
NOVEL APPROACHES TO EXPRESSION AND
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TABLE OF CONTENTS TABLE OF CONTENTS
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2.2.3 Statistical Analyses ........
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5.3 RESULTS .......................
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Furthermore, automated software was
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LIST OF FIGURES Figure 1.1 Hormonal
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LIST OF TABLES Table 1.1 Trends in
Page 15 and 16:
LIST OF ABBREVIATIONS ˚ ˚C μM 2D
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CHAPTER 1 - Introduction & Literatu
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the past 50 years and duration of o
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Oestrus growing follicle (Staigmill
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ecomes the main inhibitor of FSH an
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calf at 40-50 days post partum; inv
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al., 2006). However, aged sperm hav
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can occur within 2-3 days, but if t
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educes the incidence of problem cow
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oestradiol, the LH surge and ovulat
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The secondary signs of oestrus can
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There are also changes in normal be
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engage in more natural behaviours i
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1983). Exact explanations and mecha
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2006) and disruption of LH secretio
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1.4.3.2 Milk Yield and Nutrition Di
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influence the ability of the ovary
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cyclicity can be delayed if dietary
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indication for the optimal time to
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calving), 3) pre-breeding heat date
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1.5.2 Physiological Changes Physiol
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1.5.2.3 Body and Milk Temperature T
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physical activity and stage of the
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caused by the general environment t
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may be gained. This is because data
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In summary the objective was to for
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diet, with concentrates at milking.
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oestrus was defined as 3 consecutiv
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The interaction between parity and
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individual oestrus was not signific
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Table 2.4 The effects of the intera
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(361 vs. 578 points, respectively,
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oestrous expression with increasing
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the blood (Sangsritavong et al., 20
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CHAPTER 3 - Single Nucleotide Polym
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population owing to previous select
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Table 3.1 Cont. Follicle Stimulatin
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3.2.4 Sequencing of DNA in the Labo
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was achieved. PCR products were rem
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3.4 DISCUSSION The objectives of th
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fertility and oestrous expression.
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CHAPTER 4 - Development of a Novel
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In summary UWB seems a good option
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Initial tests were carried out to i
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Therefore this demonstrates that X
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which is most important for achievi
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Figure 4.9 Horizontal - Vertical Di
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that UWB is matching the ‘truth
Page 111 and 112: mounting cow. For example height ch
Page 113 and 114: Backpack 1 st put on in AI stalls E
Page 115 and 116: Cows’ behaviour was assessed at 5
Page 117 and 118: or that the cows were in an area of
Page 119 and 120: plane, but mostly in achieving high
Page 121 and 122: Develop techniques for analysis of
Page 123 and 124: in elastic silicone moulded over a
Page 125 and 126: and time of mount, duration of moun
Page 127 and 128: observed matched with increases in
Page 129 and 130: 5.3 RESULTS Results demonstrate pos
Page 131 and 132: Figure 5.2 Graph showing mounting b
Page 133 and 134: Table 5.1 Results from POC 2 showin
Page 135 and 136: Table 5.3 Efficiency and accuracy o
Page 137 and 138: oestrus and oestrus it is clear to
Page 139 and 140: P4 Concentration, ng/ml P4 Concentr
Page 147 and 148: Activity Activity Activity Activity
Page 153 and 154: 5.4 DISCUSSION The aim of this work
Page 155 and 156: averaged 70% which was lower than t
Page 157 and 158: when the cows lie down, especially
Page 159 and 160: the level of oestrous activity was
Page 161: technology larger herds must be mon
Page 165 and 166: antennae which could affect accurac
Page 167 and 168: predispose cows to illness e.g. met
Page 169 and 170: CHAPTER 6 - Overall Discussion & Co
Page 171 and 172: oestrus, h 2 =0.27 (Lovendahl et al
Page 173 and 174: The final section of this thesis de
Page 175 and 176: ate. By using UWB in a commercial s
Page 177 and 178: provides an insight the potential o
Page 179 and 180: BLOWEY, R. (2005) Factors associate
Page 181 and 182: ovine gonadotrophin releasing hormo
Page 183 and 184: GINTHER, O. J., KOT, K., KULICK, L.
Page 185 and 186: associated with embryonic survival
Page 187 and 188: ody condition at parturition on end
Page 189 and 190: Signs in a 24-h Tie-Stalled Dairy H
Page 191 and 192: SHERMAN, E. L., NKRUMAH, J. D., MUR
Page 193 and 194: Influence of negative energy balanc
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