Prepartum Behaviour as a Predictor of Postpartum Disease

refers to how many calves a cow has had e.g. a first parity cow = has had one calf

cow who has recently calved

• aprox 3 weeks pre to 3 weeks post calving - transition from late preg (dry period = no milk production) to early lactation

• depressed immune funtion • opening of teat canals • changes in hormone expression (e.g. to relax pelvic ligaments) • body condition loss after calving

• negative energy balance = caloric requirements for fresh cow are so high that many dont consume enough feed to compensate for calories they burn • subclinical ketosis affects = 11-49% fresh cows (high serum ketone conc but no clinical signs) • hyperketonaemia = abnormally high ketone bodies in blood

= inflammation of the uterus usually caused by bacterial infection • enlarged uterus • watery brown discharge • reduced milk yield, dullness, toxaemia, high fever • most common in first 10 days post calving • 21-40% cows affected • Risk factors = heifer cows that have experienced dystocia, retained placenta (failure to expel fetal membrane w/in 24 hours) or other calving problems (e.g. milk fever, ketosis)

• collected feeding/ drinking behaviour measurements from 101 dairy cows • from 2 weeks before calving up until 3 weeks post calving • feed intake data included • electronic monitoring system • collected social behaviour data assessed from video recordings • metritis diagnosed based upon body temperature and vaginal discharge until 21 days post partum

• for every 10 min decrease in daily feeding time = odds of severe metritis after calving increased by 1.7x • for every 1kg decrease in dry matter intake = odds metritis after calving increased by 3x • cows later diagnosed with severe metritis = engaged in fewer aggressive (agonistic) interactions • first research showing social behaviour plays role in transition cow health

• indicator for subclinical ketosis • for every 10 min decrease in daily feeding time = risk sub clinical ketosis increased by 1.9x • for every 1kg decrease in daily DMI = risk of SCK increased by 2.2x • animals later diagnosed with SCK = initiated fewer displacements at feed bunk

• Insentec food bins able to record individual feed intake and feeding behaviour • an electronic monitoring system • depend on RFID system ~ cows wear radio frequency identification ear tags ~ bin records which cow is eating and for how long and how much each cow consuming ~

Yes • competitive interactions can be quantified using data from electronic feeding system • optimal interval for predicting replacements at feed bunk = 26 seconds (specificity = 82%, sensitivity = 86%) • validate results to feeding video recordings

= proportion of positives correctly identified as being positive (e. g. proportion of sick animals correctly identified as being sick - 86% replacemens at feedbunk (as identified in video) were correctly identified as replacements by the electronic feed bin algorithm

= proportion of negatives correctly identified as being negative (e. g. proportion of healthy animals correctly identified as being healthy) - 82% of times cows switched places at feedbunks NON-aggressively (without displacing and replacing another cow) were correctly identified as non-aggressive

• Mcdonald, von Keyserlingk and Weary 2019 • optimal interval to identify replacement = 29 seconds (82% sensitivity, 83% specificity • Foris et al 2019 validated a replacement detection algorithm using combined data from electronic water and feed bins • recal and precision of algorithm very high (average >0.8) comparable to human trained observers

• conducted on single farms designated for research purposes = how can they be generalised to the commercial context? need to be used on several commercial farms • considered full day data on feeding and agonistic behaviours = farmers wont have time to analyse days worth of data • considered either ketosis or metritis, not both = do cows later diagnosed with both behave differently to those with one disease and healthy animals

• assess if measures of feeding and competitive behaviour prepartum were associated with hyperketonaemia and metritis on commercial farms • 5 commercial farms • specifically looked at first 90 mins after fresh feed delivery to evaluate feeding behaviour and agonistic interactions • actor (bully) and reactor (bullied) interactions

• cows with both postpartum diseases had significantly less interactions than health and one condition cows • very small difference between healthy and one condition cows for the no. interactions • not much pattern in prepartum interactions and whether or not the cow remained health in reactor cows • ## actor = healthy more likely to be involved in more agonistic interactions than one condition and 2 condition cows

• animals that did not eat during observation period (90 mins after fresh feed delivery) = 89% chance of becoming sick with at least one condition after calving • cows that spent only 30 mins eating = >82% chance of being sick with at least one condition post calving • drop of feeding time could indicate development of at least one condition

Prediction: - aims to predict future events by analysising patterns likely to forecast future results - based upon past behaviour, what is likely to happen in the future Association: - understanding a phenomenon - simple relationships between variables and outcomes

• most of the data are used to develop the model = training data • remainder of data used to test the model = test data • typical split 60:40 (training:testing) more training than testing

• incidence of transition cow disease has remained relatively unchanged over time, despite efforts to improve monitoring and adjust management to reduce disease risk • risk factors can be assessed using a variety of sources (e.g. health records, feed intake, BCS, milk production data) ~ bring together most important variables and their interactions

• try to determine relationship between BCS and post partum disease status • But what if the effect of BCS on postpartum disease status depends on cows parity = interaction ( 2 variables but relationship between them depends upon the influence of third variable • cows with fisrst parity dont see much of a difference on probability of disease for low or high BCS • But 2nd or above parity = cows with higher BCS far more likely to become sick • cross over interaction = first parity w/low BCS more likely to get sick but 2nd parity less likely to be sick if low BCS

• recorded behaviour of 318 cows using electronic feeding system • after calving, cows monitored for signs of metritis hyperketonaemia and mastitis • split data using stratified random method: 70:30 • model showed that animals at risk of becoming sick postpartum can be identified with reasonable success = se=73% sp=80% • found a no. of interactions between number of visits to the feed bins, time spent feeding & kg feed consumed on probability of becoming sick post partum

• cost per case of hyperketonemia = £88 • cost per case of metritis = £80 • according to model: the 60% cows with highest probability of becoming ill postpartum, will include 97% of cows that will actually become sick • if model can be automated for farm use = used to predict which cows are at highest risk of becoming sick • offers opportunity for farmers to prevent illness from occurring = allow intervention and save money on treating sick animals • allow quick data input from farmers and get a result instantly (may lead to healthy animals being treated as no model is perfect representation of reality) • need a cost analysis to see if worth cost for future studies