@conference {3678,
	title = {Practical implications of theoretical modelling of in-line milk meters},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {74},
	year = {2014},
	month = {06/2014},
	pages = {35-40},
	address = {Napier},
	abstract = {In-line milk meters are devices which measure the milk yield of dairy cows at every milking; however, they are also susceptible to error. This results in frequent, but inaccurate, measurements. This paper attempts to model a lactation curve and accumulated milk yields from these devices. Wood{\textquoteright}s model of a lactation curve was used to predict milk yield. This was modified by adding both systemic and random daily variation. The meters were modelled by taking the modified Wood{\textquoteright}s lactation curve for each cow and altering it with various error terms. Along with precision, each sensor also had a rate of drift, and a rate at which its variance grew. It was found that the precision of a sensor can be low, however by aggregating multiple records it is possible to obtain an accurate value for total yield. One strategy that improved the total yield accuracy was to scale the measurements obtained for a particular sensor in a given milking. Due to the uncertainty surrounding the errors involved, and the large assumptions that were made, this model is a good starting point for future work. It is able to identify the relative effect of errors on meter accuracy, and test strategies to improve accuracy of daily and total milk yield.},
	issn = {1176-5283},
	author = {Dowson O and Anderson G and Hempstalk K}
}
@conference {89261,
	title = {Verification of an automated camera-based system of oestrus detection in dairy cows},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {73},
	year = {2013},
	month = {Jan},
	pages = {26-28},
	publisher = {New Zealand Society of Animal Production},
	organization = {New Zealand Society of Animal Production},
	address = {Hamilton},
	abstract = {<p>Accurate oestrus detection is critical in New Zealand dairy farming, where many farms operate a seasonal farming model and, therefore, rely on a compact calving spread and a short mating period to farm efficiently. Automated oestrus detection systems are available that automatically identify and draft cows in oestrus. There are two types of automatic systems available commercially in New Zealand; activity-based systems that involve pedometers/accelerometers and systems that detect mounting behaviour using a sensor attached to the animal{\textquoteright}s back.</p>
},
	issn = {1176-5283},
	author = {Hempstalk K and Burke CR and Kamphuis C}
}
@conference {69460,
	title = {Automatic reading of Kamars for heat detection in dairy herds},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {70},
	year = {2010},
	month = {Jun},
	pages = {8-12},
	publisher = {New Zealand Society of Animal Production},
	organization = {New Zealand Society of Animal Production},
	address = {Palmerston North},
	abstract = {Most existing heat detection systems for dairy cattle are labour-intensive requiring manual inspection of 
every animal to maximise submission rates. Our solution is to automate heat detection using Kamar HeatMount 
Detectors and a camera-based recognition system. The goal of our system is to obtain 99\% sensitivity and 
specificity, ensuring that cows that are on heat are rarely missed and over-drafts are kept to a minimum. Trials 
of various camera prototypes have been undertaken over the past three years, most recently during the artificial 
breeding period in the Spring of 2009. Over 70,000 images from four farms were taken during the latest trials; 
these photographs were used to assess the system. When considering photo observations, the fully automated 
system had an error rate less than 1\% across all sites in the latest trial, and reaches our goal of 99\% sensitivity 
and specificity. },
	issn = {1176-5283},
	author = {Hempstalk K and Harris BL and Lopdell TJ}
}
@conference {69529,
	title = {Improving the power of activity-based heat detection using additional automatically captured data},
	booktitle = {Proceedings of the New Zealand Society of Animal Production},
	volume = {70},
	year = {2010},
	month = {Jun},
	pages = {299-302},
	publisher = {New Zealand Society of Animal Production},
	organization = {New Zealand Society of Animal Production},
	address = {Palmerston North},
	abstract = {The purpose of this study was to determine the current performance of activity monitoring devices on New Zealand pasture-based farms and investigate the potential for improved detection using additional automatically captured data. The data consisted of heat events that were assigned from pregnancy diagnosis and mating records, and from milk progesterone levels collected twice weekly during mating period. Daily milk production, milking order, milk flow rate and milk conductivity records were also collected during the mating period. The best single predictor of oestrus was 24 hour milk yield difference (P <0.01) for Herd 1 and normalised milking order (P <0.01) for Herd 2. The normalised pedometer data was the next best predictor of oestrus in both herds (P <0.01). A linear logistic regression model was fitted within each herd. The best model included normalised milk production, milking order, milk flow rate and pedometer variables. Machine learning models with balanced bagging were also fitted to the data. The machine learning models provided a better fit than traditional statistical models. Pedometer data can aid in the detection of cow oestrus, however the power of detection improves significantly with the addition of milk yield, milk flow and milking order data.},
	issn = {1176-5283},
	author = {Harris BL and Hempstalk K and de le Rue BT and Jago JG and McGowan JE}
}
