Machine learning based prediction of insufficient herbage allowance with automated feeding behaviour and activity data

peer-reviewedSensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system

MCMC Exploration of Supermassive Black Hole Binary Inspirals

The Laser Interferometer Space Antenna will be able to detect the inspiral and merger of Super Massive Black Hole Binaries (SMBHBs) anywhere in the Universe. Standard matched filtering techniques can be used to detect and characterize these systems. Markov Chain Monte Carlo (MCMC) methods are ideally suited to this and other LISA data analysis problems as they are able to efficiently handle models with large dimensions. Here we compare the posterior parameter distributions derived by an MCMC algorithm with the distributions predicted by the Fisher information matrix. We find excellent agreement for the extrinsic parameters, while the Fisher matrix slightly overestimates errors in the intrinsic parameters.Comment: Submitted to CQG as a GWDAW-10 Conference Proceedings, 9 pages, 5 figures, Published Versio

Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows

peer-reviewedFeeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC = 0.96 for grazing, CCC = 0.99 for rumination, CCC = 1.00 for standing and lying and CCC = 0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen‘s Kappa (κ) = 0.62 and κ = 0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC = 0.78 and CCC = 0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment

Automated monitoring of urination events from grazing cattle

AbstractUrine patches deposited by grazing cattle represent ‘hot-spots’ of very high nitrogen (N) loading from which environmentally important losses of N may occur (ammonia and nitrous oxide emissions, nitrate leaching). Information on the quantities of N deposited to grazed pastures as urine, the spatial and temporal distribution of urine patches and how these may be influenced by pasture management practices is limited. The objectives of this study were to assess the potential of recently developed urine sensors for providing data on urination behaviour by grazing cattle and relate this to measurements of ammonia emissions from the grazed paddocks. A total of six trials were conducted across two sites; two on a 1ha paddock at Easter Bush near Edinburgh using beef cattle (c. 630kg live weight) and four on a 0.5ha paddock at North Wyke in Devon using in-calf dairy heifers (c. 450kg live weight). Laboratory calibrations were conducted to provide sensor-specific functions for urine volume and N concentration. The quantity and quality of data from the urine sensors improved with successive trials through modifications to the method of attachment to the cow. The number of urination events per animal per day was greater for the dairy heifers, with a mean value of 11.6 (se 0.70) compared with 7.6 (se 0.76) for the beef cattle. Volume per urination event (mean 1.8, range 0.4–6.4L) and urine N concentration (range 0.6–31.5gL−1, excluding outliers) were similar for the two groups of cattle. Ammonia emission measurements were unsuccessful in most of the trials. The urine sensors have potential to provide useful information on urine N deposition by grazing cattle but suggested improvements including making the sensors lighter, designing a better method of attachment to the cow and including a more reliable location sensor