Growth of pasture plants (2012)

"Agriculture.""Dairy grazing.""Dairy grazing publication series: This publication is one in a series about operating and managing a pasture-based dairy. Although these publications often refer to conditions in Missouri, many of the principles and concepts described may apply to operations throughout the United States.""Revised by Robert L. Kallenbach, Forages State Specialist, Division of Plant Sciences.""This publication replaces Chapter 3, Growth of Forage Plants, in MU Extension publication M168, Dairy Grazing Manual. Original author: Greg J. Bishop-Hurley, University of Missouri."New 2/12/Web

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal specie

Opportunities for Improving Livestock Production with e-Management Systems

There is increased interest in hardware and software that can support e-Management for grassland-based livestock industries. Managers of grazing livestock were early adopters of radio frequency identification (RFID) technologies automatically monitoring individual animal performance. Recent developments of remote sensing, automated individual recording and management, location based systems, improved data transfer and technologies that can be used in more extensive grazing systems are providing new opportunities for the development of e-Management systems. There is a need for better data integration and systems that can provide the best available information to enable better decision-making. For greater industry adoption of more integrated e-Management systems, there needs to be a clear economic value. With increased on farm monitoring and the expansion of digital data sources, grazing livestock production systems have the opportunity to expand production efficiency through the implementation of e-Management

Economics of pasture-based dairies (2012)

"Agriculture.""Dairy grazing.""Dairy grazing publication series: This publication is one in a series about operating and managing a pasture-based dairy. Although these publications often refer to conditions in Missouri, many of the principles and concepts described may apply to operations throughout the United States. A list of the publications in this series is available online at http://extension.missouri.edu/m168.""Revised from M168, Dairy Grazing Manual, by Joe Horner, Dairy Economist, Commercial Agriculture Program, Ryan Milhollin, Project Manager, Commercial Agriculture Program, Wayne Prewitt, West Central Region Agriculture Business Specialist.""This publication replaces Chapter 14, Economics of a Pasture-Based Dairy, in MU Extension publication M168, Dairy Grazing Manual. Original authors: Stacey A. Hamilton, Greg J. Bishop-Hurley and Ron Young, University of Missouri."New 2/12/Web

A noise robust automatic radiolocation animal tracking system

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle

Energy-efficient Localization for Virtual Fencing

International audienceThis poster addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. It focuses on combining GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. The focus is on an outdoor location monitoring application for tracking cattle using smart collars that contain wireless sensor nodes and GPS modules. We use empirically-derived models to explore duty cycling strategies for maintaining position uncertainty within specified bounds. Specifically we explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off. Results show that GPS combined with radio-contact logging is effective in extending node lifetime while meeting application-specific positioning criteria

Animal behaviour understanding using wireless sensor networks

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animal's behaviour and activities successfull

Animal control - What constitutes a reliable cue to stop animal movement?

Controlling free-ranging livestock requires low-stress cues to alter animal behaviour. Recently modulated sound and electric shock were demonstrated to be effective in controlling free-ranging cattle. In this study the behaviour of 60, 300 kg Belmont Red heifers were observed for behavioural changes when presented cues designed to impede their movement through an alley. The heifers were given an overnight drylot shrink off feed but not drinking water prior to being tested. Individual cattle were allowed to move down a 6.5 m wide alley towards a pen of peers and feed located 71 m from their point of release. Each animal was allowed to move through the alley unimpeded five times to establish a basal behavioural pattern. Animals were then randomly assigned to treatments consisting of sound plus shock, vibration plus shock, a visual cue plus shock, shock by itself and a control. The time each animal required to reach the pen of peers and feed was recorded. If the animal was prevented from reaching the pen of peers and feed by not penetrating through the cue barrier at set points along the alley for at least 60 sec the test was stopped and the animal was returned to peers located behind the release pen. Cues and shock were manually applied from a laptop while animals were observed from a 3.5 m tower located outside the alley. Electric shock, sound, vibration and Global Position System (GPS) hardware were housed in a neck collar. Results and implications will be discussed

Impact of communication technologies on pastoralist societies

The rangelands cover approximately 20% of the World's land surface and provide 16% of annual food production as meat and milk for local and distant markets (Holechek, 2013). Food production from rangelands represents an important source of nutrition as global human population is projected to exceed 9 billion by 2050 (United Nations, 2015). There is pressure to increase production from the pastoralism but this has to be done sustainably to ensure the productive capacity is not eroded in the longer term for short term gains. Information technology represents a very real opportunity to improve livelihoods, increase food production and secure environmental outcomes in the pastoral lands. About 70% of the World’s pastoral lands are found in developing and emerging economies where they support indigenous human populations existing in a close synergy with their livestock (Reid et al., 2014). Such societies are driven by cultural mores that often lead to sub-optimal livestock production, over grazing and poor resilience to factors such as climate change and societal upheaval. In developed countries, pastoral lands are under threat from depopulation, loss or lack of infrastructure to support developed production systems and competition for alternative use of the rangelands, such as carbon storage, mining, ecosystem services and tourism (Roxburgh and Pratley, 2015). Against this background then, how can information technologies transform the pastoral lands from marginal production systems to those that are resilient to challenges, sustainable in the long term and deliver optimum levels of livestock production