CSIRO PUBLISHING
Animal Production Science, 2014, 54, 1687–1693 http://dx.doi.org/10.1071/AN14368
Wireless sensor networks to study, monitor and manage cattle in grazing systems L. A. González A,E, G. Bishop-Hurley B, D. Henry C and E. Charmley D A
The University of Sydney, Faculty of Agriculture and Environment, Camden, Sydney, NSW 2570, Australia. CSIRO, Animal, Food and Health Sciences, St Lucia, Qld 4067, Australia. C CSIRO, Animal, Food and Health Sciences, Werribee, Vic. 3030, Australia. D CSIRO, Animal, Food and Health Sciences, Townsville, Qld 4811, Australia. E Corresponding author. Email:
[email protected] B
Abstract. Monitoring and management of grazing livestock production systems can be enhanced with remote monitoring technologies collecting information with high temporal and spatial detail. However, the potential benefits of such technologies have yet to be realised and challenges still exist with hardware, and data analysis and interpretation. The objective of this paper was to propose analytical methods and demonstrate the value of remotely collected liveweight (LW) and behaviour of beef cattle grazing tropical pastures. Three remote weighing systems were set up at the water troughs to capture LW of three groups of 20 animals for 341 days. LW data reflected short-term effects following the first rain event (>50 mm) at the end of the dry season, which resulted in LW losses of 22 8.8 kg of LW at a rate of –1.54 0.46 kg/ day (n = 60). This period was followed by a peak daily LW change (LWC) of +2 kg/day. The remote weighing system also captured longer environmental effects related to seasonal changes in forage quality and quantity with highest LWC during the wet season and weight loss during the dry season. Effects of management on LW and LWC were observed as a result of moving animals to paddocks with more edible forage during the dry season when the negative trend in LWC was reversed after rotating animals. Behavioural monitoring indicated that resting and ruminating took place at camping sites, and foraging resulted in grazing hotspots. Remotely collected LW data captured both short- and long-term temporal changes associated with environmental and management factors, whereas remote monitoring collars captured the spatial distribution of behaviours in the landscape. Wireless sensor networks have the ability to provide data with sufficient detail in real-time making it possible for increased understanding of animal biology and early management interventions that should result in increased production, animal welfare and environmental stewardship. Additional keywords: behaviour, GPS, grazing livestock, live weight, remote sensing. Received 13 March 2014, accepted 18 June 2014, published online 19 August 2014
Introduction Studying, monitoring and managing animal-dominated landscapes poses several challenges as a result of the large and frequently variable spatial and temporal scales required to manage them. Collecting information to overcome these challenges is now possible with advances in sensors and sensor networks, and information and communication technologies (Handcock et al. 2009). The flow of information at high spatial (e.g. vegetation patch) and temporal [e.g. daily for liveweight (LW)] frequency, and in real-time is increasing our understanding of livestock systems in new and previously unexplored ways. As a result, management should be improved to enhance productivity, profitability, environmental stewardship and animal welfare of commercial livestock production systems. Evolving technologies, such as global navigation satellite systems of which the global positioning system (GPS) is the most frequently employed, has become almost standard protocol for documenting the spatio-temporal profile for Journal compilation CSIRO 2014
free-ranging livestock (Turner et al. 2000; Anderson et al. 2013). However, applying this information to production agriculture remains in its developmental stages. The electronic weighing of free-ranging cattle had its beginning in the USA in the 1960s (Martin et al. 1967), only later it was combined with electronic identification (Anderson and Weeks 1989), more recently its utility demonstrated (Charmley et al. 2006; Alawneh et al. 2011) and nowadays are available on the market. Anderson and Weeks (1989) did not find any associations between the LW profiles of individual animals and any plant or animal factors. However, there is no scientific information with data collected with sufficient regularity using the latest technology and analytical methods to be able to judge the value of the information for either research or commercial purposes in beef cattle. In contrast, Brown et al. (2014) used remote weighing systems in sheep and concluded that it had limitations in reliability, repeatability or accuracy. www.publish.csiro.au/journals/an
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The objective of this paper is to report results from automatic weighing and GPS-based monitoring collars in cattle that are part of a more comprehensive monitoring system to enhance the scientific understanding and subsequent value to the management of free-ranging beef cattle. The hypothesis of the present study was that remote weighing and behavioural monitoring systems can provide information of animals’ response to environmental and management factors in near real-time and with sufficient frequency to increase understanding of animal biology and improve productivity. Materials and methods All experimental procedures were approved by the institutional Animal Ethics Committee (Approval #A8/2011 and A11/2012). Cattle and management Ninety Brahman and Belmont Red Composite steers, a crossbred Bos indicus · B. taurus (initial full LW = 293 31 kg; initial age = 465 23 days) were blocked by breed and randomly assigned to one of three experimental groups with 30 steers each. Each group grazed three, 15-ha paddocks (45 ha in total) in a rotation from February 2013 till January 2014 (341 days) at the CSIRO Landown Research Station, Townsville, Queensland (19390 33.610 S, 146500 59.800 E). The region is characterised by a wet season (December to March) receiving 80% of annual rainfall, and a dry season. The dominant herbaceous vegetation included tropical species of Urochloa, Stylosanthes, Siratro and Rhodes. Browsing vegetation had a minimal presence in paddocks whereas tree density qualifies for openwoodland environment. Animals were moved through paddocks on a monthly basis during the wet season (till July) whereas paddocks were grazed to a residual level of ~1000 kg of DM/ha (by visual appraisal of paddocks) during the dry season, at which point cattle were introduced into another paddock. Gates in between the three paddocks for each animal group were opened at the end of the dry season to allow animals to graze them simultaneously in an attempt to maintain the animals with limiting forage. Ten steers from each experimental group were taken out of the paddocks on 4 July 2013 to reduce stocking rate in view of the upcoming dry season. Supplementation (Anipro, Performance Feeds, Kingsthorpe, Qld, Australia) was introduced on 1 December 2013 when body condition score of the herd dropped below 3 (scale 1–5 scored in the yards; Gaden 2005) to aim for an intake of 1 kg/day. Anipro is a urea and molasses-based supplement consisting of two components (sweet and sour), which are mixed in different proportions to control the amount of intake. The mixture in the present study was a 1 : 1 of sweet to sour (crude protein contents were 15% and 32% in the sweet and sour fraction, respectively, and both have 3% salt). The date of all management practices were recorded such as mustering to yards, vaccinations, paddock change, and feed supplementation. Wireless sensor network A wireless sensor network (Wark et al. 2007; Handcock et al. 2009) was deployed at the research station with a range of sensors: (1) stations to remotely measure LW; (2) sensors to ascertain body condition from 3D data (SOKUIKI sensor
L. A. González et al.
model UTM-30 LX; Hokuyo Automatic Co., Osaka, Japan); (3) monitoring collars to measure cattle location and behaviour (González et al. 2014); (4) infrared thermal cameras to measure body temperature (FLIR Systems, Inc., Wilsonville, OR, USA); (5) pasture stations to measure quantity and quality of forage (Skye Instruments, 4-channel model SKA1850, Llandrindod Wells, UK); (6) soil sensors to measure temperature and humidity (Decagon Devices 5TM, Pullman, WA, USA); (7) weather stations to measure a range of climatic variables (Vaisala WXT520, Vantaa, Finland). However, the objective of the present study was to report results regarding LW and behaviour of cattle because these two characteristics are among the most valuable information to improve productivity, environmental stewardship and animal welfare, and because data analysis and interpretation are needed for both. The information collected by the sensors is sent through a cellular network and stored on a server and archived on the devices. The information can be processed to extract relevant information of each component of the production system, and the results are later integrated and displayed through web-based user interfaces. One remote weighing station was installed at the only water point for each group of animals to record LW every time animals accessed water. A yard (25 m · 25 m) was built to enclose the only one water point located in the central paddock for each block of three paddocks assigned to each group of animals. The yard had an entry and an exit spear gate in the opposite corners to allow one-way movement of animals. The weighing station was located at the entry gate to record LW before animals drank water. The weighing station consisted of a platform mounted on two load bars, which sat on two 0.60 m · 1.20 m · 10-mm-thick aluminium plates. Two, 2.5 m-long · 1.7 m-tall metal panels were bolted into the aluminium plates at each side of the walk-through platform with a radio frequency identification reader panel mounted in the right-hand side of the animals to identify individuals walking through the station (Tru-Test WOW, Pakuranga, NZ) by means of an electronic identification ear tag (EID, AllFlex, Capalaba, Qld, Australia). The system recorded animal ID, date, time, and LW. Animals were trained to use the weighing system progressively through a series of steps by: (1) placing animals in the experimental paddocks without installing the weighing system to accustom them to using the water trough for 3 days, (2) installing the yards’ fence while leaving the entry and exit gates freely open (no spear gates) for 3 days, (3) installing the spears gates to allow one-way flow of animals when accessing the water trough and let them use for 1 week, and (4) installing the weighing station in the yards’ inner part of the entry gate to weigh animals as they walk through the access the water point. After all equipment was installed, animals were mustered daily to go through the system for 5 days while providing molasses to encourage them to continue using the system by themselves after mustering was discontinued. Cattle behaviour was measured using in-house developed monitoring collars (Wark et al. 2007; Handcock et al. 2009; González et al. 2014), which collected information of animal location from a GPS chip (U-Blox, Thalwil, Switzerland), and head position and activity from accelerometers (HMC6343 Honeywell, Plymouth, MN, USA). These data were later aggregated by calculating mean and s.d. values for every 10-s
Remote monitoring of liveweight and behaviour in cattle
intervals and classified into five behavioural activities: grazing, ruminating, resting, travelling and other active behaviours using methodology described by González et al. (2014). The methodology was able to correctly classify 90% of all data points into the correct activity and 95% of the grazing data points using a decision tree and mixed distributions methodology (González et al. 2014). These categorised location fixes were then exported to ArcMap (ArcGIS, ESRI, Dublin, Ireland) to create maps showing the location of individual animals at 10-s intervals, the corresponding activity being performed, and the relative density of data points per unit of area using the Kernel function (Silverman 1986). A search radius of 50 m and an output cell size of 2 m were set to count the number of data points per hectare for each activity. Data analyses All data was analysed for those 20 animals from each group, which were measured until the end of the trial (60 animals in total) to simplify the presentation of results and discussion. Data from the weighing stations was processed by first filtering for erroneous data and then deleting outliers. All data lines containing missing EID records, or containing EID while having LW lower than 200 kg or greater than 700 kg were deleted because all animals of the present study were within these ranges. The data was then fitted to B-splines penalised on the coefficients (Eilers and Marx 1996) for each individual animal with the smoothing parameter selected having the lowest Schwarz Bayesian criterion. Data points below or above 1.5 times the residuals for each animal were deleted and the penalised B-spline fitted again to obtain the predicted LW. Growth rate [LW change (LWC)] was calculated as the first derivative throughout the predicted LW curve. The time between two successive LW observations for each animal was also calculated. Data on LW and LWC were then averaged for each animal and day before statistical analysis with a mixedeffects linear regression model considering date as repeated factor for each animal and group as a fixed effect. Temporal changes in LW and spatial distribution of behaviours in the paddock were associated with management (e.g. paddock change, location of water point) and environmental (e.g. rainfall, season, vegetation) factors. Management factors recorded and assessed included paddock changes, opening gates to additional paddocks, and feed supplementation with Anipro. Environmental factors studied included seasonal changes and rain events greater than 20 mm in 24 h. Results There were 41 824 observations in the data recorded by all three weighing stations throughout the 341 days of the experiment. However, 18.8% of these observations contained missing EID number (data not shown), another 12.1% of observations were outliers with LW records less than 200 or greater than 700 kg, and 4.9% of observations were outliers with values outside predicted LW 1.5 times the residuals (Table 1). The final dataset contained 26 840 observations for all three groups of animals, i.e. 64.2% success rate. Liveweight of raw data contained extreme values not likely to occur in these animals which disappeared when in the dataset with no outliers (Table 1).
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Table 1. Descriptive statistics of liveweight data remotely collected in grazing cattle n Liveweight (LW, kg/steer) All data 100 kg < LW < 700 No outliers (registered) Predicted Growth rate (kg/day) Days between observations No. of observation/steer No. of observations/day No. of days LW data
Minimum Mean Maximum
s.d.
41 824 28 902 26 840 26 842 26 833 26 780
0 107 206 180 –2.46 0
336.7 419.9 416.7 416.6 0.37 0.76
934 700 580 580 2.88 70.95
178.4 65.0 46.3 46.5 0.863 1.336
26 840 15 834 15 834
276 0.0 232.0
447.3 1.7 337.6
629 7.0 341.0
83.9 0.80 11.75
On average, every animal had a successful observation in the final dataset every 0.75 days however gaps in the data were observed with one animal showing 71 days between two successive observations in the final dataset (Table 1). Up to seven successful observations per day and animal were recorded and 337.6 records per animal were registered throughout the 341-day trial on average. There was an association between experimental group of animals and the frequency of observations with Groups 1, 2 and 3 having 39.5%, 29.7% and 30.8% of all observations in the same time period between groups (Chi-square P < 0.001). There were also associations between the frequency of observations and date or EID (Chi-square
