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© Institution of Engineers Australia, 2009 Australian Journal of Multi-disciplinary Engineering, Vol 7 No 1
* Reviewed and revised version of a paper
originally presented at the 2007 Society for
Engineering in Agriculture (SEAg) National
Conference, Adelaide, 23-26 September 2007.
† Corresponding author Dr Thomas Banhazi can be
contacted at banhazi.thomas@saugov.sa.gov.au.
Precision livestock farming: A suite of
electronic systems to ensure the application of
best practice management on livestock farms *
TM Banhazi †
Livestock Systems Alliance, South Australian Research and Development Institute,
Roseworthy Campus, Adelaide University, Roseworthy, South Australia
JL Black
John L. Black Consulting, Warrimoo, NSW
SUMMARY: The sophisticated global market place for livestock products demands safe, uniform,
cheap, and environmentally- and welfare-friendly products. However, best-practice management
procedures are not always implemented on livestock farms to ensure that these market requirements
are consistently satis ed. Therefore, improvements are needed in the way livestock farms are
managed. Information-based and electronically-controlled livestock production systems are needed to
ensure that the best of available knowledge can be readily implemented on farms. New technologies
introduced on farms as part of Precision Livestock Farming (PLF) systems will have the capacity to
activate livestock management methods that are more responsive to market signals. PLF technologies
encompass methods for measuring electronically the critical components of the system that indicate
ef ciency of resource use, software technologies aimed at interpreting the information captured,
and controlling processes to ensure optimum ef ciency of resource use and animal productivity.
These envisaged real-time monitoring and control systems should dramatically improve production
ef ciency of livestock enterprises. However, as some of the components of PLF systems are not yet
suf ciently developed to be readily implemented, further research and development is required. In
addition, an overall strategy for the adoption and commercial exploitation of PLF systems needs
to be developed in collaboration with private companies. This article outlines the potential role
PLF can play in ensuring that existing and new knowledge is implemented effectively on farms to
improve returns to livestock producers, quality of products, welfare of animals and sustainability
of the farm environment.
1 INTRODUCTION – THE IMPORTANCE
OF APPLYING EXISTING KNOWLEDGE
In most developed countries, a signi cant amount of
money is spent on agricultural research, development
and related extension efforts (RD&E) to generate and
communicate the research ndings to the farming
community (Mullen, 2002; Alston et al, 2000). For
example, during 2005-06 approximately A$540
million was invested by the Rural Research and
Development Corporations on agricultural research
in Australia. Despite this considerable investment
into agricultural research, a comprehensive study by
Mullen (2002) suggested that in real terms the gross
value of agricultural production across Australia
has remained largely unchanged over the period
from 1953 to 2000 ( gure 1). However, Mullen (2002)
concluded that without productivity improvements
resulting from investment into RD&E, the real gross
value of agriculture production in Australia would
have fallen by approximately 65% over the period.
Two striking examples from Australian animal
industries show how the rate of productivity can
remain virtually unchanged over decades despite
large investments in RD&E. The first example
reveals that average reproductive performance of
pigs has hovered around 21 pigs weaned per sow
per year for the last two decades despite continuing
research funding and a realistic potential being 28-30
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pigs weaned per sow per year (Black et al, 2002). A
study of 31 farms over at least 3 years showed that
the best farm produced 24.7 pigs and the poorest
produced only 16.1 weaned per sow per year. The
second example shows that on average grazing
beef enterprises in southern Australia utilise only
around 30-35% of the pasture grown, despite several
individual farms utilising over 85% of the pasture
grown (Black & Scott, 2002).
These two examples, plus many others, con rm that
a considerable portion of the funds spent by RD&E
providers in agriculture have resulted in limited return
to their respective industries. An enormous amount of
information has been generated around the world over
preceding decades, but many of the funds invested
by the research organisations have been on projects
that “reinvent the wheel” being largely repetition of
older research with, at best, marginal improvements
in industry productivity or pro tability.
The main reasons for this lack of progress is the
inef cient adoption by the farming community of
current knowledge, which means that the same
industry “problems” keep reappearing. The funding
organisations frequently react by continuing to invest
in these “problem” areas despite the application of
existing knowledge being the major issue, rather than
a lack of knowledge.
One main issue that needs resolving to bring about
more effective returns from RD&E expenditure
is a process that ensures effective adoption of
current knowledge. The greatest advances in farm
or enterprise productivity and pro tability in the
future will come from the application of a rigorous
procedure for ensuring that the most essential
processes are carried out correctly and consistently
using well known risk control methodology (Snijders
& van Knapen, 2002). There is currently an abundance
of information available to farm managers, but it is
generally not structured in a way that can be applied
readily. The information often appears piecemeal
in many places, including rural papers, radio,
television, commercial outlets, RD&E organisations,
universities, the internet and others. The information
is frequently “dumbed-down” or sensationalised and
is not structured in a way that promotes adoption.
Many managers suffer from “information-overload”
and thus cannot readily identify the practices that are
the most important to adopt or how to apply them
correctly. Frequently managers adopt procedures in
areas of most interest or in which they have the most
expertise, and neglect other important processes
that drive overall productivity and pro tability. A
system is needed to ensure that the most important
processes are identi ed and that all these processes
are carried out correctly and consistently, with none
being neglected.
Well-defined quality control and continuous
improvement methods based on (i) Total Quality
Management (TQM) systems (Landesberg, 1999) and
(ii) Hazard Analysis Critical Control Point (HACCP)
principles are used widely in the manufacturing
industries throughout the world (von-Borell et
al, 2001; Noordhuizen & Frankena, 1999). These
processes are engaged to ensure all products from
an industrial plant meet speci cations with little
tolerance for error. The HACCP principles were
developed originally by the United States Army and
the National Aeronautics and Space Administration
(NASA) to guarantee that food poisoning would not
occur in astronauts during early space ights in the
1960s. Similar processes are now used widely for food
safety across the food processing and agricultural
industries (Petersen et al, 2002; Valdimarsson et al,
2004; Snijders & van Knapen, 2002), and have also
been applied in management of cropping systems
0
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35000
1953
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Year
GVP ( AU$ m)
Real GVP f rom product ivityReal GVP w ithout productivity
Figure 1: Gross value of Australian agricultural production (GVP) in the year 2000 in real dollar value
terms, showing the proportion due to productivity improvement (Mullen, 2002).
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Australian Journal of Multi-disciplinary Engineering Vol 7 No 1
(Aubry et al, 2005). Indeed, the HACCP principles
can be applied to any sector of agriculture to control
risk and ensure high levels of productivity and
product quality at all stages along a production
chain (Snijders & van Knapen, 2002). The HACCP
principles provide the ideal structure for ensuring
that the most important processes determining
productivity and pro tability in an animal enterprise
are adopted and performed with least chance of
failure. The HACCP principles are now being used
by several sectors of the Australian animal industries
to aid adoption of existing knowledge.
The essential steps that need to be incorporated within
a well-designed and controlled production process
are (Beattie, 2001; Cumby & Phillips, 2001; Webster,
2001): (i) integration of automated data measurement
and acquisition systems into the production chain
(Frost, 2001; Banhazi et al, 2007b); (ii) establishment
of protocols for data-integration and automated
data analysis to identify inef ciencies in processes
and to facilitate decision making (Scho eld et al,
1994; 2002); (iii) transfer of the results from data
analysis as inputs into automated decision making
processes and trigger certain management actions
(Banhazi et al, 2002; 2003; Black, 2002); (iv) activate
control systems, which could be either automated
or appropriately documented in standard operating
procedures (SOPs) (Gates & Banhazi, 2002; Gates et
al, 2001); and (v) include procedures to monitor the
outcome of control actions and documentation for
quality assurance (QA) purposes (Black, 2001).
The major bene ts from adopting a Precision Livestock
Farming (PLF) system as outlined are to ensure that
every process within a livestock enterprise, which
can have a large positive or large negative effect on
productivity and pro tability, is always controlled
and optimised within narrow limits. The system
enables near optimum use of all resources, even in
a highly variable environment, with animal growth
rates meeting predetermined speci cations through
optimal use of feed and water, and control of health
status. The system ensures consistent, high quality
products and the potential for real-time supply chain
management throughout an industry. An important
component of the system is for measurements,
interpretation of measurements and control of
processes to be conducted through electronically-
controlled technology, with limited need for human
intervention and the frequently associated inherent
human error. When fully implemented, the system
should ensure near-maximum pro tability through
continual optimisation of resource use in a highly
variable environment, as is normally encountered
in Australian agriculture.
The purpose of this paper is to describe the concepts
behind developing PLF systems for livestock
enterprises, and how they can be implemented
to ensure optimum use of variable resources and
maximisation of pro tability. Several dif culties in
implementing the systems on farms and the need for
development of new technologies are highlighted.
2 ADOPTION OF A RIGOROUS
PROCEDURE FOR THE APPLICATION
OF EXISTING KNOWLEDGE
2.1 Identi cation of important
processes on farms
The first step in developing the proposed PLF
system is to identify those processes, which if not
carried out correctly, will have a major impact on
either productivity or pro tability of an enterprise.
A large negative impact has been termed a hazard
(Landesberg, 1999). A critical control point (CCP) is
the last point in a process where the hazard can be
averted and product quality, level of productivity,
profitability and sustainability of the enterprise
can be maintained near to optimum/maximum.
The primary reason for identifying the CCP for
each process is that it helps determine the variables
that need to be measured and the frequency of
measurement required to avoid the hazard.
The most important aspect in developing the CCP
approach is to rst identify and then ensure that only
those few processes or tasks that will have a major
impact on productivity, pro tability or sustainability
of an enterprise are carried out. These are described
as the “must do” or “big hit” processes, where the
consequences of them not being carried out correctly
could have a substantial impact on the success of an
enterprise (Black, 2002; Black et al, 2001). The aim is
to reduce to a minimum the number of tasks on which
a manager needs to concentrate, but ensure that these
tasks are carried out correctly. These essential tasks
are usually identi ed by considering the relative
magnitude of the “hazard” or loss in pro t that
would result if they were not applied correctly. For
example, only 24 processes were considered essential
in a grazing beef enterprise from strategic planning
to sale of product (Black & Scott, 2002).
Identifying the essential task for speci c enterprises
can be a major challenge, and needs a great deal of
lateral thinking and analysis. Methods that can be
used to identify areas where a change could result in a
marked increase in productivity and/or pro tability
are described.
One procedure is a formal analysis of historical
data within an industry sector or across speci c
enterprises to identify major determinants of
productivity and profitability. For example, a
statistical evaluation of the reasons for low and
variable reproduction performance of sows across
31 farms in Australia showed that 45% was due to
litter size and approximately 15% due to extended
weaning to mating intervals (Black et al, 2002).
Further assessment of the information resulted
in the conclusion that developing highly-ef cient
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technologies for identifying oestrus and mating time
or reducing early embryonic loss should result in
major improvements in reproductive performance of
Australian pigs. Another example of this approach
could be the recently developed statistical models
that identi ed signi cant risk factors for sub-optimal
air quality in Australian livestock buildings (Banhazi
et al, 2008b; 2008c; 2008d; 2008e; 2008f). By managing
these risk factors, improvements in air quality can
be achieved.
Another approach is to undertake a workshop or
“think-tank” process, where experts across a whole
industry value chain identify potential areas where
change would substantially affect productivity
(Banhazi et al, 2007b; Banhazi, 2006). Some are
relatively clear, such as removing humans from the
dairy cow milking process (Bull et al, 1996; Ordolff,
2001). Others, such as several-fold increase in winter
fodder production, which commonly limits overall
grazing enterprise carrying capacity in southern
Australia, although recognised as important, are
often accepted as inevitable. However, low winter
fodder production should not be assumed to be
unchangeable.
Computer simulation models or spreadsheets can
also be used to quantify the likely effects of change on
productivity and/or pro tability. Table 1 shows the
predicted effect, using the AUSPIG simulation model,
of a range of scenarios suggested from a workshop
on the pro tability of a 500 sow piggery in Australia
(Black, 2006). The quantitative evaluation of likely
bene ts had a signi cant bearing on identifying
processes that must be carried out correctly and for
setting future research priorities.
One of the greatest opportunities for quantum
changes in productivity and profitability within
agricultural enterprises is to reduce the need for
high-cost and inef cient labour and/or increasing
its reliability.
2.2 Data collection systems
Once the main processes are identi ed that will have
a major impact on farm pro tability, the next step
is to identify for each process the farm or market
variable that must be measured, and the maximum
and minimum limits to the variable that will ensure
the process is always carried out correctly and the
potential hazard avoided. The desired frequency of
the measurements must also be set. Electronic capture
of the information is essential to improve reliability
of the information collected. Table 2 details the
areas of possible electronic data collection on farms
(Durack, 2002).
In the past, information collection on such wide
ranging parameters (especially in electronic format)
has been dif cult, costly and was often neglected
(Black et al, 2001). However, advancement in sensor,
computer technology and modern analytical tools
have made the collection and analysis of large
amounts of data highly feasible (Black et al, 2001).
A well-designed data collection system would
reduce the need for manual recording of farm
production data, and would present the producer
with management data in the most ef cient form
(Schon & Meiering, 1987). By using automated data
collection systems on farm, it would be possible to
transfer details of animal performance, labour input,
environmental performance and other essential farm
information to a central data warehouse, where
it could be stored, processed and sent to control
systems within the farm. Examples of relevant
information that can be captured electronically are
discussed below.
The weight of animals is one of the most important
measurements to obtain. Up-to-date monitoring of
the growth rate of pigs would provide producers
with valuable information on compliance with
projected growth pathways, health, and likely carcass
composition and yield (Korthals, 2001; Scho eld
Table 1: Predicted effect using the AUSPIG simulation model to examine a range of scenarios on the
pro tability of a 500 sow reference piggery (Black, 2006).
AUSPIG simulation Pro t ($/kg carcass)
Reference piggery – base simulation 0.13
Increase price paid for pig meat by $0.50/kg carcass 0.63
Decrease average cost of feed by $50/t 0.32
Increase average growth rate for male and female pigs by 50 g/day 0.19
Apply PSTa for 4 weeks at a cost of $5.00/pig 0.21
Decrease herd health and increase mortality from 3.5% to 7% –0.18
Change feed waste to 2% 0.22
Change feed waste to 22% 0.04
Halve labour costs 0.28
Increase pigs sold/sow/year to 23.9 0.34
Increase pigs sold/sow/year to 28.0 0.58
a Porcine somatotrophin
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et al, 1999; Lokhorst & Lamaker, 1996). Acquiring
suf cient data points at acceptable time intervals
and with suf cient accuracy is impractical using
conventional scales because of the increase in labour
requirements and stress on both the farmer and
stock. An automated weighing system is essential.
Image analysis technologies are being developed
to acquire weight of animals automatically as they
visit a feeder, and also for identifying fatness and
carcass yield (Banhazi et al, 2007c; Kollis et al,
2007). The information on weights of animals can
be incorporated into computer simulation models,
such as AUSPIG, to calculate the daily ration needed
to meet speci ed growth trajectories based on the
genotype of animal and the current environmental
conditions. In addition, image analysis can be used
to assess behaviour and welfare of pigs (Xin & Shao,
2002; Hemsworth et al, 1995; Shao & Xin, 2008).
Detecting irregularities in feed (Sliva et al, 2007;
Banhazi et al, 2009), water intake, body temperature
and body weight could be used as an early warning
system for detecting diseases (Pedersen & Madsen,
2001; Mottram, 1997). Disease monitoring, as well
as the traceability of the nal product, could be
dramatically enhanced by the wide spread adoption
of electronic animal identi cation tools and related
monitoring equipment (Jansen & Eradus, 1999;
Eradus & Rossing, 1994; Street, 1979; Schon &
Meiering, 1987). Some innovative approaches have
already been taken to automatically detect coughing
episodes in piggery buildings, and use the frequency
and intensity of these coughing episodes to detect
respiratory diseases (Chedad et al, 2001; Moshou et
al, 2001a; 2001b).
Airborne particles and gases are associated with sub-
optimal air quality in sheds predisposing both livestock
and staff to potential health problems (Banhazi et al,
2008d). Pollutant emissions from intensive livestock
production facilities are also associated with odour
transfer (Hartung, 1986; Bottcher, 2001). Therefore,
accurate, low-cost monitoring of these pollutants is
essential if appropriate reduction strategies are to be
implemented on farms (Banhazi, 2005; 2009).
2.3 Data analysis systems
Computer models should in the future become an
integral part of PLF management systems (Banhazi
et al, 2007b). The models would be capable in real
time of identifying individual animals that are of the
correct weight and body speci cations to maximise
payment from a wide range of alternative buyers.
Growth models would be capable of assessing
whether individual animals or groups of animals
Table 2: A summary of the range of production and environmental variables which could potentially
be measured, recorded and analysed (Durack, 2002).
Possible rate of collection
Animal parameters
Daily weight gain (DWG) Daily
Feed conversion ratio (FCR) Daily
Feed consumption (FC) Hourly
Body composition – eg. back fat (BC) Daily
Body conformation (BCo) Daily
Stress levels Daily
Antisocial/normal behaviours Daily
Oestrus (heat) detection Hourly
Environmental conditions
Ambient climatic conditions Hourly
Humidity and internal air and oor temperature Hourly
Air speed Hourly
Floor and animal wetness Daily
Gas levels, CO2, NH3Hourly
Dust levels Hourly
Air born pathogen levels Daily
Transport and supply chain management
Electronic individual animal ID and trace back N/A
Transport environmental conditions log Hourly
Full individual animal carcass performance N/A
Input material ID, such as feed, medication, etc. N/A
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have an excess or de ciency in amino acid supply for
every diet presented, whether ambient temperature
is above or below the zone of thermal comfort, and
whether the animals are under- or over-stocked
(Black, 2002). In addition, the models could assess
whether the livestock are being fed an energy intake
that is coincident with the minimum needed for
maximum protein deposition, where the ef ciency
of feed utilisation is maximised. The factors limiting
feed intake could be predicted and excessive feed
wastage identified. Diets could be reformulated
automatically at speci ed times to most economically
meet the nutrient requirements of each group
of livestock and feed intake modi ed to achieve
greatest economic efficiency of feed utilisation
(Wathes et al, 2001). Building temperatures, air
movement and humidity could be controlled to
ensure that the animals were in the zone of thermal
comfort for suf cient time each day to maximise
pro t in relation to constraints on growth and costs
of environmental control (Gates & Banhazi, 2002).
Similarly, stocking rates and diet composition could
be adjusted through the automatic movement of
animals and modi cations to diet energy density to
ensure maximum pro t.
A variety of approaches can be used for analysing
recorded information from simple graphing and
spreadsheet analysis to sophisticated statistical
analyses (such as data-mining) and computer
simulation modelling (Aerts et al, 2001; Bird et al,
2001; Durack, 2002; Schmoldt, 2001; Stafford, 2000).
Although the simpler techniques can provide useful
insights into production inef ciencies, they are often
limited and do not account for important interactions
between factors that affect livestock performance and
enterprise pro tability (Black, 2001). Computerised
models that account for total enterprise resource
use, such as AUSPIG (Black et al, 2001), could help
identify likely inef ciencies in production methods
and pro t generation.
2.4 Control systems
Each process identi ed to have an important affect
of enterprise pro tability should have an optimal
range. Keeping these processes within the optimal
range will ensure optimisation of farm pro tability.
Consequently, when these critical processes are
outside the limits, automated systems need to
be called into operation or managers/appointed
persons need to be alerted to rectify the situation.
An important outcome of either human or machine
intervention is to ensure that the key processes on
farms remain within the critical limits.
Control systems on livestock farms should be viewed
holistically and incorporate both technology-based
systems, such as automated sorting-gates, and
operator–based systems, such as moving animals.
The establishment of SOPs for individual tasks
is essential. The SOPs ensure that procedures are
carried out correctly and should keep all measured
variables within the maximum and minimum limits.
When measured variables fall outside these limits,
procedures are in place to undertake predetermined
corrective action. The SOPs in combination with
the HACCP process are designed to ensure that the
manager can be absent from the enterprise for an
extended period and be con dent that pro t will
be maximised and long-term sustainability of the
enterprise sustained (Black, 2002). There are both
high and low level SOPs. The high level SOPs de ne
each major task and when it should be undertaken,
whereas the low level SOPs describe how an
individual task is done or how to operate speci c
pieces of equipment or software designed to assist
with the data collection, data analysis or the control.
Development of SOPs for an individual enterprise
is a challenging task, but crucial for complete
uptake of the scheme proposed for improving
adoption of current knowledge. In addition, having
predetermined SOPs whenever the measurement
limits are breached, reduces substantially the stress
level for the manager because the plan of action and
when to apply it has already been established and
the consequences known.
3 IMPORTANCE OF AUTOMATION
For example, the HACCP and SOP based system
outlined is being implemented with enthusiasm
and some success in the Australian beef industry
(Black & Scott, 2002). The proposed system has also
been applied to speci c aspects of other enterprises,
including reproductive success in the pig industry
(Black, 2002). However, continuing implementation
of the system has proved difficult for many
managers. Although the system is logical, it must
have enterprise SOPs that are often time-consuming
to produce. In addition to the dif culty of preparing
precise SOPs, removal of mundane, repetitious
tasks is essential for all agricultural industries (Yen
& Radwin, 2000; Van Henten et al, 2006; Noguchi
et al, 2004; Belforte et al, 2006). Future advances in
compliance will come from automated measurement,
interpretation and control of most processes, which
can be overseen by a manager from the of ce.
The current revolution in agricultural engineering,
electronics and robotics has enormous potential
for agriculture, and is already being applied
in many areas (Ordolff, 1997; Artmann, 1997;
Noguchi et al, 2004; Zhang et al, 2006; Belforte
et al, 2006). Electronic, robotic and automatic
components have a particularly important role in
the measurement of critical variables, interpretation
of these measurements and control of processes.
A recent example from Bishop-Hurley et al (2007)
illustrated how radio technology can be modi ed to
develop virtual fencing for cattle. Narrow, straight
lines of radio signals, potentially controlled by
satellite positioning systems, form the boundaries to
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Australian Journal of Multi-disciplinary Engineering Vol 7 No 1
the area designated for grazing. As an animal tted
with a collar approaches the signal, a buzzer in the
collar starts to sound and becomes louder as the cow
nears the signal. An electric shock is delivered from
the collar if the line is breached. Trials have shown
that animals learn quickly to retreat once the buzzer
sounds (Bishop-Hurley et al, 2007).
3.1 Provision of integrated package
It is essential for the success of PLF systems to provide
all the tools necessary for making the essential
measurements, interpreting the measurements and
executing the most profitable corrective actions.
These tools are an essential component of the overall
“package” delivered to farm managers and must be
provided as part of the adoption process. The tools
may be physical instruments (for example, a meter
for measuring pasture height or mass), they may be
descriptions on how measurements are made, tables
of information, graphs, spreadsheet models, and
other forms of software or internet links to speci c
programs to retrieve essential rainfall, soil moisture
and market or satellite information. The process of
identifying these tools has proven to provide an
excellent framework for determining critical R&D
projects that need to be completed to ensure the
adoption “package” can be fully implemented. An
essential component of the “package” is that all
the information is readily available to the manager
whenever it is needed. One of the reasons for the
relatively slow adoption rate of PLF technologies
on farms is the fact that individual components of
the system are often independently developed by
different research groups without fully appreciating
the importance of integrating these technologies
into a fully functional package (Banhazi et al, 2007a;
Banhazi, 2006).
4 DEVELOPMENT OF PLF SYSTEMS
SPECIFICALLY WITHIN THE
AUSTRALIAN PIG INDUSTRY
The adoption of PLF technologies within the
Australian livestock industries should occur in
coordination of current hardware and software
components and systems to run on a standardised
data communication protocol. This approach
optimises the value of existing developments, but
also supports the utilisation of whole of industry
coordination (Banhazi et al, 2002). Figure 2 represents
a schematic presentation of the integrated system
proposed above, which has the most potential to
create a sustainable competitive advantage for the
Australian pig industry. In table 3, the likely research
programs needed to develop fully integrated PLF
systems are listed.
Note that the main barrier to this complete solution
is currently data compatibility and transfer. In
Figure 2: Australian PLF Vision (integration
of herd management tools with the
AUSPIG computer model, resulting in
a holistic decision support system).
delivering on the vision expressed in gure 2, the
industry must recognise the need for investment
in an effectively targeted R&D program to link the
different components together and optimise the
value of existing technologies (table 2). In summary,
the following key developments will be needed:
(i) Establishment of “fully instrumented piggery
buildings” to collect data from these production
sites for analysis.
(ii) Application of in-depth analysis, modelling
and investigation of the available data (using
a variety of methods) with the clear focus of
demonstrating direct financial benefits for
producers through the identification of key
production indicators (KPI).
(iii) Improvement of the “control functions” of
AUSPIG to facilitate outputs from the system
to be linked with automatic control tools.
(iv) Application of economical analysis to
demonstrate the nancial advantages of using
PLF systems and commercialisation of PLF
systems developed.
5 RESEARCH NEEDS IDENTIFIED
The main objectives of the farm implementation
project are to (i) demonstrate the economic bene ts
of the PLF system on farms, and (ii) identify
the shortcomings of the system and implement
improvements. The establishment of the so-called
“fully instrumented piggery buildings” will be the
first step in the project implementation process.
These facilities will make the real time analysis and
modelling of the available data possible with the
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clear focus of demonstrating direct nancial bene ts
for producers. The planned study will facilitate
interaction between producers, manufacturers of the
hardware devices needed for making measurements,
and developers of software that capture and process
the electronic outputs into a form that can be analysed
using traditional statistical and other approaches.
Recommendations for changes in management
practices to improve the biological ef ciency and
pro tability of the enterprises will then be made in
consultation between advisers and producers.
Figure 3 shows an overview of data ow throughout
the system. Measurement data are collected and
accumulated on farms. This temporary storage is
automatically accessed daily. The data warehouse
will access the on-farm data collection system and
request data upload.
The accumulated data will be sent to the web server
where it is compressed and stored. The data are
erased from the on-farm loggers and the system will
be ready to continue gathering data. Any authorised
owner of data can log onto a secure website and
download information. The requested data are
uncompressed and sent to consultants in a standard
format. The data received is then processed and
uploaded into speci c software such as PrimePulse,
AUSPIG and statistical analysis packages. Once
the data have been analysed, the results can be
interpreted by the project team and communicated
to producers.
To transform the information management systems
currently used on farms to a more sophisticated
system, data collection and more importantly data
analysis need to be automated (Schon & Meiering,
1987). Automated data collection, management and
analysis, together with accessible data-warehouses,
would transform the currently used segregated
systems into a powerful information based PLF
system (Enting et al, 1999). Centralised data
collection sites (data warehouses) will be an essential
component of a well-functioning PLF system. Such
centralised data management will enable uniform
data analysis and appropriate interpretation of
available data. On-farm data processing could
provide valuable support for farm managers in
everyday management, but the real gains would
come from using in-depth data analysis provided
by remote data warehouses via the internet (Geers,
1994; Petersen et al, 2002).
6 CONCLUSIONS
Funds spent by RD&E providers in agriculture
have resulted frequently in limited return to their
industries in comparison to their potential. However,
large opportunities exist to increase the returns
on investment in RD&E in the animal industries,
and to substantially improve productivity and
profitability in the 21st Century. Investment of
funds into the rigorous application of well-de ned
HACCP principles and individual enterprise SOPs,
along with essential associated tools, should ensure
that relevant existing knowledge is focused on
maximising pro tability and sustainability of animal
Table 3: Potential R&D topics within the major program areas (Durack, 2002).
Development areas
Environmental management
• On-farm measurement and documentation tools (Banhazi, 2005; Sliva et al, 2007)
Housing management
• Advanced climate control tools (Banhazi et al, 2008a; 2008f)
• Animal welfare and behaviour assessment tools (Shao & Xin, 2008)
Production management
• Real time individual pig weighing system (Kollis et al, 2007)
• Real time feed and water consumption recording (Madsen & Kristensen, 2005; Madsen et al, 2005)
• Disease monitoring tools and systems (Maatje et al, 1997; Eradus & Jansen, 1999)
• Feed disappearance measurements
• Market intelligence systems
• Integrated performance analysis of units (Heinonen et al, 2001; Pomar & Pomar, 2005)
• Online KPIs monitoring and comparison with modelled performance norms (Tukey, 1997)
• AUSPIG integration program
Supply chain management
• Information ow from slaughter houses (Petersen et al, 2002)
• Individual animal ID (Naas, 2001; 2002)
• Automated record keeping (Holst, 1999)
• Real time supply management programs (Dobos et al, 2004; Guerrin, 2004)
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Australian Journal of Multi-disciplinary Engineering Vol 7 No 1
enterprises. Further investment is essential into
technologies that automatically measure, interpret
and control these crucial systems, because many of
the system breakdowns are due to human failings.
The replacement of mundane, repetitive tasks
within many animal enterprises with automated
systems should substantially improve quality
control and job satisfaction, thereby reducing risks
commonly associated with intensi cation. Additional
investment will generally be required to develop the
tools needed to ensure effective application of all
aspects of the HACCP-SOPs systems. Agricultural
engineers will have a major role in providing these
essential hardware and software components for
animal industry PLF systems. In addition, more
critical thought is frequently needed when setting
research priorities. An important challenge is to
identify those changes in an industry sector that
are likely to result in “quantum leap” changes in
productivity and pro tability. The greatest progress
is usually made by developing new technological
tools, rather than ne-tuning existing methods of
animal production.
ACKNOWLEDGMENTS
The authors wish to acknowledge the professional
assistance of professors D. Berckmans, S. Pedersen,
C. Wathes and R. Gates, and the nancial support of
Australian Pork Limited.
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Australian Journal of Multi-disciplinary Engineering Vol 7 No 1
“Precision livestock farming: A suite of electronic systems to ensure ...” – Banhazi & Black
THOMAS BANHAZI
Dr Thomas Banhazi is a Senior Research Scientist at the South Australian
Research and Development Institute (Livestock System Alliance) and his
research interests are related to intensive livestock housing. He has undertaken
studies to investigate the relationship between environmental conditions and
management factors in livestock buildings. He has also investigated methods
for reducing the impact of poor air quality on the respiratory health of both
humans and animals. His recent research interest is related to the development
of data acquisition and data management systems for the livestock industries
to improve the precision of production management. He is the Vice-President
of the International Commission for Agricultural Engineering (CIGR) – Section
II group and the Chair of the Australian Society for Engineering in Agriculture.
JOHN BLACK
Prof John L Black (AM FTSE) received his PhD from the University of Melbourne
in 1970. He joined CSIRO Division of Animal Physiology at Prospect, Sydney,
in 1971, where he continued the study of amino acid and energy requirements
of sheep for body and wool growth. A major component of his research was
the integration of physiological and biochemical concepts into mechanistic
simulation models. He led the team that developed the AUSPIG Decision
Support Software for pigs and became Assistant Chief of the Division in 1992.
In 1996 he left CSIRO and started a consulting company specialising in research
management for government and commercial organisations.
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