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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 satisfied. 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 efficiency of resource use, software technologies aimed at interpreting the information captured, and controlling processes to ensure optimum efficiency of resource use and animal productivity. These envisaged real-time monitoring and control systems should dramatically improve production efficiency of livestock enterprises. However, as some of the components of PLF systems are not yet sufficiently 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.

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INTRODUCTION – THE IMPORTANCE OF APPLYING EXISTING KNOWLEDGE

In most developed countries, a significant amount of money is spent on agricultural research, development and related extension efforts (RD&E) to generate and communicate the research findings 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 *

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 [email protected]. © Institution of Engineers Australia, 2009

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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 (figure 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 Australian Journal of Multi-disciplinary Engineering, Vol 7 No 1

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“Precision livestock farming: A suite of electronic systems to ensure ...” – Banhazi & Black 35000 30000

Real GVPwithout productivity

Real GVPfromproductivity

GVP (AU$ m)

25000 20000 15000 10000 5000

19 53 19 56 19 59 19 62 19 65 19 68 19 71 19 74 19 77 19 80 19 83 19 86 19 89 19 92 19 95 19 98

0

Year

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).

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, confirm 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 profitability. The main reasons for this lack of progress is the inefficient 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 profitability 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 Australian Journal of Multi-disciplinary Engineering


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 profitability. A system is needed to ensure that the most important processes are identified 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 specifications 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 flights 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 Vol 7 No 1

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“Precision livestock farming: A suite of electronic systems to ensure ...” – Banhazi & Black

(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 profitability 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 inefficiencies in processes and to facilitate decision making (Schofield 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 benefits 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 profitability, 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 specifications 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 electronicallycontrolled technology, with limited need for human intervention and the frequently associated inherent human error. When fully implemented, the system should ensure near-maximum profitability 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 profitability. Several difficulties in Australian Journal of Multi-disciplinary Engineering


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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

Identification 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 profitability 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 first identify and then ensure that only those few processes or tasks that will have a major impact on productivity, profitability 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 identified by considering the relative magnitude of the “hazard” or loss in profit 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 specific 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 profitability are described. One procedure is a formal analysis of historical data within an industry sector or across specific 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-efficient Vol 7 No 1

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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 identified significant 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 profitability. Table 1 shows the predicted effect, using the AUSPIG simulation model, of a range of scenarios suggested from a workshop on the profitability of a 500 sow piggery in Australia (Black, 2006). The quantitative evaluation of likely benefits had a significant 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 inefficient labour and/or increasing its reliability.

2.2

Data collection systems

Once the main processes are identified that will have a major impact on farm profitability, 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 difficult, 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 efficient 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; Schofield

Predicted effect using the AUSPIG simulation model to examine a range of scenarios on the profitability of a 500 sow reference piggery (Black, 2006).

Table 1:

AUSPIG simulation

Profit ($/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

a

Apply PST 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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Table 2:

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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 floor temperature

Hourly

Air speed

Hourly

Floor and animal wetness

Daily

Gas levels, CO2, NH3

Hourly

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

et al, 1999; Lokhorst & Lamaker, 1996). Acquiring sufficient data points at acceptable time intervals and with sufficient 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 specified 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 final product, could be dramatically enhanced by the wide spread adoption of electronic animal identification tools and related monitoring equipment (Jansen & Eradus, 1999; Australian Journal of Multi-disciplinary Engineering


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 suboptimal 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 specifications to maximise payment from a wide range of alternative buyers. Growth models would be capable of assessing whether individual animals or groups of animals Vol 7 No 1

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have an excess or deficiency 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 efficiency of feed utilisation is maximised. The factors limiting feed intake could be predicted and excessive feed wastage identified. Diets could be reformulated automatically at specified times to most economically meet the nutrient requirements of each group of livestock and feed intake modified 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 sufficient time each day to maximise profit 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 modifications to diet energy density to ensure maximum profit. 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 inefficiencies, they are often limited and do not account for important interactions between factors that affect livestock performance and enterprise profitability (Black, 2001). Computerised models that account for total enterprise resource use, such as AUSPIG (Black et al, 2001), could help identify likely inefficiencies in production methods and profit generation. 2.4

Control systems

Each process identified to have an important affect of enterprise profitability should have an optimal range. Keeping these processes within the optimal range will ensure optimisation of farm profitability. 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 Australian Journal of Multi-disciplinary Engineering


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 confident that profit 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 define 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 specific 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 specific 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 difficulty 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 office. 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 modified to develop virtual fencing for cattle. Narrow, straight lines of radio signals, potentially controlled by satellite positioning systems, form the boundaries to Vol 7 No 1

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the area designated for grazing. As an animal fitted 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

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(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 financial 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 benefits 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

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 Australian Journal of Multi-disciplinary Engineering


delivering on the vision expressed in figure 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:

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 specific 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).

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Figure 2:

Australian PLF Vision (integration of herd management tools with the AUSPIG computer model, resulting in a holistic decision support system). Vol 7 No 1

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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 flow 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) clear focus of demonstrating direct financial benefits 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 efficiency and profitability of the enterprises will then be made in consultation between advisers and producers. Figure 3 shows an overview of data flow 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 specific 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 Australian Journal of Multi-disciplinary Engineering


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 21 st Century. Investment of funds into the rigorous application of well-defined HACCP principles and individual enterprise SOPs, along with essential associated tools, should ensure that relevant existing knowledge is focused on maximising profitability and sustainability of animal Vol 7 No 1

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Data Warehouse

Figure 3:

PLF system implementation, adapted from Banhazi et al (2007b).

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 intensification. 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 profitability. The greatest progress is usually made by developing new technological tools, rather than fine-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 financial support of Australian Pork Limited. Australian Journal of Multi-disciplinary Engineering


REFERENCES Aerts, J. M., Wathes, C. M. & Berckmans, D. 2001, “Applications of process control techniques in poultry production”, in Integrated Management Systems for Livestock, Wathes, C. (editor), Selwyn College, Cambridge, UK, BSAS, Edinburgh, pp. 147-154. Alston, J. M., Marra, M. C., Pardey, P. G. & Wyatt, T. J. 2000, “Research returns redux: a meta-analysis of the returns to agricultural R&D”, The Australian Journal of Agriculture and Resource Economics, Vol. 44, pp. 185-215. Artmann, R. 1997, “Sensor systems for milking robots”, Computers and Electronics in Agriculture, Vol. 17, No. 1, pp. 19-40. Aubry, C., Galan, M. B. & Maze, A. 2005, “HACCP m e t h o d o l o g y a n d q u a l i t y / e n v i ro n m e n t a l specifications for crop farms: Implications for the design of good agricultural practices guidelines”, Cashiers Agricultures, Vol. 14, pp. 313-322. Banhazi, T. 2005, “Improved air quality measurement procedure – BASE-Q system”, in AAPV Conference, Gold Coast, QLD, Australia, Fahy, T. (editor), Vol. 1, pp. 71-75. Banhazi, T. 2006, “Potential precision livestock farming technologies for the egg industry”, in Advances in agricultural technologies and their economic and ecological impacts, Gan-Mor, S. (editor), ISAE, Tel Aviv, Israel, Vol. 1, pp. 45-47. Vol 7 No 1

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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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