Control charts applied to pig farming data

Engler, J.; Tölle, K.-H.; Timm, H. H.; Hohls, E.; Krieter, Joachim

doi:10.5194/aab-52-272-2009

Cited by 4 publications

(9 citation statements)

References 21 publications

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“…For example, a sensitivity of 49.7 % corresponded to a specificity of 76.9 % and an error rate of 73.9 % (or a precision of 26.1 %) (Kruse et al, 2011). Some other reports on warning systems only present cases and do not report any performance measures (for example Engler et al, 2009;Madsen and Kristensen, 2005;Mertens et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Online warning systems for individual fattening pigs based on their feeding pattern

Maselyne

Nuffel²,

Briene

et al. 2018

Biosystems Engineering

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For sustainable pork production and maximum pig welfare, all health, welfare and productivity problems in the barn should be detected as early as possible. In this paper, an automated monitoring and warning system is proposed. Based on measurements of the feeding pattern, it is able to generate daily alerts for individual fattening pigs. Using historical data, the following types of warning systems were developed: (1) fixed limits that treat all pigs and all days equally; and (2) time-varying individual limits using the concept of Synergistic Control. These types of limits were constructed either for the number of registrations per pig or the average interval between feeding visits of a pig, leading to four warning systems in total. These warning systems were used to generate alerts during an online validation period. During an entire fattening period, all pigs were individually monitored to establish true alerts, false alerts and missed problems. The best performance was achieved for the Synergistic Control method on the number of registrations, with a sensitivity of 58.0 %, specificity of 98.7 %, accuracy of 96.7 % and precision of 71.1 %. Severe problems were detected on average within 1.3 days from the start of the problem. These are promising results that provide a solid basis for the development of a system for individual pigs but further improvements are warranted to make the system more practical.

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Section: Discussionmentioning

confidence: 99%

Online warning systems for individual fattening pigs based on their feeding pattern

Maselyne

Nuffel²,

Briene

et al. 2018

Biosystems Engineering

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show abstract

“…The majority of the papers handle economically relevant parameters related to animal fertility (Wrathall 1977;Wilson et al 1980;Wrathall & Hebert 1982;Dial et al 1996Dial et al , 1994de Vries et al 1997a, b;Deen 1997;Marsh et al 1997;Morrison et al 1997;Polson 1998;de Vries 2001;de Vries & Conlin 2003, 2005Engler et al 2005Engler et al , 2009Niehoff et al 2007;Krieter et al 2009) and to production performance or product quality (Akachi 1971;de Vries et al 1997a;Pleasants et al 1998;Grennstam 2005;Mertens et al 2008Mertens et al , 2009Lukas et al 2009). Parameters that reflect collective production or quality performance, such as hen-day egg production in laying hens, are less sensitive to process changes as they pool the inter-individual variability.…”

Section: Practical Relevance Of the Process Parametermentioning

confidence: 99%

“…Practical considerations are often mentioned when considering the choice between cusum and EWMA. Engler et al (2009) choose the cusum because it is more straightforward and the settings are easier to handle. Krieter et al (2009) consider that the EWMA may be easier to understand since actual values are assigned, namely an estimate of the current process mean (Montgomery 2009), in contrast to the cusum, which works with deviations.…”

Section: Choice Of the Con Trol Chartmentioning

confidence: 99%

“…Lukas et al (2009) compared their disease detection control charts with an existing software tool; their charts detected the onset of different diseases in more cases (more true positives) and earlier. Analysis of weekly pig weaning related farm data using control charts by Engler et al (2009) resulted in a detection of a process shift 2 and 4 weeks before the diagnosis of porcine reproductive and respiratory syndrome (PRRS) was made. For fertility data, a change was detected 3•5 months and 6 weeks before the diagnosis was made.…”

Section: Control C Hart Performancementioning

confidence: 99%

“…1997; Polson 1998; de Vries 2001; de Vries & Conlin 2003, 2005; Engler et al . 2005, 2009; Niehoff et al . 2007; Krieter et al .…”

Section: Data Collectionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical control charts as a support tool for the management of livestock production

et al. 2010

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SUMMARYThe concepts of control charts, an important tool in statistical process control, are commonly used for monitoring industrial production processes. In the context of precision livestock farming, their use has been demonstrated by many, although the statistical properties of livestock process data often do not comply with the basic assumptions of such control charts. The focus of the current review is on the most important aspects, recommendations, pitfalls and opportunities for the development and performance of control charts on livestock process data. An important hurdle to tackle is the statistical characteristics of the raw livestock process data which are mostly violating the control charts’ assumptions. An integrated approach, like synergistic control, appears to be promising in handling this issue. The availability of real-time on-farm validation of proposed systems will be crucial for lifting them from the potential level to direct practical relevance.

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Early Detection of PRRSV Outbreaks in Breeding Herds by Monitoring Productivity and Electronic Sow Feed Data Using Univariate and Multivariate Statistical Process Control Methods

Pedro Mil-Homens,

Jayaraman,

Rupasinghe

et al. 2024

Transboundary and Emerging Diseases

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Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most impactful viruses in swine production worldwide. Early detection of PRRSV outbreaks is the first step in rapid disease response. A syndromic surveillance system can be implemented to detect early signs of PRRSV activity in breeding herds. This study aimed to integrate multiple data sources and test univariate and multivariate statistical process control charts to identify early indicators associated with PRRSV outbreaks in sow farms. From March 2022 until May 2023, 16 breed‐to‐wean swine farms were enrolled in the study. The following key clinical and productivity indicators associated with PRRSV outbreaks were investigated: number of abortions, number of dead sows, number of off‐feed events, preweaning mortality rate (PWM), and percentage of neonatal losses. The PRRSV status for the herd was determined by reverse transcriptase polymerase chain reaction testing using processing fluid samples, and it was considered as the reference to calculate the performance of the surveillance system. The exponentially weighted moving average (EWMA), cumulative sum (CUSUM), multivariate exponentially weighted moving average (MEWMA), and multivariate cumulative sum (MCUSUM) were the methods used to detect significant changes in the aforementioned parameters following the PRRSV outbreaks. Using the EWMA model, the indicators with the highest early detection rates were PWM followed by the abortions (71% and 64%, respectively), with the models raising alarms 4 weeks earlier on average than the processing fluids, respectively. For the CUSUM model, the weekly number of PWM, followed by abortions, were the indicators with the highest early detection rates (71% and 64%, respectively), with the models raising alarms 4 weeks earlier on average than the processing fluids for both indicators. Concerning the multivariate models, the MEWMA model with higher early detection used the PWM and neonatal losses (86%), with the models raising alarms 4 weeks earlier on average than the processing fluids, with the models raising alarms 3.5 weeks earlier on average than the processing fluids. For the MCUSUM, the model with higher early detection used PWM and neonatal losses (86%), with the models raising alarms 4.3 weeks earlier on average than the processing fluids. The models with the earliest time to detect signs associated with a PRRSV outbreak and with the lowest false negative and false positive were the multivariate models, MEWMA and the MCUSUM, using the combination of PWM and neonatal losses. Thus, monitoring multiple indicators outperformed the univariate models. With that, using multivariate models is the best option for disease surveillance using indicators, and it allows the decision‐makers to investigate potential outbreaks earlier.

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Control charts applied to pig farming data

Cited by 4 publications

References 21 publications

Online warning systems for individual fattening pigs based on their feeding pattern

Online warning systems for individual fattening pigs based on their feeding pattern

Statistical control charts as a support tool for the management of livestock production

Early Detection of PRRSV Outbreaks in Breeding Herds by Monitoring Productivity and Electronic Sow Feed Data Using Univariate and Multivariate Statistical Process Control Methods

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