Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-25T12:45:08.177Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  23 December 2010

K. MERTENS ,
E. DECUYPERE ,
J. DE BAERDEMAEKER  and
B. DE KETELAERE
K. MERTENS*
Affiliation:
Division Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30 bus 2456, 3001 Heverlee, Belgium
E. DECUYPERE
Affiliation:
Division Livestock, Nutrition, Quality, Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30 bus 2456, 3001 Heverlee, Belgium
J. DE BAERDEMAEKER
Affiliation:
Division Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30 bus 2456, 3001 Heverlee, Belgium
B. DE KETELAERE
Affiliation:
Division Mechatronics, Biostatistics and Sensors (MeBioS), Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30 bus 2456, 3001 Heverlee, Belgium
*
*To whom all correspondence should be addressed. Email: kristof.mertens@biw.kuleuven.be
Get access Rights & Permissions [Opens in a new window]

Summary

The 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.

Type
Modelling Animal Systems
Information
The Journal of Agricultural Science , Volume 149 , Issue 3 , June 2011 , pp. 369 - 384
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Akachi, S. (1971). Control charts using removal rate and egg-production obtained from 1 to 29 weeks old of chickens. Japanese Journal of Veterinary Science 33(Suppl.), 284.Google Scholar
Alwan, L. C. & Roberts, H. V. (1995). The problem of misplaced control limits. Applied Statistics 44, 269–278.CrossRefGoogle Scholar
Banhazi, T., Dunn, M., Cook, P., Black, J., Durack, M. & Johnnson, I. (2007). Development of precision livestock farming (PLF) technologies for the Australian pig industry. In Precision Livestock Farming '07 (Ed. Cox, S.), pp. 219–228. Wageningen, The Netherlands: Wageningen Academic Publishers.CrossRefGoogle Scholar
Bebbington, M., Lai, C. D. & Zitikis, R. (2009). Modeling lactation curves: classical parametric models re-examined and modified. Journal of Applied Statistics 36, 121–133.CrossRefGoogle Scholar
Blackmore, B. S. (2007). A systems view of agricultural robots. In Precision Agriculture ‘07 (Ed. Stafford, J. V.), pp. 23–31. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Borror, C. M., Montgomery, D. C. & Runger, G. C. (1999). Robustness of the EWMA control chart to non-normality. Journal of Quality Technology 31, 309–316.CrossRefGoogle Scholar
Box, G. E. P. & Paniagua-Quiñones, C. (2007). Two charts not one. Quality Engineering 19, 93–100.CrossRefGoogle Scholar
Brockwell, P. J. & Davis, R. A. (1991). Time Series: Theory and Methods, 2nd edn.New York: Springer-Verlag.CrossRefGoogle Scholar
Cornou, C., Vinther, J. & Kristensen, A. R. (2008). Automatic detection of oestrus and health disorders using data from electronic sow feeders. Livestock Science 118, 262–271.Google Scholar
Cowen, P., Fernandez, D. & Barnes, H. (1994). Surveillance strategies for monitoring variation in animal health and productivity: the use of statistical process control in the turkey industry. The Kenya Veterinarian 18, 202–204.Google Scholar
Darmani-Kuhi, H., Kebreab, E., Lopez, S. & France, J. (2003). An evaluation of different growth functions for describing the profile of live weight with time (age) in meat and egg strains of chicken. Poultry Science 82, 1536–1543.Google Scholar
de Mol, R., Keen, A., Kroeze, G. & Achten, J. M. F. H. (1999). Description of a detection model for oestrus and diseases in dairy cattle based on time series analysis combined with a Kalman filter. Computers and Electronics in Agriculture 22, 171–185.Google Scholar
de Vargas, V. C. C., Lopes, L. F. D. & Souza, A. M. (2004). Comparative study of the performance of the CuSum and EWMA control charts. Computers and Industrial Engineering 46, 707–724.Google Scholar
de Vries, A. (2001). Statistical process control charts applied to dairy herd production, PhD thesis, University of Minnesota, USA.Google Scholar
de Vries, A. & Conlin, B. J. (2003). Design and performance of statistical process control charts applied to estrous detection efficiency. Journal of Dairy Science 86, 1970–1984.Google Scholar
de Vries, A. & Conlin, B. J. (2005). A comparison of the performance of statistical quality control charts in a dairy production system through stochastic simulation. Agricultural Systems 84, 317–341.Google Scholar
de Vries, A., Conlin, B. J., Marsh, W. & Reneau, J. (1997 a). Monitoring daily milk weights with statistical process control techniques. Journal of Dairy Science 80(Suppl. 1), p. 230.Google Scholar
de Vries, A., Conlin, B. J., Reneau, J., Kinsel, M. & Marsh, W. (1997 b). Some illustrations of the use of Statistical Process Control techniques in monitoring dairy herd performance. Epidémiologie et Santé Animal 31–32, 13.11.1–13.11.3.Google Scholar
Deen, J. (1997). Using statistical process control in swine production. In Proceedings of the North American Veterinary Conference, Vol. 11. pp. 987–988. Orlando, FL: NAVC (www.tnavc.org).Google Scholar
del Castillo, E. (2002). Statistical Process Adjustment for Quality Control. New York: John Wiley and Sons, Inc.Google Scholar
Deming, W. (1986). Out of the Crisis. Cambridge, MA: Massachusetts Institute of Technology.Google Scholar
DeVor, R. E., Chang, T. & Sutherland, J. W. (1992). Statistical Quality Design and Control: Contemporary Concepts and Methods. Englewood Cliffs, NJ: Prentice Hall Inc.Google Scholar
Dial, G. D., FitzSimmons, M., BeVier, G. W. & Wiseman, B. S. (1994). Systems approaches for improving the productivity of the breeding herd. In Leman Swine Conference Proceedings (Ed. Allen, D.), pp. 50–54. St. Paul, MN: Veterinary Outreach Programs, University of Minnesota.Google Scholar
Dial, G. D.Duangkaew, C. & Rademacher, C. (1996). Statistical process control - Application to swine production. In Leman Swine Conference Proceedings (Ed. Allen, D.), pp. 53–83. St. Paul, MN: Veterinary Outreach Programs, University of Minnesota.Google Scholar
Dohoo, I. R. (1993). Monitoring livestock health and production: service – epidemiology's last frontier? Preventive Veterinary Medicine 18, 43–52.Google Scholar
Emmans, G. C. & Kyriazakis, I. (1997). Models of pig growth: problems and proposed solutions. Livestock Production Science 51, 119–129.CrossRefGoogle Scholar
Engler, J., Tölle, K.-H., Timm, H. H., Hohls, E. & Krieter, J. (2005). Control charts applied to individual sow farm analysis. In Precision Livestock Farming ‘05 (Ed. Cox, S.), pp. 319–325. Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
Engler, J., Tölle, K.-H., Timm, H. H., Hohls, E. & Krieter, J. (2009). Control charts applied to pig farming data. Archiv Tierzucht 52, 272–283.Google Scholar
Fernandez, D. V. (1995). Determinants of productivity in commercial tom turkey production. PhD thesis, North Carolina State University, Raleigh, NC, USA.Google Scholar
Frost, A. R., Schofield, C. P., Beaulah, S. A., Mottram, T. T., Lines, J. A. & Wathes, C. M. (1997). A review of livestock monitoring and the need for integrated systems. Computers and Electronics in Agriculture 17, 139–159.Google Scholar
Galli, A., Signori, T. & Balduzzi, D. (1998). Statistical methods to produce ‘good’ bovine frozen semen. Reproduction in Domestic Animals 33, 125–132.Google Scholar
Grennstam, N. (2005). On Predicting Milk Yield and Detection of Ill Cows. Stockholm, Sweden: KTH, Royal Institute of Technology.Google Scholar
Hawkins, D. M. & Olwell, D. H. (1998). Cumulative Sum Charts and Charting for Quality Improvement. New York: Springer-Verlag.Google Scholar
Huirne, R. B. M. (1990). Basic concepts of computerized support for farm management decisions. European Review of Agricultural Economics 17, 69–84.CrossRefGoogle Scholar
Kniffen, T. (1994). Potential uses of SPC in a pork production system. In Leman Swine Conference Proceedings (Ed. Allen, D.), pp. 1–12. St. Paul, MN: Veterinary Outreach Programs, University of Minnesota.Google Scholar
Koketsu, Y., Duangkaew, C., Dial, G. D. & Reeves, D. E. (1999). Within-farm variability in number of females mated per week during a one-year period and breeding herd productivity on swine farms. Journal of the American Veterinary Medical Association 214, 520–524.Google Scholar
Krieter, J., Kirchner, K., Engler, J. & Tölle, K.-H. (2005). Computer-based analysis of sow herd performance. Archiv Tierzucht 48, 346–358.Google Scholar
Krieter, J., Engler, J., Tölle, K.-H., Timm, H. & Hohls, E. (2009). Control charts applied to simulated sow herd datasets. Livestock Science 121, 281–287.Google Scholar
Kutner, M., Nachtsheim, C., Neter, J. & Li, W. (2005). Applied Linear Statistical Models, 5th edn.New York: McGraw-Hill/Irwin.Google Scholar
Lokhorst, C. (1996). Mathematical curves for the description of input and output variables of the daily production process in aviary housing systems for laying hens. Poultry Science 75, 838–848.Google Scholar
Lukas, J. M., Hawkins, D. M., Kinsel, M. L. & Reneau, J. K. (2005). Bulk tank somatic cell counts analyzed by statistical process control tools to identify and monitor subclinical mastitis incidence. Journal of Dairy Science 88, 3944–3952.CrossRefGoogle ScholarPubMed
Lukas, J. M., Reneau, J. K. & Linn, J. G. (2008). Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows. Journal of Dairy Science 91, 3385–3394.Google Scholar
Lukas, J. M., Reneau, J. K., Wallace, R., Hawkins, D. & Munoz-Zanzi, C. (2009). A novel method of analyzing daily milk production and electrical conductivity to predict disease onset. Journal of Dairy Science 92, 5964–5976.Google Scholar
Madsen, T. N. & Kristensen, A. R. (2005). A model for monitoring the condition of young pigs by their drinking behaviour. Computers and Electronics in Agriculture 48, 138–154.Google Scholar
Marsh, W. E., de Vries, A., Reneau, J. K. & Kinsel, M. L. (1997). Monitoring performance: Statistical process control in dairy herd management. In Annual Northeast Dairy Production Medicine Symposium, 6th edn, pp. 34–46. Syracuse, NY: NEDPMS.Google Scholar
Mertens, K. (2009). An intelligent system for optimizing the production and quality of consumption eggs based on synergistic control. PhD thesis, Katholieke Universiteit Leuven, Belgium.Google Scholar
Mertens, K., De Ketelaere, B., Vaesen, I., Löffel, J., Ostyn, B., Kemps, B., Kamers, B., Bamelis, F., Zoons, J., Darius, P., Decuypere, E. & De Baerdemaeker, J. (2008). Data-based design of an intelligent quality control chart for the daily monitoring of the average egg weight. Computers and Electronics in Agriculture 61, 222–232.Google Scholar
Mertens, K., Vaesen, I., Löffel, J., Kemps, B., Kamers, B., Zoons, J., Darius, P., Decuypere, E., De Baerdemaeker, J. & De Ketelaere, B. (2009). An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy. Computers and Electronics in Agriculture 69, 100–111.Google Scholar
Mertens, K., Vaesen, I., Löffel, J., Kemps, B., Kamers, B., Perianu, C., Zoons, J., Darius, P., Decuypere, E., De Baerdemaeker, J. & De Ketelaere, B. (2010). The transmission color value: A novel egg quality measure for recording shell color used for monitoring the stress and health status of a brown layer flock. Poultry Science 89, 609–617.Google Scholar
Montgomery, D. C. (2009). Introduction to Statistical Quality Control, 6th edn.Hoboken, NJ: John Wiley and Sons Inc.Google Scholar
Montgomery, D. C., Jennings, C. L. & Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting. Hoboken, NJ, USA: John Wiley and Sons Inc.Google Scholar
Morrison, R. B., Dial, G. D., Bahnson, P. B., Marsh, W. E., Collins, J. E. & Polson, D. (1997). Using statistical process control to investigate reproductive failure. In Current Therapy in Large Animal Theriogenology (Ed. Youngquist, R. S.), pp. 770–775. Philadelphia, PA: W.D. Saunders Company.Google Scholar
Niehoff, D., Tölle, K.-H. & Krieter, J. (2007). Fertility monitoring in dairy herds. Zuchtungskunde 79, 275–286.Google Scholar
Noordhuizen, J., Frankena, K., Stassen, E. & Brand, A. (1992). Applied epidemiology in aid to dairy herd health programs. In Proceedings of the XVII World Buiatric Congress and the 25th American Association of Bovine Practitioners Conference (Ed. Williams, E. I.). Vol. 2, St. Paul, MN, USA, pp. 6–11.Google Scholar
Ostyn, B., De Ketelaere, B., Mertens, K., Anthonis, J., Zoons, J. & De Baerdemaeker, J. (2006). Control charts for online monitoring of non-stationary processes. In Proceedings of the 3rd IFAC International Workshop on Bio-Robotics, Information Technology and Intelligent Control for Bioproduction Systems (BIO-ROBOTICS III), 9–10 September 2006, Sapporo, Japan (Eds Kataoka, T., Noguchi, N. & Murase, H.). Laxenburg, Austria: International Federation of Automatic Control.Google Scholar
Page, E. S. (1954). Continuous inspection schemes. Biometrika 41, 100–114.Google Scholar
Pastell, M. & Madsen, H. (2008). Application of CUSUM charts to detect lameness in a milking robot. Expert Systems with Applications 35, 2032–2040.Google Scholar
Pleasants, A. B., McCall, D. G. & Sheath, G. W. (1998). Design and application of a cusum quality control chart suitable for monitoring effects on ultimate muscle pH. New Zealand Journal of Agricultural Research 41, 235–242.Google Scholar
Polson, D. (1998). SPC=statistical pig control. International Pig Letter 18, 43–46.Google Scholar
Polson, D., Baum, D. & Holck, J. T. (1999). Management of livestock systems need to be based on continuous improvement. Feedstuffs 1, 39–42.Google Scholar
Quesenberry, C. (1991). SPC Q charts for start-up processes and short or long runs. Journal of Quality Technology 23, 213–224.CrossRefGoogle Scholar
Quesenberry, C. (1995). On properties of Q charts for variables. Journal of Quality Technology 27, 184–203.CrossRefGoogle Scholar
Quesenberry, C. (1997). SPC Methods for Quality Improvement. New York: John Wiley and Sons.Google Scholar
Quimby, W. F., Sowell, B. F., Bowman, J. G. P., Branine, M. E., Hubbert, M. E. & Sherwood, H. W. (2001). Application of feeding behaviour to predict morbidity of newly received calves in a commercial feedlot. Canadian Journal of Animal Science 81, 315–320.Google Scholar
Ravindranathan, N. & Unni, A. K. K. (1990). A study on consistency in body weights of chicks using Shewhart control charts. Cheiron 19, 156–158.Google Scholar
Reneau, J. K. & Kinsel, M. L. (2001). Record systems and herd monitoring in production-oriented health and management programs in food producing animals. In Herd Health: Food Animal Production Medicine, 3rd edn (Ed. Radostits, M. O.), pp. 107–146. Philadelphia, PA: W.D. Saunders Company.Google Scholar
Reneau, J. K. & Lukas, J. (2006). Using statistical process control methods to improve herd performance. Veterinary Clinics of North America: Food Animal Practice 22, 171–193.Google Scholar
Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics 1, 239–250.Google Scholar
Roush, W. B., Tomiyama, K., Garnaoui, K. H., D'Alfonso, T. H. & Cravener, T. L. (1992). Kalman filter and an example in poultry production responses. Computers and Electronics in Agriculture 6, 347–356.Google Scholar
Sard, D. M. (1979). Dealing with data: the practical use of numerical information (14) Monitoring changes. Veterinary Record 105, 323–328.Google Scholar
Schmilovitch, Z., Shmulevich, I., Notea, A. & Maltz, E. (2000). Near infrared spectrometry of milk in its heterogeneous state. Computers and Electronics in Agriculture 29, 195–207.Google Scholar
Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Products. Princeton, NJ: D. Van Nostrand.Google Scholar
Silvestre, A. M., Martins, A. M., Santos, V. A., Ginja, M. M. & Colaço, J. A. (2009). Lactation curves for milk, fat and protein content in dairy cows: a full approach. Livestock Science 122, 308–313.Google Scholar
St-Pierre, N. R. & Cobanov, B. (2007 a). A model to determine the optimal sampling schedule of diet components. Journal of Dairy Science 90, 5383–5394.Google Scholar
St-Pierre, N. R. & Cobanov, B. (2007 b). Optimal sampling schedule of diet components: Model robustness to departure from assumptions. Journal of Dairy Science 90, 5395–5404.Google Scholar
Thysen, I. (1993). Monitoring bulk tank somatic cell counts by a multiprocess Kalman filter. Acta Agriculturae Scandinavica. Section A. Animal Science 43, 58–64.Google Scholar
Tsenkova, R., Atanassova, S., Toyoda, K., Ozaki, Y., Itoh, K. & Fearn, T. (1999). Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. Journal of Dairy Science 82, 2344–2351.Google Scholar
Tsenkova, R., Atanassova, S., Itoh, K., Ozaki, Y. & Toyoda, K. (2000). Near infrared spectroscopy for biomonitoring: Cow milk composition measurement in a spectral region from 1,100 to 2,400 nanometers. Journal of Animal Science 78, 515–522.Google Scholar
Van Bebber, J., Reinsch, N., Junge, W. & Kalm, E. (1999). Monitoring daily milk yields with a recursive test day repeatability model (kalman filter). Journal of Dairy Science 82, 2421–2429.Google Scholar
Wadsworth, H. M., Stephens, K. S. & Godfrey, A. B. (2002). Modern Methods for Quality Control Improvement. New York: John Wiley and Sons.Google Scholar
Wathes, C. M., Kristensen, H. H., Aerts, J. M. & Berckmans, D. (2008). Is precision livestock farming an engineer's daydream or nightmare, an animal's friend or foe, and a farmer's panacea or pitfall? Computers and Electronics in Agriculture 64, 2–10.Google Scholar
Wheeler, D. (1995). Advanced Topics in Statistical Process Control – the Power of Shewhart's Charts. Knoxville, TN: SPC Press.Google Scholar
Wieringa, J. E. (1999). Statistical process control for serially correlated data. PhD thesis, Rijksuniversiteit Groningen, The Netherlands.Google Scholar
Wilson, M. R., McMillan, I. & Swaminathan, S. S. (1980). Computerized health monitoring in swine health management. Pig Veterinary Society Proceedings 6, 64–71.Google Scholar
Woodall, W. H. (2000). Controversies and contradictions in statistical process control. Journal of Quality Technology 32, 341–350.Google Scholar
Woodall, W. H. & Montgomery, D. C. (1999). Research issues and ideas in statistical process control. Journal of Quality Technology 31, 376–386.CrossRefGoogle Scholar
Wrathall, A. E. (1977). Reproductive failure in the pig: diagnosis and control. Veterinary Record 100, 230–237.Google Scholar
Wrathall, A. E. & Hebert, C. N. (1982). Monitoring reproductive performance in the pig herd. Pig Veterinary Society Proceedings 9, 136–148.Google Scholar
Ziggers, G. & Bots, J. (1989). The farmer as ‘producer’ of the strategic planning process. In Managing Long-term Developments of the Farm Firm: Strategic Planning and Management. Proceedings of the 23rd Symposium of the European Association of Agricultural Economists, Copenhagen, Denmark (Eds Christensen, J., Rasmussen, S. & Stryg, P.), pp. 13–26. Kiel, Germany: Vauk.Google Scholar