Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-24T03:36:11.264Z Has data issue: false hasContentIssue false
  • English
  • Français

A review of current statistical methodologies for in-storage sampling and surveillance in the grains industry

Published online by Cambridge University Press:  25 September 2012

D. Elmouttie ,
N.E.B. Hammond  and
G. Hamilton
D. Elmouttie
Affiliation:
Cooperative Research Centre for National Plant Biosecurity, LPO Box 5012, Bruce, ACT 2617, Australia Earth, Environmental and Biological Sciences, Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
N.E.B. Hammond
Affiliation:
Cooperative Research Centre for National Plant Biosecurity, LPO Box 5012, Bruce, ACT 2617, Australia School of Veterinary and Biomedical Sciences, Murdoch University, South Street, Murdoch, WA, 6150, Australia Department of Agriculture and Food, Locked Bag 4, Bentley Delivery Centre, WA, 6983, Australia
G. Hamilton*
Affiliation:
Cooperative Research Centre for National Plant Biosecurity, LPO Box 5012, Bruce, ACT 2617, Australia Earth, Environmental and Biological Sciences, Science and Engineering Faculty, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia
*
*Author for correspondence Fax: + 61 7 3138 1535 E-mail: g.hamilton@qut.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, integrated pest management (IPM), to determine grain quality and to satisfy importing nation's biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in-storage sampling strategies concentrating on detection of pest insects within a grain bulk; however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in-storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in-storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in-storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in-storage sampling systems.

Keywords

grainssamplingsurveillancedetectionpestsIPMsurveys
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 103 , Issue 2 , April 2013 , pp. 140 - 147
Copyright
Copyright © Cambridge University Press 2012

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

Athanassiou, C.G., Kavallieratos, N.G., Sciarretta, A., Palyvos, N.E. & Trematerra, P. (2011) Spatial Associations of Insects and Mites in Stored Wheat. Journal of Economic Entomology 104, 1752–1764.CrossRefGoogle ScholarPubMed
Audigé, L., Doherr, M.G., Hauser, R. & Salman, M.D. (2001) Stochastic modelling as a tool for planning animal-health surveys and interpreting screening-test results. Preventive Veterinary Medicine 49(1–2), 1–17.Google Scholar
Audigé, L., Doherr, M.G. & Wagner, B. (2003) Use of Simulation Models in Surveillance and Monitoring Systems. pp. 49–168in Salman, M.D. (Ed.) Animal Disease Surveillance and Survey Systems: Methods and Applications. Ames, IA, USA, Wiley-Blackwell.Google Scholar
Branscum, A.J., Gardner, I.A. & Johnson, W.O. (2004) Bayesian modelling of animal- and herd-level prevalences. Preventive Veterinary Medicine 66(1–4), 101–112.Google Scholar
Branscum, A.J., Gardner, I.A. & Johnson, W.O. (2005) Estimation of diagnostictest sensitivity and specificity through Bayesian modeling. Preventive Veterinary Medicine 68(2–4), 145–163.Google Scholar
Cameron, A.R. & Baldock, F.C. (1998a) A new probability formula for surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34, 1–17.Google Scholar
Cameron, A.R. & Baldock, F.C. (1998b) Two-stage sampling in surveys to substantiate freedom from disease. Preventive Veterinary Medicine 34, 19–30.CrossRefGoogle ScholarPubMed
Cannon, R.M. & Roe, R.T. (1982) Livestock Disease Surveys: A Field Manual for Veterinarians. Canberra, Australia, Australian Bureau of Animal Health, Department of Primary Industry.Google Scholar
Czaja, R. and Blair, J. (2005) Designing Surveys: A Guide to Decisions and Procedures. Thousand Oaks, CA, USA, Pine Forge Press.CrossRefGoogle Scholar
Dominiak, B.C., Gott, K., McIver, D., Grant, T., Gillespie, P.S., Worsley, P., Clift, A. & Sergeant, E.S.G. (2011) Scenario tree rsik analysis of zero detections and the eradication of yellow crazy ant (Anoplolepis gracilipes (Smith)), in New South Wales, Australia. Plant Protection Quarterly 26, 124–129.Google Scholar
Elmouttie, D., Kiermeier, A. & Hamilton, G. (2010) Improving detection probabilities in stored grain. Pest Management Science 66, 1280–1286.Google Scholar
FAO (2009) International Standards for Phytosanitary Measures 1 to 32. (2009 edn). Rome, Italy, Food and Agriculture Organization of the United Nations.Google Scholar
Fischer, E.A.J., van Roermund, H.J.W., Hemerik, L., van Asseldonk, M.A.P.M. & de Jong, M.C.M. (2005) Evaluation of surveillance strategies for bovine tuberculosis (Mycobacterium bovis) using an individual based epidemiological model. Preventive Veterinary Medicine 67, 283–301.Google Scholar
Gardner, I.A. (2002) The utility of Bayes' theorem and Bayesian inference in veterinary clinical practice and research. Australian Veterinary Journal 80, 758–61.Google Scholar
Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2004) Bayesian Data Analysis. 2nd edn.Boca Raton, FL, USA, Chapman and Hall/CRC.Google Scholar
Green, R.H. & Young, R.C. (1993) Sampling to detect rare species. Ecological Applications 3, 351–356.Google Scholar
Hadorn, D.C., Racloz, V., Schwermer, H. & Stärk, K.D.C. (2009) Establishing a cost-effective national surveillance system for Bluetongue using scenario tree modelling. Veterinary Research 40, 57.Google Scholar
Hagstrum, D.W. (1996) Monitoring and predicting population growth of Rhyzopertha dominica (Coleoptera: Bostrichidae) environmental conditions. Environmental Entomology 25, 1354–1359.Google Scholar
Hagstrum, D.W. & Subramanyam, B. (2006) Fundamentals in Stored-Product Entomology. St Paul, MN, AACC International Press.Google Scholar
Hagstrum, D.W., Milliken, G.A. & Waddell, M.S. (1985) Insect distribution in bulk-stored wheat in relation to detection or estimation of abundance. Environmental Entomology 14, 655–661.CrossRefGoogle Scholar
Hagstrum, D.W., Subramanyam, B. & Flinn, P.W. (1997) Nonlinearity of a generic variance-mean equation for stored-grain insect sampling data. Environmental Entomology 26, 1213–1223.Google Scholar
Hamilton, G.S., Fielding, F., Chiffings, A.W., Hart, B.T., Johnstone, R.W. & Mengerson, K.L. (2007) Investigating the use of a Bayesian network to model the risk of Lyngbya majuscule bloom initiation in Deception Bay, Queensland. Human and Ecological Risk Assessment 13, 1271–1287.Google Scholar
Hammond, N.E.B. (2010) Evaluation of emergency plant pathogen surveillance and surveillance methods for demonstrating pest freedom in Western Australia. PhD thesis. Murdoch University. Perth, Australia.Google Scholar
Hayes, K.R. (2002) Identifying hazards in complex ecological systems. Part 1: Fault-tree analysis for biological invasions. Biological Invasions 4, 235–249.Google Scholar
Hellström, J.S. (2008) Opening address: biosecurity surveillance. pp. 1–9in Froud, K.J., Popay, A.I. & Zydenbos, S.M. (Eds) Surveillance for Biosecurity: Pre-border to Pest Management. Hastings. New Zealand, New Zealand Plant Protection Society, Inc.Google Scholar
Hoyland, K. & Wallace, S.W. (2001) Scenario trees for multistage decision problems. Management Science 47, 295–307.Google Scholar
Hueston, W.D. & Yoe, C.E. (2000) Estimating the overall power of complex surveillance systems. p. 339 in Proceedings of the 9th International Symposium on Veterinary Epidemiology and Economics. Surveillance & monitoring session. August 2000, Breckenridge, CO, USA.Google Scholar
Hunter, A.J. & Griffiths, H.J. (1978) Bayesian approach to estimation of insect population size. Technometrics 20, 231–234.CrossRefGoogle Scholar
Jefferies, G.M. (2000) Review of grain sampling inspection methodology. Australia Department of Agriculture, Fisheries and Forestry.Google Scholar
Jian, F., Larson, R., Jayas, D.S. & White, N.D.G. (2011) Three dimensional spatial distribution of adults of Cryptolestes ferrugineus (Coleoptea: Laemophloeidae) in stored wheat under different temperatures, moisture contents, and adult densities. Journal of Stored Products Research 47, 293–305.Google Scholar
Johnson, W.O., Su, C.L., Gardner, I.A. & Christensen, R. (2004) Sample size calculations for surveys to substantiate freedom of populations from infectious agents. Biometrics 60, 165–171.CrossRefGoogle ScholarPubMed
Jorgensen, K., Cannon, R.M. & Muirhead, I. (2003) Guidelines for the establishment of pest-free areas for Australian quarantine. Report prepared for Plant Health Australia Ltd and Agriculture, Fisheries and Forestry Australia.Google Scholar
Kean, J.M., Phillips, C.B. & McNeill, M.R. (2008) Surveillance for early detection: lottery or investment? pp. 11–17in Froud, K.J., Popay, A.I. & Zydenbos, S.M. (Eds) Surveillance for Biosecurity: Pre-border to Pest Management. Hastings. New Zealand, New Zealand Plant Protection Society, Inc.Google Scholar
Lippert, G.E. & Hagstrum, D.W. (1987) Detection or estimation of insect population in bulk-stored wheat with probe traps. Journal of Economic Entomology 80, 601–604.Google Scholar
Love, G., Twyford-Jones, P. & Woolcock, I. (1983) An economic evaluation of alternative grain insect control measures. Canberra, Australian Capital Territory, Australian Government Publishing Service.Google Scholar
Marcot, B.G., Holthausen, R.S., Rapheal, M.G., Rowland, M.M. & Wisdom, M.J. (2001) Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153, 29–42.Google Scholar
Martin, P.A.J., Cameron, A.R. & Greiner, M. (2007a) Demonstrating freedom from disease using multiple complex data sources: 1: A new methodology based on scenario trees. Preventive Veterinary Medicine 79, 71–97.Google Scholar
Martin, P.A.J., Cameron, A.R., Barfod, K., Sergeant, E.S.G. & Greiner, M. (2007b) Demonstrating freedom from disease using multiple complex data sources: 2: Case study-Classical swine fever in Denmark. Preventive Veterinary Medicine 79, 98–115.Google Scholar
Martin, T.G., Kuhnert, P.M., Mengersen, K. & Possingham, H.P. (2005) The power of expert opinion in ecological models using Bayesian methods: Impact of grazing on birds. Ecological Applications 15, 266–280.CrossRefGoogle Scholar
McCarthy, M.A. (2007) Bayesian Methods for Ecology. Cambridge, UK, Cambridge University Press.Google Scholar
McKirdy, S.J., Mackie, A.E. & Kumar, S. (2001) Apple scab successfully eradicated in Western Australia. Australasian Plant Pathology 30, 371–371.Google Scholar
McMaugh, T. (2005) Guidelines for surveillance for plant pests in Asia and the Pacific. ACIAR Monograph No. 119.Google Scholar
Opit, G.P., Throne, J.E. & Flinn, P.W. (2009) Sampling plans for the psocids Liposcelis entomophila and Liposcelis decolor (Psocoptera: Liposcelididae) in steel bins containing wheat. Journal of Economic Entomology 102, 1714–1722.Google Scholar
Rees, D. (2004) Insects of Stored Products. Melbourne, Victoria, Australia, CSIRO Publishing.Google Scholar
Salman, M., Chriél, M. & Wagner, B. (2003a) Improvement of survey and sampling methods to document freedom from diseases in Danish cattle population on both national and herd levels. Copenhagen, International EpiLab, 2003.Google Scholar
Salman, M.D., Stärk, K.D.C. & Zepeda, C. (2003b) Quality assurance applied to animal disease surveillance systems. Revue scientifique et technique (International Office of Epizootics) 22, 689–696.Google Scholar
Scott, G.E. & Zummo, N. (1995) Size of maize sample needed to determine percent kernel infection by Aspergillus flavus. Plant Disease 79(8), 861–864.Google Scholar
Stärk, K.D.C. (2003) Quality assessment of animal disease surveillance and survey systems. pp. 169–176in Salman, M.D. (Ed.) Animal Disease Surveillance and Survey Systems: Methods and Applications. Ames, IA, USA, Wiley-Blackwell.CrossRefGoogle Scholar
Stephens, K.S. (2001) The Handbook of Applied Acceptance Sampling: Plans, Principles and Procedures. Milwaukee, WI, USA, American Society for Quality.Google Scholar
Subramanyam, B.H. & Hagstrum, D.W. (1996) Integrated Management of Insects in Stored Products. New York, NY, USA, Marcel Dekker, Inc.Google Scholar
Subramanyam, B.H. & Harein, P.K. (1990) Accuracies and sample size associated with estimating densities of adult beetles (Coleoptra) caught in probe traps in stored barely. Journal of Environmental Entomology 83, 1102–1109.Google Scholar
Subramanyam, B.H., Hagstrum, D.W. & Schenk, T.C. (1993) Sampling adult beetles associated with stored grains: comparing detection and mean trap catch efficiency of types of probe traps. Environmental Entomology 22, 33–42.Google Scholar
Subramanyam, B.H., Hagstrum, D.W., Meagher, R.L., Burkness, E.C., Hutchison, W.D. & Naranjo, S.E. (1997) Development and Evaluation of sequential sampling plans for Cryptolestes ferrugineus (Stephens) (Coleoptra: Cucujidae) infesting farm-stored wheat. Journal of Stored Product Research 33, 321–329.Google Scholar
Taylor, L.R. (1961) Aggregation, variance and the mean. Nature 189, 732–735.CrossRefGoogle Scholar
Taylor, S. & Slattery, J. (2008) National Surveillence Plan for the Australian Grains Industry. Canberra, Australia, Cooperative Research Centre for Plant Biosecurity Report.Google Scholar
Vose, D. (2008) Risk Analysis: A Quantitative Guide. (3rd edn). West Sussex, UK, John Wiley and Sons, Ltd.Google Scholar
Wagner, B., Gardner, I., Cameron, A. & Doherr, M.G. (2003) Statistical analysis of data from surveys, monitoring, and surveillance systems. pp. 67–86in Salman, M.D. (Ed.) Animal Disease Surveillance and Survey Systems: Methods and Applications. Ames, IA, USA, Wisley-Blackwell.Google Scholar
Weinberg, J. (2005) Surveillance and control of infectious diseases at local, national and international levels. Clinical Microbiology and Infection 11(suppl. 1), 12–14.CrossRefGoogle ScholarPubMed
Wilkin, D.R. (1991) An assessment of methods of sampling bulk grain. HGCA Project Report No. 34. London, UK, Home Grown Cereals Authority.Google Scholar