Moving towards IPM with robust sampling strategies
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Transcript of Moving towards IPM with robust sampling strategies
Cooperative Research Centre for National Plant Biosecurity
Dr Grant Hamilton
Be/er sampling strategies for post harvest grain in Australia
Project Aims
• To review current sampling methodologies • develop a flexible, staBsBcally robust sampling system for the detecBon of post-‐harvest grain storage pests in the Australian grains industry.
1: review of sampling
• Current sampling gives a number of opportuniBes to detect infestaBons
• In the 1950’s Australia began to develop a reputaBon for infested grain
• Response -‐ Export grain regulaBons (1963) • NO live insects • Grain needed to be sampled – but how much?
– Will determine how effecBve a sampling programme is at detecBng what is there
1: review of sampling • 2.25L /33 Tonnes – based on pragmaBc consideraBons – Belt loading speeds – Smoko breaks – Size of storages and transport infrastructure – Samplers capacity to sieve sample
• sampling model reviewed by Hunter and Griffiths (1978)
• reasonable IF insects spread homogeneously
Hunter and Griffiths
• But they’re not – Grain type – Behaviour – Micro-‐climaBc condiBons – Storage type
Grant Hamilton and David Elmouee (2011). Insect distribuBons and sampling protocols for stored commodiBes. Stewart Postharvest Review
2: New sampling model
• To be more accurate sampling model needs to account for heterogeneous distribuBon
2: new sampling model
• New sampling model -‐ number of samples that need to be taken to detect (rejecBon sampling approach)
– ProporBon of grain infested p – Density of infestaBon λ – Size of sample unit
Elmouee, Kiermeier and Hamilton. (2010). Pest management Science
Advantages
• Closer representaBon of biological system-‐ greater capacity to detect infestaBons
• Parameters intuiBve • Inform parameters from range of informaBon sources (expert opinion, samples taken for other reasons)
3: Assess the accuracy
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LL HH VH HL
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Rhyzopertha dominica
Cryptolestes ferrugineus
(Density of infestaBon, ProporBon infested)
oversampling undersampling
2 bins –Parameter esBmates 1, permute and ‘sample’ other 2 10,000 simulaBons
4: Sampling for Integrated Pest Management
• Sampling integral to IPM programmes • Can inform decisions (to treat, treatment type, movement of product)
• Currently modelling rejecBon (decision to treat/fumigate) based on detecBon of single insect
• Use model for scenario tesBng– treat at some higher acBon threshold
Other outcomes
• Masters project – 3D analysis spaBal locaBon Rd – IntegraBng with sampling model
Steel, Elmouee, Hamilton. JSPR, 2012
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High
Outcomes for industry • Review • TheoreBcal framework for further work • Model can be used to establish level of confidence from number of samples
• Model structured so that different forms of informaBon can be used
• Sampling could base on fixed number of samples rather than by size of consignment
• StaBsBcal foundaBon for alternaBve acBon thresholds
Thanks • Dr. David Elmouee • Peterson family (Killarney) • Philip Burrill, GRDC • Pat Collins, Greg Daglish, Manoj Nayak • Jim Eldridge and Roderic Steel (QUT) • CBH, Graincorp, Viterra, • Dr. Andreas Kiermeier – SARDI • Dr. Paul Flinn – USDA • Prof. Bhadriraju Subramanyam & Prof. David Hagstrum –
KSU