moving towards ipm with robust sampling strategies
DESCRIPTION
Sound sampling strategies are an essential foundation for effective integrated pest management. In this project we have successfully combined data collected from bulk grain storages with ecological and statistical theory to develop robust, flexible sampling strategies for stored grains. These novel sampling methods can be adapted along the stored grains supply chain based on the differing needs of stakeholders, and can incorporate a variety of important factors, such as climate and grain moisture content, that will impact on insect infestation dynamics.TRANSCRIPT
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
30°C, 14 days
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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