Be/er sampling strategies for post harvest grain in Australia
Dr Grant Hamilton Cooperative Research Centre for National Plant Biosecurity
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: A ssess t he a ccuracy Rhyzopertha dominica oversampling undersampling
Percentage model success
1 0.9 0.8 0.7 0.6 0.5
CM
0.4
H&G
0.3 0.2 0.1
1 LL
HH
VH
HL
Type of Infestation
(Density of infestaBon, ProporBon infested)
2 bins –Parameter esBmates 1, permute and ‘sample’ other 2 10,000 simulaBons
Percentage model success
0
Cryptolestes ferrugineus
0.9 0.8 0.7 0.6 0.5
CM
0.4
H&G
0.3 0.2 0.1 0 LL
MM
HM
Type of Infestation
ML
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
2.5cm 7.5cm 12.5cm 17.5cm 22.5cm 27.5cm 32.5cm 37.5cm
10cm 5cm 0cm 5cm 10cm
Vert. dist from PoI
Horiz. dist from PoI
10cm 5cm 0cm 5cm 10cm
Vert. dist from PoI
2.5cm 7.5cm 12.5cm 17.5cm 22.5cm 27.5cm 32.5cm 37.5cm
10cm 5cm 0cm 5cm 10cm
Horiz. dist from PoI
10cm 5cm 0cm 5cm 10cm
10cm 5cm 0cm 5cm 10cm 2.5cm 7.5cm 12.5cm 17.5cm 22.5cm 27.5cm 32.5cm 37.5cm
Horiz. dist from PoI
High
10cm 5cm 0cm 5cm 10cm
10cm 5cm 0cm 5cm 10cm
Vert. dist from PoI
2.5cm 7.5cm 12.5cm 17.5cm 22.5cm 27.5cm 32.5cm 37.5cm
Horiz. dist from PoI
10cm 5cm 0cm 5cm 10cm
35°C, 1 gen.
10cm 5cm 0cm 5cm 10cm
Vert. dist from PoI
Horiz. dist from PoI
10cm 5cm 0cm 5cm 10cm
30°C, 1 gen. Horiz. dist from PoI
25°C, 1 gen. Horiz. dist from PoI
10cm 5cm 0cm 5cm 10cm
10cm 5cm 0cm 5cm 10cm
Horiz. dist from PoI
30°C, 14 days
Horiz. dist from PoI
Horiz. dist from PoI
Horiz. dist from PoI
Horiz. dist from PoI
Steel, Elmouee, Hamilton. JSPR, 2012
Low
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