Completing the Picture: Integrating Plant Disease Into Ag Systems Modelling

I present four scenarios that cover various spatial and temporal scales where plant disease models can be used to help inform growers or researchers. This was presented for the official opening of the USQ Agriculture Systems and Engineering Precinct in Toowoomba as a part of a symposium on the future of agriculture.

Transcript

1. CRICOS QLD 00244B | NSW 02225M TEQSA: PRV12081 Completing the Picture Integrating Plant Disease Into Ag Systems Modelling Adam Sparks USQ Centre for Crop Health
2. PeDiMIP: Pests and Diseases Model Intercomparison Project Holzworth et al. “APSIM - Evolution towards a New Generation of Agricultural Systems Simulation.” Environmental Modelling & Software 62 (December 2014): 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009.
3. Problems Four that modelling can help solve across different scales
4. 1. Pre-season decisions on where and what to plant 2. In-season decisions to treat disease 3. Post-season definitions of where, when and why there are yield limitations regionally 4. Future estimates of yield limiting factors nationally
5. Pre-season Crop Previous Season Low Medium Low High Medium Moderate Low Low Low Next Season Risk Dr. Paul Melloy
6. In-season
8. 5 20 3 0 18 10 20 25 27 24 25 23 3.5 3.2 3.6 3.6 Satellite-derived, georeferenced data x y y x y Georeferenced Yield Loss Estimates x y Precip Temp Tons/HA In-Paddock Survey x Crop & Disease Model Post-season
9. Map data ©2019 GBRMPA, Google & SARDI PIRSA Low Medium High Below Detection Limit Disease Risk P. thornei 2018 Future-looking
10. Crop Diseases Holzworth et al. “APSIM - Evolution towards a New Generation of Agricultural Systems Simulation.” Environmental Modelling & Software 62 (December 2014): 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009.
11. Find out more: CRICOS QLD 00244B | NSW 02225M TEQSA: PRV12081 7 4631 1948 https://www.facebook.com/CCHUSQ https://twitter.com/CCH_USQ adam.sparks@usq.edu.au Broad Acre Cropping Initiative