Related Experiment Video
Updated: Apr 15, 2026
On-Site Molecular Detection of Soil-Borne Phytopathogens Using a Portable Real-Time PCR System
Published on: February 23, 2018
Predicting Cereal Root Disease in Western Australia Using Soil DNA and Environmental Parameters.
Grant J Poole1, Martin Harries1, D Hüberli1
1First and seventh authors: South Australian Research and Development Institute, Gate 2b Hartley Grove, Urrbrae, SA 5064 Australia; second author: Department of Agriculture and Food, Western Australia, PO Box 110, Geraldton, WA 6530 Australia; third and fifth authors: Department of Agriculture and Food, Western Australia, 3 Baron-Hay Court, South Perth, WA 6151 Australia; fourth author: Department of Agriculture and Food, Western Australia, Lot 12 York Rd., Northam, WA 6401 Australia; and sixth author: CSIRO Ecosystem Sciences and Sustainable Agriculture Flagship, Wembley, WA 6913 Australia.
Australian grain growers can now better predict crop root diseases before planting. A new model uses soil pathogen DNA and environmental data to assess risks, helping plan profitable cropping programs and reduce yield loss.
Area of Science:
- Agricultural Science
- Plant Pathology
- Environmental Science
Background:
- Root diseases significantly impact Australian grain yields, necessitating pre-sowing management decisions.
- Soilborne pathogens like Rhizoctonia solani AG-8, take-all, Fusarium pseudograminearum, and root-lesion nematodes are major threats.
- Understanding pathogen interactions and environmental influences is crucial for effective disease management.
Purpose of the Study:
- To assess soilborne pathogen levels and root health in Western Australian wheat crops.
- To develop a predictive model for root health based on pathogen DNA and environmental factors.
- To improve risk assessment for root diseases to aid growers in planning profitable cropping.
Main Methods:
- A 3-year survey (2010-2012) across Western Australia assessed 260 fields.
- DNA-based services (PreDicta B) measured soilborne pathogen levels.
- Visual assessments scored root health and disease incidence; predictive models incorporated rainfall and soil temperature.
Main Results:
- Pathogen DNA explained 2-16% of root health variation; environmental parameters explained 1-22% across the years.
- Rhizoctonia solani AG-8 DNA, soil temperature, and rainfall were key predictors of root health.
- The predictive model highlighted the significant interaction between environmental conditions and pathogen presence.
Conclusions:
- Pre-sowing assessment of pathogen levels and environmental data can predict root disease risk.
- Integrating environmental factors and pathogen DNA into predictive models enhances risk assessment for growers.
- This approach supports more informed management decisions for profitable and sustainable cropping programs.
