Canola Allies data collection: enabling the greater use of beneficials in Australian canola systems

Brief description

Canola Allies: Tailoring Practices for Beneficials in Canola Systems (2023-2028). GRDC Investment CSP2309-004RTX Minimising damage of invertebrate pests in canola through a better understanding of the impact of beneficial insects. A collaborative research project led by CSIRO, in partnership with NSW DPIRD, SARDI, Murdoch University, DPIRD and Biological Services.

Canola is an important oilseed crop in Australia, with >8 million tonnes produced in 2023, valued at $6B. Australian-grown canola is subject to attack by at least 30 different species of invertebrate pests, causing estimated average annual loss of $54M.  Current management strategies rely on prophylactic use of synthetic pesticides. Approximately 90% of canola seed is sown with an insecticide treatment applied. A high proportion of the sown soil is also sprayed with an insecticide to protect emerging seedlings. This single-technology approach has negative impacts on biodiversity, and beneficial species and presents an ongoing risk of pests developing resistance to such insecticides. Beneficials (natural enemy species, such as predators and parasitic wasps) can suppress a wide range of herbivores, and thus potentially offer a sustainable management approach if integrated into canola production systems.   

Research has examined beneficial species’ presence, however relatively few research studies have directly examined the impact of beneficials in cropping systems. This makes it difficult to make informed pest control decisions that incorporate the actions of beneficials. The purpose of this project is to generate new knowledge about the ecology of beneficial arthropod species in canola growing regions, understand their impact on key canola pests, and determine ways to enhance their activities to control pests. Australian canola growers and agronomists/consultants will be able to use the new digital tools and practices that are the project outputs to help enable transition towards more active management of beneficials. The research outputs will enable stakeholders to understand complex trade-offs with the use of certain pesticides and how to optimise management actions to support beneficial population growth.   

We will collaborate with delivery partners (e.g. biologicals suppliers and Agtech industries) to explore how we can provide them with the tailored tools they need to service canola growers as customers. The long-term commercial viability of new providers of biological approaches to agripest management (e.g. inoculative release of beneficials, new technology for spreading beneficials at scale, products for attracting beneficials into canola fields) is dependent on new information tools to optimise deployment and a strong “pull” from growers demanding this technology. This project will start the process of developing these tools in collaboration with delivery partners specifically for canola but could ultimately lead to more sustainable agripest management across all grain production landscapes.

Through the project development process with GRDC we have identified four key outputs:  
1. Delivery of knowledge on the distribution, biology, and ecology of beneficial invertebrate species that impact pests of canola. Determine how to integrate information on beneficials into a surveillance system to enable active management of beneficial species.  
2. Estimate the impact of beneficials and develop implementation strategies tailored to the needs of growers.   
3. Explore strategies to manipulate and optimise habitats to conserve beneficial species in canola landscapes.  
4. Develop a spatially-explicit model that incorporates field, farm and regional dynamics. This will inform agripest risks and help tailor solutions to individual canola crop fields.  

Each year we will catalog the data collected across a range of activities here. In the final year of the project we will endevour to make most of this collection accessible to others for research purposes
Lineage: Each data set will have methods associated with its collection.