Decision support systems (DSS) are tools that provide growers with advice on how to manage various aspects of agriculture. DSS that aim to support decisions about the control of pests and pathogens can help the grower to reduce the application of unnecessary pesticides by improvements in the targeting of applications; resulting in both economic gain for the grower and benefiting the environment. However, if a DSS is used in a location in which it wasn’t calibrated, the advice may be detrimental, potentially compromising pest control, and resulting in economic or environmental losses.
It is important for growers to understand how well a DSS is likely to perform in conditions similar to their own and have knowledge of any potential risks associated with following DSS advice.
It is therefore essential that “the value” of DSS be calculated. Testing DSS by experiment is time consuming and costly. The IPM Decisions team developed a new methods which could potentially use existing sources of data to quantify the benefits of using DSS.
The methods are tested using example DSS models for the control of septoria tritici blotch disease on wheat and downy mildew on grape. DSS models must be valid to provide potential economic and environmental benefits.
The main messages from the new method are;
- Open-source, septoria models have been validated in literature.
- Grape downy mildew models have been validated in literature, but they are closed-source.
- Grape downy mildew models must be validated based on microclimate data, not weather data.
- Field trial data from fungicide efficacy testing are not well-suited for the validation of septoria models