The SpotIT project aims to provide cereal farmers in the Nordic-Baltic region with better models for predicting leaf spot diseases in wheat and barley by choosing and improving user-friendly disease prediction models, made available to the farmers through locally adapted IPM tools. SpotIT is coordinated by NIBIO, with partners from Norway, Sweden, Lithuania, Denmark and Finland.
Wheat and barley are among the most widely produced agricultural crops in the Nordic-Baltic region and provide a source of nutrition for both animals and humans. However, leaf spot diseases, caused by fungi that colonize and kill the plant tissue, can reduce the yield significantly. Improved management strategies can aid in optimizing timing of the required fungicide treatments and potentially reduce the number of fungicide applications. This can contribute to increased food production, better economy for the growers and decreased impact on the environment.
In depth interviews and questionnaires have been used to identify motivation and barriers for use of DSS among farmers and advisors in the participating countries. In response to an online survey where cereal farmers were asked if they are currently using DSS, the differences between the participating countries was evident, with 40 % DSS users in Norway, 26 % in Finland, 24 % in Sweden 24%, and 14 % in Lithuania. Four farmer motives for using DSS to support crop protection decisions on farm level were identified; i) risk aversion, ii) relationship-based decision making, i.e. together with an advisor, iii) environmental considerations and iv) consideration of neighbors and colleagues view. Interviews with farmers and advisors provided information and suggestions related to the implementation, dissemination and functionality of a DSS. These responses from the user groups provide essential input to the progress of model evaluation and technical solutions adapted to local needs.
To test and compare models, crop phenology information of spring wheat, winter wheat and spring barley in different regions was linked to corresponding weather data over five years. In general, and based only on the historical field trial data, the models tend to predict more treatments than what results in a net return. Models with a high sensitivity often carried a low specificity meaning that most predictions were to spray. To keep the false negative rate low, the results showed that a large proportion of the fields would have been treated redundantly, without the result of a positive net yield gain.
Two wheat models and two barley models were selected for testing in field trials in all countries during 2018 and 2019 seasons. The wheat models are a Danish humidity-driven model, and the model currently used by Crop Protection Online in Denmark. The barley models are the Finnish net blotch model and the Crop Protection Online model used in Denmark. In wheat, the two risk models and the reference treatments provided comparable disease control in 2019. In 2018 very few treatments were recommended by the models, saving 84-98 % of treatments compared to the references while in the wetter season 30 % fewer applications were recommended. Based on fungicide input and net yield responses the models gave correct recommendations in 96 % of the trials in 2018 and in 54 % of the trials in 2019. The reference treatments gave correct recommendations only in 5-46 % of the trials. In barley, three different risk models were tested (CPO, HM, Finnish net blotch). With few exceptions the models provided sufficient disease control and reliable yield responses in line with the reference treatment. The models have in both seasons saved input of fungicides, saving 40-97 % of the treatments. Based on net-yield evaluation and the models' ability to save input, the models have performed reasonably. As the Finnish model only was tested to a limited extent, it is too early to know how the model has performed outside of Finland.
Two models have been implemented in VIPS, a Danish Humidity model and a Finnish Net Blotch model, and automatic collection of data from online weather stations in the partner countries for use in VIPS has been established. A test page in Eurowheat is set up as demo to visualize the potential for implementation of VIPS-models in any web site. The basis is a web application that can be integrated into any web page using only a few lines of code. This application includes regional risk maps for the Nordic-Baltic regions, where the user can choose to see output data from several data sources, such as the reference humidity model, humidity model, rainy days, yield loss, and observed disease. This map service gives a regional overview of a risk situation. This is one way of visualizing a risk model for the whole region. A field-based version of a model can be supplementary. VIPS - or the validated models independently of the system - can be offered for use in Nordic and Baltic countries.
We have achieved increased and new awareness of the potential of DSS in IPM within the partner countries in the Nordic-Baltic region. The involvement of advisory services in each country did allow for interactions and direct contact with existing and potentially new users of the models tested, and systems developed as part of the SpotIT project. We have experienced increased interest in DSS from stakeholders throughout the project period.
We have gained increased understanding of model performance under varying conditions (other countries, environments).
An infrastructure (VIPS-platform) for future development and collaboration is in place.
SpotIT is a Nordic-Baltic initiative to provide farmers with better models for predicting leaf spot diseases in wheat and barley, aiming for user-friendly, locally adapted IPM-tools. Leaf diseases are a major threat in cereal crops in the Nordic-Baltic area and fungicides are used for reducing yield losses. Improved management strategies, will contribute to increased food production, better economy for the growers and minimize negative impact on the environment.
SpotIT aims to provide locally adopted disease forecasting models via a trans-national platform allowing cost efficient development of locally adapted DSS in native language, to facilitate the use of IPM. The main objectives will be to: 1) Characterize end-user groups and their preferences for models and DSS, 2) Understand the motives behind farmer decision-making in relation to IPM-tools, 3) Improve and validate disease models in cereals and control thresholds, 4) Develop IPM-tools that accommodate local user needs.
End-user preferences will be investigated to encourage broad use of local DSS and implementation of IPM tools. National DSSs usually implement a few locally developed or individually selected models, though, a common approach is a natural consequence of the similar conditions. IPM-tools will be developed with VIPS to utilize both existing DSS and facilitate joint use of a trans-national system. While this project will focus on leaf spot pathogens, the resulting platform can be used for other systems.