Serving the Farming Industry across the Midlands for 35 Years
Growers can now use super-local weather indicators combined with disease forecasting to make more efficient fungicide applications.

Growers can now use super-local weather indicators combined with disease forecasting to make more efficient fungicide applications.

Sencrop, which is known for its local weather station systems, has joined forces with the Information System for Integrated Plant production (ISIP) to provide disease forecasting for farmers based on pooled and individual data.

Taking into account local temperature, humidity and precipitation, the system forecasts risk levels for a range of cereal diseases between 15 February and 30 June. This then enables farmers to react to high disease pressure and protect crop quality and yield.

“Farmers can connect their individual crops to local weather data via their weather station and app, to receive crop-specific information and risk calculations,” says Lucie D’Haene, product manager and agronomist at Sencrop.

Common diseases covered include septoria tritici, yellow and brown rust, powdery mildew, leaf blotch, net blotch, ramularia and leaf rust.

“This will support farmers in assessing the disease infection pressure and assist them in taking the right actions.”

By integrating local weather data, the quality of the model statements is raised to a new level, says ISIP managing director Manfred Röhrig. “No other data source can make it more precise,” he adds.

Ultra-local data

Sencrop is working to provide ultra-local real-time weather data with farmers, producer groups, agronomists and consultants in 26 countries. The company says its system gives farmers on an easy and quick overview of which crops require action.

“By seeing the crop risk in real time, farmers can easily drive to the affected fields for on-site inspections, saving time and fuel,” says Sencrop business development manager Harry Atkinson.

“Given soaring inputs costs – from fuel to fertiliser – farmers want to hone efficiencies as much as possible. Combining individual and pooled data can help them to do that.”