Crops protected by bespoke disease forecasting

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Crops protected by bespoke disease forecasting

Arable farmers in the UK can make use of super-local weather indicators combined with disease forecasting to make more efficient use of control measures like fungicide applications.

Growers can use a new system which takes local temperature, humidity and precipitation, to predict risk levels for diseases affecting winter wheat, barley, rye, triticale and spring barley between February 15 and June 30. The new system can help growers to control high disease pressure, protecting crop quality and yield.

Sencrop, makers of smart local weather stations, has joined forces with the Information System for Integrated Plant production (ISIP) to provide bespoke disease forecasting for farmers based on pooled and individual data.

LucieD’Haene, productmanager and agronomist at Sencrop says, “Farmers can connect their crops and local weather data via their weather station and app, to receive crop-specific information and risk calculations. 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 to maximise crop quality.”

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

Sencrop is working with farmers, producer groups, agronomists and consultants in 26 countries worldwide to provide ultra-local real-time weather data. By integrating with ISIP, it is giving farmers an easy and quick overview of which crops require action.

Harry Atkinson, UK business development manager, SenSat says, “By seeing the crop risk in real time, farmers can easily drive to the affected fields for on-site inspections, saving time and fuel. Given the soaring cost of inputs, from fuel to fertiliser, farmers want to hone efficiencies as much as possible, and combining individual and pooled data can help them to do that.”

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