Farm Management Advice

Localised and real-time advisories to optimise farm management

Our farm management service translates localized weather information into a tailored message for agricultural planning. The service supports farmers and other stakeholders to make the right decisions for their farm management.

The number one information needed by smallholder farmers is “when to plant”, which is inheritably related to the onset of the rain season. With the start of the rain season becoming increasingly erratic, it is difficult for farmers to plan the optimal planting period. For

example, germinating seeds can dry out when there is a short dry spell after the first rains. This is devastating for a farmer whose savings have been spent on buying seeds. Our service provides farmers with crop-specific information on the optimal time to start sowing (planting date). The coupling of weather forecasts with crop growing cycles enables farmers to make optimized decisions for their farm management; sow at the onset of the rain season and harvest at the right moment.

Our advisory services inform farmers and other stakeholders in the agri-value chain about:

“The coupling of weather forecasts with crop growing cycles enables farmers to make optimized decisions for their farm management”

Example service: Grape Compass

Grape Compass, an online decision-support system for viticulturists, promotes sustainable farming and optimizes vineyard operations. It does this by combining global data with local field data. Grape Compass also provides reliable forecasts of fungal disease pressure. This allows for more efficient spraying programs, resulting in reduced fungicides, labor and environmental costs. Grape growers can access the forecasted risks of downy mildew, powdery mildew and botrytis on individualized dashboards that contain user-friendly maps, tables and graphs at field level. The major benefits of Grape Compass are:

· Optimized logistical planning and management practices
· Savings for the environment and health
· Cost optimization and learning from past experience