There is an increasing number of digital farm advisory services available to farmers. However, these services are uni-directional and based on technology push; the farmer gets advice based on satellite data, agricultural models, soil sensors, or weather forecasts. Users’ acceptance and ease of use of these services are hardly ever checked, and it is not known whether farmers actually benefit from the advice provided. SLAM! is a self-learning platform that uses farmer feedback and news items to improve the automatically generated advice to farmers based on simple agronomic models.

With SLAM!: Self-learning advice for farm managers, farmers take ownership of their digital crop management advice service and contribute to improving it over time. SLAM! creates a digital twin of every farm, making it possible to tailor advice to the farm’s specific soil and climatic conditions. SLAM! includes the following components:

• Community-based agronomic management module. Following the community-based forest management model, the crowd will be engaged to validate and provide relevant data. Farmers will be used as the community that jointly manages their own farms and the wider agricultural landscape, and can thus validate the relevance of the advice.
• Full crop management advice module, using the AgroMet Service of WEnR. In each crop development phase, tailored advices will be supplied to the farmer, using sms, voice or other messaging services.
• Learning and news mining module is based on feedback provided by farmers on the use of the service and targeted text mining of agricultural news media in the region.

Tailored and smart crop management advice provided by SLAM! is expected to empower farmers and help them improve their agricultural production, yields and thus farm income, or food self-sufficiency.