Apps for supporting farmers in performing assessment of soil health
A healthy soil is pivotal for producing healthy food. The farmers preserve soil health by maintaining and improving the soil organic matter content and the biodiversity of soil organisms and reducing the presence of substances that may be detrimental to soil fertility (e.g. salts, pollutants).
Sharing knowledge on how to manage soil health according to climatic condition, types of crops and agronomic practices could is very helpful for the farmers.
We propose as a challenge the development of a tool allowing the farmer to monitor the soil condition by integrating his/her observations with available location-specific open data and share these knowledge with other farmers.
The app should be location aware. The app can query a set of maps available from the European Soil Data Center (ESDAC, provider of maps and web services about European soil conditions). The user can associate these data with a local qualitative soil health assessment. A set of soil health features (presence of living organisms, good water presence, compaction) will be graded on a 1-10 scale. Their own data and external open source data could be integrated in a synthetic indicator. The data can also be associated with comments, pictures and information about the agronomic practices. The users should be able to share their own data with other farmers, getting info about the closest farms, or soil with similar condition.
A great challenge will be also the integration of Copernicus Remote sensing data in the app; Sentinel1 and Sentinel2 satellites can provide information to estimate the soil coverages, the vegetation indexes, and soil water content conditions.
If you are interested, please subscribe to this challenge here.
Datasets to be used
Soil data: there are several open source data sets about soil condition
- GRID-INFO: an open api providing global soil data
- ESDAC: Here are some sample interesting map:
Remote Sensing data: Sentinel2 provide updated multispectral global images that can be used to derive vegetation maps