10 & 11 APRIL 2018 


In grassland management, the largest crop in Europe, differences in management practices can have a huge impact on biodiversity. Grassland has the potential to create suitable habitats for a myriad of birdlife and other species. Grassland can also significantly reduce climate gas emissions and improve water quality. However, grassland can also be managed as a monoculture; more productive when viewed as a single crop but without the positive contributions and unwelcoming to any other species.

This hackathon we looked into the question: how can farmers be rewarded for nature-inclusive measures in grassland? 

Farmers do not oppose nature, biodiversity or climate change mitigation. However, for a farmer the viability of his farm is key. The most tangible strategy is to make the production process as efficient as possible, and  there are many KPIs, and benchmarks to record it. Nature or climate does not pay, and there are few good yardsticks, KPIs or monitoring mechanisms.  


In the mean time, lots of satellite and other data and new tech became available. What can data and tech offer, to reward for nature. In this hackathon we want explore this question. 

For the Rewarding Nature hackathon we will take a farmer-centric approach. How can we support farmers to make their sustainability process visible and measurable. Farmers will be using tech to improve their  environmental performance, not unlike tech is supporting their primary production process. It will also serve to justify labour or other investments, in order to be compensated for it. Therefore it should be farmers who own and control dataflows about their practice. Obviously, close articulation is needed with Governments (EU and national), their payment agencies and industries. 

The multistakeholder approach is reflected in the organisors:  this hackathon is powered by a partnership of WWF NLRVO and BoerenNatuur.

The hackathon took place during the CAPIGI conference on Performance Agriculture, that connects people from government, science, ag-industry and businesses to discuss the application of geospatial data and systems in agriculture. International geospatial and ag-tech experts supported teams in dedicated fixer sessions. 


How can farmers be rewarded for nature-inclusive measures in grassland? There are many possible angles to the question. How to monitor farmers' management of grasslands and the impact for 'nature'? What are yardsticks, KPIs, or benchmarking mechanisms? How to make this visible, and to whom? What is fair; rewarding the farmers' effort or the effect? How to add farmers' common sense to data? How to do efficient patrolling, without ending up in a Big Brother situation?  These are examples of the approaches we hope hackers will take.  

An excellent group of 'hackers' contributed to solutions that will give farmers the incentives and rewards to move forward on the environmental performance of his farm. From satellites to drones, from geo tagging pictures to big open data, it was all there.

For more information check:


Participants had access to the SentinelHub ( which is a powerful repository of standardised satellite data. Additional data (meteo, soil, land-use maps, etc.) was provided from different sources, and experts were present to guide through. 

The combination of artificial intelligence methods with satellite data supersedes the traditional classification by far. Machine learning techniques on satellite data have proven to be very powerful in other/similar applications. Combining artificial intelligence | machine learning | deep learning | signal & image processing with satellite data | Remote Sensing | optical and radar | multi-temporal time series can provide new insights.

A full conference with experts in satellite data processing, agronomy, land-use classification, data science, precision agriculture and more is available for consultation. Knowledge exchange and networking are a natural part of this event. Participants of the conference will be guided through hands-on participation in so called fixer sessions.