Solum

❗Problem

• The Netherlands produces a large amount of livestock manure, containing high levels of nitrogen and phosphorus, which can damage surface water and groundwater when not managed correctly
• The European Environment Agency reports that average nitrate concentration in EU groundwater has not significantly decreased from 2007 to 2023, and it is currently unlikely that the EU will meet its 50% nutrient-loss reduction target by 2030
• The EEA states that manure and mineral fertilisers are the main sources of nitrate in EU groundwater, and agriculture accounts for an estimated 80% of nitrogen discharge to the EU aquatic environment
• In the Netherlands, the issue is especially urgent because the country produces huge volumes of manure: 74.4 million tonnes of animal manure in 2024, containing 448.9 million kg of nitrogen and 146.7 million kg of phosphate
• Agrocompanies are one of the main sources of nitrate pollution in European water systems.
• From 1 January 2026, Dutch farmers can no longer use derogation. The maximum amount of animal manure that can be applied will fall to the standard 170 kg nitrogen per hectare. A 95-kilotonne nitrogen surplus is predicted for 2026, 
• Authorities already use risk-based enforcement, but inspections remain limited. In 2024, physical supervision intensity was only 4%, while the enforcement rate reached 22%, showing that violations exist, but inspectors cannot be everywhere.
• In 2024, Dutch manure administrative controls resulted in 479 fines worth €6.7 million, averaging about €14,000 per fine. This shows that non-compliance creates real financial risk for farmers and enforcement pressure for authorities
• The core problem: water authorities lack a scalable, parcel-level way to identify land and farms that are at high risk of exceeding manure policy limits before nitrogen and phosphorus reach nearby water systems.

💎 Idea

We propose a web application that helps detect and mitigate manure-related water pollution using satellite data, geospatial intelligence, and Dutch manure-policy rules.

• For governments and water authorities, Solum provides:
  • A map of land under a country's jurisdiction shows farmland, nearby water bodies, and high-risk runoff zones.
  • A risk score showing where manure over-application or dumping is most likely to threaten water quality.
  • A prioritised inspection list so authorities know where to check first, instead of relying only on random or limited physical inspections. Authorities are also given an explanation of why specific areas are suspicious and what to check in those areas through explainable AI.
  • A privacy-aware dashboard that focuses on risk zones first, not mass surveillance of every farmer.
• For farmers, Solum provides:
  • A manure compliance warning alert based on the Dutch 170 kg N/ha standard.
  • Warnings when a field is close to water, has high runoff risk, or may exceed manure limits.
  • Farmers are also given explanations of how they can reduce their usage with Explainable AI

The first stage focuses on manure-related nitrogen and phosphorus pollution, as agricultural nutrient runoff is a major water-quality issue in the Netherlands and across Europe. However, we plan to extend Solum to detect and predict other nutrient and agricultural pollution risks, including fertilizer runoff, pesticide contamination, irrigation-related pollution, and broader groundwater and surface-water quality threats. By integrating Solum with existing government, water board, and farm management systems, the platform can quickly become accessible to authorities and farmers across Europe.

💸 Business Case

Our plan is to offer Solum as a SaaS platform for governments and water authorities, with a companion compliance tool for farmers. Authorities pay for better risk-based enforcement, while farmers benefit from fewer accidental violations and clearer manure-use decisions.

Our estimated pilot cost is €15,000–18,000 per month, including €2,000–3,000 operational costs and continued development and maintenance. This fits our current early-stage deployment model. 

The value comes from helping authorities inspect smarter instead of blindly, and helping farmers avoid fines and reduce runoff risk. In 2024, Dutch manure controls led to 479 fines worth €6.7 million, showing that non-compliance already creates measurable financial pressure. Additionally the Dutch economy faces a loss of 12 to 15 billion euros annually from damage to nature and health caused by excess Nitrogen emission, By preventing even a small share of violations, helping reduce damages from nitrogen emissions, and reducing unnecessary inspections, Solum can justify its cost.

Starting in the Netherlands gives us a strong entry point because manure rules tighten further in 2026, after which the same platform can be expanded to other pollutants and other European countries, in line with our roadmap.

🛰️ EU space technologies

The manure applied to land affects nutrient content, plant growth, soil moisture retention, spectral signature of soil and organic matter reflectance in SWIR.

The datasets we are using are from: 

• Sentinel 1 for soil moisture and disturbance layer. The reason being radar is not affected by cloud cover and sensitive to surface roughness, soil wetness, compaction/disturbance, and slurry spreading patterns, i.e. distribution of liquid manure. We derive data from: VV Increase (wetter soil), VH change (vegetation structure change), VV/VH land cover differentiation, and temporal change (recent field activity). We use this data because manure causes short-term soil moisture and roughness changes.
• Sentinel 2 for monitoring vegetation, soil, and water cover, as well as observation of inland waterways and coastal areas. We derive data from: NDVI (vegetation growth from fertilizer), NDWI (moisture change), SWIR bands, and the bare soil index. We chose this data because manure changes crop vigor, soil reflectance, and moisture signature.
• HRL Imperviousness to identify barns, storage yards, and concrete pads.
• HRL Water and Wetness to help identify permanent water bodies, wet soils, drainage zones, and runoff accumulation areas. This is important because manure impact is measured by nutrient leakage into the water system. This layer defines where contamination would accumulate, where environmental impact is measurable.
• Copernicus DEM GLO-30 to identify the movement of manure via water. The elevation determines runoff direction, slope, drainage basins, and flow accumulation paths.

The workflow integrates multi-source satellite and geospatial datasets through preprocessing, feature extraction, and spatial overlay analysis to derive thematic layers representing vegetation response, soil moisture, land use, hydrology, and infrastructure, which are then combined into a spatial risk model visualized as a multi-layer geospatial map.

 🌍 EU Space for Water

• We are addressing Challenge #2: Tracking and preventing water pollution.
• Preventing nutrient pollution: Solum uses Copernicus data, water proximity, and parcel-level risk scoring to identify farmland where manure over-application could lead to nitrogen and phosphorus runoff into nearby surface water or groundwater.
• Supporting targeted inspections: Authorities cannot inspect every farm or waterway. Solum highlights high-risk zones so water authorities can prioritise inspections, sampling, and interventions before pollution spreads.
• Helping farmers stay compliant: Dutch manure rules are complex, especially after the end of derogation and the move to the 170 kg nitrogen per hectare standard. Solum helps farmers validate manure use before application, reducing accidental violations and fines.
• Using EU Space data for public benefit: By combining Copernicus Earth Observation data, Solum turns space data into a practical tool for protecting water quality, supporting agricultural compliance, and improving environmental security.

🤼 Team

Ayudh ( ongoing MSc Computer Science & Information Security Technology) - Versatile computer science skills, with a main focus on system design engineering.

Abbas (ongoing MSc Embedded Systems) - Experience with wireless sensor networks and embedded networks. Minor in technology entrepreneurship.

Divo (BSc Computer Science) - Seasoned frontend and UX developer 

Salaheldin (BSc Computer Science) - Backend & Business Integration Developer, specialist in BPMN orchestration, and facilitating complex enterprise workflows.

Thomas (ongoing MSc Computer & Embedded Systems Engineering) - Experience with user-oriented design, embedded systems, and algorithmic problem solving, with a minor in entrepreneurship

Francisco (ongoing MSc Data Science & AI) - Machine learning expert, focusing on performance optimization through visual analytics and Explainable AI. Interested in data-driven entrepreneurship

Rafi (BSc Computer Science) - Full-stack engineer with experience (and interest) in all things tech

Github reposity:

https://github.com/Ayudh-M/Solum (for access to it, please message us via email: [email protected])