Monitoring of the Swiss Agri-Environmental System (MAUS)
In order to develop agricultural policy, it is important to know how agriculture affects the environment. For this reason, the Federal Office for Agriculture (FOAG) performs an agri-environmental monitoring (AEM) service, based on the ‘Ordinance concerning the Evaluation of the Sustainability of Agriculture’ (SR 919.118). Agroscope’s Competence Centre for Agri-Environmental Indicators is responsible for coordinating and calculating agri-environmental indicators (AEIs), at both national and individual-farm level.
AEIs are easily understandable, environmentally relevant variables for representing the complex agri-environmental system. To obtain farm-specific results for 2009 to 2022, the Competence Centre relied on the Swiss Agri-Environmental Data Network (SAEDN), where detailed farm data on stock and practice (e. g. livestock, fertilisation applications) from around 300 farms were annually recorded and used to calculate AEIs.
In 2023, the SAEDN was replaced by the new Monitoring of the Swiss Agri-Environmental System (MAUS) (see diagram). First data relating to the year 2023 will be collected by MAUS in 2024.
New Monitoring of the Swiss Agri-Environmental System (MAUS)
MAUS indicators (Monitoring of the Swiss Agri-Environmental System)
MAUS will calculate various AEIs that provide information on environmental problems at local, regional and global level (see table below). For example, an increased heavy-metal balance can lead to high localised heavy metal concentrations in the soil, airborne ammonia emissions can be harmful to regional biodiversity, while greenhouse gas emissions can cause global warming.
Overview of agri-environmental indicators calculated in MAUS. The indicators are grouped according to their environmental impact.
Local
Regional
Global
Humus balance
Heavy-metal balance
Erosion risk
Ammonia emissions
Nitrogen balance
Phosphorus balance
Nitrate leaching
Use of plant protection products
Risk from plant protection products
Biodiversity
Feed-food competition and land use competition
Greenhouse gas emissions
Energy demand
MAUS data sources
The MAUS philosophy is to calculate AEIs using existing data sources as far as possible to avoid unnecessary duplication of data collection. Any data gaps will be filled with separate surveys. MAUS comprises the following data sources:
Existing data sources, e. g. FOAG data on agricultural structures and the transportation of farmyard and recycled manure. Many existing data sources have already been processed and used to calculate the indicators. In future, the FOAG will collect data on plant protection product use and nutrient flows when implementing the notification obligation under the Agriculture Law. This data may also be incorporated into MAUS in the future.
Application data from agricultural software systems. For a modest compensation payment, farmers send data from their farm software systems to Agroscope via dedicated modules. Data transfers will initially be limited to standardised field calendar data. This data source is particularly suitable for high-resolution spatial and temporal data. The MAUS module in the barto smart farming platform is scheduled to deliver the first data in 2024 (data from the 2023 season). Data delivery via other agricultural software systems is expected to follow later.
Satellite data are used to determine specific variables needed to calculate the AEIs. These include catch crops that provide winter cover, for example. The first project to integrate satellite data into MAUS was launched in early 2024.
MAUS calculations
Preliminary calculations for some indicators have already been performed using existing data (e.g., greenhouse gas emissions, nitrogen balance). It will not be possible to calculate all indicators until the first data collections for 2024 are complete and these data have been incorporated into the calculations.
MAUS uses a bottom-up approach for most of the calculations. This means that the indicators are calculated approximately for each farm, then these farm values are aggregated by region or farm type. Aggregation at national level is also possible, and particularly important for indicators without a national equivalent (e.g., heavy-metal balance).
