Partners: Wageningen Environmental Research
Objective
FAIRiCUBE Use Case 2 explores how agricultural practices impact biodiversity by applying analysis workflows built within a spatial cube-based data infrastructure. The study uses established causal models with graph-based inference methods to investigate causal effects of farm level interventions on bird species richness.
The main objectives are:
- To assess whether the integration of biodiversity, agricultural, environmental, and remote sensing data analyzed with machine learning (ML) and causal modeling can consistently generate actionable insights across agricultural regions.
- To explore how spatial data cube functionalities and ML can discover causal relationships between farm management, environmental variables, and biodiversity outcomes.
Applications
Causal inference methods were effectively applied within the FAIRiCUBE spatial data cube infrastructure to identify causal relationships between agricultural activities and farmland bird species richness. The developed methods offers a valuable framework for evaluating the ecological impacts of agricultural policies – such as the EU Green Deal – and for guiding the development of targeted, biodiversity-friendly farming measures using quantifiable indicators. The established model can be used by decision makers in agriculture and environmental protection by supporting better-informed decisions such as selecting more nature-inclusive practices promoting biodiversity through specific applications:
- Spatial categorization: The results of the observation and estimation steps for biodiversity can be used to categorize agricultural landscapes and e.g. administrative regions, based on predicted suitability.
- Casual modeling: Causal modelling allows reasoning about potential situations to answer ‘What-if?” type of questions and creating scenarios for farmland landscape development considering biodiversity favorable conditions.
- Smart tools: The presented approach aims at improved understanding of causalities between farm activities and changes in biodiversity. When results are sufficiently robust, the model could be incorporated into advisory tools for farmers or policy makers, to help assess the consequences of actions.
Approach
Three main data categories (biodiversity, environment and agriculture) are handled primarily within their individual processing flows and data cubes, which are then ultimately merged using causal machine learning. Modelling methods such as causal inference and discovery provide insights into the underlying mechanisms describing the impact of agricultural practices on biodiversity. They do not only statistically predict the correlations but also provide meaningful explanations for those predictions, enhancing the overall interpretability of the model results.
All the tools are expected to be provided within FAIRiCUBE hub as shared data infrastructure and documented.
