The Farming Systems Ecology Group (FSE) aims to design multifunctional agricultural systems that can produce sufficient amounts of nutritious food, clean water and fertile soil, whilst contributing to landscape quality and other landscape functions.
Is this a utopia?
Well, there will be trade-offs and choices that will have to be made because we usually cannot have it all. Important is that the trade-offs are visible, that the options are known and that various perspectives on agricultural systems and rural landscapes can be discussed. This way it is possible to make informed decisions that benefit society.
A central question in our work is how we can support the functioning of ecological processes for agricultural production by adjusted farming practices or landscape structure. This results in many more concrete and applied questions, such as:
- How can we manage nitrogen-fixing plants, cropping systems and organic residues to improve soil fertility and structure?
- How can we manage crops and semi-natural landscape elements to improve the contribution of natural enemies to pest and weed suppression in crops, and arrive at pest and weed suppressive landscapes?
- How do we breed and keep robust, healthy farm animals with high productivity and in conditions of high animal welfare?
What we do
- We use empirical knowledge from experiments, for instance carried out on the Droevendaal farm or in research stations, or under more controlled conditions in greenhouses or growth chambers. But we also use contextual expertise and knowledge collected on-farm or at landscape level, to identify combinations of practices that are most effective in a given context.
Time and again this turns out to be highly relevant, for instance for small mixed crop-livestock farming systems in Nepal, for large-scale grazing systems in natural grasslands of Uruguay, for ambitious low-input dairy farming communities operating under adverse dry conditions in Mexico, or for forest-based coffee producers in the highlands of Cost Rica.
- We use simulation models and optimisation algorithms to analyse windows of opportunity for improvement in farming systems performance in situations that have not been explored before. These models are always based on the experimental findings and on-farm data. They allow us to answer what-if questions when data are scarce, or experimentation is difficult or too costly, or when various scales from field to farm and landscape need to be bridged. This also allows learning in a ‘serious play’ setting.
- We analyse changes that are needed by farmers and communities to reach desirable futures. Many adjustments need action beyond the single farmer or farm gate, and require orchestration of actions of various involved stakeholders. We rely on agent-oriented approaches to increase awareness and show how the actions of people and organisations distributed within a landscape together affect the overall outcome.
- We use project management approaches that allow the representation of perspectives of diverse stakeholders, ranging from farmers, to ecologists and landscape managers, to agri-business and policy makers. Together people explore the window of opportunity, evaluate the benefits and drawbacks of different farm or landscape configurations, select the most desirable future and jointly implement the necessary changes in an adaptive manner, while staying open to continuous learning.