Integrating Agricultural and Ecological Goals into the Management of Species-Rich Grasslands: Learning from the Flowering Meadows Competition in France
2015
Current agri-environmental schemes for reconciling agricultural production with biodiversity conservation are proving ineffective Europe-wide, increasing interest in results-based schemes (RBSs). We describe here the French “Flowering Meadows” competition, rewarding the “best agroecological balance” in semi-natural grasslands managed by livestock farmers. This competition, which was entered by about a thousand farmers in 50 regional nature parks between 2007 and 2014, explicitly promotes a new style of agri-environmental scheme focusing on an ability to reach the desired outcome rather than adherence to prescriptive management rules. Building on our experience in the design and monitoring of the competition, we argue that the cornerstone of successful RBSs is a collective learning process in which the reconciliation of agriculture and environment is reconsidered in terms of synergistic relationships between agricultural and ecological functioning. We present the interactive, iterative process by which we defined an original method for assessing species-rich grasslands in agroecological terms. This approach was based on the integration of new criteria, such as flexibility, feeding value, and consistency of use, into the assessment of forage production performance and the consideration of biodiversity conservation through its functional role within the grassland ecosystem, rather than simply noting the presence or abundance of species. We describe the adaptation of this methodology on the basis of competition feedback, to bring about a significant shift in the conventional working methods of agronomists and conservationists (including researchers).The potential and efficacy of RBSs for promoting ecologically sound livestock systems are discussed in the concluding remarks, and they relate to the ecological intensification debate.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
22
References
10
Citations
NaN
KQI