Characterizing potential flexibility in grassland use. Application to the French Aubrac area
- Cite this article as:
- Martin, G., Hossard, L., Theau, J.P. et al. Agron. Sustain. Dev. (2009) 29: 381. doi:10.1051/agro:2008063
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Farmers increasingly need to adjust their management practices to accommodate unexpected events such as drought, and preserve the sustainability of their production. This flexibility requires background knowledge about where and when freedom of choice can be exercised. Here, we designed an analysis framework for grassland-based farming systems in mountainous and less-favored areas. An expert-based discrimination tree characterizes organizational flexibility by determining the range of possible types of grassland use under various topographic and farming constraints such as suitability for mechanization, and ease of access to a field. A set of time windows evaluates the timing flexibility in grassland use, each associated with a combination of a grassland community type and a type of grassland use. Our results show that the outputs of the discrimination tree match for 139 of 165 grassland fields, by comparison with field data obtained in the French Aubrac region. For a particular type of grassland use, the set of time windows proves that timing flexibility in grassland use between grassland community types can increase by 15 days over a 37-day time range. When applying the two components of the analysis framework to a farm case, it shows that 24% of the farm area offers organizational flexibility, with several possibilities for grassland use. Timing flexibility for bringing forward or delaying the use of the grassland fields is unused in the farm. Most of the dates of grassland first use are similar irrespective of the diversity of grassland communities. The application of the analysis framework offers a sound evaluation of the potential flexibility to establish where and when it is possible to adjust management practices to cope with unexpected events. It can also be helpful in setting up coherent alternatives to the observed management strategies that can then be expanded in dynamic simulation models enabling deeper analysis.