|Title||Targeting the impact of agri-environmental policy - Future scenarios in two less favoured areas in Portugal|
|Author(s)||Jones, Nadia; Fleskens, Luuk; Stroosnijder, Leo|
|Source||Journal of Environmental Management 181 (2016). - ISSN 0301-4797 - p. 805 - 816.|
Soil Physics and Land Management
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Afforestation - Benefit-cost targeting - Fire hazard - Improved pastures - Landscape fragmentation - Mediterranean ecosystem|
Targeting agri-environmental measures (AEM) improves their effectiveness in the delivery of public goods, provided the necessary coordination with other incentives. In less favoured areas (LFA) measures focusing on the conservation of extensive farming contribute to sustainable land management in these areas. In this paper we investigate the implementation of a possible AEM supporting the improvement of permanent pastures coordinated with the extensive livestock and single farm payments actually in place. Through applying a spatially-explicit mixed integer optimisation model we simulate future land use scenarios for two less favoured areas in Portugal (Centro and Alentejo) considering two policy scenarios: a 'targeted AEM', and a 'non-targeted AEM'. We then compare the results with a 'basic policy' option (reflecting a situation without AEM). This is done with regard to landscape-scale effects on the reduction of fire hazard and erosion risk, as well as effects on farm income. The results show that an AEM for permanent pastures would be more cost-effective for erosion and fire hazard mitigation if implemented within a spatially targeted framework. However when cost-effectiveness is assessed with other indicators (e.g. net farm income and share of grazing livestock) 'non-targeted AEM' implementation delivers the best outcome in Alentejo. In Centro the implementation of an AEM involves important losses of income compared to the 'basic policy'. 'Targeted AEM' tends to favour farms in very marginal conditions, i.e. targeting is demonstrated to perform best in landscapes where spatial heterogeneity is higher. The results also show the risk of farm abandonment in the two studied less favoured areas: in all three scenarios more than 30% of arable land is deemed to be abandoned.