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Highlights • Iberian dehesas undergo lack of tree recruitment and abandonment of marginal areas. • Data mining methods were used to build maps predicting areas of tree recruitment. • Afforestations performed with UE aids in Extremadura were spatially analysed. • Afforestations hardly discriminated the areas prone to natural regeneration. • Spatial models could be used in decision making to detect suitable locations. Abstract Iberian silvopastoral systems known as dehesas in Spain and montados in Portugal are undergoing a spatially polarized process by which many of the main areas lack tree recruitment, whereas marginal lands suffer abandonment and shrub encroachment. These ongoing processes should be considered when designing afforestation measures and policies. We analyzed the temporal tree dynamics in 800 randomly selected plots of 100 m radius in dehesas and treeless pasturelands of Extremadura by comparing aerial images taken in 1956 and 2012. Based on this data, spatial models that predict areas prone to undergo or lack natural tree recruitment were developed using three data mining algorithms: MARS (Multivariate Adaptive Regression Splines), Random Forest (RF) and Stochastic Gradient Boosting (TreeNet, TN). A number of 51 candidate environmental, physical and land use and cover spatial variables were used as predictors in models, from which the main 15 were selected. The statistical models developed were deployed to the spatial context of the rangelands in Extremadura and, separately, to the afforested areas performed under the UE First Afforestation of Agricultural Land program between 1992 and 2013. The percentage of area predicted as prone to tree recruitment was calculated in each case. The three data mining algorithms used showed high fitness and low misclassification rates. Although the drivers and patterns of the different models were similar, outstanding differences were observed among models attending the area prone to tree recruitment. A model ensemble was also produced as a map of agreement reflecting the majority vote among models. Despite these differences, when maps of the model results were related to the afforested surfaces, the three algorithms pointed to the similar conclusion, i.e., the afforestations performed in the studied rangelands barely discriminated between areas that already showed or lacked natural tree regeneration. In conclusion, data mining technics are suitable to develop high-performance spatial models of vegetation dynamics. These models are useful to help policy design, decision-making and assessment about the implementation of afforestation measures and could be used to improve the spatial targeting of future programs. [ABSTRACT FROM AUTHOR]

This paper proposes a change in the conditions of cross-compliance of CAP payments. Specifically, the eligibility criterion considered is compliance with minimal requirements of long-term economic and agroenvironmental sustainability. To this end, 69 range farms were surveyed in Extremadura (SW Spain). In these farms, sustainability was studied using the MESMIS framework. MESMIS is based on the evaluation of basic attributes of sustainability (adaptability, self-reliance, equity, stability, and productivity) formed from different indicators. The original indicators are then synthesized by means of qualitative, quantitative, or mixed techniques into a single value measuring the sustainability of the system (sustainability index). Alternative scenarios were then defined in which the perception of CAP subsidies was to a greater or lesser extent linked to levels of sustainability. For each of these scenarios, the economic indicators of the farms were compared with those of the baseline (present) situation. The analysis was completed using a logistic model classification to study the relationship between the maximum levels attainable by the economic indicators in terms of the sustainability indices. The results showed that including sustainability as a condition for receiving aid under the CAP can contribute to improving the economic results of traditional extensive farms. [Copyright &y& Elsevier]

Abstract: The technical efficiencies of a sample of extensive dehesa farms were analyzed using a non-parametric Data Envelopment Analysis (DEA) model. This technique creates efficiency indices by comparing the performance of each farm with the best production practices observed, which define the efficiency or production frontier. The influence of the European Union''s Common Agricultural Policy (CAP) subsidies (taken as an output) on the farms'' efficiency was determined. The measurement of the efficiency has taken place from inputs per hectare thus allowing to make a different interpretation from the scale efficiency that, instead of referring to the dimension of the farm, is referred to the intensification. Thanks to the input-oriented analysis it can be established that the farms could maintain similar production levels by introducing a 30% reduction of their inputs (e.g. with breeds better adapted to the environment, a better use of the own produced pastures to reduce the amount of feedstuff to be bought, reductions in manpower…). Those highly intensified dehesas have shown decreasing inefficiencies of scale, whereas the least intensified have shown increasing inefficiencies. With a second stage analysis, different types of farms were identified as corresponding to greater or lesser efficiencies. It has been identified that the group of farms with high stocking rates have the greatest efficiencies indices. With respect to the livestock orientation, the farms with a livestock mix including pig were the most efficient. Modifications in the stocking rate, allowing an adequate use of the natural resources available, can be a solution to improve the efficiency of dehesa farms. In addition mixed livestock farming is another useful tool towards achieving increased efficiency, and a reduced dependence on subsidies. [Copyright &y& Elsevier]