Abstract: As well as the entire Mediterranean area, the Italian Apennines have been affected by increasing temperatures, rainfall extreme events and decreases in annual precipitation due to climate change. Moreover, permanent grasslands, species-diverse ecosystems characterizing the marginal areas of the Apennines landscape, are acknowledged as very sensitive and vulnerable to climate variation. Building on these premises, statistical classification models coupled with data integration by GIS techniques, were used to territorially assess future climate change impacts on pastoral communities on the Italian Apennines chain. Specifically, a machine learning approach (Random Forest – RF), firstly calibrated  for the present period and then applied to future conditions, as projected by HadCM3 General Circulation Model (GCM), was used to simulate potential expansion/reduction and/or altitudinal shifts of the Apennine pasturelands in two time slices, centred on 2050 and 2080, under A2 and B2 SRES scenarios. RF classification model proved to be robust and very efficient to predict lands suited to pastures with regards to present period (classification error: 12%). Furthermore, according to RF simulations, relevant reductions (46 and 34%) of areas potentially suitable for pastoral resource are expected under A2 at the middle and end of the century, respectively, as depicted by the GCM and SRES scenarios. Moreover, progressive upwards shifts are predicted by the model under both SRES scenarios. These reductions will likely interest the central area of the chain threatening the typical and unique herbaceous biodiversity characterizing the Apennine pasturelands.

Abstract: Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10 to 38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.