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, a slight reduction (<15%) of areas potentially suitable for pastoral resource is expected under the future climatic conditions, as depicted by the GCM and SRES scenarios. Despite a moderate reduction of areas potentially suited to pasturelands, troubling impacts on floristic composition might be expected in the future (e.g. expansion of more xeric and thermophilous species and decline of high-altitude pastoral typologies). This might threaten the typical and unique herbaceous biodiversity characterizing the Apennine pasturelands.

Climate change impacts on natural pasturelands of Italian Apennines / Camilla Dibari; Giovanni Argenti; Francesco Catolfi; Marco Moriondo; Nicolina Staglianò; Marco Bindi. - ELETTRONICO. - (2014), pp. 67-67. (Intervento presentato al convegno Modelling European Agriculture with Climate Change for food security).

Climate change impacts on natural pasturelands of Italian Apennines

DIBARI, CAMILLA;ARGENTI, GIOVANNI;MORIONDO, MARCO;STAGLIANO', NICOLINA;BINDI, MARCO
2014

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, a slight reduction (<15%) of areas potentially suitable for pastoral resource is expected under the future climatic conditions, as depicted by the GCM and SRES scenarios. Despite a moderate reduction of areas potentially suited to pasturelands, troubling impacts on floristic composition might be expected in the future (e.g. expansion of more xeric and thermophilous species and decline of high-altitude pastoral typologies). This might threaten the typical and unique herbaceous biodiversity characterizing the Apennine pasturelands.
2014
CropM International Symposium and Workshop
Modelling European Agriculture with Climate Change for food security
Camilla Dibari; Giovanni Argenti; Francesco Catolfi; Marco Moriondo; Nicolina Staglianò; Marco Bindi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/947932
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