As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011–2040, 2041–2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change; (ii) future climate will likely reduce current extend of suitable pasture (−30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra); (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.

Expected Changes to Alpine Pastures in Extent and Composition under Future Climate Conditions / Dibari, Camilla; Costafreda-Aumedes, Sergi; Argenti, Giovanni; Bindi, Marco; Carotenuto, Federico; Moriondo, Marco; Padovan, Gloria; Pardini, Andrea; Staglianò, Nicolina; Vagnoli, Carolina; Brilli, Lorenzo. - In: AGRONOMY. - ISSN 2073-4395. - ELETTRONICO. - 10:(2020), pp. 1-21. [10.3390/agronomy10070926]

Expected Changes to Alpine Pastures in Extent and Composition under Future Climate Conditions

Dibari, Camilla;Costafreda-Aumedes, Sergi;Argenti, Giovanni;Bindi, Marco;Moriondo, Marco;Padovan, Gloria;Pardini, Andrea;Staglianò, Nicolina;
2020

Abstract

As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011–2040, 2041–2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change; (ii) future climate will likely reduce current extend of suitable pasture (−30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra); (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.
2020
10
1
21
Goal 13: Climate action
Dibari, Camilla; Costafreda-Aumedes, Sergi; Argenti, Giovanni; Bindi, Marco; Carotenuto, Federico; Moriondo, Marco; Padovan, Gloria; Pardini, Andrea; ...espandi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1199127
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