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Spesa pubblica per le Misure 8.5 e 8.6 previste dai PSR 2014-2020. Fonte: elaborazioni CREA sui PSR regionali (Sturla e Romano, 2017).
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I soprassuoli forestali governati a ceduo rappresentano una parte consistente dei boschi italiani. Al fine di una
gestione efficace dal punto di vista ambientale ed economico-finanziario è opportuna una conoscenza approfondita
delle specifiche caratteristiche di questo sistema selvicolturale. Questa nota si propone di offrire un aggiornamento
e una...
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Citations
... In order to support the development of the forestry sector in Italy, many political efforts have been undertaken on both national and regional scales (Marchetti, 2018;Quatrini et al., 2017), with several measures focusing on coppices (Mairota et al., 2016). In addition to these policy measures, the scientific community produced several works addressing this topic, including among others: the benefits deriving from the sustainable active management of coppices studied from the perspective of biodiversity (Mattioli et al., 2016;Müllerová et al., 2015), natural risk reduction (Vogt et al., 2006), logging impacts (Venanzi et al., 2016) and policy solutions (Fabbio, 2016). ...
... In the Piedmont Region, an innovative law was passed in 2017 to promote the establishment of associations of forest owners, supporting the shared management of private forest areas (Beltramo et al., 2018;Marandola & Romano, 2012). Similarly, within the application of the last period of the Rural Development Fund, 15 Italian Regions out of 20 funded measures to improve the management of coppiced forests (Quatrini et al., 2017) and a new National Forest Law was recently issued (Marchetti, 2018). Finally, decisive progress was made in the development of management guidelines for coppices in order to reduce the influence of the diseases affecting the chestnut in the achievable assortments (Fabbio, 2016;Spinelli et al., 2017). ...
In the Piedmont region, in North-West Italy, the abundance of unmanaged woods has led to negative environmental and economic consequences, generating a decrease in the ecosystem services supplied and in the provision of low-value timber products. In this context, increased logging activities could create new development opportunities for the rural areas in which most of the abandoned stands are situated.
This work analyses a forest harvest by creating a model to evaluate its timber value. The economic results were analysed to investigate the structural and logistic factors influencing the profitability of a harvest. The results obtained revealed that a small profit margin is achievable for small local logging companies, even if strongly influenced by the hourly costs of labourers.
To quantify the influence of each factor of the model on the timber value, a sensitivity analysis was performed. Then, to test the robustness of the results a Monte Carlo simulation was carried out simultaneously varying the factors involved. Finally, a scenario analysis was performed, in which the standard conditions referring to the most common private forest company typologies were examined.
Overall, these methods were found to be suitable for our aims and capable of supplying important results to analyse a forest harvest from an economic perspective.
Cost-effective sampling methods for the estimation of variables of interest that are time-consuming are a major concern. Ranked set sampling (RSS) is a sampling method that assumes that a set of sampling units drawn from the population can be ranked by other means without the actual measurement of the variable of interest. We used data on vegetation dynamics from satellite remote sensing as a means in which to rapidly rank sampling units across various land covers and to estimate their residual agroforestry biomass contribution for a small cogeneration facility located in the center of a study area in central Italy. A remote sensing map used as an auxiliary variable in RSS enabled us to cut down the photo-interpretation of the residual biomass present in sampling units from 745 to 139, increase the relative precision of the estimate over common simple random sampling, and avoid individual subjective bias being introduced. The photo-interpretation of the sampling units resulted in a 1.12 Mg ha −1 year −1 mean annual density of residual biomass supply, although unevenly distributed among land cover classes; this led to an estimate of a yearly supply of 132 Gg over the whole 2276 km 2 wide study area. Further applications of this study might include the spatial quantification of biomass supply-related ecosystem services.
