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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios

Authors: Cappelli, Giovanni Alessandro; Ginaldi, Fabrizio; Fanchini, Davide; Ceotto, Enrico; Donatelli, Marcello;

How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios

Abstract

This data were presented in the research paper “How much energy can giant reed and Miscanthus produce in marginal lands across Italy? A modelling solution under current and future scenarios”, currently accepted in the journal Global Change Biology Bioenergy (https://onlinelibrary.wiley.com/journal/17571707).The study delivers a model-based evaluation of how much energy, in the form of biomethane and bioethanol, can be produced by giant reed and Miscanthus across Italy in 2000, 2055 and 2085. Marginal lands were defined as low profitable non-irrigated lands, without mechanization and/or nature conservation limitations. Our findings offer an estimation of achievable energy yields and related stability under current/future climate, identifying critical spots and opportunities at province and regional level across Italy.This work was conducted by the Council for Agricultural Research and Economics and supported by the Italian Ministry of Agricultural, Food and Forestry Policies (MiPAAF) under i) the AGROENER project (D.D. n. 26329, April 1, 2016, http://agroener.crea.gov.it/) and ii) the AgriDigit-Agromodelli project (DM n. 36502 of 20/12/2018, https://www.progettoagridigit.it/il-progetto). The database used was split in two main datasets, one for the national case study and one for the provincial case study (Bologna province).The national dataset consists of:1) a gridded shape file (Marginal_Suitable_Areas_National.shp; 500 x 500 m resolution) including marginal lands suitable for Miscanthus and giant reed cultivation across Italy (code_nod field), together with related geographic coordinates;2) a csv file (Results_National.csv) reporting the values of key output variables for each of the marginal lands considered. Output variables are:a. USDA soil texture classification: 1= Loamy, 2=Sandy−loam, 3=Silty−loam, 4= Clay−loam, 5= Sandy−clay−loam, 6=Silty−clay−loam, 7=Loamy−sand, 8=Sandy−clay, 9=Silty−clay, 10=Silty, 11=Clay, 12=Heavy−clay, 13=Sandy.b. soil organic carbon (SOC) classification: SOC≤1.5%=low, 1.5%

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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