Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades

Risultato della ricerca: Chapter

Abstract

Soils contain about three times the amount of carbon globally availablein vegetation, and about twice the amount in the atmosphere. However, soil organiccarbon (SOC) has been reduced in many areas, while an increase in atmosphericCO2 has been detected. Recent research works have shown that it is likely that pastchanges in land use history and land management were the main reasons for theloss of carbon rather than higher temperatures and changes of precipitation resultingfrom climate change. The primary scope of this work was to estimate soil organiccarbon stock (CS) variations in Italy during the last three decades and to relate themto land use changes. The study was also aimed at finding relationships betweenSOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, andland use, but also at verifying the possible bias on SOC estimation caused by theuse of data coming from different sources and laboratories. The soil database ofItaly was the main source of information in this study. In the national soil databaseis stored information for 20,702 georeferentiated and dated observations (soil pro-files and minipits) analysed for routine soil parameters. Although the observationswere collected from different sources, soil description and analysis were similar,because all the sources made reference to the Soil Taxonomy and WRB classificationsystems, and soil analyses followed the Italian official methods. Besides horizondescription and analysis, soil observations had a set of site information includingtopography, lithology, and land use. The SOC and bulk density referred to the first50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rockfragments volume, and bulk density. A set of geographic attributes were consideredto spatialize point information, in particular, DEM (100 m) and derived SOTERmorphological classification, soil regions (reference scale 1:5,000,000) and soil systemslithological groups (reference scale 1:500,000), soil moisture and temperatureregimes (raster maps of 1 km pixel size), land cover (CORINE project, referencescale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodologyused a multiple linear regression (MLR). CS was the target variable, whilepredictive variables were the geographic attributes. Basic statistical analysis wasperformed first, to find the predictive variables statistically related to CS and to verifythe bias caused by different laboratories and surveys. After excluding the biaseddatasets, the best predictors were selected using a step-wise regression method withAkaike Information Criterion (AIC) as selection and stop criterion. The obtainedMLR model made use of the following categorical attributes: (i) decade, (ii) landuse, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime,(vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soilaridity index (dry days per year), and, (x) elevation. The interaction between decadeand land use variables was also considered in the model. Results indicated that CSwas highly correlated with the kind of main type of land use (forest, meadow, arableland), soil moisture and temperature regimes, lithology, as well as morphologicalclasses, and decreased notably in the second decade but slightly increased in thethird one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias causedby the variables like “laboratory” and “survey source” could be as large as the 190
Lingua originaleEnglish
Titolo della pubblicazione ospiteLand Degradation and Desertification: Assessment, Mitigation and Remediation
Pagine435-465
Numero di pagine31
Stato di pubblicazionePublished - 2010

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organic carbon
soil
lithology
soil temperature
land use
bulk density
soil moisture
soil analysis
soil classification
pedogenesis
carbon
raster
research work
land management
land use change
meadow
digital elevation model
interpolation
pixel
land cover

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Fantappie', M. (2010). Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades. In Land Degradation and Desertification: Assessment, Mitigation and Remediation (pagg. 435-465)

Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades. / Fantappie', Maria.

Land Degradation and Desertification: Assessment, Mitigation and Remediation. 2010. pag. 435-465.

Risultato della ricerca: Chapter

Fantappie', M 2010, Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades. in Land Degradation and Desertification: Assessment, Mitigation and Remediation. pagg. 435-465.
Fantappie' M. Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades. In Land Degradation and Desertification: Assessment, Mitigation and Remediation. 2010. pag. 435-465
Fantappie', Maria. / Factors Influencing Soil Organic Carbon StockVariations in Italy During the Last ThreeDecades. Land Degradation and Desertification: Assessment, Mitigation and Remediation. 2010. pagg. 435-465
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abstract = "Soils contain about three times the amount of carbon globally availablein vegetation, and about twice the amount in the atmosphere. However, soil organiccarbon (SOC) has been reduced in many areas, while an increase in atmosphericCO2 has been detected. Recent research works have shown that it is likely that pastchanges in land use history and land management were the main reasons for theloss of carbon rather than higher temperatures and changes of precipitation resultingfrom climate change. The primary scope of this work was to estimate soil organiccarbon stock (CS) variations in Italy during the last three decades and to relate themto land use changes. The study was also aimed at finding relationships betweenSOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, andland use, but also at verifying the possible bias on SOC estimation caused by theuse of data coming from different sources and laboratories. The soil database ofItaly was the main source of information in this study. In the national soil databaseis stored information for 20,702 georeferentiated and dated observations (soil pro-files and minipits) analysed for routine soil parameters. Although the observationswere collected from different sources, soil description and analysis were similar,because all the sources made reference to the Soil Taxonomy and WRB classificationsystems, and soil analyses followed the Italian official methods. Besides horizondescription and analysis, soil observations had a set of site information includingtopography, lithology, and land use. The SOC and bulk density referred to the first50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rockfragments volume, and bulk density. A set of geographic attributes were consideredto spatialize point information, in particular, DEM (100 m) and derived SOTERmorphological classification, soil regions (reference scale 1:5,000,000) and soil systemslithological groups (reference scale 1:500,000), soil moisture and temperatureregimes (raster maps of 1 km pixel size), land cover (CORINE project, referencescale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodologyused a multiple linear regression (MLR). CS was the target variable, whilepredictive variables were the geographic attributes. Basic statistical analysis wasperformed first, to find the predictive variables statistically related to CS and to verifythe bias caused by different laboratories and surveys. After excluding the biaseddatasets, the best predictors were selected using a step-wise regression method withAkaike Information Criterion (AIC) as selection and stop criterion. The obtainedMLR model made use of the following categorical attributes: (i) decade, (ii) landuse, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime,(vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soilaridity index (dry days per year), and, (x) elevation. The interaction between decadeand land use variables was also considered in the model. Results indicated that CSwas highly correlated with the kind of main type of land use (forest, meadow, arableland), soil moisture and temperature regimes, lithology, as well as morphologicalclasses, and decreased notably in the second decade but slightly increased in thethird one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias causedby the variables like “laboratory” and “survey source” could be as large as the 190",
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N2 - Soils contain about three times the amount of carbon globally availablein vegetation, and about twice the amount in the atmosphere. However, soil organiccarbon (SOC) has been reduced in many areas, while an increase in atmosphericCO2 has been detected. Recent research works have shown that it is likely that pastchanges in land use history and land management were the main reasons for theloss of carbon rather than higher temperatures and changes of precipitation resultingfrom climate change. The primary scope of this work was to estimate soil organiccarbon stock (CS) variations in Italy during the last three decades and to relate themto land use changes. The study was also aimed at finding relationships betweenSOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, andland use, but also at verifying the possible bias on SOC estimation caused by theuse of data coming from different sources and laboratories. The soil database ofItaly was the main source of information in this study. In the national soil databaseis stored information for 20,702 georeferentiated and dated observations (soil pro-files and minipits) analysed for routine soil parameters. Although the observationswere collected from different sources, soil description and analysis were similar,because all the sources made reference to the Soil Taxonomy and WRB classificationsystems, and soil analyses followed the Italian official methods. Besides horizondescription and analysis, soil observations had a set of site information includingtopography, lithology, and land use. The SOC and bulk density referred to the first50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rockfragments volume, and bulk density. A set of geographic attributes were consideredto spatialize point information, in particular, DEM (100 m) and derived SOTERmorphological classification, soil regions (reference scale 1:5,000,000) and soil systemslithological groups (reference scale 1:500,000), soil moisture and temperatureregimes (raster maps of 1 km pixel size), land cover (CORINE project, referencescale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodologyused a multiple linear regression (MLR). CS was the target variable, whilepredictive variables were the geographic attributes. Basic statistical analysis wasperformed first, to find the predictive variables statistically related to CS and to verifythe bias caused by different laboratories and surveys. After excluding the biaseddatasets, the best predictors were selected using a step-wise regression method withAkaike Information Criterion (AIC) as selection and stop criterion. The obtainedMLR model made use of the following categorical attributes: (i) decade, (ii) landuse, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime,(vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soilaridity index (dry days per year), and, (x) elevation. The interaction between decadeand land use variables was also considered in the model. Results indicated that CSwas highly correlated with the kind of main type of land use (forest, meadow, arableland), soil moisture and temperature regimes, lithology, as well as morphologicalclasses, and decreased notably in the second decade but slightly increased in thethird one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias causedby the variables like “laboratory” and “survey source” could be as large as the 190

AB - Soils contain about three times the amount of carbon globally availablein vegetation, and about twice the amount in the atmosphere. However, soil organiccarbon (SOC) has been reduced in many areas, while an increase in atmosphericCO2 has been detected. Recent research works have shown that it is likely that pastchanges in land use history and land management were the main reasons for theloss of carbon rather than higher temperatures and changes of precipitation resultingfrom climate change. The primary scope of this work was to estimate soil organiccarbon stock (CS) variations in Italy during the last three decades and to relate themto land use changes. The study was also aimed at finding relationships betweenSOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, andland use, but also at verifying the possible bias on SOC estimation caused by theuse of data coming from different sources and laboratories. The soil database ofItaly was the main source of information in this study. In the national soil databaseis stored information for 20,702 georeferentiated and dated observations (soil pro-files and minipits) analysed for routine soil parameters. Although the observationswere collected from different sources, soil description and analysis were similar,because all the sources made reference to the Soil Taxonomy and WRB classificationsystems, and soil analyses followed the Italian official methods. Besides horizondescription and analysis, soil observations had a set of site information includingtopography, lithology, and land use. The SOC and bulk density referred to the first50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rockfragments volume, and bulk density. A set of geographic attributes were consideredto spatialize point information, in particular, DEM (100 m) and derived SOTERmorphological classification, soil regions (reference scale 1:5,000,000) and soil systemslithological groups (reference scale 1:500,000), soil moisture and temperatureregimes (raster maps of 1 km pixel size), land cover (CORINE project, referencescale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodologyused a multiple linear regression (MLR). CS was the target variable, whilepredictive variables were the geographic attributes. Basic statistical analysis wasperformed first, to find the predictive variables statistically related to CS and to verifythe bias caused by different laboratories and surveys. After excluding the biaseddatasets, the best predictors were selected using a step-wise regression method withAkaike Information Criterion (AIC) as selection and stop criterion. The obtainedMLR model made use of the following categorical attributes: (i) decade, (ii) landuse, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime,(vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soilaridity index (dry days per year), and, (x) elevation. The interaction between decadeand land use variables was also considered in the model. Results indicated that CSwas highly correlated with the kind of main type of land use (forest, meadow, arableland), soil moisture and temperature regimes, lithology, as well as morphologicalclasses, and decreased notably in the second decade but slightly increased in thethird one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias causedby the variables like “laboratory” and “survey source” could be as large as the 190

KW - Carbon sequestration

KW - Factor of pedogenesis

KW - Land use change

KW - Multiple regression

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SN - 978-90-481-8656-3

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EP - 465

BT - Land Degradation and Desertification: Assessment, Mitigation and Remediation

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