Land use effects on soil carbon in the argentine pampas

Our objective was to establish the pattern of variation of soil organic [SOC] and inorganic [SIC] carbon stored in surface and deep soil layers of the Argentine Pampas as affected by environmental conditions and land use. Eighty two farms, widespread over the region, were used for the study. At each farm paired treatments were sampled representing common land uses: trees, uncropped controls, seeded pastures, cropped fields and periodically flooded areas. Bulk density, SOC, SIC, texture, pH and electrical conductivity were determined to 1 m depth. Rainfall and temperature were obtained from climatic records. Significant differences were detected between treatments in SOC contents. Average SOC stocks to 1 m were: 131 t ha-1 under trees more than 101 t ha-1 in uncropped control more than 90 t ha-1-1 in pastures=86 t ha-1 in cropped field more than and 70 t ha-1 in flooded sites. Compared with uncropped controls, SOC was significantly different in all soil layers under trees, to 75 cm depth in flooded sites and to 50 cm in pastures and cropped soils. Agriculture determined a reduction of 16 percent of SOC to 50 cm in sampled sites. In the 50-100 cmdepth a decrease of 9 percent was observed, though not significant. The stratification pattern of SOC in depth was not affected by the treatments; implying that land use impacted the SOC sequestered in soil, but not its allocation in depth. SIC accounted for one third of total soil carbon, average SIC stockwas 50 t C ha-1 to 1 m. Both, its stock and distribution in the profile were not affected by the treatments; with greater SIC stocks founded in deep soil layers. An artificial neural network model was developed that allowed the estimation of SOC [R2=0.64] based on climate, soil properties and land use. The model, linked to information from satellite image classification, was used for the estimation of present SOC stock of pampean soils, which accounted for 4.22 more or less 0.14 Gt in an area of 48.2 Mha. Using soil surveys performed during the 1960-1980 period we estimated a SOC stock of 3.96 more or less 0.22 Gt. Consequently, no change of total SOC stock seems to be produced in the last decades in the region. At smaller scale, counties with SOC content greater than 95 t ha-1 to 1 m depth lost carbon; increases prevailed below this threshold. Apparently, SIC reservoirs seem have not change during the last decades.

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Bibliographic Details
Main Authors: Berhongaray, Gonzalo, Alvarez, Roberto, Paepe, Josefina Luisa de, Caride, Constanza, Cantet, Rodolfo Juan Carlos
Format: Texto biblioteca
Language:eng
Subjects:CARBON SEQUESTRATION, EMPIRICAL MODELING, SOIL INORGANIC CARBON, SOIL ORGANIC CARBON, ARTIFICIAL NEURAL NETWORK MODELING, ELECTRICAL CONDUCTIVITY, EMPIRICAL MODEL, ENVIRONMENTAL CONDITIONS, SATELLITE IMAGE CLASSIFICATION, SOIL INORGANIC CARBONS, FLOODS, FORESTRY, IMAGE CLASSIFICATION, LAND USE, RAIN, SATELLITE IMAGERY, SOIL SURVEYS, SOILS, ARTIFICIAL NEURAL NETWORK, CONCENTRATION [COMPOSITION], EMPIRICAL ANALYSIS, LAND USE CHANGE, NUMERICAL MODEL, PH, SOIL CARBON, SOIL ORGANIC MATTER, CARBON, CHELATION, IMAGE ANALYSIS, SOIL, SURVEYS, ARGENTINA, PAMPAS,
Online Access:http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46992
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id KOHA-OAI-AGRO:46992
record_format koha
institution UBA FA
collection Koha
country Argentina
countrycode AR
component Bibliográfico
access En linea
En linea
databasecode cat-ceiba
tag biblioteca
region America del Sur
libraryname Biblioteca Central FAUBA
language eng
topic CARBON SEQUESTRATION
EMPIRICAL MODELING
SOIL INORGANIC CARBON
SOIL ORGANIC CARBON
ARTIFICIAL NEURAL NETWORK MODELING
ELECTRICAL CONDUCTIVITY
EMPIRICAL MODEL
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
FLOODS
FORESTRY
IMAGE CLASSIFICATION
LAND USE
RAIN
SATELLITE IMAGERY
SOIL SURVEYS
SOILS
ARTIFICIAL NEURAL NETWORK
CONCENTRATION [COMPOSITION]
EMPIRICAL ANALYSIS
LAND USE CHANGE
NUMERICAL MODEL
PH
SOIL CARBON
SOIL ORGANIC MATTER
CARBON
CHELATION
IMAGE ANALYSIS
SOIL
SURVEYS
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
IMAGE CLASSIFICATION
SATELLITE IMAGERY
SOIL SURVEYS
CONCENTRATION [COMPOSITION]
NUMERICAL MODEL
PH
SOIL ORGANIC MATTER
ARGENTINA
PAMPAS
CARBON SEQUESTRATION
EMPIRICAL MODELING
SOIL INORGANIC CARBON
SOIL ORGANIC CARBON
ARTIFICIAL NEURAL NETWORK MODELING
ELECTRICAL CONDUCTIVITY
EMPIRICAL MODEL
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
FLOODS
FORESTRY
IMAGE CLASSIFICATION
LAND USE
RAIN
SATELLITE IMAGERY
SOIL SURVEYS
SOILS
ARTIFICIAL NEURAL NETWORK
CONCENTRATION [COMPOSITION]
EMPIRICAL ANALYSIS
LAND USE CHANGE
NUMERICAL MODEL
PH
SOIL CARBON
SOIL ORGANIC MATTER
CARBON
CHELATION
IMAGE ANALYSIS
SOIL
SURVEYS
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
IMAGE CLASSIFICATION
SATELLITE IMAGERY
SOIL SURVEYS
CONCENTRATION [COMPOSITION]
NUMERICAL MODEL
PH
SOIL ORGANIC MATTER
ARGENTINA
PAMPAS
spellingShingle CARBON SEQUESTRATION
EMPIRICAL MODELING
SOIL INORGANIC CARBON
SOIL ORGANIC CARBON
ARTIFICIAL NEURAL NETWORK MODELING
ELECTRICAL CONDUCTIVITY
EMPIRICAL MODEL
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
FLOODS
FORESTRY
IMAGE CLASSIFICATION
LAND USE
RAIN
SATELLITE IMAGERY
SOIL SURVEYS
SOILS
ARTIFICIAL NEURAL NETWORK
CONCENTRATION [COMPOSITION]
EMPIRICAL ANALYSIS
LAND USE CHANGE
NUMERICAL MODEL
PH
SOIL CARBON
SOIL ORGANIC MATTER
CARBON
CHELATION
IMAGE ANALYSIS
SOIL
SURVEYS
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
IMAGE CLASSIFICATION
SATELLITE IMAGERY
SOIL SURVEYS
CONCENTRATION [COMPOSITION]
NUMERICAL MODEL
PH
SOIL ORGANIC MATTER
ARGENTINA
PAMPAS
CARBON SEQUESTRATION
EMPIRICAL MODELING
SOIL INORGANIC CARBON
SOIL ORGANIC CARBON
ARTIFICIAL NEURAL NETWORK MODELING
ELECTRICAL CONDUCTIVITY
EMPIRICAL MODEL
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
FLOODS
FORESTRY
IMAGE CLASSIFICATION
LAND USE
RAIN
SATELLITE IMAGERY
SOIL SURVEYS
SOILS
ARTIFICIAL NEURAL NETWORK
CONCENTRATION [COMPOSITION]
EMPIRICAL ANALYSIS
LAND USE CHANGE
NUMERICAL MODEL
PH
SOIL CARBON
SOIL ORGANIC MATTER
CARBON
CHELATION
IMAGE ANALYSIS
SOIL
SURVEYS
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
IMAGE CLASSIFICATION
SATELLITE IMAGERY
SOIL SURVEYS
CONCENTRATION [COMPOSITION]
NUMERICAL MODEL
PH
SOIL ORGANIC MATTER
ARGENTINA
PAMPAS
Berhongaray, Gonzalo
Alvarez, Roberto
Paepe, Josefina Luisa de
Caride, Constanza
Cantet, Rodolfo Juan Carlos
Land use effects on soil carbon in the argentine pampas
description Our objective was to establish the pattern of variation of soil organic [SOC] and inorganic [SIC] carbon stored in surface and deep soil layers of the Argentine Pampas as affected by environmental conditions and land use. Eighty two farms, widespread over the region, were used for the study. At each farm paired treatments were sampled representing common land uses: trees, uncropped controls, seeded pastures, cropped fields and periodically flooded areas. Bulk density, SOC, SIC, texture, pH and electrical conductivity were determined to 1 m depth. Rainfall and temperature were obtained from climatic records. Significant differences were detected between treatments in SOC contents. Average SOC stocks to 1 m were: 131 t ha-1 under trees more than 101 t ha-1 in uncropped control more than 90 t ha-1-1 in pastures=86 t ha-1 in cropped field more than and 70 t ha-1 in flooded sites. Compared with uncropped controls, SOC was significantly different in all soil layers under trees, to 75 cm depth in flooded sites and to 50 cm in pastures and cropped soils. Agriculture determined a reduction of 16 percent of SOC to 50 cm in sampled sites. In the 50-100 cmdepth a decrease of 9 percent was observed, though not significant. The stratification pattern of SOC in depth was not affected by the treatments; implying that land use impacted the SOC sequestered in soil, but not its allocation in depth. SIC accounted for one third of total soil carbon, average SIC stockwas 50 t C ha-1 to 1 m. Both, its stock and distribution in the profile were not affected by the treatments; with greater SIC stocks founded in deep soil layers. An artificial neural network model was developed that allowed the estimation of SOC [R2=0.64] based on climate, soil properties and land use. The model, linked to information from satellite image classification, was used for the estimation of present SOC stock of pampean soils, which accounted for 4.22 more or less 0.14 Gt in an area of 48.2 Mha. Using soil surveys performed during the 1960-1980 period we estimated a SOC stock of 3.96 more or less 0.22 Gt. Consequently, no change of total SOC stock seems to be produced in the last decades in the region. At smaller scale, counties with SOC content greater than 95 t ha-1 to 1 m depth lost carbon; increases prevailed below this threshold. Apparently, SIC reservoirs seem have not change during the last decades.
format Texto
topic_facet CARBON SEQUESTRATION
EMPIRICAL MODELING
SOIL INORGANIC CARBON
SOIL ORGANIC CARBON
ARTIFICIAL NEURAL NETWORK MODELING
ELECTRICAL CONDUCTIVITY
EMPIRICAL MODEL
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
FLOODS
FORESTRY
IMAGE CLASSIFICATION
LAND USE
RAIN
SATELLITE IMAGERY
SOIL SURVEYS
SOILS
ARTIFICIAL NEURAL NETWORK
CONCENTRATION [COMPOSITION]
EMPIRICAL ANALYSIS
LAND USE CHANGE
NUMERICAL MODEL
PH
SOIL CARBON
SOIL ORGANIC MATTER
CARBON
CHELATION
IMAGE ANALYSIS
SOIL
SURVEYS
ENVIRONMENTAL CONDITIONS
SATELLITE IMAGE CLASSIFICATION
SOIL INORGANIC CARBONS
IMAGE CLASSIFICATION
SATELLITE IMAGERY
SOIL SURVEYS
CONCENTRATION [COMPOSITION]
NUMERICAL MODEL
PH
SOIL ORGANIC MATTER
ARGENTINA
PAMPAS
author Berhongaray, Gonzalo
Alvarez, Roberto
Paepe, Josefina Luisa de
Caride, Constanza
Cantet, Rodolfo Juan Carlos
author_facet Berhongaray, Gonzalo
Alvarez, Roberto
Paepe, Josefina Luisa de
Caride, Constanza
Cantet, Rodolfo Juan Carlos
author_sort Berhongaray, Gonzalo
title Land use effects on soil carbon in the argentine pampas
title_short Land use effects on soil carbon in the argentine pampas
title_full Land use effects on soil carbon in the argentine pampas
title_fullStr Land use effects on soil carbon in the argentine pampas
title_full_unstemmed Land use effects on soil carbon in the argentine pampas
title_sort land use effects on soil carbon in the argentine pampas
url http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46992
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spelling KOHA-OAI-AGRO:469922023-10-25T10:43:50Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46992AAGLand use effects on soil carbon in the argentine pampasBerhongaray, GonzaloAlvarez, RobertoPaepe, Josefina Luisa deCaride, ConstanzaCantet, Rodolfo Juan Carlostextengapplication/pdfOur objective was to establish the pattern of variation of soil organic [SOC] and inorganic [SIC] carbon stored in surface and deep soil layers of the Argentine Pampas as affected by environmental conditions and land use. Eighty two farms, widespread over the region, were used for the study. At each farm paired treatments were sampled representing common land uses: trees, uncropped controls, seeded pastures, cropped fields and periodically flooded areas. Bulk density, SOC, SIC, texture, pH and electrical conductivity were determined to 1 m depth. Rainfall and temperature were obtained from climatic records. Significant differences were detected between treatments in SOC contents. Average SOC stocks to 1 m were: 131 t ha-1 under trees more than 101 t ha-1 in uncropped control more than 90 t ha-1-1 in pastures=86 t ha-1 in cropped field more than and 70 t ha-1 in flooded sites. Compared with uncropped controls, SOC was significantly different in all soil layers under trees, to 75 cm depth in flooded sites and to 50 cm in pastures and cropped soils. Agriculture determined a reduction of 16 percent of SOC to 50 cm in sampled sites. In the 50-100 cmdepth a decrease of 9 percent was observed, though not significant. The stratification pattern of SOC in depth was not affected by the treatments; implying that land use impacted the SOC sequestered in soil, but not its allocation in depth. SIC accounted for one third of total soil carbon, average SIC stockwas 50 t C ha-1 to 1 m. Both, its stock and distribution in the profile were not affected by the treatments; with greater SIC stocks founded in deep soil layers. An artificial neural network model was developed that allowed the estimation of SOC [R2=0.64] based on climate, soil properties and land use. The model, linked to information from satellite image classification, was used for the estimation of present SOC stock of pampean soils, which accounted for 4.22 more or less 0.14 Gt in an area of 48.2 Mha. Using soil surveys performed during the 1960-1980 period we estimated a SOC stock of 3.96 more or less 0.22 Gt. Consequently, no change of total SOC stock seems to be produced in the last decades in the region. At smaller scale, counties with SOC content greater than 95 t ha-1 to 1 m depth lost carbon; increases prevailed below this threshold. Apparently, SIC reservoirs seem have not change during the last decades.Our objective was to establish the pattern of variation of soil organic [SOC] and inorganic [SIC] carbon stored in surface and deep soil layers of the Argentine Pampas as affected by environmental conditions and land use. Eighty two farms, widespread over the region, were used for the study. At each farm paired treatments were sampled representing common land uses: trees, uncropped controls, seeded pastures, cropped fields and periodically flooded areas. Bulk density, SOC, SIC, texture, pH and electrical conductivity were determined to 1 m depth. Rainfall and temperature were obtained from climatic records. Significant differences were detected between treatments in SOC contents. Average SOC stocks to 1 m were: 131 t ha-1 under trees more than 101 t ha-1 in uncropped control more than 90 t ha-1-1 in pastures=86 t ha-1 in cropped field more than and 70 t ha-1 in flooded sites. Compared with uncropped controls, SOC was significantly different in all soil layers under trees, to 75 cm depth in flooded sites and to 50 cm in pastures and cropped soils. Agriculture determined a reduction of 16 percent of SOC to 50 cm in sampled sites. In the 50-100 cmdepth a decrease of 9 percent was observed, though not significant. The stratification pattern of SOC in depth was not affected by the treatments; implying that land use impacted the SOC sequestered in soil, but not its allocation in depth. SIC accounted for one third of total soil carbon, average SIC stockwas 50 t C ha-1 to 1 m. Both, its stock and distribution in the profile were not affected by the treatments; with greater SIC stocks founded in deep soil layers. An artificial neural network model was developed that allowed the estimation of SOC [R2=0.64] based on climate, soil properties and land use. The model, linked to information from satellite image classification, was used for the estimation of present SOC stock of pampean soils, which accounted for 4.22 more or less 0.14 Gt in an area of 48.2 Mha. Using soil surveys performed during the 1960-1980 period we estimated a SOC stock of 3.96 more or less 0.22 Gt. Consequently, no change of total SOC stock seems to be produced in the last decades in the region. At smaller scale, counties with SOC content greater than 95 t ha-1 to 1 m depth lost carbon; increases prevailed below this threshold. Apparently, SIC reservoirs seem have not change during the last decades.CARBON SEQUESTRATIONEMPIRICAL MODELINGSOIL INORGANIC CARBONSOIL ORGANIC CARBONARTIFICIAL NEURAL NETWORK MODELINGELECTRICAL CONDUCTIVITYEMPIRICAL MODELENVIRONMENTAL CONDITIONSSATELLITE IMAGE CLASSIFICATIONSOIL INORGANIC CARBONSFLOODSFORESTRYIMAGE CLASSIFICATIONLAND USERAINSATELLITE IMAGERYSOIL SURVEYSSOILSARTIFICIAL NEURAL NETWORKCONCENTRATION [COMPOSITION]EMPIRICAL ANALYSISLAND USE CHANGENUMERICAL MODELPHSOIL CARBONSOIL ORGANIC MATTERCARBONCHELATIONIMAGE ANALYSISSOILSURVEYSENVIRONMENTAL CONDITIONSSATELLITE IMAGE CLASSIFICATIONSOIL INORGANIC CARBONSIMAGE CLASSIFICATIONSATELLITE IMAGERYSOIL SURVEYSCONCENTRATION [COMPOSITION]NUMERICAL MODELPHSOIL ORGANIC MATTERARGENTINAPAMPASGeoderma