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...
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2013berhongaray1.pdf LINK AL EDITOR |
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| 024 | |a 10.1016/j.geoderma.2012.07.016 | ||
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| 245 | 1 | 0 | |a Land use effects on soil carbon in the argentine pampas |
| 520 | |a 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. | ||
| 653 | 0 | |a CARBON SEQUESTRATION | |
| 653 | 0 | |a EMPIRICAL MODELING | |
| 653 | 0 | |a SOIL INORGANIC CARBON | |
| 653 | 0 | |a SOIL ORGANIC CARBON | |
| 653 | 0 | |a ARTIFICIAL NEURAL NETWORK MODELING | |
| 653 | 0 | |a ELECTRICAL CONDUCTIVITY | |
| 653 | 0 | |a EMPIRICAL MODEL | |
| 653 | 0 | |a ENVIRONMENTAL CONDITIONS | |
| 653 | 0 | |a SATELLITE IMAGE CLASSIFICATION | |
| 653 | 0 | |a SOIL INORGANIC CARBONS | |
| 653 | 0 | |a FLOODS | |
| 653 | 0 | |a FORESTRY | |
| 653 | 0 | |a IMAGE CLASSIFICATION | |
| 653 | 0 | |a LAND USE | |
| 653 | 0 | |a RAIN | |
| 653 | 0 | |a SATELLITE IMAGERY | |
| 653 | 0 | |a SOIL SURVEYS | |
| 653 | 0 | |a SOILS | |
| 653 | 0 | |a ARTIFICIAL NEURAL NETWORK | |
| 653 | 0 | |a CONCENTRATION [COMPOSITION] | |
| 653 | 0 | |a EMPIRICAL ANALYSIS | |
| 653 | 0 | |a LAND USE CHANGE | |
| 653 | 0 | |a NUMERICAL MODEL | |
| 653 | 0 | |a PH | |
| 653 | 0 | |a SOIL CARBON | |
| 653 | 0 | |a SOIL ORGANIC MATTER | |
| 653 | 0 | |a CARBON | |
| 653 | 0 | |a CHELATION | |
| 653 | 0 | |a IMAGE ANALYSIS | |
| 653 | 0 | |a SOIL | |
| 653 | 0 | |a SURVEYS | |
| 653 | 0 | |a ENVIRONMENTAL CONDITIONS | |
| 653 | 0 | |a SATELLITE IMAGE CLASSIFICATION | |
| 653 | 0 | |a SOIL INORGANIC CARBONS | |
| 653 | 0 | |a IMAGE CLASSIFICATION | |
| 653 | 0 | |a SATELLITE IMAGERY | |
| 653 | 0 | |a SOIL SURVEYS | |
| 653 | 0 | |a CONCENTRATION [COMPOSITION] | |
| 653 | 0 | |a NUMERICAL MODEL | |
| 653 | 0 | |a PH | |
| 653 | 0 | |a SOIL ORGANIC MATTER | |
| 653 | 0 | |a ARGENTINA | |
| 653 | 0 | |a PAMPAS | |
| 700 | 1 | |9 32180 |a Berhongaray, Gonzalo | |
| 700 | 1 | |9 7830 |a Alvarez, Roberto | |
| 700 | 1 | |9 37662 |a Paepe, Josefina Luisa de | |
| 700 | 1 | |9 12474 |a Caride, Constanza | |
| 700 | 1 | |9 12817 |a Cantet, Rodolfo Juan Carlos | |
| 773 | |t Geoderma |g vol.192, no.1 (2013), p.97-110 | ||
| 856 | |u http://ri.agro.uba.ar/files/intranet/articulo/2013berhongaray1.pdf |i En reservorio |q application/pdf |f 2013berhongaray1 |x MIGRADOS2018 | ||
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| 900 | |a as | ||
| 900 | |a 20141009 | ||
| 900 | |a 20171026 | ||
| 900 | |a N | ||
| 900 | |a SCOPUS | ||
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| 900 | |a ARTICULO | ||
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| 900 | |a 10.1016/j.geoderma.2012.07.016 | ||
| 900 | |a ^tLand use effects on soil carbon in the argentine pampas | ||
| 900 | |a ^aBerhongaray^bG. | ||
| 900 | |a ^aAlvarez^bR. | ||
| 900 | |a ^aPaepe^bJ. de | ||
| 900 | |a ^aCaride^bC. | ||
| 900 | |a ^aCantet^bR. | ||
| 900 | |a ^aBerhongaray^bG. | ||
| 900 | |a ^aAlvarez^bR. | ||
| 900 | |a ^aPaepe^bJ. de | ||
| 900 | |a ^aCaride^bC. | ||
| 900 | |a ^aCantet^bR. J. C. | ||
| 900 | |a ^aBerhongaray^bG.^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av. San Martin 4453 [1417] Buenos Aires, Argentina | ||
| 900 | |a ^aAlvarez^bR.^tDepartment of Biology, University of Antwerp, Research group of Plant and Vegetation Ecology [PLECO], Campus Drie Eiken, Universiteitsplein 1, B-2610 Wilrijk [Antw.], Belgium | ||
| 900 | |a ^aDe Paepe^bJ. | ||
| 900 | |a ^aCaride^bC. | ||
| 900 | |a ^aCantet^bR. | ||
| 900 | |a ^aBerhongaray, G^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av^tSan Martin 4453 (1417) Buenos Aires, Argentina | ||
| 900 | |a ^aBerhongaray, G^tDepartment of Biology, University of Antwerp, Research group of Plant and Vegetation Ecology (PLECO), Campus Drie Eiken, Universiteitsplein 1, B-2610 Wilrijk (Antw.), Belgium | ||
| 900 | |a ^aAlvarez, R^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av^tSan Martin 4453 (1417) Buenos Aires, Argentina | ||
| 900 | |a ^aDe Paepe, J^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av^tSan Martin 4453 (1417) Buenos Aires, Argentina | ||
| 900 | |a ^aCaride, C^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av^tSan Martin 4453 (1417) Buenos Aires, Argentina | ||
| 900 | |a ^aCantet, R^tFacultad de AgronomÃa, Universidad de Buenos Aires-CONICET, Av^tSan Martin 4453 (1417) Buenos Aires, Argentina | ||
| 900 | |a ^tGeoderma^cGeoderma | ||
| 900 | |a ^n^x^o^s | ||
| 900 | |a ^l^p | ||
| 900 | |a ^n^s^l^p^f | ||
| 900 | |a en | ||
| 900 | |a 97 | ||
| 900 | |a ^i | ||
| 900 | |a Vol. 192, no. 1 | ||
| 900 | |a 110 | ||
| 900 | |a CARBON SEQUESTRATION | ||
| 900 | |a EMPIRICAL MODELING | ||
| 900 | |a SOIL INORGANIC CARBON | ||
| 900 | |a SOIL ORGANIC CARBON | ||
| 900 | |a ARTIFICIAL NEURAL NETWORK MODELING | ||
| 900 | |a ELECTRICAL CONDUCTIVITY | ||
| 900 | |a EMPIRICAL MODEL | ||
| 900 | |a ENVIRONMENTAL CONDITIONS | ||
| 900 | |a SATELLITE IMAGE CLASSIFICATION | ||
| 900 | |a SOIL INORGANIC CARBONS | ||
| 900 | |a FLOODS | ||
| 900 | |a FORESTRY | ||
| 900 | |a IMAGE CLASSIFICATION | ||
| 900 | |a LAND USE | ||
| 900 | |a RAIN | ||
| 900 | |a SATELLITE IMAGERY | ||
| 900 | |a SOIL SURVEYS | ||
| 900 | |a SOILS | ||
| 900 | |a ARTIFICIAL NEURAL NETWORK | ||
| 900 | |a CONCENTRATION [COMPOSITION] | ||
| 900 | |a EMPIRICAL ANALYSIS | ||
| 900 | |a LAND USE CHANGE | ||
| 900 | |a NUMERICAL MODEL | ||
| 900 | |a PH | ||
| 900 | |a SOIL CARBON | ||
| 900 | |a SOIL ORGANIC MATTER | ||
| 900 | |a CARBON | ||
| 900 | |a CHELATION | ||
| 900 | |a IMAGE ANALYSIS | ||
| 900 | |a SOIL | ||
| 900 | |a SURVEYS | ||
| 900 | |a ENVIRONMENTAL CONDITIONS | ||
| 900 | |a SATELLITE IMAGE CLASSIFICATION | ||
| 900 | |a SOIL INORGANIC CARBONS | ||
| 900 | |a IMAGE CLASSIFICATION | ||
| 900 | |a SATELLITE IMAGERY | ||
| 900 | |a SOIL SURVEYS | ||
| 900 | |a CONCENTRATION [COMPOSITION] | ||
| 900 | |a NUMERICAL MODEL | ||
| 900 | |a PH | ||
| 900 | |a SOIL ORGANIC MATTER | ||
| 900 | |a ARGENTINA | ||
| 900 | |a PAMPAS | ||
| 900 | |a 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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| 900 | |a 2013berhongaray1.pdf | ||
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