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| 7. |  | Pérez Gomar, E.; Bemhaja, M.; Gutiérrez, F. Evaluación de la vegetación, biomasa radicular y propiedades físico químcas del suelo en sistemas pastoriles y forestales en suelos arenosos de Tacuarembó ln: Reunión del grupo técnico en forrajeras del Cono Sur, 22., 2008, Minas, Uruguay Bioma campos: innovando para mantener su sustentabilidad y competitividad. Memorias. Minas (Uruguay): INIA; FAO; PROCISUR, 2008. p. 186 Versión impresa y en CD ROM Instituto Nacional de Investigación Agropecuaria, Uruguay; FAO; PROCISUR| Biblioteca(s): INIA Las Brujas; INIA Tacuarembó. |
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| 16. | | Suelos. Uruguay Forestal, 1995, no. 8, p. 6-8.| Biblioteca(s): INIA Tacuarembó. |
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 | Acceso al texto completo restringido a Biblioteca INIA Treinta y Tres. Por información adicional contacte bibliott@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha actual : |
21/02/2014 |
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Actualizado : |
28/06/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
B - 1 |
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Autor : |
TERRA, J.A.; SHAW, N.J.; REEVES, D.W.; RAPER, R.L.; VAN SANTEN, E.; MASK, P.L. |
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Afiliación : |
JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Soil Carbon relationships with terrain attributes, electrical conductivity, and a soil survey in a coastal plain landscape. |
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Fecha de publicación : |
2004 |
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Fuente / Imprenta : |
Soil Science, 2004, V. 169, No. 12, p. 819-831. |
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DOI : |
10.1097/00010694-200412000-00001 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received May 3, 2004 // Accepted Sept. 30, 2004, Publishing Dec. 2004. |
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Contenido : |
Soil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration. MenosSoil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased... Presentar Todo |
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Thesagro : |
MANEJO DEL SUELO; SUELO. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02522naa a2200229 a 4500
001 1032966
005 2021-06-28
008 2004 bl uuuu u00u1 u #d
024 7 $a10.1097/00010694-200412000-00001$2DOI
100 1 $aTERRA, J.A.
245 $aSoil Carbon relationships with terrain attributes, electrical conductivity, and a soil survey in a coastal plain landscape.$h[electronic resource]
260 $c2004
500 $aArticle history: Received May 3, 2004 // Accepted Sept. 30, 2004, Publishing Dec. 2004.
520 $aSoil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha?1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha?2) while regression was the worst (MSE = 20.65 Mg2 ha?2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ? 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration.
650 $aMANEJO DEL SUELO
650 $aSUELO
700 1 $aSHAW, N.J.
700 1 $aREEVES, D.W.
700 1 $aRAPER, R.L.
700 1 $aVAN SANTEN, E.
700 1 $aMASK, P.L.
773 $tSoil Science, 2004, V. 169, No. 12, p. 819-831.
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