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Spatial sampling, landscape modeling...

Venteris, Erik Ray.

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Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds./
Author:	Venteris, Erik Ray.
Description:	222 p.
Notes:	Adviser: Brian K. Slater.
Contained By:	Dissertation Abstracts International63-04B.
Subject:	Agriculture, Soil Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3049127
ISBN:	0493635289

Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds.
Venteris, Erik Ray.

Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds. - 222 p.

Adviser: Brian K. Slater.

Thesis (Ph.D.)--The Ohio State University, 2002.

High-resolution (2 m) raster spatial models of soil organic carbon were created for zero-order watersheds at the North Appalachian Experimental Watershed in Coshocton, Ohio. Soil samples, management practice, and topographic parameters obtained from a digital elevation model were used to create spatial models through multivariate regression. Soil organic carbon was studied because of its importance in the global CO<sub>2</sub> cycle and soil quality. Four small watersheds (5000 m<super>2</super> to 10,000 m<super>2</super>) were studied, representing continuous no-till corn, conventional tillage, hay, and pasture management practices. Topographic information (slope, wetness index) was derived from a 2 m digital elevation model (DEM). Nearly 500 new soil samples were collected (30 cm depth). Sample locations were chosen using a rational scheme. Legacy and initial sample sets (61 legacy, 250 initial samples) were used to obtain preliminary information on the structure and predictors of spatial variability. Such information was used to design a final sample set (185 samples) that was compatible with statistical (ANOVA and least squares regression), and geostatistical (kriging) modeling techniques.

ISBN: 0493635289Subjects--Topical Terms:

1017824
Agriculture, Soil Science.

Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds.
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005 20110426
008 110426s2002 eng d
020 $a 0493635289
035 $a (UnM)AAI3049127
035 $a AAI3049127
040 $a UnM $c UnM
100 1 $a Venteris, Erik Ray. $3 1251726
245 1 0 $a Spatial sampling, landscape modeling, and interpretation of soil organic carbon on zero-order watersheds.
300 $a 222 p.
500 $a Adviser: Brian K. Slater.
500 $a Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 1630.
502 $a Thesis (Ph.D.)--The Ohio State University, 2002.
520 $a High-resolution (2 m) raster spatial models of soil organic carbon were created for zero-order watersheds at the North Appalachian Experimental Watershed in Coshocton, Ohio. Soil samples, management practice, and topographic parameters obtained from a digital elevation model were used to create spatial models through multivariate regression. Soil organic carbon was studied because of its importance in the global CO<sub>2</sub> cycle and soil quality. Four small watersheds (5000 m<super>2</super> to 10,000 m<super>2</super>) were studied, representing continuous no-till corn, conventional tillage, hay, and pasture management practices. Topographic information (slope, wetness index) was derived from a 2 m digital elevation model (DEM). Nearly 500 new soil samples were collected (30 cm depth). Sample locations were chosen using a rational scheme. Legacy and initial sample sets (61 legacy, 250 initial samples) were used to obtain preliminary information on the structure and predictors of spatial variability. Such information was used to design a final sample set (185 samples) that was compatible with statistical (ANOVA and least squares regression), and geostatistical (kriging) modeling techniques.
520 $a The results from statistical modeling were used to identify the major challenges to carbon mapping at regional scales for inventory purposes. Soil carbon exhibited a large amount of variability (4 kg C m<super>−2</super>) over small distances (10<super>1</super> meters). Carbon values assigned to county soil survey mapping units are based on few laboratory measurements that cannot properly account for such large gradients. Geostatistical autocorrelation ranged from 30 to 100 meters. Accordingly, the maximum sample spacing permitted for kriging was <30 meters. Kriging was excluded as a viable technique for county-scale carbon inventory because of the required sample intensity. Regression-based techniques were favored because of decreased sample requirements. Management (no-till vs. the others) was the most important predictor of soil carbon (from comparison of management model (ANOVA) to a coupled management-topographic model (multiple regression). Long-term no-till fields must be identified for accurate inventory at broad scales. Finally, local rises and pits (2–10 meters in wavelength) were the most predictive topographic features for soil carbon. High-resolution (<10 m) DEMs are needed to capture this relationship.
590 $a School code: 0168.
650 4 $a Agriculture, Soil Science. $3 1017824
650 4 $a Biogeochemistry. $3 545717
690 $a 0425
690 $a 0481
710 2 0 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 63-04B.
790 $a 0168
790 1 0 $a Slater, Brian K., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3049127