Which covariates are needed for soil carbon models in Florida?

Digital Soil Assessments and Beyond - Proceedings of the Fifth Global Workshop on Digital Soil Mapping

Volumn , Issue , 2012, Pages 109-113

  Xiong, X ^a   Grunwald, S ^a   Myers, D B ^b   Kim, J ^a   Harris, W G ^a   Comerford, N B ^c  

^a UNIVERSITY OF FLORIDA   (United States)

^b U S DEPARTMENT OF AGRICULTURE   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CARBON; DECISION TREES; FORESTRY; MAPPING; REGRESSION ANALYSIS; SOIL MOISTURE; SOIL SURVEYS; SUPPORT VECTOR MACHINES; TOPOGRAPHY;

BOOSTED REGRESSION TREES; CRITICAL QUESTIONS; DOMINATING FACTORS; MACHINE LEARNING TECHNIQUES; RANDOM FOREST MODELING; REGRESSION TREES; SOIL-LANDSCAPE MODELS; TOTAL SOIL CARBON;

DATA MINING;

DECISION MAKING; FORESTRY; REGRESSION ANALYSIS; SOIL; TREES;

EID: 84866356410     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1201/b12728-23     Document Type: Conference Paper

References (10)

1. Chmura, G.L., Anisfeld, S.C., Cahoon, D.R. & Lynch, J.C. 2003. Global carbon sequestration in tidal, saline wetland soils. Global Biogeochemical Cycles 17, 1111-1122.
2. De'ath, G. & Fabricius, K.E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81, 3178-3192.
3. Grunwald, S., 2009. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152, 195-207.
4. Grunwald, S., Thompson, J.A. & Boettinger, J.L. 2011. Digital soil mapping and modeling at continental scales: finding solutions for global issues. Soil Science Society of America Journal 75, 1201-1213.
5. Lal, R., 2003. Global potential of soil carbon sequestration to mitigate the greenhouse effect. Critical Reviews in Plant Sciences 22, 151-184.
6. McBratney, A.B., Mendonça Santos, M.L. & Minasny, B. 2003. On digital soil mapping. Geoderma 117, 3-52.
7. Prasad, A.M., Iverson, L.R. & Liaw, A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9, 181-199.
8. Vasques, G.M., Grunwald, S. & Myers, D.B. 2012. Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida, USA. Landscape Ecology 27, 355-367.
9. Vasques, G.M., Grunwald, S. & Sickman, J.O. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146, 14-25.
10. Williams, P.C. 1987. Variables affecting near-infrared refl ectance spectroscopic analysis. In P. Williams and K. Norris (eds), Near-infrared technology in the agricultural and food industries: 143-167. St. Paul, Minnesota: American Association of Cereal Chemists.