PhD Confirmation of Candidature Seminar – Mr Benjamin R. Fitzpatrick

Variable Selection and Spatial Inference for Soil Carbon

Student: Mr Ben Fitzpatrick

When: Friday, 19 April 2013 10:00 AM-11:00 AM

Where: GP-M306

Supervisors:

  • Prof Kerrie Mengersen (Principal)
  • Prof Peter Grace
  • Prof David Lamb

Panel Members:

  • Prof Kerrie Mengersen (Chair)
  • Prof Tony Pettitt (Maths Science)
  • Prof Peter Grace (Environmental Science and Management, Soil Sciences)
  • Prof David Lamb (University of New England)

Abstract:

With global soils containing 3.3 times the amount of Carbon present in the atmosphere as CO2(g) [Lal, 2004] the importance of the dy-namic equilibrium between these bodies to Carbon accounting initiatives and related concerns is readily apparent. The e ort and cost associated with quantifying soil Carbon via laboratory analysis of soil cores has lead to strong interest in improving soil core based maps ofoil Carbon through statistical modeling using more readily attainable environmental variables as covariates [Mueller and Pierce, 2003,Barnes et al., 2003,Chan et al., 2008, Johnson et al., 2001, Simbahan et al., 2006, Miklos et al., 2010,Vasques et al., 2012]. This present work focuses on variable selection for soil Carbon models tted to data featuring spatially misaligned variables, anisotropic spatial dependence and collinearity among covariates. Least Angle Regression [Efron et al., 2004] is applied to the potential model space arising from considering terms for each covariate to polynomial order four and all possible pair-wise interactions thereof. A substantial degree of interaction amoung covariates and non-linear relationships are thereby detected in data from agricultural land in Northern New South Wales.

References

  1. Barnes, E. M., Sudduth, K. A., Hummel, J. W., Lesch, S. M., Corwin, D. L., Yang, C., Daughtry, C. S. T., and Bausch, W. C. (2003). Remote and Ground-Based Sensor Techniques to Map Soil Properties. Photogrammetric Engineering & Remote Sensing, 69(6):619-630.
  2. Chan, K. Y., Cowie, A., Kelly, G., Singh, B., and Slavich, P. (2008). Scoping Paper: Soil Organic Carbon Sequestration Potential for Agriculture in NSW. Technical Report September, New South Wales Department of Primary Industries, Orange, NSW.
  3. Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004). Least Angle Regression. The Annals of Statistics, 32(2):407-451.
  4. Johnson, C. K., Doran, J. W., Duke, H. R., Wienhold, B. J., Eskridge, K. M., and Shanahan, J. F. (2001). Field-Scale Electrical Conductivity Mapping for Delineating Soil Condition. Soil Science Society of America Journal, 65:1829-1837.
  5. Lal, R. (2004). Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science, 304(5677):1623-1627.
  6. Miklos, M., Short, M. G., Mcbratney, A. B., and Minasny, B. (2010). Mapping and comparing the distribution of soil carbon under cropping and grazing management practices in Narrabri, north-west New South Wales. Australian Journal of Soil Research, 48:248-257.
  7. Mueller, T. G. and Pierce, F. J. (2003). Soil carbon maps: Enhancing spatial estimates with simple terrain attributes at multiple scales. Soil Science Society of America Journal, 67(1):258-267.
  8. Simbahan, G. C., Dobermann, A., Goovaerts, P., Ping, J., and Haddix, M. L. (2006). Fine-resolution mapping of soil organic carbon based on multivariate secondary data. Geoderma, 132:471-489.
  9. Vasques, G. M., Grunwald, S., and Myers, D. B. (2012). Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida , USA. Landscape Ecology, 27:355-367.
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