Capítulo 28 Referências

Agresti, A. Categorical data analysis. 2. ed. New York: Wiley-Interscience, 2002. p. 710. http://www.stat.ufl.edu/~aa/cda2/cda.html.

Brus, D. J., Heuvelink, G. B. M. Optimization of sample patterns for universal kriging of environmental variables. Geoderma, 138:86–95, 2007. doi:10.1016/j.geoderma.2006.10.016.

Burrough, P. A., Bouma, J., Yates, S. R. The state of the art in pedometrics. Geoderma, 62(1–3):311–326, 1994. doi:10.1016/0016-7061(94)90043-4.

De Gruijter, J. J., Brus, D., Bierkens, M., Knotters, M. Sampling for natural resource monitoring. Berlin: Springer, 2006. p. 332. http://www.springer.com/environment/environmental+toxicology/book/978-3-540-22486-0.

Diggle, P. J., Ribeiro Jr, P. J. Model-based Geostatistics. 1. ed. New York: Springer, 2007. p. 228. http://www.springer.com/earth+sciences+and+geography/book/978-0-387-32907-9.

Everitt, B. S. The Cambridge dictionary of statistics. 3. ed. Cambridge: Cambridge University Press, 2006. p. 432.

Florinsky, I. V. The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication). Eurasian Soil Science, 45(4):445–451, 2012. doi:10.1134/S1064229312040047.

Hair, J. F., Black, B., Babin, B., Anderson, R. E. Multivariate data analysis. 7. ed. New Jersey: Pearson Prentice Hall, 2010. p. 760. http://www.mypearsonstore.com/bookstore/multivariate-data-analysis-9780138132637.

Hengl, T., Toomanian, N., Reuter, H. I., Malakouti, M. J. Methods to interpolate soil categorical variables from profile observations: lessons from Iran. Geoderma, 140(4):417–427, 2007. doi:10.1016/j.geoderma.2007.04.022.

Heuvelink, G. B. M. Identification of field attribute error under different models of spatial variation. International journal of geographical information systems, 10(8):921–935, 1996. doi:10.1080/02693799608902117.

Heuvelink, G. B. M. What is pedometrics? Pedometron, 13:6–7, 2003a.

Heuvelink, G. B. M. The definition of pedometrics. Pedometron, 14:2–3, 2003b.

Heuvelink, G. B. M. The definition of pedometrics. Pedometron, 15:11–12, 2003c. http://www.pedometrics.org/pedometron/pedometron15.pdf.

Heuvelink, G. B. M., Webster, R. Modelling soil variation: past, present, and future. Geoderma, 100(3-4):269–301, 2001. doi:10.1016/S0016-7061(01)00025-8.

Jenny, H. Factors of soil formation – a system of quantitative pedology. Toronto: Dover Publications, 1941. p. 281.

Kuhn, T. S. A estrutura das revoluções científicas. 10. ed. São Paulo: Perspectiva, 2011. p. 260.

Kutner, M. H., Nachtsheim, C. J., Neter, J., Lispss, W. Applied linear statistical models. 5. ed. New York: McGraw-Hill, 2004. p. 1396.

Lagacherie, P., McBratney, A. Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. In: P. LAGACHERIE, A. M.; VOLTZ, M. (Eds.).. Digital soil mapping – an introductory perspective. Developments in Soil Science. [s.l.] Elsevier, 2007. v. 31p. 3–22. doi:10.1016/S0166-2481(06)31001-X.

McBratney, A. B. Pedometrics in a sentence. Pedometron, 14:4–5, 2003.

McBratney, A. B., Mendonça-Santos, M. L., Minasny, B. On digital soil mapping. Geoderma, 117:3–52, 2003. doi:10.1016/S0016-7061(03)00223-4.

McBratney, A. B., Odeh, I. O., Bishop, T. F., Dunbar, M. S., Shatar, T. M. An overview of pedometric techniques for use in soil survey. Geoderma, 97:293–327, 2000. doi:10.1016/S0016-7061(00)00043-4.

Müller, W. G. Collecting spatial data - optimum design of experiments for random fields. Berlin: Springer, 2007. p. 242. doi:10.1007/978-3-540-31175-1.

Nussbaum, M., Papritz, A., Baltensweiler, A., Walthert, L. Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging. Geoscientific Model Development, 7(4):1197–1210, 2014. doi:10.5194/gmd-7-1197-2014.

Papritz, A. georob: Robust Geostatistical Analysis of Spatial Data. [s.l: s.n.]. https://CRAN.R-project.org/package=georob. R package version 0.2-2

Rossiter, D. G. Methodology for Soil Resource Inventories. 2. ed. Enschede: University of Twente, 2000. p. 132. https://www.researchgate.net/publication/252998954_Lecture_Notes_Principles_of_Soil_Classification. Lecture Notes

Samuel-Rosa, A. Funções de predição espacial de propriedades do solo. dissertação de mestrado—Santa Maria: Programa de Pós-graduação em Ciência do Solo, Universidade Federal de Santa Maria; Federal University of Santa Maria, 2012.http://w3.ufsm.br/ppgcs/.

Samuel-Rosa, A., Heuvelink, G. B. M., Vasques, G. M., Anjos, L. H. C. Do more detailed environmental covariates deliver more accurate soil maps? Geoderma, 243–244:214–227, 2015. doi:10.1016/j.geoderma.2014.12.017.

Troeh, F. R. Landform parameters correlated to soil drainage. Soil Science Society of America Journal, 28:808–812, 1964. https://www.soils.org/publications/sssaj/abstracts/28/6/SS0280060808?access=0&view=pdf.

Webster, R. The development of pedometrics. Geoderma, 62(1–3):1–15, 1994. doi:10.1016/0016-7061(94)90024-8.

Webster, R., Lark, R. M. Field sampling for environmental science and management. London: Routledge, 2013. p. 200.

Webster, R., Oliver, M. A. Statistical methods in soil and land resource survey. Oxford: Oxford University Press, 1990. p. 316.

Wittgenstein, L. Investigações filosóficas. São Paulo: Nova Cultural, 1999. p. 207.