Statistical models for the prediction of field-scale and spatial salinity patterns from soil conductivity survey data

Agricultural Salinity Assessment and Management: Second Edition

Volumn , Issue , 2011, Pages 461-482

  Lesch, S M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FIELD SCALE; SOIL CONDUCTIVITY; SURVEY DATA;

EID: 85030980876     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1061/9780784411698.ch14     Document Type: Chapter

References (39)

1. Atkinson, A. C., and Donev, A. N. (1992). Optimum experimental designs, Oxford University Press, Oxford, UK.
2. Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2004). Hierarchical modeling and analysis for spatial data, CRC Press, Boca Raton, Fla.
3. Bogaert, P., and Russo, D. (1999). “Optimal spatial sampling design for the estimation of the variogram based on a least squares approach.” Water Resour. Res., 35, 1275-1289.
4. Brandsma, A. S., and Ketellapper, R. H. (1979). “Further evidence on alternative procedures for testing of spatial autocorrelation amongst regression disturbances,” in Exploratory and explanatory statistical analysis of spatial data, C. P. A. Bartels and R. H. Ketellapper, eds., Martinus Nijhoff, Boston, 113-136.
5. Brus, D. J., and de Gruijter, J. J. (1993). “Design-based versus model-based estimates of spatial means: Theory and application in environmental soil science.” Environmetrics, 4, 123-152.
6. Brus, D. J., and Heuvelink, G. B. M. (2007). “Optimization of sample patterns for universal kriging of environmental variables.” Geoderma, 138, 86-95.
7. Burgess, T. M., and Webster, R. (1984). Optimal sampling strategy for mapping soil types. I. Distribution of boundary spacings.” J. Soil Sci., 35, 641-654.
8. Burgess, T. M., Webster, R., and McBratney, A. B. (1981). “Optimal interpolation and isarithmic mapping of soil properties. IV. Sampling strategy.” J. Soil Sci., 32, 643-654.
9. Cook, R. D., and Weisberg, S. (1999). Applied regression including computing and graphics, John Wiley and Sons, New York.
10. Corwin, D. L., and Lesch, S. M. (2005a). “Apparent soil electrical conductivity measurements in agriculture.” Comp. Electron. Ag., 46, 11-43.
11. Corwin, D. L., and Lesch, S. M. (2005b). “Characterizing soil spatial variability with apparent soil electrical conductivity. I. Survey protocols.” Comp. Electron. Ag., 46, 103-134.
12. Cressie, N. A. C. (1991). Statistics for spatial data, John Wiley and Sons, New York.
13. Haining, R. (1990). Spatial data analysis in the social and environmental sciences, Cambridge University Press, Cambridge, UK.
14. Haskard, K. A., Cullis, B. R., and Verbyla, A. P. (2007). “Anisotropic Matèrn correlation and spatial prediction using REML.” J. Ag. Bio. Environ. Stats., 12, 147-160.
15. Hendrickx, J. M. H., Das, B., Corwin, D. L., Wraith, J. M., and Kachanoski, R. G. (2002). “Indirect measurement of solute concentration,” in Methods of soil analysis, Part 4: Physical methods. Soil Science Society of America Book Series, J. H. Dane and G. C. Topp, eds., Soil Science Society of America, Madison, Wisc., 1274-1306.
16. Lesch, S. M. (2005). “Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties.” Comp. Electron. Ag., 46, 153-179.
17. Lesch, S. M., and Corwin, D. L. (2008). “Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests.” Geoderma 148, 130-140.
18. Lesch, S. M., and Corwin, D. L. (2003). “Using the dual-pathway parallel conductance model to determine how different soil properties influence conductivity survey data.” Agron. J., 95, 365-379.
19. Lesch, S. M., Corwin, D. L., and Robinson, D. A. (2005). “Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils.” Comp. Electron. Ag., 46, 351-378.
20. Lesch, S. M., Rhoades, J. D., and Corwin, D. L. (2000). ESAP-95 version 2.10R: User manual and tutorial guide, Research Report 146, USDA-ARS, George E. Brown, Jr., ed., U.S. Salinity Laboratory, Riverside, Calif.
21. Lesch, S. M., Strauss, D. J., and Rhoades, J. D. (1995). “Spatial prediction of soil salinity using electromagnetic induction techniques: 2. An efficient spatial sampling algorithm suitable for multiple linear regression model identification and estimation.” Water Resour. Res., 31, 387-398.
22. Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. (1996). SAS system for mixed models, SAS Institute Inc., Cary, N.C.
23. Maas, E. V., and Hoffman, G. J. (1977). “Crop salt tolerance: Current assessment.” J. Irrig. and Drainage Div., ASCE, (103 IR2), 115-134.
24. Minasny, B., McBratney, A. B., Walvoort, D. J. J. (2007). “The variance quadtree algorithm: Use for spatial sampling design.” Comput. Geosci. 33, 383-392.
25. Müller, W. G. (2001). Collecting spatial data: Optimum design of experiments for random fields, 2nd ed., Physica-Verlag, Heidelberg, Germany.
26. Müller, W. G, and Zimmerman, D. L. (1999). “Optimal designs for variogram estimation.” Environmetrics, 10, 23-37.
27. Myers, R. H. (1986). Classical and modern regression with applications, Duxbury Press, Boston.
28. Myers, R. H., and Montgomery, D. C. (2002). Response surface methodology: Process and product optimization using designed experiments, 2nd ed., John Wiley and Sons, New York.
29. Press, S. J. (1989). Bayesian statistics: Principles, models, and applications, John Wiley and Sons, New York.
30. Rhoades, J. D., Chanduvi, F., and Lesch, S. M. (1999). Soil salinity assessment: Methods and interpretation of electrical conductivity measurements, FAO Irrigation and Drainage Paper No. 57, Food and Agriculture Organisation of the United Nations, Rome.
31. Russo, D. (1984). “Design of an optimal sampling network for estimating the variogram.” Soil Sci. Soc. Am. J., 48, 708-716.
32. Schabenberger, O., and Gotway, C. A. (2005). Statistical methods for spatial data analysis, CRC Press, Boca Raton, Fla.
33. Shapiro, S. S., and Wilk, M. B. (1965). “An analysis of variance tests for normality (complete samples).” Biometrika, 52, 591-611.
34. Thompson, S. K. (1992). Sampling, John Wiley and Sons, New York.
35. Tiefelsdorf, M. (2000). Modeling spatial processes: The identification and analysis of spatial relationships in regression residuals by means of Moran’s I, Springer-Verlag, New York.
36. Upton, G., and Fingleton, B. (1985). Spatial data analysis by example, John Wiley and Sons, New York.
37. Valliant, R., Dorfman, A. H., and Royall, R .M. (2000). Finite population sampling and inference: A prediction approach, John Wiley and Sons, New York.
38. Warrick, A. W., and Myers, D. E. (1987). “Optimization of sampling locations for variogram calculations.” Water Resour. Res., 23, 496-500.
39. Zhu, Z., and Stein, M. L. (2006). “Spatial sampling design for prediction with estimated parameters.” J. Ag. Bio. Environ. Stats., 11, 24-44.