Using Indicator Kriging Technique for Soil Salinity and Yield Management
Publication: Journal of Irrigation and Drainage Engineering
Volume 137, Issue 2
Abstract
This paper presents a practical method to manage soil salinity and yield in order to obtain maximum economic benefits. The method was applied to a study area located in the southeastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The following were the objectives of this study: (1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; (2) compare the expected potential productivity of different crops based on the soil salinity conditions; and (3) assess the expected net revenue of multiple crops under different soil salinity conditions. Four crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a global position system unit. Different scenarios of crops and salinity levels were evaluated. Indicator variograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. Indicator kriging (IK) was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. Expected crop net revenue for each scenario was calculated and all the results were compared to determine the best scenarios. The results of this study show that IK can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated.
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References
Agterberg, F. P. (1984). “Trend surface analysis in spatial statistics and models.” G. L. Gaile and C. J. Willmott, eds., Reidel, Dordrecht, The Netherlands, 147–171.
Ayers, R. S., and Westcot, D. W. (1976). Water quality for agriculture. Irrigation and drainage paper 29, FAO, Rome.
Bierkens, M. F. P., and Burrough, P. A. (1993a). “The indicator approach to categorical soil data. I. Theory.” J. Soil Sci., 44, 361–368.
Bierkens, M. F. P., and Burrough, P. A. (1993b). “The indicator approach to categorical soil data. II. Application tomapping and land use suitability analysis.” J. Soil Sci., 44, 369–381.
Carr, J. R., Bailey, R. E., and Deng, E. D. (1985). “Use of indicator variograms for an enhanced spatial analysis.” Math. Geol., 17(8), 797–811.
Castrignanò, A., Goovaerts, P., Lulli, L., and Bragato, G. (2000). “A geostatistical approach to estimate probability of occurrence of Tuber melanosporum in relation to some soil properties.” Geoderma, 98, 95–113.
Cressie, N. (1993). Statistics for spatial data, Wiley, New York.
Deutsch, C. V., and Journel, A. G. (1998). GSLIB, Geostatistical software library and user's guide, 2nd Ed., Oxford University Press, New York.
Douaik, A., Van Meirvenne, M., and Toth, T. (2004). “Spatio-temporal kriging of soil salinity rescaled from bulk soil electrical conductivity.” Quantitative Geology And Geostatistics, GeoEnv IV: 4th European Conf. on Geostatistics For Enironmental Applications, X. Sanchez-Vila, J. Carrera, and J. Gomez-Hernandez, eds., Kluwer Academic, Dordrecht, The Netherlands, 13(8) 413–424.
Eldeiry, A., and Garcia, L. A. (2008). “Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing.” Soil Sci. Soc. Am. J., 72(1), 201–211.
Eldeiry, A., Garcia, L. A., and Reich, R. M. (2008). “Soil salinity sampling strategy using spatial modeling techniques, remote sensing, and field data.” J. Irrig. Drain. Eng., 134(6), 768–777.
Figueira, R., Tavares, P. C., Palma, L., Beja, P., and Sergio, C. (2009). “Application of IK to the complementary use of bioindicators at three trophic levels.” Environ. Pollut., 157, 2689–2696.
Fogg, G. E., LaBolle, E. M., and Weissman, G. S. (1999). “Groundwater vulnerability assessment: Hydrologic perspective and example from Salinas Valley, California.” Assessment of non-point source pollution in the vadose zone, American Geophysical Union, 45–61.
Froidevaux R. (1993). Probability field simulation., In Geostatistics Troia '92, Vol. 1, edited by Soares A. Kluwer Academic Publisher, Dordrecht, 73–84.
Frasier, W. M., Waskom, R. M., Hoag, D. L., and Bauder, T. A. (1999). “Irrigation management in Colorado: Survey data and findings.” Technical Rep. TR99-5, Agricultural Experiment Station, Colorado State Univ., Fort Collins, Colo.
Gates, T. K., Burkhalter, J. P., Labadie, J. W., Valliant, J. C., and Broner, I. (2002). “Monitoring and modeling flow and salt transport in a salinity-threatened irrigated valley.” J. Water Resour. Plann. Manage., 128(2), 87–99.
Ghassemi, F., Jackeman, A. J., and Nix, H. A. (1995). Salinization of land and water resources: Human causes, extent, management and case studies, CAB International, Wallingford Oxon, U.K.
Goovaerts, P. (1994). “Comparative performance of indicator algorithms for modeling conditional probability distribution functions.” Math. Geol., 26, 389–411.
Greenway, H., and Munns, R. (1980). “Mechanisms of salt tolerance in nonhalophytes.” Annu. Rev. Plant Physiol., 31, 149–190.
Guisan, A., and Zimmermann, N. E. (2000). “Predictive habitat distribution models in ecology.” Ecol. Modell., 135, 147–186.
Haberlandt, U. (2006). “Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event.” J. Hydrol., 322, 144–157.
Hillel, D. (2000). Salinity management for sustainable irrigation: Integrating science, environment, and economics, The World Bank, Washington, D.C.
Istok, J. D., and Pautman, C. A. (1996). “Probabilistic assessment of groundwater contamination: 2. Results of case study.” Ground Water, 34(6), 1050–1064.
Journel, A. G. (1983). “Nonparametric estimation of spatial distributions.” Math. Geol., 15(3), 445–468.
Juang, K. W., and Lee, D. Y. (1998). “Simple indicator kriging for estimating the probability of incorrectly delineating hazardous areas in a contaminated site.” Environ. Sci. Technol., 32, 2487–2493.
Kravchenko, A., and Bullock, D. G. (1999). “A comparative study of interpolation methods for mapping soil properties.” Agron. J., 91, 393–400.
Lin, Y. P., Chang, T. K., Shih, C. W., and Tseng, C. H. (2002). “Factorial and indicator kriging methods using a geographic information system to delineate spatial variation and pollution sources of soil heavy metals.” Environ. Geol., 42, 900–909.
Liu, C., Jang, C., and Liao, C. (2004). “Evaluation of arsenic contamination potential using indicator kriging in the Yun-Lin aquifer (Taiwan).” Sci. Total Environ., 321, 173–188.
Lyon, S. W., Lembo, A. J., Jr., Walter, M. T., and Steenhuis, T. S. (2006). “Defining probability of saturation with indicator kriging on hard and soft data.” Adv. Water Resour., 29, 181–193.
Marinoni, O. (2003). “Improving geological models using a combined ordinary-indicator kriging approach.” Eng. Geol. (Amsterdam), 69, 37–45.
McCord, J. T., Gotway, C. A., and Conrad, S. H. (1997). “Impact of geologic heterogeneity on recharge estimation using environmental tracers: numerical modeling investigation.” Water Resour. Res., 33(6), 1229–1240.
McQuarrie, A. D. R., and Tsai, C. -L. (1998). Regression and time series model selection, World Scientific, Singapore.
Miles, D. L. (1977). Salinity in the Arkansas Valley of Colorado. Region VIII, U.S. EPA, Denver.
Miller, J., Franklin, J., and Aspinall, R. (2007). “Incorporating spatial dependence in predictive vegetation models.” Ecol. Modell., 202(3–4), 225–242.
Olea, R. A. (1991). Geostatistical glossary and multilingual directionary, Oxford University Press, New York.
Oyedele, D. J., Amusan, A. A., and Obi, A. (1996). “The use of multiple-variable indicator kriging technique for assessment of the suitability of an acid soil for maize.” Int. J. Tropical Agric., 73(4), 259–263.
Panahi, A., Cheng, Q., and Bonham-Carter, G. F. (2004). “Modelling lake sediment geochemical distribution using principal component, indicator kriging and multifractal power-spectrum analysis: A case study from Gowganda, Onatario.” Geochem: Explor., Environ., Anal., 4, 59–70.
Postel, S. (1999). Pillar of sand: Can the irrigation miracle last?, W.W. Norton and Co., New York.
Pozdnyakova, L., and Zhang, R. (1999). “Geostatistical analyses of soil salinity in a large field.” Precis. Agric., 1, 153–165.
Reis, A. P., et al. (2005a). “Combining GIS and stochastic simulation to estimate spatial patterns of variation for lead at the Lousal Mine, Portugal.” Land Degrad. Dev., 16(2), 229–242.
Reis, A. P., Sousa, A. J., Ferreira Da Silva, E., and Cardoso Fonseca, E. (2005b). “Application of geostatistical methods to arsenic data from soil samples of the Cova dos Mouros Mine (Vila Verde-Portugal).” Environ. Geochem. Health, 27(3), 259–270.
Richmond, A. (2002). “An alternative implementation of indicator kriging.” Comput. Geosci., 28(4), 555–565.
Saito, H., and Goovaerts, P. (2002). “Accounting for measurement error in uncertainty modeling and decision-making using indicator kriging and p-field simulation: application to a dioxin contaminaed site.” Environmetrics, 13, 555–567.
Samra, S., and Gill, H. S. (1993). “Modeling of variation in a sodium-contaminated soil and associated tree growth.” Soil Sci., 155(2), 148.
Schloeder, C. A., Zimmermann, N. E., and Jacobs, M. J. (2001). “Comparison of methods for interpolating soil properties using limited data.” Soil Sci. Soc. Am. J., 65, 470–479.
Smith, J. L., Halvorson, J. J., and Papendick, R. I. (1993). “Using multiple-variable indicator kriging for evaluating soil quality.” Soil Sci. Soc. Am. J., 57, 743–749.
Soares, A. (1992). “Geostatistical estimation of multi-phase structures.” Math. Geol., 24(2), 149–160.
Solow, A. R. (1986). “Mapping by simple indicator kriging.” Math. Geol., 18(3), 335–352.
Tanji, K. K., ed. (1990). “Agricultural salinity assessment and management.” ASCE Manuals and Rep. on Engineering Practice No. 71, ASCE, New York
Triantafilis, J., Huckel, A. I., and Odeh, I. O. A. (2003). “Field-scale assessment of deep drainage risk.” Irrig. Sci., 21, 183–192.
Triantafilis, J., Odeh, I. O. A., and McBratney, A. B. (2001). “Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton.” Soil Sci. Soc. Am. J., 65, 869–878.
Triantafilis, J., Odeh, I. O. A., Short, M., and Kokkoris, E. (2004). “Estimating and mapping deep drainage risk at the district level in the lower Gwydir and Macquarie Valleys, Australia.” Aust. J. Exp. Agric., 44, 893–912.
Utset, A., Ruiz, M. E., Herrera, J., and Ponce de Leon, D. (1998). “A geostatistical method for soil salinity sample site spacing.” Geoderma, 86, 143–151.
Van Meirvenne, M., and Goovaerts, P. (2001). “Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold.” Geoderma, 102, 75–100.
Western, A. W., Bloschl, G., and Grayson, R. B. (1998). “How well do indicator variograms capture the spatial connectivity of soil moisture?” Hydrolog. Process., 12(12), 1851–1868.
Wittler, J. M., Cardon, G. E., Gates, T. K., Cooper, C. A., and Sutherland, P. L. (2006). “Calibration of electromagnetic induction for regional assessment of soil water salinity in an irrigated valley.” J. Irrig. Drain. Eng., 132(5), 436–444.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 137 • Issue 2 • February 2011
Pages: 82 - 93
Copyright
© 2011 ASCE.
History
Received: Sep 15, 2009
Accepted: Jul 14, 2010
Published online: Jan 14, 2011
Published in print: Feb 2011
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