HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (27) Citing Articles via Google Scholar Google Scholar Articles by Corwin, D. L. Articles by Ayars, J. E. Search for Related Content PubMed Articles by Corwin, D. L. Articles by Ayars, J. E. Agricola Articles by Corwin, D. L. Articles by Ayars, J. E. Related Collections Watershed and Landscape Processes Sustainable Agriculture Plant and Soil Interactions Cotton Field-Scale Studies Other Geophysical Methods Spatial Variability Crop Models Production Agriculture Site-Specific Analysis Soil Fertility and Productivity Spatial Distribution

SOIL MANAGEMENT

Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity

^a USDA-ARS, George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507-4617
^b USDA-ARS, Water Manage. Res. Lab., 9611 S. Riverbend Ave., Parlier, CA 92648

^* Corresponding author (dcorwin{at}ussl.ars.usda.gov)

Received for publication March 22, 2002. Crop yield inconsistently correlates with apparent soil electrical conductivity (EC_a) because of the influence of soil properties (e.g., salinity, water content, texture, etc.) that may or may not influence yield within a particular field and because of a temporal component of yield variability that is poorly captured by a state variable such as EC_a. Nevertheless, in instances where yield correlates with EC_a, maps of EC_a are useful for devising soil sampling schemes to identify soil properties influencing yield within a field. A west side San Joaquin Valley field (32.4 ha) was used to demonstrate how spatial distributions of EC_a can guide a soil sample design to determine the soil properties influencing seed cotton (Gossypium hirsutum L.; ‘MAXXA’ variety) yield. Soil sample sites were selected with a statistical sample design utilizing spatial EC_a measurements. Statistical results are presented from correlation and regression analyses between cotton yield and the properties of pH, B, NO₃–N, Cl^-, salinity, leaching fraction (LF), gravimetric water content, bulk density, percentage clay, and saturation percentage. Correlation coefficients of -0.01, 0.50, -0.03, 0.25, 0.53, -0.49, 0.42, -0.29, 0.36, and 0.38, respectively, were determined. A site-specific response model of cotton yield was developed based on ordinary least squares regression analysis and adjusted for spatial autocorrelation using maximum likelihood. The response model indicated that salinity, plant-available water, LF, and pH were the most significant soil properties influencing cotton yield at the study site. The correlations and response model provide valuable information for site-specific management.

Abbreviations: EC_a, apparent soil electrical conductivity • EC_e, electrical conductivity of the saturation extract • GIS, geographical information systems • GPS, global positioning systems • LF, leaching fraction • OLS, ordinary least squares • SP, saturation percentage • _g, gravimetric water content • _b, bulk density

This article has been cited by other articles:

				J. Sawchik and A. P. Mallarino Evaluation of Zone Soil Sampling Approaches for Phosphorus and Potassium Based on Corn and Soybean Response to Fertilization Agron. J., November 6, 2007; 99(6): 1564 - 1578. [Abstract] [Full Text] [PDF]

				D. L. Corwin, J. Hopmans, and G. H. de Rooij From Field- to Landscape-Scale Vadose Zone Processes: Scale Issues, Modeling, and Monitoring Vadose Zone J., March 8, 2006; 5(1): 129 - 139. [Abstract] [Full Text] [PDF]

				P. J. Shouse, S. Goldberg, T. H. Skaggs, R. W. O. Soppe, and J. E. Ayars Effects of Shallow Groundwater Management on the Spatial and Temporal Variability of Boron and Salinity in an Irrigated Field Vadose Zone J., March 8, 2006; 5(1): 377 - 390. [Abstract] [Full Text] [PDF]

				J. A. Terra, J. N. Shaw, D. W. Reeves, R. L. Raper, E. van Santen, E. B. Schwab, and P. L. Mask Soil Management and Landscape Variability Affects Field-Scale Cotton Productivity Soil Sci. Soc. Am. J., December 2, 2005; 70(1): 98 - 107. [Abstract] [Full Text] [PDF]

				S. Goldberg, D. L. Corwin, P. J. Shouse, and D. L. Suarez Prediction of Boron Adsorption by Field Samples of Diverse Textures Soil Sci. Soc. Am. J., August 4, 2005; 69(5): 1379 - 1388. [Abstract] [Full Text] [PDF]

				H. Shahandeh, A. L. Wright, F. M. Hons, and R. J. Lascano Spatial and Temporal Variation of Soil Nitrogen Parameters Related to Soil Texture and Corn Yield Agron. J., April 27, 2005; 97(3): 772 - 782. [Abstract] [Full Text] [PDF]

				D. B. Lobell, J. I. Ortiz-Monasterio, G. P. Asner, R. L. Naylor, and W. P. Falcon Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape Agron. J., January 1, 2005; 97(1): 241 - 249. [Abstract] [Full Text] [PDF]

				J. L. Ping, C. J. Green, K. F. Bronson, R. E. Zartman, and A. Dobermann Identification of Relationships between Cotton Yield, Quality, and Soil Properties Agron. J., November 1, 2004; 96(6): 1588 - 1597. [Abstract] [Full Text] [PDF]

				D. A. Robinson, I. Lebron, S. M. Lesch, and P. Shouse Minimizing Drift in Electrical Conductivity Measurements in High Temperature Environments using the EM-38 Soil Sci. Soc. Am. J., March 1, 2004; 68(2): 339 - 345. [Abstract] [Full Text] [PDF]