|Lesch, Scott - UC RIVERSIDE, CA|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 15, 2004
Publication Date: March 1, 2005
Citation: Corwin, D.L., Lesch, S.M. 2005. Characterizing soil spatial variability with apparent soil electrical conductivity: II. Case Study. Computers and Electronics in Agriculture. 46:135-152. Interpretive Summary: Spatial measurements of apparent soil electrical conductivity (ECa) are a quick, easy, and reliable means of mapping and monitoring changes in various soil properties (e.g., salinity, water content, organic matter, etc.). As such, maps of ECa measurements can be used to assess soil quality. A set of detailed protocols has been developed to guide researchers in conducting an ECa survey and to assist researchers in understanding and interpreting ECa survey measurements. To demonstrate the usefulness of these recently developed protocols, a soil quality assessment was conducted on a 42.4-ha (80-acre) saline-sodic field located on the westside of California's San Joaquin Valley. The assessment, which was based upon an intensive ECa survey to direct soil sample, resulted in maps showing the distribution of properties influencing soil quality on a field growing forage for livestock. The assessment showed high levels of sodium, salinity, and molybdenum. The salinity and sodium levels pose a potential threat to forage productivity, while the high molybdenum levels pose a potential threat to the digestive tract of ruminant animals. The protocols provided the detailed guidelines that resulted in information that was reliable, will be compatible with future assessments, and can be readily interpreted. In addition, the resulting maps of soil quality provided the farmer with valuable information for managing the field to reduce salinity, sodium, and molybdenum levels in order to improve forage production and quality.
Technical Abstract: Geospatial measurements of apparent soil electrical conductivity (ECa) are recognized as a means of characterizing soil spatial variability at field and landscape scales. However, inconsistencies in the measurement and interpretation of field- and landscape-scale geosptial ECa measurements have resulted in data sets that are unreliable and/or incompatible. These inconsistencies are, in part, a consequence of the lack of ECa-survey protocols that provide standardized guidelines to assure reliability, consistency, and compatibility. It is the objective of this paper to apply ECa-survey protocols to a soil quality assessment to demonstrate their utility in characterizing spatial variability. The soil quality assessment was conducted on a 32.4-ha field on the westside of central California's San Joaquin Valley where a mobile electromagnetic induction (EM) survey was performed following outlined protocols. The EM survey consisted of ECa measurements taken at 22,177 locations in April 2002. A response-surface sampling design was used to identify 40 sites where soil-core samples were taken at 0.3-m increments to a depth of 1.2 m. Duplicate samples were taken at 8 sites to evaluate the local-scale variability. Soil samples were analyzed for a variety of physico-chemical properties associated with soil quality for an arid zone soil. Analysis characterized the soil as montmorillonitic, saline, and sodic with ECe (electrical conductivity of the saturation extract) varying from 4.83 to 45.3 dS m-1, SAR (sodium adsorption ratio) from 5.62 to 103.12, and clay content from 2.5% to 48.3%. Spatial trends showed high areas of salinity and SAR in the center of the southern half of the study area. Strong correlation was obtained between ECa and the soil properties of ECe; Cl-, HCO3-, SO42-, Na+, K+, and Mg2+ in the saturation extract; exchangeable Na+; and SAR, while the properties of qv (volumetric water content); 'b (bulk density); % clay; SP (saturation percentage); ESP (exchangeable sodium percentage); Mo; CaCO3; gypsum; total N; Ca2+ in the saturation extract; and exchangeable K+, Ca2+, and Mg2+ were poorly correlated. The spatial distribution of the poorly correlated properties is not as well represented with a response-surface sampling design suggesting the need for a complementary stratified random sample design.