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Soil electrical conductivity classification: Applications for sustainable management in semiarid cropping systems
Abstract
Methods are needed to identify, monitor, and advance economically and ecologically sustainable management practices in semiarid cropping systems at the field scale. On-farm research offers the greatest potential for appropriate and influential investigations, although replication is often unfeasible. Large-scale apparent electrical conductivity (ECa) sensors are now available to map spatial patterns in soil condition. We hypothesized that the classification of ECa maps can divide fields into distinct (management) zones of production potential applicable to these sustainability issues. A contiguous section of farmland (250 ha) in northeast CO, managed in a no-till winter wheat (Triticum aestivum L.)—corn (Zea mays L.)—millet (Panicum miliaceum L. )—fallow rotation, was ECa mapped and separated into four management classes. Geo-referenced surface residue and soil samples were collected within classes and evaluated. Microbial-scale measurements were made utilizing C16:1(cis)11 fatty acid methyl ester biomarker and glomalin immunoassay techniques. Soil tests and two years of yield maps were geo-aligned with ECa maps for comparative statistical analyses. Classification, based on ECa, effectively integrated soil edaphic and biological characteristics to define distinct zones of soil condition (yield potential) across a field. Soil properties associated with crop productivity were negatively correlated with ECa; those associated with erosion were positively correlated. Within-field variance, based upon ECa classification, approximated experimental error for most soil attributes. While wheat yields were negatively correlated with ECa, similar relationships were not found for corn probably due to the confounding effects of weather (drought stress). Classification using ECa is an effective basis for directing soil sampling, monitoring ecological trends, and site-specific management. Classes can also be used as a point of reference through which data collected at smaller or larger levels of scale can be related. Temporal analysis of these data will allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes. Lastly, ECa classification may promote on-farm, farm-scale research by providing bases for statistically evaluating non-replicated experiments and managing soil spatial variability.* *This dissertation includes a CD that is compound (contains both a paper copy and a CD as part of the dissertation). The CD requires the following applications: Microsoft Excel, ArcView.
Subject Area
Agronomy|Geophysics|Soil sciences
Recommended Citation
Johnson, Cinthia K, "Soil electrical conductivity classification: Applications for sustainable management in semiarid cropping systems" (2001). ETD collection for University of Nebraska-Lincoln. AAI3035051.
https://digitalcommons.unl.edu/dissertations/AAI3035051