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PRECISION MAPPING

Using Electrical Conductivity Classification and Within-Field Variability to Design Field-Scale Research

^a USDA-ARS, 119 Keim Hall, Lincoln, NE 68583-0934
^b Univ. of Nebraska, 103 Miller Hall, Lincoln, NE 68583
^c Colorado State Univ., C130 Plant Sci., Ft. Collins, CO 80523
^d Usda-Ars, Aerc-Csu, Ft. Collins, Co 80523-1325

^* Corresponding author (cjohnso2{at}bigred.unl.edu)

Received for publication February 14, 2002. Agronomic researchers are increasingly accountable for research programs and outcomes relevant to producers. Participatory research—where farmers assume leadership roles in identifying, designing, and managing on-farm field-scale research—addresses this directive. However, replication is often unfeasible at this level of scale, underscoring a need for alternative methods to estimate experimental error. We compared mean square errors to evaluate: (i) within-field variability for estimating experimental error (in lieu of replication) and (ii) classified within-field variability, using apparent electrical conductivity (EC_a), for estimating plot-scale experimental error. Eight 31-ha fields, within a contiguous section of farmland (250 ha), were managed as two replicates of each phase of a no-till winter wheat (Triticum aestivum L.)–corn (Zea mays L.)–millet (Panicum miliaceum L.)–fallow rotation. The section was EC_a–mapped (approximately 0- to 30-cm depth) and separated into four classes (ranges of EC_a). Georeferenced sites (n = 96) were selected within classes, sampled, and assayed for multiple soil parameters (0- to 7.5- and 0- to 30-cm depths) and residue mass. Within-field variance effectively estimated experimental error variance for several evaluated parameters, supporting its potential application as a surrogate for replication. Comparison of data from the field-scale experimental site to that from a nearby plot-scale experiment revealed that EC_a–classified within-field variance approximates plot-scale experimental error. We propose using this relationship for a systems approach to research wherein treatment differences and their standard errors, derived from EC_a–classified field-scale experiments, are used to roughly evaluate treatments and identify research questions for further study at the plot scale.

Abbreviations: EC, electrical conductivity • EC_a, apparent electrical conductivity • FICS, Farm-Scale Intensive Cropping Study • MS, mean square • OM, organic matter • SDAMP, Sustainable Dryland Agroecosystem Management Project

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