Using electrical conductivity classification and within-field variability to design field-scale research

Cinthia K. Johnson, Kent M. Eskridge, Brian J. Wienhold, John W. Doran, Gary A. Peterson, Gerald W. Buchleiter

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

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 (ECa), 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 ECa-mapped (approximately 0- to 30-cm depth) and separated into four classes (ranges of ECa). 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 ECa-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 ECa-classified field-scale experiments, are used to roughly evaluate treatments and identify research questions for further study at the plot scale.

Original languageEnglish (US)
Pages (from-to)602-613
Number of pages12
JournalAgronomy Journal
Volume95
Issue number3
StatePublished - May 1 2003

Fingerprint

electrical conductivity
on-farm research
Panicum miliaceum
leadership
millets
research programs
fallow
no-tillage
winter wheat
agricultural land
Triticum aestivum
Zea mays
researchers
farms
corn
soil
methodology

ASJC Scopus subject areas

  • Agronomy and Crop Science

Cite this

Johnson, C. K., Eskridge, K. M., Wienhold, B. J., Doran, J. W., Peterson, G. A., & Buchleiter, G. W. (2003). Using electrical conductivity classification and within-field variability to design field-scale research. Agronomy Journal, 95(3), 602-613.

Using electrical conductivity classification and within-field variability to design field-scale research. / Johnson, Cinthia K.; Eskridge, Kent M.; Wienhold, Brian J.; Doran, John W.; Peterson, Gary A.; Buchleiter, Gerald W.

In: Agronomy Journal, Vol. 95, No. 3, 01.05.2003, p. 602-613.

Research output: Contribution to journalArticle

Johnson, CK, Eskridge, KM, Wienhold, BJ, Doran, JW, Peterson, GA & Buchleiter, GW 2003, 'Using electrical conductivity classification and within-field variability to design field-scale research', Agronomy Journal, vol. 95, no. 3, pp. 602-613.
Johnson CK, Eskridge KM, Wienhold BJ, Doran JW, Peterson GA, Buchleiter GW. Using electrical conductivity classification and within-field variability to design field-scale research. Agronomy Journal. 2003 May 1;95(3):602-613.
Johnson, Cinthia K. ; Eskridge, Kent M. ; Wienhold, Brian J. ; Doran, John W. ; Peterson, Gary A. ; Buchleiter, Gerald W. / Using electrical conductivity classification and within-field variability to design field-scale research. In: Agronomy Journal. 2003 ; Vol. 95, No. 3. pp. 602-613.
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