Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/20/2003
Publication Date: 3/1/2003
Citation: JOHNSON, C.J., ESKRIDGE, K.M., WIENHOLD, B.J., DORAN, J.W., PETERSON, G.A., BUCHLEITER, G.W. USING APPARENT ELECTRICAL CONDUCTIVITY CLASSIFCATION AND WITHIN-FIELD VARIABILITY TO DESIGN FIELD-SCALE RESEARCH. AGRONOMY JOURNAL 95:602-613. 2003. Interpretive Summary: Prior to adopting new practices, farmers require evidence of odds that favor success, where success is measured in terms of profit, yield, and soil conservation effects. For many this evidence must be in the form of experiments conducted under farm-scale conditions similar to those they encounter on their own farms. However, because traditional statistical methods for scientifically evaluating experiments are often unfeasible in experiments of this size, new approaches must be developed. An on-farm, farm-scale experiment (610 ac) comprised of eight fields planted to four different crops, was mapped for electrical conductivity (EC) using a commercially available EC meter. Maps from each field were separated into four classes (ranges) of EC from which soil and surface residue samples were taken for testing. We found that EC classes can be used to distinguish differences in most soil characteristics thereby providing a way to compare different treatments statistically. Classes of EC can also be used as a basis for comparing statistical relationships in farm-scale experiments to comparable experiments conducted in traditional experiment station- sized plots. These findings may support farm-scale research and farmer interest by promoting field designs and methods for data analysis that provide an acceptable degree of experimental control at the farm-scale.
Technical Abstract: Agronomic researchers are held increasingly accountable for research directions and outcomes relevant to the producer. Participatory research, where farmers contribute to long-term research agendas and assume leadership roles in the identification, design, and management of on-farm farm-scale research supports this aim. However, because experimental replication is often unfeasible at this level of scale, alternative methods are required for estimating experimental error. We use mean-square error comparisons to evaluate: (1) farm-scale within- field variability as an estimate of experimental error (in lieu of replication) and (2) classified within-field variability, derived from apparent electrical conductivity (ECa), as an estimate of 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 site was ECa mapped (approximately 0-30 cm depth), separated into four ECa classes (ranges), and sampled (0-7.5 and 7.5-30 cm depths) from three geo- referenced soil-sampling sites per class (n=96). Several soil parameters and surface residue mass were appraised. Within-field variance is an effective measure of experimental error for most soil attributes indicating that their experimental error can be based on one replicate per treatment. Plot-scale experimental error can be approximated using ECa classified within-field variance. These relationships may be used to reverse research direction wherein farm- scale experiments serve to identify questions for more plot scale study