FIGURE 20.3 (a) Map of research plots and subplots in the irrigated Montana field, (b) mean ECa and yield (with error bars), and (c) coefficient of variation (CV) for each of the nine research plots across the Montana field.
First, plots M6 to M9 are the most productive (highest yield) but have the lowest ECa values. In other words, soil ECa and yield correlation is not necessarily positive. Generally speaking, a yield map represents a crop response surface that integrates the effect of a host of influences including soil, water, nutrients, pests, climate, and management. Because of that, a yield response map could be specific to a growing season and may change over time. On the other hand, a soil ECa map is strictly a soil phenomenon, a reflection of highly complex interactions of soil physical and chemical properties of texture (for instance, clay content), cation exchange capacity, organic matter, and soil water. The main distinction between a soil ECa and a yield map is that the former is known to be temporally stable (Farahani and Buchleiter, 2004), providing a useful base map for multiple years.
Second, Figure 20.3c shows that within-plot variability was as pronounced as within-field and within-farmland variability. Figure 20.3 is an attempt to illustrate an example of the usefulness of baseline ECa and yield information. This information can aid researchers in placing their particular study across a desirable level of soil and productivity variance. As shown in Figure 20.3c, variability ranged from less than 20 percent CV to about 60 percent in Montana plots. Choice of plots with least soil variance is offered by plots M6 to M9. For small plot research, the baseline information may be further refined to infer variability within the subplots.
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