Results And Discussion

Although soil heterogeneity arises from interactions among the soil-forming factors (Jenny, 1941), it is confounded by management such as tillage and fertilization (Bouma and Finke, 1993). At the FICS, it is interesting to note that mapped ECa reflects both historical and recent management. The V-shaped patterns in the four corner fields of the section are consistent with the plow path followed in the 1930s when the site was farmed as two half sections (Figure 18.3 and Figure 18.4). In addition, varying mean levels of conductivity, for the eight fields in the study, distinguish among different crops grown the year before ECa mapping.

18.3.2 Phase ii: ECA versus Soil Edaphic Properties (Johnson et al., 2001)

Sampling-site scale analyses of surface residue and nineteen soil physical and chemical parameters (0 to 7.5 and 0 to 30 cm) were compared with ECa maps (0 to 30 cm) to "ground-truth" ECa and ECa zones. Except for NO3- and NH4-N, which exhibited a narrow range of variability across the site, all residue and soil parameters were significantly different among ECa productivity zones at one or both depths of analysis (0 to 7.5 and 0 to 30 cm). Surface residue mass and soil properties related to yield potential were negatively correlated with ECa, and properties associated with soil erosion were positively correlated (Table 18.1). These relationships are a function of the calcareous soils at the FICS, where clay content and CaCO3 salts dominate measured ECa (0 to 30 cm). The loss of top-soil in less-productive eroded areas of each field has exposed underlying clay and CaCO3 horizons, which increase ECa. These horizons are also characterized by associated elevations in bulk density and pH, soil properties positively correlated with ECa and negatively correlated with productivity.

At the FICS, strong correlations were not found between ECa and individual soil properties at point sources. This is because ECa integrates multiple soil properties, wherein changes in one may be buffered by corresponding changes in another. In this semiarid environment, ECa is most useful for delimiting overall soil productivity and for defining distinct zones of within-field yield potential. Therefore, soil and residue sampling based upon ECa productivity zones appears to be a useful basis for (1) zone soil sampling, (2) tracking the temporal impact of farm management on soil productivity, and (3) assessing soil parameters to calculate fertilizer and herbicide inputs in site-specific management.

18.3.3 Phase iii: ECA versus Crop Yields (Johnson et al., 2003b)

Field-scale measures of crop yield were evaluated for significant relationships to both ECa and the sampling-site scale soil properties integrated by ECa (Phase II) using two years of geo-referenced yield maps for wheat and corn. Winter wheat yields were negatively correlated with ECa (0 to 30 cm) and positively correlated with soil properties, indicative of production potential. Correlations between ECa and yield have been corroborated by investigators in other regions (Corwin et al., 2003b; Kitchen et al., 2003). A wheat-yield response curve for 1999, a high-yielding year, revealed a boundary line of maximum yield that decreased with increasing ECa (Figure 18.5), an effective basis for identifying yield goals.

Although crop yield maps provide the most realistic picture of yield heterogeneity, they integrate all factors driving crop yields, including weather variations. Productivity zones, based on ECa, distinguish only the soil-based factors underlying yield heterogeneity that can be managed. These zones offer a basis for three key aspects of site-specific management: (1) yield goal determination,

(2) soil sampling to assess residual nutrients and soil attributes affecting herbicide efficacy, and

(3) prescription maps for metering fertilizer, pesticide, and seed inputs.

No consistent associations were found between ECa (0 to 30 cm) and corn yields probably due to high drought stress in corn during the 2-year study. However, both wheat and corn yields were positively correlated with ECa (0 to 90 cm), a reversal of the negative relationship between ECa (0 to 30 cm) and wheat yield. This is likely due to differences in the impact of soil clay content on

FIGURE 18.5 Scatter plot of 1999 winter-wheat yield as a function of apparent soil electrical conductivity (ECa) measured at approximately 0 to 30 cm soil depth. The red line is a "boundary line" of maximum potential yield defined as yield points falling at the ninetieth percentile of yield frequency for each 0.01 increment of ECa.

measured ECa with depth. Although clay content controls ECa (0 to 30 cm), soil water and salts (NO3 and NH4) may drive ECa (0 to 90 cm) and yield. Hence, the positive correlation.

18.3.4 Phase iv: EcA versus Microbial-scale Measurements (Johnson et al., 2004)

Soil edaphic and biological (community composition, diversity, and activity) variability occurs across multiple levels of scale. It is useful to evaluate this variability, as it relates to sustainable management within the context of a farm. This can be conceptualized as originating with the microbe (micron scale) and continuing upward to sampling site, within-field, field, and farm levels (multiple-ha scale) (Figure 18.1). Microbial-scale analyses of VAM fungi were evaluated for significant correlations with ECa, and with sampling-site scale soil properties and field-scale yield data integrated by ECa (Phases II and III). Concentrations of glomalin were negatively correlated with ECa and positively correlated with soil quality and winter wheat yields, across crop treatments. The C16 bio-marker and wet aggregate stability were different among crop treatments as fallow < wheat < corn < millet and were negatively correlated with ECa in the fallow treatments (effect of crop removed). Due to significant partitioning of these microbial-scale measures among ECa productivity zones, they can be linked to both soil chemical and physical characteristics and crop yields. Thus, ECa zones offer a pivotal point of reference through which microbial-, sampling-site-, and field-scale data can be related.

18.3.5 Phase v: Eca for Experimental Design and Analysis (Johnson et al., 2003a, 2005)

In classic experimentation, small plots are arranged in a randomized complete block design where blocks serve to increase precision by reducing experimental error due to soil heterogeneity. Blocks are placed in areas of similar production potential that have been identified by analyzing soil samples for yield-significant properties. Topography, soil fertility, and soil series exemplify traditional blocking factors, and ECa productivity zones were examined for this purpose at the FICS. Figure 18.6 illustrates the relationship between ECa productivity zones and plot-scale blocking. The 32 ha field shown on the left is separated into four classes of ECa (a), three of which form blocks in the traditional plot-scale experiment set in a randomized complete block design (b). Because blocks are homogeneous, plots need not be adjacent but could be placed anywhere in field (a) within assigned blocks. Thus, the entire 32 ha field can be conceptualized as an enlarged version of the plot-scale experiment, where variance across ECa-delineated productivity zones is equivalent to experimental error in the plot-scale experiment.

To test this, soil and residue data from the FICS were compared with those taken from a nearby plot-scale experiment (Peterson et al., 1993). For each soil and residue measurement, within-field

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