Soil sampling survey for agriculture

The basic steps of a field-scale ECa survey for characterizing spatial variability include (1) ECa survey design, (2) geo-referenced ECa data collection, (3) soil sample design based on geo-referenced ECa data, (4) soil sample collection, (5) physicochemical analysis of pertinent soil properties, (6) stochastic or deterministic calibration of ECa to soil properties, (7) determination of the soil properties influencing the ECa measurements at the study site, and (8) GIS development. Details on ECa-directed soil sampling protocols are presented by Corwin and Lesch (2005b, 2005c). Outlined protocols are provided in Table 4.2. Of the eight basic steps, ECa-directed soil sample design, stochastic or deterministic calibration of ECa, and determination of the soil properties influencing the geospatial ECa measurements are the least understood and yet are crucial for correctly understanding and interpreting spatial ECa data. Ideally, efforts must be directed toward mapping ECa when the soil property of interest is expected to have its greatest influence on ECa values. This maximizes the likelihood of inferring the spatial patterns of the soil property of interest from the ECa map. For instance, the effect of texture (or clay content) on ECa is more pronounced at higher water contents (Dalgaard et al., 2001), suggesting ECa field mapping when the soil is wet rather than dry.

4.3.1 eca-directed Soil Sample Design

An ECa survey of a field is most often conducted with either mobile ER or EMI equipment that has been coupled to a GPS. Depending on the level of detail desired, from 100 to several thousand spatial measurements of ECa are taken generally in regularly spaced traverses across the field of interest. The use of mobile EMI equipment has one slight advantage over the use of mobile ER equipment due to the fact that EMI is noninvasive, which is the ability to take measurements on dry and stony soils.

Once a geo-referenced ECa survey is conducted, the data are used to establish the locations of the soil core sample sites for (1) calibration of ECa to a correlated soil sample property (e.g., salinity, water content, and clay content) and (2) delineation of the spatial distribution of soil properties correlated to ECa within the field surveyed. Currently, two different sampling schemes are used to establish the locations where soil cores are to be taken: design-based and model-based sampling schemes. Design-based sampling schemes have historically been the most commonly used and, hence, are more familiar to most research scientists. Design-based methods include simple random sampling, stratified random sampling, multistage sampling, cluster sampling, and network sampling schemes. The use of unsupervised classification by Fraisse et al. (2001) and Johnson et al. (2001) is an example of design-based sampling. Model-based sampling schemes are less common. Specific model-based sampling approaches that have direct application to agricultural and environmental survey work are described by McBratney and Webster (1983), Russo (1984), and Lesch et al. (1995a, 1995b, 2005).

The sampling approach introduced by Lesch et al. (1995a, 1995b, 2005) is specifically designed for use with ground-based soil ECa data. This sampling approach attempts to optimize the estimation of a regression model (i.e., minimize the mean square prediction error produced by the calibration function), while simultaneously insuring that the independent regression model residual error assumption remains approximately valid. This, in turn, allows an ordinary regression model to be used to predict soil property levels at all remaining (i.e., nonsampled) conductivity survey sites.

There are two main advantages to the response-surface approach. First, a substantial reduction in the number of samples required for effectively estimating a calibration function can be achieved, in comparison to more traditional design-based sampling schemes. Second, this approach lends itself naturally to the analysis of remotely sensed ECa data. Many types of ground-, airborne-, and satellite-based remotely sensed data are often collected specifically because one expects this data

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