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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #126600
Title: ANALYSIS OF SOIL, TOPOGRAPHIC, AND YIELD DATA USING NESTED VARIOGRAMS

Author
item CHUNG, SUN-OK - UNIV OF MO
item Sudduth, Kenneth - Ken
item Drummond, Scott
item Kitchen, Newell

Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Proceedings
Publication Acceptance Date: 11/7/2001
Publication Date: 11/7/2001
Citation: Chung, S., Sudduth, K.A., Drummond, S.T., Kitchen, N.R. 2001. Analysis of soil, topographic, and yield data using nested variograms. In: Proceedings Third International Conference on Geospatial Information in Agriculture and Forestry. Ann Arbor, Michigan. CDROM.

Interpretive Summary: Precision farming relies on efficient and economical collection and interpretation of data describing the variations within cropped fields. Analysis of such data is most often done using geostatistical methods. Geostatistics is a branch of statistics where the analysis considers not only the value of a parameter (for example, soil pH) but also its location in relation to other data points. Standard geostatistical approaches do not always do a good job of representing complex data. Because of this, statisticians have developed a more complex approach, called the "nested variogram." In this study, we applied the nested variogram approach to better understand the variability present in two Missouri corn/soybean fields. Parameters studied included soil chemical properties, soil electrical conductivity, topography, and crop yield. We found that the nested variogram approach was not successful with soil chemical property data, possibly because the soil samples were too far apart. However, dense sensor-derived data (topography, soil electrical conductivity, and crop yield) were well-represented by the nested variogram approach. This allowed us to better understand how sensor data varied within a field and its potential causes. These research results will benefit scientists and data analysts who wish to more thoroughly examine precision farming datasets as a first step in developing management zones for site-specific field operations.

Technical Abstract: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. The structure of variability determines the required spatial intensity of data collection and is related to the delineation of management zones. In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity, soil chemical property, crop yield, and elevation data for two fields in central Missouri. Some, but not all, variables which were highly correlated or which were strongly expressed in the same principal component exhibited similar spatial ranges when fit with a single variogram model. Nested models generally yielded a better fit than single models for sensor-based data, and multiple scales of spatial structure were apparent. Gaussian- spherical or Gaussian-exponential nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components.