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Variability Structure of On-the-go Soil Strength Sensor Data

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  051039,  2005 ASAE Annual Meeting . (doi: 10.13031/2013.19882) @2005
Authors:   Sun-Ok Chung, Kenneth A. Sudduth, Jinglu Tan
Keywords:   Soil Sensor, Soil Strength, Compaction, Variability, Variogram Analysis, Spectral Analysis

As agricultural machinery has become larger and tillage practices have changed in recent decades, compaction as a result of wheel traffic and tillage has caused increasing concern. If strategies to manage compaction, such as deep tillage, could be applied only where needed, economic and environmental benefits would result. For such site-specific compaction management to occur, compacted areas within fields must be efficiently sensed and mapped. We previously developed an on-the-go soil strength profile sensor (SSPS) for this purpose. The SSPS measures within-field variability in soil strength at five soil depths up to 50 cm. Determining the spatial structure of SSPS data is useful in site-specific field management since the variability structure determines the required spatial intensity of data collection and is related to the delineation of compaction management zones. In this paper, soil bin and field data were analyzed by spectral and variogram analysis techniques to determine the variability structure of the SSPS data, and to investigate causes and implications of this variability. In the soil bin, we observed a repeating pattern due to soil fracture with an approximate 12- to 19-cm period, especially at the 10-cm depth. In the field, variograms at 10- and 20-cm depths were similar between the two sites, while sill variances were higher at 30- and 40-cm depths and lower at the 50-cm depth in sandy soils than in finer-textured soils. Spatial periods between 3 and 15 m were predominant, due to soil strength variability within transects. These findings will facilitate interpretation of soil strength data and enhance application of the SSPS.

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