Characterizing soil spatial variability with apparent soil electrical conductivity

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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CHARACTERIZATION OF SPATIAL VARIABILITY OF SOIL ELECTRICAL CONDUCTIVITY AND CONE INDEX USING COULTER AND PENETROMETER-TYPE SENSORS

Soil Science ◽

10.1097/01.ss.0000228037.80270.1d ◽

2006 ◽

Vol 171 (8) ◽

pp. 627-637 ◽

Cited By ~ 9

Author(s):

Jay David Jabro ◽

Robert G. Evans ◽

Yunseup Kim ◽

William B. Stevens ◽

William M. Iversen

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Soil Electrical Conductivity ◽

Cone Index

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Integration of Soil Electrical Conductivity and Indices Obtained through Satellite Imagery for Differential Management of Pasture Fertilization

AgriEngineering ◽

10.3390/agriengineering1040041 ◽

2019 ◽

Vol 1 (4) ◽

pp. 567-585 ◽

Cited By ~ 2

Author(s):

João Serrano ◽

Shakib Shahidian ◽

José Marques da Silva ◽

Luís Paixão ◽

José Calado ◽

...

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Satellite Imagery ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Stability ◽

Soil Characteristics ◽

Low Fertility ◽

Soil Electrical Conductivity ◽

Potential Productivity

Dryland pastures in the Alentejo region, located in the south of Portugal, normally occupy soils that have low fertility but, simultaneously, important spatial variability. Rational application of fertilizers requires knowledge of spatial variability of soil characteristics and crop response, which reinforces the interest of technologies that facilitates the identification of homogeneous management zones (HMZ). In this work, a pasture field of about 25 ha, integrated in the Montado mixed ecosystem (agro-silvo-pastoral), was monitored. Surveys of apparent soil electrical conductivity (ECa) were carried out in November 2017 and October 2018 with a Veris 2000 XA contact sensor. A total of 24 sampling points (30 × 30 m) were established in tree-free zones to allow readings of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). Historical time series of these indices were obtained from satellite imagery (Sentinel-2) in winter and spring 2017 and 2018. Three zones with different potential productivity were defined based on the results obtained in terms of spatial variability and temporal stability of the measured parameters. These are the basis for the elaboration of differentiated prescription maps of fertilizers with variable application rate technology, taking into account the variability of soil characteristics and pasture development, contributing to the sustainability of this ecosystem.

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Comparison of Sampling Strategies for Characterizing Spatial Variability with Apparent Soil Electrical Conductivity Directed Soil Sampling

Journal of Environmental and Engineering Geophysics ◽

10.2113/jeeg15.3.147 ◽

2010 ◽

Vol 15 (3) ◽

pp. 147-162 ◽

Cited By ~ 19

Author(s):

D. L. Corwin ◽

S. M. Lesch ◽

E. Segal ◽

T. H. Skaggs ◽

S. A. Bradford

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Soil Sampling ◽

Sampling Strategies ◽

Soil Electrical Conductivity

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Soil Electrical Conductivity (Ec) Mapping Using Real-Time Soil Sensor

Jurnal Ilmiah Teknologi Pertanian Agrotechno ◽

10.24843/jitpa.2020.v05.i01.p02 ◽

2020 ◽

Vol 5 (1) ◽

pp. 9

Author(s):

Ni Nyoman Sulastri ◽

Sakae Shibusawa ◽

Masakazu Kodaira

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Real Time ◽

Near Infrared ◽

Spectroscopic Method ◽

Soil Electrical Conductivity ◽

Capacitance Sensor ◽

Capacitance Method ◽

Spectroscopic Sensors ◽

Commercial Applications

The development of soil electrical conductivity (EC) recently to generate soil EC spatial variability map is increasingly attractive because of its application for site-specific crop management. Several commercial applications have been developed and marketed. The purpose of this paper is to compare soil EC spatial variability map produced by capacitance and spectroscopic sensors. The two sensors (capacitance and spectroscopic sensors) was mounted in a Real-time soil sensor. The spectrophotometer was used that has linearly arrayed photodiodes of 256 channels for 400 to 900 nm for visible (Vis) lights and 128 channels for 900 to 1700 nm for near infrared (NIR) lights. For two capacitance sensors were embedded in soil penetrator (front/ECF and side/ ECS), which its tip with a flat plane edge to make uniform soil cuts and the soil flattener behind produced a uniform surface texture. It was found that spectroscopic method performed better compared to capacitance sensor. Using linear regression, the spectroscopic method has shown a correlation of 0.75 with soil EC generated from laboratory analysis (ECL). While, the capacitance method shows significant different compared to soil ECL. The primary cause of the extreme differences between ECL, ECF and ECS values is likely related to the calibration of the capacitance sensor itself.

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