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

2010 ◽  
Vol 15 (3) ◽  
pp. 147-162 ◽  
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

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

Soil Science ◽  
2006 ◽  
Vol 171 (8) ◽  
pp. 627-637 ◽  
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

Identifying Soil Properties that Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity

2003 ◽  
Vol 95 (2) ◽  
pp. 352-364 ◽  
Author(s):  
D. L. Corwin ◽  
S. M. Lesch ◽  
P. J. Shouse ◽  
R. Soppe ◽  
J. E. Ayars
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Soil Sampling ◽  
Soil Electrical Conductivity ◽  
Cotton Yield

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

2019 ◽  
Vol 1 (4) ◽  
pp. 567-585 ◽  
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.

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

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.    

Soil Sampling Strategies for the Characterization of Spatial Variability Under Two Distinct Land Uses

2021 ◽  
pp. 1-24
Author(s):  
Sunshine A. De Caires ◽  
Mark N. Wuddivira ◽  
De Shorn E. Bramble ◽  
Melissa Atwell ◽  
Ronald Roopnarine ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Sampling ◽  
Sampling Strategies ◽  
Land Uses

scholarly journals Mapping of yield, economic return, soil electrical conductivity, and management zones of irrigated corn for silage

2018 ◽  
Vol 53 (12) ◽  
pp. 1289-1298 ◽  
Author(s):  
Alberto Carlos de Campos Bernardi ◽  
Oscar Tupy ◽  
Karoline Eduarda Lima Santos ◽  
Giulia Guillen Mazzuco ◽  
Giovana Maranhão Bettiol ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Precision Agriculture ◽  
Temporal Variability ◽  
Production Cost ◽  
Spatial And Temporal Variability ◽  
Corn Yield ◽  
Soil Electrical Conductivity ◽  
Economic Return ◽  
Management Zones

Abstract: The objective of this work was to evaluate the spatial and temporal variability of the dry matter yield of irrigated corn for silage, as well as its economic return. The study was conducted in an irrigated silage corn field of 18.9 ha in the municipality of São Carlos, in the state of São Paulo, Brazil. The spatial variability of the yield of three crop seasons, normalized yield indexes, production cost, profit, and soil electrical conductivity (EC) were modeled using semivariograms. Yield maps were obtained by kriging, and management zones were mapped based on average yield, normalized index, and EC. The results showed a structured spatial variability of corn yield, production cost, profit, and soil EC within the irrigated area. The adopted precision agriculture tools were useful to indicate zones of higher yield and economic return. The sequences of yield maps and the analysis of spatial and temporal variability allow the definition of management zones, and soil EC is positively related to corn yield.

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