Applying Soil Electrical Conductivity Technology to Precision Agriculture

2015 ◽  
pp. 1089-1100 ◽  
Author(s):  
Eric D. Lund ◽  
P.E. Colin ◽  
D. Christy ◽  
Paul E. Drummond
Keyword(s):  
Electrical Conductivity ◽  
Precision Agriculture ◽  
Soil Electrical Conductivity

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

scholarly journals Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology

2001 ◽  
Vol 1 ◽  
pp. 767-776 ◽  
Author(s):  
E.D. Lund ◽  
M.C. Wolcott ◽  
G.P. Hanson
Keyword(s):  
Electrical Conductivity ◽  
Case Studies ◽  
Crop Yield ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Management Practices ◽  
N Availability ◽  
Soil Electrical Conductivity ◽  
Variable Yield ◽  
Wide Range

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

scholarly journals SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1

Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 1-15
Author(s):  
Iug Lopes ◽  
Abelardo A. A. Montenegro
Keyword(s):  
Electrical Conductivity ◽  
Soil Moisture ◽  
Precision Agriculture ◽  
Spatial Dependence ◽  
Spatial Information ◽  
Gravimetric Method ◽  
Research Unit ◽  
Soil Electrical Conductivity ◽  
Agricultural Engineering ◽  
Space Dependence

SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1     IUG LOPES2 E ABELARDO ANTONIO DE ASSUNÇÃO MONTENEGRO3   1Paper extracted from the doctoral thesis of the first author. 2Department of Agronomy, Instituto Federal de Educação, Ciência e Tecnologia Baiano, BR 349, Km 14 - Zona Rural, CEP: 47600-000, Bom Jesus da Lapa - BA, Brazil; [email protected] - ORCID: 0000-0003-0592-4774. 3Department of Agricultural Engineering, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900, Recife - PE, Brazil; [email protected] -ORCID: 0000-0002-5746-8574.     1 ABSTRACT   Spatial information on soil characteristics is essential to proper decision-making regarding to the environment and land use management. The objective of this work was the investigation of cross - variance between soil moisture and apparent soil electrical conductivity (CEa), under different land uses in an alluvial valley of Pernambuco. The study was developed at the Advanced Research Unit of Universidade Federal Rural de Pernambuco (UFRPE), located at  Brígida River Basin, municipality of Panamirim-PE. Soil samples were collected in a regular mesh of 20 x 10 m, for soil moisture by gravimetric method and, following a regular 10 x 10 m mesh, CEa measurements were performed using EM38® device. Cross-semivariograms were assessed and spatial dependence was verified by geostatistical procedures. It was verified in geostatistical procedures  low variation for soil moisture and intermediate variation for CEa. The use of geostatistics allowed identification of covariance between soil moisture and ECa, as well as spatial dependence for both variables, for agricultural areas. It was verified that soil moisture, even at levels close to residual, constitutes a relevant secondary component for increasing soil salinity maps precision, and hence to precision agriculture.   Keywords: geostatistics, semi-arid, precision agriculture     LOPES, I. E MONTENEGRO, A. A. DE A. DEPENDÊNCIA ESPACIAL DA UMIDADE DO SOLO E CONDUTIVIDADE ELÉTRICA EM REGIÃO ALUVIAL     2 RESUMO   Informações espaciais sobre as características do solo são essenciais para uma tomada de decisão adequada em relação ao meio ambiente e ao gerenciamento do uso do solo. O objetivo deste trabalho foi investigar a variância cruzada entre a umidade do solo e a condutividade elétrica aparente do solo (CEa), sob diferentes usos do solo em um vale aluvial de Pernambuco. O estudo foi desenvolvido na Unidade de Pesquisa Avançada da Universidade Federal Rural de Pernambuco (UFRPE), localizada na bacia do rio Brígida, município de Panamirim-PE. As amostras de solo foram coletadas em uma malha regular de 20 x 10 m, para a umidade do solo pelo método gravimétrico e, seguindo uma malha regular de 10 x 10 m, as medidas de CEa foram realizadas usando o dispositivo EM38®. Os semivariogramas cruzados foram avaliados e a dependência espacial foi verificada por procedimentos geoestatísticos. Verificou-se procedimentos geoestatísticos, uma baixa variação da umidade do solo e variação intermediária para CEa. O uso da geoestatística permitiu identificar a covariância entre a umidade do solo e o CEa, bem como a dependência espacial para ambas as variáveis, para as áreas agrícolas. Verificou-se que a umidade do solo, mesmo em níveis próximos ao residual, constitui um componente secundário relevante para o aumento da precisão do mapeamento da salinidade do solo e, consequentemente, para a agricultura de precisão.   Palavras-chave: geoestatística, semiárido, agricultura de precisão

Development and Testing of a Low-Cost Portable Apparent Soil Electrical Conductivity Sensor Using a Beaglebone Black

2020 ◽  
Vol 36 (3) ◽  
pp. 341-355
Author(s):  
Daniel M. Queiroz ◽  
Emanoel D. T. S. Sousa ◽  
Won Suk Lee ◽  
John K. Schueller
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Low Cost ◽  
Satellite System ◽  
Mountainous Area ◽  
Soil Electrical Conductivity ◽  
Resistivity Method ◽  
Conductivity Sensor ◽  
Global Navigation Satellite

Abstract.The adoption of apparent soil electrical conductivity (soil ECa) sensors has increased in precision agricultural systems, especially in systems pulled by vehicles. This work developed a portable soil sensor for measuring soil ECa that could be used without vehicles in mountainous areas and small farms. The developed system was based on the electrical resistivity method. The system measured the electrical conductivity by applying a square wave signal at frequencies defined by the user. The acquired data were georeferenced using a low-cost global navigation satellite system (GNSS) receiver. The sensor system was developed using a BeagleBone Black, a low-cost single-board computer. A user interface was developed in C++, and a touch screen with a resolution of 800×480 pixels was used to display the results. This interface performed statistical analysis, and the results were used to guide the user to identify more field locations to be sampled to increase mapping accuracy. The system was tested in a coffee plantation located in a mountainous area and in a sugarcane plantation in Minas Gerais, Brazil. The system worked well in mapping the soil ECa. The apparent soil electrical conductivities measured using frequencies of 10, 20, 30, and 40 Hz were highly correlated. In the sugarcane field that had more variation in soil texture, a greater number of soil properties presented a significant correlation with the soil ECa. Keywords: Electrical conductivity, Geostatistics, Precision agriculture, Soil properties, Soil sensing, Spatial variability.

Efficacy of Variable Rate Soil-applied Herbicides Based on Soil Electrical Conductivity and Organic Matter Differences

2017 ◽  
Vol 8 (2) ◽  
pp. 277-282
Author(s):  
G. J. Gundy ◽  
J. A. Dille ◽  
A. R. Asebedo
Keyword(s):  
Electrical Conductivity ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Weed Control ◽  
Precision Agriculture ◽  
Visual Assessment ◽  
Grain Sorghum ◽  
Variable Rate ◽  
Soil Electrical Conductivity ◽  
Flat Rate

Soil application of herbicides for preemergence (PRE) weed control in grain sorghum is vital to control weeds. Efficacy of soil-applied herbicides is impacted by herbicide adsorption which is influenced by soil organic matter (SOM) and texture. With precision agriculture technologies, variable rate applications (VRA) can be utilized to maximize herbicide effectiveness. In 2016, algorithms were developed for two locations to use VRA of two tank mixed herbicides based on SOM and soil electrical conductivity (EC) collected by a Veris MSP3 system. Drone imagery provided an effective way to evaluate the efficacy of herbicide applications along with visual assessment. VRA applications of herbicide tank mixes provided equal weed control compared to flat rate applications.

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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