Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture

Sudduth, K. A.; Drummond, S. T.; Kitchen, Newell R.

doi:10.1016/s0168-1699(00)00185-x

Cited by 313 publications

(193 citation statements)

References 25 publications

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“…This yielded seven predictive regression equations across the whole soil profile (Table 1). Readers are referred to Sudduth et al (2001), (2005), Harvey and Morgan (2009) and Herrero et al (2011) for detailed descriptions of similar approaches.…”

Section: Laboratory Analysismentioning

confidence: 99%

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Yao

Yang

Shao

2012

Environ Monit Assess

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Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC e 4-8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.

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Section: Laboratory Analysismentioning

confidence: 99%

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Yao

Yang

Shao

2012

Environ Monit Assess

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“…The EMI technique can measure apparent soil electrical conductivity (ECa) by inducing an electrical current in the soil [7]. This method provides a relatively accurate, non-invasive, fast and inexpensive measurement of ECa [8][9][10]. Traditional soil sampling and EMI techniques together can provide better understanding of spatial variability in soils [1,11].…”

Section: Introductionmentioning

confidence: 99%

Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

2016

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Use of electromagnetic induction (EMI) sensors along with geospatial modeling provide a better opportunity for understanding spatial distribution of soil properties and crop yields on a landscape level and to map site-specific management zones. The first objective of this research was to evaluate the relationship of crop yields, soil properties and apparent electrical conductivity (ECa) at different topographic positions (shoulder, backslope, and deposition slope). The second objective was to examine whether the correlation of ECa with soil properties and crop yields on a watershed scale can be improved by considering topography in modeling ECa and soil properties compared to a whole field scale with no topographic separation. This study was conducted in two headwater agricultural watersheds in southern Illinois, USA. The experimental design consisted of three basins per watershed and each basin was divided into three topographic positions (shoulder, backslope and deposition) using the Slope Position Classification model in ESRI ArcMap. A combine harvester equipped with a GPS-based recording system was used for yield monitoring and mapping from 2012 to 2015. Soil samples were taken at depths from 0-15 cm and 15-30 cm from 54 locations in the two watersheds in fall 2015 and analyzed for physical and chemical properties. The ECa was measured using EMI device, EM38-MK2, which provides four dipole readings ECa-H-0.5, ECa-H-1, ECa-V-0.5, and ECa-V-1. Soybean and corn yields at depositional position were 38% and 62% lower than the shoulder position in 2014 and 2015, respectively. Soil pH, total carbon (TC), total nitrogen (TN), Mehlich-3 Phosphorus (P), Bray-1 P and ECa at depositional positions were significantly higher compared to shoulder positions. Corn and soybeans yields were weakly to moderately (<±0.75) correlated with ECa. At the deposition position at the 0-15 cm depth ECa-H-0.5 was weakly correlated (r < ±0.50) with soil pH and was moderately correlated (r = ±0.50-±0.75) with organic matter (OM), calcium (Ca) and sulfur (S). Slope variation from 1%-20% at the research site had a strong influence on soil properties at watershed scale. When data from all topographic positions were combined together in all basins spatial interpolation between Mehlich-3 P and ECa-H-0.5 resulted in a larger cross validation RMSE compared to individual shoulder and backslope positions. Results demonstrated that topographic position should be considered while making correlations of ECa with soil properties. Methods of delineating topography positions presented in this paper can easily be replicated on other fields with similar landscape characteristics and EMI sensor based survey techniques can certainly improve and help in making detailed prediction maps of soil properties.

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“…El autor recalca en que el consumo de energía de este tipo de sensores debe ser el mínimo posible para aumentar la vida útil de la red y prolongar la duración de las baterías. Sudduth et al [19] presentan resultados de investigación acerca del uso de sensores de inducción electromagnética para la obtención de la conductividad eléctrica del suelo. Los autores presentan posibles errores de medición al momento de utilizar dichos sensores y la correspondiente salinidad, humedad, profundidad y arcilla del suelo estudiado, y proponen técnicas de calibración para la medición de estas variables en cultivos.…”

Section: Sensores Remotos Y Arquitecturas De Redunclassified

Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión

Sarasti

Ramírez

2016

Rev. ing. univ. Medellín

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ResumenLos cultivos de caña de azúcar son una de las principales actividades económicas en Colombia; por ende, son esenciales para el desarrollo agrícola del país. Además, las tecnologías de la información y las comunicaciones (TIC) se han empezado a utilizar e implementar en todo el ciclo de vida del cultivo. Consecuentemente, las TIC son importantes al momento de definir sistemas basados en Agricultura de Precisión (AP), capaces de incrementar el rendimiento del cultivo y optimizar el uso de recursos económicos como fertilizantes, agua y pesticidas, entre otras funciones. Este artículo presenta una revisión acerca de sistemas de información basados en AP y aplicables a cultivos de caña de azúcar, haciendo énfasis en las tecnologías utilizadas, la gestión de datos y sus arquitecturas. Además, se presenta la propuesta de los autores: un sistema de información integral de tres capas basado en AP, capaz de facilitar la optimización en distintas etapas del ciclo de vida de la caña de azúcar. El artículo concluye describiendo el trabajo futuro y el desarrollo de la implementación del sistema propuesto.

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Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture

Cited by 313 publications

References 25 publications

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

Sistemas de Información enfocados en tecnologías de agricultura de precisión y aplicables a la caña de azúcar, una revisión

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