Estimation of Soil Physico-chemical Properties by On-the-go Measurement of Soil Electrical Conductivity

For modern crop management practices, like precision farming, is crucial information about detailed spatial distribution of soil properties. A study was conducted to evaluate the on-the-go measurement of soil electrical conductivity for mapping of agronomical relevant soil properties. The experimental work was carried out on the eight fields of Rostenice a.s. farm enterprise, located in the South Moravia region of Czech Republic. The measurement of apparent electrical conductivity of soil was done by using CMD-1 and CMD-6L instruments (GF Instruments, Czech Republic) in 2013 (117 ha) and 2016 (359 ha). Soil properties were obtained by soil sampling in irregular grid with the density of 1 sample per 3 ha. Soil samples were taken from the depth of 30 cm and analyzed for soil texture (percentage of clay, silt and sand particles), content of available nutrients (P, K, Mg, Ca) and soil organic matter (SOM) content. The results of correlation analysis showed differences in main sensitivity of EMI to the soil properties across observed fields. Most frequent correlation was found in the percentage of clay particles smaller than 0.002 mm (r = 0.598). The correlation between EMI and nutrients content in soil and pH value was significant only for few fields. These results were obtained for individual fields, the aggregated evaluation showed lower relationships to EC. These outcomes showed, that rather than predictor of soil properties could be on-the-go measurement of soil EC used for identification of main zones within the fields at high spatial level

Estimation of Soil Physico-chemical Properties by On-the-go Measurement of Soil Electrical Conductivity

For modern crop management practices, like precision farming, is crucial information about detailed spatial distribution of soil properties. A study was conducted to evaluate the on-the-go measurement of soil electrical conductivity for mapping of agronomical relevant soil properties. The experimental work was carried out on the eight fields of Rostenice a.s. farm enterprise, located in the South Moravia region of Czech Republic. The measurement of apparent electrical conductivity of soil was done by using CMD-1 and CMD-6L instruments (GF Instruments, Czech Republic) in 2013 (117 ha) and 2016 (359 ha). Soil properties were obtained by soil sampling in irregular grid with the density of 1 sample per 3 ha. Soil samples were taken from the depth of 30 cm and analyzed for soil texture (percentage of clay, silt and sand particles), content of available nutrients (P, K, Mg, Ca) and soil organic matter (SOM) content. The results of correlation analysis showed differences in main sensitivity of EMI to the soil properties across observed fields. Most frequent correlation was found in the percentage of clay particles smaller than 0.002 mm (r = 0.598). The correlation between EMI and nutrients content in soil and pH value was significant only for few fields. These results were obtained for individual fields, the aggregated evaluation showed lower relationships to EC. These outcomes showed, that rather than predictor of soil properties could be on-the-go measurement of soil EC used for identification of main zones within the fields at high spatial level

Predictive modelling of grain-size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Author Posting. © The Authors, 2018. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 215 (2018): 460–473, doi:10.1093/gji/ggy152.In this work, we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine-learning techniques. Non-negative matrix factorization is used to determine grain-size end-members from sediment surface samples. Four end-members were found, which well represent the variety of sediments in the study area. A radial basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.This work was funded through DFG Research Center/Cluster of Excellence ‘The Ocean in the Earth System’ and was part of MARUM Research Area S

Predicción de contenido de arcilla superficial utilizando conductividad eléctrica aparente y esquemas de muestreo basados en modelos

La predicción espacial del contenido de arcillas (As) a escala de lote es requerida para la implementación de agricultura de precisión y modelos de simulación hidrológica. Sin embargo, la brecha de técnicas de cartografía que permitan establecer la heterogeneidad de As limita la capacidad para determinar su variabilidad. En este estudio, se utilizó cokriging ordinario, conductividad eléctrica aparente (CEa) como variable auxiliar y dos esquemas de muestreo basados en modelos (EBM) (Hipercubo latino condicionado (HCL) y fuzzy c-medias (FCM)) para predecir contenido de As superficial en un lote agrícola experimental de 25,18 ha. Los resultados soportan los supuestos que HCL y FCM capturan adecuadamente la distribución total de la CEa; y que As está cerradamente relacionado con CEa en condiciones del sudeste bonaerense. A partir de los resultados se determinó que (i) el tipo de EBM afecta la eficiencia de la interpolación para predecir As; (ii) una reducción considerable de muestras es posible cuando se aplica la metodología propuesta, logrando mapas precisos de As (R2>0,69); (iii), un conjunto de muestras de suelo independiente es lo más adecuado para validar la metodología propuesta; y (iv) la Interpolación espacial a partir de CEa y HCL proporcionó una leve mejora en la predicción espacial de As (R2= 0,78, RMSE=1,50%) que interpolación espacial a partir de CEa y FCM (R2= 0,69, RMSE=1,69%). La metodología propuesta proporcionó una mejora significativa de información de As en comparación con los costos y el tiempo que demandan las técnicas de cartografía convencional. Además, la metodología propuesta es sencilla de replicar para otros lotes o condiciones edáficas, lo cual puede ser significativo para la implementación de manejo sitio específico de cultivos y para modelos de simulación hidrológica.Spatial prediction of clay content at field scale is needed to implement precision agriculture and hydrological models. However, the lack of techniques that can detect clay content heterogeneity limits the ability to determine its variability. In this study, we tested the use of geostatistical interpolation (ordinary cokriging), apparent electrical conductivity (CEa) as auxiliary information and two model-based soil sampling schemes (EBM) (conditioned Latin hypercube (HCL) and fuzzy K-means (FCM) to predict clay content in an 25.18 ha agricultural field. Results support the underlying assumptions that both HCL and FCM capture adequately the full distribution of CEa; and that clay content was closely related to the CEa. Also, suggested that (i) the type of EBM affects the clay prediction model efficiency; (ii) a considerable soil sample reduction is possible when the proposed methodology is applied; (iii) an independent data set is most adequate to validate the proposed methodology; and (iv) the geostatistical interpolation based on CEa and HCL provided a slight improvement in the clay content prediction (R2 = 0.75, RMSE = 1.50%) compared to the geostatistical interpolation based on CEa and FCM (R2 = 0.73, RMSE = 1.69%). The proposed methodology provided a significant improvement of information on clay content with respect to soil survey techniques and is easy to replicate in other farm fields. Therefore, it can be significant to implement these findings in site-specific managements or hydrological simulations.Fil: Castro Franco, Mauricio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Díaz, Hernán Julio. Universidad Nacional de Mar del Plata. Facultad de Ciencias AgrariasFil: Quiroz Londoño, Mauricio. Universidad Nacional de Mar del Plata. Instituto de Geología de Costas y del Cuaternario; ArgentinaFil: Ciccore, Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentin

Apparent electrical conductivity mapping in managed podzols using multi-coil and multi-frequency EMI sensor measurements

The research focused on utilizing apparent electrical conductivity (ECa) survey protocols in characterizing the spatial and temporal variability of soil physical and hydraulic properties in Western Newfoundland, Canada. In this study, two different non-invasive multi-coil and multi-frequency EMI sensors; CMD Mini-explorer and GEM-2, respectively were used to collect ECa data under different nutrient management systems at Pynn’s Brook Research Station, Pasadena. Results showed that due to the differences in investigation depths of the two EMI sensors, the linear regression models generated for SMC using the CMD Mini-explorer were statistically significant with the highest R² = 0.79 and the lowest RMSE = 0.015 m³ m⁻³ and not significant for GEM-2 with the lowest R² = 0.17 and RMSE = 0.045 m³ m⁻³. Furthermore, there is a significant relationship between the ECa mean relative differences (MRD) versus SMC MRD (R² = 0.33 to 0.70) for both multi-Coil and multi-Frequency sensors. In addition, the spatial variability of the ECa predicted soil properties are relatively consistent with lower variability compared to the measured soil properties. Conclusively, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for soil physical and hydraulic properties at the field scale

EXPLORING SPATIAL AND TEMPORAL VARIABILITY OF SOIL AND CROP PROCESSES FOR IRRIGATION MANAGEMENT

Irrigation needs to be applied to soils in relatively humid regions such as western Kentucky to supply water for crop uptake to optimize and stabilize yields. Characterization of soil and crop variability at the field scale is needed to apply site specific management and to optimize water application. The objective of this work is to propose a characterization and modeling of soil and crop processes to improve irrigation management. Through an analysis of spatial and temporal behavior of soil and crop variables the variability in the field was identified. Integrative analysis of soil, crop, proximal and remote sensing data was utilized. A set of direct and indirect measurements that included soil texture, electrical conductivity (EC), soil chemical properties (pH, organic matter, N, P, K, Ca, Mg and Zn), NDVI, topographic variables, were measured in a silty loam soil near Princeton, Kentucky. Maps of measured properties were developed using kriging, and cokriging. Different approaches and two cluster methods (FANNY and CLARA) with selected variables were applied to identify management zones. Optimal scenarios were achieved with dividing the entire field into 2 or 3 areas. Spatial variability in the field is strongly influenced by topography and clay content. Using Root Zone Water Quality Model 2.0 (RZWQM), soil water tension was modeled and predicted at different zones based on the previous delineated zones. Soil water tension was measured at three depths (20, 40 and 60 cm) during different seasons (20016 and 2017) under wheat and corn. Temporal variations in soil water were driven mainly by precipitation but the behavior is different among management zones. The zone with higher clay content tends to dry out faster between rainfall events and reveals higher fluctuations in water tension even at greater depth. The other zones are more stable at the lower depth and share more similarities in their cyclic patterns. The model predictions were satisfactory in the surface layer but the accuracy decreased in deeper layers. A study of clay mineralogy was performed to explore field spatial differences based on the map classification. kaolinite, vermiculite, HIV and smectite are among the identified minerals. The clayey area presents higher quantity of some of the clay minerals. All these results show the ability to identify and characterize the field spatial variability, combining easily obtainable data under realistic farm conditions. This information can be utilized to manage resources more effectively through site specific application

Electromagnetic Imaging of the marine subsurface : a novel approach to assess sediment patterns and dynamics on clastic shelf systems

Electromagnetic (EM) imaging is a new approach to investigate marine near-surface sediments. The EM data provide information about electric conductivity and magnetic susceptibility of the sediments. Both are important physical parameters in exploration geophysics. Electric conductivity of marine sediments is a function of porosity, tortuosity and chemistry of the pore fluid. Magnetic susceptibility indicates the magnetic particle concentration and is hence related to the mineral composition of the sediment. In this thesis data processing, inversion and machine learning methods for a novel marine EM profiling system are developed, with the goal to explore the internal structure and spatial variability of sediment patterns in coastal and shelf regions. The investigated EM data were acquired on the NW Iberian shelf during the Meteor cruise M84/4b with the bottom towed electromagnetic profiler MARUM NERIDIS III. This non-conductive, non-magnetic fiberglass sled accommodates a controlled source electromagnetic system based on a frequency-domain concentric-loop EM induction sensor. In order to estimate quantitative seafloor sediment properties from the NERIDIS III EM data, the approach developed in this thesis follows three main steps: The first step is to calibrate the EM data such that instrument related bias is removed and the EM response is solely controlled by the frequency of the source signal, the system geometry, the electric conductivity and magnetic susceptibility of the seawater and the sediment. Calibration is necessary to make data from different measurements and surveys comparable and to enable solving of the ill-posed inverse problem for electric conductivity and magnetic susceptibility. This thesis shows that calibrating the primary EM field alone, by means of independently measured water conductivity and constant water susceptibility, is not sufficient. Therefore, a calibration methodology is developed which firstly calibrates the recorded EM data to compensate for bias in the primary EM field followed by a secondary EM field calibration by means of ground-truth data. The second step involves the inversion of the EM data, which can be subdivided into a half-space and 1-D inversion. The half-space inversion aims for the reconstruction of bulk sediment conductivity and susceptibility of the uppermost approximately 0.5 to 1 m. It is demonstrated that recovered half-space conductivity and susceptibility well reflect the main sediment patterns on the NW Iberian shelf and allow the reconstruction of sediment pathways. The 1-D inversion can be used to reconstruct the vertical conductivity structure of the subsurface. An algorithm is developed which employs the half-space susceptibility as a priori information and hence allows the utilisation of the in-phase component of the complex earth response increasing the depth of investigation. It is shown that vertical conductivity variations down to approximately 3 m below the seafloor can be reconstructed revealing the internal structure of the Galician Mud Belt. The third step covers the predictive modelling of grain-size from the electric conductivity and magnetic susceptibility of the sediment. Correlation analyses are carried out which reveal a strong relation between the electromagnetic and textural sediment properties. A radial basis function network is developed which predicts the entire grain-size distribution for each EM measurement location along shelf wide survey lines. The predicted grain-size distributions are used to identify the well-known sediment facies on the NW Iberian shelf and give new insights into their distribution and transitions