The four-electrode method modified for laboratory use was employed for electrical conductivity measurements of eight Scottish and two Greek Ca-saturated soils (< 2 mm fraction) over a range of soil solution conductivities (0.5 - 10 dS m^-1 as CaCℓ2) and for a range of tensions (saturation to 8 bars). Soil bulk conductivity, κb(dS m^-1), was linearly related with the soil solution conductivity κw (dS m^-1) for the tensions used, the relationship being of the form, κb = 1/F_f κw + κs, where κs (dS m^-1) is the adsorbed Ca conductivity and 1/F_f accounts for tortuosity and available pore volume fraction for conductance. For each soil, 1/F_f was linearly related with the volumetric moisture content θ. It was found that for the type of the soils used, non-montmorillonitic soils and mean organic matter content 5% for the Scottish soils, the adsorbed Ca molar conductivity is only a small fraction (1 - 4%) of its infinite dilution value. Also κs was related to soil properties like clay content, total surface area, CEC and organic matter. Diffusion coefficients for Ca and Cℓ for the soil solution and for adsorbed Ca were calculated by the Nernst-Einstein relationship from the measured solution conductivities and from the κs values. For θ= 0.57 to 0.15 and soil solution concentration of 0.0025 - 0.0045M as CaCℓ2, Ca diffusion coefficients ranged from 2.67 to 0.062 x 10^-10m²s^-1 and Cℓ diffusion coefficients from 6.9 to 0.16 x 10^-10m^2s^-1. A concentration correction was applied. Adsorbed Ca diffusion coefficients ranged from 11.8 to 0.045 x 10^-12m^2s^-1 for θ= 0.52 tp 0.14. A bulk density effect was introduced by consolidating and compacting four of the soils with a static load of 0.45 kg cm^-2. Soil bulk conductivities were measured at the same range of soil solution conductivities and for a tension range of 40 cm to 2 bars. Diffusion coefficients for Ca and Cℓ in the soil solution calculated using the Nernst-Einstein relationship were higher than the non-compressed soils on a tension basis, while for each soil on a common θ basis diffusion coefficients for Ca and Cℓ tended to increase slightly as bulk density was increased.

The overall goal of this research is to develop ground-based technologies for disease detection and mapping which can maximize the effectiveness and efficiency of cotton root rot (CRR) treatments. Accurately mapping CRR could facilitate a much more economical solution than treating entire fields. Three cotton fields around CRR-prone areas of Texas have been the sites for three years of data collection. A complete soil apparent electrical conductivity (ECa) survey was conducted for each field with an EM38DD sensor. Multiple linear regression was used to relate physical and chemical soil properties to the ECa values obtained from the EM38DD. The variability in soil ECa measurements can be best accounted for using calcium carbonate levels as well as clay and sand contents in the soil. T-tests were used to determine that soil pH, clay, sand, and inorganic carbon content were significantly related to CRR incidence as determined by aerial images of each location. Spectral data were obtained for freshly picked cotton leaves from healthy, disease-stressed, and dying or dead plants using an ASD VisNIR spectroradiometer. The leaf spectra were evaluated using linear discriminant analysis (LDA), the receiver operator characteristic, and wavelet analysis to relate them to classifications of infection level. It was determined that healthy and infected leaves can be correctly classified 85% of the time based on the spectral data. The results from this study suggest that differences in soil characteristics may not be pronounced enough to accurately map CRR in the soil; however, the precision treatment of CRR may possible using an optoelectronic sensor to diagnose infected plants based on leaf reflectance.

Digital soil mapping (DSM) provides a cost-effective approach for characterizing the spatial variation in soil properties which contributes to inconsistent productivity. This study utilized Random Forest (RF) models to facilitate DSM of apparent soil electrical conductivity (ECa), estimated cation exchange capacity (CEC), and soil organic matter (SOM) in agricultural fields across the Lower Mississippi Alluvial Valley. The RF models were trained and tested using in situ collected ECa, CEC, and SOM data, paired with a bare soil composite of Landsat 9 imagery. Field data and imagery were collected during the study period of 2019 through 2023. Models ranged from fair to moderate in accuracy (R2 from 0.27 to 0.68). The contrasting performance between CEC/SOM and ECa models is likely due to the dynamic nature of soil properties. Accordingly, models could have benefitted from covariates such as soil moisture, topography, and climatic factors, or higher spectral resolution imagery, such as hyperspectral.

O trabalho teve o objetivo de investigar técnicas de sensoriamento de solo e analisar o potencial de utilização das mesmas diretamente no campo. Quatro etapas distintas foram desenvolvidas para atender aos seguintes objetivos: a) comparar e avaliar o potencial de predição de atributos do solo de três espectrômetros portáteis (vis-NIR) em ambiente controlado; b) avaliar a predição em movimento de pH, P e K utilizando técnicas de sensoriamento óptico (reflectância vis-NIR) e eletroquímico (eletrodos íon-seletivos de pH e K) em uma área experimental com variabilidade induzida; c) montar e testar uma plataforma de campo com sensores elétricos, eletroquímicos e ópticos; d) avaliar o potencial de predição de textura do solo utilizando um equipamento portátil de espectroscopia de fluorescência de raios X. Na primeira etapa as leituras de reflectância espectral vis-NIR dos três equipamentos avaliados mostraram-se muito semelhantes, com coeficientes de correlação acima de 0,86 na faixa de 400 a 1800 nm. Quando comparados nesta região espectral os equipamentos produziram modelos de predição muito semelhantes, com leve superioridade para o sistema FieldSpec. Os modelos mostraram-se mais promissores para a predição da textura do solo em relação aos atributos químicos. Na segunda etapa as leituras de campo utilizando eletrodos íon-seletivos de pH e K apresentaram uma alta correlação com as análises de laboratório. As avaliações permitiram observar que condições de solo com baixa umidade prejudicam sensivelmente as leituras. Apesar da alta correlação, os valores de campo precisam ser corrigidos para a metodologia de laboratório desejada. Os modelos de predição de P, K e pH utilizando espectrometria vis-NIR no campo apresentaram baixa precisão. Os testes da Plataforma Multisensores de Solo (PMS) na terceira etapa demonstraram ser possível a utilização de sensores elétricos, eletroquímicos e ópticos em uma mesma plataforma. As leituras de condutividade elétrica aparente do solo (CEa) demonstraram que este parâmetro está relacionado com a textura, atuando como um indicador da variabilidade e possibilitando a identificação dos locais de transição de textura. O pH mensurado pela PMS exibiu correlações abaixo das verificadas na segunda etapa. No entanto, alguns resultados atípicos foram verificados, como a correlação maior entre pH em CaCl2 com a PMS do que em relação ao método em H2O. As leituras de reflectância vis-NIR utilizando a PMS apresentaram bons modelos de predição de areia e argila, permitindo a criação de mapas de alta resolução destes parâmetros. A espectroscopia de fluorescência de raios X portátil foi eficiente para estimar a textura do solo. Os teores de areia e argila foram estimados tanto por meio de regressões lineares simples como regressões múltiplas com valores de R2 acima de 0,60. O Fe total foi o principal elemento utilizado nesses modelos de regressão. / The objective of this work was to investigate soil sensing techniques and to analyze the potential for their use directly in the field. Four distinct steps were developed to meet the following objectives: a) to compare and evaluate the potential of predicting soil attributes with three portable spectrometers (vis-NIR) in a controlled environment; B) to evaluate the on-the-go prediction of pH, P and K using optical sensors (vis-NIR reflectance) and electrochemical techniques (ion-selective pH and K electrodes) in an experimental area with induced variability; C) assemble and test a field platform with electrical, electrochemical and optical sensors; D) to evaluate the potential of predicting soil texture using a portable X-ray fluorescence spectroscopy equipment. In the first step the vis-NIR spectral reflectance readings of the three equipments evaluated were very similar, with correlation coefficients above 0.86 in the 400 to 1800 nm range. When compared in this spectral region, the equipment produced very similar prediction models, with slight superiority for the FieldSpec system. The models showed to be more promising for the prediction of soil texture in relation to chemical attributes. In the second step the field readings using ion-selective pH and K electrodes presented a high correlation with the laboratory analyzes. The evaluations showed that soil conditions with low moisture significantly affect the readings. Despite the high correlation, the field values need to be corrected for the desired laboratory methodology. Prediction models of P, K and pH using field-vis-NIR spectrometry showed low precision. The tests of the Multisensors Soil Platform (MSP) in the third stage demonstrated that it is possible to use electric, electrochemical and optical sensors in the same platform. The electrical conductivity (EC) readings showed that this parameter was related with soil texture, acting as an indicator of variability and allowing the identification of texture transitions. The pH measured by MSP exhibited correlations below those verified in the second step. However, some atypical results were verified, such as the higher correlation between pH in CaCl2 and MSP than in H2O. The vis-NIR reflectance readings using the MSP resulted in good sand and clay prediction models, allowing the creation of high resolution maps of these parameters. Portable X-ray fluorescence spectroscopy was efficient for estimating soil texture. The sand and clay contents were estimated both by simple linear regressions and multiple regressions with R2 values above 0.60. Total Fe was the main element used in these regression models.

O trabalho teve o objetivo de investigar técnicas de sensoriamento de solo e analisar o potencial de utilização das mesmas diretamente no campo. Quatro etapas distintas foram desenvolvidas para atender aos seguintes objetivos: a) comparar e avaliar o potencial de predição de atributos do solo de três espectrômetros portáteis (vis-NIR) em ambiente controlado; b) avaliar a predição em movimento de pH, P e K utilizando técnicas de sensoriamento óptico (reflectância vis-NIR) e eletroquímico (eletrodos íon-seletivos de pH e K) em uma área experimental com variabilidade induzida; c) montar e testar uma plataforma de campo com sensores elétricos, eletroquímicos e ópticos; d) avaliar o potencial de predição de textura do solo utilizando um equipamento portátil de espectroscopia de fluorescência de raios X. Na primeira etapa as leituras de reflectância espectral vis-NIR dos três equipamentos avaliados mostraram-se muito semelhantes, com coeficientes de correlação acima de 0,86 na faixa de 400 a 1800 nm. Quando comparados nesta região espectral os equipamentos produziram modelos de predição muito semelhantes, com leve superioridade para o sistema FieldSpec. Os modelos mostraram-se mais promissores para a predição da textura do solo em relação aos atributos químicos. Na segunda etapa as leituras de campo utilizando eletrodos íon-seletivos de pH e K apresentaram uma alta correlação com as análises de laboratório. As avaliações permitiram observar que condições de solo com baixa umidade prejudicam sensivelmente as leituras. Apesar da alta correlação, os valores de campo precisam ser corrigidos para a metodologia de laboratório desejada. Os modelos de predição de P, K e pH utilizando espectrometria vis-NIR no campo apresentaram baixa precisão. Os testes da Plataforma Multisensores de Solo (PMS) na terceira etapa demonstraram ser possível a utilização de sensores elétricos, eletroquímicos e ópticos em uma mesma plataforma. As leituras de condutividade elétrica aparente do solo (CEa) demonstraram que este parâmetro está relacionado com a textura, atuando como um indicador da variabilidade e possibilitando a identificação dos locais de transição de textura. O pH mensurado pela PMS exibiu correlações abaixo das verificadas na segunda etapa. No entanto, alguns resultados atípicos foram verificados, como a correlação maior entre pH em CaCl2 com a PMS do que em relação ao método em H2O. As leituras de reflectância vis-NIR utilizando a PMS apresentaram bons modelos de predição de areia e argila, permitindo a criação de mapas de alta resolução destes parâmetros. A espectroscopia de fluorescência de raios X portátil foi eficiente para estimar a textura do solo. Os teores de areia e argila foram estimados tanto por meio de regressões lineares simples como regressões múltiplas com valores de R2 acima de 0,60. O Fe total foi o principal elemento utilizado nesses modelos de regressão. / The objective of this work was to investigate soil sensing techniques and to analyze the potential for their use directly in the field. Four distinct steps were developed to meet the following objectives: a) to compare and evaluate the potential of predicting soil attributes with three portable spectrometers (vis-NIR) in a controlled environment; B) to evaluate the on-the-go prediction of pH, P and K using optical sensors (vis-NIR reflectance) and electrochemical techniques (ion-selective pH and K electrodes) in an experimental area with induced variability; C) assemble and test a field platform with electrical, electrochemical and optical sensors; D) to evaluate the potential of predicting soil texture using a portable X-ray fluorescence spectroscopy equipment. In the first step the vis-NIR spectral reflectance readings of the three equipments evaluated were very similar, with correlation coefficients above 0.86 in the 400 to 1800 nm range. When compared in this spectral region, the equipment produced very similar prediction models, with slight superiority for the FieldSpec system. The models showed to be more promising for the prediction of soil texture in relation to chemical attributes. In the second step the field readings using ion-selective pH and K electrodes presented a high correlation with the laboratory analyzes. The evaluations showed that soil conditions with low moisture significantly affect the readings. Despite the high correlation, the field values need to be corrected for the desired laboratory methodology. Prediction models of P, K and pH using field-vis-NIR spectrometry showed low precision. The tests of the Multisensors Soil Platform (MSP) in the third stage demonstrated that it is possible to use electric, electrochemical and optical sensors in the same platform. The electrical conductivity (EC) readings showed that this parameter was related with soil texture, acting as an indicator of variability and allowing the identification of texture transitions. The pH measured by MSP exhibited correlations below those verified in the second step. However, some atypical results were verified, such as the higher correlation between pH in CaCl2 and MSP than in H2O. The vis-NIR reflectance readings using the MSP resulted in good sand and clay prediction models, allowing the creation of high resolution maps of these parameters. Portable X-ray fluorescence spectroscopy was efficient for estimating soil texture. The sand and clay contents were estimated both by simple linear regressions and multiple regressions with R2 values above 0.60. Total Fe was the main element used in these regression models.