USING HYDROGEOPHYSICS & XRF TO PRODUCE A HIGH-RESOLUTION 3-DIMENSIONAL SOIL CADMIUM MAP FOR EVALUATING HYBRID WHEAT TRIALS

Cadmium (Cd) accumulation in wheat decreases germination, growth, grain yield, and in higher concentration leads to adverse effects on human health (Liu et al, 2018). Due to wheat cultivars variation in Cd accumulation, wheat breeders aim to select those at low Cd concentration lines in a field. Hence the need to quantify the concentration of Cd at different parts of a field and visually represent on a high resolution Cd distribution map. Various ways to quantify the concentration of soil Cd exist. However, the cost of equipment required make the process quite expensive and labor intensive. This work studied the feasibility of predicting the concentration of Cd and other soil chemical elements based on readily available environmental covariates collected at the site. These are electrical conductivity in shallow and deep zones (ECaS, ECaD), total gamma counts and elevation. Soil samples were collected from Havelock farm, analyzed in the lab and then results were used to train and test different statistical models to predict the occurrence of chemical elements in the soil. Showed statistical correlation between Geo-covariates and some soil element data (i.e. Zn & Fe) providing proof-of-concept for technique and warranting further investigation • At Havelock Cd was below level of detection • Adding VNIR to Geo-covariates improves prediction accuracy in nonlinear statistical model

USING HYDROGEOPHYSICS & XRF TO PRODUCE A HIGH-RESOLUTION 3-DIMENSIONAL SOIL CADMIUM MAP FOR EVALUATING HYBRID WHEAT TRIALS

Cadmium (Cd) accumulation in wheat decreases germination, growth, grain yield, and in higher concentration leads to adverse effects on human health (Liu et al, 2018). Due to wheat cultivars variation in Cd accumulation, wheat breeders aim to select those at low Cd concentration lines in a field. Hence the need to quantify the concentration of Cd at different parts of a field and visually represent on a high resolution Cd distribution map. Various ways to quantify the concentration of soil Cd exist. However, the cost of equipment required make the process quite expensive and labor intensive. This work studied the feasibility of predicting the concentration of Cd and other soil chemical elements based on readily available environmental covariates collected at the site. These are electrical conductivity in shallow and deep zones (ECaS, ECaD), total gamma counts and elevation. Soil samples were collected from Havelock farm, analyzed in the lab and then results were used to train and test different statistical models to predict the occurrence of chemical elements in the soil. Showed statistical correlation between Geo-covariates and some soil element data (i.e. Zn & Fe) providing proof-of-concept for technique and warranting further investigation • At Havelock Cd was below level of detection • Adding VNIR to Geo-covariates improves prediction accuracy in nonlinear statistical model

Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory-constructed samples

Accurate quantification of petroleum hydrocarbons (PHCs) is required for opti- mizing remedial efforts at oil spill sites. While evaluating total petroleum hydro- carbons (TPH) in soils is often conducted using costly and time-consuming lab- oratory methods, visible and near-infrared reflectance spectroscopy (Vis–NIR) has been proven to be a rapid and cost-effective field-based method for soil TPH quantification. This study investigated whether Vis–NIR models calibrated from laboratory-constructed PHC soil samples could be used to accurately estimate TPH concentration of field samples. To evaluate this, a laboratory sample set was constructed by mixing crude oil with uncontaminated soil samples, and two field sample sets (F1 and F2) were collected from three PHC-impacted sites. The Vis–NIR TPH models were calibrated with four different techniques (partial least squares regression, random forest, artificial neural network, and support vector regression), and two model improvement methods (spiking and spiking with extra weight) were compared. Results showed that laboratory-based Vis– NIR models could predict TPH in field sample set F1 with moderate accuracy (R2 \u3e .53) but failed to predict TPH in field sample set F2 (R2 \u3c .13). Both spik- ing and spiking with extra weight improved the prediction of TPH in both field sample sets (R2 ranged from .63 to .88, respectively); the improvement was most pronounced for F2. This study suggests that Vis–NIR models developed from laboratory-constructed PHC soil samples, spiked by a small number of field sam- ple analyses, can be used to estimate TPH concentrations more efficiently and cost effectively compared with generating site-specific calibrations