Date of this Version
Poster presentation, UCARE Research Symposium, University of Nebraska-Lincoln, Spring 2020.
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 models