U.W.A. Vitharana

Agronomic consequences of potential management zones delineated on the basis of EM38DD measurements

This study was conducted to investigate the feasibility of dual dipole apparent electrical conductivity (ECa) measurements obtained with an EM38DD sensor as an explicit information source to delineate potential management classes in an agricultural field in the Polder area of Belgium. The success of class delineation was evaluated in relation to soil physical-chemical properties and sugar beet yield. The average apparent electrical conductivity (ECa-A) derived from vertical and horizontal dipole measurements was capable of delineating 3 relatively large management classes. The analysis of variance of soil properties indicated that topsoil sand and lime and subsoil clay, sand and lime were largely different across these classes (all having a proportion of the variance accounted for by a classification of >0.5). During the growing season of 2005, we monitored topsoil NO - 3 -N and moisture content and found strong differences among classes. As a result, the crop biomass at harvest (roots plus leaves) was strongly variable between classes (ranging from 105 Mg ha -1 to 153 g ha -1 ), as well as the sugar content (ranging from 15.4% to 17.2%). However, due to a compensation effect between the crop biomass and sugar accumulation, differences in sugar yield and financial income between classes were relatively small (the income ranged from 3950 € ha -1 to 4230 € ha -1 ). However, these income values resulted from strongly different growing conditions, calling for a class-specific management. The image of the average ECa was found to be a reliable basis for delineating agronomically relevant management zones.

A neural network approach to topsoil clay prediction using an EMI-based soil sensor

High-resolution proximal soil sensor data are an important source of information for optimising the prediction of soil properties. On a 10.5 ha arable field, an intensive EM38DD survey with a resolution of 2 m × 2 m resulted in 19,694 measurements of ECa-H and ECa-V. A large textural variation was present in the subsoil due to the presence of former water channels. Nevertheless, both ECa-V and ECa-H data displayed the same spatial variability. This spatial similarity indicated the strong influence of the subsoil heterogeneity on the ECa-H measurements. Using variography, two scales of ECa variability were identified: short-range (∼35 m) variability associated with the channel pattern and wider within-field variability (∼200 m). Using artificial neural networks (ANNs), prediction of the topsoil clay content was optimised (i) by using an input window size of 3, 5, 7, 9, and 11 pixels to account for local contextual influence and (ii) by including both ECa-H and ECa-V in the network input layer to isolate the response from the topsoil. The models were evaluated using R 2 and the relative mean squared estimation error (rMSEE) of the test data. The most accurate predictions were obtained using both orientations of the EM38DD sensor without contextual information (R 2 = 0.66, rMSEE = 0.40). The importance of ECa-V on the topsoil clay prediction was expressed by a relative improvement of the rMSEE of 29%. For comparison, a multivariate linear regression (MVLR) was performed to predict the topsoil clay content based on the two orientations. The ANN models up to a window size of 5 pixels outperformed the MVLR, which resulted in an R 2 of 0.42 and an rMSEE of 0.63. ANN analysis based on both orientations of the EM38DD appears to be a useful tool to extract topsoil information from depth-integrated EM38DD measurements.

Identifying the Sources and Contamination Status of Potentially Toxic Trace Elements in Agricultural Soils

ABSTRACT Elevated concentrations of potentially toxic trace elements in agricultural soils contribute to soil pollution affecting food quality and safety. We assessed pollution levels in agricultural systems, lowland rice (LL) and highland cash crops (HL), by comparing with non-agricultural soils (NA). Correlation analysis and principal component analysis (PCA) were performed, and geo-accumulation index (Igeo) and pollution loading index (PLI) were calculated. Zinc in LL, and Cd in LL and HL, were significantly higher than in NA. The Igeo values of cooper (Cu), lead (Pb), nickel (Ni), zinc (Zn), and cadmium (Cd) ranged from uncontaminated to moderately contaminated (Class 0 to 2) for LL, HL, and NA. Overall, trace element levels were categorized as unpolluted based on PLI. Soil properties significantly correlated with Cu, Pb, Ni, and Zn concentrations but not with Cd. Based on PCA, sources of origin for Cu, Pb, Ni, and Zn were lithogenic, while the sources for Cd was anthropogenic in the studied agricultural soils.