Variability in Sorption Coefficients of Metolachlor on a Captina Silt Loam

Abstract

Spatial variability of soil properties is important in determining pesticide behavior in soils. A study was conducted on a 4‐ha field to measure the spatial variability in sorption coefficients (K) for the herbicide metolachlor [2‐chloro‐N‐(2‐ethyl‐6‐methylphenyl)‐N‐(2‐methoxy‐1‐methylethyl)acetamide] and to correlate these K values to other soil properties. The soils in this field were Captina and Johnsburg (Typic Fragiudults). The rectangular field was sampled on a grid with 135 nodes. At each node, samples of the Ap, Bt, and Btx horizons were taken for determinations of water content at 10 and 1500 kPa, pH, particle size analysis, sorption coefficients (K), and organic matter content. Means, standard deviations, minimum values, maximum values, and coefficients of variation (CV) were determined on all properties. In addition, multiple linear regression analyses were performed to determine the maximum correlation between K and the soil properties. Soil organic matter content was the soil property most highly correlated with the adsorption of metolachlor in the surface soils (Ap). Adsorption was also significantly correlated with clay content, water content at 1500 kPa, and pH. Semivariograms were constructed for each horizon for the organic matter content and K. Semivariograms for organic matter indicated a range of spatial dependency of 110 m in the Ap and no spatial structure for the Bt and Btx horizons. The range for K was 110 m in the Ap and 42 m in the Bt with no spatial structure evident in the Btx horizon. The data for K and organic matter were normalized by dividing each semivariance by the mean squared for each parameter, which resulted in similar semivariograms in the Ap horizon. Contour plots of kriged values for normalized organic matter and normalized K in the Ap horizon were also similar.

Degradation and persistence of metolachlor in soil: Effects of concentration, soil moisture, soil depth, and sterilization

“…Soil-bound residues increased from 17.2 to 27.6% in unsaturated surface soil and from 3.8 to 8.3% in unsaturated subsurface soil at 60 and 120 d. Greater quantities of soil-bound residues formed in the surface soil relative to the subsurface soil, presumably as a result of the greater quantity of organic carbon in the surface soil. Surface soil often contains more organic matter than subsurface soil [29,30]. The organic carbon content of the soil has been found to be the most important soil property influencing the adsorption of metolachlor [30][31][32][33][34][35].…”

“…Surface soil often contains more organic matter than subsurface soil [29,30]. The organic carbon content of the soil has been found to be the most important soil property influencing the adsorption of metolachlor [30][31][32][33][34][35]. The main adsorption mechanisms of acetanilide herbicides result from multifunctional H-bonds and charge-transfer bonds between humic acids and the herbicides [35] and from hydrogen bonds between carboxyl or hydroxyl groups of organic substances and the carbonyl oxygen of metolachlor [36].…”

Evaluating the use of digital terrain modelling for quantifying the spatial variability of 2,4-D sorption by soil within agricultural landscapes

“…Evaluating soil-water partitioning coefficients at the large-scale has been done for the herbicides atrazine (Novak et al 1997), imazethapyr (Oliveira et al 1999), metolachlor (Wood et al 1987), and 2,4-D (Farenhorst et al 2001). Spatial analyses of soil-water partitioning coefficients are traditionally performed using kriging, but this approach requires intensive soil sampling and herbicide analysis (Novak et al 1997).…”

An Overview of Spatial Variability of Soil Microbiological Properties using Geostatistics

“…Spatial analysis of soil microbiological properties has added advantage of assessing soil quality and health, as well as for developing appropriate sampling strategies. Besides these the geostatistical analyses have been used to estimate the spatial variability soil biochemical properties (Bonmati et al, 1991;Sutherland et al, 1991), pesticide distribution in soil (Rao and Wagenet, 1985;Wood et al, 1987), soil microbiological processes (Ailken et al, 1991;Rochette et al, 1991), and ecological parameters (Robertson, 1987;Rossi et al, 1992). Geostatistics can be applied to detect, estimate and map the spatial patterns of micribiological variables in a field.…”