A method is presented for developing probability density functions for parameters of soil moisture relationships of capillary head [h(θ)] and hydraulic conductivity [K(θ)]. These soil moisture parameters are required for the assessment of water flow and solute transport in unsaturated media. The method employs a statistical multiple regression equation proposed in the literature for estimating [h(θ)] or [K(θ)] relationships using the soil saturated water content and the percentages of sand and clay. In the absence of known statistical distributions for either [h(θ)] or [K(θ)] relationships, the method facilitates modeling by providing variability estimates that can be used to examine the uncertainty associated with water flow or solute transport in unsaturated media.

Developing joint probability distributions of soil water retention characteristics

https://pubag.nal.usda.gov/download/6636/PDF

Apparent soil electrical conductivity measurements in agriculture

Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil-water-plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.

/pdf/application-of-soil-electrical-conductivity-to-precision-58ofkxwsi3.pdf

Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines

The soil sorption coefficient Kd and the soil organic carbon sorption coefficient KOC of pesticides are basic parameters used by environmental scientists and regulatory agencies worldwide in describing the environmental fate and behavior of pesticides. They are a measure of the strength of sorption of pesticides to soils and other geosorbent surfaces at the water/solid interface, and are thus directly related to both environmental mobility and persistence. KOC is regarded as a 'universal' parameter related to the hydrophobicity of the pesticide molecule, which applies to a given pesticide in all soils. This assumption is known to be inexact, but it is used in this way in modeling and estimating risk for pesticide leaching and runoff. In this report we examine the theory, uses, measurement or estimation, limitations and reliability of these parameters and provide some 'rules of thumb' for the use of these parameters in describing the behavior and fate of pesticides in the environment, especially in analysis by modeling.

Pesticide soil sorption parameters: theory, measurement, uses, limitations and reliability.

Partition coefficients for metals in surface water, soil, and waste

Characterizing the uncertainty of pesticide leaching in agricultural soils

Expected values and standard deviations of order statistics in samples from a normal parent distribution are developed. Values were coinputed on the basis of a Gauss-Legendre quadrature technique to 25 decimal places for sample sizes of 2(1)50, 60(10)200, and 225(25)500. These values are more precise than those of other available tables and are more accurate for higher sample sizes. An abbreviated table of expected values is presented covering sample sizes up to 50.

Computing expected values of normal order statistics

A variety of predictive models are being applied to evaluate the transport and transformation of pesticides in the environment. These include well known models such as the Pesticide Root Zone Model (PRZM), the Risk of Unsaturated-Saturated Transport and Transformation Interactions for Chemical Concentrations Model (RUSTIC) and the Groundwater Loading Effects of Agricultural Management Systems Model (GLEAMS). The potentially large impacts of using these models as tools for developing pesticide management strategies and regulatory decisions necessitates development of sound model validation protocols. This paper offers guidance on many of the theoretical and practical problems encountered in the design and implementation of field-scale model validation studies. Recommendations are provided for site selection and characterization, test compound selection, data needs, measurement techniques, statistical design considerations and sampling techniques. A strategy is provided for quantitatively testing m...

Conducting Field Studies for Testing Pesticide Leaching Models

A technique for computation of variances and covariances of normal order statistics is presented. This method provides the means to extend the precision of and correct errors in current tables. A numerical integration approach is employed for the calculations and associated error bounds are developed. Tables were constructed for samples sizes up to M with precision as follows: 25 decimal places (d.p.) for samples sizes of 2(1)20; 20 d.p. for 21(1)30; 15 d.p. for 31(1)40; 10 d.p. for 41(1)50. A table of variances and covariances for sample sizes up to 20 and a table of product moments of normal order statistics for samples sizes of 20(10)50 are presented.

Rudolph S. Parrish

Papers

Characterizing the uncertainty of pesticide leaching in agricultural soils

Computing expected values of normal order statistics

Conducting Field Studies for Testing Pesticide Leaching Models

Computing variances and covariances of normal order statistics

An estimate of the variability in biotransformation kinetics of xenobiotics in natural waters by aufwuchs communities