Derivation and predictive accuracy of regression-based pedotransfer functions

Speaker’s Name: Dr Subhash Chandra

Speaker’s Institution: Chief Biometrician, Agriculture Research division, Department of Environment and Primary Industries

Abstract

Direct measurement of many soil properties is time consuming and expensive. Pedotransfer functions (PTFs) help overcome these limitations through, usually regression-based, derivation of an empirical relationship of any such costly-to-measure soil property with other potentially correlated but more easily and economically measurable soil properties. The data for derivation of these PTFs emanate from soil samples that are usually collected using a nested spatial sampling design. Soil science literature review suggests that derivation of PTFs has not been based on accounting for this nested spatial sampling structure in data. Sound scientific modelling of data, the derivation of PTFs being no exception, needs to recognize and to appropriately account for this structure in data in order to generate scientific inferences that the data actually support. This talk, taking field capacity (FC) as an example PTF, aims to demonstrate this simple principle using mixed effects regression models to derive PTFs that the data actually support and an assessment of their predictive accuracy using Akaike information criterion.

Seminar Convenors: Andriy Olenko

AGR IT Support: Darren Condon