*Osvaldo Gargiulo and Kelly T. Morgan,

Osvaldo Gargiulo, Agricultural and Biological Engineering Department, University of Florida, Gainesville, Fl. 32601; and Kelly T. Morgan, Soil and Water Science Department, University of Florida, Gainesville, Fl. 32601.

Key words: Soil, PTFs, Missing Soil Parameters

Abstract

In this study, we used the Florida Soils Characterization Database (FSCD) as source of physical and chemical soil parameters for the development of a soils characteristics dataset usable by crop growth models. Unfortunately, as with other soil surveys, this database has missing soil parameters within the various soil horizons. Three main objectives of this study were to: (1) develop procedures to extract soil characteristics data from available soil data sources, (2) convert soil profile data into a format usable by simulation models, and (3) create and evaluate multiple regression models to simulate missing soil parameters. We used the conditional distribution model to investigate empirical relationships within available measured soil data. A multi regression model, as the one described in this paper, is a valuable tool that can be used to fill singular or multiple missing soil data in the incomplete list of the FSCD. Statistical indexes as RMSE and Nash-Sutcliffe-efficiency were used to test the behavior of multi regression model in simulating missing soil values in the 0-20cm horizon. The results showed that silt, clay, organic carbon, cation exchange capacity, bulk density, and sand can be predicted with a model performance that ranges from very good to satisfactory. However, pH and saturated hydraulic conductivity are not simulated with satisfactory performance levels. The approach taken in this paper to simulate missing soil parameters is universal; meaning that it can be developed anywhere having available representative source of measured soil data of the study area.