Prediction of Soil Temperature at Various Depths Using a Mathematical Model

Ja Oyewole,T Olasupo,Eo Faboro,Ja Akinpelu

doi:10.4314/jasem.v22i9.09

Abstract

This study discusses variation of soil temperature with depth using mathematical models at Ataoja School of Science, Osogbo, Osun State, Nigeria. Average conditions were assumed. The annual soil temperature cycles were modeled with fairly good accuracy. Differences in measured and predicted soil temperatures were determined at annual levels at depths 0cm (top soil), 10cm, 30cm and 50cm. For the annual cycle, the absolute errors ranged from 0.5oC to 7.8 oC with an average of 2.7 oC at the soil surface (0cm). At the 10cm depth, the errors ranged from 0.1 oC to 4.5 oC with an average value of 2.0 oC . At the 30cm depth, the absolute errors ranged from 0.05 oC to 2.9 oC with an average of 1.7 oC. The highest average absolute error was 2.7 oC while the lowest average absolute error was 1.7 oC.Keywords: Soil temperature, transient heat flow, simulation, homogenous.

Highlights

To enhance agricultural operations and food production in the country, good prediction techniques are needed for adequate monitoring of soil temperature variations
The amplitude of the annual temperature wave at depth z is given from equation (4), as;
The measured and predicted soil temperatures at different depths for the annual cycle are shown in figures 2-5

Summary

Introduction

To enhance agricultural operations and food production in the country, good prediction techniques are needed for adequate monitoring of soil temperature variations. Some earlier works in this area have been done, e.g, Penrod, et al (1960) studied the temperature variation of sod-covered soil at Lexington, Kentucky from 1952 – 1956. Toy et al (1978) predicted mean monthly soil temperature solely from mean monthly air data, using simple linear models. Gupta et al (1981) developed a predictive model for temperatures of bareand residue – covered soil using daily maximum and minimum air temperatures, initial soil temperature and thermal diffusivity profiles as inputs.

Results

Conclusion