In an agricultural ecosystem, soil temperature can affect the growth of plants and organisms, the fate and
transport of chemicals, and many other natural phenomena. Simulation of soil temperature is essential to support many
agricultural models. Modeling the fluctuations of soil temperature at different depths is complicated considering the great
number of variables. In this study, a simple model, based on an artificial neural network (ANN), was developed to
simulate daily soil temperatures at 100, 500 and 1500 mm depths, in a soil from Ottawa, Ontario, Canada, in an attempt
to develop a simple, fast, and more accurate ANN model than the conceptual models currently used to simulate soil
temperature. The inputs for the ANN model included: daily rainfall, potential evapotranspiration, maximum and minimum
air temperature, and the day of the year. These input factors are all easy to obtain and are measured at most weather
stations world-wide. The parity between the measured and the simulated data, resulting from ANNs, shows the ability of
simple ANN models to simulate soil temperature. The results obtained from ANN models varied within a root-meansquare
difference range from 0.63 to 1.39°C, standard deviations from 0.61 to 1.39°C and coefficients of determination
(r2) from 0.937 to 0.985. The accuracy of the simulations shows the simplicity with which ANNs can be used to model
complicated phenomena in agricultural systems. The short time of execution (a few seconds for a one-year simulation) is
another benefit of ANN models. Many simulation models, such as for pesticide fate and transport, nutrient movement in
soils, and soil bioremediation, require timely fluctuations of soil temperatures. For such uses, the fast execution of ANNs
is very helpful. Therefore, this technology could prove very useful for decision support systems which require real-time
control in agricultural applications.