Predicting tthe Effect of Farmyard Manure and Number of Passes of Tractor Tire on Clay Soil Compaction Using Multilayer Perceptron Neural Networks

One way to reduce soil compaction is to add organic matter and to manage the field traffic. In this research, farmyard manure was incorporated into clay soil with rates of 0, 45, 60, 90 ton ha-1. After 6 months (September to March), at different numbers of tyre passes of 1, 6, 11 and 16, and three soil moisture contents of 8%, 11% and 14% soil compaction was evaluated measuring soil bulk density and soil sinkage. Experiments were conducted in the soil bin at the Urmia University under a single trector’s tire 220/65 R 21l under a constant load of 4 kN, inflation pressure of 110 kPa and at a forward velocity of 2.88 km hr-1. It was found that at manure rate of of 90 ton ha-1, comparing to no-manure treatment, soil bulk density and soil sinkage decreased by 14.7 and 6.94 percent, respectively. Also, increasing the number of tyre passes from 1 to 16 and increasing soil moisture content from 8 to 14 percent, increased soil bulk density 7.21% and 7.92%, respectively. For neural network modeling multilayer perceptron network with six neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was used. Comparison of neural network output and experimental results showed high correletion with correlation coefficient of R= 0.99 between them. The mean square error (MSE) of the model and mean absolute percentage error of the system (MAPE) were 0.0119071 and 0.0009641,respectivly, which showed high accuracy of neural network to model soil compaction.