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One of the most important soil degradation agents in agriculture is compaction. To rehabilitate compacted fields, farmers usually use subsoilers to break compacted layers. Moreover, the required draft force of a subsoiler dictates the amount of energy needed to operate it. Therefore, measurement, calculation or prediction of the draft force of this machine is vital for designing an efficient subsoiler. In the present study, an analytical model was developed to calculate the subsoiler draft force. To verify the model developed herein, model outputs regarding the draft force of a subsoiler were compared with corresponding results from the American Society of Agricultural Engineers (ASAE) standard, as well as the results of literature studies. Moreover, the output of the model was compared with the measured draft force of subsoiling a silty clay loam soil. Furthermore, the results obtained regarding the quantitative effect of model inputs on the draft force of a subsoiler were checked from the viewpoint of compatibility with the expected trends or observed results in other studies. The data obtained from the developed model were compatible with those of the ASAE standard. Moreover, the draft force of a single-shank subsoiler was almost 10 kN, which is approximately 14% higher than the result obtained by the model (8.73 kN). Therefore, the model developed herein can be used to calculate the subsoiler draft force with reasonable accuracy. Of the machine parameters, subsoiler wingspan had an adverse effect on the specific draft of this machine. Moreover, for the range of working depths between 30 and 50cm, the minimum values of specific draft took place.

The objective of this study was to predict celeriac drying curves using artificial neural networks (ANNs). The experimental data for vacuum drying kinetics of celeriac slices reported by other researcher in the previously published article was used. The air temperature, chamber pressure and time values were used as ANN inputs. To predict the moisture content, the multilayer feed forward back propagation neural network, as a well-known network, was used. The network with Levenberg-Marquardt learning algorithm, hyperbolic tangent sigmoid transfer function, and 3-6-9-1 topology provided the superior results.