Purpose: This study was aimed at developing a tractor-driving simulator for the safety training of agricultural tractor operators. Methods: The developed simulator consists of five principal components: mock operator control devices, a data acquisition and processing device, a motion platform, a visual system that displays a computer model of the tractor, a motion platform, and a virtual environment. The control devices of a real tractor cabin were successfully converted into mock operator control devices in which sensors were used for relevant measurements. A 3D computer model of the tractor was also implemented using 3ds Max, tractor dynamics, and the physics of Unity 3D. The visual system consisted of two graphic cards and four monitors for the simultaneous display of the four different sides of a 3D object to the operator. The motion platform was designed with two rotational degrees of freedom to reduce cost, and inverse kinematics was used to calculate the required motor positions and to rotate the platform. The generated virtual environment consisted of roads, traffic signals, buildings, rice paddies, and fields. Results: The effectiveness of the simulator was evaluated by a performance test survey administered to 128 agricultural machinery instructors, 116 of whom considered the simulator as having potential for improving safety training. Conclusions: From the study results, it is concluded that the developed simulator can be effectively used for the safety training of agricultural tractor operators.

Purpose:In order to develop strategies to prevent farm-work accidents relating to agricultural machinery, influential factors were examined in this paper. The effects of these factors were quantified using logistic regression. Methods: Based on the results of a survey on farm-work accidents conducted by the National Academy of Agricultural Science, 21 tentative independent variables were selected. To apply these variables to regression, the presence of multicollinearity was examined by comparing correlation coefficients, checking the statistical significance of the coefficients in a simple linear regression model, and calculating the variance inflation factor. A logistic regression model and determination method of its goodness of fit was defined. Results: Among 21 independent variables, 13 variables were not collinear each other. The results of a logistic regression analysis using these variables showed that the model was significant and acceptable, with deviance of 714.053. Parameter estimation results showed that four variables (age, power tiller ownership, cognizance of the government's safety policy, and consciousness of safety) were significant. The logistic regression model predicted that the former two increased accident odds by 1.027 and 8.506 times, respectively, while the latter two decreased the odds by 0.243 and 0.545 times, respectively. Conclusions: Prevention strategies against factors causing an accident, such as the age of farmers and the use of a power tiller, are necessary. In addition, more efficient trainings to elevate the farmer's consciousness about safety must be provided.

Purpose:The goal of this study was to develop a methodology for the demand forecast of tractor, riding type rice transplanter and combine harvester using an ARIMA (autoregressive integrated moving average) model, one of time series analysis methods, and to forecast their demands from 2012 to 2021 in South Korea. Methods: To forecast the demands of three kinds of machines, ARIMA models were constructed by following three stages; identification, estimation and diagnose. Time series used were supply and stock of each machine and the analysis tool was SAS 9.2 for Windows XP. Results: Six final models, supply based ones and stock based ones for each machine, were constructed from 32 tentative models identified by examining the ACF (autocorrelation function) plots and the PACF (partial autocorrelation function) plots. All demand series forecasted by the final models showed increasing trends and fluctuations with two-year period. Conclusions: Some forecast results of this study are not applicable immediately due to periodic fluctuation and large variation. However, it can be advanced by incorporating treatment of outliers or combining with another forecast methods.

Abstract. Annually, tractor accidents are estimated to account for more than 100 deaths in South Korea. Periodic accident surveys have served as an essential means for the National Institute of Agricultural Sciences (NAS) to develop strategies to prevent tractor accidents. In this study, hazards leading to accidents were identified, and their risks were assessed based on survey results to establish a more effective accident prevention strategy. Risk assessment for hazards proceeded as follows: hazard identification, frequency estimation, number of equivalent fatalities (NEF) estimation, and finally risk evaluation. Hazards were identified by analyzing 588 accident cases from NAS surveys and performing an expert review of the analysis results by implementing a Delphi survey. The frequency and NEF of each hazard were estimated by multiplying its probabilities and the statistical results of the NAS surveys. Each hazard was plotted in a frequency-NEF (FN) diagram and evaluated according to its position. Fifty-four hazards were identified, and their frequencies and NEF values were estimated. The risk evaluation results, based on the FN diagram, revealed that no hazard was located in the “unacceptable” area, and two hazards (carelessness and not looking ahead carefully) were in the “as low as reasonably practicable” area. Thus, it is critical to mitigate the effects of these two hazards. With the risk assessment method used in this study, personnel who are engaged in the prevention of tractor accidents, such as policymakers, extension specialists, and researchers, can quantitatively predict how many cases or fatalities can be reduced by eliminating a certain hazard. Keywords: Equivalent fatality, Frequency estimation, Hazard identification, Risk assessment, Tractor accident.

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