The research of strategic accounting information systems of technology and targeted industry groups to Thailand 4.0 was aimed at studying 1) strategic accounting information systems of technology and targeted industry groups to Thailand 4.0 and 2) factors related to strategic accounting information systems of technology and targeted industry groups to Thailand 4.0. The questionnaires were constructed as a research tool for collecting data from 400 entrepreneurs in technology and targeted industry groups. The data was statistically analyzed by frequency distribution percentage mean standard deviation and multiple classification analysis: MCA. The research findings indicated that most entrepreneurs in the technology and targeted industry groups were in food agriculture and biotechnology. Their capital to run business at the present time was between baht 1000001-1500000. The average number of employees was 4.75. They had income of baht 5000001-7500000 per year. Most of them employed companies or organizations to develop their own accounting information systems and their practicable period was 7.61 years. The capacity accounting information systems was totally at high level ( =3.64). Also the usability of accounting information systems was totally at high level ( =3.67) whose data collection was at =4.00 data processing at =3.66 data controlling at =3.53 and information generation at =3.51 respectively. The hypothesis testing results at statistical significance of 0.05 showed that the capacity of accounting information systems was the vital factor relating to data collection data processing data controlling and information generation and the annual income of entrepreneurs in strategic accounting information systems to Thailand 4.0 was related to data collection data processing and strategic accounting information systems to Thailand 4.0.

The objectives of this research were to propose a model with some functions including CAR spatial effects and to select a proper model for predicting rince and rubber yields in southern provices of Thailand. A linear mixed model (LMM) with spatial effects and dummy varables and Fourier seasonal effects was proposed. Bayesian estimation was used and the estimated rice and rubber yields were used for prediction. The dependent variables were rice and rubber yields in southern provinces of Thailand. Factors considered were rain temperature soil fertilizer soil organic matter trend and also sesonal effects. The results show that rain temperature soil fertilizer soil organic matter trend and sesonal effects influence on rice and rubber yields. The proposed model with Fourier seasonal term is the most appropriate comparing to the model with dummy variable for season effects and the Holt-Winters model. Therefore the proposed model with Fourier sesaonal term should be the first consideration for prediction.

The objectives of this research were to propose a spatial analysis using Bayesian generalized linear mixed model for predicting drought in Thailand. The spatial effect was assumed to be a conditional autoregressive model (CAR model). The dependent variables was a drought or non-drought occurrence which was assumed to have a Bernolli distribution with logistic link function. Factors considered were rain temperature number of rainy days number of large reservoirs and number of medium reservoirs. The results show that the rain temperature number of rainy days number of large reservoirs and number of medium reservoirs influence on the drought occurrence. This study is useful for planning making decision preventing and remedying the drought problem.

The objectives of this research were to create a model for time sereries data prices and yields of natural rubber to be more accurate prediction and to compare the proposed model with the classical time series models such as the Holt-Winters Additive Exponential smoothing model (Holt-Winters ES) and the seasonal autoregressive integrated moving average model (SARIMA). The proposed model was a linear mixed model (LMM) consisting with trend autoregression outliers and sesonal terms. Dummy varialbles and Fourier seasonal effects were studied. Bayesian estimation was used and the estimated prices and yields were used for prediction. The results show that the proposed model with a Fouries seasonal effect is the most appropriated comparing with the dummy seasonal effect the Holt-Winters ES and the SARIMA having the least mean absolute values (MAE) both fiting and validating parts. The proposed model with sesaonal dummy variables should be the first consideration for forecasting spatial time series with a seasonal component.

The objectives of this research are to propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating equation (GEE) and a linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) were adopted. The dependent variables are the cassava and rubber yields collected each month in every province of Thailand. The factors considered are rainfall averaged temperatures and regions. The results from GEE and LMM show that the factors influencing on the cassava and rubber yields are rainfall averaged temperature and region. Both GEE and LMM fit the correlated data. The GEE is used to explain the influence of factors on the yields in all provinces while the LMM is used to explain the influence of factors on the yields in each province.