The objectives of this research are to propose an approate and efficient model for analyzing rice corn and sugar yields data. A linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) was adopted. The results show that for rice yields if the amount of rainfall in a harvest season increases 1 mm the yields will decease by 25.74 tons. if the temperature in the harvest season increases 1 Celsius the yields will decease by 212.60 tons and according to the trend the yield will increase by 15.80 tons each year. For sugar cane yields if the amount of rainfall in the harvest season increases 1 mm the yields will decease by 51.35 tons. if the temperature in the harvest season increase 1 Celsius the yields will incease by 121.30 tons and according to the trend the yield will increase by 29.93.80 tons each year. For corn yield if the amount of rainfall in the havest season increases 1 mm the yields will incease by 73.45 tons. if the temperature in he havest season increases 1 celsius the yields will decease by 40.44 tons and according to the trend the yield will increase by 1.74 tons each year.

The objectives of this research are to propose a proper model for risk assessment for drought in Thailand. A hidden Markov model is adopted. The results indicate that the amount of rainfall depends on the hidden states drought or non-drought state. If the state changes from drought to non-drought the amount of rain will increase. The probability of changing from drought state to non-drought state is higher than from drought state to drought state. The probability of drought occurrence in February is highest followed by in November March December January July April June October September August and May respectively. When the temperature increases the probability of drought occurrence will increase.

The objectives of this research are to propose a proper model for detecting defective machines in auto parts factories to apply the proposed model to the defective product data and to investigate the factors influencing on the machine producing defective products. A Bayesian hidden Markov model (HMM) is adopted. The results show that if the number of defective products increases the machine will be in the hidden bad state. The probability of changing from a hidden bad state to a hidden good state is higher than the probability of changing from a hidden good state to a hidden good state. The highest probability that the machine will be in a good state is at the work process 4 followed by 1 3 and 2 respectively. The highest probability that the machine will be in hidden good state is at the Worker no. 10 followed 9 3 12 7 2 1 6 11 4 8 and 5 respectively and The highest probability that the machine will be in the hidden good state is at the product type 1 followed by 2 and 3 respectively.

This research aims to apply the principle of stochastic process for modeling. The parameters are estimated using Bayesian methods. The monthly average real price of paddy rice 15% and yield of paddy rice in Thailand were studied. The price and the yield of paddy rice which are time series data consisting of four components autocorrelation an exponential cumulative distribution function for trend outliers and two different types of seasons: the dummy seasons and Fourier function seasons. Writing algorithms programming in OpenBUGS and evaluating the performance of models from simulation programming in R were conducted. After that Bayesian methods with dummy seasons and Fourier function season were compared using RMSE MSE and MAE as the criteria. The results show that the Bayesian model with an exponential cumulative distribution function for trend and dummy seasons give the lowest RMSE MSE and MAE for both model fitting and model validating.

This research is intended ; 1.To analyze the experts perspective affecting green logistics model on inventory management of corporate community in agricultural and food product for inventory sustainable management. 2.To determine and examine the future trend possibility model of green logistic on inventory management of corporate community in agricultural and food products for inventory sustainable management. The key informants of the expert consisted of the expert in corporate community management 2) entrepreneurs 3) relevance persons who related to green logistics and 4) intellectual persons 21 persons in total with the selection through purposive method. The research was conducted the future research method using the Delphi technique involving Three-round questionaires including interviews are research instrument. The descriptive statistics including median interquartile rang (IR) for desired agreement and consensus answer of the key informants from the expert. The research result found that 1)In the first objective consisted of 1.1 Green logistics performance 1.2 Green organization performance 1.3 Competitive advantage building Green logistics performance were 1. Green inventory management efficiency 2. Personal efficiency 3. Follow and evaluation Green organization performance were 1. Expected based on green logistics 2. Image building for green supply chain organization Competitive advantage building were 1. Competitive strategy 2. Green logistics strategy 2) In the second objective from the above research finding and led to determine and examine acceptance model approach in the future possibility of green logistics model on inventory management of corporate community in agricultural and food products for inventory sustainable managements follows : 3 performance of 1) green logistics performance on inventory management of corporate community in agricultural and food products for inventory sustainable management. 2) organization or corporate community performance and 3) competitive advantage building performance.