This research aims to develop the coconut coir ceiling board mixed with natural latex. The 6 ratios of gypsum plaster: coconut coir: sodium silicate: natural rubber: tap water are equal to 1: 0.15: 0.03: 0.00: 1 1: 0.15: 0.03: 0.01: 1 1: 0.15: 0.03: 0.02: 1 1: 0.15: 0.03: 0.03: 1 1: 0.15: 0.03: 0.04: 1 and 1: 0.15: 0.03: 0.05: 1 by weight. The ceiling boards were produced by casting in normal temperature (30 – 35 degree of Celsius). The properties testing of coconut coir ceiling board mixed with natural latex followed the TIS 219-2009 standard (gypsum plasterboard) including: breaking load nail pull resistance deflection water absorption density thermal conductivity coefficient and real usage. From the results 1: 0.15: 0.03: 0.03: 1 ratio is the most suitable ratio of coconut coir ceiling board mixed with natural latex. This developed ceiling boards can reduce the quantity of coconut coir waste and get the good thermal insulation ceiling board products.

The purposes of this research were to developed the tea Product from Ya Nang Melientha suavis and safflower. The objective was to study the optimum drying conditions of Ya Nang tea leaf and Melientha suavis 2) to study the optimum ratio between Ya Nang tea leaf and Melientha suavis and Safflower in the tea formulation and 3) to study the acceptance of Ya Nang tea products with Melientha suavis and safflower flowers from consumers It was found that the suitable drying time of tea leafe was 4 h at 60o c with moisture content lower than 10% that was in agreement with standard. The proper ratios between Ya Nang Melientha suavis and Safflower such as 100:0:0 60:20:20 60:30:10 and 60:10:30 were evaluated the sensory properties by 30 panelists. It was found that the highest acceptability sure was found in formulation 3. In addition the DPPH radial scavenging activity and phenolic content were 66.51 μM Trolox/ml and 14.46 mg GAE/ml respectively for the accepted tea formulation. For the consumer test (120 consumer) the data relating to product interest acceptability and antioxidant properties were 59.20% 96.70% and 75% respectively. This results indicated that tea Ya Nang Melientha suavis and Safflower is accepted by consumer.

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.