Models for Analyzing Economical Crop Yields in Thailand

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Pitsanu Tongkhow
Pornpit Sirima
Krisada Lekdee
Rachadasak Supengcum


This research is to propose an appropriate and efficient model for analyzing economical crop yields, rice, sugarcane and corn, in Thailand. The data were collected from provinces in the central region of Thailand, 22 provinces for rice, 9 provinces for corn and 14 provinces for sugarcanes, from 2007 to 2019. A linear mixed model (LMM) including spatial relationship explained by a conditional autoregressive (CAR) model was adopted. The Bayesian method was used for parameter estimation.  The factors related to the production of rice, sugarcane and corn were also investigated. The results showed that the amount of rain, temperature, region and spatial effects affected the yields.  When comparing the LMM with spatial relationship and the one without spatial relationship, it was found that the LMM model spatial relationship was more suitable than the one without spatial relationship. For each yield, the mean square error (MSE) of  the LMM was greater than the MSE of the proposed model (rice yields: 6.07 vs 1.59, corn yields: 7226.11 vs 6287.44 and sugarcane yields: 76.76 vs 46.69).

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