An Efficient Analysis of Cassava and Rubber Yields in Thailand using GEE and LMM models with Spatial Effects

Rachadasak Supengcum, Watcharin Sangma, Pitsanu Tongkhow, Sompong Chueaprakha

Abstract


The objectives of this research are to propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating quation (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 Thai-land.  The factors considered are rainfall, averaged temperature, and regions.  The results from GEE and LMM showed that the factors influencing on the cassava and rubber yields were rainfall, averaged temperature, and regions. Both GEE and LMM fitted 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.

Keywords


Crop yields, Linear mixed model (LMM), generalized estimating equation (GEE), Conditional autoregressive model (CAR), Spatial relationship, Bayesian estimation

Full Text:

PDF

References


Yield data. the Office of Agricultural Economics [updated 2019 Aug 19; cited 2019 Aug 9]. Available from: http://www.oae.go.th/

Liang, KY. and Zeger, SL. Longitudinal data analysis using generalized linear models. Biometrika, 1986; 73: 13-22.

Limmun, W. and Ingsrisawang, L. Study of factors affecting road traffic accidents using generalized estimating equations and generalized linear models. Journal of KMUTNB. 2010; 20(2): 311-321.

Amnarttrakul.R and Ingsrisawang.L.2012.A Model for Prediction of Efficiency of Induced Draft Counterflow Cooling Tower Using Generalized Estimating Equations, Burapha science journal,pp.87-96.

Lekdee, K. and Ingsrisawang, L. Risk factors for malaria in Thailand using generalized estimating equations (GEE) and genalized linear mixed model (GLMM). Journal of Health Science. 2010; 19: pp. 364-373.

Saengseedam, P. and Kantanantha, N. Spatio-temporal model for crop yield forecasting. Journal of Applied Statistics, 2017; 44(3): 427-440.

Sammatat, S. and Lekdee K. Estimation and Detection of Rice Yield in Thailand Using Spatial and Longitudinal Data Analysis, Applied Mathematical Sciences, 2019; 13(18) : 877 - 884.

] Fox, J.-P., Veen, D., & Klotzke, K. (2019). Generalized linear mixed models for randomized responses. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 15(1), 1–18.

Islam,M.A.and Chowdhury,R.I,2017, Analysis of Repeated Measures Data, Springer, Singapore,pp.161-167. [10] Pedroza, C. A Bayesian forecasting model: predicting U.S. male mortality. Biostatistics. 2006; 7(4): 530-550.

Banerjee, S. Carlin, B.P. and Gelfand, A.E. Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC Press. FL. Janeway CA, Travers P, Walport M, Shlomchik M. Immunobiology. 5th ed. New York: Garland Publishing. 2004.

Congdon, P. Bayesian Statistical Modelling, 2nd ed., John Wiley & Sons: New York(2006)1-56.

Cowles,MK. Applied Bayesian statistics: with R and OpenBUGS, New York, springer. 2013.

Temperature data. the thai meteorological department [updated 2019 Aug 19; cited 2019 Aug 9]. Available from: https://www.tmd.go.th/en/.




DOI: http://dx.doi.org/10.52155/ijpsat.v22.1.2005

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Rachadasak Supengcum, Watcharin Sangma, Pitsanu Tongkhow, Sompong Chueaprakha

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.