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Spatio-temporal model for crop yield forecasting

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  • Panudet Saengseedam
  • Nantachai Kantanantha

Abstract

This paper proposes a linear mixed model (LMM) with spatial effects, trend, seasonality and outliers for spatio-temporal time series data. A linear trend, dummy variables for seasonality, a binary method for outliers and a multivariate conditional autoregressive (MCAR) model for spatial effects are adopted. A Bayesian method using Gibbs sampling in Markov Chain Monte Carlo is used for parameter estimation. The proposed model is applied to forecast rice and cassava yields, a spatio-temporal data type, in Thailand. The data have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The proposed model is compared with our previous model, an LMM with MCAR, and a log transformed LMM with MCAR. We found that the proposed model is the most appropriate, using the mean absolute error criterion. It fits the data very well in both the fitting part and the validation part for both rice and cassava. Therefore, it is recommended to be a primary model for forecasting these types of spatio-temporal time series data.

Suggested Citation

  • Panudet Saengseedam & Nantachai Kantanantha, 2017. "Spatio-temporal model for crop yield forecasting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(3), pages 427-440, February.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:3:p:427-440
    DOI: 10.1080/02664763.2016.1174197
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    References listed on IDEAS

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    1. Rahman, N.M.F, 2010. "Forecasting of boro rice production in Bangladesh: An ARIMA approach," Journal of the Bangladesh Agricultural University, Bangladesh Agricultural University Research System (BAURES), vol. 8.
    2. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
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