Yield and price forecasting for stochastic crop decision planning

Nantachai Kantanantha, Nicoleta Serban, Paul Griffin

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


The primary objective of this paper is to develop yield and price forecasting models employed in informed crop decision planning-a key aspect of effective farm management. For yearly yield prediction, we introduce a weather-based regression model with time-dependent varying coefficients. In order to allow for within-year climate variations, we predict yearly crop yield using weekly temperature and rainfall summaries resulting in a large number of correlated predictors. To overcome this difficulty, we reduce the space of predictors to a small number of uncorrelated predictors using Functional Principal Component Analysis (FPCA). For detailed price forecasting, we develop a futures-based model for long-range cash price prediction. In this model, the cash price is predicted as a sum of the nearby settlement futures price and the predicted commodity basis. We predict the one-year commodity basis as a mixture of historical basis data using a functional model-based approach. In both forecasting models, we estimate approximate prediction confidence intervals that are further integrated in a decision planning model. We applied our methods to corn yield and price forecasting for Hancock County in Illinois. Our forecasting results are more accurate in comparison to predictions based on existing methods. The methods introduced in this paper generally apply to other locations in the US and other crop types. The supplemental materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)362-380
Number of pages19
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number3
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Agricultural and Biological Sciences (miscellaneous)
  • General Environmental Science
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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