Coffee prices, long range forecasts and drought

The tropical highlands of Minas Gerais, Brazil are responsible for 25% of the world’s Arabica coffee production. In 2014, the region experienced drought during the critical austral summer months of January and February. World coffee prices moved sharply higher at the end of January. By early March, prices had nearly doubled.

In an efficient market the price of a commodity reflects all available information. Did the coffee price assimilate long-range weather forecast information available in 2014 ?


The above chart show monthly rainfall (crosses) and average rainfall (black line). Rainfall totals were extracted from ERA-interim reanalysis. Periods of deficit relative to average rainfall are indicated in red. Rainfall was less than 50% of average in both January and February 2014.


Despite dryness, coffee futures actually trended slightly lower during January 2014 (above). However this situation reversed dramatically after January 29 (indicated by the red arrow). It is as though the market abruptly woke up to the fact that drought would continue well into February and that this would impact Arabica coffee fruit development.

In fact, well in advance of January 29, long range weather forecasts were indicating a high probability of continued drought in February. The graph below shows a large ensemble of CFSv2 rainfall forecasts for Minas Gerais for December, January and February. Such forecasts[*] are made every 6 hours up to 9 months prior to the forecast month. A high probability of anomalous rainfall for the months of January and February is evident some 2-3 weeks in advance.



This analysis points to a surprising conclusion. For perhaps two weeks, world coffee market prices did not properly reflect probabilistic information available from long range weather forecasts.

[*] Raw forecasts, not bias corrected.



  1. The seasonal forecasts shown in the Figure are issued 9 months before? What about the forecasts with a shorter lead-time, are they better in predicting such events?

  2. The dates on the x-axis are forecast dates (lead-times running from 9-0 months). For example, the forecasts for the month of February 2014 (bottom panel) are shown from April 2013 to the end of January 2014.

    So you are right, there is no evidence of an anomaly at lead-times longer than 3-4 weeks in this instance.


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