Weather Data and the Supply Chain

There is a lot of environmental data available at the public's disposal. If structured and viewed in the right way, that data can provide insights on the supply side of many basic commodities. Assessing food production potential for basic necessities is the first true step in understanding the global agricultural supply chain.

There's no such thing as a perfect forecast. Even with forward-looking metrics that can be deemed 100-percent accurate, there are variables that go into determining, for instance, how many bushels per acre a particular region's wheat crop will yield. Forecasting is part art, part science.

From the science perspective, the more clean, reliable data that we can obtain and plug into a model, the better we may get at determining production potential, or more importantly, highlighting areas that may be susceptible to a weather risk.

A simple example of the climate/food relationship: Casual weather observers are probably familiar with the El Niño Southern Oscillation cycle (referred to as ENSO). This particular phase of this large-scale physical weather driver often governs the global pattern. While an El Niño dictated much of the US pattern in 2009 (remember snow being trucked in for certain Olympic events in Vancouver?), the opposite La Niña has developed in 2010, which brings its' own set of variables.

The current La Niña is one reason that our January-February outlook at Weather Trends is for a little cooler than last year for the western US. We also can use the ENSO index to assess yield potential for a commercially important commodity: milk.

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