Sunspots Stir the Lynx-Hare Cycle

The data from the Greenwich Observatory, specifically the long-period sunspot counts, have always held my attention. Not merely as astronomical curiosities, but as potential drivers, or at least correlates, of terrestrial phenomena. The latest bulletin on the Lynx-hare cycle, confirming Lotka-Volterra oscillations with a 9.8-year period, stirs a thought. This 9.8-year cycle is remarkably close to the average sunspot cycle of approximately 11 years. The null hypothesis is that no causal or correlative link exists between solar activity and the population dynamics of Canadian fauna.

Consider the price of wheat in English markets, recorded meticulously in historical ledgers. A dataset I have been examining. The price fluctuations exhibit periodicities. A spectral analysis, applying the same techniques used to identify cycles in the lynx-hare data, reveals certain dominant frequencies. One such frequency approaches the decadal. This is not a direct correspondence, but a proximity. The question then becomes: is this a mere coincidence, a statistical artifact of common underlying environmental drivers, or does the sun, through its influence on weather patterns and thus agricultural yields, impose a predictable rhythm on market prices?

The initial correlation coefficient between detrended sunspot numbers and the inverse of wheat prices (reflecting abundance) is weak, perhaps 0.2. This is a description of covariation in this sample. The t-test for this correlation yields a p-value of 0.18. We fail to reject the null hypothesis at the conventional alpha of 0.05. This does not mean the null is true. It means the data do not provide sufficient evidence to reject it. The sample size for reliable, consistent sunspot data and comparable wheat price series is limited, particularly for pre-18th century records. The power of this test to detect a small but real effect is low. Further investigation requires more granular data, perhaps regional price series which might show greater sensitivity to local climate variation. The decision is to disaggregate the series and re-examine.