Managing crop revenue risk is of critical importance for financial success by agricultural producers and a central theme of many government commodity and insurance programs. Debate surrounding the farm bill for example, includes various programs intended to limit revenue variability that arises from low crop prices, production declines as might happen under a drought, and so forth.
Crop insurance is critical for most commercial scale producers to protect against the consequences of poor relative crop performance or price declines, but is remains debated whether price risk or yield risk is more likely to influence insurance payments. In general, farm-level crop revenue risk results from price variability, yield variability, relationships between prices and yields, and relationships among the crops produced. It is important to first understand the underlying causes of crop revenue risk to better assess the effectiveness of various strategies and programs that might be used to mitigate crop revenue risk. Improving the understanding of the relative influences of price and yield risk is the intent of this farmdoc daily post.
To begin, it is clear that in the heart of the Corn Belt, prices and yields tend to move in opposite directions. This negative correlation is particularly evident this year as evolving prospects for lower yields due to widespread drought have led to commensurate market price increases resulting from efforts to balance supply and demand factors. Likewise, each crop production report release, acreage or yield estimate revision, or update to estimates of world production and use is met with price responses of the opposite direction compared to the understood impact on production. Aggregate level effects are relatively most pronounced, and while intense production areas have generally similar effects, the strength of the relationship tends to decline down to the individual producer level, but overall maintains the negative relationship between price and yield. This effect is sometimes referred to as the "natural hedge" and the size of its impact could be useful to better understand. There are various technical methods for decomposing variability into source components, but applications to farm revenue series are complicated by the evolving yield levels (trending upward) and by price regimes that seem to have epochal differences in general levels through time due to new markets such as ethanol, and year-to-year effects from carryover supplies and acreage shifts through time. Measuring the correlation at a point in time is thus an inexact idea, but there are useful approximations that can be used.
To begin to address this issues, yield data for each of the counties in Illinois were collected and detrended, or put onto a current basis using methods similar to those used by the Federal Crop Insurance Corporation to detrend yields under the Trend-Adjusted APH Endorsement. Failure to control for the trends through time can result in substantially overstated measures of risk. In essence, the impact of yield increases through time is added back to historic yields so that deviations around a current yield that would be expected from the historic deviations from trend can be used to proxy yield risk at a point in time.
Prices present a bit more of a challenge. For the analysis presented below, the perspective of the modern crop insurance program provides a useful guide. Each year, the Projected Price is established at roughly the time of planting decisions using the futures markets to determine. The Harvest Price is then determined during the month of October (historically November, as well), as a proxy for actual available price to a farmer. A constant basis is assumed through time, and while that may not be a good assumption for many local markets, it does not materially affect the conclusions related to sources of risk.
To construct the associated "current" price distribution, each year's percentage price change from Projected to Harvest price was computed and the distribution of price changes applied to a base price from this year's experience. All results are presented for corn. Soybeans have similar but somewhat more muted features and slightly higher shares of price risk and slightly lower correlation effects. The graph below helps illustrate the resulting measures and show the movements in prices and yields within a year are generally opposite in sign and of roughly similar magnitudes in most cases.