The markets await each USDA Crop Progress report with bated breath. Every crop year repeats an endless cycle; too wet and cold to plant, followed by too hot and dry to pollinate, ending with white knuckles about an early frost. The amount of price volatility these different stages generate depends on how close you are to the bottom of this year’s bin.
This year’s cold and wet start for the crop year would have generated some real fireworks if the U.S. and the world weren’t beginning the crop year with heavy carry-out stocks. The following table shows the USDA’s estimates for finishing stocks to total usage. With U.S. corn and soybean stocks relatively high, the market doesn’t see anything to get excited about even with plenty of poor emergence and replanting. If this weather had followed the 2012 crop year, price volatility would be running white hot.
It’s axiomatic that buyers want to buy low and sellers want to sell high. The truth is that both can be accommodated if they are willing to strike when the iron is hot. The December 2017 corn futures hit its historical high of nearly $5 per bushel in January of 2014 when trading began, and, hit its current historical low of $3.60 per bushel in September of 2016. The only sellers and buyers who stood a chance of catching the highs and lows were hedgers working with some rule driven by their likely production or usage. In all likelihood, they would not have covered all of their production or consumption at those highs and lows; however, they could have covered some significant portion based on targeted profitability or risk reduction. Those who stood no chance of hitting the highs and lows were those trying to forecast the future.
The USDA’s current 2017 yield forecast for corn and soybeans is 171 bushels of corn and 48 bushels of soybeans per acre respectively. Looking at the graph below, it’s clear that the USDA isn’t going out on a limb with these guesses. The trend model reinforces a couple of key takeaways. First, crop technology continues to deliver predictable annual yield improvement (1.5% for corn and 1.2% for soybean). Second, it’s much easier to fail big than to outperform big, and years 2002–2004 show that what hurts or helps corn doesn’t always impact soybeans the same way.
The model also emphasizes something that is almost always skipped in most statistics courses, and that is that the errors are more important than what was predicted. Too often, modelers focus on what they can explain as justification for their modeling, but what hurts the users of the models is what happens when modelers miss the mark. Both corn and soybeans show that 70% of the yield variation can be explained by where you stand in time. The other 30% is basically a swing and miss. Should a serious weather event occur, corn varies by 12% and soybeans 11%. Otherwise, the trend model will put you within a couple of percentage points of the right yield without further adjustments or worries.
Source: USDA, Wells Fargo
The question most relevant for the producer, buyer, and speculator parallels the infamous Watergate question. “What did you know, and when did you know it”? Looking at crop progress statistics from the years that had big misses (either low or high); nobody knew anything until mid-July of the respective year. And, all the weather events that matter always occur by mid-August. So, this reinforces the fact that just four weeks out of every year make or break the crop, and all the talk and trading prior to those weeks is just for fun and games. Likewise, everything afterwards is just sweeping up the broken pieces.
No doubt, this lack of enthusiasm for predicting the future runs counter to some traders “raison d’être” (French for “the reason I exist”). The real problem isn’t that people fail at achieving the impossible, such as attempting to predict the future, but rather that they were tasked to do so in the first place. Whether a family farm or a large, multinational operation, or anything in-between, all should question whether they are asking their employees for the impossible specific to predictions. If so, they should reformulate the challenge that they’re trying to solve. No doubt, they will be met with a lot more enthusiasm for goals that are achievable based on historical analysis and discipline. Remember who makes the money in the Las Vegas casinos.