The Surprising Costs of Bad Crop Yield Estimates
July 29, 2016
Forecasting is a cornerstone of farming: making accurate crop yield estimates in the face of uncertainty is part of the job description. In a previous article, we looked at the uncertainty of crop prices. In this post, we’ll examine yield risk, a second major source of uncertainty on the farm.
We generally find that most farms keep pretty detailed records of their historical crop yields, often with field-level crop yield data that goes back ten, sometimes even twenty, years. However, most of these farms aren’t fully taking advantage of this information when estimating their future yield. Before going into techniques for estimating crop yield, let’s first establish why estimating crop yield is so important.
Crop Yield Estimates for Decision Making
Poor crop yield estimates can easily lead to poor decision-making. Let’s look at two common examples where it is critical to have good yield forecasts:
Crop Decisions: We see that growers’ crop yield estimates typically differ from robust statistical estimates by 10 to 20 bushels/acre, but even smaller differences than that can have big effects. For example, one of our customers was overestimating corn yields by only 4 bu/acre and underestimating soybean yields by 7 bu/acre. When calculating the relative profitability of those two crops, these estimates led to underestimating the profitability advantage of soybeans by about $90/acre.
Land Rent Decisions: In another example, a customer overestimated his non-irrigated yields by 49 bu/acre for corn and 5 bu/acre for soybeans. That means that they overestimated the revenue on their fields by somewhere between $50 – $190/acre, which easily makes the difference between making or losing money on those fields.
Common Mistakes When Estimating Crop Yield
We see a few common mistakes that growers make when estimating their yields:
While it can be tempting to put in crop yield estimates that give nice bottom-line revenue numbers, the first step of making good estimates is to base your numbers on real historical data – not your gut, not your expectations. Unlike grain price,s the best predictor of future yield performance is past performance.
Using Only Recent Data
We find that many growers are hesitant to use all of their historical yield data when forecasting next year’s yield. We often hear that varieties and practices have changed enough that data from ten years ago is no longer a good predictor of future yields. We disagree. With proper “de-trending” (taking into account the fact that yields have increased on a farm over time), data from ten years ago is still very relevant when estimating next year’s yield. You want to make sure that you are capturing the risk of “unusual events”, and that can’t be done if you are only looking at the past five years.
ARC payments have made popular the idea of “Olympic averages”, which means throwing out the highest and lowest data points. While this may make sense when judging figure skating, it is not a good practice when trying to forecast long-term expected yields. Consider the following 5-year yield scenario:
An Olympic average would be equal to 150 bu/acre. If you take into account the bad years, which have a 20% chance of happening, you should really expect 130 bu/acre per year in the long run. Those 20 bu/acre per year over the long term make a pretty big difference when figuring out which rent you can afford.
The Power of Good Crop Forecasts
The good news is that by using some relatively straightforward techniques, it is possible to build robust working estimates of yield.
Good methods when creating crop yield estimates;
- Take into account a farm’s full history and don’t “throw” out unusually good or bad years
- Don’t penalize a field for the vagaries of nature. Just because you happened to grow corn on that field during a drought year doesn’t mean it is a bad field. This means that you should take into account historical weather effects when estimating future yield.
- Quantify the yield risk for both individual fields and your entire farm: you should be able to know what the probability is for specific yield outcomes. For example, we show below an analysis with real data for two real fields on the same farm. Not only can we see that Field 2 is much better for growing soybeans, but we can quantify the chances of different outcomes. For example, we can determine:
- Field 1 has a 77% chance of yielding less than 50 bu/acre
- Field 2 has a 0.4% chance of yielding less than 50 bu/acre
- Field 1 has a 0.5% chance of yielding more than Field 2.
To wrap it up: crop yield forecasting is a critical component of decision making on the farm, and there are some common mistakes people make even if they base their forecasts off of their historical data. The good news is that with proper analysis, a farm’s historical yield can provide powerful insight into important decisions.
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