TEST CODE HERE

What would you do if your agronomist told you that planting your crop at 1,000 ft in elevation was a sure way to increase your yield by 5 bu/acre? You’d probably step back and wonder if you’re getting your money’s worth from your consultant. What if, instead, the benchmarking program you pay for told you that? For many farms, that is exactly the kind of results they’re spending money on.

Screen Shot 2016-06-01 at 10.18.19 AM
Example 1. Judging by the red “error bars”, this analysis suggests that someone is very confident that yield changes dramatically by changing elevation by 300 ft.
In the last few years, a lot of time and money has been spent publicizing the benefits of cross-farm comparisons and analyses. “Benchmarking” has claimed its spot in ag’s top buzzwords, and while a lot of benefit can come with understanding how your operation stacks up against peer farms, conclusions need to be drawn accurately and carefully. Poor benchmarking can encompass many problems, ranging from data quality (such as aggregating incomplete or mislabeled data) to inaccurate analysis. Many readers will probably be familiar with “as planted” files that have the wrong hybrid listed, but there are also many examples of incorrect analysis that are harder to catch.
Issues with analysis methodology and visualization are often the easiest to spot. Here are some more examples we’ve seen in the market – we’ve recreated the original data to preserve the anonymity of the companies involved:
Screen Shot 2016-06-01 at 10.20.25 AM
Example 2. This suggests that yield goes up at 130k and 170k seeds/acre, but somehow 160k seeds per acre is bad for your field.
Screen Shot 2016-06-01 at 10.20.34 AM
Example 3. This plot suggests that a) yields go down after you reach a soil productivity index of 0.3 and b) some people are farming ground that has a negative productivity index – something that doesn’t even exist in reality!
Most farmers looking at the charts above would realize this issues and not make a decision based on the visualization output. However, it is difficult or often impossible to judge the methodology or the data quality that went into the analysis. For example, one of the most common analyses published is a ranking of yield by variety, which typically shows up as a table like this:
Screen Shot 2016-06-01 at 10.20.46 AM
Example 4. Yield by Variety Analysis
In this example, the farmer typically has no way to tell if the data or analysis used to make the table was sound. They thus need to trust that the data they are are using is high quality data that has been analyzed properly…a trust that is sometimes misplaced. Here are some practical takeaways to keep in mind when you are looking at benchmarking data:
1. Find companies that you trust. Finding bad data is like finding a mouse: if you spot it once, there is probably a lot more that you haven’t noticed.
2. Make sure the results you see pass the “common sense” test: do the results make logical sense? Hint: several of the conclusions you could draw from the examples shown above don’t.
3. Understand the sample set and make sure you are looking at data that is representative and relevant for your particular operation. “Yield by variety” that includes irrigated land in Nebraska, for example, isn’t that helpful if you are a dryland farmer in Kansas.
4. Remember that the value of benchmarking can be positive or negative: making a poor decision from questionable analysis can be a lot more expensive than what you paid to subscribe to the benchmarking service
5. Correlation is not causation. Here is a simple example: people plant slower on hills. If yield is lower on steep slopes, an analysis of yield vs. planting speed will make it look like planting slower is bad for yield. Also, adding more farms to the sample won’t fix the problem, so don’t fall for the line “more farms average out potential errors”.
6. Make sure you understand why the company is providing you benchmarking: is their business based on providing accurate data, or do they have another way of making money? Good benchmarking takes a lot of work and focus – it shouldn’t be a company’s afterthought.
Despite the preceding examples, we think that benchmarking is here to stay, and that it can provide a lot of real value. There is a lot of well analyzed, useful benchmarking programs out there (you can read about some of our at work at Granular here and here). However, the quality of data in the marketplace currently varies tremendously, and benchmarking as a whole runs the risk of ending up with a bad reputation. Before you make important business decisions based on a benchmarking or cross-farm analytics program, look closely at where the data comes from and how it was analyzed – not just where you show up relative to others!

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Everyone knows there is a lot of risk in commodity prices right now. However, people often struggle to figure out exactly how much. We can borrow some basic finance concepts and publicly available information to quantify this risk.

Right now, market prices indicate that Dec’16 corn has a 15% chance of going below $3.00, and Nov-16 soybeans have a 13% chance of dropping below $8.00.
The Chances of Sub-$3 Corn (And How to Reduce Them)
The Chances of Sub-$3 Corn (And How to Reduce Them) (1)

Figure 1 These are corn and soybean price risk distributions (as of April 21st). You can analyze the areas under the curves (you can read more details here) to determine that there a 15% chance of corn going below $3.00/bu and a 13% chance of soybeans going below $8.00/bu.

 
For this analysis, we leverage the fact that known options prices reflect the uncertainty in the price of the underlying asset. For example, on April 21st, Dec-16 corn futures were trading at $4.03. An option to buy Dec-16 corn for $4.30 was trading at $0.24. What determines the price of that option? In part, the uncertainty in corn price. If corn prices are very uncertain, then there is a greater chance that corn prices will go up, and therefore the option would be worth more. If corn prices were absolutely certain (i.e. we knew that corn prices were going to stay at exactly $4.03), then the option wouldn’t be worth anything, since you wouldn’t ever exercise it . Using options prices to calculate price risk is a commonly used procedure in finance, and it is even used to determine crop insurance rates (a great summary article on how it is used to calculated crop insurance premiums can be found here). For now, we’ll just stick with the punchline: option prices can be used as a measure of the market’s estimation of price risk.
One striking thing about the price ranges in Figure 1 is their width: the fact that corn prices could range between $2 and $6 is what makes farming one of the riskiest businesses. However, farmers have many tools to reduce that risk: forward contracts, hedging, and insurance are some of the more common ones. As a simple example, let’s examine the effect of forward contracts on our risk model:
Many growers at this point have already forward-sold a good portion of their 2016 crop. We can use our risk model to calculate their final price risk by combining the known value of their sold crop with the risk associated with the unsold crop. Before the sale, there is a 15% chance that your final price would be under $3/bu. After the sale, you’ve reduced the risk your final crop price being below $3/bu to 0.4%. Of course, you have also limited your potential upside while reducing your downside risk.
The Chances of Sub-$3 Corn (And How to Reduce Them) (2)

Figure 2 Effect of forward sales on price risk on Dec-16 corn, before and after selling 50% of the crop at today’s price for 2016, assuming the same April 21st prices used above.

 
Managing risk is a key component of successful farming – as important as the agronomics and operational aspects. Market prices of futures and options contain a lot of useful information about price risk, and you can use this data to drive real marketing decisions. It is clear that with the current market conditions, the farmers who know how to evaluate risk will be ahead of their peers, and more quickly earn the trust of their lenders.

Latest

How a Potato Grower Found 11% More Profit Using Granular

Learn how Granular helped a real farm discover that their variety choice was costing them $800 per acre

Karl Wozniak, Role
  |  
September 11, 2017

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Our Continued Committment to Independence and Data Privacy

Karl Wozniak, Role
  |  
September 11, 2017

Our Continued Committment to Independence and Data Privacy

Karl Wozniak, Role
  |  
September 11, 2017

Our Continued Committment to Independence and Data Privacy

Karl Wozniak, Role
  |  
September 11, 2017

Industry Insights

Three Ways to Run a Successful Harvest

Three Ways to Run a Successful Harvest

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Events

Grow 2018

Granular's Annual Customer CEO Summit

March 21-33, 2018

California Academy of Sciences, San Francisco, CA

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