Category: Data Decisions

Increasing return on investment for every input is clearly on the mind of every grower. They are in an economic squeeze, but that doesn’t mean they won’t invest more in the crops. It does mean they are scrutinizing every dollar they spend. The current success measure now goes beyond yield increase to include which inputs offer better return on investment.

During Premier Crop’s 20 years in business, one of the most dramatic changes has been the evolution of the bio-tech seed industry. University of Illinois research shows per-acre seed investments have increased 4 to 5-fold in that time. While yields have increased, seed cost per bushel produced has also increased 4-5 times. Part of the increased cost/bushel has been offset by reduced herbicide and insecticide/acre investments.

But this is not your father’s seed investment! The times and economics associated with managing your seed investment have changed and deserve more attention.

The return on investment for managing your seed investment within fields through variable rate seeding has never been higher. How do you know your ROI on variable rate seeding? For Premier Crop, the answer has always been tying input cost to the as-planted or as-applied files, dding all input, land, management costs to generate a cost per bushel map for each field. Why cost/bushel versus a profit map? Our experience is that the first step in a solid grain marketing plan is knowing your cost per bushel of production. Grain marketing in most operations can be a 12-month process and we want to be able to deliver results and analysis as soon as the yield file is processed.

Consider this field’s story:

 

Originally published in Corn and Soybean Digest.

Is it possible that 3 and 2 can equal more than 5? That’s the concept of synergy — when the “whole equals more than the sum of the parts”. Within our company, we talk a lot about agronomic synergies. We see it in data analysis and we believe that discovering and capitalizing on agronomic synergies is an exciting part of our future in using data to make better decisions.

Listen to Dr. Scott Murrell, director of International Plant Nutrition Institute, talk about nutrient research. “Most of the studies I read look at plant responses to one nutrient, and of those, nitrogen (N) gets most of the attention. But occasionally, more accurately, rarely, I come across a study that looks at two or more nutrients.” He goes on to cite two studies.

Both are great examples of the power of agronomic synergies.

Because nitrogen gets the most attention, our customers have been using their data to refine their N rates from our beginning 17 years ago. Any deep dive into their data reveals what appears to be the “right” rate that differs dramatically within parts of fields. One of the earliest realizations we had is just how fundamentally “wrong” our historic approaches had been. Remember the old formula — 1.2# of N per bushel of yield goal less credits for manure, legumes, and other sources of applied N.

Using that approach would lead us to put more applied nitrogen on our best and frequently highest organic matter soils because they have the highest yield potential. But our finding years ago, showed that our best soils have more ability to furnish soil-supplied N from mineralization than our lighter soils. The data would show that we actually need more applied N per bushel produced on our lighter soils than our best soils.

But even that statement is a generalization that captures only one (yield by organic matter by applied rate) of the many synergies that are part of the nitrogen puzzle. We’re excited to empower growers and advisors to discover and capture all the other agronomic synergies that are waiting to be unlocked in their fields and their data!!

Our goal is to make 3 and 2 turn into 32 = 9!!!

 

Originally published in Corn and Soybean Digest.

Sometimes “average” can be your worst enemy.

Everyone has heard far-fetched examples that illustrate the problem with “average.” Picture standing with one foot in ice-cold water and the other foot in steaming-hot water — the average of the two is OK. Sometimes that’s what using averages can do in your farming operation.

An example would be using average as you consider your fall nutrient budget. Tighter, even negative, margins will cause many to scrutinize input spend- ing this crop year with much more intensity. Unfortunately, too many suppliers will simply calculate the most nutrients that can be applied for your per-acre budget and apply those rates on every crop acre. For all the headlines about precision agriculture, the majority of nutrient applications are still the same blend applied across every acre.

Agronomically, a better answer is to focus your input dollars in the site-specific areas of individual fields that will provide you with the most economic return on your nutrient investment.

At some point in our farming history, all of us have seen a nutrient response curve. Applied nutrient response curves tend to have these similar characteristics — steeper at lower levels and flatter at higher levels. For example, most of us understand that generally we get more yield response for the first 50 pounds of nitrogen applied than the last 50 pounds. Finding maximum economic return is the basis for nutrient response curves. Think about response curves as you review the soil test phosphorus map above (expressed in parts per million — double the numbers if you are used to pounds per acre). If you composite-sample this field, the average is 20 ppm of P — running parallel with many university threshold levels where a response to applied P can be expected.

Using university recommendations as a reference, virtually any type of georeferenced soil sampling will reveal that in this field, spreading the same rate of P on every acre will result in no yield response on 50% of the acres (the green part of the map). As bad as that might seem, there is a decision even more economically harmful. Making a decision to not apply any phosphorus because the field’s average soil test level is 20 ppm would likely result in lost yield in 50% of the field (red, orange and yellow areas).

None of us can control or even predict all the curves Mother Nature can throw our way. But using data from your fields opens the door for you to manage the crop production variables that are manageable. This fall’s tight economics are a great time to put all your data to work — to stay away from averages. If you don’t have much data, now is the time to get started. The cost of a good grid soil sample spread over the normal four-year life equals less than one-third of 1% of your production cost.

Collecting and using your data can move you away from defaulting to using averages and getting average results.

 

Originally published in Corn and Soybean Digest.

Some people remember phone numbers or calendar dates; I remember farm fields. Before the 2005 crop year, the program leaders for Central Advantage from Central Valley Cooperative in southern Minnesota asked me to help generate variable-rate planting prescriptions. The primary question was, “Agronomically, what makes sense?”

Field by field, I looked at the data collected for historic yields, soils, fertility levels, cation-exchange capacities, etc., and generated variable-rate prescriptions for each field. But I didn’t stop there. I wanted to prove that what I thought made sense agronomically would truly work.

To do so, I put 1- to 2-acre check blocks within each area/ population rate of the field. That fall, I carved out the yield for each acre check block and compared it to its surrounding area. I still remember those fields and the stories the check blocks told as we learned and proved concepts.

Since those days, we [Premier Crop] have trade-marked the name Learning Blocks and have automated the process for analyzing yield inside Learning Blocks compared to the surrounding area via planting, fertilizer and other inputs. In my entire career, I have never “sold” anything as popular as Learning Blocks! Growers love them for many reasons. Most notably, they make sense — comparing 2 treated acres to 4 non treated acres within a management zone is a great apples-to-apples comparison. And, the technology does all the work — the grower does not need to slow down planting or harvesting to learn from the data.

Growers enjoy not being told “trust us, this recommendation works”; but rather are given the ability to check and validate the recommendation. In the case of planting, growers can see Learning Blocks by downloading them to their smartphone and physically walking to a Learning Block, verifying the population change. Learning Blocks are a low-risk strategy for implementing changes. For example, planting 39,000 plants per acre or increasing nutrient rates across an entire management zone might be a stretch, but every grower would risk a 2-acre Learning Block to test the limits.

Premier Crop has done plenty of strip trials in the past — running different treatments across the entire length of a field. For variable populations, the strip trial approach means running high populations across all management zones. Everything we gained from increasing populations in an A zone, we lost when the strip ran across a C zone.

When Premier Crop talks about using data to make decisions, Learning Blocks are a vital part of that decision-making strategy. They give growers the knowledge necessary to refine further the prescriptions in their fields, with confidence and data to back them up.

 

Learning Blocks are small 1- to 3-acre blocks within a management zone that create a low-risk way to learn in real-world agronomic situations.

 

Ask yourself

1. How do you discover what is and is not working in a management zone? Does your trial match the variability within each zone?
2. How would you implement Learning Blocks into your operation to better understand yield response to applied nutrients?

 

Originally published in Corn and Soybean Digest.

Throughout Premier Crop’s nearly 20-year history, we’ve perhaps been the most diligent at communicating that what we do — big data analysis — would be considered “observational data analysis” by those in the scientific community.

Early on, my slide deck featured this chart — a photo on the left from my earlier years versus today — to explain that observational data analysis can show us relationships and correlations. But, it stops short of proving cause and effect.

Within crop production and agronomy scientific circles, making decisions using observational data analysis has been viewed as inferior and some would argue it’s an informed guess. It’s only been in the last few years, with the dramatic investments by major ag companies, that millions of yield observations have been validated as valuable in crop production decision-making.

For decades, the foundations of agronomic knowledge have been the results of small randomized replicated plots. That experimental design and the statistical analysis dates back to 1930’s, with Sir Ronald Fisher’s analysis of variance (ANOVA), whom many consider the father of modern statistics. All universities and industry companies have adopted and use replicated plots as the gold standard for conducting trials and proving the a change in treatment actually causes a change in yield.

Premier Crop has now added randomization and replication to our Learning Block concept with an offering we are calling Enhanced Learning Blocks. It allows us to move beyond correlation and experimentally establish causation.

With over 500 successful trials in the 2016 crop year, we are now able to scientifically prove the value of using variable rate technology in a grower’s operation. And the results are dramatic. In several examples, customers with 5 replications of 3 different planting rates were able to prove that the ideal seeding rate varied over 9,000 seeds per acre, with resulting yield results greater than 40 bushels per acre. That’s a swing of over $30/acre in seed cost and $140/acre in revenue.

The beauty of these experiments is that the technology you’ve already purchased in the cab does all the work — there is no extra work needed for this experiment. You plant, apply and harvest the experiment with the same technology used on your farm. We refer to Enhanced Learning Blocks as “knowledge creation at the speed of farming”!

 

Originally published in Corn and Soybean Digest.

Virtually every precision ag survey done with growers and industry over the last 20 years would rank “getting different systems to work together” as the greatest frustration and obstacle to growing the overall market.

It has seemed like each piece of software, each monitor and each system had their own unique file format. Sometimes “precision ag experts” could be defined simply as people that could make all of the technology talk to each other and actually work in the cab. Their value to growers wasn’t measured on whether the prescription made sense agronomically or economically — but just that they could make it work.

I want to take a break from this column’s normal message, using your data to make better agronomic decisions, to high- light a major industry effort to solve this daunting problem. The industry effort is called ADAPT and the ADAPT project is organized as part of AgGate- way, an industry standard organization. For the past two years, two teams from participating industry companies have been meeting online every week. One team focused on technical issues and one team focused on business issues. ADAPT’s purpose: eliminate the major pain points to the broad use of precision agriculture data by easily enabling interoperability between different software and hardware applications.

Now, I’m not quite geek enough to understand some of terms and descriptions these folks use so I’ll try to explain their work as simply as possible.

The ADAPT solution allows each equipment manufacturer to keep their own proprietary software and technology in cab and monitor (Mobile Implement Control System) but all participating companies will “export to” and “import from” a common open-source ADAPT file format! Farm Management Information Software (FMIS), the industry’s term for companies like Premier Crop, will be able to program “one-time” and be able to receive data from all companies using the ADAPT format, including other FMIS companies. And program “one-time” to be able to export a prescription to any monitor that is using ADAPT.

This isn’t something that will happen — it’s something that IS happening right now! The ADAPT team’s hard work has now paid off. Virtually all the major equipment companies and software companies have committed to using ADAPT. A formal press release will be issued soon.

You can lend your support to drive the effort even faster. Since companies respond to what growers (customers) want and need, you can add your support by logging on to www.adaptframework.org and follow the link to “CLICK HERE TO TAKE THE DATA MANAGEMENT SURVEY”. Let them know how important this effort is to you and your operation.

If you’ve been looking for the “Easy” button — it’s on the way!!!

 

Originally published in Corn and Soybean Digest.

Hunters and soil scientists may seem like an odd pairing but they have at least one thing in common — they know and appreciate that nature has an aversion to straight lines. Hunters spend a lot of time in and observing the great outdoors and getting an up-close look at the variability Mother Nature molded upon our landscape. Soil scientists not only spend time looking at the curvy contour lines that represent the transition from one soil type to another but their academic training is about the “how’s and why’s” of soil formation over the centuries.

Unlike nature, humans have figured out how to perfect designing making straight lines! From early days of the very first mechanical planters and “cultivator blight”, the straightness of our rows was something that created neighborhood envy. Nowadays auto-steer has made it easy.
Straight lines are one of the first “gotcha’s” when studying a yield map. The cause for yield differences that follow straight lines are always man-made! It can be a variety change, a different nutrient application, a crop protection treatment, an equipment performance issue, a tillage pass or even something like a manure application we did years earlier. Seeing a straight line on a yield map instantly leads to digging deeper!

Yield maps are an awesome way to visualize data differences however a second “gotcha” is not paying attention to the map legend! Years ago, I had a college friend send me an image of one of his yield maps, with a note that said “you see, we really don’t have much variability in our area”. But as I studied his map, I noticed that there was the predominant green color on his yield map had a 40-bushel- per-acre range.

The two maps above use the exact same yield data.

There is not a “perfect” way to set map legends. The key is to also LOOK AT THE LEGEND — not just the map!

 

Originally published in Corn and Soybean Digest.

Telling your customers they are under-performing never seemed like a great business model to me. Benchmarking can have that exact effect — 60% aren’t performing well if you remember teachers grading on the curve back in school. Those at the top might enjoy the satisfaction of knowing they are the stars but how do you gently push the below average customers to step up their game?

I understand that bench-marking can be used effectively, but I also know sometimes it hurts. An example of benchmarking that many can relate to is the medical community’s use of BMI — Body Mass Index. At my height of 6’2″, my weight bounces from 220 to 214 lbs. so I move from one benchmarking category to another, leading my very slender, young and blunt doctor to proclaim “congratulations, you’ve moved from obese to grossly overweight”. Using the BMI, I need to get below 190 lbs. to reach “normal”.

Benchmarking can serve the purpose of providing us a kick in the pants to exercise more and eat less. The problem with agronomic benchmarking is that the solutions to reaching better numbers aren’t as obvious as exercise and eating less.

When seed companies tell you the genetic potential of a bag of seed corn is 500 bushel per acre and it starts going downhill once you open the bag, it’s almost implied that they’ve done their part and you are the one that is failing to perform.

Comparing my yields to those in the counties I farm in might be okay unless I farm the poorest soils in each county, then I might resent someone pointing out the obvious — that my yields are below the average. Comparing my yields to those of others that farm the same soils in my part of the state is better but being labeled as being in the bottom quartile isn’t fair if I didn’t get similar rainfall. Even if both soils and weather are similar, what about rotations? What about cost of production? Maybe all those higher yields came at a high production cost?
So what are the keys to meaningful agronomic bench-marking? I’d suggest these as a few of the important keys:

1. Realistically quantify the growing environment to get closer to apples to apples comparisons. Look longer-term — look for trends over multiple years. Everyone has a great or a bad year once in a while but looking at longer-term trends are more meaningful.
2. Look longer-term — look for trends over multiple years. Everyone has a great or a bad year once in a while but looking at longer-term trends are more meaningful.
3. The more depth of data, the more value in the benchmarking. Depth will provide you with more confidence in the comparison as well as more answers.

The best benchmarking services don’t just tell you where you rank — but they tell you why. What does the data say you need to change to perform better or to keep doing to stay on top?

 

Originally published in Corn and Soybean Digest.

1 pound into a sample bag, send it off to the lab, get the results back, and then pretend that what was on that sheet of paper accurately represented the nutrient levels for that entire field. While that may have been the best we could do then, we can do much better now — but many are treating entire fields the same. The economics of grid or small-zone sampling fields and variable-rate applying lime, P and K need to be revisited by many growers and their advisers. Nutrient prices have tripled since grid sampling was first introduced, and grain prices have escalated from the $2.50 per bushel days — meaning the reward for managing your nutrient investment intensely has never been higher.

In the early days of grid sampling, too many preached we would even out nutrient levels within fields by applying P and K on the low-testing areas while mining down the high-testing areas. The pitch was that in a few years, the map that previously showed so much variability would be one color, with each part of the field in the same nutrient range. However, 15 years and seven to 10 variable-rate applications later, very few fields have uniform nutrient levels. Trying to create uniform fields was the wrong goal — the goal needed to be higher yields, not ppm’s on a map. Grid sampling and variable-rate nutrient applications are needed foundations for getting started in precision agronomy.

In many fields, a comparison of yield and fertility data will show the lower-testing parts of the field are the highest yielding. These high-yielding parts of the field are what we define as “A zones,” A standing for aggressive. A zones can test lower in nutrients because consistently higher yields remove more nutrients (see table). Even though variable-rate applications might put more nutrients on these A zones, it is frequently not enough to catch up with increasing nutrient removal through even higher yields.

Think of A zones within your fields as Olympic swimmers — able to consume 8,000 calories per day and remain lean and fit. A zones are so productive, they warrant an all-you-can-eat buffet.

Just as A zones call for being even more aggressive, for us, C zones are the parts of the field that justify being more conservative. In fields that have had straight-rate blends applied for years, it is easy to pick out C zones in the data. These consistently lower-yielding areas can become nutrient obese from lack of productivity. For Premier Crop, variable-rate apply- ing nutrients is not about saving money; it is about reapportioning a higher nutrient investment within each field to maximize nutrient efficiency. One rate does not fit all.

Got data?

1. What changes have you implemented with your nutrient application investment since the prices of nutrients, corn and soybeans have escalated over the past several years? Have you seen a positive return on that investment?
2. Do you see your yields “leveling off”? In what areas of your field could you push more? Sit down with your agronomic adviser to discuss ways you can get a yield bump for your 2014 A zones.
3. How do you proportion your nutrient investment from field to field, and acre to acre.

 

Originally published in Corn and Soybean Digest.

Difficult economic times tend to bring out skepticism or at least a review of current practices. Recently, I was asked “Do variable rate applications of any crop input really pay?” To some it might be surprising that 20 plus years after variable rate technology was first brought to the market, there are still so many that haven’t “bought in” to the concept.

It doesn’t surprise me though. As we grow into new markets, it’s very common to find that virtually no one is doing any variable rate applications — not lime, not phosphorus or potassium, not nitrogen, not seeding or crop protection products. Reality is, across the country there are by far more flat rate applications of crop inputs than variable rate applications. It’s not really that hard to figure out why this is the case. Flat rate applications are easy — no files to move, no prescriptions to create and a shorter discussion for an input supplier to get the order.

In some parts of the country, growers that used to variable rate crop inputs no longer do. When you ask these growers why they quit — frequently the answer is “I didn’t know if it paid.”

But all that has changed. Using your yield file, you can now quantify results and answer the did-it-pay question. Since 2005, we’ve been checking our work with our trademark Learn- ing Blocks and we have proved, and continue to prove every year, that variable rate applications frequently pay. It’s not 100% but given the complexity of our modern crop production the results from millions of acres are amazing. In this example, the grower used Learning Blocks as a low-risk way to test whether the nitrogen prescription was correct.

Our experience has been that growers love the Learning Block concept. They love the idea of advisors who are willing to check their work. Still today, that is uncommon. With regard to most variable rate prescriptions sold today there is no validation of the results. Rather, growers are sold a prescription for a crop input and told that a model or algorithm is so sophisticated that it just works. But without a check area of higher or lower treatment to compare to the prescription rate, there isn’t any way to know if it worked.

 

Originally published in Corn and Soybean Digest.