Time to Scrutinize ROI

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 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 biotech 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.

hybrid selection cost per bushel

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 fields, adding 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:

precision agriculture field history with cost report

Trust… But Verify

Every week I see ads using the latest marketing buzzwords to describe how a company is going to use your/their big data to revolutionize how you farm. Estimated rainfall using radar images is being claimed as “hyper-local”. UAV’s, imagery and crop models being claimed to replace scouting for diseases and insects. Proprietary algorithms use big data to manage all your inputs so efficiently it will be “game-changing.” That’s one of my favorite buzzwords- “game-changing.”

I do believe that using your data can and will be game-changing. But I would suggest the “game-changing” may be different from what many companies are expecting.

When Premier Crop (PCS) first began in the late 90s, I drew the comparison between what we’re doing and the Dairy Herd Improvement Association (DHIA). DHIA is a record-keeping database system that documents production by the individual cow – the same as we’re doing within fields. DHIA allowed dairy farmers to benchmark each cow’s performance not just to others in the herd but also to other cows in the database. Genetic selection, nutrition and herd management evolved rapidly as the entire industry moved to data based decision making. DHIA is now the industry standard operating procedure.

So, what are the effects of data based decision-making? Initially, it creates a huge economic advantage for the growers using data to make decisions. Long-term, once a practice becomes industry standard, the advantage is lost and becomes the norm. The most significant outcome is that data based decision-making empowers growers.

data based decision making for corn yield per acre

We are entering a new era in crop production when growers will frequently have better information on product performance than the company producing the product or the selling retailer. The day has arrived when growers, using PCS or a similar system, can evaluate hybrids by yield and profitability in their fields and local geography. Testimonials and “trust me, it works” simply won’t cut it.

Sound harsh? Think about what’s happened to all the feed companies that existed 30 years ago. Most disappeared because the data didn’t prove their products/delivery model provided an economic advantage.

In 2005, PCS introduced our Learning Block concept. We wanted to know whether our VR seeding prescriptions worked, so we added 1-2 acre higher and lower population Learning Blocks in each management zone. We’ve now extended that concept of “checking our work” to VR nutrients and it’s used extensively, especially with nitrogen. If we’re going to put more nitrogen in an area of the field, we can add higher and lower N rate Learning Blocks to validate if we did the right thing. Learning Blocks have proven that our real-world and humble approach to agronomy is warranted.

I’d suggest that companies line up to offer you solutions based on their proprietary algorithms that you adopt the same position as President Reagan did in negotiating arms treaties with the Former Soviet Union – “trust, but verify”.

  1. It’s easy to use your technology to “trust, but verify”. If a company tells you that their proprietary algorithm says you need 50 lbs. of additional N, use your technology to verify.

More Data Helps Data Driven Decisions

At this time of the year, it’s easy to feel like yields are largely a function of weather – temperature and rainfall. Over the years in hundreds of grower meetings, I’ve heard that sentiment repeatedly. If you are inclined to think that way, think about this scenario.

Imagine a flat 160-acre field in your area, farmed by the same grower for 30 years, is going to be auctioned to the highest bidder. The field is unique in that it is all one soil type (I know there is no such field in most acres – but we’re pretending so please play along). Pushing for the highest value, the auctioneer splits the field into two side-by-side 80 acre tracts – selling the field first as two 80’s and then as a 160.

The price received as two 80’s is higher, so the next year two different growers farm each of the 80’s. The entire field was soybeans the year before, so both growers plant corn in their first year farming their new purchase.

Both will receive the same growing degree units and virtually the exact same rainfall. How much yield difference could there be between each of these two 80’s the following harvest?

Over the years, I’ve used this example with growers in small group meetings and usually the answer is in the 40-50 bushel per acre range – sometimes as high as 75-80 bushels per acre difference!

How can there be that much difference? Simple. It’s because management matters!! And the purpose of this column is to encourage you to use your agronomic and economic data to make better management decisions.

We’ve seen it over and over again – similar soils and weather but dramatic differences in results. Usually it’s not one decision but the combination of multiple decisions. This chart is one example:

determine seed selection by soil type

Hybrid and variety selection – it is common to find 20-30 bushels per acre differences on the same soil type and same weather events. A starting place is looking at your own hybrid and variety performance data by soils – both at a field level and across all your entire operation.

Your data can be a guide for not only making next year’s hybrid and variety selection but also where to place specific genetics.

The more data you collect, the more you can make data driven decisions! Applied fertility rates, planting dates, planter performance, trait packages, soil test levels and planting populations are examples of some of the critical agronomic decisions you make every year.

You might be able to hold Mother Nature accountable for the first 50% or even 75% of your yield results, but the other half or less (and all the profit) is your responsibility!

Best Average Rate Costs Profits

Long before GPS was part of our acronym vocabulary, my early agriculture career started in eastern Iowa and northern Illinois. On one of those scorching hot July days, as you were driving through the area, every so often the road would be higher than the fields and you could visually capture a birds-eye view of the fields below. You could see parts of the fields, where the corn was rolled up tight as the plants went in to “protection” mode – while other parts of the field look perfectly normal. Images like that help make me an advocate that managing parts of the fields differently would make agronomic and economic sense.

Now those “pictures” can be captured in detail with a drone, a plane, or a satellite and further documented with your yield file. Since 80% of the US corn and soybean crop is rain-fed, we can’t solve the problem of running low on moisture in those drought-prone parts of fields. But we can manage those areas by changing the seeding rate – spreading the plants out a few inches can provide more room for the root mass needed to maximize yield and economic returns per acre.

Premier Crop’s new Enhanced Learning Blocks allow users to use the equipment they have already invested in to do randomized and replicated trials. In this dryland Kansas field in 2016, two trials with 3 different planting rates and 5 replications each were conducted. In best area of the field, yields climbed as population climbed. We don’t know how high we should have gone but know that in the A zone (green), 24,000 returned $24 per acre more vs 21,000, including the additional seed investment.

But the results in the B zone (yellow) were much different.

The B zone trial mirrors results from many other 2016 trials – that pushing population too high can not only lead to lower yields but because of higher seed investment, can be very costly.

continuous learning with seeding population with on farm trials

Consider what these results suggest about farming by the best average rate. If the grower plants 20,000 – the best rate for the B zone on the entire field – the grower loses at least $25 per acre on the best half of the field. Obviously, planting 24,000 on the entire field would be even worse economically. Picking 22,000 as the best “average” rate – mid way between 20,000 and 24,000 – would results in losing $18/acre on the A zone and $25/acre on the B zone.

If you think this might be worse case, consider that as you move east in the Corn Belt, both seeding rates and yields are higher. The higher the investment (higher seed costs) and the more return (higher yields) equates to even larger benefits to getting the rate correct. The story with nutrients in the same; the ideal rate changes within the field and economic reward or penalty is dramatic. Remember the example of having one foot in ice water and the other in hot water and the suggestion of being just right on average?

Farming by averages is costing you profits! We can do better than choosing the best average rate.

Every Acre Is Unique

Foundations of agronomy and geography are the starting place for data-driven decisions.

I believe data driven decisions will power change in every aspect of crop production. Your data can be a valuable business asset that leads to greater profitability.

There are some key foundational principles in using data to drive decisions in crop production that are worth reviewing. The first is centered on uniqueness. Just as we each have unique fingerprints and DNA, each part of every field is unique.

Much of your data that can be used for making better decisions is being collected with a device connected to a GPS receiver. Most of the software that reads the data files is a version of a geographic information system (GIS). The difference between this software and a database you might use for your livestock operation or some other aspect of your farming operation is the first word – geographic. Your data is stored tied to a unique geographic place in the world. While there are other areas that are very similar, none of those are exactly the same.

Why does a product work so well in one place, but not at all in another? Why does the ideal rate of an input change within parts of your field? Many times, the answer is as simple as “geography matters”! If you treat all you acres as if they are same, you’ll lose out on efficiencies and profits.

The second principle to consider is best illustrated by the rain barrel. The rain barrel, with staves of varying heights, is a visual way to illustrate the real-world reality of what limits yield in any one place within a field changes. Nitrogen is limiting in the southeast corner of the field but not the center. Population is limiting one place but not another.

The rain barrel concept is easy to talk about but challenging to put into practice. Our goal is to maximize return on every dollar invested. Ideally, we are adjusting every input to not only match the uniqueness of the geography but also to match the combination and limitations of the other staves in the rain barrel in each part of the field.

use data analytics to determine yield limiting factors

The irony of our leap forward in planter technology is that, in many cases, we now have more uniformly-spaced, nutrient-deficient plants that anyone would ever have imagined. A one-time investment in upgrading planters has been easier to justify than the continuous re-investment in fertility, especially on rented acres.

An appreciation for these two principles will lead you to collect as much geo-referenced data as possible.

Understand why Hybrid and Variety Yields Vary

Yield results are in and every plot or trial has an overall winner! Winning a plot isn’t easy. Sometimes, luck is involved. Years ago, one of our customers measured the impact of “shading” in a plot. If your company’s hybrid was placed next to taller hybrids, how much did being shaded by a taller hybrid affect yield in the outside rows that were shaded? They found the difference could range from 7.7 to 33 bu/acre penalty from being shaded! In some company’s trials protocols, they discard the outside two rows and only count the results from the inside two rows.

Consistently winning is so difficult that companies frequently report percentage wins. Hybrid ABC won 83% of head-to-head trials vs. Hybrid XYZ. It’s hard to blame seed salespeople for using plot results to sell seed, but that frequently results in promoting the plot winners when they many not be the best fit for your fields.

When using plot data to select hybrids and varieties, the fact that there is only one winner does not mean you scrap other genetics from your line up. Big data analytics, like those used by Premier Crop, has been referred to as a way of “crowd sourcing” hybrid and variety selection. Same as with plot results, it’s important to look deeper than what hybrid or variety appears to have “won.” This chart is an example of real results of a local data pool – with hybrid identity masked with a letter. In the table, the number of “sites” is the number of unique farm fields and the area column is acres.

Which of these is the best number to plant? Hybrid A is impressive, averaging over 240 bu/acre on three different fields. In order to average 243 bu/acre, there must have been many times the yield monitor was reading over 260 or 270 bu/acre. But I instantly gravitate to Hybrid B – not that much different in yield, but results occurred on twice the acres and on four times more fields!

What do you think of Hybrid F? Many more sites and acres listed, but over 25 bushel per acre less yield.

use data for hybrid selection

Hybrid and variety results that are calculated over more acres always show up down the list. Why? It’s not because they aren’t great performers. It’s because they get placed in more diverse and negative growing environments. There are a lot of plot winners that don’t shine when they gain market share. In reality, not all of your fields match the well-drained, high-fertility areas that make for ideal plot location.

The more you can sort results to match your fields and management practices, the better your own experience with specific hybrids and varieties will be. The reality is there is no right way to analyze results, but it is important to examine the data results beyond yield. Number of acres and fields data help you understand why hybrid and variety yields vary. It easier for a hybrid to look either phenomenal or horrible if it is only on a few fields. Conversely, if a variety can stay in the top third as it gains significant market share, it’s likely one of the real winners!

Does Variable Rate Anything Pay?

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 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 hat 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 input no longer do. When you ask these growers why they quit – frequently the answer is “I didn’t know if it paid.”

does variable rate pay use learning blocks to find out

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 Learning 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.

Moving Beyond Correlation

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 form 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.

observational data analysis

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 investment 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 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 that 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.

on farm scientific data trials

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 4 different planting rates we are 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”!

Measuring Yield Efficiency Using a Visualization Platform

We are excited to announce the launch of our new Data Visualization platform, part of our three-tier technology strategy. This new platform allows growers to gain key insights that give them the ability to make informed decisions based off economics, seed, crop protection, fertility, operations and management.

The Data Visualization platform is focused on grower dashboards and group benchmarking ultimately showcasing a Yield Efficiency Score for growers’ operations.

 

yield efficiency as an ag tech disruption driver

 

“Data Visualization is a key to helping growers understand their yield efficiency as their measure of success using a Yield Efficiency Score,” stated Darren Fehr, director of sales and marketing.

A Yield Efficiency Score, by Premier Crop, similar to a credit FICO score, is a single number derived from multiple factors. Its purpose is to determine a grower’s return on investment on a per acre basis but from a spatial perspective.

Yield Efficiency is rapidly becoming the most important metric to measure grower’s success in order to enhance a grower’s operation. “Our ultimate success is the grower’s success. We are constantly looking to improve growers’ operations to maximize efficiency and help them be more profitable on the acres they have,” said Tony Licht, business development manager in Iowa.

Premier Crop’s Data Visualization platform and Yield Efficiency Score allows a grower to visually see anonymous group data and grower benchmarking. It provides a benchmarking score how a grower is performing against others as well as against their own fields. Using the grower’s actual data in five of their most important decision-making categories (economics, seed, crop protection, fertility, operations) to prove efficiency and effectiveness of crop production.

bradhagan_centraladvantage

“Group data is powerful over that many acres, allowing us to benchmark with other producers anonymously, which is invaluable information to my operation,” said Brad Hagen, Minnesota corn producer. Brad works with Premier Crop’s partner, Central Advantage GS.

One Rate Doesn’t Fit All

When I began my career in crop production, we would routinely pull 20 soil sample cores, mix the cores in a bucket, pour one pound into a sample bag, send it off to the lab, get the results back and then pretend that what was on the sheet of paper accurately represented the nutrient levels for the 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, phosphorus and potassium 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 level within fields by applying phosphorus and potassium 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 variation 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 PPMs on a map. Grid sampling and variable rate nutrient applications are needed foundations for getting started in precision agronomy.

how to use data for fertility management

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 divine as “A” zones, A standing for aggressive. A zones can test lower in nutrients because consistently higher yields remove more nutrients. 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 more 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 form lack of productivity. For Premier Crop, variable-rate applying 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

2. Do you see your yields “leveling off”? In what areas of your field could you push more? Sit down with your agronomic advisor to discuss ways you can get a yield bump for your A zones.

3. How do you proportion your nutrient investment from field to field, and acre to acre?