Data-Driven Variability Management

Thursday, November 12, 2020  •  Episode 7

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Managing crop production systems in accordance with spatial variability is the primary goal of site-specific crop management, which is closely linked with digital agriculture. Digital agriculture technologies have enabled many site-specific management practices whether through variable rate application control or decision algorithms executed in digital systems. Effectively addressing variability for optimal crop performance requires the use of data quantifying soil spatial variability. This episode of the FarmBits podcast is the first episode in a series that will focus on quantifying and responding to soil spatial variability. Mike Manning, an Agronomic Information Advisor with Premier Crop Systems, joins the FarmBits podcast to provide an overview of data-driven variability management. Mike begins the episode discussing the challenges of getting started with proper data organization and management, then discusses data quality, and how data can be effectively used to assess spatial variability. Following this discussion of data, Mike dives into soil sampling and discusses fundamental differences between grid and zone sampling, and why he believes 2.5-acre grid sampling is a foundational practice in site-specific crop management. The episode concludes with Mike talking about the value of farm data and offering key advice for growers seeking to manage their farms better in the digital ag landscape. Throughout this episode, Mike presents concepts and directives in a straightforward and concise manner that is clear for all to understand. The topics discussed in this episode bridge the gap between the previous episode series' focus on yield data and the new episode series' focus on soil data. Mike's discussion of traditional soil sampling concepts in this episode segue directly into the next episode of the FarmBits podcast which will feature Tyler Lund of Veris Technologies discussing on-the-go soil mapping technologies. "In my opinion, farm data is only as valuable, is only worth as much as the gain it creates for the individual grower that came from." - Mike Manning "Put that information to work. Get things structured, get things cleaned up, make it so it is accessible. Make it so it's accessible, available, actionable." - Mike Manning

Opinions expressed on FarmBits are solely those of the guest(s) or host(s) and not the University of Nebraska-Lincoln.

On this episode

host Samantha Teten
host Jackson Stansell
guest Mike Manning
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Mike Manning Contact Information:
Twitter: @DataManning 
Premier Crop Systems Information:



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Read Transcript

Jackson: Welcome to the FarmBits podcast, a product of Nebraska Extension digital agriculture. I'm Jackson Stansell.

Samantha: And I'm Samantha Teten.

Jackson: And we come to you each week to discuss the trends, the realities and the value of digital agriculture.

Samantha: Through interviews and panels with experts, producers and innovators from all sectors of digital technology, we hope that you step away from each episode with new practical knowledge of digital agriculture technology.

Jackson: As we transition into a series of episodes on a new topic beginning with this episode, we'd like to thank all of you for taking the time to listen to this podcast. We want to make this podcast as beneficial for you as possible, so if you have feedback do not hesitate to contact us. You can find our email and twitter handles in the description of the podcast if you'd like to get in touch.

Samantha: This episode, episode 7 marks the beginning of a new series of episodes focusing on quantifying and responding to soil spatial variability. Our guest for this episode is Mike Manning, an agronomic information advisor with Premier Crop Systems. Mike graduated with an agronomy degree from UNL in 2009 and has since had a range of experiences in precision and production ag.

Jackson: He was originally from Western Nebraska, he now lives in Lincoln with his wife and two children. In this episode, Mike will address several topics including data quality, soil sampling best practices, and the value of multiple spatial data layers. The goal of this episode is to transition us from thinking about yield data to addressing how digital ag can be used to quantify and respond to soil spatial variability that is often the driving force behind yield variability.

Samantha: For those of you who haven't heard of Premier Crop before, Premier Crop is a precision ag service provider that was founded in 1999 to enable data-driven solutions from the new agronomic layers generated through precision ag technologies. Premier Crop's platform provides the data backbone for many retail partners and co-ops throughout the U.S. and internationally.

Jackson: Premier Crop also offers grower direct advising that leverages their precision data platform, and Mike's role as an agronomic information advisor he has the opportunity to work directly with farmers to use Premier Crop's platform to drive profitability on their farms.

Samantha: For those of you who listen to episode 6, you may recall that we discussed profitability mapping with Terry Griffin. This week, we asked Mike similar questions regarding yield data and spatial profitability maps to get a crop consultant's perspective. One aspect of his role is helping farmers organize and manage their data to make it easier to work with. We are going to drop you into this interview with Mike talking about getting started with data management.

Mike: Farmers are generally strong, independent folks and that's a good thing we like that. Some of them are able to challenge or tackle that challenge on their own. What we find commonly is a lot of guys need help. It's you know it's not their wheelhouse and it's not fun to try and keep things strict and neatly organized in a grower farm field hierarchy. You know, I like to joke but it's not. I would bet large money that this is very accurate that if you were to pull up My John Deere on the state of Nebraska the number one product that goes through a sprayer is called Tank Mix. The number one fertilizer would be nitrogen. But that doesn't tell me if I applied 32 percent urea, ammonia anything. And yeah Tank Mix that's our favorite pre-applied chemical post-applied chemical for your chemical it's all Tank Mix.
I really encourage grower, growers insightful growers, they realize they have this information, they know they need to get organized they know it has value particularly for their reform is working with a trusted advisor working with somebody that has the technical skills to support that. Cloud-based you know it's having a strict farm field hierarchy and making sure things get accounted for correctly. You run into the same problem same problem with some of the cloud solutions out there. You might have four different versions of the home form or with you know 2014 and 2015 or under version one. The 2016 nitrogen maps in version two but the yield map for 2016 is in version three and it can be a challenge for some people to manage you know manage that consistency to specific field documentation just on an ongoing basis because in the heat of the moment of okay we're in the middle of planting we need to go.

Same way with harvest hey we just need to go, it's probably what I've seen has been most effective is having a good, trusted advisor that's knowledgeable and helping keep growers organized.

That also ties into you know documentation, you know documentation throughout the year. Making sure you're plugging your hybrids and varieties right into your planting monitor. Making sure we're changing fields properly on the combine making sure we're calibrated well. We're calibrating between moistures we're calibrating between crops. Just kind of those little check-ins where you have when you do have that good trusted advisor somebody that's helping you kind of check mark your progress through the season. You know, for instance when I pull planting maps, say middle of May I generate that. I generate those back out to the grower, and you know guys the producers we're working with and instantly say oh guess what we forgot to change see the 20 acres on each side of the field, we've got to change that. That's actually this hybrid. We try to catch that stuff as early as possible and same you know same thing applies for fertility and chemical.

Samantha: I think that's very common even just the farmers that I've talked to this fall tell me stuff like that all the time forgot to switch fields, forgot to put in the right crop, forgot to put the right header width. It's yeah very common so.

When you talk about documentation and making sure that you have that all recorded right, what platforms or other companies does Premier Crop's technology interface with? So does it talk to JD Link, does it talk to some of those others to make that documentation easier for farmers?

Mike: Yes so, yes we do we do interface with some industry- other cloud services out there. We do have API with my John Deere. I believe we still have API with Syngenta Agra Edge program and maybe a couple a couple of uh other people out there. Probably been probably the most common way that we interface with the rest of the world. We generate prescription shape files out of our out of our system that is kind of you know ubiquitous universal standard for the time being. Most controllers and monitors that have been manufactured in the last 10 years or by and large can accept generic chain files. So when we talk fertility prescription going out to a controller, planting prescription going out to a controller- that's our, that's prior primary delivery means from a documentation perspective. Having those, having those
APIs. Having those connections with other people isn't necessarily a easy button for getting documentation right. A lot of times telematics is just a way to move bad data faster.

Samantha: That's an awesome, way to put it that's true.J

Jackson: It really is and that kind of is a great segue into where we're going with the interview now. So, when you think about data use mistakes so if we're making data driven decisions, particularly when it comes to yield data what do you see as being one of the biggest mistakes there? For example, maybe growers rely on only one year of yield data when they're making a decision instead of multi-year averages or for example that that yield efficiency score like what you've already talked about. Or is that they're using some of that poor quality data that we know is often getting transmitted because something was put in wrong in the combine, something was getting put in wrong during planting something like that.

Yeah, one year of yield data is one year of yield data. That's exactly what it is. I like to say let's triangulate. Well let's triangulate data. We start observing trends in the field let's triangulate that over two or three years, let's see if that's really a trend. One of the reports we generate is a correlation to dry yield. What correlates to yield across the field spatially? Obviously, correlation does not equal causation and but in a real world field setting, agronomically it's a good place to start looking. Or it's a good call out to see that trend. But I also want to see more information behind that to support that inference. Making a decision making management zones changing up an entire fertility program or chemical program based off of one year yield data is a poor choice. You might hit the lottery one out of ten and do something good but you want more information behind that before you start making major decisions. That's another aspect of having a precision advisor that can help you along the way in that process is being able to review field by field. Yes, we can move that data quicker we still need to review each field's individual yield data. Classic case, multiple, two machines in the same field. If they were harvesting in the same pattern we might have a nice candy stripe pattern because machine one is 20 bushels higher than machine two. That's actually where the telematics have been beneficial, because the growers I work with that do run multiple machines within anywhere from 15 minutes to an hour of them being in a field, I can pull up the mapping from 100 miles away and say you guys are off let's stop and calibrate again. So, there is some benefit of that but it does and don't get me wrong there are growers out there that would want to spend the time doing it. So very few of them actually do but actually reviewing making sure that data set from each field is active.

Samantha: How much of the data collected on farms today would you consider actually high quality data? And like what are some factors that you're using to evaluate that high quality data and maybe what you're doing about it?

Mike: I love that question because I really think the vast majority of the data coming out of the farm is pretty high quality. Yield growers that are paying attention to their yield monitors and their yield maps.
Just about everyone has woken up to the idea of hey we actually need to take some time to make sure this is calibrated. I just this year I had a grower get done with a thousand acres of soybeans, he was off a total of like 43 bushels on his total count versus what went across the scale that's doing that's doing a great job. I have a number of other growers I work with that well they're keeping pretty good tabs on it. Other data types that's what's been a lot you know the planting data is very high quality right now. It's incredibly dense, it's almost too dense. You know we're getting five hertz readings by 24 rows by 40 different attributes. You go plant a quarter section that might be 50 megs almost 100 meters worth of data. Might be more than that. We're getting really high dense that's good information. Great soil samples that's you know that's a good data set as long as we know that the sampling method in the field was good. And it's, you know good qualified sampler out in the field taking good samples. You still have some room for error at the lab. You might have you know he might have hit a cow pie. He might have hit a ammonia band. That's going to flag once we get our maps back we go what's this you know we've got flyer points here. Why do I have zero parts, why do I have zero phosphorus right here? Well, that doesn't make sense, or why do I have 600 parts per million phosphorus right here? So by and large, I really do a lot of the data coming off the farm now is pretty good. Especially with the folks that are paying attention to realizing that it's part of the management of their enterprise. That's great to hear.

Jackson: That is great to hear. So right now, we're recording this podcast we're sitting here in the middle of October. So, what exactly is  Premier Crop busy with this time of year as they you know are performing services for farmers?

Mike: Good question, it's you know with as quick as harvest has progressed this time here we've got a big focus on good sampling. We have a lot of partners that make grid sample anywhere from 100,000 to 200,000 acres every single fall. So, we're getting that data pulled in so they can get ready to make that make their variable rate prescriptions both for fall or spring applications. This time of year, from a grower perspective we are making sure we've got all of our season data collected. We know what's happened to that field from the fertility, chemical, seed and everything aspect. Because really at the end of the season once we capture that yield map that's our end of year report card.
A lot of our analysis is driven off of having that yield map at the end of the year. So, a few different moving pieces. Harvest has moved rather quickly with some of the cloud solutions we try to pull. Yield data as actively in real time as possible just to keep things up to date. Make sure we're ready and once we have that we can start generating results and begin making A: evaluate how we did this year, and B: start making plans for next year.

Samantha: Awesome, so that leads right into the next topic or kind of area. What you've talked about the soil sampling and this report card type of thing. But can you talk a little bit about some of these services that Premier Crop offers?

Mike: We back from a lot of partners that run their own run their own precision programs. Our term you know, we have Premier Precision, Premier Decision. We also have what we call Premier Intelligence that gets incorporated within Premiere Decision. When we talk about Premier Precision we're talking about the ability for growers or retail partners to enter their soil samples, spatial soil samples and generate variable rate fertility prescriptions. When we get into Premier Decision, we started talking about our full analytics package. And that's again driven tracking all the agronomics, incorporating spatial soil samples all flat rate and variable rate applied fertility. All of our chemical. We do incorporate a year's worth of weather, the soil maps, the cerebral maps. Essentially by the way I talk about it, anything that happens to that field or has a potential impact on that field if it happens to the field we can check mark that or account for it. Account for it within the system that's what falls under Premier Decision. We also have you know, growers love to experiment. Growers love to trial. About five years ago we developed a concept called Enhanced Learning Blocks, which is actually a randomized and replicated trial built into a prescription within the field. So, we have a randomized complete block built in, and we can say we want to put that exactly here. We can use that to test anything from seeding rates, fertility rates on/off treatments. Say you want to do an on/off fungicide trial, an on/ off starter trial. We can do that but be able to also tie statistical confidence back to that, back to those yield values at the end of the season. So that's kind of the buckets of you know services or products that we offer within the system. But you know it's really about comprehensive farm management and helping growers make more informed, insightful decisions up from data that's coming off of their own farms.

Jackson: So, in terms of that comprehensive farm management is there a particular aspect of the farm management that growers tend to find the most useful for them? Or is it just generally the entire package that farmers buy into all the way?

Mike: It's really case by case. One thing I've got to touch on there. You know profitability mapping for some growers it's really about meeting growers where they're at in case by case. If it's someone who's never taken a spatial soil sample, we need to get them started on that path. We can still measure profitability and especially field variability across the farm, but if we're really going to start refining management we need to get- we want to be able to understand what that spatial fertility looks like. If we've been yield mapping and doing a good job with that we understand where our spatial productivity is. We tie economics to that we can actually generate a map field by field but this break even cost per bushel across that field.
Before that becomes a lot more powerful a lot is if we start assigning management zones within that field. We can show field after field
after field say we have an A zone, B zone and a C zone where we might have an overall higher per acre spent in our A zone, but our break-even cost per bushel was much less than our C zone.

Another really fun thing that we've done we've spent most of 2019 developing out is what we call a yield efficiency score. Yield efficiency score at the at the core of it is a dollar per acre return to land and management. That if I take the sum take my yield times the benchmark selling price, less my fertility my chemical, my seed, my operations, input costs. I have a dollar per acre return to land and management. And that's a metric that instantly clicks with growers because they now understand- if I have four hundred dollars per acre for my yield efficiency score on this field whereas my whole lot maybe on my core enterprise. I have 400 per agent left over to pay for my rent and to pay for management.
And the reason we went this way with this metric is some items that fall under that management can be hard numbers to define and some growers may or may not be willing to share that information because it can be very sensitive. You know management costs might be how much you spend on crop insurance depending on it also might be household living expenses and that changes significantly grower to grower. By and large, our advisors have very close relationships with our growers. We're privy to that information you know the home or not the home place you know the east place we're paying x dollars per acre cash rent. So, we know what that figure is, but the yield efficiency metric really is a good benchmark for that grower to be able to compare their fields, all their fields together and really understand where profit is coming on their farm.

Samantha: When you talk about capturing the soil variability or the spatial variability with soil samples is grid sampling the predominant way to soil sample or do you guys move into zone sampling later on after you've done? Just out of curiosity like what's the most common soil sampling technique?

Mike: Great question. I get asked my most asked question of the fall in my experience, what I've seen in Nebraska predominantly spatial samples are great soil samples. I'm typically at two and a half acre grids. There is zone stamp and this you do this is kind of an age-old debate this almost goes all the way back to the early 90s.

My two cents on it, I still call it two-and-a half acre grid sample one of those foundational, just good fundamental precision pieces to have on your farm. There are cases where a tighter resolution maybe a 1.5 or 1.1 acre grid. I've actually seen some quarter acre grids on like a quarter section that's a lot of samples. You do if you have at a two and a half acre grid. The reason I like that so much it just fits a lot of scenarios, it provides us enough economic resolution to really get a good idea of what's happening in the field. Also being you know cost friendly to the grower, you know going from 2.5 acres to 1.1 acre grids potentially adds five to depending on the provider five to ten dollars an acre in sample costs.

The reason I like grid sampling better than zone sampling. And it varies you gotta be care a little careful with it. There is some good zone sampling out there, as long as you have enough samples you're taking enough true sample points across that field to get good resolution. We've done a number of case studies with some other with some partners over the years and just too often the zone sample is a way to very slightly reduce your sample costs by pulling significantly less soil from the field actually collecting less physical soil and having less information to really make good decisions on. You know last time we ran the numbers on it if you think a two-and-a half acre grid sample you can say ballpark is about ten dollars per acre. Historically we look at that as being valid for four years. I probably you know predominantly running about a three year cycle on a grid sample. We're spending 2.50 to maybe three dollars per acre per year. You run that against the crop enterprise budget almost 90-99% of the time that's going to be less than one percent. That three dollars per acre is less than one percent of our total spend on that field in a given year. I really challenge growers is what information are we using to make that one hundred dollar an acre 200 an acre fertility decision. The other aspect I like about grid sampling, it's you know it's an objective approach to the field we're not biasing our sampling regimen. There can be again there can be good zone samples out there too often, and especially I don't know if it's so much still happening today. But too often a zone sample was well what's the easiest, what's the most accessible data layer out there. It's the SSURGO maps.
Some growers are consultants when advisors want to come out and take a zone sample on the field they're carving up the zones before we've taken a single taking a single core out of the field and so essentially we've introduced bias into our sampling protocol before we've even taken a single sample. And this is out of another case study in the zones by soil type you know take a number of cores out of specific uh soil type zone or take a number of cores out of a single zone, arrive at a single maybe as a single soil fertility value or a single soil test. So, across all you know your N, P, K, Ph, sulfur, zinc. That you're assigning to maybe a 15 or 20 acre zone. That's the kicker right there. We're assigning one or two values for an entire zone that might be a large area of the field. You go take a higher density sample you'll see there's variability within that zone. It can be pretty significant, you can have two point swings in Ph. You can have 20 per million swings and phosphorus. And that's just the type of thing that you don't pick up with the zone sample in a lot of cases.

It's with the zone sample growers are slightly lowering the cost they might be saving two or three dollars an acre by doing his own sample, but the amount of information that comes back from it is far less than what we get with the grid sample.
Jackson: What are some of the biggest challenges that you've faced in terms of trying to match up these high-resolution data sets like imagery for example your you know medium level resolution like yield and maybe your soil sampling data which is probably at your lowest resolution. What are your challenges in matching up data layers at these different resolutions and then managing at a basically composite resolution so to speak?

Mike: A couple items there. So yeah. I well, in terms, in relative terms- yes an image is going to be an image is going to be a lot more high resolution than a two and a half acre grid sample. I'd probably say the crudest or the coarsest resolution we have is within the actual U.S.- the circle maps SCS circle maps. That's probably the coarsest data that's not very well validated and especially in Nebraska. Particular areas in Nebraska, we've spent 50 years moving it around so we can irrigate better. So that is always kind of an iffy data layer  but in simple terms it really comes down when we mention this together it's what you know at the end of the day regardless if we're taking we have a pixel per foot, or a yield reading state, a yield reading every second. We have to have that information and something that can be usable, and most importantly executable by a machine. So even though we have let's say we have a pixel per foot of this image we're not going to be able to get a fertilizer applicator or even a planter to execute at that sort of level so we do need to. The meshing is actually not as difficult as you might think. It's really about getting it more in an executable form for making a management decision or application change.

Samantha: As a consultant working with a precision ag data company, we want to get your thoughts on the value of farm data. Both the value directly to a farmer and the value of the data when aggregated across the region.

Mike: In my opinion, farm data is only as valuable as only worth as much as the gain it creates for the individual grower. that came from. A raw yield data, raw planting data sitting out there somewhere in my opinion does not have inherent value in and of itself. It has to be used, it has to be able to use for some sort of insightful decision. The second aspect of that as growers explore data management and things like that is being selective about your partner. Since 2012, climate corporate excuse me Monsanto purchased this company called the Climate Corporation and there was a tidal wave of big data hype and a lot of venture capital that poured into the industry. At this point now we filtered out some of you know we filtered out some of the companies that were underperforming it we'll say. I caution growers to be aware of how companies intend to use their information, how do they intend to use that data, what's their privacy policy, does that data become their data once they aggregate it with others. It's something to be mindful of. But the overall value of the data really comes down to- can I use my data in a meaningful way that's going to benefit me. There isn't going to be a data auction, well maybe. I don't foresee a data auction in the near future.

But again, you know that could always change. Some partner programs involved with Premier Crop you know on our data, privacy data policy. We sign off all the way through and our partners sign off all the way through. The grower owns the data. We do have aggregation methods within the system. We build things up from the ground up being able to look spatially how yield changes across each individual field at ten thousand feet, being able to look at an entire farm and then at 30,000 feet in the regional group. And we can carve groups within the system to begin asking questions- like what was yield by hybrid? You know by hybrid by plant date, by hybrid by plant day, by maturity. You know yield by hybrid by soil test phosphorus. Fungicide timing, nitrogen rate, nitrogen. So, and growers love being able to see beyond their own farm but you also have to have they also have to be able to verify and prove that this is reliable information.

I guess when you think about potential ways that data can be used in aggregate, I mean have you seen any traction with specific technologies, specific data mining technologies, machine learning and overcoming some of that data quality piece? I mean at what resolution is it appropriate to be applying some of these machine learning techniques? Is it sub-regionally, is it regionally is it nationally?

Mike: I can probably just really speak to some of the things we're doing at Premier. I'd say when it comes to aggregation it again it all starts at ground level. You can't you know, you've got to be able to ensure you have quality data at the absolute lowest level before you can start aggregating it up the chain. One of the things we've been working on the last couple of years since we've introduced enhanced learning blocks, those randomized replicated trials. We began using some machine learning to aggregate those results because we know we have statistical quality data behind them, and it's something we'll be we're looking at in terms of what we're going to call next-gen RX. And it's at a sub-eco region type of grouping being able to analyze, characterize like agronomic environments. You know we from our management zone perspective we talked about A zones, B zones and C zones. Well guess what, not all A zones are created equally.

This yield is not created equally. A zones are not created equally. A zones, B zones are A zones in different fields for different reasons. So, I can really try to speak to what we've been doing and that is really just trying to characterize like agronomic environments. So again, provide that benefit back to the grower, again tying back into that data privacy piece. We vote we really look at it as you know the grower. This is the grower's information, this is the grower's data for the vehicle to help transform that, improve that raw data into something that's actually actionable in the field.

I'm really excited about this next question. We've talked a lot about where we currently are with data management and what you guys are doing as a company. But thinking about the future, what do you see as the future of data management? So a couple things that come to mind for us with people, conversations we've had will- it be used for transparency with consumers, will it help us manage land better. I know a lot of people are striving for carbon neutral or carbon negative farms. What do you see as the future of data management and how we're going to use that data?

Mike: I would say a lot of it is that's happening right now, variable rate applications, variable rate applications and most of our fertility we can be a lot more resource efficient and prove that we're maintaining or even more higher yielding or more and or more profitable using that using variable rate technologies. Sustainability is something that ties directly into the sustainability discussion. We can prove that growers are being more efficient actually and even within enhanced learning blocks and some of the nitrogen data we can prove what the optimum nitrogen rate is on the field versus what might be a statewide nutrient comprehensive, nutrient management plan directive.
Actually Dr. Jeremy McGrath, The University of Kentucky, has a great presentation some slides on what's the environmentally optimum nitrogen rate or phosphorus rate and the crop optimum nitrogen or phosphorus rate. Guess what, they're different and that's you start measuring the world of what you know walking the line between likelihood and potential regulations and that's best management practices are usually the foundation for future regulation. So helping growers be sustainable, more efficient in their own farms but also be able to prove and statistically, scientifically prove that what they're doing is working and also good for the environment. I'd say some of that's happening right now.

Samantha: What you said about the nitrogen, that is I don't think you could have said it any better. You know we talk about that stuff all the time both of our research is in nitrogen management, and how you said that was awesome.

Yeah yeah, it's trying to understand environmental costs, the actual farm costs, social costs- I mean there's so many different ways that you can frame what an optimal nitrogen rate is and also frame sustainability. You know that's a conversation we've had many times.

Samantha: All right Mike, the last question. What piece of advice do you have for our listeners that you want to leave them with?

Mike: I've got three good bullet points here. Number one- growers out there, put your data to work for you. There's a number of things you can learn by objectively diving into your data. If you don't know how to do that, do reach out, seek help. There are a lot of good folks in the industry that can really make sense of data. Rule number two- there is no easy button. There is no silver a bullet. A Veris map is not going to solve all your problems. A tissue sample is not going to solve all your problems. A grid sample, hydraulic downforce is not going to solve all your problems. There's not a silver bullet out there. But it is about accumulating all the different pieces of the puzzle and being able to get them in a form that well really gets them in a form that you're able to make sense of. Agronomy is complex. It's not about trying to simplify the complexity,
it's about making it easier to execute. Actually, understand that complexity but also be able to execute a plan easier without removing the simplicity, removing the complexity. So, I encourage growers to you know work with a good trusted advisor, work with somebody you know bring objective results to the table. A lot of times when I sit down with growers for the first time, we look at what we call a field top 10 report. Correlation to dry yield, all of our different here's the different, here's six different buckets of yield as our yield changed across the entire field? What trends are we seeing and that's across our soil tests, across our fertility. Whether that's flat rate applied or variable rate applied and it can be very eye opening to say a-ha, that's exactly why that field does that. So, I really do I encourage growers put your data to work for you, get it in an organized fashion. And again, back to that data as a commodity. How much is my data worth? I again say it's only worth as much as how it's going to help you directly, help you make better decisions, help you manage your own farm matters.

Samantha: Thank you to Mike Manning for joining us to discuss the importance of using multiple data layers to quantify the soil spatial variability that drives yield outcomes.

Jackson: We covered quite a few different bases in this episode, but the concepts covered are important as we proceed through this series of episodes that will cover a variety of data layers that contribute to characterizing spatial variability. One aspect of this episode that really stood out to me is Mike's perspective that working with data across multiple resolutions has become pretty straightforward for growers and advisors using modern digital agriculture platforms. I think this is really a testament to how far we have come with automation and the quality of Premier Crop's platform- that challenging computational processes are now executable with just a click of a button. That's enabled use of this data for management purposes at broad scales, which is really exciting for growers out there who benefit from these technologies.
Absolutely. My favorite part of this episode was Mike explaining management zone delineation and then more importantly how not all like A zones are created equally. So, comparing zones between farms is not an effective way to aggregate results to find trends of products or practices. Mike explained, like many of our speakers have, the importance of on-farm trials to find what works for your specific fields and then being sure to use that quality data to analyze the results. And it sounds like if that interests you, that Premier Crop will help you with all that.

Jackson: For sure, so we look forward to you joining us next week as we continue in this series of episodes on quantifying soil spatial variability, as we hear from Veris Technologies as they're going to discuss the technology and benefits of on-the-go soil mapping.

Thank you for taking the time to join us today on the FarmBits podcast. If you enjoyed this episode, please subscribe to the podcast on Spotify, Apple Podcasts, YouTube, or wherever you listen to podcasts to be informed about the latest content each week.

Samantha: We welcome your feedback, so if you have comments or questions for us please reach out to us over email, on Twitter or in the review section of your favorite podcast platform. Our contact information can be found in the show notes.

Jackson: We would like to thank Nebraska Extension for their support of this podcast and their commitment to providing high quality informational material to members of the agricultural community in Nebraska and beyond.
Samantha: The opinions expressed by the hosts and guests on this podcast are solely their own and do not reflect the views of Nebraska Extension or the University of Nebraska-Lincoln. We look forward to you joining us next week for another episode of FarmBits.

Transcripts are generated using a combination of speech recognition software and human transcribers, and may contain errors. Please check the audio before quoting in print and write to report any errors.