Episode 006: Leveraging Yield Data
Nebraska Extension Digital Agriculture
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11/05/2020
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Yield maps have been around for several decades and are becoming ubiquitous. But what should you do to capitalize on your yield data? Data can be incredibly valuable, but data that goes unused for better understanding precision agriculture technologies, characterizing hybrid performance within a field, or generally improving a farming operation is not valuable at all. In this episode, Dr. Terry Griffin - Assistant Professor at Kansas State University - joins the FarmBits podcast to talk about leveraging yield data. This episode is the perfect conclusion to the harvest series of the FarmBits podcast as it touches on almost every topic discussed so far. As an agricultural economist, Terry has a particularly good perspective on value. In this episode, Terry addresses a trend that is gaining some popularity - profitability mapping. Terry discusses who the profitability approach might help, how profitability maps are generated, potential challenges with profitability mapping, and why data quality is critical to accurate profitability mapping. According to Terry, one of the most valuable uses of yield data and profitability maps is for on-farm research. Terry explains that on-farm research is the only way to truly ground-truth the impact of precision agriculture technologies and production practices on your farm. He also suggests that data analytics automation is the future of digital agriculture. The practical perspective that Terry approaches digital agriculture with shines throughout this episode. Though this episode concludes the harvest series, that doesn't mean the podcast is taking a break. Next week, we begin a series on quantifying soil spatial variability with Episode 007 featuring Mike Manning of Premier Crop.
"If you have a farmer that is struggling to do the agronomy 101 things - planting on time, choosing good varieties and hybrids, being able to operate machinery without it breaking down - if they're struggling with those things, adding technology such as variable rate, and yield monitors, and farm data will not make their operation better. So it depends on the who."
- Dr. Terry Griffin
Terry Griffin Contact Info:
Twitter - @SpacePlowboy
Website - spaceplowboy.com
On-Farm Research Resources:
https://cropwatch.unl.edu/ssm
https://digitalag.teachable.com/
Podcast Contact Info:
E-Mail: farmbits@unl.edu
Twitter: @NEDigitalAg
Samantha's Twitter: @SamanthaTeten
Jackson's Twitter: @jstansell87
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.
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- [99:59:59.999]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. Samantha: Welcome back to the FarmBits podcast for our sixth episode focusing on leveraging your yield data for profitability. We got the chance to sit down with Dr. Terry Griffin, a faculty member of Kansas State University whose work focuses on cropping system economics digital agriculture precision technologies and spatial econometrics. Jackson: This episode covers a range of topics including the challenges of looking at profitability versus yield and farming operations, data quality and the future of technology and farm profitability. He even discusses the role of data in on-farm experimentation and the importance of on-farm experimentation to technology and profitability improvement. Samantha: This episode touches on nearly every topic that we've discussed in our harvest episode series, and it is the perfect concluding episode to wrap everything together. So, we are going to kick off this episode with Terry talking a bit about his background and how that relates to his digital persona the Space Plowboy. Jackson: (here we go) I saw your twitter handle is @spaceplowboy and I'm pretty curious as to what the backstory is behind that because it's a pretty unique twitter handle. Terry: Yeah, about three o'clock in the morning, I woke up sat up in bed and you know it just came to me in a dream and I jumped up and grabbed that handle while it was still available. So, you know we mentioned spatial statistics my doctorate at Purdue was in agriculture economics with a thrust of spatial econometrics. Okay, so my training is in spatial econometrics. I consider myself to be a practitioner of applied spatial econometrics. There are some people who you know don't work with real data they just come up with with the theories of the true especially competitions. I'm a practitioner of it but it has little meaning, too. One of my hobbies is astronomy, space and you know when I was a child that was sort of why I wanted to really do but in sixth grade you may remember what you may have read about the Challenger explosion in 1986. Well, that kind of diverted me from wanting to go down that path, but I still had a strong interest in space and strong I mean. So, the space part of my handle has double meaning it's astronomy and spatial statistics, spatial analysis. So, I wanted to put those together and plowboy it kind of made some sense. So, I come from a row crop farm- undergrads in agronomy and all of my work in economics is all about how farmers make optimal decisions so space plowboy had a few different meanings and kind of fit together and it's memorable, right? You notice it and you kind of remember and there's a song that kind of sticks in your head. (yep yeah yeah) So, it kind of makes it more memorable. So, people either love the handle or like my wife they hate the handle. It's memorable. Jackson: Yeah, I'm in the love it camp- especially after hearing the story behind that because it seems like it definitely embodies how your career was shaped, which is really interesting. Samantha: So, we're really focusing on wrapping up our harvest series so the first thing is- how valuable do you perceive precision agriculture data specifically yield data to be to farmers within their operation and even on a broader scale using aggregated data? Terry: All right, is it okay for me to say some things that may not make everybody happy (absolutely) say things that people want me to say? And I'm serious that's what I do when I speak to groups- you know I ask them that same question do you want me to tell you what you want to hear or do you want me to tell you what you need to hear? It's going to depend and farm data as well as most of the precision ag technologies, the probability there of is not as simple up or down yes or no the one of the biggest factors in making technology profitable, we're talking about digital ag and farm data is the human capital part. We forget that in the equation okay so just like our cell phones is your cell phone profitable to you? Well, it depends on how you use it right? But, if you left the house this morning and you realized you forgot it what do you do you turn around and go get it it has value to you. Okay let me say it this way if you have a farmer that is struggling to do the agronomy 101- things planting on time, choosing good varieties and hybrids being able to operate machinery without the machinery breaking down if- they're struggling with those things adding technology such as variable rate and yield monitors and farm data will not make their operation better. So, it depends on the who. A lot of products in agriculture depends on the where like herbicides you know the field, the weather, when it's applied and in the weed spectrum well ag technology is a little more complicated especially when it comes to the data side of the technologies that require human intervention. Is there adequate human capital either on the farm or at arm's length with crop consultants, extension agents other third party services who are willing and able to devote the necessary effort into making it work? Jackson: It's a really interesting perspective. I- you know we don't necessarily think about that a lot, I think a lot of engineers at least are thinking you know how can we automate this process more and more instead of actually thinking about you know who's implementing it how are they implementing it? Samantha: Or even this morning we had a conversation like oh technology could fix this, but I think you bring an awesome perspective that it's not going to be the self like saving everything you have to have technology in the right hands can do some really good things it sounds like. Terry: Exactly. You know when we people blindly ask me well should I adopt x y and z and essentially the question I need to ask them is how smart are your advisors or how much time do they have to devote to this and it's an uncomfortable situation a lot of times. You know, it's a lot like you know UAVs are special like this it depends on how that technology is used rather than how much fun is it to use the technology. Absolutely, you mentioned automation Jackson. I'm a big big fan of automation, anytime we can remove the requirement on the human we're better off you know automatic guidance is the poster child for this right you know we used to a lot of people in their 40s and 50s spent their childhoods learning how to drive straight that skill set has been replaced with an algorithm and a satellite signal right so that that's no longer even a factor. When we do the same thing to data it's not as easy but I would suggest we can. One good example is telematics, pushing data from a yield monitor to the cloud without humans removing flash media. Back in the day as MCIE cards, it's amazing how highly educated university researchers could lose that card between the combine and the laptop. Okay so we have to automate the process, remove the requirement for the human to be involved. Jackson: Sure, and so speaking to that in conjunction with kind of your you know your discussion of how human capital is critical to a lot of these precision ag technologies, when we're thinking about leveraging yield data specifically how can farmers do that either with an advisor or with an automated process to better understand their performance and inform their farm management decisions? Terry: So, starting about 2002 which seems like a long-long time ago, USDA started asking a question during their armed survey the urban survey many farmers are familiar with this it's the ag resource management survey that's done every year, but each year they focus on a different crop. I think 2002 was soybeans and back then for several years almost 10 years they asked the question farmer if you have a yield monitor which of these on this list which of these are the main ways you use that yield monitor, and for I think we looked at soybeans, cotton, corn, wheat, barley for the crops during that time period. And the number one use was for corn and soybean was to for harvest logistics they were using the moisture data from a yield monitor which is actually the separate as engineers you know that's a separate sensor. But, it's usually associated with it and that was the number one use of yield monitor data in the early 2000s. The second one was to document yields which you know it's kind of yeah we knew that. But the third one for third highest use of a yield monitor for corn, soybean farmers and the number one use for cotton was to conduct on farm experiments. So, farmers were telling us that conducting on-farm experiments was a leading use of yield monitoring technology and we'll come to back to that. I want to have several more comments about that other things on the list may not apply to Nebraska or Kansas necessarily, but in the eastern corn belt drainage decisions...How do we negotiate with the landowner about adding improvements to the lands for drainage and that kind of thing but yeah on-farm experiments is kind of where a lot of the action is, and I know that's been some of your interest with this podcast. Samantha: So, thinking about you know farming being a business and always wanting to be profitable from your experience what is most farmer's strategy when it comes to maximizing farm profitability is it always going for the highest yield or do you think we've kind of gone beyond that to become more profitable? Terry: I see a lot of different strategies out there. You know in economics 101 you know if you've ever said in college class in an ag economics course we talk about the difference between yield maximization and profit maximization if your inputs are not free then yield maximization is different than profit maximization; although, that's kind of in the back of everyone's minds. I do feel like the strategy is usually to maximize yield to a certain extent and I like to play you know mind games no that's not correct I like to play mental challenge games. And so, the question is okay would you be willing to follow these instructions if I could guarantee that you could make 350 bushel corn and usually the response is no I can't afford that stuff well you kind of just made the point that yield maximization is not the same as profit maximization and but yeah I think it's a good point that although I think people intuitively understand this since they've been taught these things. The strategy seems to be yield maximization when we have our conversations you know if you watch Twitter, which I do you know you mention my Twitter handle, I tweet out about ag tech, but I also listen and pay attention to what farmers are tweeting about and a lot of times you know the discussion is all about yield you know you don't hear people comparing their break-even prices or that kind of thing. Jackson: Yeah, and and that really rings a bell with us because in our research you know we're doing a lot of sensor-based nitrogen management and so for us there's a lot of discussion around marginal net return and usually that's not on a really high resolution basis it's usually on a treatment by treatment basis and so you know for us when we're looking at the effectiveness of these nitrogen management strategies we say okay well even if we reduce yield a little bit, if we cut costs more then we are still coming out more profitably and we see that as researchers as a positive outcome from the research in at least one context. And so for our producers a lot of times maximizing yield can you know be more of their focus as we just talked about so why might it be a strategy for farmers to push for higher yields as their strategy for increasing profit like does that idea have some traction and exactly how how far should that go? Terry: I don't think we should go very far but one example where it did play out in an economic context is you know depends on the farm program you know sometimes the government payments are based upon previous yield history well that yield history may be more important than saving a few dollars on some inputs from time to time but that's not a good strategy because we do not know what the next farm bill will say. (It's actually interesting the policy changes is kind of interesting) Samantha: That you actually brought up an exact comment we got once from a farmer they said well for like insurance reasons, I don't want to ever risk yield and you know you brought up that point. But yeah we don't know really what the future has in store, so that's awesome. Earlier you mentioned that there's not a lot of conversation on break even price- how many farmers do you think actually know their breakeven price? Terry: You're gonna put me on a spot on that one. I'm just going to throw this, I want to play devil's advocate. I don't think any of them do right because you have no idea what your yields are going to be when you're making purchase decisions for seed and fertilizer in the winter before you plant right? So what do you need to know for break even price well you know where your yield is you need to know what your input costs are. There was a segment- I didn't watch it closely. I think it was in Iowa with the political debate and they were you know what I'm talking about and you know the question was what's the break-even on I think on the soybean and one person didn't know another person gave an exact answer down to the penny. Like no there's that and and every producer has a different break-even price right and and we're talking about spatial variability well every spot in your field has a different breakeven price you know especially if you do variable rate applications and it's not even purely based on yield it's also based upon how much product you put out, so the knowing break even price is nebulous. But, I do not want to diminish the value in having a really good sharp pencil when farmers are tracking cost and expected yields to to have an expectation for what that is. Jackson: So, you know we've mentioned several times now the difficulty of understanding that break-even price and you mentioned it's especially difficult when you've got spatially variable applications and inputs in your farm operation, so why might the fact that you have spatially variable inputs need to change the mentality of maximizing yield and how might profitability maps for example help people to better understand what that break-even price might be and or trying to maximize their profitability on their farm? Terry: So, I like to think about this your students right yes so in class you get grades you get an a maybe A, B you know people who made C's, hopefully more A's and B's. So, I like to think of what first step is. I like to think about every spot in the field, let's you know envision a grid you know throw a fish in it over the field and every grid has a grade you know this has a high yield give it an A, but it's not as simple as that because you're really asking you know it's like a sports team you want everybody to perform at 110% right? Or let's just say 100 well some places in the field cannot produce 300 bushel corn under current technology just not going to happen some places can so the question I'd like to ask in a spatial way is- this part of the field even though it's producing 40 bushels corn is it the best that it can do it might be performing at 100 if so that's good to know, your 300 bushel area may not be producing to its full extent maybe underperforming and so that's really important to understand as well you know we're talking about on a field, subfield level that if we zoom out it's on like on google maps just zoom out for a little bit you know a farmer may have 40 or 50 fields and each field has a different expectation for yield right. And also different expectations for inputs. Well, choose one of those fields and zoom down into it and it's the same concept you know different parts of the field have different yield expectations and need for inputs and if a poor area of the field is performing best it's going to be then that's that's good information to have and we shouldn't treat it like it should be producing 300 bushel corn when it can only produce 40. Samantha: Absolutely so can we dive into this profitability mapping a little bit more. So, with the availability of high resolution data for most people operations on a farm and reasonably high resolution yield data we can really analyze farming spatially as you just talked about. So can you talk about how this profit mapping works like what are some potential errors and what type of data quality is necessary? Can you just kind of go into that process a bit? Terry: Yeah, I've got a lot of comments about that. So, making you know the first time I made a profit map was in the 1990s and oddly would you believe that the profit map looked identical to the yield map. The legend was different it was just a linear transformation multiplied yield by the possible crop right. It wasn't and since it was all uniform applications of products and when we subtracted that off you know the maps were you know identical. So, if yield comes across well you know if we start doing a variable rate you know you okay you have yield times price minus the cost of the inputs and application. Which may differ for every spot in the field, so now it may look a little bit different than the yield map did originally. I'm surprised those aren't used more than they have and then last five years or so some commercial vendors services have offered a profit map and it's like they discovered gold or something it was amazing to me that occurred. But, Samantha the real thing I want to talk about when you ask that question when it comes to profit maps or anytime we want to use yield monitor data for deciding on if we need to do nutrient management plans specifically. NRCS with USDA is really interested in this for making a profit map from yield maps. We need to make sure that yield monitor data are a sufficient quality to do so. Okay would you believe I know I'm sure you do, I'm sure you've been knee-deep in this. Would you believe that about a third of the yield monitor data points that we get off a combine are not accurately measured? Samantha: We have to go through a cleaning process every year when we get our own farm research data back and I assume that's what you're going to talk about a little bit. Terry: Exactly and I want our listeners to, I mean they know that this as well, so there's some circumstances that machine the combine or specifically the yield monitor cannot make accurate measurements and this is usually having to do with the dynamics of the combine or cotton picker. For our listeners in southern Kansas how they've been operated right so we calibrated do you calibrate yield monitor do our listeners calibrate yield monitors? Samantha: If they listened to our episode a couple weeks ago yeah. Terry: That's step one, and I always try to you know make the point and I don't have a good answer other than follow manufacturer's recommendations. But, y'all may have a better recommendation on that. But, I do firmly believe that we need to begin with an accurately calibrated yield monitor if we're going to use it for decision making purposes. But even then the way the combine is operated leads to about a third of the data points are measured erroneously and they're erroneous enough that we need to flag them and not include those for further analysis. So, I'm a big fan of data quality and not only that but I need assurance that data were handled properly. What I mean by that is I like to have as analysts who may not have ever been in that field I want to have confidence that no one has manipulated that data before it gets to me. So, I request the raw data as it comes from the combine Jackson: So, we've talked about this concept of especially variable moisture especially variable yields basically variable profit but again that's all spatial variability and we also have temporal variability that we look at in agriculture and a lot of that is related to year to year variations in climate. And so how many years of yield data do you think it really takes to truly characterize spatial variability, particularly as it relates to profit and how does data need to be treated across different years in order for those data sets to be combined? Terry: So, my preference would be we need about a thousand years of data for each field accurately reflect the each weather scenario that would be possible maybe a hundred but definitely not three or ten you know. If we truly want to characterize the spatial variability because you know even with 10 years of data I'm sure that you're saying the same thing I am is that- okay we got these areas of stable high yields and areas of stable low yields then you have these huge areas of instability. They're not always high, they're not always low, and we really know why sometimes you know is that leading us to ask really good questions. You know one thing is I don't think we have enough years of data to really characterize the variability here. Yeah, so even if you're 80 years old and you've farmed for 60 years as the main operator okay you've got 60 weather years of information it seems like a lot but ask some of these 70 year olds and 80 year olds how many years have you seen that were exactly the same. So, you know when we think about trying to do this temporal analyses you know that's great. But, you know back in the day I remember you know I made the statement back 20 years ago they need at least three or need at least five like you know that no you need a thousand you know but the fact is at best you're gonna get 60, 70 years of observations as an individual farmer and you have decisions to make for 2021 and sometimes all you got to go off of is 2020 you get one year that's better than having no years. Jackson? How is big data coming in and kind of shaping how we're able to do that faster because I would imagine if we've got these aggregated data sets from multiple different climates we're seeing a lot more of this weather variability in a year-to-year basis, so does that going to play into helping us make decisions with fewer years of data just by having such a broader swath of data? Terry: Exactly, so big data you know we've been tossing that term around for maybe about a decade in agriculture it's very fascinating we've seen huge amounts of venture capital money chasing this notion of farm data. All right, so smart wealthy people are interested in this it's a real thing you know it's it's not just an academic thought. Let's let's think about it from still with small data okay some of the biggest farms in Kansas and Nebraska have upwards of 20 plus thousand acres right. So, they get lots of observations you know 50,000 data points per acre each year and you know they're spread out across two, three counties. They're getting lots of observations from different weather regimes because you know it may rain in one county or part of the county and not the other. So, you know they've got a lot of things going on and collecting a lot of information that they're using to make decisions next year. Also in Kansas, Nebraska we have a lot of 500 acre farms okay and so many of these may not be big enough to even own their own harvest equipment and so what they do, custom operators come in and harvest for them and same is true some of the big farms out west Kansas as well. You know, those custom operations come through but regardless a lot of those custom operators will have the technology they'll have yield monitors, they'll have be able to provide maps and and the original spatial data back to the farmers. So, if you're a 500 acre farmer you're sort of at a disadvantage for a lot of things because most technology has initial cash outlays and with higher acreage on your farm you can spread those costs out across those acres and a 500 acre farm may not be able to do that. But, what if your 500 acre farm joins forces with another a dozen you know a couple dozen other farms about the same size from across two or three counties you know same general area but you know wide enough for you're getting different weather events and so forth. You know, I think that particular group has a particular advantage in forming local groups to to make not big data but you know let's call it smallish big, medium-ish you know data groups in order to gain insights that they normally would not be able to get about how these products are performing not only under their management style but under their cohorts management style on a slightly different environment. So, I like to use that example because you know nearly all cases, technology favors larger acreage farms because of spreading that fixed cost, well that's one example where smaller farms may have a relatively better advantage than the larger farms. Samantha: Yeah and seeing the different management practices across the different farmers. We've talked a lot about the challenges with making spatial maps, we talked about how the yield quality has to be good. We talked about how many years it's going to take, so why are what's the real value or why are they important why are we talking about spatial profitability mapping?
- [99:59:59.999]Terry: Oh, let's go back to an earlier comment. Spatial profitability mapping isn't for everybody. Okay, so if you look at some of the data that I published from the KFMA farmer members about how ag tech is utilized and adopted in Kansas, you know a lot of people really argue with me about now that can't be right you know, you only have you know 70 of Kansas farmers using automated guidance and 40 using yield monitors, and I firmly believe that that is accurate for 2020 not everybody has all technology and definitely not everybody even has you know some of them. We have automated guidance, is one of the things that is it profitable the answer is it doesn't matter it makes people the operator whether it's the farmer or hired labor it makes their life better the quality of life of the operator is so much better it doesn't matter if we can reduce overlaps and prevent skips and so forth with seating you know it just makes life better. Does the yield monitor make your life better? No, can it maybe and so as analysts I suspect I'm calling you analysts. I don't know if you get offended by that but you're engineering grad students correct? So, you deal with data and you probably enjoy that more than the typical person does but most people do not enjoy having mounds of data thrown at them especially if they'd rather be doing something else. It's not for everybody, but there are people who really enjoy this. You know farmers or you work with who are not typical farmers they really enjoy digging down not only looking at a printed map but really enjoy spatially wading through the electronic digital data on computers. Can you know those farmers well those are the type of farmers who are going to make use of these spatial profitability maps you know in the eastern corn belt. The example was negotiating with landowners about installing drainage structures. It could be other you know farm improvements in Nebraska and Kansas it depends so much on the farmer and also on their advisors, whether it's crop consultants, extension agents the ag service you know call them sales agronomists may be a good term. It depends on how much expertise and interest. When I say human capital, I'm talking about do they have time available do they have the energy and the desire to work with spatial data? That's our biggest thing to consider and those who are interested will find ways of making use of it. You know I can't even fathom and so for these farmers who do have access to the technology that we're talking about and who really are interested in using their data better. Jackson: Is it possible that these spatial profitability maps may be a better performance metric than yield at this point for their operations in terms of evaluating certain operational practices or ag technologies that they're using or even just you know measuring how well their operation is doing on a year-to-year basis? Terry: So, if someone brings me a yield map you know I can't really judge how well that field has performed or how well the farmer has managed that field without knowing how that field could have performed. That's the question how could this field have performed under the best combination best bundle of inputs and timing and practices. On-farm experiments is the answer and this is something I firmly believe that farmers who are interested in spatial analyses and ag tech and yield monitor data should be doing. It's going to have the highest return on investment of any other use of a yield monitor that I can think of and that is you know take a you know field a typical field that you would have and test some stuff out whether it's timing of an application of a fungicide or you know comparing two different herbicides or tillage treatments or this whatever you're interested in. And, I do finally believe it has to be something the farmer is interested in, in order for this to work out well and making a decision that will impact a thousand acres next year or some farms may implement this on five thousand, ten thousand acres well that's the the summation of all the profitability from all those acres is huge compared to the investment of the on-farm experiment although there are costs of doing experimentations and. You know, I've written some about that as well it's not costless especially if you have to devote all of your equipment to and take it away from farming for a day to put in an experiment that may take longer but with variable rate controllers we've kind of minimized the amount of downtime implementing those experiments but that's to me that's where the action is that's where this ag technology you know yield monitors, variable rate controllers you know automated boom shutoffs and automated guidance collectively used as a system will have the greatest return on investment. Samantha: We love to hear that. So, first I would also like to say that I really respect your like realistic and really practical knowledge you're not trying to be idealistic of where we're going. But, I do want to ask you about the future or where do you think we're going so what do you believe is the most exciting potential impact of digital agriculture relating to farm economics or profitability? Terry: Automation, and in economics we talk a lot about profitability which is part of it but also we talked about utility and in economic terms that means satisfaction and we look at utility in the rural household or the joint utility function of the rural household how is the farmer or the farm equipment operator and their spouse how much happiness do they have? Well, with automated items it was an example of increasing utility of the rural household people were happier, so profitability didn't matter as much and just like automa just like the steering process was automated beginning in the 1990s with automated guidance. The next exciting thing to me would be watching how academia research in a private sector would automate some of these more tedious data processes not just you know collecting the data from the sensors and passing it to the cloud, but actually analyzing the data without human intervention and as analysts it's kind of bothersome. We've got to let loose of this thing that we're trying to clasp onto, but I would encourage- I tell my students and I encourage all the academics out there who are listening how can you replace yourself with an algorithm to do the things that you're really good at doing and that's why I think would be so exciting in the next several years coming up. Jackson: I'm going to tell you right now that I love hearing you say that because that's I don't know, I feel like that's what I'm working on and I get really excited when we start thinking about automation and algorithms, and I don't know speeding up what humans can do and doing it better, more accurately. Terry: Well, it may not be better but you know the fact is with the cleaning yield monitor data you know I give the example it takes about if you don't hit the automated button if you do it manually it takes about 45 minutes per field that's doable if we're going to do a field. The problem is we don't have a field you know a farmer will have 50. If you're a sales agronomist, you may have 500 well you're not going to spend the next six months doing this you need to rely upon automated algorithm. Consistency is really important but it doesn't have to be better than a human it just needs to be faster than a human. Jackson: There you go, I think this has been a phenomenal episode to kind of wrap up our harvest series. I think we've literally touched on pretty much every aspect of what we talked about in our other episodes. But, just to to completely wrap us all together here one thing that we like to end every episode with is a piece of advice last piece of information or a next action step that you would like to offer to our listeners to kind of conclude the episode either on this topic or some topic that matters to you? Terry: Yeah, so the thing I leave all my presentations with that is somewhat controversial and a lot of people hate me saying this as a farmer you feel rushed to join one or more data companies every single day. They're like telemarketer pressure that you're feeling but farmer- you are not in a hurry if you do not see a clear benefit outweighing all the costs today your best bet is to wait you are not in a hurry the data companies and so forth they are in a hurry they're in a race with each other to get the most acres as soon as possible to win this big data race. Farmers, you're not in a hurry sit back and wait until the answer is obvious to you on who what groups you want to join. Samantha: Thank you to Dr. Terry Griffin for joining us on this episode of the FarmBits podcast. Terry provided us with exactly the kind of practical and realistic perspective that we are seeking to offer through this podcast. Jackson: There were two things that Terry said that really stood out to me during this podcast. One thing that Terry talked about is that the greatest value of yield data and potentially even the most exciting future potential of precision ag data in general is on-farm research. So, when we think about understanding profitability from a certain practice or maybe even just the benefit of a certain practice in your production, on-farm research is critical to being able to distinguish the effect of that practice versus your typical practice and how that may benefit your operation. Connected to that, there are a lot of intensive analyses that have to go on with these on-farm experiments and so Terry also talked about the value of automation particularly this automated data analysis as being a really exciting future opportunity in precision ag basically he said as analysts we need to think about making ourselves an algorithm and that the time intensive analytical processes are great but they're better when they're faster and that's exactly why automation is so beneficial. Samantha: That's exactly right, and one of the things that I found the most interesting was his discussion on value so you would think that with him being an ag economist that he would really be focused on profitability of each input action or technology but he also talked about how some of the most important and successful technologies are not the things that provide profit but are actually things that provide a utility and improve the quality of life for those who use them. Jackson: Absolutely and like I said in the intro this is the perfect wrap-up to our harvest series and it does conclude our harvest series so we look forward to having you join us next week as we dive into our next topic of characterizing soil spatial variability. Samantha: Thank you for taking the time to join us today on the FarmBits podcast. 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: If you enjoyed this episode, and it sounds like something you'd listen to each week subscribe to the podcast and set your notifications to let you know each time we release a podcast. Jackson: We would love to hear from you with your feedback so if you have comments or questions for us please reach out to us over email at @NEDigiatalAg unl.edu, on twitter, or in the reviews section of your favorite podcast platform. Samantha: See you next week on another episode of FarmBits.
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