Episode 033: Making of a Model
Nebraska Extension Digital Agriculture
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05/20/2021
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Description
Models are one class of predictive tool for nitrogen management. They offer many advantages, but also carry inherent risks associated with attempting to quantify and predict how a complex system will respond to nitrogen. Our guest for this episode, Dr. Laila Puntel, is an Assistant Professor in the Department of Agronomy and Horticulture at the University of Nebraska - Lincoln. Her expertise is in soil and water sciences - particularly soil fertility and precision agriculture - and she has extensive experience working in nitrogen and crop model development. This episode makes the complex idea of a nitrogen model much more approachable, connects the dots between the abstract concept of a model with commercially available tools, and takes a deep dive into the efficacy of models from multiple angles. If you've ever wondered what models really are and how they may be used, this episode is for you.
Resources:
CIG Project: https://cropwatch.unl.edu/precision-nitrogen-management-farm-research-project
Clarion Precision Ag: http://www.precisionclarion.com/
Laila Puntel Information:
Website: https://agronomy.unl.edu/puntel
Twitter: https://twitter.com/PuntelLab
LinkedIn: https://www.linkedin.com/in/laila-puntel-1955092a/
FarmBits Team Contact Info:
E-Mail: farmbits@unl.edu
Twitter: https://twitter.com/NEDigitalAg
Samantha's Twitter: https://twitter.com/SamanthaTeten
Samantha's LinkedIn: https://www.linkedin.com/in/samanthateten/
Jackson's Twitter: https://twitter.com/jstansell87
Jackson's LinkedIn: https://www.linkedin.com/in/jacksonstansell/
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.
Searchable Transcript
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- [00:00:09.762]Jackson: Welcome to the FarmBits podcast, a product of Nebraska Extension digital
- [00:00:13.360]agriculture, I'm Jackson Stansell Sam: And I'm Samantha Teten,
- [00:00:16.960]and we come to you each week to discuss the trends, the realities,
- [00:00:20.400]and the value of digital agriculture. Jackson: Through interviews and panels with experts,
- [00:00:25.200]producers and innovators from all sectors of digital technology- we hope that you
- [00:00:29.360]step away from each episode with new practical knowledge of digital agriculture technology.
- [00:00:34.400]Hello and welcome back to the FarmBits podcast for the second episode in our
- [00:00:37.600]nitrogen management technology series. Sam: In this episode, we will turn our focus
- [00:00:41.520]to nitrogen models- one group of tools that has been proposed for improving nitrogen management.
- [00:00:46.080]Jackson: Dr. Laila Puntel, assistant professor of soil and water sciences at the University of
- [00:00:50.720]Nebraska-Lincoln joins us for this episode to discuss the basics of models, why models work
- [00:00:56.000]when they work and what shortcomings they have. Sam: This episode sets us up for a follow-up
- [00:01:01.200]episode featuring a commercially available nitrogen modeling tool,
- [00:01:04.720]but let's start off getting the expertise in our interview with Dr. Puntel.
- [00:01:11.840]Jackson: What are some of the challenges that producers are facing right now in regards to
- [00:01:17.040]making these nitrogen management decisions and what sort of research are you hoping to contribute
- [00:01:21.520]to that's helping them make better decisions? Laila: Sure well, nitrogen is my favorite,
- [00:01:26.960]all my work my previous work has been in nitrogen since my master's, my phd and then I got into a
- [00:01:33.120]position at Nebraska that is mostly related to nitrogen management, so it's pretty exciting and
- [00:01:43.200]really kind of my motivation is around the fact that nitrogen is still very challenging
- [00:01:47.120]very challenging right. So, and I think the challenges that producers are facing is
- [00:01:53.760]the uncertainty around that nitrogen right, so basically there's a lot of variability.
- [00:01:59.680]The nitrogen that maximizes your profit and minimizes the impact environment could be
- [00:02:05.760]very different year by year as well as within the field. So, kind of picking up what's the
- [00:02:09.920]best nitrogen rate that you want to select for a field is still being a challenge right, so
- [00:02:17.200]to begin with that and then along with that kind of what tool is the best to help you
- [00:02:23.520]to predict that nitrogen right. so correct yeah Sam: So, we've had another episode that focused
- [00:02:29.760]on reactive or responsive nitrogen management tools but can you talk about more maybe predictive
- [00:02:34.800]nitrogen models and what's out there as options? Laila: Sure, yeah so in terms of tools
- [00:02:41.920]as you said like active canopy sensors or more the sensor the sensing technology it's more
- [00:02:48.480]reactive and uses the plant right as kind of our living sensor to tell us how much nitrogen is in
- [00:02:55.200]the soil how much nitrogen is needed by the plant. Now, some other tools like crop model based tools
- [00:03:02.080]kind of allow the producer to cover some of the risk related with weather and how the weather
- [00:03:07.600]interacts with the crop management as well as some of the soil characteristics, so I think there's
- [00:03:14.000]plusses and minuses but just the complexity of a crop model allowed us to kind of contemplate some
- [00:03:20.560]other aspects of the system for people who maybe technology's a little bit intimidating or they're
- [00:03:26.800]not very familiar with these models. Sam: Can you just give
- [00:03:30.480]just a really basic overview of how simple a model can be to maybe how complex it can be
- [00:03:35.920]and some examples, so that way they know if they've heard of these before. Laila: Sure,
- [00:03:40.320]yeah so probably we should have started there yeah what is the crop model mm-hmm. So, if you think
- [00:03:46.800]about it every day you can simulate right like a virtual reality of how the plant is growing,
- [00:03:54.960]so every time you're fixing radiation and put it into biomass right so that's how your plant grows,
- [00:04:02.720]so now imagine if you want to make it a little bit more complex right? You started there now you try
- [00:04:08.640]to assess like how much nitrogen is in the soil and for that you need the organic matter. You need
- [00:04:13.680]to know how it decomposes and how much nitrogen is being released, so that's one piece but then
- [00:04:18.640]the plant on one side, if it grows too much then it's going to try to uptake more nitrogen or less
- [00:04:23.840]nitrogen right sure, so models account for all that. Every day you kind of have a balance like
- [00:04:29.520]a bank account sounds equation it's a mass balance equation, but that's when the weather kind of gets
- [00:04:35.920]into play right because if it rains more if you have cloudy days if you have more radiation then
- [00:04:42.320]that potential growth is getting bigger and bigger. Right and then like the supply of
- [00:04:47.360]nitrogen as well, so that's kind of like a simple way of putting that the thing is that there's a
- [00:04:56.000]lot of other factors that can be included into the model to make it more and more complex
- [00:05:01.120]some of those are losses right so we know that nitrogen leeches in the ground and so we're
- [00:05:06.480]starting to add the hydrology concepts right yeah. What texture, how that rate of nitrogen is being
- [00:05:13.600]lost from the soil profile, how much nitrogen is run off like volatility. If, we get into the
- [00:05:20.560]complexity of the nitrogen cycling interactions and everything that's the way that you can make
- [00:05:27.040]the model more complex and hopefully more accurate and hopefully more accurate right but you know gas
- [00:05:33.040]emissions right like all volatilization things like that. Our process is that
- [00:05:39.120]we don't have that much data either right it comes back to the data it goes back to the data right.
- [00:05:44.880]Sam: Okay, and what are some examples? Laila: There are some property models that for
- [00:05:50.720]example we are partnered and collaborating with Granular. They have their own model,
- [00:05:56.880]but we know that it's a process-based model. What I mean by that is exactly what I just explained
- [00:06:01.440]as the more simplistic way but pluses and minus. That's what they run into the background right
- [00:06:10.160]and then we are also working with AdaptN, which that's a model that it's actually the basis dsat,
- [00:06:17.760]which is a very it's open source model that was adapted in Cornell University
- [00:06:23.440]to make recommendations of nitrogen and that model then was acquired by Yada a few years ago and then
- [00:06:30.960]got commercial, so in the background they run a process-based model with all these components
- [00:06:36.320]that we discussed today including nitrogen losses as well. So, they incorporate all the complexity
- [00:06:42.560]even cover crops or residue removal things like that, we are also aware of Fluorosat, is another
- [00:06:52.080]software that we are testing in this project that I mentioned and they use another open source
- [00:07:00.400]model that is very complex. it accounts for all the mechanisms from losses and
- [00:07:05.840]and other factors of the management so that is also another alternative. Farmer search
- [00:07:12.880]we know that also runs a model in the background and probably I can keep naming, and I probably
- [00:07:18.320]won't forget and in the next years I feel like we're going to see more and more coming up.
- [00:07:23.360]Jackson: So, what are some of the strengths and weaknesses that we have there between those two
- [00:07:27.440]particular, I mean you talked about how the the sensors kind of incorporate the plant.
- [00:07:31.680]What are some of the strengths of that versus like what are the risks that you're running
- [00:07:35.040]when you're kind of using only the plant to tell you everything that is going on out there
- [00:07:39.600]in the field that maybe a model makes up for? Laila: Sure, I think the the biggest part of that
- [00:07:45.360]is that the crop model will interpret the weather and what is going to happen
- [00:07:52.560]into the future as well as what the grower is going to be looking for. So, in the case on a
- [00:07:59.520]fertigation event or maybe a a round flowering application or things like that, you can
- [00:08:05.760]already account for those as well as account with what the weather will look like later.
- [00:08:11.600]I think that's kind of what they complement each other because I feel like canopy sensors or some
- [00:08:16.960]sensing technology will give you kind of a picture of today, right, how the crop looks like but maybe
- [00:08:23.520]crop models come into play to see how the growing season will look like and see if that nitrogen
- [00:08:28.960]that you put on is actually gonna end it up you know yielding more, responding more and give you
- [00:08:34.640]more profit. Sam: Yeah and building on that maybe we can step back a bit and talk about the goals of
- [00:08:40.320]nitrogen management and then how effective these models are in those goals? So, there's different
- [00:08:44.160]philosophies can you go into that a bit? Laila: Sure yeah, so I guess your goal
- [00:08:52.320]I would say is trying to hit the optimal end rate that maximize the yield response
- [00:09:00.160]and that is because you want to make sure that you are completing the yield response at a point where
- [00:09:08.320]it yields the high profit to your field and so ideally the goal is to hit that point in
- [00:09:14.960]every part of the field right. Let's think about an idea where you have sure highly resolution
- [00:09:20.880]right and we want to target each pixel or each part of our field. Our optimal end rate that
- [00:09:27.760]would be kind of our main goal now we know that we have all this complexity and the weather, the
- [00:09:35.280]soil the management only interacting. So, I think that's kind of the challenge
- [00:09:41.600]that is still there right and just to clarify Sam: For people listening, the optimal nitrogen
- [00:09:47.360]rate does not always equal yield maximization. Laila: That is I mean that's pretty interesting.
- [00:09:56.160]We tend to think that we want to apply nitrogen to maximize yield and the thing is that maybe
- [00:10:03.360]that increase in bushels doesn't pay the extra amount of nitrogen that you put on,
- [00:10:09.040]and that ties back to you know these models and how do they perform well.
- [00:10:16.000]Sometimes it's really hard to capture the response to nitrogen rather than like hitting the maximum
- [00:10:22.800]yield right. So, there are two different things. Jackson: So, you talked about how models
- [00:10:28.960]basically give you a view at what the growing season is going to look like
- [00:10:32.480]and for a lot of people out there they probably see the variability year after year and they're
- [00:10:36.160]like how could you possibly know what the season is going to look like? So, what sort of data has
- [00:10:40.320]to go into these models in order for them to work appropriately and really give you
- [00:10:45.040]that season-long picture that you're looking for? Laila: Sure, models let's start from the beginning
- [00:10:50.640]right. Models are not perfect and even you know if a statistical model an equation or
- [00:10:57.680]even a crop model they're not perfect, so they're things that they don't contemplate. A risk like a
- [00:11:05.280]weather event or a hail event right and a disease or a pest those things usually tend to be out of
- [00:11:14.480]what the scope of a model at least for now. Let's see what happens in the future right.
- [00:11:21.280]But, the way that they contemplate all these things happening is because
- [00:11:26.480]you input, soil growers can either utilize the data that they have been gathering for the last
- [00:11:32.960]few years either agreed sampling or some soil mapping or sometimes we can rely on what the
- [00:11:40.000]open sources of of soil data are available to kind of feed that model. But, then we will also
- [00:11:46.240]contemplate all the management inputs for that grower like planting day right. What kind of
- [00:11:51.200]variety or hybrid they are using for the crop things like that . And then we can also utilize
- [00:11:57.520]whatever weather sources they have. They might have their own weather station that we can fit
- [00:12:02.400]in or there might be other networks available that could be included. So, I think those are
- [00:12:08.400]the three major ones. All the crop management, soil data and weather to make that prediction.
- [00:12:14.960]Sam: Can you dive into the weather data a little bit more about
- [00:12:18.400]the predictive part of it which I'm sure is the most challenging part but also how you're using
- [00:12:22.320]the historical weather data and if whether that's many years worth of historical yield
- [00:12:26.160]data but also what's already happened up to that point in the season, why is all that data really
- [00:12:30.800]important to make an accurate recommendation? Laila: Yeah, that's whether data is key right
- [00:12:36.640]we tend to rely a lot on what's going to happen and those predictive models. So, what happened up
- [00:12:45.120]to the day that you're going to do the prediction that is key right because that kind of detects,
- [00:12:50.480]dictates how much nitrogen you have available in the soil, how much water your soil is holding and
- [00:12:57.280]you know how much growth you have up to that point. So, that's what I always say that
- [00:13:02.080]you can maximize the power of crop models the later you are into the season right or the more
- [00:13:08.160]chances you have to put nitrogen on because the more you can utilize whatever happened. Now,
- [00:13:16.240]it has been shown and actually was part of my research work a few years ago
- [00:13:21.120]where we did an assemble of weather data, and I think that's very powerful. So, usually we tend
- [00:13:28.400]to use whatever comfortable forecast we have even if it's seven day three days ten days that that
- [00:13:36.880]period of time and then kind of assemble that with historical data and then what we found is
- [00:13:43.120]that that assemble of historical data is actually tend to be very representative on average right
- [00:13:49.520]on what what could potentially happen the rest of the growing season. There has been some approaches
- [00:13:56.320]to kind of divide the data into let's say more drier years, more weather years right like tend
- [00:14:03.200]to do predictions with those two, and I think it will depend on where you are in the country
- [00:14:09.440]to know if those approaches are better or worse right, but historical weather is very powerful
- [00:14:16.240]to at least kind of have a probability of what are my chances you know that my yield is going to get
- [00:14:23.040]into this trend above 200 bushels so below 200 bushels. So, sometimes we look into this
- [00:14:29.280]predictive models more like to guide us on what's the trend rather than what is the exact value of
- [00:14:35.920]yield that I'm going to get. Sam: And, you kind of hit on this a little bit with the regional
- [00:14:40.560]information you said what works best for your region what about other characteristics as well
- [00:14:44.000]like the soil data. A lot of these models may have been built for a certain region
- [00:14:48.960]what are some of those challenges and how can we better inform it for other areas? Laila: Totally
- [00:14:54.560]totally and actually you will see that some models were created in other parts of the world
- [00:15:01.920]and then they were bringing it into U.S., right, but they has been tested and accommodated and
- [00:15:08.560]validated in multiple environments that all that research and science had been put it in. So,
- [00:15:14.400]today you can maybe click on a public available model and you might even find hybrids that are
- [00:15:20.320]grown in United States even though the model might come from another part of the country.
- [00:15:25.040]So, I would say that the science behind is very robust and they have been validated however
- [00:15:31.360]as you mentioned we have models that might be approved or very well proved in the northeast
- [00:15:38.320]region of the United States but then when you come to midwest the limitations are different.
- [00:15:43.120]The weather conditions are different and one of the things that we learn is that models tend to
- [00:15:50.800]perform the worst when you have extreme events, and so a flood right or a
- [00:15:57.920]heat or stress during the growing season, water table drainage systems different factors that
- [00:16:05.920]are really challenged to capture in a model. So, really at a regional level I think that's part of
- [00:16:11.360]my excitement in my current position is trying to find out those things that actually can make
- [00:16:16.800]those models better for our regional conditions. Jackson: So, I guess kind of on that on that
- [00:16:26.800]regional scope as well, thinking about weather data a little bit more instead of soil data
- [00:16:31.440]what sort of resolution do you need to have in your weather data to really have a reliable model
- [00:16:36.800]and how reliable is it to get that from historical weather data because I imagine we're just starting
- [00:16:42.000]to get into having really high resolution weather data with the kind of proliferation
- [00:16:45.760]of weather stations and that sort of thing in our modern era, can you speak to that a little bit?
- [00:16:50.720]Laila: Yeah, so when you mean resolution it's like if you need hourly daily both temporal as
- [00:16:56.720]well as spatial you know if we're talking about doing having a one weather station for every I
- [00:17:01.680]don't know 10 square miles or something like that versus a weather station for an entire
- [00:17:05.520]county. You know, because you think about how intermittent weather stations can be out there.
- [00:17:14.960]Let's divide the conversation into right one is the temporal models in general they run into
- [00:17:22.080]with daily data, so every day at least you have to have a record of what happened right. So, and
- [00:17:29.840]that's kind of pretty a standard you can configure your weather stations for like getting that type
- [00:17:35.200]of data now in terms of the spatial distribution I think one of the things that help especially
- [00:17:41.520]for growers that are thinking about adapting these tools is have a rain gauge. So, precipitation is
- [00:17:48.240]one of the ones the variables that could vary so much right and in a spatial grid. And so,
- [00:17:54.720]I think that kind of validating or accounting for that will make our predictions much better,
- [00:18:00.880]radiation and temperature those things kind of tend to vary less within the space. So, I think
- [00:18:08.240]kind of having your own measurement on precipitation is very valuable.
- [00:18:13.040]Sam: You mentioned about how the soil and everything can vary or that response nitrogen,
- [00:18:19.120]response curve can vary a lot even within a field or that economically optimal nitrogen rate can
- [00:18:23.680]vary a lot. So, what kind of data are you using to potentially help inform how that variability is in
- [00:18:30.880]the field especially for nitrogen management. Laila: Yeah so, there's two worlds right one
- [00:18:37.280]is the one that exists in commercial available tools and the other world is the one that exists
- [00:18:42.400]in academic world where you can tweak and access to a lot of data right and make a more complex
- [00:18:49.120]configuration. So, focusing on more commercial versions and what goes into those tools, I think
- [00:18:58.480]right now we can utilize a lot of graded information from soil mapping with electrical
- [00:19:05.360]conductivity some elevation maps to account for more specific slope or curvature in the field
- [00:19:11.600]rather than just relying on two three values from our public available data.
- [00:19:17.760]We now have those options now that transition is just happening or I see it happening where
- [00:19:24.560]every year there is a new release within the industry, companies updating or making
- [00:19:31.440]those inputs more precise because what happened is that if you use you know
- [00:19:36.880]just one unique value for maybe configuring your slope let's say that affects all your hydrology,
- [00:19:43.600]right all the water movement how much water you are getting in some parts of the field,
- [00:19:48.720]and so going back to your point if you want to capture the yield response to nitrogen correctly
- [00:19:55.120]you have to make the best to kind of represent those different environments right. So, soil
- [00:20:02.080]data I think is a big one and I'm tending to see that other indirect measurements like electrical
- [00:20:09.680]conductivity is being used to characterize how the the soil looks like or maybe what's the
- [00:20:16.720]distribution of texture right. So, I think that's becoming more and more available and some other
- [00:20:23.520]are using even remote sensing to characterize some of the initial conditions of the model. And so,
- [00:20:31.680]maybe with an image you can know what happened last year right and how your yields were and how
- [00:20:36.640]much residue you have, we know all those things will affect how your crop corresponds to nitrogen.
- [00:20:42.400]Sam: And, on the podcast before we have interviewed Veris technologies and swat maps
- [00:20:47.440]yeah how do potentially those types of maps which you know use a lot of technology you just
- [00:20:52.480]mentioned but how do you make those specific for nitrogen or is it the same type of idea?
- [00:20:56.160]Laila: Sure, I can speak on the electrical conductivity in particular. There's two ways that
- [00:21:04.400]are currently being used, and we are currently testing as well on the research side of things.
- [00:21:09.360]The first one is that you can implement those to make a better like division of your field where
- [00:21:15.040]you might have different yield potentials and different yield response. So, that's kind of a
- [00:21:19.600]more high resolution- I will say discrimination of how different is your field, so that's one
- [00:21:25.440]part and the model can take that in to simulate different areas of the field. So, that's one key
- [00:21:32.960]the other one is that if you have other data or the other layers of information like a grid
- [00:21:38.320]sampling you can actually couple your electrical conductivity with some of those variables
- [00:21:43.840]and kind of make it as a factor of how they vary across the field,
- [00:21:48.400]and so those will help you to configure better your soil profile you know in each of the
- [00:21:53.600]particular field. So, there's multiple ways that this indirect measurements are feeding the models.
- [00:22:00.160]Jackson: That's all really really good info. One other thing that kind of affects models,
- [00:22:06.000]we've talked about beyond just these soil characteristics and weather characteristics,
- [00:22:10.000]natural environment there are also management variables we've talked about right
- [00:22:13.120]so how challenging is it to accommodate all these different management variables that go in from
- [00:22:18.080]timing of applications to products that are being applied and kind of you know whether people are
- [00:22:22.720]using inhibitors whether biologicals are getting mixed into the system, tillage, cover crops
- [00:22:28.800]how do those go into a model and how can I make up for them?
- [00:22:31.360]Laila: Yeah, I would say the easy the easiest one are about management. I think those are
- [00:22:38.240]pretty well adapted. I would say like in terms of you know your planting day and what kind of
- [00:22:42.400]variety or hybrid you're using or previous crop things like that it gets a little bit
- [00:22:48.400]more complicated. When we get into manure application, cover crops and you mentioned
- [00:22:54.880]inhibitors, biological stuff like that. So, if you think about it- I think the biologicals are the
- [00:23:02.480]toughest ones just because models per se do not have any biology of the micro,
- [00:23:10.320]so that's a new world. It's coming up right at some point we're going to incorporate those where
- [00:23:17.200]you're going to think about it, what biological product you're applying and you may know
- [00:23:22.560]what certain bacterias you have there. You might be able to incorporate in your model
- [00:23:26.960]right and so those living microbes are going to have their own pool into these models, so we were
- [00:23:33.600]able to simulate them and and there's models that do that but separate from the ones that
- [00:23:38.480]maybe growers have access to. So, I think that's coming and that's probably the toughest one, but
- [00:23:44.000]I would say that manure as long as you have some sort of idea or values about how much nitrogen
- [00:23:52.880]is there how much phosphorus how much organic material there. The models will be certainly
- [00:23:59.760]able to take those values in. Cover crops, I think there's more advances on that because we
- [00:24:07.040]have more data too. So, we have more experiments where we know how much nitrogen maybe they will be
- [00:24:15.760]releasing throughout the growing season and those are values that we need to calibrate.
- [00:24:20.080]The models right so if a grower knows for example what kind of mixture did they use for their fields
- [00:24:28.720]like if it was a 50/50 rye and maybe a legume that information will be an input termination date. How
- [00:24:37.440]did they terminate or how much did they take the residue out they leave their residue in. So, I
- [00:24:44.320]think they're trying to simplify how to describe a cover crop, so then we can take it in and simulate
- [00:24:52.400]it. Sam: What do you think are some of the most common mistakes when it comes to incorporating all
- [00:24:58.400]this data and using it into form a nitrogen prescription or a nitrogen recommendation?
- [00:25:05.040]Laila: I would say first is not being aware of what kind of data
- [00:25:15.120]do you really need to make a good use of the tool right? We tend to do a lot of assumptions
- [00:25:24.080]on configuring these tools, but I'm certain that there's a lot of support, and I feel like it's
- [00:25:29.280]going to be more and more support. So, growers can get you know a crop consultant and advisor that
- [00:25:35.600]helps them to make sure that the inputs and the configuration is correct. So, I feel like impetus
- [00:25:41.520]is first the second one is the interpretation. So, I feel like big part of it is understanding
- [00:25:48.960]what is this showing like what is this figure saying right, and I think in that aspect industry
- [00:25:56.560]is doing a great job at trying to be a little bit more friendly with the interface and actually
- [00:26:02.480]showing things that are like more easily adapted to their common practices, right. It tends to
- [00:26:13.680]bemore intuitive just to go and look into yields right. What is my yield is going to
- [00:26:18.400]be well if you think about nitrogen management you actually want to know how much nitrogen is
- [00:26:24.240]still in my soil right and maybe how many bushels I can kind of support with what I have right now,
- [00:26:31.680]turning that data into something that the grower can take into account
- [00:26:36.400]that's you know valuable, and I think that it stills needs some help to
- [00:26:42.960]get a good understanding of what's the output of the models. So, I think those two and maybe
- [00:26:50.080]I will name a third that is considering that the tool is not perfect. And so if it doesn't
- [00:26:59.760]seem right it's like because most of the times it's going to be right but maybe you might
- [00:27:05.200]have a very particular growing season you know and I feel like everything is evolving and so
- [00:27:11.120]it's going to get better and better at you know trying to hit that optimal end rate.
- [00:27:16.400]But, acknowledging that is a good step. Jackson: So, we've talked you just kind
- [00:27:21.680]of brought up two great points right that you have a lot of data that goes into it. You got
- [00:27:25.520]to realize that there are you know potential errors there but it also has to really be easy
- [00:27:29.360]for somebody to pick up and use if you're going to use this on a on a wide scale. So, how do you
- [00:27:34.160]balance that is there are there any sacrifices that can be made on one side or the other and
- [00:27:38.640]where are they, I mean where are the trade-offs? Laila: Totally yeah, that's a great question and
- [00:27:44.400]it's part of the motivation behind my research and and our team is to find out you know what are the
- [00:27:52.400]things that you can sacrifice. Do we ask growers to have eight inches you know organic matter value
- [00:28:01.840]or are we okay with a smart farmer type of shallow organic matter. Can we feed the model anyways.
- [00:28:08.880]There are so many questions that I think we just still need to work on, and as you mentioned like
- [00:28:15.760]sensitivity analysis type of thing where you know which ones are causing the the biggest you know
- [00:28:23.040]weaknesses in the model and what others are really needed.
- [00:28:28.640]I would say that most of the models still relied on yield goals right and that's a
- [00:28:35.760]very critical aspect because even the model will track what's going on during the growing season.
- [00:28:42.560]Some models will still rely at the end of day on what was your yield goal determined it has
- [00:28:49.280]to have a target, it has to have a target right. So, I like the part that we're still incorporating
- [00:28:57.120]some of the growers type experience into the decision but I'm actually like
- [00:29:06.000]very careful about that because it's like we're trying to simplify something that is complex. But,
- [00:29:12.240]the problem is complex so nitrogen is complex per se, and we have these crop models that
- [00:29:19.920]attempt to capture all the pieces of the system and now we are trying back to simplify it
- [00:29:26.880]right. So, before we may have even captured everything with the complex models themselves,
- [00:29:35.120]and sometimes you know as is the data that is still not quite there. There are some particular
- [00:29:42.960]fields that may behave very average right there are conditions that are you know pretty
- [00:29:48.480]simple to simulate but again there might be some aspects or interactions that are very complex.
- [00:29:53.600]Jackson: So, what do you think I mean this has kind of come up a few times in terms of
- [00:29:59.360]how do we actually accumulate enough data to get started with this?
- [00:30:02.560]Do you have any idea what the lead time might be for somebody who wants to get into
- [00:30:06.720]modeling, how far out does a farmer need to look or an advisor need to look
- [00:30:11.360]at his clients and say you know I really think I need to bring a model to my clients,
- [00:30:16.960]but I don't know if I have the data that's stored up yet. I mean is it three years that
- [00:30:20.240]they really need to get the right amount of yield data to get everything done on a practical level,
- [00:30:26.400]how much time do you think you need? Laila: Do you want the sciency answer or you want
- [00:30:30.960]the practice? I want a practical answer. I want a practical answer from somebody that does extension
- [00:30:35.440]and you know does a lot of on-farm research. Laila: Totally, no I feel like ideally you
- [00:30:41.280]want to have some idea on your yield data right, so yield money if you're going to use the model
- [00:30:47.360]for doing a variable-rate application so intended to vary the amount of nitrogen across the field,
- [00:30:53.520]I'll say that accounting for a few years of yield monitor data will get a better
- [00:30:59.920]you know idea about your yield targets, and how the how the field responds to every year.
- [00:31:10.320]The minimum data is five probably just because I mean you might hit corn, let's say that you
- [00:31:16.240]are planning you're making a management plan for corn and you have a corn/soybean rotation so at
- [00:31:21.360]least you might have two three years of corn. You can incorporate in soybean but we tend to prefer
- [00:31:27.520]corn to kind of tell us the story about what's happening in there as far as actual yield goes.
- [00:31:34.240]Or, what was the response to your nitrogen management that you have done in the past.
- [00:31:39.120]So, I would say that's kind of my advice and on time. But, the other data layers I mean
- [00:31:47.680]you can get those if you really invested, you can get those in a year. You can and are
- [00:31:53.760]things that you do it once and pretty much you know you can utilize it multiple times
- [00:31:59.040]with some of the new tech that we have out there like Smart Firmers like you mentioned earlier and
- [00:32:02.880]you know some of the EC mapping units you can throw on a planter at this point.
- [00:32:07.040]There's a way to get it done, again I feel like that time or waiting time to get implemented
- [00:32:17.680]is gonna get shorter just because I think we might be getting more benefits out of the remote sensing
- [00:32:25.280]world where you know well maybe with a couple of yield monitor data sets plus remote sensing,
- [00:32:32.000]we can kind of get it started at least. It's not going to be perfect right but get it started.
- [00:32:37.280]Sam: And building off of this amount of time thing but how much time does it actually take to
- [00:32:43.440]run the model because I know it definitely depends, but this sounds really complex
- [00:32:47.520]when people are listening to this is some of it automated- can you talk about how you actually
- [00:32:52.560]get from all this data to a recommendation? Laila: Totally, well first to clarify there
- [00:32:59.280]are some softwares available out there where the representative will configure your field for you,
- [00:33:08.800]so you just need to provide information, so the time requirement there will be just to make
- [00:33:14.400]sure that you give them good yield data right and you inform them what were your management
- [00:33:20.160]practices or what you are planning to do with your nitrogen right. So, in terms of sources timing
- [00:33:26.960]and any kind of future event that you are planning I feel so in that sense that might be kind of
- [00:33:33.840]easier right because then you can just someone will help you to configure your account. They
- [00:33:40.000]ran the prescription for you and then you just follow your field and try to interpret you know
- [00:33:45.360]how your field is behaving. There are some other levels of software where
- [00:33:53.040]you really have to put more hands on and so in that sense models can
- [00:33:59.600]can take you an hour or so to configure you know everything. Incorporating the yield data,
- [00:34:06.800]incorporating soil information the management and everything so it will depend what kind of tool
- [00:34:13.680]you pay right. So, I guess in that sense we still have a long ways to go right because
- [00:34:20.560]there's a degree of simplicity to complexity, and it will depend on the growers right,
- [00:34:29.120]how much time they have to dedicate to it. Jackson: I've talked to an advisor here
- [00:34:35.200]in Nebraska who said he was spending about eight to ten hours a field to get everything
- [00:34:38.880]set up as far as getting all the data in the right place, getting it in the right format I mean it's
- [00:34:43.360]the upfront cost is big but then once you have everything up front it's easy to rerun
- [00:34:48.960]over and over again right? So, it's more the startup cost that's the issue more than the
- [00:34:54.160]continual time input right? Laila: Right, totally yeah.
- [00:34:57.600]Jackson: Along those lines, where do you think we are in terms of adoption? How many people are
- [00:35:02.080]actually using the using models out there actually implementing them on their farm,
- [00:35:08.080]are we in early stages are we even on that kind of uptick where do you think we are?
- [00:35:13.760]Laila: I think we are in the uptake okay and maybe I'm basing my my opinion, this is just an opinion
- [00:35:20.800]on how much services are coming out from the market. If you look at the at the last you know
- [00:35:27.920]kind of reports across the country, we see like a very steep curve you know kind of the providers
- [00:35:36.080]on all these cloud-based what we call cloud-based services is increasing a lot. So, that means that
- [00:35:43.120]a lot of crop advisors are being exposed to all these variety of tools.
- [00:35:48.640]So, I would say that will kind of get some traction on how many growers are adopting that,
- [00:35:56.320]but currently I mean with my experience at least in Nebraska what we are seeing is that there's
- [00:36:01.840]not a lot of growers that are actively using these platforms, so there's a lot of room for
- [00:36:08.960]for increasing and we are actually working on the extension side of things to try to promote these
- [00:36:13.840]tools among them and actually help them to get on board and see that it actually is not that complex
- [00:36:20.080]and that you know as you said it once and then once you understand what you need is easier.
- [00:36:28.000]Sam: And where do you see us going in the next five to ten years and whether
- [00:36:33.360]that be an adoption, but also in how we continue to develop these models?
- [00:36:37.440]Laila: Totally, what I see going I think I mentioned it a little bit before,
- [00:36:42.960]but I think all the automatic input or information coming from remote sensing will be a big player
- [00:36:51.280]in the future where I see the improvement on some of the configurations being automatic in the
- [00:37:00.080]background you know without having to get that much input from the grower,
- [00:37:06.480]so that would be kind of solving our issue between the trade-off. You know, sacrificing some inputs
- [00:37:12.720]for quality and again that will get a better recruit representation about what's going on in
- [00:37:20.080]the field. So, I see us going there. To be honest, we still need to test you know all these tools and
- [00:37:29.840]have a very good understanding on where we are at. Jackson: So, along that vein would you mind tell
- [00:37:36.000]us a little bit about the CIG project that we're doing here in Nebraska that's going
- [00:37:40.800]to help growers understand models a little bit better and what resources
- [00:37:44.320]might be available to them through this project? Laila: Sure yeah, the CIG project is supported
- [00:37:49.680]by the NRCS and USDA we started last year in the fall as a pilot because that was like what
- [00:37:59.920]the time allowed, so we are kicking off this year with wheat growers and corn growers, so the main
- [00:38:07.760]idea is that it gives growers the opportunity to test any existing tool for nitrogen management.
- [00:38:14.960]So, that that contemplates not only crop model based tools but also remote sensing tools like
- [00:38:21.280]canopy sensors or any other image provider that could be translated into a nitrogen management.
- [00:38:27.840]So, we see two main outputs for the grower- one is to get exposed to a new tool
- [00:38:34.320]with no risk right because we will compensate the grower for participating in our study as
- [00:38:40.240]well as we will cover any costs from either an extra application the extra cost of
- [00:38:46.240]you know getting into the software or getting an account and the other one is we'll test
- [00:38:52.960]if that tool actually works for them. So, yeah we are helping a lot of growers that want to
- [00:38:59.360]test these crop model tools to set up the field and get the information that they need.
- [00:39:03.440]Jackson: Sure, and will the information from that just basically be available through the
- [00:39:06.560]on-farm research network, I mean is it going to be publications how is
- [00:39:09.680]that information going to kind of be expanded out beyond the growers that are participating
- [00:39:13.280]to maybe other people in Nebraska and beyond? Laila: Sure yeah, the information is going to be
- [00:39:19.360]of course shared through the on-farm research network
- [00:39:23.440]we have an annual publication where all the results from the network that is going to get
- [00:39:28.160]published as well as there's going to be a lot of winter meetings where we're going to be recording
- [00:39:33.600]but also in person meetings to share the results of each of the growers and maybe where growers
- [00:39:38.960]also have the opportunity to tell how was the experience which is always great which is also
- [00:39:42.720]good. And then we are going to keep posting like on Twitter and some other media to see
- [00:39:48.160]the results that are coming up from this and then of course for the growers that are participating,
- [00:39:54.720]we are collecting a lot of intense data on these sites, so from images source sampling and other
- [00:40:00.800]analysis so they get to also share that and share it with other neighbors that might be
- [00:40:06.240]interested also in learning about their fields. Sam: Yeah so, we wrap all of our interviews up
- [00:40:11.600]with what one piece of advice do you have for our listeners when it comes to nitrogen management?
- [00:40:16.240]Laila: Oh, I would say try them all out try all the tools out and see which one fits you better
- [00:40:25.680]at least if you know if you have an extension educator or if you have some extension help
- [00:40:30.720]out there that can you know help you to set it up a test. I feel like that's the
- [00:40:35.920]the best way of kind of getting a conclusion about what's the tool and if you have questions you know
- [00:40:42.800]reach out to people that has been working with these tools before,
- [00:40:46.640]so you can get a better understanding but don't miss that opportunity I think
- [00:40:51.200]a lot of the nitrogen issues that we have you know not knowing that right amount or right timing
- [00:40:59.600]these complex tools are coming to solve that issue. It's just we need to
- [00:41:03.920]kind of make it more friendly in a way and get it closer to the growers, so to the growers
- [00:41:10.400]they can access to us or anyone working on extension and research to test them out.
- [00:41:15.040]Sam: Thank you to Dr. Puntel for joining us today on the podcast and
- [00:41:19.440]talking more about nitrogen management. Jackson: Yeah, it's cool to have her
- [00:41:23.040]on. We interact with her a lot so it's nice to actually sit down and do it a little bit more.
- [00:41:27.680]Sam: Yeah, that was a really fun interview and my favorite part was
- [00:41:31.360]she did such a great job of really describing all the components of a model.
- [00:41:35.520]It makes us think a lot about how growers are currently deciding how to apply their nitrogen
- [00:41:40.720]and if whether they're really thinking about all those components and how complex it is,
- [00:41:44.320]but also balancing with that with making sure it's easy to use and making sure it's practical. So, I
- [00:41:49.520]thought she did a great job of breaking that down. Jackson: Yeah and I think i think she did a really
- [00:41:53.360]good job too of explaining why exactly data is so important to models. I mean at the end of the day
- [00:41:58.800]model is only as accurate as the data that's going into it, and it is the quality of the data that's
- [00:42:02.960]being collected is pretty much equivalent to the quality of the model, and I think with weather
- [00:42:06.880]data it's kind of where that is the most almost every part of a model that we're putting so much
- [00:42:12.160]reliance on weather in terms of the modeling side of things, so just interesting to think about.
- [00:42:18.960]Next week we'll have an opportunity to hear a little bit more about a commercial offering
- [00:42:22.880]for modeling tools that's available through Granular.
- [00:42:25.920]We'll have Bob Gunzenhauser, who's going to talk to us about about what Granular is doing,
- [00:42:30.800]and we're pretty excited to bring that episode to you. So, we hope we'll tune in next week.
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- Tags:
- Models
- N modeling
- crop modeling
- digital agriculture
- precision agriculture
- weather data
- Granular
- Adapt-N
- University of Nebraska - Lincoln
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