Video 7 - 2020 Virtual Nebraska Soybean Day and Machinery Expo
Announcement of Private Industry Sponsors, Keith Glewen and Learning from Your Fields to Improve Nebraska’s Soybean Yield and Quality Patricio Grassini, UNL Associate Professor and Cropping Systems Specialist
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[00:00:17.050]Okay, I wanna take the opportunity to cut
[00:00:20.020]into our break here a little bit
[00:00:24.510]for the sake of time and just recognize
[00:00:27.970]some industry sponsors that over the years
[00:00:30.150]have been with us year in and year out.
[00:00:33.170]And I just wanna, even though they're not participating
[00:00:36.690]this year I wanna thank them for their support.
[00:00:41.010]And here you see a list of the AgWest Commodities,
[00:00:43.920]Soresco bag, Alliance for theFfuture of Vague in Nebraska,
[00:00:50.640]Frontier Cooperative, Bennett Service Company
[00:00:54.990]Gene Steffy Auto Group, Butler Ag Equipment,
[00:00:59.010]LG Seeds, Nebraska Ethanol Board,
[00:01:04.210]New Pride Genetics Network,
[00:01:06.620]Pioneer Hybrid International,
[00:01:10.100]Nutrient Ag Solution of Ashlyn,
[00:01:13.620]Platte Valley Equipment Company,
[00:01:16.060]Nebraska Soybean Association of course, PNR Sales.
[00:01:20.210]Spitchken Insurance, LLC, Titan Machinery,
[00:01:24.590]Fremont Paint Contend Equipment on Nebraska
[00:01:30.060]Strong Recovery Project, Union Bank and Trust
[00:01:34.110]Ward Laboratories and Wilbur Ellis company.
[00:01:36.840]We thank those individuals for their support in past years.
[00:01:41.940]And like I've said before,
[00:01:43.720]hopefully we can come together next year again
[00:01:46.900]and face to face and conduct
[00:01:52.856]our soybean day.
[00:01:55.790]Also, I'd like to bring to your attention
[00:01:58.900]after our next presentation,
[00:02:02.825]the Nebraska Soybean Board
[00:02:04.100]is going to be announcing the winners,
[00:02:07.350]of the drawing for the Yeti cooler
[00:02:09.510]and the $200 a pork as well as the card clothing
[00:02:15.410]and all like grease,
[00:02:18.760]So allay grease.
[00:02:20.860]So be sure to hang around for that
[00:02:23.460]to find out who the lucky individuals are.
[00:02:27.600]With that, I'm going to stop sharing my screen,
[00:02:30.680]I'm gonna turn it over to our next speaker,
[00:02:32.550]who was a Patricio, Dr. Patricio Grassini.
[00:02:36.210]And I've had the pleasure of working with Patricio
[00:02:39.020]for a number of years.
[00:02:40.690]And we've been collaborating with him and other individuals
[00:02:46.270]on collecting soybean samples
[00:02:49.610]at harvest time from growers fields.
[00:02:52.160]Many of them might be watching here today,
[00:02:55.220]looking into the production factors
[00:02:59.180]that go into affecting the quality and the yield,
[00:03:02.590]and Patricio is going to join us
[00:03:04.840]and share with us his findings.
[00:03:07.450]So welcome, Patricio.
[00:03:10.389]Thank you very much Keith, for the introduction
[00:03:13.190]and also for the invitation to...
[00:03:15.270]Can you turn your volume up a little bit on your end
[00:03:17.580]All right, how of now?
[00:03:22.680]Can you see my screen?
[00:03:24.110]I see your screen, it looks great.
[00:03:26.410]All right, great.
[00:03:27.700]Well again, thank you very much for the introduction
[00:03:29.986]and also for all your help over the past years
[00:03:33.770]and always being willing to help with our projects.
[00:03:36.570]And this is a very nice ,
[00:03:42.710]time of the year to present about this
[00:03:44.510]because it's a time when farmers
[00:03:46.820]can consider to do something different next season.
[00:03:48.970]So I'm, I'm really excited about presenting things
[00:03:51.794]that the eventually farmers can take home
[00:03:54.180]and eventually consider for the next season.
[00:03:58.450]Again, my name is Patricio Grassini,
[00:04:00.260]I'm professor of the department of agronomy
[00:04:03.580]still in Nebraska, I haven't moved to Wisconsin yet
[00:04:07.120]but you can see here from the news of course,
[00:04:10.170]or from the lowest at the bottom that I have been working
[00:04:12.340]with people from Wisconsin over the past years
[00:04:15.980]but so far I'm planning to remain in Nebraska.
[00:04:20.675]All right, I'm gonna present a little bit today
[00:04:22.250]about our work in collecting farmer's data
[00:04:25.950]and trying to use that information to identify options
[00:04:29.651]for farmers to increase their production
[00:04:32.550]as well as the quality of their soybeans.
[00:04:37.410]Then, we are really feeling the work,
[00:04:40.070]hear in the Midwest producing about 30%
[00:04:42.960]of the global soybean, that's to me, is amazing.
[00:04:45.920]And in the case of the Midwest region
[00:04:48.510]it accounts for 80% of the US soybean production.
[00:04:52.610]And the good news is that producers are not yet
[00:04:57.630]producing at the potential level.
[00:04:59.320]There is still some room there to exploit.
[00:05:01.520]And when we look at the average producer
[00:05:04.100]he or she is typically about 70 to 80%
[00:05:07.260]of their yield potential, which means that there is still
[00:05:09.840]some, some room there to produce more.
[00:05:12.450]If we are able to identify what are the limiting factors
[00:05:15.486]that are cost straining our yields
[00:05:17.570]to reach that potential.
[00:05:20.420]Amidst along those lines that we started
[00:05:22.660]this benchmarking project where the main goal
[00:05:25.070]was to identify key management practices,
[00:05:28.160]encourage producers to increase their yields
[00:05:31.420]and their input-use efficiency.
[00:05:33.350]And by doing so, also increase their profit.
[00:05:37.360]This is our team work, which I have been leading
[00:05:40.950]to where with my colleagues from Wisconsin
[00:05:43.070]for the past seven years, but also includes partners
[00:05:46.980]from our eight States across the Midwest.
[00:05:50.760]And I have also to recognize the tremendous hype
[00:05:53.280]from the Nebraska NR system,
[00:05:55.850]as well as from Nebraska Extension
[00:05:59.400]which has been a key
[00:06:03.890]to complete this project.
[00:06:06.030]So the people like Keith and Werle
[00:06:08.020]have given us a tremendous help in collecting this data.
[00:06:13.020]And what's unique about this project,
[00:06:15.670]is that here we are not talking about field experiments.
[00:06:18.974]We are not gonna, at least during the first phase
[00:06:22.530]of the approach that we did not attempt
[00:06:24.670]to run any field experiments,
[00:06:28.580]but instead, our purpose was to use producer data
[00:06:33.100]to identify suites combinations of management practices
[00:06:37.080]that consistently lead to higher yield
[00:06:39.970]and/or input-use efficiencies for a given climate
[00:06:43.417]and for a given soil type.
[00:06:45.470]So we're not running field experiment here
[00:06:47.740]but instead we are collecting farmer data from their fields,
[00:06:52.060]and we're using that information to identify those factors
[00:06:55.060]that are limiting soybeans yields.
[00:06:57.970]And this is our original approach, not only for Nebraska
[00:07:00.240]but try across the whole US North Central region.
[00:07:04.890]And at the beginning of this project
[00:07:07.730]during 2015 to around year 17,
[00:07:12.590]we collected data from farmers fields
[00:07:14.980]and overall our database included information
[00:07:18.510]from more than 8,000 fields
[00:07:21.290]which will cover coarsely half million acres,
[00:07:25.310]they planted with soybean.
[00:07:27.620]This map shows the locations of all the fields
[00:07:30.380]from which we collected data across the Midwest region.
[00:07:34.440]As you can see are quite impressive data base
[00:07:36.080]in terms of number of fields.
[00:07:38.030]And remember these are all farmer fields,
[00:07:40.450]we are not talking here about experimental fields
[00:07:43.040]or research institution farms.
[00:07:45.000]These are all information that comes from actual farmers.
[00:07:50.410]And these are the kind of information
[00:07:51.660]that we collected from those fields.
[00:07:54.140]As you can see, it included
[00:07:55.570]a quite large number of parameters,
[00:07:57.860]that goes from yield, irrigation type, planting date,
[00:08:01.910]variety, seeding rate, row spacing,
[00:08:03.990]tillage, fertilize and so forth.
[00:08:06.350]And perhaps more importantly is to look
[00:08:10.000]at the top of this survey, in which you can see
[00:08:13.410]that we have asked the farmer,
[00:08:14.880]to precisely indicate what was the location of their fields.
[00:08:19.130]And as I'm gonna explain it, this is very important
[00:08:21.730]because understanding where the field is located
[00:08:24.960]is crucial to understand the context associated
[00:08:28.350]with each of these fields.
[00:08:29.800]Are there many fields located in places
[00:08:31.930]with high or low rainfall,
[00:08:33.533]as well as with different soil types.
[00:08:36.439]So for this kind of analysis, context of a field
[00:08:40.340]in terms of soil type and climate is very important.
[00:08:43.805]And to be able to retrieve that context
[00:08:46.442]we need to know the field location.
[00:08:47.950]And that's why we hammer so much farmers to provide us
[00:08:51.100]with the exact location of their fields.
[00:08:55.750]And the reason why we are so picky,
[00:08:59.150]why we are so obsessed about understanding
[00:09:01.340]what the location of the field is,
[00:09:04.040]is related to the fact that,
[00:09:06.930]the influence of management practices on yield
[00:09:09.990]will depend upon the environmental context.
[00:09:14.310]So, a combination of management practices
[00:09:17.050]applied to a rainfed field in central Nebraska
[00:09:19.970]may have a different impact on yield
[00:09:22.470]compared with the same set of management practices
[00:09:24.360]applied to a rainfed field in central Iowa.
[00:09:28.500]And therefore, when we're looking at this data
[00:09:31.650]and trying to make sense out of them
[00:09:33.830]it's very important to group fields into,
[00:09:37.470]to group them so that they have
[00:09:39.050]the same weather and soil background.
[00:09:41.220]So that, one can start looking at management practices
[00:09:44.910]that can help to increase yields
[00:09:46.570]for a given climate and soil type.
[00:09:50.250]As you know, already, there are not many silver bullets
[00:09:53.840]in management, at least they have to be site specific.
[00:09:57.600]So again, grouping these fields into climate and soil types
[00:10:01.340]will help us to understand what works
[00:10:03.120]for each type of environment.
[00:10:06.860]In this particular project, what we did was to,
[00:10:10.620]consider all this survey field from our survey,
[00:10:14.370]and because we knew their location
[00:10:16.370]we could associate that location
[00:10:18.160]with a particular climate and soil type.
[00:10:21.090]And we group these more than 8,000 fields
[00:10:25.670]into 10 and climate and soil type combinations.
[00:10:31.430]And we call and we called
[00:10:34.770]and we referred to each climate in assorted combination
[00:10:37.840]as a technology extrapolation domain.
[00:10:39.510]So, during the rest of the presentation,
[00:10:41.550]I'm gonna refer to TEDs and TEDs means
[00:10:44.020]a combination of climate and soil type that is unique.
[00:10:48.150]As you can see in this model, we group fields
[00:10:50.572]into a total of 10 TEDs all across the Midwest.
[00:10:54.360]In each of these TEDs have at least 100 fields.
[00:10:58.288]And when you sum up all the soybean area
[00:11:01.170]planted within these 10 TEDs,
[00:11:03.010]we are talking about 70 million acres planted with soybean.
[00:11:07.830]Now, the idea is that if we can understand
[00:11:10.980]what are the management practices,
[00:11:14.170]I can help farmers to raise soybean yields
[00:11:17.150]for each of these TEDs that has implications
[00:11:19.890]that are relevant for, 70 million acres
[00:11:22.610]planted with soybeans.
[00:11:23.450]This is a power of using farmer data,
[00:11:27.750]for understanding causes for yield gaps
[00:11:30.430]and to try to increase yield.
[00:11:32.200]So again, we group all these fields into 10 regions
[00:11:36.380]that have similarity of the climate and soil type.
[00:11:41.940]All right, so this is a figure that shows
[00:11:44.399]the range of soybean yield for each of these 10 TEDs.
[00:11:50.500]So in the case of Nebraska,
[00:11:52.710]if you remember from the previous map,
[00:11:54.750]we have three, four TEDs, TEDs nine, eight and seven.
[00:12:00.050]In the case of TED seven we have desegregated
[00:12:02.580]that fits into rainfed and irrigated.
[00:12:05.950]So if we look at this field now,
[00:12:07.700]you can see the four TEDs for Nebraska,
[00:12:09.990]shown on the right hand of the field.
[00:12:13.510]Why this, very interesting for me
[00:12:14.970]when I look at this figure for first time,
[00:12:17.180]was that if we take, for example
[00:12:19.820]the TED number seven or the TED number eight
[00:12:22.450]you can see that even though these fields
[00:12:25.140]are located within the same soul type
[00:12:27.930]and within the same type of climate
[00:12:30.130]there is still quite wide variation in yield
[00:12:32.750]with some yields reaching up to 74, 73, bushel per acre
[00:12:37.760]while others even only about a 40 bushels per acre.
[00:12:42.110]And the question is why?
[00:12:43.500]Why there such high yield variation,
[00:12:46.530]when all these fields are located
[00:12:48.430]in an area that has quite similar climate and soil type.
[00:12:53.490]And the reason is probably.
[00:12:54.690]in the cause for that variation is probably management.
[00:12:58.006]There is probably something that the high
[00:13:01.570]that is applied in the high fields
[00:13:03.765]that is not being applied or follow
[00:13:05.960]in the low yield fields.
[00:13:07.670]And that therefore, give us an opportunity
[00:13:08.710]to understand what can be limiting soybean yields
[00:13:14.020]in each of these domains.
[00:13:15.390]And eventually you set as a starting point
[00:13:18.330]for a solution agenda in relation to what farmers can do
[00:13:21.780]to raise their productivity.
[00:13:23.620]So what we did next was to try to understand,
[00:13:25.980]what was going on in terms of management
[00:13:28.080]in the high yield fields compared with the low yield fields
[00:13:31.368]for each of these 10 TEDs.
[00:13:37.090]All right, and this looks like a very complicated table
[00:13:40.110]but it's very easy to interpret.
[00:13:43.368]In the columns, you will find all the ten TEDs
[00:13:46.910]from one to 9I and here the box,
[00:13:50.720]the red box shows those TEDs, those climates soil types
[00:13:53.840]that corresponds to Nebraska.
[00:13:56.070]And in the rows, corresponds to different
[00:13:58.790]management practices like planting date,
[00:14:00.710]maturity groups, seeding rate, row spacing and so forth.
[00:14:04.390]The important thing to know here is that,
[00:14:06.920]whenever you see that there is a cell
[00:14:09.960]highlighted in green color as it is a case here,
[00:14:13.930]that means that the high yield fields
[00:14:18.117]and the low yield fields were different for that variable.
[00:14:20.970]So for example, if we consider the TED eight for Nebraska
[00:14:25.100]here and for planting date, the fact that this cell
[00:14:28.400]is shown in green, it means that the planting date
[00:14:31.490]is different in the high yield fields
[00:14:33.090]compared with the low yield fields.
[00:14:35.720]And although these are kind of a very simple analysis
[00:14:38.810]is quite powerful at helping us understand
[00:14:42.440]what are the likely factors that explains
[00:14:45.920]why some fields are yielding more than others
[00:14:48.360]when in the same climate in the soil type.
[00:14:51.210]It gives us a starting point to try to understand that.
[00:14:55.360]And as you can see, there are some management factors
[00:14:57.660]that are explaining yield differences
[00:15:01.060]across pretty much all environments,
[00:15:02.900]like for example planting date, fungicide application.
[00:15:07.700]But there are other factors,
[00:15:09.530]that only explain yield differences,
[00:15:11.260]for a specific environments
[00:15:12.740]but not for ours like row spacing,
[00:15:15.040]or seed treatment or things like seeding rate.
[00:15:19.400]So as you can see, you can see things that seems to work out
[00:15:23.000]across the board and other things are very site specific.
[00:15:27.190]So let's start now to explore some of these
[00:15:30.400]management practices that look candidate
[00:15:33.080]and explain yield variation in more detail.
[00:15:38.150]This is slide shows the response of
[00:15:41.855]soybean producer year to planting date,
[00:15:44.930]And the planting date is shown here as days after April 1st.
[00:15:49.380]And each of these partners corresponds
[00:15:51.610]to a different TED in this month.
[00:15:55.606]In our cases as you can see that
[00:15:56.750]if we fit a frontier boundary,
[00:15:59.040]there are frontier regression.
[00:16:01.420]In our cases, there is a yield decline with late planting
[00:16:04.470]which means that there is a yield penalty
[00:16:06.160]associating with delayed planting after late April.
[00:16:12.190]Now you can see that there is quite a variation,
[00:16:13.647]in the yield penalty.
[00:16:15.480]In some cases, the yield penalty is almost non-existent
[00:16:19.120]in some cases could be as high as half bushel per day
[00:16:22.600]of delay in planting after late April.
[00:16:26.410]And so the question is why?
[00:16:28.610]Why there are some places where delaying planting date
[00:16:32.410]cause such a big penalty on soybeans yields
[00:16:35.140]and why they are other places where there is no
[00:16:37.140]so much yield penalty.
[00:16:41.220]And that was explained by the degree of water limitation.
[00:16:45.160]And this field here shows yield penalty
[00:16:48.540]associated with delay sewing
[00:16:50.090]in relation to the water balance
[00:16:53.080]during the pod-setting stage.
[00:16:57.488]As you go to the left of this field
[00:16:59.220]that means that, water was limiting.
[00:17:02.332]As you go to the right of this field,
[00:17:05.460]that means water was abundant.
[00:17:08.470]And this field shows that, when water is very limiting,
[00:17:12.460]you're not gonna see too much difference
[00:17:14.070]if you plant early or late,
[00:17:16.080]but if when water is not limiting,
[00:17:18.330]as it is the case of Nebraska irrigated fields,
[00:17:21.810]when you see a huge penalty associated with late planting.
[00:17:26.570]And this information has practical implications
[00:17:29.360]for Nebraska farmers, in deciding which fields
[00:17:32.628]to plant first.
[00:17:34.610]If I were a farmer and I have to plant an irrigated
[00:17:37.890]or dryland field first,
[00:17:39.660]I will probably go first with irrigated field,
[00:17:42.060]because I know that if I plant that field later
[00:17:44.480]it's gonna have a much greater penalty on yield
[00:17:47.110]compared with planting a rain fed field later.
[00:17:50.376]All right so, then the take home message is plant early,
[00:17:55.610]and if you can choose please try to plant
[00:17:59.030]the irrigated fields first
[00:18:00.690]and then move into the rain fed fields.
[00:18:05.560]Now, there are other factors that we also explore
[00:18:09.130]in relation to their influence on soybean seed yield.
[00:18:14.027]And these fields are one-to-one plots,
[00:18:16.840]which means that if anything falls above the one-to-one line
[00:18:20.790]it means that there is a yield advantage associated
[00:18:24.090]with the practice that is shown on the vertical axis.
[00:18:26.943]If the points fall below the one-to-one line
[00:18:29.440]it means that there is yield advantage associated
[00:18:32.080]with the practice shown in the horizontal axis.
[00:18:34.683]So for example, in this particular case on the left panel
[00:18:37.320]we are comparing fields that follow conventional tillage,
[00:18:42.390]against fields that were no-till
[00:18:44.870]and each of these points represents till
[00:18:47.100]one specific soil type, climate environment.
[00:18:51.320]And in almost all cases, you can see
[00:18:52.805]that the points fall out well, they want to align
[00:18:55.972]That means that we found a kind of yield advantage
[00:18:59.473]in fields that follow conventional tillage
[00:19:01.561]compare with no-till.
[00:19:04.120]Now, if we move to the field shown in the right hand,
[00:19:08.633]now you can see the same type of comparison
[00:19:11.159]but in this case between,
[00:19:12.649]fields that receive a foliar fungicide
[00:19:15.699]or insecticide application around R3
[00:19:19.119]compared with those that did not.
[00:19:21.122]And again, you can see that in most of the cases that TEDs,
[00:19:24.539]that all of the points fall above the one-to-one line,
[00:19:27.230]which means that we found that in general,
[00:19:29.160]fields that the receive a foliar fungicide
[00:19:32.140]or insecticides application around R3,
[00:19:34.667]tend to yield more compared with those
[00:19:36.670]that did not receive foliar application
[00:19:40.090]of fungicide or insecticide.
[00:19:42.380]Then now, here, the recommendation
[00:19:45.270]should be a little more cautious
[00:19:46.420]because it's not just about recommending
[00:19:48.220]to do conventional tillage to apply more fungicide
[00:19:51.380]because we know that there are trade-off.
[00:19:53.420]So there may be good reasons for following no-till
[00:19:56.780]that are not associated with a yield benefit.
[00:19:59.160]For example, with no-till you can control soil erosion,
[00:20:03.810]you can also minimize fuel and labor
[00:20:06.880]and also you can reduce the irrigation requirements.
[00:20:10.460]And in the case of fungicide application,
[00:20:12.460]we know that prophylactic applications
[00:20:15.040]over time can lead to resistance.
[00:20:17.330]So it is good to know these,
[00:20:20.024]the influence of these factors on yield,
[00:20:22.628]but we also need to be there a little bit cautious
[00:20:24.850]about implement them and understand
[00:20:28.070]what are the potential strainers.
[00:20:32.710]Now, it is also interesting,
[00:20:35.980]not only to look at things that can increase yield,
[00:20:38.610]but also to look at things that can help us
[00:20:40.440]to reduce a cost and by reducing cost,
[00:20:43.110]increase our net profit.
[00:20:45.750]In this case, you can see a field that is similar
[00:20:49.850]to the one that I showed before for planting late,
[00:20:52.310]but in this case, what I'm plotting
[00:20:53.510]is the yield against the seeding rate.
[00:20:57.710]And in this case, you can see that
[00:20:58.760]there is no relationship there,
[00:21:00.750]but you can still see that there is a lot of variation
[00:21:03.550]in seeding rates, which means that maybe there is room
[00:21:06.310]for improving your profit by cutting down on seeding rates
[00:21:10.880]and maintaining the same yield level.
[00:21:13.440]So if we take as an example
[00:21:15.140]this particular TED in Nebraska,
[00:21:17.510]which corresponds to Southeast Nebraska,
[00:21:20.950]you can see that on average,
[00:21:22.660]farmers are growing 166 seeds, sorry
[00:21:27.370]166 K seeds per acre.
[00:21:30.870]When we know from our university recommendation
[00:21:34.110]that with 125,000 seeds per acre,
[00:21:37.490]that's enough to maximize a profit.
[00:21:40.070]So you can see though that there is quite a large room
[00:21:42.660]to reduce costs and therefore to increase profit.
[00:21:45.450]And in this particular example for our farmer
[00:21:48.530]who is now throwing 166,000 seeds per acre.
[00:21:53.640]That farmer can save up to $15 per acre
[00:21:57.770]by bringing the actual,
[00:21:59.310]the average seeding rate,
[00:22:00.950]to a more less seeding rate around 125,000 seeds per acre.
[00:22:06.500]So as you can see, from this analysis
[00:22:09.120]we can identify opportunities to increase yield
[00:22:12.497]but also opportunities to increase the profit
[00:22:15.270]by reducing on cost.
[00:22:19.700]Now, as you can see, all this information was derived
[00:22:22.410]from analysis of the farmer's data
[00:22:25.070]but the question is, okay, are all the things,
[00:22:30.000]I mean if you put together all the things
[00:22:32.100]and you put them on a farmer field
[00:22:33.980]are they effective at increasing farmers profit?
[00:22:37.860]So what we did was a validation of this approach
[00:22:41.467]and to do so, we perform about 50 replicated field trials
[00:22:46.200]in 2019 across seven of these TEDs
[00:22:50.003]that are shown here in the map.
[00:22:51.470]So, each of these green circles in the maps
[00:22:54.300]represents a replicated field trial
[00:22:57.120]where we compare two treatments.
[00:23:00.070]One will be the reference treatment,
[00:23:02.410]where we just let the producers
[00:23:03.870]to continue doing whatever they were doing in the past.
[00:23:07.160]And then we have these other improved management treatment
[00:23:11.260]that was explicitly designed based on the results
[00:23:14.660]from the analysis of the farmer data,
[00:23:19.342]And these improve a treatment, will be defined according
[00:23:23.832]to what the analysis of our farmer data will tell us
[00:23:28.830]is effective at increasing yield or at increasing profit.
[00:23:32.080]So in this particular case of this experiment in Nebraska
[00:23:36.060]what we did in improved treatment was to plant earlier
[00:23:39.480]compared with a farmer practice
[00:23:42.530]to apply for foliar fungicide around R3
[00:23:45.780]and also to lower the seeding rate a little bit
[00:23:48.570]down 150,000 seeds per acre.
[00:23:51.320]So as you can see, we are not only about increasing inputs
[00:23:56.080]but in some cases it's about reducing inputs to cut on costs
[00:23:59.870]but understanding which input to increase
[00:24:02.760]or which input to bring down
[00:24:05.290]or what to change like planting date is kind of key
[00:24:09.000]so that all that converge into higher profit for the farmer.
[00:24:13.930]So again, a nice example of combining inputs,
[00:24:17.810]fine tuning of the crop management
[00:24:19.620]in a lot of knowledge inputs as well.
[00:24:23.270]These are a comparison of the yield
[00:24:25.097]in the improved treatment, following our recommendation
[00:24:28.800]and the yield in the reference plot managed by the farmer.
[00:24:32.620]This again, the one-to-one line
[00:24:33.970]is shown here with this red dotted line.
[00:24:36.010]And you can see that most of the trials that are shown
[00:24:40.590]with this symbols, are of the one-to-one line,
[00:24:44.950]which means that our, in general, our improved treatment
[00:24:48.540]yielded more compared with the reference treatment.
[00:24:51.697]And on average, the improved treatment
[00:24:53.840]yield six bushes per acre more
[00:24:56.180]compared with the reference treatment.
[00:24:58.680]Now, someone may ask,
[00:25:01.107]Patricio, can you show us what's the benefit
[00:25:03.770]in terms of profit, not only in yield?
[00:25:06.540]And that is what this slide shows,
[00:25:09.674]in this particular case you can see the additional profit
[00:25:12.740]that was the derived with from the improved treatment
[00:25:15.450]is basically the difference in profit
[00:25:17.370]between the improved treatment and the reference treatment.
[00:25:20.710]And you can see here, in most of the cases
[00:25:24.638]the profit was higher than,
[00:25:27.290]I mean, the difference was larger than zero,
[00:25:29.640]which means that there was a benefit
[00:25:32.437]in the improved treatment
[00:25:33.720]compared with the reference treatment.
[00:25:35.880]On average, the improved treatment
[00:25:37.600]has a net profit over $55 per acre higher
[00:25:40.940]compared with the reference treatment.
[00:25:42.700]And the extra profit was higher than $10 per acre
[00:25:45.770]in 85% of the cases.
[00:25:47.660]So as you can see, this price are very nice validation
[00:25:51.000]about the research that we found via analysis
[00:25:53.750]of the farmer's data
[00:25:55.170]and show that this is a valid approach
[00:25:56.680]to 85 factors that are limiting a yield
[00:25:59.847]and 25 management options for farmers
[00:26:02.510]to increase both yields and profit.
[00:26:07.780]Now let's go a little bit beyond profit and yield.
[00:26:13.120]We were also interested to analyze differences
[00:26:15.450]in seed quality.
[00:26:16.860]And in this particular case, we focus on protein and oil.
[00:26:20.350]So these are the data from those same 50 trials conducted
[00:26:23.780]across the Midwest region.
[00:26:26.120]And we wanted to compare how the protein
[00:26:29.080]and oil concentration will differ
[00:26:31.550]between the improved and the reference treatments.
[00:26:34.580]As you can see from the chart on the left hand,
[00:26:37.790]in general, the protein concentration
[00:26:39.397]tended to be a little bit lower in the improved treatment
[00:26:43.260]compared with a reference treatment.
[00:26:45.490]On average, we found minus 0.3% lower seed protein
[00:26:49.930]concentration in the improve treatment
[00:26:52.480]compared with the reference treatment.
[00:26:54.400]And in the case of the old concentration
[00:26:57.480]we found the opposite.
[00:26:58.390]I slight increase in the improved treatment
[00:27:01.240]compared with the reference treatment.
[00:27:04.740]So in general, we found that application
[00:27:07.090]of these yield improving options,
[00:27:10.240]will tend to increase yield, increased profit
[00:27:12.680]but also we will have a slight penalty on seed protein.
[00:27:19.060]And we saw that looking into more detail in these,
[00:27:24.210]protein concentration, a penalty is kind of important
[00:27:28.830]because I'm not sure how many of you are aware
[00:27:32.230]of these friends at national level
[00:27:34.600]but our seed protein concentration is going down over time.
[00:27:38.860]And this is a figure that shows how the average seed protein
[00:27:42.710]for soybean in the United States is going down over time
[00:27:46.720]from 1985 up to now.
[00:27:51.036]And you can see that this decline
[00:27:52.560]in seed protein concentration is occurring
[00:27:55.230]at the same time that yield is increasing.
[00:27:58.500]Which suggests that there is a kind of a trade-off
[00:28:01.480]between increase in yield and seed protein.
[00:28:04.560]The higher we go with yield
[00:28:07.030]we have to pay our price in terms of protein.
[00:28:09.900]Protein will go down.
[00:28:12.510]And know, this is important for Nebraska
[00:28:16.630]because as you all know Nebraska soybean production
[00:28:20.610]rely a lot on irrigated land
[00:28:23.180]about 50% of the Nebraska soybean production
[00:28:25.270]come from irrigated fields.
[00:28:27.467]And therefore, considering that the yield
[00:28:30.437]is typically higher in irrigated land
[00:28:32.900]compared with dryland, wouldn't that mean
[00:28:35.550]that the protein concentration is lower
[00:28:37.930]under irrigated conditions compared with dryland conditions
[00:28:41.320]because if that's true, then that has implications
[00:28:44.210]in terms of the competitive advantage of
[00:28:46.940]irrigated Nebraska soybean farmers
[00:28:49.840]in relation to dryland producers
[00:28:53.808]or to dryland producers in other States.
[00:28:56.960]So, along those lines we started with this new project
[00:28:59.410]last year finding bio Nebraskas solving model.
[00:29:02.660]where we tried to understand what was the influence
[00:29:05.610]of water regime as determined by the irrigation
[00:29:10.480]on the soybean seed quality parameters.
[00:29:12.720]With an exclusive focus on protein and oil concentration.
[00:29:16.020]Basically, we wanted to know if protein
[00:29:18.913]and oil concentration would be different between farmers
[00:29:22.360]that have irrigated fields versus farmers
[00:29:25.240]that grow soybeans in dryland conditions.
[00:29:30.290]And again, here we work closely with keys
[00:29:33.230]and we are a UNL extension educators
[00:29:35.900]to collect a soybean samples.
[00:29:38.140]And again, those soybean samples were collected
[00:29:40.010]from farmer's fields, not from experiments.
[00:29:42.550]We are interested to learn what's going on
[00:29:45.315]in farmer's fields in this project.
[00:29:47.960]So what our first step here was 25 farmers
[00:29:51.540]that will manage both dry and an irrigated fields.
[00:29:55.680]And we will ask them to identify a three
[00:29:58.080]irrigated fields and three dryland fields.
[00:30:01.427]And these dryland fields have to be real fields,
[00:30:04.240]not just corners, okay.
[00:30:05.290]So we are comparing true dryland fields
[00:30:09.100]against irrigated fields.
[00:30:10.920]And subsequently we ask farmers
[00:30:13.780]to collect three sub samples of soybean seeds
[00:30:17.240]at harvest time from each of these fields.
[00:30:20.090]So, for a given farmer in Nebraska, we have identified
[00:30:23.260]and have accepted to collaborate in this project.
[00:30:25.780]We have collected three sub samples
[00:30:27.920]from each of these six fields.
[00:30:31.350]So six times three, that means 18 samples
[00:30:34.810]of soybean seed collecting from each producer field.
[00:30:41.103]And in total, we have collected samples
[00:30:42.900]from a total of 172 irrigated and dryland fields
[00:30:45.680]across the state in 2019.
[00:30:48.070]And we have repeated the sampling during 2020.
[00:30:52.520]And you can see in these maps, the location
[00:30:54.750]of the fields where we have collected those samples.
[00:31:00.710]We collected those sample, I mean,
[00:31:01.990]those samples were collected by the farmers.
[00:31:04.570]They submit them to our lab.
[00:31:05.597]And in our lab, we determine, see the quality parameters
[00:31:09.250]including protein oil concentration, test weight
[00:31:13.397]and other things that are relevant
[00:31:14.940]in terms of soybean commercialization.
[00:31:18.600]Okay, let's go into the nature of the data.
[00:31:21.590]And I'm gonna show you next,
[00:31:23.820]a number of charts that are quite similar
[00:31:25.910]in terms of format and interpretation.
[00:31:31.383]On the vertical axis, you will see the irrigated fields.
[00:31:36.050]And on the horizontal axis,
[00:31:37.800]you will see the dryland fields.
[00:31:40.740]And in all cases, what we are comparing
[00:31:43.070]is the three irrigated fields,
[00:31:46.750]versus the other three dryland fields,
[00:31:48.803]that were managed by the same farmer.
[00:31:50.740]So, each of these points corresponds to the same farmer.
[00:31:53.570]And what we are comparing is three real fields
[00:31:56.920]against three irrigated fields managed by that farmer.
[00:31:59.650]And we did that for a total of 68 farmers.
[00:32:03.020]So by compare irrigated versus dryland fields
[00:32:06.465]across 68 farmers.
[00:32:09.040]In the case of yield, you can see it on average
[00:32:11.060]as expected yield was five bushes higher in irrigated
[00:32:15.014]versus dryland conditions.
[00:32:16.507]But in the case of a test weight there was no difference
[00:32:20.010]between dryland and irrigated fields.
[00:32:23.170]And I forgot to mention here that this is a one-to-one chart
[00:32:25.360]similar to the ones I presented before.
[00:32:27.670]If the points are above the one-to-one line
[00:32:29.981]that means that there is an advantage
[00:32:31.441]of irrigated fields, in relation to dryland fields.
[00:32:34.749]If the point fall below the one-to-one line
[00:32:36.641]that, that means higher values in dryland fields
[00:32:39.534]compared with the irrigated fields.
[00:32:40.980]So again, the fact that almost all of their points,
[00:32:43.590]fall above the one-to-one point,
[00:32:45.203]above the one-to-one line for CDL
[00:32:48.750]means that yield tended to be higher in irrigated fields
[00:32:51.870]compared with dryland ones.
[00:32:53.210]And again, no different for test weight.
[00:32:55.940]Now, in the case of protein and oil concentration,
[00:32:59.320]we found that in general protein concentration
[00:33:02.137]tended to be higher in irrigated fields
[00:33:04.533]compared with dryland fields.
[00:33:06.007]And on average protein concentration was higher,
[00:33:09.220]was 0.4% higher in irrigated fields
[00:33:12.690]compared with dryland fields.
[00:33:14.490]So good news, because despite yields are higher
[00:33:18.070]in irrigated conditions, the protein that's not declined,
[00:33:20.930]inequity is also higher.
[00:33:22.470]So a combination of both high yield and high protein too
[00:33:26.950]which is kind of surprising, but I alternatively,
[00:33:29.530]this are good news Nebraska farmers.
[00:33:32.260]In contrast, in the case of oil
[00:33:33.810]we could not find any statistical significant difference.
[00:33:37.630]So in other words, it seems like there is no trade-off
[00:33:40.240]between higher yields and protein concentration,
[00:33:43.360]when we look at irrigated soybean fields in Nebraska.
[00:33:47.680]And moreover, when we look at the amino acid composition
[00:33:50.226]of these seed samples, we found that in general,
[00:33:53.449]amino acids composition tend to increase
[00:33:55.720]following the same trend as in protein.
[00:33:57.780]That means the amino acids increase in irrigated conditions
[00:34:02.880]compared dryland conditions.
[00:34:05.870]All right, so I wanna give you a few take home messages
[00:34:08.300]from all these set of slide,
[00:34:09.790]so we kind of recognize the main messages
[00:34:12.310]from the presentation.
[00:34:13.690]The first one is that, there is tremendous value
[00:34:15.520]in using the farmer's data to identify
[00:34:17.330]yield increasing options for a given climate and soil type.
[00:34:21.180]In the case of Nebraska, there are many options
[00:34:23.130]to increase yield and one of them planting dates.
[00:34:25.130]So try to plant early especially in irrigated conditions.
[00:34:28.870]There are also options to cut on costs.
[00:34:30.980]For example, seeding rates, which will lead to higher profit
[00:34:34.110]and validation of our approach in field trials shows
[00:34:36.660]that on average farmers gonna increase their yields
[00:34:39.010]by six bushels and increase their profit by $55 per acre.
[00:34:45.060]We found that increasing yield in some cases has a penalty
[00:34:49.200]in terms of seed protein concentration.
[00:34:51.680]However, when we explore these trade-off
[00:34:53.690]in irrigated fields in Nebraska,
[00:34:55.640]we could not find in such a trade-off.
[00:34:57.370]And indeed we found that irrigated fields have higher yields
[00:35:00.730]together with a higher protein concentration.
[00:35:04.280]All these information is available
[00:35:05.880]through a number of extension publications.
[00:35:08.400]I will be very happy to share.
[00:35:10.920]And also, all this approach has been documented
[00:35:14.370]through a number of articles,
[00:35:16.890]published in pure review journals.
[00:35:20.230]So that's all, thank you very much again, Keith
[00:35:23.110]for the invitation and I'm open for questions.
[00:35:26.207]Yes, Patricio, excellent work.
[00:35:28.870]Your team should be congratulated
[00:35:32.960]for conducting this research cause it's very relevant.
[00:35:37.190]A question in the chat box was,
[00:35:39.350]could it be possible for the higher protein
[00:35:41.980]in irrigated fields be due to nitrate
[00:35:45.270]in the irrigation water?
[00:35:48.440]And the answer is, yes if you have a lot of
[00:35:53.680]nitrate coming from irrigation water,
[00:35:55.960]but in most of these cases, the nitrates coming from
[00:35:59.970]irrigation water was very low.
[00:36:02.410]And remember, we're talking about 2019,
[00:36:05.100]2019 was a year in which farmers actually
[00:36:07.770]have apply not that much irrigation.
[00:36:10.330]So my answer is that probably that doesn't
[00:36:14.390]play a big role at explaining
[00:36:16.850]the higher protein in irrigated fields
[00:36:18.397]compared with dryland fields.
[00:36:21.470]Okay, and another question,
[00:36:23.460]did you see any correlation
[00:36:25.080]with oil or protein and soybean variety?
[00:36:33.380]There is a good correlation between oil and protein
[00:36:37.690]in the sense that, when you increase protein
[00:36:40.110]you tend to decrease oil
[00:36:42.403]and that comes very much determined by the genetics.
[00:36:45.530]So if you put a pool of varieties
[00:36:47.370]and you look at protein versus oil
[00:36:49.527]you will find this trade-off very clearly.
[00:36:51.410]Now most of the varieties operate
[00:36:54.370]within a quite narrow range of protein concentration.
[00:36:58.670]So yes, it's to some degree it's driven
[00:37:03.530]by the variety, but I have also to say that
[00:37:06.880]the range across commercial varieties is quite narrow.
[00:37:09.320]So again, I don't think that there is out there
[00:37:12.860]a magic variety that you can achieve
[00:37:15.680]both high yields with high protein.
[00:37:17.564]I don't think that the variety exists
[00:37:19.560]and I don't think it's gonna exist for quite a long time.
[00:37:24.700]Okay, did you, you may have covered this
[00:37:27.660]and I missed it my apologies,
[00:37:29.150]but how about maturity groups?
[00:37:32.860]All right, so that's an important question.
[00:37:35.509]Thank you for raising it
[00:37:36.503]because I kind of forgot about that,
[00:37:38.420]but you know, once, I mean like,
[00:37:42.580]like anything else, everything is important
[00:37:44.830]but it's also important to recognize what's more important.
[00:37:47.940]And so in terms of maturity group,
[00:37:52.640]my suggestion for a farmer will be,
[00:37:54.480]please make sure at first to adjust your planting days right
[00:37:57.460]and once you cover adjusting your planting date
[00:37:59.230]then try to do the fine tuning with a maturity group.
[00:38:02.740]So for a farmer who is, for example
[00:38:05.200]considering to shift towards area planting date
[00:38:09.190]then the farmers should also consider to perhaps
[00:38:11.500]use I slightly longer maturity group.
[00:38:14.630]But again, first I will suggest to try to optimize
[00:38:18.660]the planting date and then go into the optimization
[00:38:20.960]of the maturity group.
[00:38:24.260]Okay and do we have any other questions for Patricio?
[00:38:32.100]If not, thank you again, Patricio.
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