Irrigation Across the Great Plains

DEREK HEEREN, associate professor and irrigation engineer, Department of Biological Systems Engineering, University of Nebraska–Lincoln SALEH TAGHVAEIAN, associate professor and irrigation and water resources engineer, Department of Biological Systems E Author

12/12/2022 Added

12 Plays

Description

Irrigation plays a critical role in agricultural production across the Great Plains. A water resources perspective underscores the need for good irrigation engineering and management. Irrigation research and extension activities in Oklahoma and Nebraska will be discussed, along with the status of irrigated agriculture in Nebraska and opportunities for collaboration.

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[00:00:00.780]The following presentation is part of the
[00:00:02.940]Agronomy and Horticulture seminar series
[00:00:05.820]at the University of Nebraska Lincoln.
[00:00:08.492]Hey.
[00:00:10.569]We good?
[00:00:12.447]Welcome everyone. Thanks for being here this afternoon.
[00:00:15.420]We're kicking off this Agronomy,
[00:00:18.027]Department of Agronomy Horticulture for this seminar series
[00:00:21.060]this is number 10 this season
[00:00:24.941]and we are having to (indistinct)
[00:00:27.071]Dr. Derek Heeren and Dr. Saleh Taghvaeian
[00:00:31.393]Taghvaeian?
[00:00:32.520]Sorry for my pronunciation.
[00:00:34.710]And they will be talking about
[00:00:36.609]Irrigation Across the Great Plains.
[00:00:38.077]So Derek Heeran is an associate professor,
[00:00:41.220]and an english engineer
[00:00:43.750]in the Biological System Engineer department here at UNL.
[00:00:47.706]He has 60% research and 40% of the teaching department
[00:00:53.605]for little bit of service too.
[00:00:55.650]His teaching plan prepares students to be wise managers,
[00:00:59.370]so food ration, water resources and recruiter systems.
[00:01:03.300]And his research interests are
[00:01:05.190]irrigation engineering and management,
[00:01:08.220]sprinklers and irrigation systems
[00:01:11.550]and irrigation management based on remote senses.
[00:01:13.920]He's received his PhD in Oklahoma State University.
[00:01:17.370]So we're happy to have you here.
[00:01:19.841]And this is kind of a shared part this Friday
[00:01:23.280]we're testing a new methodology.
[00:01:27.383](indistinct)
[00:01:29.630]We thought it was great idea to have to participate
[00:01:34.121]to Dr. Saleh Taghvaeian because he's an Associate Professor.
[00:01:39.420]He joined the university recently in August,
[00:01:44.160]to the same department
[00:01:45.196]and previously he was another professor
[00:01:50.208]in Operation Grass studies
[00:01:52.380]in the school of Green studies and partnership
[00:01:54.960]and apprenticeship.
[00:01:56.190]And also a professor an Extension Specialist
[00:01:59.064]in biosystem agricultural engineer
[00:02:00.540]at Oklahoma State University.
[00:02:02.860]His SoCo has 60% research responsibility and teaching
[00:02:07.560]responsibilities.
[00:02:09.030]His academic interest include remote sensing, application,
[00:02:12.150]irrigation, scheduling technologies
[00:02:14.608]and the design maintenance evaluation systems.
[00:02:17.310]He's currently teaching because irrigation in donuts,
[00:02:21.810]systems engineers, engineering.
[00:02:24.690]He got PhD in Utah State University
[00:02:27.510]before in the beginning.
[00:02:28.650]So we're welcome both of you.
[00:02:31.487]So they will be sharing this presentation
[00:02:33.457]where you have great questions at the end
[00:02:36.342]so for those at home
[00:02:37.175]they can submit the questions in the chat
[00:02:40.260]and we can address them at the end.
[00:02:42.570]So thank you for being here.
[00:02:50.040]Good evening everybody.
[00:02:51.510]Thanks for coming here to this seminar.
[00:02:55.067]It's a great pleasure for me to be here
[00:02:59.250]speaking to you guys.
[00:03:00.083]My first time in this building too.
[00:03:01.680]So great to be here.
[00:03:05.505]So what we're gonna do, Derek,
[00:03:06.679]is I'm gonna talk a little bit about some of my projects at
[00:03:10.412]Oklahoma since I've been here only three months,
[00:03:12.780]hopefully next time I get invited we'll talk about some of
[00:03:15.660]their basket projects and then I'll turn over to Derek to
[00:03:18.540]talk about some of his projects here.
[00:03:20.580]And then I have some irrigation statistics from Nebraska
[00:03:23.460]that I'll share with you.
[00:03:24.987]You guys probably know already about those statistics,
[00:03:28.320]but these are some of the things that I found interesting.
[00:03:30.540]We can talk about 'them a little bit.
[00:03:34.263]So my talk, we'll have kind of two sections.
[00:03:38.687]One will be on irrigation efficiency and irrigation
[00:03:42.810]adequacy and irrigation uniformity.
[00:03:45.330]And then the next one will be on the impact of the accuracy
[00:03:49.260]of some of the input data that we're using to irrigation
[00:03:52.650]scheduling and some irrigation models.
[00:03:56.070]So with that, I think I'll tell you a little bit about
[00:03:59.820]some of the Irrigation Uniformity work that we did.
[00:04:03.433]So as you see here, when we use the laser pointer here,
[00:04:08.970]so folks online can see.
[00:04:11.070]So we conducted a line of irrigation uniformity test
[00:04:14.157]in western Oklahoma.
[00:04:16.320]Set pivot like Nebraska is by far
[00:04:18.990]the dominant type of irrigation system.
[00:04:21.810]So you see one example here,
[00:04:23.280]we have catch scans there,
[00:04:24.840]the pivot is working and general researcher,
[00:04:28.556]we rely heavily on good graduate students. So they are here,
[00:04:32.250]graduate students quite at work reading those CATCH scans,
[00:04:36.270]writing notes has weeks like this or any other issues.
[00:04:41.910]Maybe we go back and look at the data.
[00:04:43.660]So we conducted these tests quite a number of centimeters.
[00:04:47.820]They, they take some time,
[00:04:48.870]but they're very valuable to us and to the researchers.
[00:04:51.969]And we have two questions was really, really, really
[00:04:56.167](indistinct)
[00:04:57.125]and one that was really, really good.
[00:05:00.960]So this on the left, if you look at the graph here,
[00:05:05.630]I have distance from the pivot point and on the running axis
[00:05:10.103]I have the application amount in inches.
[00:05:13.409]The green line shows the average and you see that from these
[00:05:16.530]keepings water all over the place.
[00:05:20.010]It's the coefficient of uniformities CU is fairly poor
[00:05:25.260]distribution uniformity of low quarter,
[00:05:27.679]which is one of the indicators we use 14%. That's terrible.
[00:05:32.730]Then water convey efficiency. We also estimated it was 89%.
[00:05:37.530]The system builder is really good even though
[00:05:41.356]it was also a fairly large system.
[00:05:42.957]You see that some of those uniformity numbers reach 92% and
[00:05:46.956]86% and maybe one of these systems or research system are
[00:05:52.530]all commercial systems operated and managed by growers.
[00:05:56.370]And you have this level of variability
[00:05:59.880]between some poor managed
[00:06:02.476]and maintained and some really good managed and maintained.
[00:06:05.070]So we'll also make that the effect of all of these
[00:06:08.397]uniformities and what that means for scarce water resources
[00:06:13.110]and also for nutrient leaching.
[00:06:15.180]Because if you have this irrigation manager,
[00:06:18.476]this is the application pattern of the nozzles on the pivot
[00:06:23.606]and you go out to the field and maybe you see the spot where
[00:06:26.610]you have totally clogged nozzles
[00:06:29.702]or partial clogged nozzles and
[00:06:31.590]you see the crop is not doing good,
[00:06:33.270]maybe you tend to try to increase irrigations for these
[00:06:37.330]areas to catch up.
[00:06:38.550]And what will that do to some of these other areas.
[00:06:43.370]So I think from both extension,
[00:06:46.020]which was a main objective of this and research perspective,
[00:06:50.348]the uniformly of irrigation application was something
[00:06:53.310]interesting. Whether it's like I said,
[00:06:55.440]nutrient leaching or just irrigation use water conservation,
[00:07:01.110]it's something very important.
[00:07:10.890]So this is the summary of with all the systems we did,
[00:07:15.420]we tried to put them in different concepts based on the zoom
[00:07:18.773]form indicators.
[00:07:20.383]So more of the systems that we tested had poor uniformity
[00:07:25.100]and then the 30 to 33% that had fail uniformity
[00:07:30.690]less than 10% were excellent.
[00:07:33.810]So that's uniformity.
[00:07:36.600]We also looked at other question
[00:07:38.417]and you can see in this watershed in west central Oklahoma,
[00:07:42.660]this is called a Fort Cubb experimental watershed.
[00:07:45.800]And the reason we picked this watershed is because of
[00:07:49.680]this fork reservoir over the years
[00:07:52.380]has had some water quality issues with sedimentation,
[00:07:55.830]with phosphorus in the water.
[00:07:57.810]So there has been some studies there has been mentioned in
[00:08:01.800]several research papers and publications out of
[00:08:05.032](indistinct),
[00:08:05.972]and USGS as as one potential source of
[00:08:09.390]contamination in the lake.
[00:08:11.010]So we just wanted to find out is that
[00:08:12.707]curious what is the extent of the irrigation contribution.
[00:08:17.790]So we picked out these site see on the map and we did a
[00:08:21.437]study over three years.
[00:08:22.751]We monitored irrigation applications, the timing, the stuff,
[00:08:27.210]irrigation applications and this is the result.
[00:08:31.847]So we have all the actual irrigation amounts.
[00:08:34.260]So these are seasonal values and the one says we estimated
[00:08:40.230]for the crops that were there, what would be the amount
[00:08:44.071]if you wanted to keep him under water condition,
[00:08:47.040]so a full irrigation amount.
[00:08:49.710]And as you see the distribution,
[00:08:52.497]all the sites were under irrigated, none of them came close.
[00:08:56.280]So if they were to rely
[00:08:58.511](indistinct)
[00:09:01.477]water to keep it under water irrigation
[00:09:04.285](indistinct) (audio cutting off)
[00:09:09.843]So that tells us a lot about the impact
[00:09:13.943]of that irrigation on some of the solution limits
[00:09:19.223]through surface runoff or deep preparation
[00:09:21.900]or to what extent the irrigation is playing a role here.
[00:09:26.112](indistinct)
[00:09:28.080]into too much detail.
[00:09:29.520]Just to give you an example,
[00:09:31.170]so this is what we call a stress coefficient and we use this
[00:09:36.572](audio cuts off)
[00:09:39.280](indistinct)
[00:09:43.800]water condition cadence should be always one
[00:09:47.753](indistinct)
[00:09:50.237]So these are all P is peanut different years,
[00:09:53.460]they're cotton, different years, soybean
[00:09:55.706](audio cuts off)
[00:09:56.557]different years.
[00:09:58.380]You see they're many fields and many times in a year that
[00:10:01.590]that KS is not one
[00:10:02.977](audio cuts off)
[00:10:03.810]is another way of telling us that these
[00:10:05.850]fields were under some level of water stress.
[00:10:08.760]They're no levels when you look at them
[00:10:10.732]but being underwater just not well irrigated.
[00:10:16.320]So let me switch gears a little bit
[00:10:18.213]and talk about the accuracy of
[00:10:19.796]some of the input data that we use in irrigation models. So,
[00:10:23.206](indistinct)
[00:10:28.410]in productions for irrigation of scheduling our sort of
[00:10:33.197]different applications and particularly for irrigation
[00:10:37.780]we estimate crop ET
[00:10:39.207]And the way we do that is based on reference CT.
[00:10:42.415](indistinct)
[00:10:45.251]So we have the temperature, wind,
[00:10:46.563]sput humidity and a key factor here is we need a reference
[00:10:52.170]surface.
[00:10:53.003]So the measurement of the weather variables must be taking
[00:10:57.991]place over a reference surface. So we get E tl.
[00:11:01.650]So the questionnaire is sometimes like this nice,
[00:11:04.440]the mascar station.
[00:11:06.030]You could have something that is very close to reference
[00:11:09.930]condition,
[00:11:10.913]which is a fully transpiring vegetation surface without
[00:11:17.078]any water or stress or any diseases, anything.
[00:11:20.449]And if you have that condition, good, good,
[00:11:24.071]great and sometimes we don't have that condition and we're
[00:11:26.621]visited a lot further stations in the great Queens area,
[00:11:30.653]not just Oklahoma.
[00:11:32.019]Well that's not the condition we have red grass or dry
[00:11:34.676]grass.
[00:11:35.548]So one the question is what what is the impact of that on
[00:11:39.269]the ETL and irrigation demand estimation?
[00:11:45.361]So for Western Oklahoma where we did this study
[00:11:51.841](indistinct)
[00:11:59.217]these are different into details,
[00:12:01.424]but if you wanted to discuss it,
[00:12:03.847]these are different indicators of weather station serenity
[00:12:08.260]and he showing percent of things that they wasn't above the
[00:12:12.690]threshold. So they were aired.
[00:12:14.940]And if you look at this first indicator,
[00:12:17.242]it's more than like Western Oklahoma than half of the
[00:12:20.310]period.
[00:12:21.703]45% to 54% are about 30 to 36% based on this other indicator
[00:12:27.307]and 73 to 90% based on this other indicator.
[00:12:30.630]So from nine periods of time,
[00:12:34.129]reference reference weather stations do not have the
[00:12:37.649]reference condition.
[00:12:39.484]And we show one example of two stations that are adjacent
[00:12:43.230]Altus and Tipton for this period of time.
[00:12:46.450]Altus as reference condition Tipton is now and we can see
[00:12:50.970]that in this case Tipton was about three inches,
[00:12:54.876]three millimeters per day larger than Altas station.
[00:12:58.716]These are very close within a few miles, same elevation.
[00:13:02.951]So this difference in ETO is just based on the fact that
[00:13:06.289]they are irrigated or not.
[00:13:08.732]Now if you assume that the three millimeters per day stays
[00:13:14.119]around for 90 days,
[00:13:15.376]which is the other case I that's extreme case,
[00:13:17.400]we're not gonna have that as the graph shows here.
[00:13:19.620]But if your CDI would be the condition you're talking about
[00:13:22.800]10 inches over the season,
[00:13:25.350]that would be because of the issues with estimated reference
[00:13:29.760]ct.
[00:13:31.080]So translating to irrigation demand.
[00:13:35.319]And then another thing that we looked at in terms of input
[00:13:38.100]data accuracy was soil data.
[00:13:40.752]So we used U S D A CS survey that our web survey and we
[00:13:46.394]completed with what we measured.
[00:13:47.853]We took soil and took it back to the lab and determined soil
[00:13:52.140]texture and all of that to be using irrigation of scheduling
[00:13:55.410]gaps and models.
[00:13:57.567]So this bar chart here,
[00:14:00.348]this scattered plot here box brought here shows the
[00:14:03.973]differences what we was that SGO data,
[00:14:10.631]fiber soil textures
[00:14:12.412]in terms single data, it's not for flood waste,
[00:14:19.770]irrigation scheduling most out they using.
[00:14:24.180]So really were just curious to see what would be the effect
[00:14:27.738]the irrigations. So you region,
[00:14:32.547]you see large negative numbers with of almost minus 25% when
[00:14:37.830]you subtract the WSS minus isis. So that's, that's for sand.
[00:14:42.210]And then you see that the overestimated seal,
[00:14:45.570]I also have total available water based on the three main
[00:14:49.290]components.
[00:14:50.640]So with these errors we estimated irrigation demands
[00:14:57.159]over 15 year period and the box spots here show the
[00:15:01.440]differences there for different regions. So panhandle, corn,
[00:15:04.963]panhandle, som, southwest, west, central,
[00:15:07.740]different crop that the crops of those regions.
[00:15:11.589]What's interesting was in some extreme cases the difference
[00:15:15.296]was really, really big.
[00:15:19.328]In half of the cases when we looked at sites and years,
[00:15:21.823]the difference over the season,
[00:15:24.120]over one season was less than an inch of irrigation.
[00:15:28.373]Now insomnia's that still significant but anyway our as made
[00:15:33.130]as were thinking hypothesizing,
[00:15:36.288]we were thinking that we would get a based on the errors we
[00:15:39.930]had in soil texture.
[00:15:43.050]That's it for me.
[00:15:57.780]All right, thank you audio coming through all.
[00:16:02.218]So,
[00:16:06.830]so we're leaving from the planes up to the central Great
[00:16:09.684]Plains now appreciate the the opportunity to be here.
[00:16:14.100]So good afternoon and it's a good week by the,
[00:16:16.680]the conference I had the chance to visit us around in people
[00:16:21.479]the last couple days downtown for the first time. So,
[00:16:26.070]so that was good.
[00:16:30.034]Really wanted to give ideas projects that has
[00:16:35.297]been past some of the current things we're doing.
[00:16:39.480]So
[00:16:41.652]details wanted to look forward so hopefully but also an
[00:16:48.120]opportunity to discuss potential collaboration.
[00:16:50.580]So I'm trying to put ideas on the table.
[00:16:52.839]For example,
[00:16:54.060]we talking of cover crops and irrigation as something that
[00:17:00.210]has not been explored yet.
[00:17:02.995]So spaces thes that I've been involved in the past couple
[00:17:05.700]years.
[00:17:06.533]Some basic pivot performance very similar to some of the
[00:17:09.660]things that were talked about earlier.
[00:17:12.008]This is funny from an extension perspective,
[00:17:15.570]the producers have a lot of interest and very practical
[00:17:17.803]aspects of pivot performance and not surprising a lot of
[00:17:21.181]pivots mostly below pressure.
[00:17:24.360]And so there's opportunities to improve management of
[00:17:27.600]irrigation.
[00:17:28.560]This is one these projects that it's a little bit of
[00:17:32.723]preliminary and it's been a wish list of something we'd like
[00:17:35.550]to ramp up someday.
[00:17:38.400]This is a project that's of irrigation which allows us to
[00:17:44.520]put out different of water in different parts of the field.
[00:17:47.260]So to that are on and off. So there's a second
[00:17:55.089]the tractors, but you know,
[00:17:56.370]tractor use a second to engage the starter goes back and
[00:18:01.031]forth you to reduce the amount of reation, you know,
[00:18:05.050]maybe the sprinkler is off for 20 seconds out of 60 seconds.
[00:18:09.211]But we then our regulators were failing in the field much
[00:18:13.319]faster than they expected.
[00:18:15.152]They started to think maybe this on and off was starting to
[00:18:18.758]impact the regulators.
[00:18:20.305]So this is same project we did in our and show industry
[00:18:26.730]writes us to what
[00:18:31.664]sure enough we could observe that over time this is
[00:18:35.717]cumulative bit of time and moving some every few seconds.
[00:18:39.600]So there's a lot of iterations but that's where the pressure
[00:18:43.607]of the there which hopefully is constant, right?
[00:18:45.527]If it regulates water pressure
[00:18:48.604]increase from one PSI to 11 psi and you can't see it here,
[00:18:53.163]but they were starting to develop leak then exam weeks and
[00:18:58.353]the testing stopped.
[00:19:01.920]Things that people have lot of interest in
[00:19:07.110]Presentation magazine sir.
[00:19:10.230]That's how we get.
[00:19:13.620]The thing I wanted to mention is we have published a
[00:19:17.833]textbook on irrigation management and excluded research and
[00:19:22.230]technology,
[00:19:23.063]but a lot of times the basics are still really important and
[00:19:27.210]so we publish this is open access,
[00:19:29.120]so hopefully it's useful to,
[00:19:32.475]we sit in our irrigation management class but has probably
[00:19:36.220]half forgotten students every year when I teach it.
[00:19:39.356]And then have finally recognize system students to really
[00:19:42.450]enjoy that.
[00:19:43.283]This out management practices everything from monitoring
[00:19:46.332]foam systems for pumping variable frequency driving,
[00:19:50.643]doing infield system evaluations, soil sensors, ions,
[00:19:55.230]et cetera.
[00:20:01.170]Smaller project we did was not really a resource
[00:20:04.920]perspective. We focus very much on field scale,
[00:20:10.897]scale flex watersheds, right?
[00:20:14.010]So somebody shared watershed,
[00:20:15.652]which the field sites were and making that connection is not
[00:20:19.633]nearly as intuitive as what you would think.
[00:20:22.590]So when we conserve reduce pumping at the field scale,
[00:20:27.143]that doesn't automatically translate into watershed savings
[00:20:30.990]from a natural resource district perspective.
[00:20:33.900]So our stakeholders had a lot of interest in this,
[00:20:35.985]the department of natural resources for example,
[00:20:38.700]this is a concept we call consumptive use,
[00:20:40.561]but it's really difficult to grasp with spending a lot of
[00:20:44.160]time thinking about it. So we developed a conference paper,
[00:20:46.877]we got an extension that's nearly published that we some
[00:20:53.120]illustrations and definitions from audience to help bring
[00:20:57.030]some clarity for this concept.
[00:21:08.252]So some of the interesting nuances,
[00:21:11.880]so we've had in the past few years that we've just wrapped
[00:21:16.080]up that we
[00:21:20.008]direction
[00:21:21.116]irrigation is a good thing if we want to pursue water
[00:21:37.050]security and food security,
[00:21:38.220]we'll connective our water whether we irrigate,
[00:21:41.276]I know Patricia's not here.
[00:21:42.631]Input use sufficiency and, and it's concerning.
[00:21:49.259]The culture might seem like subsistence follow,
[00:21:53.370]it's actually much more efficient, right?
[00:21:55.050]When we're managing the system, we get more yield,
[00:21:57.681]pretty much of water whether we're irrigating,
[00:22:02.012]we like to improve application efficiency,
[00:22:04.326]that's especially water quality
[00:22:10.034]and sending nitrates down past the root zone.
[00:22:13.933]Efficiency and sensors, better variation,
[00:22:16.290]scheduling is a good thing
[00:22:20.640]to adoption for good irrigation scheduling is the time
[00:22:23.970]commitment to do every day.
[00:22:30.541]So to become adopted we have to get to a point of automating
[00:22:34.337]center pivots and the industry has that goal too us pulled
[00:22:40.403]off yet there's so enough uncertainty that to actually make
[00:22:45.157]the decision.
[00:22:50.860]We focused on putting sensors on the center pivot itself.
[00:22:55.320]So taking advantage of the center pivot has a moving
[00:22:58.746]platform if you field
[00:23:08.993]from satellites and aircraft as well.
[00:23:13.562]Data,
[00:23:14.395]the first one has multi spectral data and we use that to
[00:23:17.370]calculate vegetation in there. Familiar with n d probably.
[00:23:23.561]Okay, so the,
[00:23:26.641]if we sensors model does that do compared methods.
[00:23:31.980]So this is an example.
[00:23:33.811]Some of the results this is at in east central Nebraska
[00:23:41.850]Saturday data reused is called Planet.
[00:23:45.609]When I finished going here, we used a lot of
[00:23:51.913]the populars. There you go.
[00:23:53.139]And then these,
[00:23:57.180]these private company products started coming too good to be
[00:24:01.290]true, but we should, they performed well.
[00:24:05.256]Solution three solution on basis.
[00:24:11.038]If there's not cloud cover significant advancements for
[00:24:15.030]agriculture in the past several years from
[00:24:25.560]center obviously they find the same truth.
[00:24:28.826]We are happy with that.
[00:24:29.883]Also they picked up the difference between irrigated and
[00:24:32.670]waiting fed. So that was good.
[00:24:35.850]We also used unlimited aircraft system and they were in the
[00:24:39.510]same range as well.
[00:24:43.145]And last year we did.
[00:24:43.984]And aircraft and we still use them, the collect data
[00:24:55.787]about deviation management,
[00:24:58.350]they're perfect for research. They might be a puzzle piece.
[00:25:01.050]But the ease of getting satellite data,
[00:25:03.390]the ease of getting data from sensors.
[00:25:05.100]Now again to pivot is just so much easier than the,
[00:25:08.627]the overhead cost of investing and operating adrenaline for,
[00:25:12.510]for most producers infrared,
[00:25:18.390]which gives us the temperature of the canopy you can
[00:25:26.940]and your sweat. The sweat helps keep you cool,
[00:25:29.850]especially if there's a breeze.
[00:25:31.950]If you're in canned sweat, that's a pretty,
[00:25:34.268]you get pretty hot and you clean. So the same way.
[00:25:37.170]So we use the temperature basically as we need to detect
[00:25:41.100]stress. So if we can estimate canopy temperature should be,
[00:25:47.568]if it has an amada supply, then we can clean it.
[00:25:49.710]If it's written within that,
[00:25:51.491]that's an indication that it's under some sort of stress.
[00:25:55.408]We used dynamax sensors,
[00:25:57.270]we put some stationary post that's control. So you know,
[00:26:04.680]the sim compared to moving part is we put sensors on the
[00:26:09.630]senate
[00:26:13.331]is a big deal cause the coming in at a different angle we
[00:26:17.646]could straight down, which is a controlled condition,
[00:26:21.465]but you see is pretty small, right?
[00:26:24.330]So wanted to put them at an angle,
[00:26:26.874]the s d agricultural research service in Texas inland,
[00:26:31.920]this is the way that sensors on the center pivot basically.
[00:26:38.160]So that sensing the same from opposing effect of the angle
[00:26:43.890]kinda cancels out. So that's the logic
[00:26:49.573]things that helped this project.
[00:26:53.447]We had a valley irrigation system that had a a guide unit
[00:27:00.090]center signal pretty slow, right?
[00:27:02.280]And if you're coming to me,
[00:27:05.253]it takes a long time to move across the field,
[00:27:08.053]take eight hours to do a full circle.
[00:27:10.500]So these new transmissions,
[00:27:12.453]a lot more pivot to go in a circle in only five hours,
[00:27:14.910]sometimes even less than that. So first that's good news.
[00:27:18.000]That means we can, without stopping irrigation for too long,
[00:27:22.640]does it stop irrigating for sure I wanna stop for eight
[00:27:26.070]hours. But for our canopy temperature,
[00:27:28.680]if we can get the canopy, that's good news for us.
[00:27:32.653]Collecting that represents the dry crop.
[00:27:36.570]So we did dry scans we.
[00:27:40.953]Between lunch and mid-afternoon when the canopy was most
[00:27:43.950]likely to experience stress.
[00:27:47.760]One of the,
[00:27:48.780]the things that we were looking at is can we detect stress
[00:27:53.280]before we get a yield loss?
[00:27:54.870]So one of the common assumptions in our discipline in
[00:27:59.400]irrigation management is that if you can detect stress by
[00:28:03.390]canopy temperature, then you've already reduced the,
[00:28:08.876]the crop water use and along with that carbon dioxide coming
[00:28:11.160]in. So you've al you've already had some yield loss.
[00:28:13.530]So the,
[00:28:14.790]the assumption in the field is that this will work well for
[00:28:17.910]deficit irrigation when you know you're gonna have some
[00:28:20.640]yield loss anyway,
[00:28:21.914]but probably not gonna work for irrigation when you're
[00:28:25.380]trying to avoid stress altogether.
[00:28:28.320]So we developed a hypothesis,
[00:28:30.630]this is one of those where in the real life,
[00:28:32.100]the hypothesis was developed after we started puzzling over
[00:28:34.740]the data,
[00:28:35.573]but that under no water stress then the photosynthesis
[00:28:42.330]is energy limiting. So in other words,
[00:28:44.730]if a SSAT is open a lot or a little bit doesn't make as much
[00:28:48.120]different. It's the solar energy that limits the rate of
[00:28:50.340]photosynthesis.
[00:28:51.750]But then there's a range of low water stress where that
[00:28:55.320]stama is closed a little bit.
[00:28:57.240]So we've reduced the conductance of the S stama and because
[00:29:03.240]of that we're reducing the, the water vapor leaving,
[00:29:06.930]we're reducing the et,
[00:29:08.280]we're reducing that cooling effect from sledding.
[00:29:11.160]And so there's a stress signal that we can detect with a
[00:29:13.604]warm canopy,
[00:29:15.210]but the carbon going into the SMA is still limited by the
[00:29:19.110]solar energy.
[00:29:20.820]And so we have not reduced carbon assimilation yet,
[00:29:26.070]but then eventually we get to high stress where we're
[00:29:28.170]reducing both,
[00:29:29.003]we're reducing the water flux through the SMA as well as the
[00:29:33.157]carbon dioxide flux coming in.
[00:29:36.630]If, if you're a person who's into climatology,
[00:29:39.840]you might think that the climate would have an impact on
[00:29:42.600]this and it certainly does.
[00:29:44.340]I think this is more likely to be possible in subhuman areas
[00:29:49.620]and very erred areas. I think this doesn't hold up.
[00:29:53.160]So anyway, when we looked at our data,
[00:29:55.800]it supported this working model.
[00:29:58.590]In this graph we show is seasonal et crop water use.
[00:30:02.160]And the horizontal axis we have the thermal stress.
[00:30:06.780]In this case we used a version of the crop water stress
[00:30:09.750]index.
[00:30:11.280]We took average across the season to get some,
[00:30:14.100]some overall data here.
[00:30:16.290]But you can see in our full irrigation treatments we have
[00:30:20.190]deficit irrigation and then rain fed as you'd expect.
[00:30:23.670]We have reducing crop water use and increasing crop water
[00:30:27.720]stress index.
[00:30:28.650]So there's that relationship between a warmer canopy,
[00:30:32.610]but the yield is consistent from our over irrigation to our
[00:30:37.020]fully irrigation to our deficit irrigation.
[00:30:39.480]We're in the 77 to 75 bushel range and it's not until we get
[00:30:43.832]a large amount, a larger amount of crop water stress,
[00:30:47.117]that yield comes down to 68.
[00:30:50.340]So we think that supports this working concept and merits
[00:30:53.280]further research. So, okay,
[00:30:57.810]so one last slide on this project and then one forward
[00:31:00.390]looking slide and I'll turn it back over to Salk.
[00:31:05.370]One other thing I was gonna mention.
[00:31:07.140]So Bruno, one of our new extension educators,
[00:31:09.900]we've had some fun discussions about this project and he has
[00:31:13.140]some stamato conductance data that he has collected from
[00:31:17.550]past years.
[00:31:18.383]And so we're gonna see if we can bring that in to help
[00:31:21.120]better understand this.
[00:31:23.700]Okay,
[00:31:24.533]we think sensors mounted on the pivot are a good thing.
[00:31:27.180]That rapid dry revolution is really important.
[00:31:30.900]We think there is a range where we can detect stress and use
[00:31:33.690]it to manage irrigation.
[00:31:35.490]We have a a crop watch article if you're interested in
[00:31:38.700]learning some more about it.
[00:31:42.120]Okay, so my last slide in our ongoing research and looking
[00:31:47.280]forward, we're in a data driven world, right?
[00:31:51.450]So think about precision agriculture.
[00:31:54.240]We have an amazing amount of data that wasn't imaginable 10
[00:31:57.600]or 20 years ago. Farmers are overwhelmed with data, right?
[00:32:00.900]Way too much data to know how to make sense of it and turn
[00:32:04.230]it into an actionable decision.
[00:32:08.700]So for example,
[00:32:09.720]this is satellite data and unmanned aircraft data.
[00:32:13.440]So this is neat. We have much higher resolution, right?
[00:32:16.680]We can see what's going on in the field a lot better,
[00:32:19.980]but there's so much data, what do we do with all of it?
[00:32:22.800]Machine learning keeps coming up as a way to integrate data
[00:32:26.670]from various sources,
[00:32:29.520]especially if you have disparate types of data.
[00:32:31.710]And one field has more types of data than the other industry
[00:32:36.390]is ahead of us on this.
[00:32:38.820]Using machine learning for not only irrigation but
[00:32:42.990]agronomics in general.
[00:32:45.450]And of course when I talk to industry they don't tell us all
[00:32:48.120]their secrets and I'm very much a process-based scientist.
[00:32:52.230]I'm very, very much like mechanistic model.
[00:32:54.660]So it makes me a little bit nervous. However,
[00:32:57.180]I appreciate machine learning as a tool.
[00:32:59.640]So this is some of our ongoing research yen.
[00:33:02.100]She as the lead on this one,
[00:33:03.510]giermo is involved as well as Lila.
[00:33:06.900]And we're incorporating that machine learning.
[00:33:10.530]The cloud is becoming a big part of what we do.
[00:33:13.050]Edge cloud computing is something we think might play a
[00:33:15.840]role. So that is part of this project as well.
[00:33:18.780]This project is looking at the the nutrient management as
[00:33:21.570]well as the water management.
[00:33:23.910]We have not solved the problem in this project yet.
[00:33:26.310]So check back in a couple years and I'll let you know how it
[00:33:28.470]goes.
[00:33:29.550]Wanna give you a little idea the direction we're going.
[00:33:32.880]Okay.
[00:33:37.710]Five. How about that?
[00:33:39.900]Too bad. You wanna stop for questions.
[00:33:42.450]Or you have two questions that have been online.
[00:33:45.750]Okay.
[00:33:47.130]Go ahead, microphone.
[00:33:50.640]Just use that one. Great.
[00:33:54.180]So the first question, Solay is for you.
[00:33:57.840]And it says,
[00:33:58.673]do you suspect the soil data errors between web soil survey
[00:34:03.366]versus NNC two R due to the map units being assigned to the
[00:34:08.430]dominant soil series, 50% predicted coverage.
[00:34:12.870]And then the second part to that,
[00:34:13.950]do you think greater accuracy will be achieved if minor soil
[00:34:18.632]series inclusions reduced?
[00:34:20.970]I think so. I, I, that's not my area of expertise.
[00:34:24.660]I,
[00:34:25.493]I've talked a lot to our soil physicists at Oklahoma State
[00:34:28.140]and we think that could help improve it a little bit.
[00:34:32.085]Like I mentioned,
[00:34:33.210]that was not really the intention of developing the SGO
[00:34:37.890]dataset and it's very expensive to modify it if that was the
[00:34:43.260]goal. But yeah, I, to answer the question,
[00:34:46.230]I think that would help with it a little bit.
[00:34:48.090]It might have been very well to the dominant component.
[00:34:51.188]Okay.
[00:34:52.179]And then the second question from Erin, for both of you.
[00:34:54.750]Are there advantages of using subsurface drip irrigation
[00:34:57.780]technologies since there's a high prevalence of poor and
[00:35:00.570]fair uniformities in your pivot surveys and ample amounts of
[00:35:04.020]technology needed to improve pivot performance?
[00:35:08.040]Repeat the question please so we can all.
[00:35:11.010]The, the question is,
[00:35:12.990]are there benefits to subsurface drip irrigation,
[00:35:16.080]particularly in light of our observations of some of the low
[00:35:19.230]pivot performance,
[00:35:20.340]some of the non-uniformity and some of the effort or
[00:35:24.210]investments it takes to get to good uniformity?
[00:35:27.600]We had a really interesting discussion a day or two ago with
[00:35:30.030]someone in the industry about this.
[00:35:33.840]You wanna take the first stab?
[00:35:35.610]Sure. And hopefully our answers are similar.
[00:35:39.680]I don't know that that would be my answer.
[00:35:42.388]I'm not sure.
[00:35:43.410]A lot of the things that I saw with center pivots is not the
[00:35:47.220]technology,
[00:35:48.150]it's poor management and poor maintenance and we have more
[00:35:52.170]subsurface drip irrigation in Oklahoma than in Nebraska.
[00:35:55.740]And I've seen poor management with subsurface strip with
[00:36:00.060]extracted cores and I had observed a lot of deep
[00:36:03.330]percolation.
[00:36:04.163]We've seen lots of leaks that have not been fixed and water
[00:36:08.790]is shooting to the surface coming up to the surface.
[00:36:11.940]I've seen a lot of poor uniformity issues with S D I too.
[00:36:15.240]So think it a lot of that is management and maintenance.
[00:36:19.110]Yeah, sounds excellent.
[00:36:20.580]Link STI sounds really good but it's not a silver bullet.
[00:36:23.640]It takes a lot of effort to manage it well.
[00:36:26.654]So any other questions.
[00:36:27.487]Jim?
[00:36:28.410]Relative to your ET reference word?
[00:36:33.060]Did you try the ET gauge that? That little gauge,
[00:36:39.364]I dunno how, I know how it works, but does it, is it good?
[00:36:45.420]I did not do a whole lot of ET gauge study in Oklahoma.
[00:36:49.500]The main reason was we had Oklahoma missing it and if you
[00:36:52.770]notice in the map, but all those circles,
[00:36:55.410]we have 121 estates across the state. So it's,
[00:36:59.400]it's a beautiful network. Beautiful one.
[00:37:02.400]I did some work on it before Oklahoma in Colorado.
[00:37:06.210]And based on, and I know that some work has been done here,
[00:37:09.210]SWAT did some work here. They seem to be working fun,
[00:37:13.140]especially when you don't have access to nearby weather
[00:37:16.560]stations or the condition of the weather station is far from
[00:37:21.030]a standard of reference condition. When we did,
[00:37:24.120]and I think most of the ET gauges are made by a company in
[00:37:27.150]Colorado, they work fine when we tested.
[00:37:30.420]Them, video cost about $280 or.
[00:37:34.366]There, there are two versions.
[00:37:35.310]There's one,
[00:37:36.143]it's manual read and then there's another one that is,
[00:37:38.704]that has a little sensor that that logs the changes in water
[00:37:43.380]level. I think the,
[00:37:44.787]the one that's manual read is 200 something dollars.
[00:37:49.200]Now the question, when you do your,
[00:37:53.715]your reflectance measurements,
[00:37:54.910]does the effect or does water on the leaves have an effect?
[00:37:59.790]Yes it does.
[00:38:03.060]For the multi-spectral data that we use to get the growth in
[00:38:06.300]disease, the N D B I,
[00:38:08.040]we would also collect that during a dry run to avoid that
[00:38:12.300]scenario.
[00:38:14.040]So I think it does have any effect.
[00:38:15.180]I'm not familiar with the details or how much that is.
[00:38:19.560]I will tell you.
[00:38:21.218]Good, good.
[00:38:22.633]Lemme know.
[00:38:23.840]So what happens when there's water on the leaves?
[00:38:26.520]It decreases the near in reflectants and increases the
[00:38:30.870]visible reflectance so it screws up in dpi.
[00:38:34.340]Sure, yeah, yeah.
[00:38:36.630]Excellent.
[00:38:37.463]We even tried mounting our sensors far enough ahead to get
[00:38:41.850]ahead of the weed pattern of the sprinklers.
[00:38:45.144]And I think for the N D V I,
[00:38:47.610]we got just far enough where it was still dry,
[00:38:49.980]but for the thermal data we were not successful.
[00:38:52.740]I think there's enough mist in the air that,
[00:38:54.600]that we could see the difference in when we,
[00:38:58.142]we ran up wet with that mount to front.
[00:39:02.110]Yeah. In the same way,
[00:39:06.090]was it an issue trying to correct for,
[00:39:07.860]so say you're in the inter, you know,
[00:39:09.390]you're closer to the first tower,
[00:39:11.100]second tower pivot as opposed to out on maybe the sixth or
[00:39:14.220]seventh tower.
[00:39:16.590]You know that difference,
[00:39:18.690]that's probably a very specific engineering question,
[00:39:22.440]but was that, was that an issue?
[00:39:24.600]I was just wondering if that was a challenge or if it was
[00:39:27.180]straightforward to address.
[00:39:29.580]Yeah, that's a really good question.
[00:39:32.340]So from a research perspective,
[00:39:33.630]we made sure we had sensors in each plot that we wanted to
[00:39:37.170]collect data on. From a practical perspective,
[00:39:40.560]we put more sensors on the end of the pivot because it
[00:39:43.410]covers so many more acres, right?
[00:39:45.210]Right.
[00:39:46.067]So it's more representative in terms of variability along
[00:39:50.190]the pivot that is real. It's true.
[00:39:53.520]We could invest in zone control variable radio irrigation to
[00:39:56.820]accommodate that.
[00:39:57.653]So that's where some of the high tech research is in
[00:40:00.870]practice that's not common.
[00:40:03.450]What is more common in practice is movement towards sector
[00:40:06.990]control vri,
[00:40:08.070]where you simply control the speed and it gives you pie
[00:40:10.140]slices. And if you're gonna manage it in pie slice,
[00:40:13.860]you don't need to fully characterize that variability along
[00:40:16.260]the pivot anyway,
[00:40:17.280]what you do wanna characterize is the circle,
[00:40:19.020]and that's what we do. But sensors mounted on the pivot.
[00:40:21.660]Interesting.
[00:40:22.980]Thank you.
[00:40:24.797]It's a two dimensional problem with radial units, radial.
[00:40:36.583]So we thought that maybe just sharing
[00:40:39.150]some of the statistics of the year,
[00:40:41.910]get it a in Nebraska it would be interesting to wrap this
[00:40:45.326]up. I guys probably know a lot of these statistics already,
[00:40:48.600]but this is the number of irrigated acres from U S D A,
[00:40:53.070]NASA and this is a census.
[00:40:56.110]So the years are oh 2, 0 7, 12 and 17 and we've passed
[00:41:01.350]California in terms of the number of irrigated acres,
[00:41:05.070]I think close to 15% of all irrigated acres in the US are in
[00:41:10.560]Nebraska and Nebraska is number one. Yeah.
[00:41:14.310]For the rest of the results,
[00:41:16.050]they're from the irrigation and water management survey of
[00:41:19.620]na. So it's one year after the census,
[00:41:22.650]but it provides us more detailed information.
[00:41:27.450]No surprise that a sprinkler systems are the dominant
[00:41:30.330]systems. 8% gravity systems are in western Nebraska,
[00:41:34.620]as you guys know. And out of the sprinkler systems,
[00:41:37.910]99% of them are center pivots in Nebraska.
[00:41:42.780]This is top crops corn, grain corn, 62%,
[00:41:47.430]30% soybean in terms of irrigated acres and then alfalfa
[00:41:54.060]hay.
[00:41:56.970]So this was something I found interesting.
[00:41:59.220]This is water sources used for irrigation based on acres
[00:42:03.330]irrigated. So 90% of irrigated acres use groundwater.
[00:42:09.090]8% of irrigated acres used surface water from an off farm
[00:42:14.340]source, not on farm.
[00:42:15.500]On-farm is only 2%. Now if I showed you the same thing,
[00:42:19.170]not based on acres irrigated,
[00:42:21.210]but based on volume applied that percentage that the
[00:42:24.900]proportion changed. So let me go back and forth.
[00:42:27.420]So this is based on acres irrigated based on volume applied
[00:42:32.850]off farms, surface irrigation is large.
[00:42:35.580]What do you guys think that's for, that's duty
[00:42:42.690]further west. All the gravity systems there,
[00:42:45.420]even though like per acre basis, more, more water use.
[00:42:49.400]So in terms of the water conservation,
[00:42:51.300]total water use at 12, 12%.
[00:42:55.417]So we had about 67 irrigation, irrigation wells in 2018.
[00:43:00.180]And this is a typical, well,
[00:43:02.370]so that's to the bottom of the well to the bowls and or
[00:43:06.750]pillars and to the water table.
[00:43:08.670]So this is some of those estimates. Average well depth,
[00:43:11.910]average depth to bowl and average water depth,
[00:43:15.270]78 feet average pumping capacity was 760 gallons per minute.
[00:43:21.480]And this is how much folks said they spent on energy for
[00:43:25.590]pumping 234 million.
[00:43:28.380]So if you do the mask, that's about $3,500 of,
[00:43:31.950]well for that year for irrigation.
[00:43:35.160]So that's about the average energy cost in 2018.
[00:43:39.360]I think natural gas and diesel were cheaper than you are
[00:43:43.224]now. So that number is probably higher, but that's,
[00:43:45.000]that's and the budget,
[00:43:46.350]that's how much folks are paying on average,
[00:43:50.700]sticking with irrigation wells,
[00:43:52.980]about 57% of wells in Nebraska have flow meters.
[00:43:57.180]43% often don't have flow meters.
[00:43:59.970]And with backflow prevention devices,
[00:44:03.330]15% of them don't have backflow prevention devices.
[00:44:06.960]I'm thinking that the 15% that don't do not do ation or I'm
[00:44:11.940]hoping that that's the case,
[00:44:14.130]but 85% and that number is fairly large enough comfort,
[00:44:17.550]somebody would say 85% is pretty big.
[00:44:19.770]What percent are actually being used?
[00:44:22.020]Yeah, great question.
[00:44:24.480]Wish that was on the, on the survey too.
[00:44:28.230]I mean for for ation or for any of these things.
[00:44:32.856]Regulation of nds in terms of their amounts and stuff like
[00:44:35.272]that. Right.
[00:44:37.815]They're inspected, right?
[00:44:38.850]So if they're there and they're inspected,
[00:44:41.070]they're not poorly maintained, they should do the job.
[00:44:44.504]Inspectors they have is pretty low.
[00:44:51.250]About 77 million expenditure on irrigation equipment,
[00:44:55.200]facilities and technology.
[00:44:57.030]Most of that over here was for replacement or maintenance of
[00:45:00.477]the irrigation systems. But it's interesting,
[00:45:03.000]you do see money is spent on water conservation,
[00:45:06.510]a little bit on energy conservation than 18% on new
[00:45:09.660]expenditures.
[00:45:10.890]So we're still expanding at least in 2018 and we're
[00:45:14.430]expanding and this is the methods that folks are using.
[00:45:18.990]So people use more than one method.
[00:45:21.000]That's why if you add 'them up, it's more than 100%.
[00:45:25.020]One thing I wanna mention is use of soil moisture sensors.
[00:45:29.280]About a third of folks surveyed said they use soil moisture
[00:45:33.090]sensors. That's the highest in the us.
[00:45:36.540]The next state I think is Mississippi and then California
[00:45:40.920]and they're like 20 something percent,
[00:45:42.480]but 31% is the number one, that's the largest number,
[00:45:46.620]which is amazing when you think about sensor technologies
[00:45:49.290]and how many decades if not centuries.
[00:45:53.160]So moisture sensors have been around and plant,
[00:45:55.957]plant moisture sensors and some of the other technologies.
[00:46:01.860]That's it.
[00:46:03.930]Thank you.
[00:46:11.277]Well just keep.
[00:46:13.237]Before questions.
[00:46:14.760]I realize you've just, this a small snapshot,
[00:46:25.680]my heels that the efficiencies
[00:46:40.110]waters and so forth, I I just find it difficult.
[00:46:45.437]Efficiencies are so low, it's that operator error.
[00:46:50.207]That's a good question. I'll, I'll comment on the,
[00:46:53.940]the pressure data that I showed.
[00:46:56.400]Many systems are under pressure,
[00:46:59.070]thankfully if they're under pressure,
[00:47:00.177]the efficiency is probably still decent, but the,
[00:47:03.690]the uniformity is not quite as good, right?
[00:47:07.604]And they plus,
[00:47:09.570]but I think that's a lack of effort on maintenance, right?
[00:47:13.470]You know,
[00:47:15.690]pivots designed and set up for a particular pressure and
[00:47:18.026]then perhaps over time, you know,
[00:47:20.760]the well stream gets fogged or whatever,
[00:47:23.190]there's reasons that that might decrease.
[00:47:26.100]So I think it's just lack of effort to maintain the system
[00:47:28.830]to keep it working well on your data, on your property.
[00:47:32.860]Yeah. So efficiency is difficult to deal with.
[00:47:36.750]And you estimate,
[00:47:37.583]so there are a lot of paper they talked about the paradox of
[00:47:40.590]irrigation efficiency and how you estimated in the example I
[00:47:44.297]showed central Oklahoma that efficiency was pretty high.
[00:47:49.560]They were so severely under irrigating that the non
[00:47:52.573]uniformities in the sprinkler system don't make much of a
[00:47:56.040]difference to irrigation efficiency.
[00:47:58.770]But that was the case in Oklahoma.
[00:48:00.450]I guess it would be different here in the same degree of
[00:48:04.230]non-uniformity would result in the en larger inefficiency in
[00:48:07.800]the system. If you under irrigate, your yield goes down,
[00:48:13.620]if you're right on with what's required because of the
[00:48:16.710]weather conditions and all the,
[00:48:17.983]the instrumentation that you talked about,
[00:48:21.717]and you're probably in the middle, but 60% under pressure,
[00:48:28.830]that means they're probably under irrigating hose.
[00:48:32.580]Right? Or
[00:48:34.729]one of our hypotheses on that is their under pressure,
[00:48:38.517]the the uniformity is not as good, right?
[00:48:41.220]So you've got a little more high and low.
[00:48:43.368]So probably what happens is they're keeping an eye on the,
[00:48:45.717]the dry spot in the field and they're saying we're gonna put
[00:48:48.450]on at least enough water so that dry spot some on the oven.
[00:48:51.810]So then you're probably getting a good yield at the cost of
[00:48:55.290]pumping more water than you need to because of the,
[00:48:59.010]so then you've got energy costs, you know,
[00:49:00.990]nitrate loss and so on. 50,
[00:49:02.730]60 years ago when pivots came in,
[00:49:04.860]it was like turn on and let them run and just dump the
[00:49:07.680]water on. Man.
[00:49:08.513]That's kind of comes from the gravity irrigation model,
[00:49:11.550]but all that over irrigation, whereas it end up,
[00:49:14.730]it takes the nitrogen and all the excess water goes into
[00:49:17.029]the, into the groundwater supply.
[00:49:19.650]So the nitrogen goes through the room. I, it's just, it's,
[00:49:23.010]it's just,
[00:49:24.090]it's incomprehensible to me that we're this far along and
[00:49:28.560]there's not more controls on that.
[00:49:30.733]Yeah. Yep.
[00:49:32.304]Definitely a need out there.
[00:49:33.870]Did you read the newspaper article about,
[00:49:38.700]so I was pretty sad.
[00:49:42.999]I noticed you gave data about drip irrigation and it was a
[00:49:49.110]tiny amount of percentage wise.
[00:49:52.050]Would you predict that since drip irrigation and this
[00:49:54.966]certainly very efficient that there will be a trend in the
[00:49:59.430]future toward tur irrigation or similar, similar systems?
[00:50:04.020]That's good question.
[00:50:05.624]I think you have that data here at the end
[00:50:10.328]change that and there are lots of other issues within and
[00:50:13.920]ownership place, role investment, cost plays role, they,
[00:50:19.438]they've been kind of like, it's all motion sensors.
[00:50:22.140]They've been around for
[00:50:29.100]The irrigated acres in the US under the drip or any type of
[00:50:33.150]microbe. A two third owner in California cash.
[00:50:39.840]Logistics.
[00:50:43.800]Cost is that if you don't own the land and you are DC you
[00:50:47.550]can take the pivot with you,
[00:50:49.260]but you cannot take the sub a strip with you there.
[00:50:54.240]Different elements. So the, the,
[00:50:57.379]one of the interesting things with the investment cost is
[00:50:59.400]the size of the field makes a big difference. So, you know,
[00:51:02.317]if you're in California growing high value vegetable crops
[00:51:06.270]in smaller fields, then drip is the way to go.
[00:51:09.030]It's an easy answer.
[00:51:10.980]However,
[00:51:12.000]if you have a quarter section of land with 160 acres,
[00:51:16.890]the area that a pivot covers, you pay for it by span, right?
[00:51:20.280]Maybe 10 or $50,000 per span,
[00:51:22.800]but the area covered is pi or square, right?
[00:51:25.470]So when you get to a bigger field,
[00:51:27.180]your cost per acre plummets.
[00:51:29.250]And so by the time you get to Nebraska,
[00:51:31.410]scale yield drip is probably twice the cost per acre
[00:51:36.660]compared to the center. Yep.
[00:51:38.520]But that, that gets back to the old thing of,
[00:51:40.707]and I remember Don Stein when he was extension,
[00:51:43.080]horticultures would get calls from farmers that wanna put in
[00:51:46.440]strawberries, high value crop.
[00:51:48.330]Can you imagine 133 acres of strawberries in Nebraska?
[00:51:52.320]I mean, you couldn't get the labor to take the things,
[00:51:55.140]right? So you,
[00:51:56.280]you take that to its ultimate viewpoint and it it just falls
[00:52:00.270]apart, right?
[00:52:01.890]Well, yeah, I mean that, that's a really good question.
[00:52:04.387]The, an alternate view on that would be 50 years from now
[00:52:10.290]California's auto water, right?
[00:52:15.222]Long time. So,
[00:52:16.113]so 50 years given time to allow the changes and the labor
[00:52:20.730]supply and the market delivery, you know, where,
[00:52:23.700]where do we have good soils and water in our country right
[00:52:26.340]here in Nebraska end up producing some of those high valley
[00:52:31.050]high value costs? Well, I dunno not, not for one,
[00:52:35.940]I promise they did a study in Ventura accounting with
[00:52:38.700]strawberries.
[00:52:39.533]They found out that when you started seeing strawberries in
[00:52:42.420]a field, it was right for development.
[00:52:45.180]The next thing, the next crop in there was condominiums.
[00:52:56.010]Israel and Greece where they have quite bit of drip
[00:52:59.010]irrigation, they'll irrigate every other row they have huge,
[00:53:07.620]but it's because of shortage of water excess irrigation.
[00:53:15.330]Right? And, and one of the differences there is,
[00:53:18.810]well back to the climate and the humidity in the air, right?
[00:53:23.190]The air is so dry there that the water evaporation sprinkler
[00:53:28.719]drops is much higher. Where here in eastern Nebraska,
[00:53:29.850]that loss is much smaller. So, so in that case it,
[00:53:32.390]it makes a pretty big difference in terms of the efficiency.
[00:53:36.643]Would you comment about,
[00:53:37.910]you talked about people using soil as sensor versus plants.
[00:53:44.430]Is is that because of the 12 sensors only monitor a very
[00:53:48.420]small area compared to if you're out there looking at the
[00:53:51.330]plant, we know how many acres?
[00:53:59.010]Oh, that's my,
[00:54:00.568]that that would be what my preference is with. We've tested,
[00:54:03.118]like you guys, many, many different types of soil sensors.
[00:54:06.630]They all sensors, temperature N the bi savvy,
[00:54:13.170]red edge, larger area.
[00:54:15.960]That would be my preference. But there's all,
[00:54:18.053]there's still challenges how you,
[00:54:21.113]how busy all the growers are,
[00:54:23.700]how you transfer that into easy irrigation decisions.
[00:54:27.327]All kind use the soil water sensors in terms of when it get
[00:54:31.940]the, I put on a depth of water,
[00:54:35.010]the plant takes more work to get into management decisions,
[00:54:38.640]but there's so many benefits can be,
[00:54:40.569]I think you drive by the road and see the leaves curling,
[00:55:14.777]improve and soybean breed accompanied by an increased model
[00:55:17.700]product monitor, still called virus,
[00:55:20.640]notably in the timeframe.
[00:55:21.960]Subsequent rainfall irrigation events ion is inevitably
[00:55:27.480]linked to synthesis, frequently linearly.
[00:55:30.270]The plants must exchange water oxide with open snow as a
[00:55:34.260]consequence restricting conductance as then be restricting
[00:55:39.960]the opportunity for modern soybean cultivatable yield
[00:55:43.530]response about
[00:56:07.290]popular soil moisture sensor used in Nebraska. Yeah,
[00:56:10.860]China sensor system is considered to be the best accuracy,
[00:56:13.710]but it's more difficult to install and extensive.
[00:56:16.440]What is your recommendation as to the best system at the low
[00:56:20.100]cost and mc fan of the, the climate,
[00:56:26.280]but obviously there insurance?
[00:56:29.790]What's your take on it?
[00:56:31.800]Yeah, we used a lot of folks and, and yeah, our difficult,
[00:56:38.160]you need one sensor per monitor.
[00:56:41.057]Monitor probes are the way to go on how all the probes out
[00:56:44.700]there.
[00:56:45.533]CEC was the one that we ended up using more than
[00:56:58.691]As well. Okay.
[00:57:00.210]What right now is a soil moisture sensors that the
[00:57:02.823]researcher I think about what's the true water content and
[00:57:06.357]the soil and my soil moisture sensor.
[00:57:09.090]Tell me that and next Sure, no, there's all these problems.
[00:57:12.240]Spatial variability.
[00:57:14.010]However, from a practical management perspective,
[00:57:18.030]I'm really more concerned about the trend in soil water than
[00:57:21.960]the absolute value. And so the trend is really good.
[00:57:25.440]Even if there's some spacial variability,
[00:57:27.840]the trend is probably pretty consistent, right?
[00:57:30.540]And so any effect,
[00:57:32.070]that's part of the reason they work so well for producers is
[00:57:34.710]they're, they're paying attention to that trend.
[00:57:36.540]They figure out where the threshold is and they're,
[00:57:38.850]they're very effective for that related discussion on add
[00:57:42.810]with colleagues that one,
[00:57:45.570]the best accuracy that care about is losing nutrients below
[00:57:51.840]the root zone.
[00:57:53.430]Maybe you don't even need to know the exact volumetric water
[00:57:56.550]content.
[00:57:57.383]As long as you get a signal from the center that tells you
[00:58:00.270]the water has reached that depth and could carry it.
[00:58:03.660]That's all you need. So it doesn't need to be,
[00:58:06.090]some of 'them say.
[00:58:15.420]Presentation. I,
[00:58:17.121]I'm wondering about the best management practices for center
[00:58:20.670]pivots. Like you guys tackle little things about, like,
[00:58:23.860]about transpiration and also some maintenance that will
[00:58:27.930]improve the cover accuracy, right?
[00:58:30.330]But also I'm wondering a little bit more into like the
[00:58:32.700]detecting probably some, eh,
[00:58:36.845]variability in the canopy temperature.
[00:58:39.030]Trying to find like other elements to lower down that
[00:58:43.890]evaporation like at Right.
[00:58:46.800]The apple evapotranspiration and I don't know,
[00:58:50.000]it will be timing of using the center fever and like,
[00:58:54.060]I don't know the best time of the day to run these so we can
[00:58:57.960]like, I don't know, like increase that pt.
[00:59:01.080]Yeah.
[00:59:03.090]A V from crop water use or from the sprinklers when we apply
[00:59:07.680]the water.
[00:59:08.513]Crop.
[00:59:10.756]From the crop.
[00:59:13.290]If, if you're managing for a max meal,
[00:59:16.440]it's really hard to reduce ET that's pointed out.
[00:59:20.610]That's such a close relationship usually between the ET and
[00:59:24.570]the carbon assimilation per field.
[00:59:28.800]Things like no-till help keeping some residue on the ground.
[00:59:32.310]Then early in the season when there's more bigger soil,
[00:59:34.740]it's covered by residue.
[00:59:35.760]So that reduces the evaporative losses in terms of timing of
[00:59:41.190]irrigation.
[00:59:43.200]One of the ideas that my students always come up with is,
[00:59:46.147]why don't we just irrigate at night?
[00:59:48.090]Cause then we'd have less losses and,
[00:59:50.490]and that's really good until you think about when it's
[00:59:54.120]really hot. My system can't keep up in the worst weeks.
[00:59:57.330]I have to believe that 24 hours a day, right?
[00:59:59.970]And so from that perspective,
[01:00:01.950]you don't have a lot of liberty to choose in those periods.
[01:00:07.240](sound cuts off)
[01:00:28.680](applause)

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Irrigation Across the Great Plains

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