MSU Extension MI Ag Ideas to Grow With

Session will cover the irrigation updates, the research update from MSU team. First, about 30 minutes, Brendon Kelley, prepare a presentation for master project which focusing on improving irrigation water efficiency for corn and soybean productions. And Brandon is the master student right now in biosystem agriculture engineering department. I'm going to go start playing his presentation. Hello, thanks for joining us today. I'm Brendan Kelley and I'll be sharing a bit on improving irrigation water use efficiency using soil moisture monitoring and irrigation scheduling. This is a three year project that is part of my master's thesis under Dr. Youngsul Dong. This project is aimed at being a demonstrational example of some irrigation strategies and their impacts on yield and water use. The data comes from private individual farms who were kind enough to share and work with us. We're hoping that this can provide a reference of sorts for irrigators in the local area to use for their benefit. Crop production and irrigation are arguably one of the most targeted water uses outside of the household. In 2015, over 40% of the United States freshwater withdrawals were utilized for irrigation, according to the USDA in 2017. The same article also goes on to say that irrigation is one of the top water uses globally, with most countries using the majority of their water withdrawals for irrigation in 2020. About 50% of the United States experienced drought like conditions at some point in the year. However, the need for irrigation and irrigated crops is also critical. Irrigated crops accounted for 54% of the United States crop sales in 2017, according to the USDA. To summarize this, it's important that we look at this issue as a balance or optimization of yield and water. As I previously mentioned, all of the data for this study was collected from farms in the southwest Michigan area for the benefit of local producers. All of these farms were under sandy loam soils so that they could be directly compared against each other. We are hoping to demonstrate the irrigation scheduling and soil moisture monitoring as an approach to improving yield and saving water at the same time. Here's a bit of conceptual background on the matter. Water holding capacity is the maximum amount of water the soil can hold without becoming over saturated. If anyone was to compare this to an analogy, it would be the maximum fuel level of your car available water is the soil moisture, usually in percent times the applicable depth range. The depth range should be compared to the root zone of your crop and is extended throughout the year to match the crop's needs. There's not much point in pushing water down below what your crop can access. Back to the car analogy, this would be closer to the fuel gauge and what you're at right now, say half a tank or three quarter tank. The irrigation threshold is simply the 0.1 decides to apply irrigation specifically in terms of soil moisture or available water. This would be compared to when your car is at E. You don't want to go below this number. As a measure of success, we are using irrigation water use efficiency, which is simply the improvement of yield divided by the volume of irrigation applied throughout the growing season to the crop. As I'm sure you're all aware, there are many variables that go into crop production and irrigation. For the purposes of this study, we chose to focus on changing two key variables, irrigation threshold and irrigation volume. As I said before, the irrigation threshold is simply how dry we let the soil profile get before we decide to apply more irrigation. Irrigation volume is simply how much we apply to the soil to fill the profile up. Ideally, filling the profile should be the most efficient since we are making less total applications and not causing drainage. Additional applications are known by many to be subject to additional transformation losses, not to mention additional electrical bills and whatnot. For the methods of the study, we designated five irrigation management prescriptions. Prescription is simply a set of an irrigation volume and an irrigation threshold. These different prescriptions were applied to different areas within the field, Four within the radius of the pivot, and one outside of it. The first area is the producer's irrigation management. This is simply up to the grower how much and when to apply. It gives us an idea of how it's been managed in the past and what the farmer has had success with. The second area is the theoretically optimal irrigation treatment zone. This is what we think should be the best irrigation management prescription based on the soil characteristics and the crop type. The third and fourth treatment zones are under irrigated and over irrigated zones. This is simply increasing and decreasing the irrigation volume and irrigation threshold. The last area is the unirrigated or control area. This area gives us an idea of what the crop did under the field conditions that were provided that year. Each of these zones had three or more sensors installed in and below the crops root zone. This allows us to see in real time what the soil moisture was doing and gives us a better basis than just weather based alone. In terms of scheduling, once the harvest is complete, we compare the yields to the total water applied using the irrigation water use efficiency equation below. It's simply the management areas yield or whichever section we're looking at subtracting the dry areas yield. All of this divided by the volume of irrigation applied gives us the irrigation water efficiency for each sector. To be a little more specific about the irrigation prescriptions applied, here's a table of what we did. The producers management is not included here because the growers chose to apply at a different threshold and apply different volumes throughout the year. The data is quite lengthy for this. For the optimal irrigation zone, we applied at a threshold of 50% soil moisture. Meaning we let the soil moisture drop to 50% or less. Before we turned on the irrigation, we applied 1 " in corn and 7,500 soybeans in the over irrigated area. We increased the threshold to 70% It should have stayed a little wetter throughout the whole year. We applied a volume of an inch and a quarter and 1 " in soybeans in the under irrigated area, we decreased the threshold to 40% and decreased the volume in corn to 7,502 an inch soybeans. It's worth noting that in the under irrigated area, we were not able to sustain the 40% threshold, just due to the demand it posed on the irrigation systems themselves. This threshold was low enough that the crop was deprived and required more than what was being applied every time we applied it. To elaborate on the equipment and resources used in this study, here are the major components. On the bottom right, you can see the locomis data logging system developed by Dr. Youngsuk Dong and the irrigation lab here at MSU. Second from the right, you can see the moisture sensors used. This is the soil watch ten by Pino Tech. All irrigation systems used in this study had to have variable rate control. This allows us to separate the different areas and apply different volumes to them. Lastly, we had to collect harvest data from the combines. This allows us to spatially separate the different zones to get a yield for each zone on average. Although we intend to continue collecting data over the 2024 growing season upcoming. Here's what we have for the last three years. The table below shows the average water use efficiency for all corn plots in the study over the last three years. As you can see, the under irrigated area outperformed all other areas by quite a bit. We suspect this is due to the frequent, and maybe not predicted, rainfall events throughout the growing seasons. This suggests that a slightly lower irrigation threshold and a slightly lower irrigation volume might be better. Particularly in the case of a lower irrigation volume. It leaves a little extra room in case you get unpredicted or heavy rainfall. It's also worth noting that we had to increase the under irrigated area to a threshold of 50% This was done simply because we could not keep up with the lower irrigation threshold. The crop was demanding more water than could be supplied at any given irrigation event. This created the effect that we almost had to run the irrigation constantly. So we upped the threshold to 50% and maintained the volume at 7,500 Here we have effectively the same data for soybeans. As you can see, the optimal irrigation treatment out yielded the other areas. While the difference is smaller, I'll note that the scale this is on is slightly different than for corn. Since soybeans have a lower yield, our water use efficiency equation is going to produce a lower number even with a little less volume of irrigation. That much smaller yield is going to shrink the scale and difference that you see. While the number is small. I don't think that discredits this data. Rather I think we need to consider this as a valid reason to maximize water use efficiency and use this as a basis. Some key takeaways from this study. Utilizing soil moisture monitoring and irrigation scheduling methods instills confidence in producers. This isn't to say that the producers are not confident in the irrigation management that they had been doing, but rather that having hard numbers provided by soil moisture and irrigation scheduling tends to make for a little more black and white decision of when to turn the irrigation on or when to wait. Secondly, soil moisture monitoring is effective in preventing drought stress. Having real time data from soil moisture probes placed throughout the soil column makes it really easy to see where you're at. Back to my analogy on the car is having a fuel gauge in real time that you can check whenever you want. Once again, this makes it very easy to say it's time to turn on the irrigation or I should wait. Thirdly, supplemental irrigation makes better use of land and water than no irrigation. At some point, you have to justify that the land is worth something to that. If you have invested the time and money into installing irrigation, you should be applying water to make a higher yield. Lastly, any methodical approach to irrigation management is better than a wild guess. This is just to say if you're struggling or have second thoughts, irrigation scheduling and moisture monitoring are really good way of quantifying these things so that it's fairly easy to decide when to irrigate before I wrap it up. I'd like to thank the US DA NRCS and Michigan Soybean Commission for their support of this project. Without it, we wouldn't have been able to do any of this or provide this data to you. I hope this presentation wasn't enjoyable. Thank you for your attention. If you have any questions, feel free to e mail me at kelley162@msu.edu contact Dr. Dong. Thank you. Brendon has been working on the improving irrigation water efficiency for consorban. I thought maybe this might be good to talk about where we are with the irrigated acres in Michigan. Lyndon and I can't wait to see that November of this year, 2024. There might be newer data to add to this graph and the figures. But based on the last 15 years from 2,002. to 2017 irrigated acres in Michigan has been continuously increased. There's a big jump after 2012, which some of you probably know because of the drought that we had in 2012. Based on the USDA Survey, in 2017 in Michigan, we had about 26,000 acres acres of irrigated field in Michigan. And the right chart shows the irrigated acres by counties. As you can see, that Cass County, St. Joe, are the two county and then Branch, Kalamazoo and Van Buren County, those are the southwest part of Michigan, are the major irrigated acres irrigated fields in Michigan. There is another location, the county is called Montcalm, also Mecosta also the county that has high irrigated fuel which produced a lot of potato there as well, when we look at the by crops. Crop types. Majority irrigated acres in Michigan are growing the corn and soybean. Potato always been pretty steady at 50,000 acres. But one thing to note for potato production in Michigan is about almost the percent potato production are under irrigation. We have been seeing the trends of putting irrigations in the fruit and vegetable, especially fruit industries. Apple, blueberries, maybe some cherries. Because especially for those who are the blueberry and apples in sandy soil and for apple, if they're adopting the newer system, it's called high density apple planning system. Which has the shallow root system And trees are much closer which may compete for water. Those chart be adding irrigation system to mitigate the effect of some of climate change that we're experiencing. Earlier Lyndon talked about the more erratic presentation is what we're seeing in a lot of places in Michigan. Some years we have observed the more than 20 days that we had no rain in summer, during growing seasons to mitigate that those climate change. In fact, the irrigation has been continuously adding to the farms to increase the resiliency of crop production to the climate change. Um, the sum up thing that we've been working on, heavily working on, are the irrigation scheduling. Earlier Lyndon talked about different irrigation scheduling method. One is the weather based irrigation scheduling. Another type of scheduling method we, we've been working on is sensor based irrigation scheduling. Bruce Mackellar, he put the irrigation scheduler program together into the Excel version, which has been used for quite a long time. The scheduler is really helpful to keeping track on the moisture level in the field and make sure the level is at the optimal level between the fed capacity and the working point so that plant doesn't go under water stress or make sure we're not wasting our over irrigating basically over irrigation. I think we've mentioned earlier that if you're over irrigating that you're increasing the risk of nitrate leaching below the root zone. That's something to concern. Lately, we've been working on converting this Excel MSU Irrigation Scheduler version to the mobile app. Because I see there's a lot of farmers are now using the smartphone app. This is what we've been working on. I think we're very close to share the beta for the first prototype of the app with the probably number of growers to have them to use the app and provide feedback. If you're interested in it, please drop your e mail address on here. In the chat or in the Q&A I'll collect the e mail address. Once the smartphone, App is ready, we can share the link. Someone asked me about whether we're going to discontinue the Excel based MSU irrigation scheduler program. No, we will keep the excel version. So if you've been using the Excel or if you're preferred to use Excel based MSU irrigation scheduler, you can continue to use the software. In the smartphone app, we have a crop data solo type data. If you collect the crop data, it, it will show you all the crops that can be used for this irrigation scheduler and also show the crop coefficients for different crop type. The soil type data, we include all the soils that are available or that are in the Michigan. For example, if you have spinks soil, Oshtemo soil. If you're interested in looking at the water hooting capacity, you can just click in the app, the Solar type data. It will provide the water holding capacity inch per inch for a different type. What we're trying to do is providing one the app. That has all the information you need to know for the irrigation scheduling. This is what the smartphone app is going to have it. But in the smartphone app, what we're asking is what soil type you have for that field? What crops you're growing, length of growing season, emergen state. The emerging date is so important compared to the planting date because the emergent date is when the schedule kick start. You could have planting day early in the season, but depending on the temperature, it may take weeks and weeks emergent stage. That's why we've been using the emergent date for the irrigation scheduling because that's the time we can accurately start tracking the stage of the crop. Yeah. So if you're interested, just let us know, we can share the first version with you. Another thing that we've been working at, sensor based irrigation scheduling, this weather base is good enough, right? But some people been asking, is there any way we can have a ground? These sensors can be installed on your, feel your soil. That really helps to understand exactly what's going on in your field, right? The technology has, has been around for many, many years, but one thing we're seeing is adoption of this technology is pretty limited. Lyndon earlier talked about, showed the USDA the survey results that shows the method to decide when to irrigate using sensor base is pretty low. This article published a couple of years ago, they talked about Michigan. Only 7.3% 7.3% irrigators are using the sensor technology to determine when to water and how much to water. One benefit of using the sensor is especially when you place the sensor at multiple depth of soil, you can really see whether you're putting enough water or maybe putting too much water. I believe this is one of the new planet Christmas tree. Their goal is to wedding on 24 inch depth. And we install the sensors at six inch deep. 12 inch which is green, 6 " red, and the gray is 24 inch deep. As you can see, the six inch depth sensor are decreasing over time. It looks like a step. This is what a lot of people, a lot of researchers claim. That is how the plants are using water, taking water. But after irrigation, this producers supply a little over 1 " application. And that really push the water that really wet the soil all the way down to 24 inch depth. The way we can see that after apply irrigation, the six inch depth sensor respond it up. And then a little later, the 12 inch depth sensor also a spike. It takes time, you know, move water in soil depending on the soil, right to the great line right here. It respond. It went up. It tells the irrigation water that push the water all the way down to 24. In this case, if the producers goal goal is only within the top 24 inch, then this is probably apply little more than what it should be, probably would recommend to reduce the irrigation application. One thing to point out is because this is a rear time, the sensor data, it really helps to better understand, better provide informed data and when to water has to water. Another thing really good is the it tells you what is the initial moisture content. For example, let's say this is a little over 1 " application. But depending on the initial moisture content before irrigation, The 16 step sensor was about maybe 9% but what if this is about 11% Or 12% might show a little different data. The sensor data really keep track of the moisture content and provide the rear time the data. Here's some other example of irrigation systems as you can see, clearly see when the irrigation start start later in the season around the July 20 to July 25, 25th. And you can see that when the irrigation start all the way down 2012 inch deep sensor spike. But 24 was somewhat steady, so this is a really good application for wedding on top, 24 inch deep. One thing I want to point out here is this is really good to monitor how rainfall has been impact that solar moisture. As you can see, after about 1 " rainfall, the moisture level all the way down to three feet responded 0.6 application, 610 inch rainfall on the one 1,500 in the rainfall didn't do much down in 1 ft. Depth sensor moisture, lot of rain probably got caught in the canopy. Maybe there's 500 or a little more might got into soil. Probably only really shallow depth of soil, but the water probably went into the soil. The sensor data is really helps to keep track of the moisture levels in the field. Something we've been working on is developing the low cost sensors because one of the barrier for technology adoption. We have seen the current commercial system are not easy to use and expensive. We've been working on trying to come up with a low cost sensor. Hope that more farmer can utilize this technology to make improve under irrigation management. The technology been demonstrated in a lot of different crops. High density apple production which we just started the project. Soybean, potato like corn drying, aspargus tomatoes. What we've been doing is going around the coast state to help farmers to improve irrigation management. Basically, what we have been doing is doing is on farm demonstration, right? We're not just going to provide technology to try, we're going to actually use their field, use the farmer's field and divide it into different irrigation strategy. And see whether the treatment irrigation scheduling really helps to improve water efficiency. Earlier Brendon's presentation covers the field here, Constantine, Sturgis Union Cities. But we also been doing the potato demonstration study, Irrigation Demonstration over in Mecosta and Lakeview. Here's some photos of install the sensors. You can also think this as a micro weather station because we track the rainfall, the temperature, humidity, leaf wetness. The reason we track the leaf wetness and temperature where we work with the plant pathologist Dr. Jamie Willbur and potato Dr. Martin Chilvers. And the fruit crop, a corn soy bean, Trying to better understand how improper irrigation management could increase risk of plant disease potential. Because that's why they're seeing if we don't properly water, in other words, if we just water once a week, you might actually increase the risk of plant disease. That's why we've been collaborating with the plant pathologist here. I ask you to better understand and how we can improve irrigation management to maximize water use efficiency, which is our goal, while minimizing the plant disease, which is pathologist their goal, right? Um, that's where we've been working at. Here's some data we collect from 2022. This is pretty simple demonstration. What we've been doing was comparing the grower base versus sensor base. There's one side of the field, we've been water based on the sensors recommendation. The other field is been irrigating what the whatever they've been doing. This was wet year. Irrigation frequency was about similar. The sensor base was only one application less than the grower base. But when we look at the yield, yields were really similar, 426 versus 425. It's hard to argues it is different, right? Same. But when we look at the irrigation water efficiency, the sensor base was higher because we didn't apply about 1.2 inch application compared to grow base. One of the goal was to, like I said earlier, trying to demonstrate how irrigation scheduling method and the tool can improve water efficiency. And this was very clearly, we can see it can help to improve water efficiency. One thing we found, they have dry corner. They planted potato potato. The yield was 192. This is clearly why almost 100% maybe over 90% of potato production in Michigan are under irrigation, because they can really need irrigation. We also collect the 2055 pounds of potato for five location. In each treatment we look at the size, look at the color distortationser internal qualities. But overall we did not see any statistical difference between those. Conclusion was the yield and the quality were same. But sensor based irrigation treatment area, we apply about 1.2 inch less water than the grower base. Our goal is more crop per drop, right? So how can we utilize the water maximum the crop production? We also been doing this demonstration study at the tomato field over Heart in Michigan. Same set up like what Brandon study. Treatment number one would be grower based treatment 2.32 would be 100% get schedule recommendation T three was less than treatment, four was more. But overall at the end, we didn't see an significant difference between the treatment in terms of marketable market of a tomato number and the tomato weights. But we apply about 30% less water and 100% irrigation scheduling when we compare to the farmers existing practice so far for the scheduling method can be applied for potato, corn, soybean, and the tomato. The sum up thing we've been working on was we get a lot of questions about, yeah, how many senses do I need for my field? If I got 40 acre, 30 acre, 20 acres, that's really good questions. And something we have done was looking at the variability within the field. This is one of the trial that we did here, South campus, here MSU, that we had a 14 sensors were strutically placed in the blue or chart. What we do, we looked at the moisture level, the differences at different depths. We had 6, ", 12, ", 24 ". And we compare the, the moisture level, The moisture data from all these sensors, all had about same similar type and the same irrigation method. And the crops were at the very similar stages, but as you can see, there are the difference between the sensor values. What we've done was we collect all the data, we find the medium values, and we find the difference between the sensor to the median values. Then we start extracting only the sensors that were within the 5% differences once we've done it and then we start color code. These three locations are most represents the soil condition for the six inch deep. It's a centimeter, six inch deep. And these five locations most location for the 12 in the sword depth. And these are for 24 inch de, one thing we found from the study was this one location are covering the 61224 inch depth. If this is the case the we have one sensor system, then we probably recommend putting here this location that will be the most represent location for the orchard. This is our first trial we plan to do for next year and the fruit crops, the center pivot, much larger fruit side. See if you can find some similar study. Yesterday, I think at the Michiana Irrigated Corn and Soybean meetings, someone asked about what really drives for this most represents the condition, I think. So type is definitely one major factor, but there are other factors. Crop stage organing matters and there are the management practical impact and the variability within the field. That's something we'll continue to explore. What is really contributing the variability of moisture level within the field, the GIS tool we utilize to predict the moisture variability within the field. I want to briefly talked about blue dye tests that we have done for a number of years. The blue dye is really effective methods to look at the water filtration in the field or look at the wetting zone from the drip tape. As you can see the tomato, the drip tape is about a couple inches from the surface. And you can see how much the wetting zone can be created from this one emitter. Here's another case study we've done and one of the commercial blue chart to evaluate whether the producer spend irrigating too much or not. The blue dye is another way to evaluate irrigation practice in addition to the sensor data. But blue dye typically takes good amount of time and labor. This is really good for demonstration. But I don't think it s be, it will be something that, that producers or will be doing multiple times to optimize the irrigation application amount. That's something to think about. The blue dye test to be done at Christmas tree this year was trying to understand how different amount of irrigation application really impact the most level in the soil. We had to drive tapes and we apply different irrigation mounds. In the middle of this photo shows we apply about quarter inch application, which pushed the water all the way down to, I'm sorry, 6 ". The quarter inch application, we the top six inch deep. When we apply a half inch application, the water blue die moves all the way down to 12 inch depth. Some demonstration we've done with the blue dye. You can look at the spacing, the required for different crops. Right here, 18 spacing, there will be about seven in the dry area. If you have a 12 inch spacing because the emitter much tighter, then you only have the three area here between the red zone. Here's comparing the surface irrigation to the subsurface irrigation. I believe we apply the same amount of water, but as you can see, if you got subsurface irrigation, you're more likely pushing water deeper than surface irrigation. But another thing we want to show is cap reaction, which is basically pulling water from the top. Even the sandy soil, you get about couple inch effect, the wetting zone, you can really see. Using the blue dye demonstration, we reopen and also working on the blue dye irrigation study. This is somewhat similar to the tomato and corn soybean, potato treatment. Number one, we follow the industry standards to be just supply 1 " per week. And the 1 " as experimental. 2.3 we apply based on the irrigation scheduling. The treatment two is 100% Recommendation number four was less application. We apply slightly less than 100% The left. This figure is showing the yield, total yield per plan. As you can see, the scheduling improve the yield when we compare to the growers just 1 " per week scheduling actually to improve the yield which may vary from year to year. But I think overall what we're seeing is really gets, it really helps to improve water efficiency and maintain the production and the quality, and some years you can actually improve the yield. The right figure shows 50 by per weight. So basically we collect. We randomly pick the 50 berry and we weigh them and you can see a higher weight. We see the size and the treatment number 2.3 This is fun. We worked with Dr. Josh Vander Weide from the Holticulture Department. This machine actually measured the frimness of a blueberry. All the collect data, the blueberries, we measure the firmness in firmness data. We didn't see significant difference between the treatment, but we did see in the yield and the 50 berry weight. Here's some new projects we've been working with the Dr. Emily Lavely over at the West Central Research Station. We have honey, crisp and Gala the variety. These are about two years old new trees. Newly planted trees. What we're comparing here is what we're demonstrating here is evapid transpiration base, which is the weather basics scheduling, the Sobs sensor, basic scheduling and canopy temperature based rig scheduling. You probably heard a lot about the weather based and so bolita but canopy is something, something new. But the reason we're doing the canopy temperature is somewhat, has been more demonstrated in old chart like apple in other state. That's why we include this Can temperature based in the study the Can temperature as we can schedule is basically looking at be temperature to calculate crop water stress index. Based on the index whether you're under stress or you're at the right level. This is new thing that we're trying here. Apple. We're pretty exciting to compare the treatment and hope that we can. Here's our E mail address and the irrigation website. We are currently updating our irrigation website to be more efficient. Please stay tuned. But you'll still be able to access the irrigation website to get all the information that Lyndon has been working on and I have been working on. I think that I really wanted to talk about some of future study. If you have any suggestions on what you'd like to see from us can be a topic or it can be some of the thing that you were interested in. One example is we start demonstrating the night time irrigation versus day time which was clearly driven by you. The farmers been interesting, is there any benefit of irrigating at night time and daytime? There at the textbooks are saying there might be benefits because of the last evaporation at night time. But we really like to see the true data right on the form, that's something we just started comparing, the daytime and nighttime. Maybe next year we can share some of the those data.