Mike Howell (00:08):
The Dirt with me, Mike Howell, an eKonomics podcast where I present the down and dirty agronomic science to help grow crops and bottom lines. Inspired by eKonomics.com farming’s go-to informational resource, I’m here to break down the latest crop nutrition research use and issues helping farmers make better business decisions through actionable insights. Let’s dig in.
(00:38)
Welcome back to the Dirt everyone. We’ve got another episode we’re bringing to you today, changing topics just a little bit from what we currently talk about. We usually talk about agronomy and soil fertility. Today we’ve got a guest here with us and we’re going to spend a few minutes talking about statistics in agriculture and why statistics are so important to help me go through that. We’ve got Dr. Darrin Dodds joining us today. Darrin, welcome to the Dirt.
Dr. Darrin Dodds (01:01):
Well, thank you very much. Mike, I appreciate you having me.
Mike Howell (01:04):
Darrin and I go way back. We both worked at Mississippi State for a number of years together. Darrin was a cotton specialist about the same time I was working with peanuts here in Mississippi and Darrin was actually on my research committee. At one time we have quite a history and still work together when we get a chance. Darrin, if you would introduce yourself to our listeners and tell them what you’re doing now.
Dr. Darrin Dodds (01:24):
Yeah. I’d be glad to. My name is Darrin Dodds. I’m the head of the Department of Plant and Soil Sciences here at Mississippi State. I’ve been doing that a little over four years now, but for the previous, I don’t know, Mike, 13, 14 years before that I served as the extension cotton agronomist here at Mississippi State. So much like extension any other state, my job was to be in the truck every day or on the phone every day and working with growers and retail and basic suppliers and all this other stuff and basically to help our growers just become more productive, more profitable. I don’t know where time goes, Mike, but I was thinking about this the other day. This is my 21st year at Mississippi State. I can’t believe it’s been that long, but it has been.
Mike Howell (02:05):
Yeah, time definitely flies by. It’s been quite a road we’ve went down. I just turned 50 this past year and it seems like just yesterday I was in school there at Mississippi State, so time flies. Darrin, today we wanted to spend a few minutes talking about statistics and the importance of statistics and I travel around and I meet with a lot of growers and even retailers and manufacturers and that type of stuff and it’s always said, I hear a lot… Well, I did a side by side on my farm or we conducted a side by side and this one outperformed the other one by 20 bushels and we’re just going to do that wall to wall next year. There is some benefit to doing side by side comparisons, but side by side is just like flipping a coin and I’m sure we’ll get into that here in just a minute, but let’s start this off and just give a few definitions and get some stuff out of the way. First off, when we talk about statistics, what exactly is statistics?
Dr. Darrin Dodds (02:57):
Yeah. Well, let me say this. If there are any statisticians listening, please forgive me if I butcher any part of your side. You got a guy that was born and raised in Illinois and worked cotton in Mississippi and plant soil sciences talking to you about statistics. So let’s put that into perspective real quick.
Mike Howell (03:12):
Well, I know a lot about statistics cause you marked up all of my statistics when I was trying to write some papers. You definitely know a little bit about it.
Dr. Darrin Dodds (03:20):
Well, I just done unto you as was done unto me.
Mike Howell (03:22):
There we go.
Dr. Darrin Dodds (03:23):
But at the end of the day, when you think about statistics, you are a true statistician, somebody that’s truly a hardcore trained statistician could make something as complex or really as simple as they wanted to do that. But at the end of the day, I mean really statistics is nothing more than, I don’t know if you want to call it a practize or if you want to call it a science or really however you want to term that, but it’s really nothing more than collecting data and then doing some statistics for lack of a better term on that data. And what you’re trying to do is draw some reliable conclusions.
(03:59)
In a real simple basic nutshell, that’s what it is and in agriculture, Mike and what we do, if we’re being honest with ourselves, we probably just barely even scratch the surface of what a real hardcore statistician could do with the data set. So really it’s just statistics in a nutshell for what we do is collecting the data, analyzing the data, and trying to draw some reliable conclusions that we can bring to our growers or our retail partners or our basic suppliers or whoever it is.
Mike Howell (04:28):
That’s right, Darrin. And we were talking before we started recording here about how long it had been since we had been in a stats class, but one thing I remember from one of the very first statistics class I took, they started talking about probability and that always seemed to be where we started. So let’s get another definition out of the way and tell us what probability is and how that works into statistics and why that’s important for a grower to understand that.
Dr. Darrin Dodds (04:52):
At the most basic level, probability is nothing more than how likely something is to happen and it essentially lets us and I’ll use us collectively, statisticians us that play in that area or whatever. It lets us calculate some results from a random experiment is essentially what it does. So we take these observations whether it’s yield or some variable of soil fertility or whatever, and we measure those and we analyze the data and then we end up with basically a probability value. We see P-values on all these things and that’s essentially a probability value is what that is and that’s calculated from the data collected and it basically lets you determine if the differences that we see did not basically occur to sheer chance.
(05:38)
Now you mentioned flipping a coin. A lot of times flipping a coin is 50/50, a lot of that sheer chance, I don’t know how you really have a lot of strategy to flipping a coin. But the probability in those P-values really help give us an idea of how whatever variable we’re looking at occurred because of sheer chance or not.
Mike Howell (05:55):
That’s right, Darrin, and there’s different P-values that we look at if we’re talking about the medical profession, a lot of times they’ll have P-values. They want something 99% confidence that something’s going to work or even higher sometimes, and when I first started looking at statistics, they wanted everything set at 95% confidence limits and now we stretch that out a little bit. I’m seeing more and more people reporting 90% confidence. Talk a little bit more about that and what those numbers mean.
Dr. Darrin Dodds (06:21):
A lot of times those significance levels, if you want to call them that, that’s really at the discretion of whoever’s doing the work and really what the field is. In agriculture, you mentioned the medical profession. I would suggest we’ve probably got a little bit more wiggle room if we make a mistake than somebody does in the medical profession. I think we all understand that our growers’ livelihoods depend on us giving them good information, but by the same token, we’re going to make a mistake. It doesn’t matter what we do, mistakes are going to happen, but in the medical profession, if you make a mistake that’s very likely someone’s life, they have much tighter significance levels for obvious reasons. I would say day in, day out, a lot of those significance levels, a lot of times they’re really at the sole discretion of who’s doing the work. And still a lot of the work that I do in cotton, whether it’s variety evaluations or plant growth regulators or fertility or irrigation or harvest aids, a lot of times I still use a 95% significance level and it’s not uncommon.
(07:24)
I agree with you. I think you’re starting to see a lot more 90 creep into that and really when you see that manipulation if you will or you see that adjustment of that significance level, basically what you’re doing is just you’re measuring how much of the effects that we see are due to sheer chance or not, and you’re basically just loosening up the restrictions for lack of a better term just a little bit. If you and I were looking at some data, Mike and you said, “Hey, I got a 90% significance level here,” and these treatments are different and you’ve got 95 and they may not separate at 95. Basically my estimates or my numbers in that situation are going to be maybe a little bit more conservative than yours are. The higher you go with those significance level, the more conservative you’re getting when you’re saying, Hey, one treatment is truly different than another.
Mike Howell (08:10):
I don’t remember a whole lot about my statistics classes, but one thing I do remember, they also taught us that it doesn’t matter what number you pick for your confidence interval, as long as you determine that before you run any numbers, you can pick a 99 or a 95 or a 90, but don’t start changing it after you run the numbers just to make the separation work. That’s something you need to determine beforehand and stick with that all the way through.
Dr. Darrin Dodds (08:32):
What you don’t want to do is say hey, I’m going to look at these data and I want to see if treatment one is different than treatment two and I want to be 95% certain they’re different and then get you numbers back and see that the significance level is 0.08 or 0.09 and then start adjusting. You don’t want to adjust based on your significance level. You want to set that significance level from the get go and then basically your date or your date after that.
Mike Howell (08:57):
That’s right. We started this conversation talking about doing side-by-side trials and there’s definitely a place for those in agriculture, but what statistics can we get from a grower going out and doing a side-by-side, planting one variety on one half of the field and one half on the other side of the field?
Dr. Darrin Dodds (09:13):
Let me say this and I maybe should have prefaced our whole conversation with this. Like you, I’m confident that side by side comparisons have some validity or they have some utility in our world, but I would also say this when it comes to any statistics, I don’t care if they’re your statistics, my statistics whoever. The thing to always remember about statistics, trash in is trash out. In other words, if the numbers you put in run statistics on are not good, your outcome is not good. What I’m really saying is whatever numbers you look at, you want to make sure that they are from a reliable reputable source and that they were collected in the proper manner. Because if something is amiss and all that and the numbers that go into an analysis are not right, then the numbers that come out of it are not going to be right. I think it’s important to make that distinction.
(10:02)
The thing about side by sides and we see them all the time, we see them with highs or varieties. We see them with fertility, we see them here, there in yonder. In my opinion, they’re really good to see a visual difference. You could go do some nitrogen work in corn and probably see a visual difference and if each half of the field that you split was completely uniform would probably have some level of utility, but you live in the same world. I live in a lot of these fields that you and I walk in the soil, texture changes from one end to the other. How that field drains changes from one end to the other, whether it’s surface drainage, internal drainage, whatever. Oftentimes fertility levels in that field change dramatically depending on where you are.
(10:51)
Topography changes. There are so many things that can be different from one half of a field to another. While I think they’re good to see visual differences in things, I tend to put not a tremendous amount of stock in side by side when it comes to truly making large decisions. For the money I’m going to spend, I want to make sure the data that I’m looking at are very solid, very reputable data, and I would encourage folks to maybe look at whatever replicated data. It could be strip trial data. I really like strip trial data as long as the strips are replicated in a field.
Mike Howell (11:26):
Darrin, you finally got to the one word that I was really hoping to pull out of you the whole time and you talked about replication. Tell us what replication is and why as researchers, we want to replicate studies in a field and how we can replicate studies.
Dr. Darrin Dodds (11:40):
Replication really at the very basic level is just having a given treatment in an area multiple times. So let’s just say we were looking at nitrogen rates in corn and you were going to look at whatever, 100 pounds, 150 pounds, 200 pounds, 250, 300, replication would be having that 100 pound rate multiple times in a given area or 150 pound rate multiple times within a given area. The reason I think, at least in my opinion that matters is because the more replications you have, the more powerful it makes your data.
(12:17)
Now you have to be careful with replication, and we hit on it a few minutes ago. Not a lot of the fields that you’re in or I’m in are the same from one end to the other. So if I go back to that corn nitrogen example, if you have some soil textures in a field of corn that change. And let’s just say you’re a silt loam on one part of the field and you put out 150 pounds of nitrogen and it moves into a clay loam on the other end of the field, that 150 pounds of nitrogen is probably not going to react the same way on a clay loam as it does a silt loan.
(12:50)
If you had two replications of that 100 or 150 pound rate on a silt loam and you had two more replications on a clay loam, you start introducing variability that’s going to muddy the waters, if you will, of your numbers. Replication is really important, but when you do a replication of any treatment, you want it to be under as close as you can, the exact same circumstances for how many ever replicates that you have.
Mike Howell (13:17):
That’s right, and you brought in another term the variability, and as researchers, we want to do everything we can to take out that variability and that’s where the replication, and another word that comes in there is randomization. Talk a little bit about why we randomize these treatments within replications.
Dr. Darrin Dodds (13:34):
The randomization thing that really goes back to that variability as well. When we do small plot research, and I know if you really want to open up a can of worms, you start talking to a room full of growers about small plot research and you’ll get all manner of opinion from, they love it to they hate it, to they think it’s the greatest thing ever, to they think it’s the stupidest thing ever. You tend to get a lot of funny looks when you talk about small plot research. But the whole goal of randomization and replication really, the goal of the two of those together is to make sure that your data are reliable. If we use that nitrogen example, you don’t want an area that’s got 150 pounds of nitrogen and then directly behind it have the same thing and directly behind it have the same thing because there may be something happen in that small area.
(14:21)
It could be you could have a border effect if you’re up against a different crop. You could have a soil texture change, you could have some differences in water on that one area or another. So really when you start randomizing, you want those rates, but you want them within the confines of the area. You’re doing the work in different places among them, and really what you’re trying to capture is how much inherent variability is there in a nutshell. There may be some variability that gets introduced. Again, if you’re on different soil textures or different water holding capacity and you try to minimize that as much as you can, but the reason that you want to randomize is to capture how much ever natural variation there’s going to be in whatever number you’re looking at. It could be yield, it could be plant high, it could be whatever.
Mike Howell (15:05):
Anything we can do to control that variability is going to help control what’s going into these statistics. You mentioned trash in and trash out. We’re cleaning up that trash if we randomize these and replicate them like we’re supposed to.
Dr. Darrin Dodds (15:17):
Yeah, no, that’s right. Probably the thing that makes agriculture so much fun to work in but also makes it so complicated is that no two days or no two things are really going to be the same. You can go to two different fields, turn row to turn row and oftentimes have differences. And really we’re trying to capture with randomization and replication, we’re trying to capture some of that natural variation and put a value to it and then figure out can we repeat that on a different farm that’s under similar circumstances is what we’re trying to do. And you could think about hybrids or varieties. If I come in and tell you, “Hey Mike, I got this hybrid that’s a rockstar hybrid and I’ve looked at it on a silt loam soil and it’s going to walk the dog and do all these things.” I want you to be able to go put on your silt loam soil and do that.
(16:03)
And if I haven’t done my homework ahead of time with respect to the data collection and analysis and I don’t do my randomization correctly and my replication correctly, and I tell you that, and then you go plant this, and if how many ever hybrids you’re planting that comes in dead last, I’ve suddenly lost whatever credibility that I have with you. So it behooves us as research folks or extension folks or industry folks or whoever to give the best data that we can because really at the end of the day, you’re given that farmer, that grower, that producer, you’re giving them an opinion on what they should do, but at the end of the day, it’s their livelihood on the line for that. And for me in my job at the university, I take that super seriously because if somebody calls me and asks my opinion, it’s because they believe I’m going to give them the right answer. All this stuff we’re talking about goes into help giving them the right answer, and that’s why I think it’s so important for folks to understand it.
Mike Howell (16:54):
That’s exactly right, Darrin. If we’re not giving the right answers when people call us, they’re not going to be in business to keep calling us very often and then we’re not going to have a job. It’s really important that we make sure we get it right and know what we’re talking about when we go talk to growers and make recommendations for what works for those. Darrin, I’ve been around a long time and back when we were both working with Extension, we had a grower meeting and one of our counties down here in South Mississippi and everybody was talking about this new product. Everybody was saying it was the greatest thing ever. And I showed some data, I showed two graphs, put them up on the screen, one of them, the bars, there was a lot of difference. I mean you could look at it and see huge differences on those graphs.
(17:36)
And then I showed another one and it looked like it was straight across and there was no statistical difference on either one of those. And I got to asking the growers, what’s going on with this? Why can you see this big difference on one and not the other? And after about 15 minutes, they finally figured it out looking at both of those graphs. One of the graphs had the scale changed on one side it went zero to 100 and the other went from 95 to 100. It made it look like there was a big difference when really there was very little difference there. Talk a little bit about how graphs should be set up and what growers need to look at when they’re looking at graphs that get published all over the place.
Dr. Darrin Dodds (18:13):
Yeah. I’ll share a story with you too. I had a similar experience. I was doing some work and looking at some products and cotton and got the data, ran the stats on it, sent it to the folks I was working with. In my data there were no differences in terms of yield. Well, yield pays the bills, so there’s no differences. Well, this person showed up to my office and had graphed up my own data and they had manipulated the scale and showed these bars that were different and started telling me while my data were wrong, and I said, “Look, I see your graph.” I’m like, “But there’s one thing that you’re not taking into account in this whole thing.” I said, “That’s at P-value right there.” And that P-value, I think for that date was 0.5, which basically means there’s a 50% probability that the differences that were there were due to sheer chance.
(19:04)
I said, “I’m not willing to bet my grower’s livelihood on a 0.5 P-value.” I’ll never forget if they respond like, “Well, you just don’t understand how this particular product works.” And I said, “Well, I understand how farm eKonomics works and if you lose more money than you make, you’re out of business. And again, I’m not willing to put my reputation on the line with that,” but when you brought that up about the graph, I still chuckle when that person showed up to my office and had manipulated my own bars on me.
(19:31)
Now on graphs, the biggest thing I would say is I think there’s a time you can manipulate that scale and there’s a time where you may not need to. I think about something like corn yield. If you’re talking about corn yield and you’ve got some hybrids topping out at 300 bushels or 290 bushels and you go from zero to 350, those differences may not be super apparent just because of the limitations of the screen you’re looking at or whatever. I’m not necessarily against manipulating that scale to show from 150 to 350 or 200 to 350. I don’t necessarily have a problem with that, but what I would really encourage people to look at regardless of the scale on the graph is there’s some numbers on there they probably ought to see if they’re showing, and one of those is that P-value that we talked about.
(20:17)
If somebody pops a graph up and you see a P-value and it says P equals 0.0002, you have a lot of confidence in that data that those differences they’re showing you are probably realistic differences. But if somebody pops up a graph and it’s got these bars and these huge differences there and they don’t show you a P-value at all, I would start asking some questions about that. Because really that P-value, again, that’s that probability value that’s telling you how likely that the differences that you’re seeing are there because of sheer chance.
(20:51)
And basically the lower, if you will, of that P-value is the more reliable those data are. If you start seeing a P-value of 0.1 or 0.05 or 0.02, make sure you look at that. If you start seeing the 0.25 or a 0.5 or a 0.75, that’s telling you that those data really are less and less reliable all the time. But the biggest thing, look for that P-value number and just see what it says. And if it looks like it’s a 0.002 or something, I’d have a lot of confidence in that data. And then there’s probably going to be a number there called an LSD or there’s going to be some letters on those bars or something like that. If you see letters on those bars, if you see an A and a B, it’s telling you that 90 or 95, you have your significance level set at percent of the time that you can tell the difference in those two treatments.
(21:42)
I sometimes see scale manipulated, and sometimes it may have a little bit of nefarious intent, but sometimes folks just do it because the differences come out better and they’ll show you those P-values and those LSDs. In those cases, I really don’t have a big issue with it. I just think you need to go into seeing those graphs, knowing what to look for when you see them.
Mike Howell (22:01):
That’s right, and that’s what we’re hoping to get across here today. Just let everybody understand a little bit more about what they’re looking at on these graphs and what the statistics mean behind it. Darrin, I think one thing that we’ve probably got across to our listeners today that neither you are, I or expert statisticians, we know just enough to be dangerous, but I hope we got enough information across that somebody learned something out of this today and can help make more informed decisions on their farm. Darrin, before we wrap this segment up, is there anything else that you want to touch on about statistics?
Dr. Darrin Dodds (22:32):
In my career as an extension specialists, a lot of times when I would walk into a room of growers and start talking about small plot research, I’ve heard the comment more times than I can probably count of less small plot research that doesn’t really apply to my farm. And growers really like to see split fields and they like to see strip trials, but I would tell you don’t necessarily discount small plot research because it’s small plot research. It’s not uncommon to see yields be a little bit higher in small plot research because it’s a super controlled environment
Mike Howell (23:02):
And there’s a multiplication effect in there as well. You’re dealing with a very small area, and when you multiply it out to get those yields, you’re naturally going to have some higher yields a lot of times.
Dr. Darrin Dodds (23:11):
That’s exactly right. But I would encourage you don’t necessarily get lost, so to speak, in just the absolute yield number. Look at what variety or hybrid perform best under circumstances that are similar to the circumstances in which you farm. And don’t think, because Mike Howell has some data saying variety one’s going to yield whatever, 100 bushels of beans to the acre. His situation’s going to be different than yours. But if he has some data that says variety one yield is the best yielder on a silt loam, clay loam, and a sandy loam, basically what that’s telling you is that’s a very versatile variety that goes across a lot of different soil textures that you can have confidence on your farm to go do that, and it’ll be one of the highest performing varieties. Understand that what the yields you make on your farm may be different than those that Mike sees in his work. That’s the biggest thing.
Mike Howell (24:00):
Well, Darrin, we appreciate you being on today and giving us a little insight into statistics and how statistics can benefit these growers. Growers, we appreciate you tuning in and listening to us today. And if you’ve been listening for very long, it’s time for our second segment today.
(24:17)
In our second segment, we start talking about a famous person in agronomy. We’re going to change that up just a little bit today. I’ve actually got several people that we want to talk about all at one time, and Darrin’s agreed to help me talk about these people. Darrin, without these people, it’d be very unlikely that you would have the job that you have today.
(24:35)
So let’s dig in and we’ll start off talking about John Morill. Now, John was from Vermont. He served in the US House from 1855 to 1867, then moved over to the Senate where he served from 1867 to 1898. Earlier in his life, John was a merchant in Vermont, and he invested the profits from his stores back into farms, banks, railroads, real estate, and built his fortune. While in Congress, he was the chair of the House Ways and Means Committee, and one of his feature legislations was the Moral Land Grant Act. Darrin, tell our listeners what the Moral Land Grant Act is and why that’s so important.
Dr. Darrin Dodds (25:13):
The Moral Act created land grant colleges in every US state, and the first one was in 1862, and then there was another one in 1890, and then there was another one in 1994 that established the land grant college or university system with the goal to teach agriculture, mechanical arts. And think about our country at the time that this came about, we were very much agrarian society, a very rural country at those times. So the folks would come to colleges or universities because more than likely they were going to go back and farm or do mechanical type of work. And it basically provided a way to provide higher education to those people. And if you think about the land-grant University today, agriculture is at them, engineering’s at them, and I would argue, maybe I’m biased, Mike, but I would argue that agriculture is every bit as important today as it was in 1862 when that first act was created.
Mike Howell (26:12):
I’ll take it one step further, Darrin, and I think it’s more important. We’ve got fewer and fewer farmers trying to feed more and more people, so we’ve got to be more productive. And without the land grant institutions, we don’t know which way to go and what to do. And you said the land grant institutions were to educate about ag agriculture. It’d be hard to go in and teach a class about agriculture if you didn’t have some research and know what to do. That’s where William Henry Hatch came in. He was a congressman from Missouri that served from 1879 to 1895. He was a chairman of the Senate Ag Committee and he introduced the Hatch Act in 1887. Darrin, tell us what the Hatch Act did.
Dr. Darrin Dodds (26:51):
It helped establish agricultural experiment stations in conjunction with those land grant universities in our country. It established a framework to provide funding and investment in the future of our country and help us develop and train personnel. So if you think about our state, Mike, our experiment station is the Mississippi Agricultural and Forestry Experiment Station, and that was created by out of the Hatch Act of 1887. And all US states have an agricultural experiment of some kind. Like I think about Arkansas’s, I just remember because their old email address UAES, university of Arkansas Experiment Station, and there’s several others, but it basically established that.
(27:34)
And if you think about the incarnation of that today, I think about our institution because that’s one I’m most familiar with. I know several others are very similar. We have a research facility for agriculture here on campus. We have one 30 minutes down the road. We have several all over our state, one near you on the coast, Mike, one in the Delta Central Mississippi, up in North Mississippi, and all that was born out of that Hatch Act of 1887.
(27:59)
Many other institutions the same way. I mean Tennessee is a good example, university of Tennessee’s in Knoxville, but they got an experiment station not terribly far from Mississippi State up in Jackson, Tennessee that was also created out of that Hatch Act. It helped create, for a lack of a better term, the university research farms that a lot of us do our work on.
Mike Howell (28:17):
That’s right. And not every state has as many research farms as we do here in Mississippi. I don’t remember exactly how many we have in Mississippi, 16 or 17, something like that.
Dr. Darrin Dodds (28:27):
Yeah. It’s a pretty good number. And you think about it though, the goal of a lot of that is to try to capture as many different environments in our state as you can. And when you were doing peanuts, Mike, I still think about those days you were working a lot on the Gulf Coast. Well, that Gulf Coast growing environment is vastly different than the environment in Northeast Mississippi, and both of those environments are vastly different than the Mississippi Delta. And even within those regions, you have other little areas. We may have one of our farms, I think 30 minutes from here is predominantly a clay loam soil texture farm, and there’s value to that because we have a very substantial farming presence in that area. It gives us a way to go do some work in those areas that benefit those growers.
Mike Howell (29:12):
So Darrin, we talked about the Moral Act that it set up the Land-Grant universities and the Hatch Act that set up the experiment stations that gets the students at the universities educated, but that still didn’t get the word out to the people in the local population. At that time, not everybody was going to college and furthering their education. The next two people I want to talk about are Hoax Smith and Frank Lever. They came up with the Smith-Lever Act. Now, Hoax Smith was an attorney and a politician, a newspaper owner, resided in the state of Georgia. He was the 58th governor of the state of Georgia. He served as Secretary of State for a brief time, and he was a US Senator from 1911 to 1920.
(29:53)
Now, Frank Lever was a US representative from South Carolina from 1913 to 1919. He was a member of the education committee and the AG Committees. And Frank Lever was also instrumental in setting up the Cotton Futures Act of 1914 and the Federal Farm Loan Act of 1916, which created the Farm Credit Administration that all of our growers are well familiar with today. Darrin, if you would talk a little bit about the Smith-Lever Act that we’re talking about today.
Dr. Darrin Dodds (30:22):
Smith-Lever, that act was in 1914, and it established the system of cooperative extension services that again are connected to Land Grant University. I think back to your and I history, Mike, when I first got done with school, I was hired as the extension cotton agronomist here at Mississippi State. My appointment was 100% extension. My job was to go out and educate and help our producers, retailers, whatever, be as productive as they could be for the least amount of money that we could do that. That act, really the goal all those years ago was for it to inform citizens about current developments and things like agriculture, home eKonomics, public policy and government, leadership, 4-H, economic development, even coastal issues to some degree.
(31:13)
That Smith-Lever Act of 1914 set the stage for extension at our land grant universities. We have two land grant universities and basically two set of extension folks here in Mississippi, one here at Mississippi State University, the other one at Alcorn State University. There are a few other states that also have two land grants in there as well we do here. But basically established anything from the county agent, whether they be 4-H or they’d be ag and natural resources or whatever, all the way up to faculty at universities like I used to be that covered cotton or soybeans or corn or livestock or whatever that may be.
Mike Howell (31:50):
That completed the circle and made sure that we had a place to do the research. We had somebody educating students, and we were getting the information back out to the growers where it could benefit them.
(32:01)
Listeners, I hope you’ve enjoyed the time today going through this brief history lesson and realise the importance that these men have in agriculture, not only back then, but in today’s world as well. Darrin, we appreciate you joining us today. We couldn’t bring these episodes to our listeners without guests like you coming on and helping us, and listeners, we’re doing all of this for you, so any feedback that you want to give us, please do that. And for any more information on the topics we covered today, you can always go to our website. That’s nutrien-ekonomics.com. Until next time, this has been Mike Howell with the Dirt.
