1 00:00:01,480 --> 00:00:04,280 Speaker 1: Welcome to Stuff you should know, a production of I 2 00:00:04,360 --> 00:00:13,760 Speaker 1: Heart Radio. Hey, I'm welcome to the podcast. I'm Josh Clark, 3 00:00:13,840 --> 00:00:17,520 Speaker 1: and there's Charles W Chuck Bryant. Jerry's with us as well, 4 00:00:18,360 --> 00:00:21,400 Speaker 1: and we all have a nice They're a hay sticking 5 00:00:21,440 --> 00:00:27,360 Speaker 1: out of our teeth. Got a hat on. It's a derby. 6 00:00:27,760 --> 00:00:30,920 Speaker 1: Doesn't quite fit right, but is it still a hat? 7 00:00:31,520 --> 00:00:35,240 Speaker 1: And I'm wearing overalls with nothing underneath and no shoes 8 00:00:35,240 --> 00:00:42,239 Speaker 1: and socks. Have I told you my uh Olan Mills story? No? 9 00:00:44,080 --> 00:00:47,479 Speaker 1: I can't not. When I lost my two front teeth, 10 00:00:48,240 --> 00:00:52,440 Speaker 1: my mom promptly took me to Olan Mills. And there 11 00:00:52,560 --> 00:00:54,640 Speaker 1: is a picture in existence. I'll try and find it 12 00:00:54,680 --> 00:00:59,360 Speaker 1: and put it on my Instagram um of me in 13 00:00:59,440 --> 00:01:01,840 Speaker 1: front of a like a lazy river scene or a 14 00:01:01,920 --> 00:01:07,680 Speaker 1: lake or something, with overall shorts and you know, cut 15 00:01:07,720 --> 00:01:11,680 Speaker 1: off overalls, I guess, nothing else on, no shirt, no shoes, 16 00:01:12,720 --> 00:01:15,319 Speaker 1: and like, uh, I love to see it. I feel 17 00:01:15,360 --> 00:01:18,960 Speaker 1: like I had like a cane pole and maybe a straw. Hateez? 18 00:01:19,560 --> 00:01:22,399 Speaker 1: Did they did they have the the the engraving and 19 00:01:22,440 --> 00:01:26,800 Speaker 1: stone mountain and as the background? No, thank goodness, I 20 00:01:26,920 --> 00:01:30,399 Speaker 1: just it was straight up cosplay it's very embarrassing, but 21 00:01:30,880 --> 00:01:33,280 Speaker 1: I'll see if I can find that and throw it 22 00:01:33,360 --> 00:01:37,959 Speaker 1: up on Chuck the Podcaster Instagram. You should, Chuck. I 23 00:01:38,000 --> 00:01:43,280 Speaker 1: think that would really garner some likes, you know, putting 24 00:01:43,280 --> 00:01:45,000 Speaker 1: a few old pictures up there every now and then. 25 00:01:45,040 --> 00:01:49,520 Speaker 1: It's fun. That's very cute. Um. So, I guess you're saying, 26 00:01:49,560 --> 00:01:52,640 Speaker 1: you're we're talking about wearing nothing but overalls because we're 27 00:01:52,640 --> 00:01:55,200 Speaker 1: talking about farming, right, Yeah, I mean that that picture 28 00:01:55,240 --> 00:01:57,840 Speaker 1: Basically I learned all I needed to know about farming 29 00:01:57,880 --> 00:02:02,440 Speaker 1: that day, which is I'm not cut out for it. 30 00:02:02,480 --> 00:02:04,600 Speaker 1: I'm not cut out for it. I am cut out 31 00:02:04,640 --> 00:02:09,880 Speaker 1: to be photographed for money. Whoa, Um, that was me 32 00:02:09,960 --> 00:02:12,919 Speaker 1: doing my impression of you. Yeah. I didn't get paid. 33 00:02:13,520 --> 00:02:15,840 Speaker 1: Uh yeah, I guess that's the opposite. Happened Your mom 34 00:02:15,840 --> 00:02:19,360 Speaker 1: paid somebody take pictures of you regardless, Um, none of 35 00:02:19,400 --> 00:02:22,880 Speaker 1: that really happens in farming. Maybe people running around with 36 00:02:22,919 --> 00:02:26,560 Speaker 1: overalls on probably has something underneath the overalls. But for 37 00:02:26,600 --> 00:02:30,200 Speaker 1: the most part, this is a gross misconception of of 38 00:02:30,280 --> 00:02:33,680 Speaker 1: what farming is. Especially now that I've done some research 39 00:02:33,720 --> 00:02:37,360 Speaker 1: on the current state of farming, We're pretty far from 40 00:02:37,440 --> 00:02:40,480 Speaker 1: the whole idea of people running around with hey in 41 00:02:40,520 --> 00:02:46,200 Speaker 1: their and their mouths and wearing overalls and nothing. But sure, 42 00:02:46,280 --> 00:02:48,040 Speaker 1: I mean, well, it depends on who you are. Like, 43 00:02:48,760 --> 00:02:50,639 Speaker 1: have you ever seen and I've talked about it before 44 00:02:50,680 --> 00:02:55,320 Speaker 1: the documentary The Biggest Little Farm. Yeah, I think you 45 00:02:55,360 --> 00:02:57,320 Speaker 1: have talked about that. I don't remember what episode it 46 00:02:57,360 --> 00:02:59,600 Speaker 1: was in there, And we'll get to that. That's coming 47 00:02:59,639 --> 00:03:02,160 Speaker 1: up on the the last part of this episode about 48 00:03:02,200 --> 00:03:06,120 Speaker 1: agro ecology, but that those are people that are adherents 49 00:03:06,160 --> 00:03:09,320 Speaker 1: of bringing it back to what farming used to be, 50 00:03:09,400 --> 00:03:14,519 Speaker 1: which was ecologically sound, harmonious with nature, that kind of thing, right, 51 00:03:14,720 --> 00:03:17,519 Speaker 1: which makes a tremendous amount of sense. But we'll see 52 00:03:17,560 --> 00:03:21,440 Speaker 1: if that's even possible in the future, because here's the thing. 53 00:03:22,400 --> 00:03:28,919 Speaker 1: There's a huge um boom in demand for agriculture that 54 00:03:29,000 --> 00:03:31,079 Speaker 1: we are on the presiposs of it. Actually, I guess 55 00:03:31,080 --> 00:03:33,080 Speaker 1: you could make a pretty good case that we're in 56 00:03:33,120 --> 00:03:35,760 Speaker 1: the midst of it right now. Um, by the year 57 00:03:37,320 --> 00:03:41,720 Speaker 1: there's a predicted somewhere around nine to ten billion. It's 58 00:03:41,720 --> 00:03:43,720 Speaker 1: a pretty big gap. But let's say nine to ten 59 00:03:43,840 --> 00:03:47,880 Speaker 1: billion people are expected to be running around on planet Earth, right, 60 00:03:48,640 --> 00:03:51,080 Speaker 1: and all those people are going to need to be fed. 61 00:03:51,960 --> 00:03:55,800 Speaker 1: But the thing is is we're not exactly sure how 62 00:03:56,040 --> 00:04:00,880 Speaker 1: we're going to reach that increased demand. And because there 63 00:04:00,960 --> 00:04:04,200 Speaker 1: is a study by the Food and Agriculture Organization of 64 00:04:04,200 --> 00:04:07,800 Speaker 1: the u M that said that we're basically going to 65 00:04:07,840 --> 00:04:11,440 Speaker 1: have to increase food production UM compared to two thousand 66 00:04:11,480 --> 00:04:15,440 Speaker 1: seven levels by seventy to feed all those people. And 67 00:04:15,600 --> 00:04:17,560 Speaker 1: that's that's if we hit the low and the nine 68 00:04:17,600 --> 00:04:23,880 Speaker 1: billion UM in population by Yeah, and you mentioned agriculture, Um, 69 00:04:23,920 --> 00:04:27,800 Speaker 1: we're also talking meat in protein because not only is 70 00:04:27,839 --> 00:04:32,320 Speaker 1: the population increasing, but income is on the rise worldwide, 71 00:04:32,360 --> 00:04:37,200 Speaker 1: especially in developing countries. And as you are developing country 72 00:04:37,200 --> 00:04:40,440 Speaker 1: that gets a little more money in your pocket as individuals, 73 00:04:40,920 --> 00:04:44,240 Speaker 1: you want to eat more meat in protein. So um, 74 00:04:44,279 --> 00:04:47,760 Speaker 1: I think they said global meat consumption is going to 75 00:04:47,800 --> 00:04:51,920 Speaker 1: arise by about sevents. Well, yeah, and when you're talking 76 00:04:51,960 --> 00:04:57,039 Speaker 1: about meat um consumption as far as agriculture goes, like, 77 00:04:57,080 --> 00:05:01,000 Speaker 1: you're talking about agriculture times too, because not only do 78 00:05:01,040 --> 00:05:03,800 Speaker 1: you have to use all those inputs to grow the 79 00:05:04,560 --> 00:05:08,800 Speaker 1: cattle themselves, the livestock themselves, so um you can eat them, 80 00:05:08,839 --> 00:05:11,039 Speaker 1: you have to feed them to get them ready to 81 00:05:11,080 --> 00:05:13,159 Speaker 1: be eaten, right, So you have to grow the food 82 00:05:13,560 --> 00:05:15,960 Speaker 1: to feed to the cattle that you're going to grow 83 00:05:16,080 --> 00:05:18,640 Speaker 1: that you're going to eventually eat. So there's a lot 84 00:05:18,680 --> 00:05:22,280 Speaker 1: of agriculture that's going to have to be going on. Um. 85 00:05:22,320 --> 00:05:26,120 Speaker 1: But here's the thing. Agriculture has handled this before. There 86 00:05:26,200 --> 00:05:30,200 Speaker 1: was a time you and I have talked about countless times, um, 87 00:05:30,240 --> 00:05:34,600 Speaker 1: the Green Revolution back in the early mid twentieth century. 88 00:05:34,600 --> 00:05:37,240 Speaker 1: There are a lot of people saying, UM, we're not 89 00:05:37,360 --> 00:05:39,440 Speaker 1: exactly sure that agriculture is going to be able to 90 00:05:39,520 --> 00:05:43,640 Speaker 1: keep pace with the growing population. And we think probably 91 00:05:43,640 --> 00:05:46,560 Speaker 1: about a billion people are going to starve. And that 92 00:05:47,000 --> 00:05:50,040 Speaker 1: may have happened, we will never know, but we know 93 00:05:50,120 --> 00:05:53,840 Speaker 1: that it didn't happen thanks to the green revolution that 94 00:05:53,960 --> 00:05:58,840 Speaker 1: was hastened by scientists like Norman Borlog. Yeah, we've been 95 00:05:58,839 --> 00:06:01,640 Speaker 1: talking about that guy for a year. And the irony 96 00:06:01,680 --> 00:06:04,920 Speaker 1: of the green revolution is is today's terminology. You might 97 00:06:04,960 --> 00:06:08,560 Speaker 1: think that has something to do with, um, environmentally sound practices. 98 00:06:08,680 --> 00:06:10,440 Speaker 1: It was kind of the opposite of that in a 99 00:06:10,440 --> 00:06:13,159 Speaker 1: lot of ways. Uh, they've meant green just like a 100 00:06:13,200 --> 00:06:17,400 Speaker 1: lot of plants. Uh, it was really harmful to the environment. 101 00:06:17,640 --> 00:06:20,800 Speaker 1: It did feed a lot of people, and there's a 102 00:06:20,880 --> 00:06:24,680 Speaker 1: lot of mixed um mixed reviews on Yelp about the 103 00:06:24,680 --> 00:06:28,360 Speaker 1: green revolution. I guess, uh, that's an easier way to 104 00:06:28,400 --> 00:06:32,320 Speaker 1: say controversy because what happened is a lot of the 105 00:06:32,360 --> 00:06:36,479 Speaker 1: greenhouse gas emissions that we see uh in the world 106 00:06:36,480 --> 00:06:40,440 Speaker 1: today come from food production. We've talked about methane coming 107 00:06:40,560 --> 00:06:46,240 Speaker 1: from cowpoots before, We've talked about it's real big problem. 108 00:06:46,240 --> 00:06:50,800 Speaker 1: We've talked about deforestation. Um obviously transportation getting this food 109 00:06:51,120 --> 00:06:54,040 Speaker 1: and transporting it, you know, because the idea we kind 110 00:06:54,040 --> 00:06:56,920 Speaker 1: of went away from the idea of local farms feeding 111 00:06:57,080 --> 00:07:01,680 Speaker 1: regions into shipping food across the globe if we need to, 112 00:07:02,520 --> 00:07:05,240 Speaker 1: and that's just a lot of pollution. I think raising 113 00:07:05,279 --> 00:07:11,240 Speaker 1: livestock and fish accounts for about of agricultures greenhouse emissions, 114 00:07:11,600 --> 00:07:17,440 Speaker 1: with livestock being about Yeah, so this whole So we've 115 00:07:17,480 --> 00:07:21,160 Speaker 1: we've reached this an inflection point that really resembles the 116 00:07:21,240 --> 00:07:23,920 Speaker 1: last inflection point where Okay, we're about to have a 117 00:07:23,920 --> 00:07:26,320 Speaker 1: big increase in population. We need to make sure that 118 00:07:26,360 --> 00:07:29,600 Speaker 1: agriculture can keep up with food production to feed everybody, 119 00:07:29,680 --> 00:07:32,080 Speaker 1: or else we're gonna have big problems. But this time 120 00:07:32,080 --> 00:07:34,760 Speaker 1: there's an added twist and that we know the last 121 00:07:35,000 --> 00:07:38,880 Speaker 1: thing we did kind of wreck to the environment. So 122 00:07:38,920 --> 00:07:40,880 Speaker 1: now we have to figure out, Okay, how are we 123 00:07:40,920 --> 00:07:44,480 Speaker 1: going to meet this challenge this time without further wrecking 124 00:07:44,520 --> 00:07:47,040 Speaker 1: the environment and then maybe even figuring out a way 125 00:07:47,040 --> 00:07:50,960 Speaker 1: to um help the environment through food production. That's kind 126 00:07:50,960 --> 00:07:53,800 Speaker 1: of where we're at right now. And agriculture has gone 127 00:07:53,840 --> 00:07:57,760 Speaker 1: through different iterations, and right now supposedly we're in agriculture 128 00:07:57,840 --> 00:08:00,800 Speaker 1: three doto And what every but he's trying to figure 129 00:08:00,800 --> 00:08:04,679 Speaker 1: out is what comes next? What's agriculture for dotto? Yeah? 130 00:08:04,840 --> 00:08:10,320 Speaker 1: One one point oh? You say dot? Yeah, okay, one 131 00:08:10,360 --> 00:08:13,280 Speaker 1: point oh was from Neolithic to the nineteen twenties. So 132 00:08:13,360 --> 00:08:17,160 Speaker 1: that was boy, that had a good long run, didn't it. Yeah, 133 00:08:17,920 --> 00:08:20,520 Speaker 1: that's a good long stretch, a very good long stretch. 134 00:08:20,600 --> 00:08:24,320 Speaker 1: But that included a lot of labor from human hands 135 00:08:24,360 --> 00:08:27,880 Speaker 1: and animal hoofs uh two point oh? Was that green 136 00:08:27,920 --> 00:08:31,000 Speaker 1: revolution we were talking about? Three point I was about 137 00:08:31,080 --> 00:08:33,800 Speaker 1: ten or eleven years ago when big data kind of 138 00:08:33,840 --> 00:08:39,080 Speaker 1: came in to help maximize yields. And it's uh, they're 139 00:08:39,080 --> 00:08:41,480 Speaker 1: saying like four point needs to start happening now, and 140 00:08:41,559 --> 00:08:44,080 Speaker 1: sort of is We're just not exactly sure what the 141 00:08:44,080 --> 00:08:46,319 Speaker 1: final iteration is gonna look like yeah, and a lot 142 00:08:46,360 --> 00:08:48,640 Speaker 1: of the stuff that's going on in three Datto is 143 00:08:48,679 --> 00:08:51,400 Speaker 1: going to make an appearance in four Duto. But it's 144 00:08:51,559 --> 00:08:55,240 Speaker 1: gonna be that's a way to say it. Sometimes points 145 00:08:55,240 --> 00:08:57,880 Speaker 1: so yeah, of course, although it reminds me. Have you 146 00:08:57,920 --> 00:09:01,719 Speaker 1: watched Cobra kai Uh? We watched the first like four 147 00:09:01,800 --> 00:09:03,480 Speaker 1: or five episodes and then we're like, I get it. 148 00:09:04,600 --> 00:09:08,800 Speaker 1: Same here. Um, But that one where Johnny's handing out 149 00:09:09,280 --> 00:09:11,800 Speaker 1: flyers for his new website, He's like, check out this 150 00:09:11,920 --> 00:09:18,360 Speaker 1: cool website. H T T P colon slash slash w 151 00:09:18,400 --> 00:09:24,920 Speaker 1: w W period Cobra guy period c O M. They 152 00:09:25,000 --> 00:09:27,200 Speaker 1: just spelled it out while he was handing out a flocker. 153 00:09:27,400 --> 00:09:30,600 Speaker 1: But he says period. He didn't say dot. That's really funny. 154 00:09:30,640 --> 00:09:35,480 Speaker 1: Well two period of three periodo he uh it was. 155 00:09:35,559 --> 00:09:37,320 Speaker 1: That was great. Yeah, it was a great premise that 156 00:09:37,320 --> 00:09:40,280 Speaker 1: they really pulled off for a little while there. So 157 00:09:40,360 --> 00:09:43,560 Speaker 1: now whenever you say to Dot, oh, I'll say do 158 00:09:43,640 --> 00:09:48,440 Speaker 1: you have a problem, Mr Clark, Yeah, and I'll say 159 00:09:48,840 --> 00:09:53,760 Speaker 1: no mercy, no mercy, Yeah, okay, Um, that's the new 160 00:09:53,800 --> 00:09:56,160 Speaker 1: response to that question. But you don't say it like that. 161 00:09:56,240 --> 00:10:01,120 Speaker 1: You don't go no, mercy. Oh you don't say it, director, 162 00:10:01,520 --> 00:10:06,240 Speaker 1: No mercy. Uh, okay, let me try keep them ready. 163 00:10:07,960 --> 00:10:14,199 Speaker 1: No mercy. Very intimidating. Thank you. Where were we? We 164 00:10:14,200 --> 00:10:18,120 Speaker 1: were talking about what four dotto is going to look like? Yeah, 165 00:10:18,120 --> 00:10:22,520 Speaker 1: because here's the deal. Uh, farmers themselves, the human beings 166 00:10:22,559 --> 00:10:26,439 Speaker 1: are getting older. Um, farmers over sixty five years old 167 00:10:26,960 --> 00:10:30,600 Speaker 1: out number uh those under forty five years old by 168 00:10:30,600 --> 00:10:33,800 Speaker 1: two to one, actually a little more than two dot 169 00:10:33,840 --> 00:10:37,640 Speaker 1: one to one dot one. Know those are Colin's my friend. 170 00:10:38,200 --> 00:10:44,000 Speaker 1: Know there's a dot to dot one. Oh man, I 171 00:10:44,200 --> 00:10:47,400 Speaker 1: feel like this is gonna happen. Um. So one of 172 00:10:47,440 --> 00:10:50,680 Speaker 1: the first kind of things that people think may happen, 173 00:10:51,120 --> 00:10:54,800 Speaker 1: and that we're already seeing some is consolidation of farms. 174 00:10:54,840 --> 00:10:58,520 Speaker 1: Instead of a lot of medium, too small sized farms, 175 00:10:59,160 --> 00:11:03,320 Speaker 1: how about few are really big farms. And you know 176 00:11:03,400 --> 00:11:06,400 Speaker 1: that's already kind of been happening. Yeah. And like in 177 00:11:06,440 --> 00:11:09,920 Speaker 1: a normal industry, let's say the kazoo manufacturing industry, if 178 00:11:09,960 --> 00:11:13,520 Speaker 1: like the kazoo makers were way old and there weren't 179 00:11:13,559 --> 00:11:16,320 Speaker 1: very many young kazoo makers, that wouldn't fare very well 180 00:11:16,360 --> 00:11:19,120 Speaker 1: for the kazoo industry, but no one would really care. 181 00:11:19,280 --> 00:11:22,559 Speaker 1: We wouldn't miss kazoos all that much, would be Okay, 182 00:11:22,559 --> 00:11:28,360 Speaker 1: farming does not really fall within that same category as kazoos, 183 00:11:28,440 --> 00:11:32,360 Speaker 1: Like we need food. So rather than farming just going away, 184 00:11:32,480 --> 00:11:35,200 Speaker 1: they're just going to figure out how to consolidate it 185 00:11:35,200 --> 00:11:40,000 Speaker 1: with fewer, younger farmers with bigger farms under their belt, right, 186 00:11:40,240 --> 00:11:43,360 Speaker 1: which you know, if you think there's fewer farmers, so 187 00:11:43,400 --> 00:11:46,199 Speaker 1: you consolidate the farms. That makes sense, But you're like, 188 00:11:46,240 --> 00:11:49,400 Speaker 1: you still need people because these farmers are getting older 189 00:11:49,920 --> 00:11:53,520 Speaker 1: and ostensibly, you know, farm hands are getting older as well. 190 00:11:54,320 --> 00:11:59,240 Speaker 1: But here's where uh four dot four period oh comes 191 00:11:59,240 --> 00:12:04,720 Speaker 1: in is robots. As John Hodgman would say, yeah, oh man, 192 00:12:05,080 --> 00:12:07,480 Speaker 1: I didn't hear it in any other way every time 193 00:12:07,520 --> 00:12:10,719 Speaker 1: I read it. Yeah, because there's a lot of inefficiencies 194 00:12:10,800 --> 00:12:15,360 Speaker 1: in traditional farming with farm hands. I mean, just one 195 00:12:15,400 --> 00:12:19,680 Speaker 1: example is when you fertilize an area. You know, you 196 00:12:19,679 --> 00:12:23,160 Speaker 1: can fertilize like a platt, but you fertilize that whole plat. 197 00:12:23,880 --> 00:12:26,439 Speaker 1: If there's a part of that plat that doesn't need fertilizing, 198 00:12:26,760 --> 00:12:30,040 Speaker 1: it's probably gonna get fertilized anyway, just because you know, 199 00:12:30,080 --> 00:12:34,560 Speaker 1: they just run the fertilizer over that area, right exactly. 200 00:12:34,559 --> 00:12:38,400 Speaker 1: It's just that's just what is the most efficient. And 201 00:12:38,440 --> 00:12:40,400 Speaker 1: as we'll see, like that's a that's a real problem. 202 00:12:40,440 --> 00:12:42,400 Speaker 1: That's sad that that's the current way to do it, 203 00:12:42,440 --> 00:12:46,560 Speaker 1: but that's conventional farming practices. You just fertilize the whole 204 00:12:46,600 --> 00:12:49,200 Speaker 1: field and go on and do something else, because there's 205 00:12:49,200 --> 00:12:51,400 Speaker 1: a million other things that need to be done as well. 206 00:12:52,040 --> 00:12:54,640 Speaker 1: But one of the things, um that's going to be 207 00:12:54,920 --> 00:12:58,040 Speaker 1: uh kind of saved in that way by by robots 208 00:12:58,160 --> 00:13:03,040 Speaker 1: is um They're going to take these different steps that 209 00:13:03,080 --> 00:13:07,599 Speaker 1: are involved in UM farm work and kind of um 210 00:13:07,720 --> 00:13:11,959 Speaker 1: break them down into uh, um, what's the word I'm 211 00:13:12,000 --> 00:13:16,240 Speaker 1: looking for, chuck where where you know, Oh, specialties. So 212 00:13:16,280 --> 00:13:19,520 Speaker 1: a robot specializes in a certain task or whatever. And 213 00:13:19,559 --> 00:13:21,800 Speaker 1: because you'll have a bunch of different robots they doing 214 00:13:21,840 --> 00:13:24,400 Speaker 1: the same task, they'll be able to kind of give 215 00:13:24,440 --> 00:13:28,080 Speaker 1: more personal tailored care to the to the plants. Say 216 00:13:28,160 --> 00:13:31,079 Speaker 1: like some plants need fertilizer, those plants will get fertilizer. 217 00:13:31,160 --> 00:13:33,920 Speaker 1: Plant doesn't need fertilizer, it's not going to get fertilizer. 218 00:13:34,120 --> 00:13:36,800 Speaker 1: And that's going to save a lot of UM inputs. 219 00:13:37,080 --> 00:13:38,720 Speaker 1: Is kind of what you talk about when you're in 220 00:13:39,040 --> 00:13:43,559 Speaker 1: talking agriculture UM, which kind of is generally a good thing, 221 00:13:43,640 --> 00:13:46,200 Speaker 1: not just financially, but when it comes to the environment 222 00:13:46,200 --> 00:13:49,400 Speaker 1: as we'll see. Yeah, and you know, you think about 223 00:13:49,440 --> 00:13:53,360 Speaker 1: a tractor that requires a human to drive that tractor. Uh, 224 00:13:53,440 --> 00:13:57,880 Speaker 1: they already have tractors that can drive themselves with GPS 225 00:13:58,040 --> 00:14:01,240 Speaker 1: accuracy involved. And you know that's been going on for 226 00:14:01,240 --> 00:14:04,520 Speaker 1: a little while now. And the idea I think is UM. 227 00:14:04,559 --> 00:14:06,400 Speaker 1: And you know, some of these tractors are something called 228 00:14:06,440 --> 00:14:09,840 Speaker 1: the lettuce bot, which is kind of cool, where basically 229 00:14:09,840 --> 00:14:13,400 Speaker 1: have a tractor at least that's sort of the current iteration, 230 00:14:13,960 --> 00:14:15,880 Speaker 1: and on the back of that tractor is a big 231 00:14:16,000 --> 00:14:20,040 Speaker 1: row of I mean, we call them robots. It's not 232 00:14:20,120 --> 00:14:23,520 Speaker 1: like you know, George Jetson type of stuff. A robotis 233 00:14:23,560 --> 00:14:27,760 Speaker 1: just means it's a mechanical, you know, automated system. Right, 234 00:14:27,800 --> 00:14:32,160 Speaker 1: it's not they're not looking for a husband like. So 235 00:14:32,200 --> 00:14:34,640 Speaker 1: the lettuce bot is pulled along behind the tractor and 236 00:14:34,640 --> 00:14:36,960 Speaker 1: it's got you know, just a big row of little 237 00:14:37,040 --> 00:14:41,120 Speaker 1: robots that can do everything from UM kind of custom 238 00:14:41,160 --> 00:14:45,440 Speaker 1: fertilization to picking out a weed using the same technology 239 00:14:45,480 --> 00:14:49,800 Speaker 1: that they use in facial recognition like there's a ragweed 240 00:14:49,880 --> 00:14:52,400 Speaker 1: or something, let's get rid of just that weed instead 241 00:14:52,440 --> 00:14:54,480 Speaker 1: of like let's just spray the whole field with round 242 00:14:54,560 --> 00:14:57,360 Speaker 1: up or whatever. Uh. And it's you know, it's going 243 00:14:57,400 --> 00:15:01,520 Speaker 1: to increase efficiencies. And I think that's the first iteration. 244 00:15:01,560 --> 00:15:04,680 Speaker 1: And what they're looking at in the future is instead 245 00:15:04,680 --> 00:15:07,200 Speaker 1: of even a big tractor that still costs a lot 246 00:15:07,240 --> 00:15:10,240 Speaker 1: of money, Yeah, like hundreds of thousands of dollars for 247 00:15:10,240 --> 00:15:12,560 Speaker 1: a new track. I think, like it doesn't really dawn 248 00:15:12,640 --> 00:15:18,440 Speaker 1: on city slickers how incredibly expensive farm equipment is. Yeah, 249 00:15:18,480 --> 00:15:22,000 Speaker 1: those big, big tractors, not like you're sort of fun tractor. No, 250 00:15:22,160 --> 00:15:24,920 Speaker 1: not a fun tractor, sure, that's like fifty grand, who cares, 251 00:15:24,960 --> 00:15:27,920 Speaker 1: but like a really big tractor. Yeah, it's a lot 252 00:15:27,960 --> 00:15:30,640 Speaker 1: of money. Um. So I think the idea is, or 253 00:15:30,640 --> 00:15:34,320 Speaker 1: at least what I saw, there's conceptual drawings of smaller 254 00:15:35,120 --> 00:15:38,120 Speaker 1: uh robot tractors that are I don't know, it looked 255 00:15:38,160 --> 00:15:40,640 Speaker 1: like the size of like a like a six foot 256 00:15:40,640 --> 00:15:43,800 Speaker 1: folding table or something just kind of going over like 257 00:15:43,840 --> 00:15:46,000 Speaker 1: a row of lettuce, Like it's that it's in charge 258 00:15:46,000 --> 00:15:48,800 Speaker 1: of that one row. Probably, Yeah, But what I wonder 259 00:15:48,960 --> 00:15:51,160 Speaker 1: is how expensive are those and do you have to 260 00:15:51,200 --> 00:15:53,840 Speaker 1: get fifty of those to equal one tractor? And how 261 00:15:53,880 --> 00:15:57,240 Speaker 1: does it suss out financially? I mean, I'm sure that 262 00:15:57,360 --> 00:16:01,200 Speaker 1: like there's a maximum number that you would possibly need, 263 00:16:01,320 --> 00:16:03,480 Speaker 1: or else you're like, I have more than I can use. 264 00:16:03,960 --> 00:16:06,440 Speaker 1: And then you have you have a farm that like 265 00:16:06,880 --> 00:16:08,880 Speaker 1: doesn't make quite as much money as another farm, so 266 00:16:08,920 --> 00:16:12,240 Speaker 1: they make do with half the number of robots, and 267 00:16:12,240 --> 00:16:14,920 Speaker 1: that maybe they have to supplement that with humans or whatever, 268 00:16:15,360 --> 00:16:17,600 Speaker 1: um or it takes longer for that to be done, 269 00:16:17,720 --> 00:16:21,440 Speaker 1: or they can grow less lettuce. But I'm sure that 270 00:16:21,480 --> 00:16:24,160 Speaker 1: like there's from what I read, there's a price point 271 00:16:24,240 --> 00:16:28,760 Speaker 1: where like having robots is going to be financially way 272 00:16:28,800 --> 00:16:32,840 Speaker 1: better than having to sink hunt several hundred grand into 273 00:16:32,880 --> 00:16:34,880 Speaker 1: a new tract or even like a hundred and fifty 274 00:16:34,920 --> 00:16:37,640 Speaker 1: grand for a use tractor. And then the other thing, 275 00:16:37,640 --> 00:16:40,280 Speaker 1: and this is really important to number one, you don't 276 00:16:40,280 --> 00:16:42,400 Speaker 1: need a human to drive these things, which frees the 277 00:16:42,440 --> 00:16:44,200 Speaker 1: human up to do other things, or you don't have 278 00:16:44,200 --> 00:16:46,480 Speaker 1: to pay the human anymore, So that's gonna save you 279 00:16:46,520 --> 00:16:50,480 Speaker 1: some money. And then secondly, um, if one of those 280 00:16:50,520 --> 00:16:55,960 Speaker 1: robots breaks down, you can still work all the other rows. 281 00:16:56,320 --> 00:16:59,120 Speaker 1: If the tractor breaks down, You're you're done. You have 282 00:16:59,160 --> 00:17:01,360 Speaker 1: to wait until the try just fixed, and then all 283 00:17:01,400 --> 00:17:03,840 Speaker 1: of that work has to wait. This is just one 284 00:17:03,840 --> 00:17:07,280 Speaker 1: single say row that isn't getting attended to right then 285 00:17:07,480 --> 00:17:09,720 Speaker 1: while that one robot's broken down. So that's a huge 286 00:17:09,720 --> 00:17:12,600 Speaker 1: advantage right there. Yeah, and you can just tell robot 287 00:17:12,680 --> 00:17:15,560 Speaker 1: number two to scoot over and cover the ground that 288 00:17:15,640 --> 00:17:18,840 Speaker 1: robot number one is missing exactly, and he's like, I 289 00:17:18,880 --> 00:17:22,520 Speaker 1: gotta pull a double Shut up. You're a machine. You 290 00:17:22,600 --> 00:17:26,360 Speaker 1: can't talk. The other thing robots can potentially do is harvest. 291 00:17:27,040 --> 00:17:31,280 Speaker 1: Harvesting is very labor intensive, and it's also kind of inefficient, 292 00:17:31,400 --> 00:17:35,240 Speaker 1: especially when it comes to something like um, maybe like strawberries, 293 00:17:35,800 --> 00:17:39,760 Speaker 1: which you harvest one time during the year, and you 294 00:17:39,800 --> 00:17:42,280 Speaker 1: know there there's still a little bit of leeway in there. 295 00:17:42,320 --> 00:17:46,040 Speaker 1: Like it's it's not an exact science in that some 296 00:17:46,200 --> 00:17:49,040 Speaker 1: of those plants will have ripe fruit before you go 297 00:17:49,119 --> 00:17:51,840 Speaker 1: to harvest and it'll rot and drop off. Some of 298 00:17:51,880 --> 00:17:54,359 Speaker 1: them may need to wait a little bit and ripen afterward, 299 00:17:54,880 --> 00:17:57,520 Speaker 1: so you're wasting a lot of fruit there on the ground. 300 00:17:57,680 --> 00:18:01,840 Speaker 1: And robot harvesters would just constantly kind of patrol these 301 00:18:01,960 --> 00:18:04,840 Speaker 1: rosa plants and harvests the berries when they're ready to 302 00:18:04,840 --> 00:18:08,360 Speaker 1: be harvested, right, which would make it a lot more 303 00:18:08,400 --> 00:18:11,119 Speaker 1: money for the farmers who are growing that stuff. It's 304 00:18:11,160 --> 00:18:14,040 Speaker 1: pretty pretty awesome. And then as we kind of evolve 305 00:18:14,080 --> 00:18:17,439 Speaker 1: further and further along in our technology UM and we 306 00:18:17,520 --> 00:18:21,440 Speaker 1: finally reach the capability of nanotechnology, one of the things 307 00:18:21,440 --> 00:18:25,399 Speaker 1: that they are hoping that nanobots, which are currently just 308 00:18:25,480 --> 00:18:29,800 Speaker 1: hypothetical robots on the scale of UM like a strand 309 00:18:29,800 --> 00:18:33,080 Speaker 1: of DNA or an atom or something, they'll be able 310 00:18:33,080 --> 00:18:37,199 Speaker 1: to manipulate matter about UM like on that scale. So 311 00:18:37,240 --> 00:18:41,280 Speaker 1: what they're hoping for is UM with agriculture, nanobots will 312 00:18:41,320 --> 00:18:45,040 Speaker 1: eventually be able to deliver nutrients directly to the roots 313 00:18:45,040 --> 00:18:48,240 Speaker 1: of a plant, like right when it needs it, not 314 00:18:48,240 --> 00:18:51,120 Speaker 1: not from some humans saying like, hey, nanoboto, go take 315 00:18:51,160 --> 00:18:53,600 Speaker 1: this nutrient, this little bit of nitrogen over to that 316 00:18:53,640 --> 00:18:56,080 Speaker 1: plant right there. It will be all of this stuff 317 00:18:56,080 --> 00:19:00,440 Speaker 1: will be guided and directed by computers that are paying 318 00:19:00,480 --> 00:19:03,639 Speaker 1: attention to the plants through sensors and then directing the 319 00:19:03,720 --> 00:19:06,640 Speaker 1: nanobots to go take this nutrient to this particular plant 320 00:19:06,680 --> 00:19:09,679 Speaker 1: because it needs it right now. And this kind of 321 00:19:10,000 --> 00:19:13,439 Speaker 1: this kind of attention. This tailored to individualize attention is 322 00:19:13,440 --> 00:19:16,840 Speaker 1: what's called precision farming, and that seems to be something 323 00:19:16,880 --> 00:19:19,400 Speaker 1: that's looming on the horizon that will be a big 324 00:19:19,440 --> 00:19:24,000 Speaker 1: part of for for period. Oh yeah, I think the 325 00:19:24,080 --> 00:19:27,760 Speaker 1: NANO is like serious future farming when we get to 326 00:19:27,800 --> 00:19:30,320 Speaker 1: that point, But a lot of this stuff is on 327 00:19:30,359 --> 00:19:34,120 Speaker 1: the imminent horizon. Um Nutrient waste is a really big 328 00:19:34,119 --> 00:19:37,600 Speaker 1: deal and a big problem. I think about six of 329 00:19:37,640 --> 00:19:41,040 Speaker 1: fertilizer that you apply to a field is lost to 330 00:19:41,200 --> 00:19:44,439 Speaker 1: run off. So there's a big cost factor there that 331 00:19:44,440 --> 00:19:48,280 Speaker 1: you're losing and it just rereaks havoc on water sheds 332 00:19:48,280 --> 00:19:50,040 Speaker 1: that are nearby, which we talked about a little bit 333 00:19:50,040 --> 00:19:55,840 Speaker 1: in the watershed episode. And fertilizer production and then transporting 334 00:19:55,840 --> 00:19:58,200 Speaker 1: that where you need it is a big, big part 335 00:19:58,240 --> 00:20:02,080 Speaker 1: of CEO two emissions. I think five to eleven kilograms 336 00:20:02,720 --> 00:20:06,280 Speaker 1: of CEO two are admitted through the lifestyle life cycle 337 00:20:06,320 --> 00:20:11,560 Speaker 1: of one keg I'm sorry, one kilogram gig tepe kega. 338 00:20:11,680 --> 00:20:16,480 Speaker 1: That fertilizer man keg stand uh one kilogram of fertilizer. 339 00:20:17,280 --> 00:20:20,000 Speaker 1: And once you fertilize the plant, Uh, it gets in 340 00:20:20,040 --> 00:20:23,200 Speaker 1: that soil and these microbes you know, have to convert 341 00:20:23,240 --> 00:20:26,639 Speaker 1: that fertilizer into something that's useful. And when it does that, 342 00:20:26,800 --> 00:20:29,399 Speaker 1: it emits uh n O two or I'm sorry in 343 00:20:29,480 --> 00:20:33,359 Speaker 1: two O nitrous oxide and that's number three behind CEO 344 00:20:33,400 --> 00:20:36,719 Speaker 1: two and methane is a big problem gas. Right, So 345 00:20:36,760 --> 00:20:39,280 Speaker 1: there's a lot to be saved by by cutting down 346 00:20:39,359 --> 00:20:43,639 Speaker 1: on that six of waste fertilizer. A lot of stuff 347 00:20:43,640 --> 00:20:45,840 Speaker 1: would be helped by that. And the more you can 348 00:20:45,920 --> 00:20:50,480 Speaker 1: precisely tailor agriculture, UM, the less waste you're going to have. 349 00:20:51,000 --> 00:20:53,640 Speaker 1: And I would say that UM for anybody who's interested 350 00:20:53,640 --> 00:20:58,320 Speaker 1: in hearing how colossally wrong deploying nanobots into crop land 351 00:20:58,480 --> 00:21:01,760 Speaker 1: could go, I would direct you to UM my tempart 352 00:21:01,840 --> 00:21:04,800 Speaker 1: series The End of the World with Josh Clark, specifically 353 00:21:04,800 --> 00:21:07,600 Speaker 1: the AI episode. It's pretty good, kind of eye opening. 354 00:21:08,080 --> 00:21:10,760 Speaker 1: I think that's my favorite one. Actually. Oh, thanks Chuck. 355 00:21:11,280 --> 00:21:14,040 Speaker 1: All right, so let's take a break and we'll come 356 00:21:14,040 --> 00:21:42,600 Speaker 1: back and talk about big data right after this. Okay, 357 00:21:42,640 --> 00:21:47,359 Speaker 1: we're back, and we're talking about Chuck big data and 358 00:21:50,160 --> 00:21:54,800 Speaker 1: three period oh, which was already or is already using 359 00:21:54,840 --> 00:21:57,600 Speaker 1: a lot of data. UM. We talked about the GPS 360 00:21:57,600 --> 00:22:01,400 Speaker 1: guided tractors that have been around for a while. Um. 361 00:22:01,440 --> 00:22:04,600 Speaker 1: They do use things like drones and satellites to get 362 00:22:04,880 --> 00:22:07,320 Speaker 1: like literal big pictures of farms that can be really 363 00:22:07,440 --> 00:22:12,359 Speaker 1: useful UM in determining like areas that are patchy or dry, 364 00:22:12,480 --> 00:22:15,680 Speaker 1: or hey, this looks ready for harvest. But they're gonna 365 00:22:15,720 --> 00:22:20,000 Speaker 1: bring light into the mix, which is something I know 366 00:22:20,040 --> 00:22:23,480 Speaker 1: we've talked about before. L I D A R. When 367 00:22:23,480 --> 00:22:25,640 Speaker 1: do we talk about that? I don't know. It might 368 00:22:25,680 --> 00:22:30,400 Speaker 1: have been. It's used most famously for m mapping dense 369 00:22:31,080 --> 00:22:35,399 Speaker 1: jungle ruins. UM. Like. I was reading about it in 370 00:22:35,520 --> 00:22:37,960 Speaker 1: this book called The Lost City of the Monkey God 371 00:22:38,040 --> 00:22:41,360 Speaker 1: by Douglas Preston, which I actually heard about in researching 372 00:22:41,359 --> 00:22:45,080 Speaker 1: our episode on Finn Treasure, because Douglas Preston was the 373 00:22:45,400 --> 00:22:50,640 Speaker 1: friend of Forest Finn, who UM vouched for having seen 374 00:22:50,680 --> 00:22:54,920 Speaker 1: the treasure in person before his closet, And I was like, Oh, 375 00:22:54,920 --> 00:22:56,720 Speaker 1: that book sounds pretty interesting. I went and read it, 376 00:22:56,720 --> 00:22:59,320 Speaker 1: and it's really interesting. But he talks a lot about 377 00:22:59,400 --> 00:23:03,640 Speaker 1: light are being used to map these these these ruins 378 00:23:03,680 --> 00:23:06,200 Speaker 1: that have been overgrown by jungle that you normally would 379 00:23:06,200 --> 00:23:08,920 Speaker 1: never be able to see overhead, but the light are 380 00:23:09,320 --> 00:23:13,280 Speaker 1: UM is able to basically get beams of lasers through 381 00:23:13,440 --> 00:23:15,919 Speaker 1: the brakes and like leaves and all that in the 382 00:23:16,040 --> 00:23:19,280 Speaker 1: canopy to hit the ground below and then bounce back up. 383 00:23:19,640 --> 00:23:21,760 Speaker 1: And so you get a picture of the understory too, 384 00:23:21,760 --> 00:23:24,640 Speaker 1: which would come and handy big time for for crops, 385 00:23:24,720 --> 00:23:29,880 Speaker 1: especially tall crops that are growing closely together, like say corn. Right, 386 00:23:30,080 --> 00:23:33,440 Speaker 1: So you bundle all that together in a handy little 387 00:23:33,960 --> 00:23:38,119 Speaker 1: app for Mr. Future Farmer and Mrs Future Farmer or 388 00:23:38,400 --> 00:23:41,680 Speaker 1: Miss Future Farmer or a Miss Future Farmer or dr 389 00:23:41,760 --> 00:23:47,280 Speaker 1: Future Farmer or future farmer, they or they the future farmers. 390 00:23:47,960 --> 00:23:50,880 Speaker 1: That's that's great. I think we covered all the base. 391 00:23:50,960 --> 00:23:53,840 Speaker 1: I think so. Um, So you have an app there, 392 00:23:53,880 --> 00:23:58,600 Speaker 1: and machine learning becomes more intelligent, the Internet of Things 393 00:23:58,720 --> 00:24:01,159 Speaker 1: kind of gets a little more robot and then you 394 00:24:01,200 --> 00:24:06,000 Speaker 1: have farmers that don't have to constantly make these tiny 395 00:24:06,040 --> 00:24:10,000 Speaker 1: little decisions, these micro decisions that they have to make 396 00:24:10,040 --> 00:24:13,200 Speaker 1: every day about keeping their farm healthy. They can kind 397 00:24:13,200 --> 00:24:15,639 Speaker 1: of rely on this AI technology to figure it out 398 00:24:15,680 --> 00:24:18,160 Speaker 1: and do it for them. Uh, and I guess they 399 00:24:18,160 --> 00:24:21,320 Speaker 1: can spend their time building future weapons to fight the 400 00:24:21,400 --> 00:24:26,320 Speaker 1: eventual robot uprising. That's right. But I mean, like think 401 00:24:26,320 --> 00:24:28,720 Speaker 1: about it, all of this stuff is just using things 402 00:24:28,800 --> 00:24:33,360 Speaker 1: that are popping up in other sectors right now, machine learning, UM, 403 00:24:33,560 --> 00:24:36,399 Speaker 1: sensors that are connected to the Internet of Things, and 404 00:24:36,400 --> 00:24:40,280 Speaker 1: then integration of all this stuff that UM to oversee 405 00:24:40,320 --> 00:24:42,560 Speaker 1: this so that the farmer doesn't have to make these decisions. 406 00:24:42,600 --> 00:24:45,199 Speaker 1: And when you combine all this stuff together, you have 407 00:24:45,320 --> 00:24:49,480 Speaker 1: like a farm that could be humming along UM just 408 00:24:49,520 --> 00:24:54,440 Speaker 1: an absolute peak performance with minimal inputs that are delivered 409 00:24:54,520 --> 00:24:56,840 Speaker 1: just at just the right time and just the right amount, 410 00:24:56,920 --> 00:25:00,360 Speaker 1: with minimal waste UM, with the farmer having to make 411 00:25:00,480 --> 00:25:04,119 Speaker 1: minimal decisions. And if you take this to its you know, 412 00:25:04,440 --> 00:25:08,680 Speaker 1: eventual conclusion. I mean the there won't be like young 413 00:25:08,880 --> 00:25:12,040 Speaker 1: farmers running and you know, huge farms. It'll be like 414 00:25:12,720 --> 00:25:15,000 Speaker 1: somebody who owns the farm. But really it's like an 415 00:25:15,040 --> 00:25:19,080 Speaker 1: AI that's overseeing the entire farm, communicating with everything through 416 00:25:19,080 --> 00:25:22,760 Speaker 1: the Internet of Things, directing this nanobot over there, UM 417 00:25:22,760 --> 00:25:26,399 Speaker 1: this let us bot over here. And then potentially is 418 00:25:26,480 --> 00:25:31,400 Speaker 1: we grow as a UM an advanced society, there may 419 00:25:31,480 --> 00:25:34,920 Speaker 1: just be one AI that we we rely on to 420 00:25:35,080 --> 00:25:37,840 Speaker 1: run all the farms everywhere around the world and then 421 00:25:38,320 --> 00:25:41,600 Speaker 1: handle distribution and all of that stuff. So I don't know, 422 00:25:41,640 --> 00:25:44,560 Speaker 1: maybe that's agriculture five auto. Who knows, maybe we'll never 423 00:25:44,640 --> 00:25:47,240 Speaker 1: get there. There's some people that certainly hope we that's 424 00:25:47,280 --> 00:25:49,520 Speaker 1: not the direction we go. But we'll talk about them 425 00:25:49,520 --> 00:25:52,960 Speaker 1: in a little bit. Yeah. Like if you're screaming right now, 426 00:25:53,359 --> 00:25:55,840 Speaker 1: how awful this sounds, Well, we'll get to you later, 427 00:25:55,880 --> 00:26:01,159 Speaker 1: don't worry. UM. Another part of four point oh is 428 00:26:01,640 --> 00:26:04,520 Speaker 1: um trying to grow crops where it doesn't seem like 429 00:26:04,520 --> 00:26:07,840 Speaker 1: you should be able to grow crops. The desert is 430 00:26:07,840 --> 00:26:11,440 Speaker 1: obviously one of those places. Uh. Saludias are already investing 431 00:26:11,440 --> 00:26:16,120 Speaker 1: a lot in trying to UM figure out the genomic 432 00:26:16,200 --> 00:26:22,840 Speaker 1: codes or genomic genomic. I think both work. One seems British. 433 00:26:22,960 --> 00:26:26,600 Speaker 1: Well it's a genome, so it's probably genomic gentemic. That's 434 00:26:26,600 --> 00:26:31,000 Speaker 1: how the British had saved the genomic codes. Uh. These 435 00:26:31,000 --> 00:26:34,719 Speaker 1: plants that can withstand the desert conditions and figure out 436 00:26:34,720 --> 00:26:36,879 Speaker 1: how to grow stuff there. And this is kind of 437 00:26:36,880 --> 00:26:43,040 Speaker 1: where we wander gently into GMOs, which I think we've 438 00:26:43,440 --> 00:26:45,520 Speaker 1: have been dodging this one as a full topic for 439 00:26:45,560 --> 00:26:48,080 Speaker 1: a while to do it, we should at some point 440 00:26:48,119 --> 00:26:51,359 Speaker 1: because it is very controversial. It has a bad rap, 441 00:26:51,840 --> 00:26:55,320 Speaker 1: some people say, rightfully, so it has a bad rap 442 00:26:56,200 --> 00:27:01,640 Speaker 1: I think about Americans say they think GMOs um are 443 00:27:01,720 --> 00:27:05,720 Speaker 1: safe to eat, which is, you know, pretty pretty decent 444 00:27:05,760 --> 00:27:09,400 Speaker 1: minority there. Science says that they are safe to eat. 445 00:27:09,960 --> 00:27:13,320 Speaker 1: But for all this sort of bluster about GMOs, they 446 00:27:13,359 --> 00:27:17,639 Speaker 1: haven't really done a lot with GMOs yet, except for 447 00:27:17,680 --> 00:27:22,560 Speaker 1: a couple of a few little kind of dirty, underhanded things. Well, yeah, 448 00:27:22,600 --> 00:27:27,720 Speaker 1: like um creating patented um seeds that grow plants that 449 00:27:27,760 --> 00:27:31,000 Speaker 1: don't produce more seeds, so farmers are forced to buy 450 00:27:31,080 --> 00:27:34,680 Speaker 1: the seeds every single year. Yeah, that's a big one. 451 00:27:34,840 --> 00:27:38,399 Speaker 1: There's another one that only responds or responds best to 452 00:27:38,880 --> 00:27:43,720 Speaker 1: a specific brand of pesticide UM, so you have to 453 00:27:43,720 --> 00:27:46,480 Speaker 1: buy that brand of pesticide, which happens to be manufactured 454 00:27:46,520 --> 00:27:48,800 Speaker 1: by the same company that owns the patent on the plant. 455 00:27:49,280 --> 00:27:52,359 Speaker 1: It's kind of shady stuff like that. The thing is, 456 00:27:52,400 --> 00:27:55,120 Speaker 1: it's not like that's all they've tried to do. They've 457 00:27:55,160 --> 00:27:58,919 Speaker 1: also tried to have breakthroughs in you know, um like 458 00:27:59,040 --> 00:28:03,440 Speaker 1: plants that can withstand like horrible droughts, and they haven't 459 00:28:03,440 --> 00:28:06,000 Speaker 1: been able to break through in that that sense, or 460 00:28:06,480 --> 00:28:10,240 Speaker 1: plants that you know, produce double the yield with minimal inputs. 461 00:28:10,280 --> 00:28:12,600 Speaker 1: They haven't had that breakthrough. That doesn't mean they're not 462 00:28:12,680 --> 00:28:16,560 Speaker 1: going to break through that there won't be huge advances 463 00:28:16,600 --> 00:28:20,919 Speaker 1: and plants science. But even if we do reach that point, like, 464 00:28:21,000 --> 00:28:23,320 Speaker 1: there's going to have to also be like a public 465 00:28:23,359 --> 00:28:26,520 Speaker 1: information campaign that basically says like this stuff will not 466 00:28:26,680 --> 00:28:29,320 Speaker 1: mutate your children, it's safe to eat, it won't make 467 00:28:29,359 --> 00:28:34,080 Speaker 1: you glow. And there's definitely an enormous amount of fear 468 00:28:34,119 --> 00:28:37,760 Speaker 1: of science from what I can tell involved in GMOs. 469 00:28:37,880 --> 00:28:39,960 Speaker 1: I haven't done the research yet, so my opinion might 470 00:28:40,040 --> 00:28:42,840 Speaker 1: change when we actually do the episode, but from the 471 00:28:42,960 --> 00:28:45,160 Speaker 1: minimal research I did on it, it seems like a 472 00:28:45,560 --> 00:28:48,480 Speaker 1: fear of science. And as far as science is concerned, 473 00:28:48,520 --> 00:28:51,600 Speaker 1: it's it's everything we know about it, it's it's safe 474 00:28:51,680 --> 00:28:55,120 Speaker 1: to eat. Um. I don't know that will remain to 475 00:28:55,120 --> 00:28:57,200 Speaker 1: be seen. Let me do some more research first before 476 00:28:57,200 --> 00:29:00,040 Speaker 1: you quote me on that. Yeah, and I think of 477 00:29:00,120 --> 00:29:02,240 Speaker 1: the bad rep too. Is just like we were talking 478 00:29:02,280 --> 00:29:05,760 Speaker 1: about the couple of three uses so far that have 479 00:29:05,840 --> 00:29:09,920 Speaker 1: allowed certain companies to really take advantage of the situation. 480 00:29:10,040 --> 00:29:13,320 Speaker 1: Let's just yeah, it's not like giant mega corporations have 481 00:29:13,680 --> 00:29:16,000 Speaker 1: you know, garnered a lot of trust from the general 482 00:29:16,000 --> 00:29:18,960 Speaker 1: public over the years and in fact have squandered it 483 00:29:19,040 --> 00:29:24,720 Speaker 1: pretty pretty efficiently. Actually. Uh So, seawater farming is another 484 00:29:24,760 --> 00:29:27,680 Speaker 1: thing on the horizon, um, and there are a couple 485 00:29:27,760 --> 00:29:31,360 Speaker 1: of iterations of that, one of which is actually using 486 00:29:31,440 --> 00:29:35,280 Speaker 1: seawater to farm. And when we're not talking about spraying 487 00:29:35,280 --> 00:29:40,320 Speaker 1: plants obviously, we're talking like farming shrimp things that this working. 488 00:29:40,720 --> 00:29:43,880 Speaker 1: It's like idiocracy where they were using gatorade to water 489 00:29:43,960 --> 00:29:49,000 Speaker 1: the plants. Um. Yeah, like growing shrimp, farming shrimp. Um. 490 00:29:49,040 --> 00:29:51,440 Speaker 1: Because that protein demand that we were talking about, as 491 00:29:51,680 --> 00:29:55,240 Speaker 1: as developing nations get more money and they're gonna they're 492 00:29:55,240 --> 00:29:58,520 Speaker 1: gonna want more shrimp. I'll eat more shrimp. Remember when 493 00:29:58,520 --> 00:30:01,000 Speaker 1: you were allergic to shrimp? Yes, And I was like, 494 00:30:01,160 --> 00:30:02,640 Speaker 1: I am not going to spend the rest of my 495 00:30:02,680 --> 00:30:05,800 Speaker 1: life allergic to shrimp. And you figure it out sort 496 00:30:05,800 --> 00:30:09,760 Speaker 1: of right, I handled it. Um. There's a Japanese snack 497 00:30:09,960 --> 00:30:14,800 Speaker 1: called shrimp chips, and they're like little fried kind of 498 00:30:14,800 --> 00:30:19,160 Speaker 1: like French fries, but they're crispy um chips and they're 499 00:30:19,240 --> 00:30:23,000 Speaker 1: dusted with shrimp flavoring that include shrimp. And I just 500 00:30:23,120 --> 00:30:29,560 Speaker 1: kind of immunotherapy therapy or therapized myself immunize them myself 501 00:30:29,600 --> 00:30:32,040 Speaker 1: to them to shrimp so that I could eat them 502 00:30:32,040 --> 00:30:34,560 Speaker 1: again and it worked. You know, we should quickly thank 503 00:30:34,600 --> 00:30:38,320 Speaker 1: our scallop buddy. Oh, I think that's a great idea, man, 504 00:30:38,440 --> 00:30:42,960 Speaker 1: So huge, huge, huge thanks to our pal tog Braun 505 00:30:43,960 --> 00:30:47,120 Speaker 1: who just hooked us up, man, I mean hooked us 506 00:30:47,200 --> 00:30:52,120 Speaker 1: up with some amazing scallops. Yes, fresh ones. They have 507 00:30:52,200 --> 00:30:54,280 Speaker 1: been in the in the water like the day before 508 00:30:54,320 --> 00:30:57,560 Speaker 1: we got them, I believe, right. Yes, it was very fresh. 509 00:30:57,760 --> 00:31:00,480 Speaker 1: And I think her boat is the downiest day boat 510 00:31:00,560 --> 00:31:03,640 Speaker 1: out of main that's her company for sure. Yeah yeah, yeah, 511 00:31:03,640 --> 00:31:05,560 Speaker 1: but you can you can order this stuff and like 512 00:31:05,800 --> 00:31:08,080 Speaker 1: you can get the best scallops in the world sent 513 00:31:08,200 --> 00:31:11,200 Speaker 1: right to your door very quickly. And boy were they good. 514 00:31:11,400 --> 00:31:13,400 Speaker 1: I mean they were so fresh, dude, that the first 515 00:31:13,440 --> 00:31:17,480 Speaker 1: two three, alright, seventeen scallops I ate or raw, Like 516 00:31:17,520 --> 00:31:21,200 Speaker 1: I just ate them. I ate them raw. They were amazing. Yeah, 517 00:31:21,280 --> 00:31:23,760 Speaker 1: it was. It's really good. So I strongly recommend them 518 00:31:23,760 --> 00:31:26,280 Speaker 1: to hats off. Thanks a lot to I'm not the 519 00:31:26,280 --> 00:31:29,280 Speaker 1: biggest raw scalop person, but um, there was a lot 520 00:31:29,360 --> 00:31:32,160 Speaker 1: of butter and garlic involved in my scene. Yeah, and 521 00:31:32,200 --> 00:31:35,720 Speaker 1: they cooked up so nicely down the perfectly. Uh so, yeah, 522 00:31:35,800 --> 00:31:39,160 Speaker 1: thanks for that diversion since we were talking shellfish. But yeah, 523 00:31:39,240 --> 00:31:43,560 Speaker 1: farming shrimp for seawater farming. And another is where they 524 00:31:43,600 --> 00:31:47,240 Speaker 1: actually have greenhouses built that will you see water that 525 00:31:47,280 --> 00:31:50,800 Speaker 1: evaporate uh, the salt out of it into fresh water. 526 00:31:51,040 --> 00:31:53,479 Speaker 1: They can sell that salt, which is great and then 527 00:31:53,560 --> 00:31:56,600 Speaker 1: have that great delicious fresh water to irrigate their crops. Right, 528 00:31:57,360 --> 00:32:00,400 Speaker 1: and then um land uses a big problem to people 529 00:32:00,400 --> 00:32:02,200 Speaker 1: are like running out of lane. We need it to 530 00:32:02,240 --> 00:32:04,520 Speaker 1: live on and do other stuff on. Some people have said, 531 00:32:04,520 --> 00:32:08,320 Speaker 1: well how about this, we'll just grow stuff indoors vertically 532 00:32:08,440 --> 00:32:12,640 Speaker 1: rather than outdoors horizontally. I actually have a friend who's 533 00:32:12,640 --> 00:32:16,000 Speaker 1: engaged in this endeavor up in Jersey. He broke his teeth, 534 00:32:16,040 --> 00:32:20,080 Speaker 1: my friend Matt Um. He broke his teeth, not literally, 535 00:32:20,400 --> 00:32:25,720 Speaker 1: but he gained experience working on space lettuce for NASA. UM. 536 00:32:25,920 --> 00:32:29,520 Speaker 1: Like he's right, that's that's right. I was like, it 537 00:32:29,600 --> 00:32:33,520 Speaker 1: doesn't sound right. He cut his teeth. UM, and he's 538 00:32:33,520 --> 00:32:37,440 Speaker 1: an expert in UM like the like light spectrums, like 539 00:32:37,600 --> 00:32:42,360 Speaker 1: artificial light spectrums to grow plants in space. It's pretty awesome. Yeah, 540 00:32:42,360 --> 00:32:45,920 Speaker 1: it's amazing. Yes, So hey, Matt, should we take another 541 00:32:45,960 --> 00:32:47,760 Speaker 1: break and then talk about the other other side of 542 00:32:47,760 --> 00:32:56,240 Speaker 1: the coin. Here, hold on, let me think, yes, we'll 543 00:32:56,240 --> 00:33:24,840 Speaker 1: be right back, all right. So if we were talking 544 00:33:24,880 --> 00:33:31,400 Speaker 1: about all the benefits of consolidation of farms, bigger farms, 545 00:33:31,480 --> 00:33:35,920 Speaker 1: automation of farms, future farming, there is another group of 546 00:33:35,920 --> 00:33:39,520 Speaker 1: people that have been screaming for years. We don't want 547 00:33:39,560 --> 00:33:42,000 Speaker 1: to go that way. We should go back to to 548 00:33:42,160 --> 00:33:45,040 Speaker 1: the neat not know within necessary, but we should go 549 00:33:45,080 --> 00:33:49,959 Speaker 1: back to one period, oh and practice agro ecology. Uh. 550 00:33:50,040 --> 00:33:52,000 Speaker 1: And that's what I was that documentary I saw about 551 00:33:52,000 --> 00:33:53,880 Speaker 1: the biggest little farm. These people that moved from l 552 00:33:53,920 --> 00:33:58,200 Speaker 1: a too very impossibly or I guess, and probably start 553 00:33:58,240 --> 00:34:02,480 Speaker 1: their own farm where everything lives in harmony. There's livestock, 554 00:34:02,520 --> 00:34:06,240 Speaker 1: and there's wild animals, and there's pests that they say 555 00:34:06,240 --> 00:34:09,600 Speaker 1: are beneficial that they let live. And they really have 556 00:34:09,680 --> 00:34:13,960 Speaker 1: tried to figure out this idea that you know, you 557 00:34:14,000 --> 00:34:17,359 Speaker 1: can have a small farm that feeds people locally with 558 00:34:17,800 --> 00:34:20,640 Speaker 1: giving people fresh food and not transporting it halfway across 559 00:34:21,000 --> 00:34:24,480 Speaker 1: the country or the world. And that is the way forward, 560 00:34:24,640 --> 00:34:27,840 Speaker 1: not what we're heading towards with four point Oh. Yeah. 561 00:34:27,920 --> 00:34:30,759 Speaker 1: People who are proponents of agrocology are looking at the 562 00:34:30,840 --> 00:34:34,520 Speaker 1: other proposals for for for auto and they're like, you're 563 00:34:34,560 --> 00:34:37,799 Speaker 1: talking about genetically modifying plants so you can grow them 564 00:34:37,840 --> 00:34:40,920 Speaker 1: in the desert. Do you really not see that? We've 565 00:34:41,400 --> 00:34:44,600 Speaker 1: like we've really lost our way here, like let's figure 566 00:34:44,600 --> 00:34:47,440 Speaker 1: out something else. And they're saying like, look, you know, 567 00:34:47,480 --> 00:34:52,239 Speaker 1: we've really tried to UM. We tried this green revolution, 568 00:34:52,640 --> 00:34:56,480 Speaker 1: which basically the a good definition I saw for the 569 00:34:56,480 --> 00:35:01,879 Speaker 1: green revolution is where you take UM ecosystem services, which 570 00:35:01,920 --> 00:35:05,759 Speaker 1: is like natural pest control, like predatory insects or the 571 00:35:05,840 --> 00:35:10,799 Speaker 1: natural nutrient cycle UM, and you you manipulate them. You 572 00:35:10,800 --> 00:35:13,759 Speaker 1: you you create an artificial version of it that you 573 00:35:13,800 --> 00:35:16,400 Speaker 1: can control a lot more easily, and you use the 574 00:35:16,400 --> 00:35:18,080 Speaker 1: heck out of it to grow the heck out of 575 00:35:18,200 --> 00:35:21,319 Speaker 1: some plants. That's the green revolution there, Like we tried this, 576 00:35:22,040 --> 00:35:23,920 Speaker 1: it worked for a little while, but now we know 577 00:35:24,520 --> 00:35:28,240 Speaker 1: for sure that it is really harmful to the environment, 578 00:35:28,680 --> 00:35:31,200 Speaker 1: so we need to dial that back, not double down 579 00:35:31,280 --> 00:35:34,839 Speaker 1: on it. And that's UM. That's kind of that. That's 580 00:35:34,840 --> 00:35:37,680 Speaker 1: where this there's a tension. Now there's this split, there's 581 00:35:37,680 --> 00:35:40,160 Speaker 1: a we've reached a fork in the road, and we're like, 582 00:35:40,200 --> 00:35:42,479 Speaker 1: which way do we go. Do we just really keep 583 00:35:42,520 --> 00:35:45,879 Speaker 1: hammering this traditional farming because we know that we can 584 00:35:45,920 --> 00:35:50,200 Speaker 1: coax um enough food to feed some people or ten 585 00:35:50,239 --> 00:35:53,319 Speaker 1: billion people, or do we say no, we actually need 586 00:35:53,360 --> 00:35:56,400 Speaker 1: to go the agro ecology route because we have to 587 00:35:56,480 --> 00:35:59,920 Speaker 1: take into account basically just as much as our ability 588 00:35:59,960 --> 00:36:03,279 Speaker 1: to feed ten billion people, UM, the idea that we're 589 00:36:03,320 --> 00:36:06,680 Speaker 1: not harming the earth with our agricultural practices. That's the 590 00:36:06,719 --> 00:36:09,319 Speaker 1: split that we're looking at right now while we're trying 591 00:36:09,360 --> 00:36:12,400 Speaker 1: to figure out what four datto is going to be. Yeah, 592 00:36:12,440 --> 00:36:15,439 Speaker 1: and it seems like and Emily is way way into 593 00:36:15,520 --> 00:36:18,960 Speaker 1: this stuff with agroecology for years now, and it seems 594 00:36:18,960 --> 00:36:22,719 Speaker 1: like it's really all about the soil and the devastating 595 00:36:22,760 --> 00:36:26,920 Speaker 1: effects on the actual soil that UM, I guess the 596 00:36:26,960 --> 00:36:30,919 Speaker 1: green Revolution has has caused by all that manipulation. And 597 00:36:31,000 --> 00:36:33,880 Speaker 1: she has made an effort just in our little backyard 598 00:36:33,920 --> 00:36:36,160 Speaker 1: over the past, like whatever, how long we've been here 599 00:36:36,200 --> 00:36:41,440 Speaker 1: fourteen years to reclaim that soil and to make it 600 00:36:42,000 --> 00:36:45,799 Speaker 1: good soil again. And we've got great soil now. And 601 00:36:45,840 --> 00:36:48,839 Speaker 1: it's uh, it takes a long time because so much 602 00:36:48,920 --> 00:36:51,440 Speaker 1: damage has done over so many years. It's not the 603 00:36:51,480 --> 00:36:52,840 Speaker 1: kind of thing where you can just be like, all right, 604 00:36:52,840 --> 00:36:54,880 Speaker 1: we're gonna stop doing that, and then the soil is 605 00:36:54,920 --> 00:36:57,879 Speaker 1: gonna be great again. Right. Um. It takes many, many 606 00:36:57,960 --> 00:37:00,799 Speaker 1: years of really caring for that soil to get it 607 00:37:00,840 --> 00:37:04,560 Speaker 1: back where it began or as close to it as possible. Yeah, 608 00:37:04,600 --> 00:37:07,000 Speaker 1: and in some cases too, when you're talking about what 609 00:37:07,080 --> 00:37:11,359 Speaker 1: conventional and agriculture does to the soil as far as 610 00:37:11,480 --> 00:37:15,239 Speaker 1: like crop production is concerned, it's never going to get 611 00:37:15,360 --> 00:37:17,680 Speaker 1: okay again. It's never going to come back. And so 612 00:37:17,760 --> 00:37:21,839 Speaker 1: there's a process in conventional agriculture where you use up 613 00:37:21,960 --> 00:37:25,160 Speaker 1: a plot of land and you move on to the 614 00:37:25,160 --> 00:37:26,799 Speaker 1: next one, and when you run out of land, you 615 00:37:26,840 --> 00:37:31,200 Speaker 1: bring more crop land online into the food production sector. 616 00:37:31,600 --> 00:37:34,120 Speaker 1: And that's what you do. You you use up land 617 00:37:34,160 --> 00:37:37,240 Speaker 1: until you have to replace it by taking over more land. 618 00:37:37,600 --> 00:37:40,719 Speaker 1: That's the current iteration. Agro ecologies like you don't have 619 00:37:40,760 --> 00:37:42,840 Speaker 1: to do that if you just treat the soil like Emily. 620 00:37:43,000 --> 00:37:46,120 Speaker 1: This is their motto, Um, you where it's going to 621 00:37:46,239 --> 00:37:49,080 Speaker 1: be all good like you. You you don't have to 622 00:37:49,200 --> 00:37:52,439 Speaker 1: keep replacing land with more land because you don't use 623 00:37:52,600 --> 00:37:56,200 Speaker 1: up the land. You actually leave the land better off 624 00:37:56,280 --> 00:37:59,400 Speaker 1: than it was before you started. Using it. That to 625 00:37:59,480 --> 00:38:02,439 Speaker 1: me is thing that just makes my eyes pop open 626 00:38:02,480 --> 00:38:05,040 Speaker 1: and my heart just swell for I grow a college. 627 00:38:05,040 --> 00:38:08,520 Speaker 1: He's like, you're actually improving the land. And there have 628 00:38:08,640 --> 00:38:12,240 Speaker 1: been studies. I ran across a study of a place 629 00:38:12,320 --> 00:38:16,600 Speaker 1: called um white Oak Pastures down in how I can't 630 00:38:16,600 --> 00:38:18,880 Speaker 1: remember where it is. It's like south central Georgia, not 631 00:38:18,920 --> 00:38:23,680 Speaker 1: too far from albany Um. And they have actually they 632 00:38:23,840 --> 00:38:27,120 Speaker 1: they've hired independent researchers to come in and look at 633 00:38:27,320 --> 00:38:31,160 Speaker 1: the environmental impact of what they do, which is regenerative grazing. 634 00:38:31,680 --> 00:38:37,320 Speaker 1: And the study turned up findings that literally made international news. 635 00:38:37,520 --> 00:38:40,920 Speaker 1: That's how eye opening that what they found was. Yes, 636 00:38:41,080 --> 00:38:45,080 Speaker 1: so just a little backstory, reginatry Man, I knew I 637 00:38:45,120 --> 00:38:49,160 Speaker 1: was going to do that. Regenerative grazing is a very 638 00:38:49,200 --> 00:38:53,759 Speaker 1: simple premise. Basically, don't let your livestock eat all the 639 00:38:53,840 --> 00:38:57,399 Speaker 1: grass down to the nub where it will probably die, 640 00:38:57,920 --> 00:38:59,920 Speaker 1: and then just move them on to another area to 641 00:39:00,040 --> 00:39:03,879 Speaker 1: do the same thing. Um, move them more often, they 642 00:39:03,880 --> 00:39:06,680 Speaker 1: won't eat down to the nubs. Those plants and those 643 00:39:06,680 --> 00:39:10,439 Speaker 1: grasses will grow back even better probably, and you'll have 644 00:39:10,840 --> 00:39:12,680 Speaker 1: you know, you, like you said, you won't be using 645 00:39:12,719 --> 00:39:17,600 Speaker 1: up the land. So this study they looked at they 646 00:39:17,640 --> 00:39:20,960 Speaker 1: wanted to compare like the c O two costs of 647 00:39:21,040 --> 00:39:24,720 Speaker 1: industrial beef production, which is something that we've talked about before, 648 00:39:24,960 --> 00:39:29,040 Speaker 1: but traditional grazing emits and this is just astounding and awful. 649 00:39:29,960 --> 00:39:33,480 Speaker 1: Amidst thirty three pounds of c O two to raise 650 00:39:33,520 --> 00:39:37,560 Speaker 1: a single pound of meat. Uh, it's nuts. So you 651 00:39:37,600 --> 00:39:41,239 Speaker 1: go to plant based meat, um like a like a 652 00:39:41,320 --> 00:39:45,000 Speaker 1: beyond situation, right, which I still haven't tried. I want 653 00:39:45,000 --> 00:39:48,400 Speaker 1: to try that. There's a good, impossible and beyond all right, 654 00:39:48,880 --> 00:39:52,480 Speaker 1: So these meat alternatives made from plants, they really reduce 655 00:39:52,600 --> 00:39:54,520 Speaker 1: that to about three to three and a half to 656 00:39:54,600 --> 00:39:57,680 Speaker 1: four pounds of c O two first a single pound. 657 00:39:58,280 --> 00:40:01,279 Speaker 1: But at the kok that's good, you're on the right track. 658 00:40:01,320 --> 00:40:07,719 Speaker 1: But white oak is actually sequestering CEO two. It's amazing. Yeah. 659 00:40:07,920 --> 00:40:12,959 Speaker 1: So if meat alternatives emit about three and a half 660 00:40:13,040 --> 00:40:15,359 Speaker 1: kilograms or three and a half pounds of CEO two 661 00:40:15,400 --> 00:40:18,840 Speaker 1: per pound of meat alternative, white Oak pastors is raising 662 00:40:18,920 --> 00:40:23,560 Speaker 1: beef like like actual beef. And when they do, they 663 00:40:23,600 --> 00:40:28,279 Speaker 1: are sinking sequestering three point five pounds of c O 664 00:40:28,440 --> 00:40:31,680 Speaker 1: two for every pound of beef that's produced. There's like 665 00:40:31,800 --> 00:40:37,759 Speaker 1: having solar power and creating more energy than you use exactly. Yeah, 666 00:40:37,880 --> 00:40:40,920 Speaker 1: it's insane. These findings where and they've been like looked 667 00:40:40,960 --> 00:40:43,560 Speaker 1: at and studied and looked at again, and everybody's like, 668 00:40:43,600 --> 00:40:46,799 Speaker 1: this can't make sense. Apparently some of the UM meat 669 00:40:46,800 --> 00:40:50,640 Speaker 1: alternative companies out there were like, you know, this is 670 00:40:50,680 --> 00:40:52,759 Speaker 1: all wrong, this can't be right. And they were like, 671 00:40:53,040 --> 00:40:58,120 Speaker 1: it's actually right. Regenator and regenerative grazing produces livestock that 672 00:40:58,360 --> 00:41:03,200 Speaker 1: actually capture carbon and store it. It's insane. What are 673 00:41:03,239 --> 00:41:06,359 Speaker 1: you doing. We're just moving the cows a little more. Yeah, 674 00:41:06,400 --> 00:41:09,000 Speaker 1: that's basically it. And then they move them to another 675 00:41:09,080 --> 00:41:11,520 Speaker 1: meadow and and like when it's belly high, and then 676 00:41:11,520 --> 00:41:13,640 Speaker 1: they do it again. And like here's the thing. The 677 00:41:13,880 --> 00:41:16,399 Speaker 1: reason that everybody is not doing this already is because 678 00:41:16,400 --> 00:41:21,040 Speaker 1: it's way more expensive to regenera regeneratively graze. UM. If 679 00:41:21,080 --> 00:41:23,000 Speaker 1: you look at white Oak Pastures, you can order their 680 00:41:23,000 --> 00:41:27,680 Speaker 1: stuff online. Um, it's very expensive. It's not ridiculously expensive. 681 00:41:28,120 --> 00:41:32,720 Speaker 1: Like there's definitely like um long established mail order beef 682 00:41:32,800 --> 00:41:36,120 Speaker 1: companies that are three or four times of the price. 683 00:41:36,600 --> 00:41:39,400 Speaker 1: You know what I mean. It does sound gross, but 684 00:41:40,239 --> 00:41:43,040 Speaker 1: mail order beef. But it's some of those companies are 685 00:41:43,040 --> 00:41:45,359 Speaker 1: way more expensive, but it's still more than you're going 686 00:41:45,440 --> 00:41:47,240 Speaker 1: to go pay if you just go to the grocery 687 00:41:47,239 --> 00:41:50,680 Speaker 1: store and get whatever beef they have. But in buying that, 688 00:41:50,760 --> 00:41:53,800 Speaker 1: if you can afford it, you're actually helping to save 689 00:41:53,840 --> 00:41:58,040 Speaker 1: the earth. Um, it's it's pretty impressive stuff. Well yeah, 690 00:41:58,080 --> 00:42:00,200 Speaker 1: and that also helps solve the issue if you need 691 00:42:00,239 --> 00:42:04,840 Speaker 1: to feed uh, however, nine billion people but in the 692 00:42:04,880 --> 00:42:08,080 Speaker 1: next you know, thirty years. One of the big issues 693 00:42:08,200 --> 00:42:11,600 Speaker 1: is like do we even have enough land to do that? Like, well, yeah, dummies, 694 00:42:11,640 --> 00:42:13,960 Speaker 1: we might if we don't just use up land and 695 00:42:14,040 --> 00:42:16,400 Speaker 1: move on to new land. Because that's a big critics 696 00:42:16,520 --> 00:42:19,960 Speaker 1: criticism of regenerative grazing is it requires two point five 697 00:42:20,040 --> 00:42:23,239 Speaker 1: times the land that conventional grazing does, which is why 698 00:42:23,280 --> 00:42:26,040 Speaker 1: it's so much more expensive. But yeah, if if if 699 00:42:26,080 --> 00:42:27,600 Speaker 1: you're not using up the land and having to bring 700 00:42:27,640 --> 00:42:30,919 Speaker 1: more land in his crop land, then that issue might 701 00:42:30,960 --> 00:42:34,439 Speaker 1: not actually exist. That might not be a problem, right, Yeah, 702 00:42:35,080 --> 00:42:38,919 Speaker 1: pretty impressive, super impressive. I think the last thing here 703 00:42:39,760 --> 00:42:41,680 Speaker 1: as far as future farming goes as we need to 704 00:42:41,719 --> 00:42:45,040 Speaker 1: hit on food waste, which is I mean, if they could, 705 00:42:45,480 --> 00:42:48,719 Speaker 1: if they could reduce food waste by thirty that would 706 00:42:48,719 --> 00:42:51,799 Speaker 1: be a game changer for feeding the world. I think 707 00:42:51,920 --> 00:42:55,440 Speaker 1: right now, it takes a land mass larger than China 708 00:42:55,680 --> 00:43:00,600 Speaker 1: to grow food that goes uneaten ultimately une eaten. The 709 00:43:00,640 --> 00:43:05,520 Speaker 1: size of China, it's like it's hard to even talk 710 00:43:05,520 --> 00:43:08,919 Speaker 1: about without getting like super upset. So food waste also 711 00:43:09,000 --> 00:43:11,439 Speaker 1: needs to be an episode that we have to do 712 00:43:11,640 --> 00:43:14,360 Speaker 1: because it is just so mind boggling. But from what 713 00:43:14,440 --> 00:43:17,759 Speaker 1: I saw, up to fifty of the food that America 714 00:43:18,080 --> 00:43:22,200 Speaker 1: produces is thrown away, or if that Americans buy maybe 715 00:43:22,560 --> 00:43:25,920 Speaker 1: I'm not quite sure, but in America about thirty in 716 00:43:25,920 --> 00:43:29,759 Speaker 1: the world overall, a ton of water is wasted. I 717 00:43:29,800 --> 00:43:32,680 Speaker 1: saw as much as um. A quarter of the world's 718 00:43:32,680 --> 00:43:36,480 Speaker 1: water is wasted through this wasted food our fresh water 719 00:43:37,000 --> 00:43:42,480 Speaker 1: intake um. And if you can just dial back a 720 00:43:42,520 --> 00:43:46,279 Speaker 1: significant portion of that food waste um, not only are 721 00:43:46,280 --> 00:43:47,600 Speaker 1: you gonna save a lot of money and a lot 722 00:43:47,640 --> 00:43:50,240 Speaker 1: of environmental harm, you're gonna feed a lot of people. 723 00:43:50,480 --> 00:43:53,520 Speaker 1: Because it's like you said earlier, our foods, food supply 724 00:43:53,640 --> 00:43:57,560 Speaker 1: or food chain is globally interconnected. So we make enough 725 00:43:57,560 --> 00:44:01,560 Speaker 1: food already for a lot of people, probably everybody. It's 726 00:44:01,600 --> 00:44:04,680 Speaker 1: just some people go hungry because we waste so much 727 00:44:04,719 --> 00:44:07,920 Speaker 1: food and we're terrible at distributing it equitably. If we 728 00:44:07,960 --> 00:44:10,560 Speaker 1: can figure that out, we may not have a problem 729 00:44:10,600 --> 00:44:15,560 Speaker 1: at all. And it's possible that agro um ecological farming 730 00:44:15,880 --> 00:44:19,680 Speaker 1: could supply food for ten billion people. That's a big one. 731 00:44:19,680 --> 00:44:22,640 Speaker 1: There's studies underway right now to figure out just what 732 00:44:22,760 --> 00:44:26,480 Speaker 1: kind of gap we're talking about between uh say, organic 733 00:44:26,560 --> 00:44:31,200 Speaker 1: or agro ecological crop yields and conventional crop yields. And 734 00:44:31,239 --> 00:44:32,680 Speaker 1: for a long time where he's like, yeah, you just 735 00:44:32,719 --> 00:44:35,399 Speaker 1: get way more food from conventional farming, and some people 736 00:44:35,440 --> 00:44:37,959 Speaker 1: are actually doing the studies and they're finding like, yeah, 737 00:44:38,000 --> 00:44:40,440 Speaker 1: that's true in some cases, not true in other cases. 738 00:44:40,480 --> 00:44:43,080 Speaker 1: And if we can quantify exactly what the gap is, 739 00:44:43,360 --> 00:44:45,040 Speaker 1: we can figure out how to close that gap. And 740 00:44:45,080 --> 00:44:48,680 Speaker 1: then yeah, we can just use agro ecology. Yeah, and 741 00:44:48,719 --> 00:44:50,319 Speaker 1: I think this, you know, the fork in the road 742 00:44:50,360 --> 00:44:54,319 Speaker 1: where we are hopefully what it will look like. It's 743 00:44:54,400 --> 00:44:56,399 Speaker 1: not a hard left turn or a hard right turn, 744 00:44:57,080 --> 00:45:02,160 Speaker 1: but maybe a gentle turn on both sides that eventually 745 00:45:02,160 --> 00:45:05,719 Speaker 1: come back together down the road where there's a mix 746 00:45:05,760 --> 00:45:09,400 Speaker 1: of both, where there is precision farming um used in 747 00:45:09,480 --> 00:45:13,520 Speaker 1: agroecology because they're not. I mean, I suppose there are 748 00:45:13,520 --> 00:45:17,880 Speaker 1: some really back to basics agro uh ecological farmers that, 749 00:45:18,480 --> 00:45:21,360 Speaker 1: you know, I want to have an oxen pulling a 750 00:45:21,400 --> 00:45:27,000 Speaker 1: plow the Amish. Yeah, probably just them. The point is 751 00:45:27,000 --> 00:45:29,439 Speaker 1: they're they're into it, man. I mean, they don't mind 752 00:45:29,520 --> 00:45:33,560 Speaker 1: the idea of a robot and in precision weeding and 753 00:45:33,600 --> 00:45:38,440 Speaker 1: stuff like that. Uh, it's it's these massive farms and 754 00:45:38,520 --> 00:45:40,719 Speaker 1: all this waste is what they're trying to combat. So 755 00:45:40,760 --> 00:45:43,480 Speaker 1: hopefully they can there can be a marriage and that 756 00:45:43,520 --> 00:45:46,080 Speaker 1: can be the best way forward. Yeah, that's what I'm 757 00:45:46,080 --> 00:45:50,160 Speaker 1: hoping to Pretty cool stuff, man, who knew? Great? I 758 00:45:50,200 --> 00:45:53,160 Speaker 1: love it, um and we got two episode ideas out 759 00:45:53,160 --> 00:45:56,799 Speaker 1: of it. So there you go. Uh, if you want 760 00:45:56,840 --> 00:45:59,520 Speaker 1: to know more about the future of farming, to start 761 00:45:59,520 --> 00:46:01,800 Speaker 1: reading a if. There's a lot of really interesting stuff 762 00:46:01,800 --> 00:46:03,960 Speaker 1: out there. And since I said that, it's time for 763 00:46:04,000 --> 00:46:09,359 Speaker 1: a listener mail. This is kind of fitting. Actually this 764 00:46:09,400 --> 00:46:13,600 Speaker 1: is about hydrology. Hey, guys, have a PhD in hydrology 765 00:46:14,360 --> 00:46:18,200 Speaker 1: and teach hydrology and water resources at a university. One 766 00:46:18,239 --> 00:46:21,520 Speaker 1: of the misconceptions I'm constantly battling and my courses is 767 00:46:21,560 --> 00:46:24,400 Speaker 1: that water is a renewable resource, which is something we 768 00:46:24,440 --> 00:46:28,759 Speaker 1: said in the episode on hydro power, because it's only 769 00:46:28,800 --> 00:46:31,680 Speaker 1: renewable if we use it as such. I often use 770 00:46:31,680 --> 00:46:34,760 Speaker 1: the money analogies to teach this point to my students. 771 00:46:35,560 --> 00:46:39,440 Speaker 1: Imagine your load your local hydrologic water balance is like 772 00:46:39,480 --> 00:46:42,279 Speaker 1: having a rather large inheritance, and you are unable to 773 00:46:42,320 --> 00:46:45,120 Speaker 1: ever work again or make money. As long as you 774 00:46:45,120 --> 00:46:46,960 Speaker 1: live off the interest, you and your family can live 775 00:46:46,960 --> 00:46:49,920 Speaker 1: forever without ever working. But if you spend the principle, 776 00:46:50,320 --> 00:46:52,799 Speaker 1: you'll eventually run out of money. It's the same with water. 777 00:46:53,280 --> 00:46:55,880 Speaker 1: If you only use the renewable water, you won't have 778 00:46:55,920 --> 00:46:59,440 Speaker 1: an issue, as Josh noted, But if you lower lake levels, 779 00:46:59,440 --> 00:47:02,520 Speaker 1: deplete ground water and melt ice caps, you will eventually 780 00:47:02,600 --> 00:47:06,040 Speaker 1: run out of renewable water. You might ask, but it'll 781 00:47:06,120 --> 00:47:09,239 Speaker 1: just rain again, right, And that's true, but not enough 782 00:47:09,239 --> 00:47:12,000 Speaker 1: to refill the pot or the interest to refill the 783 00:47:12,040 --> 00:47:15,280 Speaker 1: bank account. The money isn't gone, it's just in someone 784 00:47:15,320 --> 00:47:18,360 Speaker 1: else's pocket. Similarly, the water isn't gone, it's just in 785 00:47:18,400 --> 00:47:22,080 Speaker 1: someone else's watershed. Were most likely the salty oceans which 786 00:47:22,120 --> 00:47:29,239 Speaker 1: we can't drink uh. That is Dr Pete Whittington, Associate 787 00:47:29,280 --> 00:47:35,239 Speaker 1: professor at Brandon University, Dr Pete. Dr Pete. Dr Pete 788 00:47:35,280 --> 00:47:38,640 Speaker 1: sounds like one of those people who still continue to 789 00:47:38,760 --> 00:47:42,440 Speaker 1: insist that climate change is real even though it's cold outside. 790 00:47:43,680 --> 00:47:48,560 Speaker 1: You know, right, smart, Well, thanks a lot, Dr Pete. 791 00:47:48,600 --> 00:47:51,719 Speaker 1: We appreciate being set straight. Thank you. Um, and that 792 00:47:51,880 --> 00:47:55,400 Speaker 1: is an excellent point. Um. Yeah, I don't I didn't 793 00:47:55,400 --> 00:47:59,120 Speaker 1: mean to get across this idea that you know, just 794 00:47:59,360 --> 00:48:01,080 Speaker 1: there's water. If we're wearing, we don't need to worry 795 00:48:01,080 --> 00:48:04,000 Speaker 1: about water. No, I think you got it mostly right, 796 00:48:04,000 --> 00:48:08,600 Speaker 1: he said. Good. I love hearing that. So. Um, you 797 00:48:08,640 --> 00:48:12,719 Speaker 1: got anything else from Dr Pete? Nothing else? Okay? Well then, everybody, 798 00:48:12,800 --> 00:48:14,120 Speaker 1: if you want to get in touch of this, like 799 00:48:14,239 --> 00:48:17,120 Speaker 1: Dr Pete did, you can send us an email. Send 800 00:48:17,160 --> 00:48:23,400 Speaker 1: it off to stuff podcast at iHeart radio dot com. 801 00:48:23,480 --> 00:48:25,680 Speaker 1: Stuff you Should Know is a production of iHeart Radio. 802 00:48:26,120 --> 00:48:28,360 Speaker 1: For more podcasts for my heart Radio, visit the iHeart 803 00:48:28,400 --> 00:48:30,920 Speaker 1: Radio app, Apple Podcasts, or wherever you listen to your 804 00:48:30,920 --> 00:48:31,600 Speaker 1: favorite shows.