1 00:00:04,104 --> 00:00:07,374 - I can't believe how pricey these spicy peppers were. 2 00:00:07,374 --> 00:00:11,144 Get ready for some hot facts next on "Real World." 3 00:00:11,144 --> 00:00:17,150 [futuristic music] 4 00:00:18,886 --> 00:00:21,855 Why are some foods more expensive than others? 5 00:00:21,855 --> 00:00:23,724 And why do some places have more 6 00:00:23,724 --> 00:00:25,759 of one type of food than others? 7 00:00:25,759 --> 00:00:28,295 It all boils down to food supply 8 00:00:28,295 --> 00:00:30,864 and what plants need in order to grow. 9 00:00:30,864 --> 00:00:33,166 - So there are many things that a plant needs 10 00:00:33,166 --> 00:00:34,868 in order to grow. 11 00:00:34,868 --> 00:00:36,770 They need water, 12 00:00:36,770 --> 00:00:37,838 they need air, 13 00:00:37,838 --> 00:00:38,939 they need nutrients that 14 00:00:38,939 --> 00:00:40,807 they largely get from the soil. 15 00:00:40,807 --> 00:00:41,975 They need sunlight or sunshine. 16 00:00:41,975 --> 00:00:43,677 They need heat to grow. 17 00:00:43,677 --> 00:00:45,245 They need time, of course. So they don't grow in one day. 18 00:00:45,245 --> 00:00:47,181 They grow through time. 19 00:00:47,181 --> 00:00:48,949 And what they do is, they use, through their roots, 20 00:00:48,949 --> 00:00:50,717 they will carry up the nutrients 21 00:00:50,717 --> 00:00:52,619 into the various parts of the crop 22 00:00:52,619 --> 00:00:54,254 that will let them grow, develop, 23 00:00:54,254 --> 00:00:55,789 and eventually create the fruit, 24 00:00:55,789 --> 00:00:57,891 which is then what we harvest. 25 00:00:57,891 --> 00:01:00,928 All of these are what we call natural resources. 26 00:01:00,928 --> 00:01:03,463 Our Earth provides with a lot of natural resources, 27 00:01:03,463 --> 00:01:06,633 and all of these are really important for life on Earth, 28 00:01:06,633 --> 00:01:10,037 whether that's human life or the ecosystem 29 00:01:10,037 --> 00:01:11,905 and crops or animals. 30 00:01:11,905 --> 00:01:15,542 So NASA has a really important Earth-observing fleet 31 00:01:15,542 --> 00:01:17,377 of satellites that are continuously 32 00:01:17,377 --> 00:01:18,979 going around the world and providing 33 00:01:18,979 --> 00:01:20,614 different types of information. 34 00:01:20,614 --> 00:01:23,917 Satellite data are a great tool for us 35 00:01:23,917 --> 00:01:26,420 to monitor this kind of information 36 00:01:26,420 --> 00:01:28,121 and monitor that on a daily basis 37 00:01:28,121 --> 00:01:29,857 to give us a global picture, 38 00:01:29,857 --> 00:01:31,391 but that can give us information 39 00:01:31,391 --> 00:01:32,759 all the way down at the field scale 40 00:01:32,759 --> 00:01:34,094 around how crops are developing, 41 00:01:34,094 --> 00:01:36,496 what we can expect the productivity is. 42 00:01:36,496 --> 00:01:39,433 And the important part then is to convert that data 43 00:01:39,433 --> 00:01:42,769 into information that decision-makers can use. 44 00:01:42,769 --> 00:01:46,840 So crop yields are the harvested production 45 00:01:46,840 --> 00:01:50,043 per unit of a harvested area of a crop. 46 00:01:50,043 --> 00:01:51,912 You know, thinking about why is it important for us 47 00:01:51,912 --> 00:01:54,348 to measure a ton of yield. 48 00:01:54,348 --> 00:01:57,317 That's essentially giving us the measure of productivity 49 00:01:57,317 --> 00:01:58,852 of a crop, 50 00:01:58,852 --> 00:02:02,022 and when we multiply the yield 51 00:02:02,022 --> 00:02:03,624 by the area of where it's produced, 52 00:02:03,624 --> 00:02:05,092 that'll give us the total production, 53 00:02:05,092 --> 00:02:06,927 which is generally-- oftentimes measured 54 00:02:06,927 --> 00:02:08,762 in tons, themselves. 55 00:02:08,762 --> 00:02:10,264 And that's giving us, essentially, 56 00:02:10,264 --> 00:02:12,766 if we think about what our food supply is, 57 00:02:12,766 --> 00:02:13,901 we need to care not only 58 00:02:13,901 --> 00:02:15,469 what's happening in one country, 59 00:02:15,469 --> 00:02:18,739 but our food system is really interconnected, 60 00:02:18,739 --> 00:02:20,440 and so it's really important for us to understand 61 00:02:20,440 --> 00:02:21,909 actually globally what's going on. 62 00:02:21,909 --> 00:02:24,278 So if there's a drought, for example, in Russia, 63 00:02:24,278 --> 00:02:25,913 it's one of the biggest exporters 64 00:02:25,913 --> 00:02:30,417 of wheat to Egypt, and therefore, for bread. 65 00:02:30,417 --> 00:02:32,319 So if Russia has a shortfall, right, 66 00:02:32,319 --> 00:02:35,088 if Russia produces less than it normally would 67 00:02:35,088 --> 00:02:36,490 or less than expected, 68 00:02:36,490 --> 00:02:38,258 that has big implications for all the people 69 00:02:38,258 --> 00:02:40,561 that are importing food from Russia, 70 00:02:40,561 --> 00:02:42,396 and actually has implications globally 71 00:02:42,396 --> 00:02:45,165 because when we have less food than what we expect 72 00:02:45,165 --> 00:02:46,800 or less grain than what we expect, 73 00:02:46,800 --> 00:02:49,069 that can have implications also across the world 74 00:02:49,069 --> 00:02:50,671 in terms of increasing prices. 75 00:02:50,671 --> 00:02:52,773 And vice-versa, when you have a lot of supply, 76 00:02:52,773 --> 00:02:54,575 then the prices will go down 77 00:02:54,575 --> 00:02:56,176 because you can meet all that demand 78 00:02:56,176 --> 00:02:57,878 and more than what you have. 79 00:02:57,878 --> 00:03:01,381 - So that's the data that NASA collects about food. 80 00:03:01,381 --> 00:03:05,118 But how do we use that data, and what does it look like? 81 00:03:05,118 --> 00:03:07,321 - There are a lot of data sets that come together 82 00:03:07,321 --> 00:03:10,424 to help us forecast potential food shortages. 83 00:03:10,424 --> 00:03:14,494 The most commonly-used band index is NDVI, 84 00:03:14,494 --> 00:03:17,464 which is Normalized Difference Vegetation Index. 85 00:03:17,464 --> 00:03:19,399 And it tells us something 86 00:03:19,399 --> 00:03:21,768 about how healthy the crops are 87 00:03:21,768 --> 00:03:23,737 and how green they are, 88 00:03:23,737 --> 00:03:27,074 how much chlorophyll is in their leaves. 89 00:03:27,074 --> 00:03:30,644 So high NDVI means really healthy crops; 90 00:03:30,644 --> 00:03:35,115 low NDVI means very little vegetation. 91 00:03:35,115 --> 00:03:36,950 Outliers are data points 92 00:03:36,950 --> 00:03:38,685 that are substantially different 93 00:03:38,685 --> 00:03:40,287 than the rest of the data set. 94 00:03:40,287 --> 00:03:42,623 And maybe this is a sensor error, 95 00:03:42,623 --> 00:03:45,259 or maybe a cloud got in the way 96 00:03:45,259 --> 00:03:47,961 and resulted in a much different value 97 00:03:47,961 --> 00:03:49,830 than the rest of the points. 98 00:03:49,830 --> 00:03:51,665 But we also look at other variables 99 00:03:51,665 --> 00:03:54,601 like the weather data, which might include 100 00:03:54,601 --> 00:03:57,070 rainfall or temperature data. 101 00:03:57,070 --> 00:04:01,074 We look at soil moisture, since healthy and moist soils 102 00:04:01,074 --> 00:04:03,510 are important for crop growth. 103 00:04:03,510 --> 00:04:06,780 The model might take in all of this evidence 104 00:04:06,780 --> 00:04:08,849 and predict that food production 105 00:04:08,849 --> 00:04:10,784 will be low in that region, 106 00:04:10,784 --> 00:04:13,320 and then we can then pass that information along 107 00:04:13,320 --> 00:04:15,522 to decision makers who can help 108 00:04:15,522 --> 00:04:17,591 to prevent or mitigate the effects 109 00:04:17,591 --> 00:04:20,093 of the potential food shortage. 110 00:04:20,093 --> 00:04:23,230 Even though we can observe variables like NDVI, 111 00:04:23,230 --> 00:04:25,799 weather, and soil moisture from space, 112 00:04:25,799 --> 00:04:28,235 we need to use ground truth data 113 00:04:28,235 --> 00:04:30,204 to know how what we're seeing 114 00:04:30,204 --> 00:04:32,706 from space and the satellite images 115 00:04:32,706 --> 00:04:34,775 matches up with what's actually happening 116 00:04:34,775 --> 00:04:36,109 on the ground. 117 00:04:36,109 --> 00:04:38,478 Many people use this information-- 118 00:04:38,478 --> 00:04:42,049 farmers are always looking to maximize their production; 119 00:04:42,049 --> 00:04:45,385 governments care about how much food is produced, 120 00:04:45,385 --> 00:04:47,187 where the food is being produced, 121 00:04:47,187 --> 00:04:50,290 and the economic impacts of food production. 122 00:04:50,290 --> 00:04:52,459 Much of this data is open source 123 00:04:52,459 --> 00:04:56,930 and available to the public through NASA resources online. 124 00:04:56,930 --> 00:04:58,298 - Sounds like a lot of math, 125 00:04:58,298 --> 00:05:00,634 but it all adds up to understanding 126 00:05:00,634 --> 00:05:02,669 how the world's food supply works 127 00:05:02,669 --> 00:05:04,571 and how NASA's eyes in the sky 128 00:05:04,571 --> 00:05:06,807 can help put food on your plate. 129 00:05:06,807 --> 00:05:07,908 [bell dings] 130 00:05:07,908 --> 00:05:09,142 Sounds like it's time for me 131 00:05:09,142 --> 00:05:10,878 to put some food on my plate. 132 00:05:10,878 --> 00:05:13,547 See you next time on "Real World."