WEBVTT 00:00:08.380 --> 00:00:09.340 For lesson two, 00:00:09.340 --> 00:00:11.890 I'm going to split it up into two videos 00:00:11.890 --> 00:00:15.270 because there is a lot of information to present. 00:00:15.270 --> 00:00:17.210 So for this first video, we're going 00:00:17.210 --> 00:00:20.290 to view the whole buffalograss experimental plot. 00:00:20.290 --> 00:00:24.050 And we're going to learn where three different genotypes are 00:00:24.050 --> 00:00:26.070 for each repetition. 00:00:26.070 --> 00:00:28.260 We'll learn about the special software 00:00:28.260 --> 00:00:30.470 used to score the buffalograss plot, 00:00:30.470 --> 00:00:33.743 and then we'll identify bad data that can occur when using 00:00:33.743 --> 00:00:35.373 that special software. 00:00:37.520 --> 00:00:42.150 So in lesson one I did mention that all the genotypes used, 00:00:42.150 --> 00:00:45.140 all 64 genotypes are in a different location 00:00:45.140 --> 00:00:47.100 for each of the repetitions. 00:00:47.100 --> 00:00:48.980 And so here you can kind of see 00:00:48.980 --> 00:00:50.690 where three different ones are placed. 00:00:50.690 --> 00:00:55.567 So the black square is for genotype Ne-9-3565 00:00:56.430 --> 00:00:59.020 and repetition one it's in a corner. 00:00:59.020 --> 00:01:00.920 Repetition two and three, 00:01:00.920 --> 00:01:03.380 you can see it's in a different location. 00:01:03.380 --> 00:01:06.840 And the same for the other two different genotypes 00:01:06.840 --> 00:01:08.670 that are shown here. 00:01:08.670 --> 00:01:13.630 And then you can see, this is April 10th, 2015. 00:01:13.630 --> 00:01:17.910 And then we will have a photo of those same genotypes 00:01:18.860 --> 00:01:20.840 on May 26th. 00:01:20.840 --> 00:01:24.460 And then we will also see them on June 22nd. 00:01:24.460 --> 00:01:26.500 And so having these videos is a, 00:01:26.500 --> 00:01:28.730 or these pictures is a good way 00:01:28.730 --> 00:01:31.753 to see how they green up each month. 00:01:33.740 --> 00:01:35.010 So in lesson one, 00:01:35.010 --> 00:01:39.430 we scored a small section for one of the repetitions 00:01:39.430 --> 00:01:41.730 of the buffalograss research area 00:01:41.730 --> 00:01:44.390 using the NTEP guidelines. 00:01:44.390 --> 00:01:47.860 And Dr. Amundsen did not use that method 00:01:47.860 --> 00:01:52.860 to score his plot area because it is a rather large area, 00:01:52.940 --> 00:01:56.080 it'd be very time consuming and there would be a lot 00:01:56.080 --> 00:01:58.970 of variability among evaluators. 00:01:58.970 --> 00:02:03.850 So Dr. Amundsen would have aerial drones take these photos. 00:02:03.850 --> 00:02:08.490 And then what would happen is these photos would be cut down 00:02:08.490 --> 00:02:10.420 to an individual plot. 00:02:10.420 --> 00:02:15.180 And then only the inner 50% of that plot area 00:02:15.180 --> 00:02:19.513 would be given a score based on their percent greenness. 00:02:20.860 --> 00:02:23.700 Quality of the photos taken by the aerial drones 00:02:23.700 --> 00:02:25.640 can create bad data. 00:02:25.640 --> 00:02:29.330 And so we can see how June 8th, 00:02:29.330 --> 00:02:31.890 this photo does not have as high quality 00:02:31.890 --> 00:02:35.610 as the photo taken on June 22nd. 00:02:35.610 --> 00:02:39.980 And so when we have photos that have bad quality like this, 00:02:39.980 --> 00:02:43.980 it's going to affect our scores for our genotypes. 00:02:43.980 --> 00:02:47.140 So if we look at this one genotype here, 00:02:47.140 --> 00:02:50.210 and this table is all of its greenness scores 00:02:50.210 --> 00:02:54.010 for all three repetitions in 2015. 00:02:54.010 --> 00:02:56.160 If we just look at repetition three, 00:02:56.160 --> 00:03:01.160 we can see how in May, it had 95.6% greenness. 00:03:02.290 --> 00:03:06.480 And then in June, the score dropped down to 34.5, 00:03:06.480 --> 00:03:10.600 and this is June 8th because of this photo. 00:03:10.600 --> 00:03:13.020 And then again, June 22nd, 00:03:13.020 --> 00:03:15.790 a high quality photo was captured. 00:03:15.790 --> 00:03:19.990 And so the score went back up to 99.1. 00:03:20.940 --> 00:03:24.630 And you can really see how the bad quality photos 00:03:24.630 --> 00:03:29.630 affected this genotype if you look at this line graph here. 00:03:30.730 --> 00:03:34.930 And so this data was eliminated from the experiment. 00:03:34.930 --> 00:03:37.000 The color range could have been adjusted 00:03:37.000 --> 00:03:38.600 to rescue the image, 00:03:38.600 --> 00:03:41.603 but it was easier to eliminate the data. 00:03:43.230 --> 00:03:45.300 Problems with bluegrass and weeds 00:03:45.300 --> 00:03:49.820 in the research experimental area can also create bad data. 00:03:49.820 --> 00:03:52.180 Since buffalograss is a warm season grass, 00:03:52.180 --> 00:03:53.420 it thrives in the summer 00:03:53.420 --> 00:03:56.190 and it's dormant in the spring and fall. 00:03:56.190 --> 00:03:59.700 During the dormant periods, weeds and cool season grass 00:03:59.700 --> 00:04:01.800 like bluegrass will develop rapidly 00:04:01.800 --> 00:04:04.340 and take over the plot area. 00:04:04.340 --> 00:04:06.490 And so you can see in photo A, 00:04:06.490 --> 00:04:09.360 there is a weed infestation around the edge 00:04:09.360 --> 00:04:11.340 of the experiment area. 00:04:11.340 --> 00:04:15.670 And in photo B, you can see weed or bluegrass infestation 00:04:15.670 --> 00:04:18.550 around each individual plot. 00:04:18.550 --> 00:04:23.550 And so weed infestations and bluegrass infestations 00:04:24.700 --> 00:04:28.540 are going to affect each genotype differently. 00:04:28.540 --> 00:04:31.870 So if we look at this genotype, Ne-9-3565, 00:04:33.114 --> 00:04:35.030 if we just look at the line graph, 00:04:35.030 --> 00:04:38.803 we can't tell any kind of weed infestation occurred. 00:04:39.740 --> 00:04:41.600 If we look at the table, 00:04:41.600 --> 00:04:46.083 we can see that it had a small infestation. 00:04:47.090 --> 00:04:51.740 And so if we look at this genotype, Ne-6636, 00:04:51.740 --> 00:04:53.180 we can see in the fall 00:04:53.180 --> 00:04:56.600 that it had a much bigger infestation. 00:04:56.600 --> 00:05:00.690 And we can also see that in the table as well. 00:05:00.690 --> 00:05:02.850 And so this data, 00:05:02.850 --> 00:05:06.630 any kind of bad data that occurs due to weed 00:05:06.630 --> 00:05:09.710 and bluegrass infestation was eliminated 00:05:09.710 --> 00:05:11.163 from the experiment.