Understanding the effects of water stress on winter wheat in Colorado

Sarah Grogan Author

03/19/2019 Added

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Sarah Grogan from Colorado State University discusses the effect of water stress on winter wheat.

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[00:00:00.560]Hello.
[00:00:01.560]My name is Sarah Grogan.
[00:00:02.560]I'm a graduate student at Colorado State University, and I'm going to talk with you today about
[00:00:07.120]understanding the effects of water stress on winter wheat in Colorado.
[00:00:14.320]Wheat is the leading export crop in the U.S. and the fourth leading field crop.
[00:00:19.000]This is a breakdown of the different classes of wheat grown in the U.S. in 2012, with each
[00:00:23.320]class being used for different types of food products.
[00:00:26.840]In 2012, hard winter wheat comprised almost half of the total wheat production in the
[00:00:31.860]U.S.
[00:00:32.860]I'm going to show you a couple of graphs that will help you understand the impact drought
[00:00:36.940]has on winter wheat.
[00:00:41.520]In 2012, the U.S. produced just under 1.7 billion bushels of winter wheat, which is
[00:00:47.440]shown in the chart on the left.
[00:00:49.760]You can see that production varies a lot among states, with the most wheat being produced
[00:00:54.020]in Kansas.
[00:00:55.740]The chart on the right shows you the amount of land wheat was grown on.
[00:00:59.820]In general, states with the highest wheat production also had the largest areas harvested.
[00:01:05.820]The U.S. Great Plains, from Texas to North Dakota, is especially important in winter
[00:01:11.160]wheat production.
[00:01:13.780]However, understanding how much land is planted and how much wheat is produced is only part
[00:01:20.440]of the picture.
[00:01:21.440]Here, we're looking at the productivity of the land in bushels per acre.
[00:01:25.640]This map is almost the inverse of the other two, with the U.S. Great Plains having among
[00:01:31.020]the lowest productivity and higher yields being achieved along the coasts and in the
[00:01:35.800]Midwest.
[00:01:36.800]So, there is a lot of land in the U.S. Great Plains that's used for winter wheat production,
[00:01:42.420]but this land is not very productive.
[00:01:44.520]However, because these states have so much land, they're very important to U.S. wheat
[00:01:49.880]production.
[00:01:51.540]One could speculate a lot of different reasons for low yields
[00:01:55.540]in the Great Plains. One explanation is because of the low and variable precipitation.
[00:02:04.040]So this map shows the average annual precipitation in each county across the U.S.
[00:02:09.820]Those same states that had low yield per acre also have low moisture.
[00:02:14.620]The yellow regions here [rocky mountain region] only have 10 to 20 inches of precipitation annually.
[00:02:22.500]The specific research I'm presenting on is
[00:02:25.440]working to improve drought tolerance in Colorado wheat.
[00:02:29.940]Annual precipitation in Colorado is only 16 inches, and the eastern part of the state
[00:02:34.780]where most of the wheat is grown is even drier.
[00:02:38.040]The amount of precipitation is also very variable, as shown in the graph, which has more than
[00:02:44.540]100 years of precipitation data.
[00:02:47.900]Despite the low amounts of moisture, very little irrigation is used for Colorado wheat.
[00:02:55.340]One of our research objective is to identify winter wheat lines that have enhanced tolerance to
[00:03:00.360]water stress based on their yield in water-stressed or non-stressed treatments.
[00:03:07.200]To do this, we have a group of 300 different wheat varieties that we grew in side-by-side
[00:03:12.380]fully irrigated and partially irrigated treatments.
[00:03:16.400]We grew this panel for two years and controlled moisture using different types of irrigation,
[00:03:21.900]either drip irrigation tubes in 2012 or linear irrigation tubes in 2012.
[00:03:25.240]We also used wet irrigation sprinklers in 2013.
[00:03:30.920]This is a picture of our side-by-side treatments in 2013, with the wet, fully irrigated treatment
[00:03:36.800]on the right.
[00:03:38.120]You can see that early in the season the two treatments looked similar, but as the summer
[00:03:42.780]progressed, the wet treatment grew taller, matured more slowly, and stayed green longer,
[00:03:48.480]which meant that the wheat kernels had more time to get plump.
[00:03:55.140]Ultimately our wet treatment had much higher yields. The dry treatment yielded only half as much in most plots.
[00:04:03.140]By comparing yields between the two treatments, we were able to identify wheat varieties that
[00:04:07.840]perform well in both treatments, as well as those with especially high yields in the dry
[00:04:12.900]treatment.
[00:04:14.620]These varieties might have beneficial genes that could be useful for Colorado wheat production.
[00:04:22.200]A second objective is to determine whether yield or
[00:04:25.040]other yield components can be predicted indirectly through a process called canopy spectral reflectance,
[00:04:31.640]or CSR.
[00:04:33.940]Field measurements using instruments like this one are much quicker and easier to measure
[00:04:39.000]than labor-intensive hand measurements, and are also less expensive.
[00:04:45.540]If they are strongly associated with yield, we might be able to use these higher-throughput
[00:04:50.540]methods instead of hand measurements.
[00:04:54.940]There are a couple of things related to plant-light interactions that you need to know to understand
[00:05:01.080]some of the theory behind CSR.
[00:05:03.640]First, light reaching the plant canopy can either be absorbed, reflected, or transmit
[00:05:09.220]through a leaf or tissue.
[00:05:11.640]When measuring CSR, we are measuring the amount of light reflected back up at many different
[00:05:16.540]wavelengths.
[00:05:18.180]The amount of light reflected depends on what compounds are present, for example, pigments like chlorophyll or orange carotenoids that absorb visible light, or how much water is present in the tissue.
[00:05:30.360]The amount of light reflected also varies with morphological features, such as whether a leaf is curled or wilted in response to desiccation, or how wide the leaves are.
[00:05:45.180]This is an example of what a typical reflectance spectrum might look like.
[00:05:49.120]There's low reflectance in the visible light, from 400 to 700 nanometers, due to absorption by pigments like chlorophyll.
[00:05:57.320]There's higher reflectance in the near-infrared, from about 700 to 1400 nanometers, and low reflectance above 1400 nanometers, due to the presence of water in the leaf tissue.
[00:06:12.180]These are the sensors that we use to measure the amount of reflectance.
[00:06:18.180]Here there are four sensors. Two top sensors measure the amount of irradiance from the sun reaching the plant canopy, and two bottom sensors measure the amount of reflectance back off of the plant canopy.
[00:06:31.180]Each pair of sensors measures a different range of light, but only one pair of sensors is used at a time, because of the amount of time required to take a sample.
[00:06:41.180]Usually a single pair of sensors is used for an entire day.
[00:06:46.180]So this is how we would measure canopy spectral reflectance in our field. We're able to walk with the instrument, scanning each individual field plot.
[00:06:58.180]You can see our two treatments. Each treatment has 330 field plots, so there's 660 plots total, but only one treatment is measured per day.
[00:07:10.180]Each measurement creates two text files, because there are two sensors that contain reflectance data.
[00:07:20.180]Each of these text files has reflectance data from more than 2,000 wavelengths.
[00:07:26.180]And all of these files from a single day can be combined into a single Excel document with a macro.
[00:07:34.180]Plots are measured many times during the summer, so in total we have 10's of thousands of data points per day.
[00:07:39.180]Plots are measured many times during the summer, so in total we have 10's of thousands of data points per day.
[00:07:40.180]From our reflectance data, we can calculate spectral
[00:07:46.640]indices, which specify relationships between the amount of light at different wavelengths.
[00:07:53.740]If the spectral indices correlate to important physiological traits like yield or yield components,
[00:07:59.720]they can be very useful in plant breeding because they are very quick and inexpensive
[00:08:03.740]to measure. Some spectral indices related to drought tolerance include the water index,
[00:08:11.700]which is the ratio of light reflected at 970 to 900 nm, and the normalized water indices
[00:08:19.340]that use different relationships between the water band at 970 nm and other wavelengths.
[00:08:27.780]The water index and normalized water indices rely on reflectance between 850 and 920 nm,
[00:08:33.720]which is the plateau on the spectra to the left of the 900 nm line,
[00:08:41.260]and reflectance at 970 nm, which you can see is in the dip of the spectra.
[00:08:48.520]Together, these wavelengths provide useful insight to the water status of the plant,
[00:08:54.940]that is, how much water is present in the tissues, which is an important part of understanding
[00:09:01.100]water stress tolerance.
[00:09:03.700]Finally, a third objective is to identify which spectral indices, and at what growth
[00:09:10.380]stages, best correlate with yield and yield components.
[00:09:15.500]I showed you some water indices, but in reality there are dozens of published spectral indices
[00:09:20.560]that we could use.
[00:09:22.040]We're not yet sure which of these indices are most important, most strongly correlated
[00:09:26.880]with our traits, so we want to look at separate correlations between each of these many indices
[00:09:33.680]and different growth stages.
[00:09:37.980]We measured the date of five different growth stages in each plot: jointing and booting
[00:09:43.040]are vegetative growth stages, heading and anthesis are reproductive stages, and maturity
[00:09:49.940]indicates the date at which grain is no longer changing.
[00:09:55.380]We also want to look at correlations for different traits.
[00:09:58.680]Some of the yield components we measured included the length of the spike,
[00:10:03.660]how many spikelets were present on each spike, how many kernels, or wheat grains, were inside
[00:10:09.800]of each spikelet, how big those kernels were, and also the spike fertility, or how many
[00:10:17.200]of those spikelets had any grain inside of them.
[00:10:22.340]I hope this presentation was useful.
[00:10:24.280]I'd like to acknowledge many members of my lab [Byrne Lab], as well as the CSU Wheat Breeding Program,
[00:10:31.600]the [CSU Soil and Crops] Graduate Program, and the
[00:10:33.640]community. Thank you.
[00:10:35.340].

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Understanding the effects of water stress on winter wheat in Colorado

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