US Long-Term Ecological Research Network

WSC - Yield and water table depth shapefiles from Wibu field site

Abstract
Yield data from the Wibu field site combined with a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages). It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Dataset ID
317
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Yield data was collected at the time of harvest following the 2012 and 2013 growing season using a John Deere 9660 combine equipped with a Greenstar yield monitoring system. Yield data was cleaned by removing any polygons collected during pre-harvest check strips, turn-around at the end of rows, any polygons less than 6 m2, and any polygons where yield exceeded the record reported yield for Dane County WI (327 bu ac-1). Yield was normalized by calculating z-scores (the number of standard deviations away from the mean) within each field and each year. 2012 data was resampled to the 2013 polygon boundaries by taking the mean of all 2012 polygons with their centroid within each 2013 polygon. For each polygon, 2013 interpolated groundwater metrics were extracted using ArcMAP 10.2 software. A full description of this methodology is contained in Zipper et al. (in review).
Version Number
16

WSC - Leaf area index (LAI) at various points within Wibu field site, 2012-2014

Abstract
Leaf area index (LAI) measurements collected at various points within the Wibu field site between 2012-2014. Measurements were collected approximately weekly from plant emergence until appr. 1 month past the onset of senescence. The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons; therefore, these are all LAI values for corn. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site and use of the LAI data.
Dataset ID
314
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Measurements were made using a Licor-brand LAI-2200 device at approximately weekly intervals during the 2012, 2013, and 2014 growing seasons. At each point, the measurements are the average LAI value calculated from 20 subcanopy measurements ranging from in-line with planted rows to the area between rows. In 2012 and 2013, measurements were taken exclusively during diffuse light conditions (sunrise, sunset, or full cloud cover). In 2014, measurements were taken during daylight hours and corrected for light scattering using the LAI-2200C correction described on their website. This means that 2014 data is less accurate than 2012-2013 data. Note that, for each growing season, a date is listed with 0 LAI – this corresponds to the date prior to emergence at each site.After installation of wells and soil monitoring gear in each of the 2012, 2013, and 2014 growing seasons, we used a Topcon GR5 RTK-GPS to collect the location of each relevant point within the field. In 2014, a gridded set of soil sampling points was also measured.
Version Number
13

WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters

Abstract
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 &amp; 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (<em>in review</em>). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper &amp; Loheide (2014) <em>Ag. For. Met. </em>for more information about the field site.
Dataset ID
312
Data Sources
Date Range
-
Maintenance
completed
Metadata Provider
Methods
A gridded set of points distributed at appr. 30 m resolution over the Wibu field site were established during the 2014 growing season. At each of these points, a disturbed soil sample from a depth of 5 cm was collected and used to measure the soil organic content. At a randomly selected subset of these points, an additional undisturbed soil core was collected from a depth of 5-7.5 cm and used to measure porosity, bulk density, and organic content (measured by burning for 24 hrs at 440&deg;C). Undisturbed soil cores were then split into 3 subsamples and a Beckman-Coulter LS-230 laser particle size analyzer was used to measure a continuous particle size distribution. The subsample with the median d50 particle size was used to calculate a variety of soil texture metrics, described in the data table below. These soil texture parameters were used as input to the Rosetta pedotransfer function (Schaap et al., 2001) to estimate soil water retention properties. At each of these points, the 2013 interpolated groundwater metrics and the yield data described in the Yield dataset were extracted using ArcMAP 10.2 software. A full description of this methodology is contained in Zipper et al. (in review).
Version Number
18

Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007

Abstract
Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape."; The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation :Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Core Areas
Dataset ID
285
Date Range
-
Metadata Provider
Methods
Data were collected from two lakes, Sparkling Lake (46.008, -89.701) and Peter Lake (46.253, -89.504), both located in the northern highlands Lake District of Wisconsin and the Upper Peninsula of Michigan over a 10 day period on each lake in July and August of 2007. Refer to Van de Bogert et al. 2011 for limnological characteristics of the study lakes.Measurements of dissolved oxygen and temperature were made every 10 minutes using multiple sondes dispersed horizontally throughout the mixed-layer in the two lakes (n=35 sondes for Sparkling Lake and n=27 sondes for Peter Lake). Dissolved oxygen measurements were corrected for drift.Conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were also measured by a subset of sensors in each lake. Of the 35 sondes in Sparkling Lake, 31 were from YSI Incorporated: 15 of model 600XLM, 14 of model 6920, and 2 of model 6600). The remaining sondes placed in Sparkling Lake were 4 D-Opto sensors, Zebra-Tech, LTD. In Peter Lake, 14 YSI model 6920 and 13 YSI model 600XLM sondes were used.Sampling locations were stratified randomly so that a variety of water depths were represented, however, a higher density of sensors were placed in the littoral rather than pelagic zone. See Van de Bogert et al. 2012 for the thermal (stratification) profile of Sparkling Lake and Peter Lake during the period of observation, and for details on how locations were classified as littoral or pelagic. In Sparkling Lake, 11 sensors were placed within the shallowest zone, 12 in the off-shore littoral, and 6 in each of the remaining two zones, for a total of 23 littoral and 12 pelagic sensors. Similarly, 15 sensors were placed in the two littoral zones, and 12 sensors in the pelagic zone.Sensors were randomly assigned locations within each of the zones using rasterized bathymetric maps of the lakes and a random number generator in Matlab. Within each lake, one pelagic sensor was placed at the deep hole which is used for routine-long term sampling.Note that in Sparkling Lake this corresponds to the location of the long-term monitoring buoy. After locations were determined, sensors were randomly assigned to each location with the exception of the four D-Opto sensor is Sparkling Lake, which are a part of larger monitoring buoys used in the NTL-LTER program. One of these was located near the deep hole of the lake while the other three were assigned to random locations along the north shore, south shore and pelagic regions of the lake. Documentation: Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Version Number
17

North Temperate Lakes LTER: Chlorophyll - Trout Lake Area 1981 - current

Abstract
Chlorophyll and phaeopigments are measured at our permanent sampling station in the deepest part of each lake. Chlorophyll samples are collected from the seven primary study lakes (Allequash, Big Muskellunge, Crystal, Sparkling, and Trout lakes and bog lakes 27-02 [Crystal Bog], and 12-15 [Trout Bog]) in the Trout Lake area at two to 10 depths depending on the lake and analyzed spectrophotometrically. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 7
Core Areas
Dataset ID
35
Date Range
-
LTER Keywords
Maintenance
ongoing
Metadata Provider
Methods
Spectrophotometer:A. Chlorophyll Extraction (using tissue grinder at DNR Research Station) 1. Dim the lights and keep the sample tubes in the freezer: Because chlorophyll degrades when exposed to light and heat, this procedure and all others associated with analyzing chlorophyll should be carried out in dim light conditions. Only one sample tube should be out of the freezer at any one time while the pre-grinding or grinding procedure is occurring. Return each tube to the freezer as soon as its filter has been ground. 2. Pre-grind filters: Use the sharpened stainless steel probe to chop up the filter into small pieces. This should take approximately 2 minutes. 3. Grind filters: The teflon tip on the tissue grinder should be sanded after grinding approximately 5 filters. Grind each filter for 2 minutes. Do not lift the teflon tip out of the test tube while the grinder is rotating. Grind the filters by attempting to keep the teflon tip in the acetone solution and pressing the tip against the filter and the tube. 4. Return the sample tubes to the freezer for 24 hours: Most protocols call for extracting the samples in the refrigerator (at 4 degrees C). However, after extracting duplicate samples in the freezer and refrigerator (after grinding) there was no significant difference in the chlorophyll results. Because past samples have been extracted in the freezer, this is the current procedure being used. B. Centrifuging the Samples: The samples should be centrifuged as close as possible to 24 hours after extraction. Before centrifuging the samples, turn on the spectrophotometer and enter the correct program number to be sure that it is working properly. Perform the procedures below in dim light. 1. Checking acetone volume: In dim light, use an identical tube as those used for the samples but with mL marked on it, to measure the volume of the acetone in the samples. Measure to the nearest 0.5 mL. If the sample has any other volume than 5 mL, write the volume on the sample label and remember to enter the volume later into the spreadsheet. 2. Loading the centrifuge: Making sure that the rubber stoppers are on tight, put tubes with equal acetone volumes opposite each other in the centrifuge. If there is an odd tube remaining or a tube with a different volume, put a spare tube opposite the sample with the same volume of water to counterbalance the centrifuge. 4. Running the centrifuge: Turn the speed dial below 40. Turn the timer past 15 minutes. Slowly turn up the speed allowing time for the centrifuge to increase in speed. If there is an imbalance in the centrifuge (or any other problem), the centrifuge will run much louder than normal. In this case, stop the centrifuge and attempt to locate the imbalance. If the centrifuge is running smoothly, set the speed at 90 and the timer at 15 minutes. Previously, the numbers on the dial were believed to correspond to revolutions per second; however, this is not the case, for the centrifuge will only reach rpms of approximately 2500. 5. Unloading the centrifuge: Allow the centrifuge to come to a stop on its own. Carefully take each sample tube out of the centrifuge with minimal mixing. If the filter paper is mixed with the liquid, it will be necessary to re-centrifuge the sample. Transport the samples to the spectrophotometer in a rack that has tinfoil on the sides in order to block out the light. C. Running a Sample: 1. Select the test: Allow the spectrophotometer to warm up for at least 15 minutes. Select the proper program by pressing the test number followed by Select. 2. Rinse the cuvettes 3 times with acetone. It is most efficient to rotate 4 matching 1 cm cuvettes. Try to touch the cuvettes only on the opaque sides avoiding touching the clear sides especially on the lower half of the cuvette. 3. Run a blank and check that all cuvettes read near 0: Add acetone to the 4 matching cuvettes (at least half full), wipe them clean with a tissue, and insert them into the spectrophotometer with the labeled sides all facing the same direction (always put the tops on the cuvettes when they are in the spec). Press Run and the spec. will ask for a blank. Use one of the cuvettes filled with acetone as the blank. Once the blank is run, run all of the cuvettes (the cuvette position is changed by pulling out the metal rod to the next notched position). All of the readings at all wavelengths should be within .001 of 0. If this is not the case, remove the suspect cuvette and rinse, wipe, add acetone, and rerun it. Make sure that the correct program is being run by checking the wavelengths. The LTER samples should be run at 750, 665, 664, 647, and 630 nm. 4. Rinse the pipette tip: Before adding sample to a cuvette, the pipette tip should be rinsed with acetone. You should have 2 different sized beakers, one for waste and one for acetone rinse. Set the 10-1000uL pipette to 1000 uL (1 mL) and pipette 1mL of acetone from the rinse beaker and dispose of it in the waste beaker. Be sure that the pipette tip is firmly on the pipette (press it on the bottom of the rinse beaker). 5. Add sample to a cuvette: Before bringing the samples into the spectrophotometer room, turn off the overhead light and turn on the desk light in the corner. Carefully remove a sample from the rack and pipette approximately 2 mL of sample into a cuvette. Use caution not to suck up any filter paper into the pipette; tilt the sample to the side and submerge the pipette tip only just below the fluid level. If the pipette tip is getting close to the filter paper when removing the second mL of sample, stop pipetting and add the partial mL to the cuvette (it is possible to read approximately 1.5 mL of sample). 6. Check the 750 nm reading and run the sample: Insert the cuvette into the spec. (making sure that the labeled side is always facing in the same direction). The default reading on the spec is 750 nm. Check to make sure that this reading is less than 0.010 A. If the reading is higher, remove the cuvette and re-wipe it with a tissue. If the reading is still high, pour the sample back into the tube and re-centrifuge it. To run the sample press Run. 7. Acidify the sample: Once the sample has been run, remove it from the spec and add 60 uL of 0.1 N HCl (30 uL per 1 mL of sample). Gently shake the sample and wait 90 seconds to run it. 8. Check the acidification ratio: The before acidorafter acid ratio of the LTER samples is usually between 1.3 and 1.7. Compare the two readings to make sure the ratio fits in this range. If the ratio is higher than 1.7, re-acidify the sample and run it again (the acid probably did not make contact with the sample). 9. Rinse the cuvette: After checking the acidification ratio, dispose of the sample in the waste beaker and rinse the cuvette 3 times with acetone. Be sure to fill the cuvette to the top with acetone during each rinse to be sure that there is not any trace of acid left. Running Multiple Samples: 1. It may be more efficient to run 2 samples before acidification and then run them both after acidification. If this is done, take caution to add the correct sample to the correct cuvette and not to mix up the samples when they are removed from the spec. for acidification. Recording the Results: 1. Write the spec. id number located on the left of the printout onto the label of the corresponding sample. Each sample should have a before and an after acidification spec. id number written on its label. After all of the samples have been run, enter the date of analysis onto the spec. printout. This date will be used to identify the spec. printout when the data is proofread (after which proofed from spec. printout should be written on the spreadsheet). Clean-up: 1. Rinse the cuvettes 3 times with acetone, allow them to dry for several minutes in the cuvette rack, and return them to their box. 2. Solutions of less than 20percent Acetone can be disposed of down the drain followed by at least 10 volumes of water. Fill the waste beaker with water and pour the waste down the sink with the water running. Leave the water running for several minutes 3. Rinse the beakers and pipette tips 3 times with tap water followed by 3 rinses with distilled water. Hang the beakers on the drying rack. &nbsp;
Short Name
NTLPL01
Version Number
30

North Temperate Lakes LTER: Physical Limnology of Primary Study Lakes 1981 - current

Abstract
Parameters characterizing the physical limnology of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish) are measured at one station in the deepest part of each lake at 0.25-m to 1-m depth intervals depending on the lake. Measured parameters in the data set include water temperature, vertical penetration of photosynthetically active radiation (PAR; not measured on lakes Mendota, Monona, Wingra, and Fish), dissolved oxygen, as well as the derived parameter percent oxygen saturation. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11
Core Areas
Dataset ID
29
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
Light (PAR) extinction coefficient is calculated by linearly regressing ln (FRLIGHT (z)) on depth z where the intercept is not constrained. FRLIGHT(z) = LIGHT(z) or DECK(z) where LIGHT(z) is light measured at depth z and DECK(z) is light measured on deck (above water) at the same time. For open water light profiles, the surface light measurement (depth z = 0) is excluded from the regression. For winter light profiles taken beneath the ice, the first light data are taken at the bottom of the ice cover and are included in the regression. The depth of uppermost light value is equal to the depth of the ice adjusted by the water level in the sample hole, i.e., the depth below the surface of the water. The water level can be at, above or below the surface of the ice. If the water level was not recorded, it is assumed to be 0.0 and the calculated light extinction coefficient is flagged. If ice thickness was not recorded, a light extinction coefficient is not calculated. For light data collected prior to March, 2007, light values less than 3.0 (micromolesPerMeterSquaredPerSec) are excluded. For light data collected starting in March 2007, light values less than 1.0 (micromolesPerMeterSquaredPerSec) are excluded. Except for bog lakes before August 1989, a light extinction coefficient is not calculated if there are less than three FRLIGHT values to be regressed. For bog lakes before August 1989, a light extinction coefficient is calculated if there are least two FRLIGHT values to be regressed. In these cases, the light extinction coefficient is flagged as non-standard. FRLIGHT values should be monotonically decreasing with depth. For light profiles where this is not true, a light extinction coefficient is not calculated. For samples for which light data at depth are present, but the corresponding deck light are missing, a light extinction coefficient is calculated by regressing ln (LIGHT (z)) on depth z. Note that if actual deck light had remained constant during the recording of the light profile, the resulting light extinction coefficient is the same as from regressing ln(FRLIGHT(z)). In these cases, the light extinction coefficient is flagged as non-standard. Oxygen and Temperature: We sample at the deepest part of the lake, taking a temperature and oxygen profile at meter intervals from the surface to within 1 meter of the bottom using a YSI Pro-ODO temporDO meter. We sample biweekly during open water and approximately every five weeks during ice cover. Protocol Log: Prior to 2011, we used a YSI Model 58 temporDO meter.
Short Name
NTLPH01
Version Number
28
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