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1,648 results for “farm”
Methane drone flights over a cattle farm in Tjøtta, Norway
<p>This dataset includes observations of atmospheric methane concentrations at a cattle farm in Norway.</p> <p>The data was collected by using a DJI Matrice 300 RTK drone equipped with an Aeries MIRA Strato LDS methane sensor.</p> <p>The files contain the raw data from the methane sensor with an additional column, 'OSD.height [m]', for the estimated height of the drone relative to its home location next to the barn. The height data comes from the drone and is resampled (linear interpolation) to the datetimes of the methane sensor. The datetimes are in UTC.</p> <p>The data was collected on 11.08.2023 and 12.08.2023 in Tjøtta, Norway. The cattle farm is located at 65.902044 N, 12.541739 E.</p> <p>The following flights were performed:</p> <table> <tbody> <tr> <td>20230811_101700</td> <td>Flight along the approximate mean wind direction at ~16m and ~28m height.</td> </tr> <tr> <td>20230811_140340</td> <td>Flight in horizontal plane at ~14m, hovering for ~1min at specific locations.</td> </tr> <tr> <td>20230811_144650</td> <td>Flight in vertical plane at ~16m and ~32m, hovering for ~1min at specific locations.</td> </tr> <tr> <td>20230811_153040</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230811_161430</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_073100</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_075830</td> <td>Lawnmower flight at ~25m height.</td> </tr> <tr> <td>20230812_082530</td> <td>Continuous vertical profile to ~100m height above the chimney of the barn.</td> </tr> <tr> <td>20230812_102930</td> <td>Lawnmower flight at ~25m height.</td> </tr> </tbody> </table>
Data and code from Rodkey et al. (2024): "Sod farms drive habitat selection of a migratory grassland shorebird during a critical stopover period"
<p>The data and code in this repository was used to fit the final models for the submitted manuscript "Sod farms drive habitat selection of a migratory grassland shorebird" by Rodkey and coauthors.</p> <p><strong>Abstract:</strong></p> <p>Migratory shorebirds are one of the fastest declining groups of North American avifauna. Yet, relatively little is known about how these species select habitat during migration. We explored the habitat selection of Buff-breasted Sandpipers (<em>Calidris subruficollis</em>) during spring and fall migration through the Texas Coastal Plain, a major stopover region for this species. Using tracking data from 118 birds compiled over 4 years, we found Buff-breasted Sandpipers selected intensively managed crops such as sod and short-stature crop fields, but generally avoided rangeland and areas near trees and shrubs. This work supports prior studies that also indicate the importance of short-stature vegetation for this species. Use of sod and corn varied by season, with birds preferring sod in spring, and avoiding corn when it is tall, but selecting for corn in fall after harvest. This dependence on cropland in the Texas Coastal Plain is contrary to habitat use observed in other parts of their non-breeding range, where rangelands are used extensively. The species' almost complete reliance on a highly specialized crop, sod, at this critical stopover site raises concerns about potential exposure to contaminants as well as questions about whether current management practices are providing suitable conditions for migratory grassland birds.</p> <p> </p> <p><strong>Data and code for model fitting </strong><em>bbsa_sodfarm_selection.zip</em><br>Following files are used to fit final models for the Texas study and the range-wide study.</p> <p><strong>script_tx </strong>is the script used to fit models on Texas dataset. <strong> </strong><em>script_tx.R</em></p> <p><strong>script_rw </strong> is the script used to fit models on range-wide dataset. <em>script_rw.R</em></p> <p><strong>data_tx </strong>is the datafile for containing model input data for the Texas models. data<em>_tx.csv</em></p> <p><strong>data_rw </strong>is the datafile containing model input data for the rangewide models. <em>data_rw.csv</em></p> <p>Columns in the datafiles for both Texas and rangewide models (data_tx and data_rw) are organized as follows:</p> <p>ID - unique identifier for tracked birds</p> <p>year - year in which bird was tracked</p> <p>season - season in which bird was tracked (spring or fall)</p> <p>fromTexas - (rangewide dataset only) whether or not the tracked bird was captured in the study region, the Texas Coastal Plain (T = bird was caught within study region; F = bird was caught outside of study region)</p> <p>night- (Texas dataset only) whether or not the location fell within daytime or nightime hours (y = nightime, n = daytime)</p> <p>droad - distance of location in meters from nearest road</p> <p>dwoody - distance of location in meters from nearest woody cover</p> <p>corn_prop250 - proportion of corn/sorghum landcover type within a 250m buffer around location</p> <p>cotton_prop500 - proportion of cotton/soybean landcover type within a 500m buffer around location</p> <p>herb_prop250 - proportion of herbaceous (i.e., grassy) landcover type within a 250m buffer around location</p> <p>rice_prop1000 - proportion of rice landcover type within a 1000m buffer around location</p> <p>sod_prop250 - proportion of sod landcover type within a 250m buffer around location</p> <p>presence - identifying used versus available locations (1 = used, as in, an actual GPS location of tracked bird; 0 = available, as in, a randomly generated location within the bird's area of use)</p> <p> </p> <p> </p> <p><strong>Tracking data </strong><em>bbsa_txStudy_movebank_20240731.csv</em></p> <p>Tracking data used for Texas study. Lotek PinPoint GPS data were imported and decoded via Movebank and relevant columns were selected. Columns are organized as follows:</p> <p> </p> <p>timestamp - time of GPS location in UTC (format = YYYY-MM-DD HH:MM:SS.SSS)</p> <p>location.long - longitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>location.lat - latitude of GPS location in decimal degrees, projected in the WGS 84 datum</p> <p>lotek.crc.status.text - value of Lotek's cyclical redundancy check (OK = good data, OK(corrected) = message was corrected with Lotek's error correction algorithm, Fail = data has been corrupted on transmission, and therefore cannot be trusted)</p> <p>sensor.type - type of sensor, all rows are "gps"</p> <p>tag.local.identifier - Argos ID of the attached Lotek PinPoint GPS transmitter</p> <p>individual.local.identifier - unique individual identifier, in this case the USGS-issued band number attached to each tracked bird</p> <p>individual.taxon.canonical.name - species taxonomical name, all rows are "Calidris subruficollis"</p> <p>sex - sex of tracked bird (M = male, F = female)</p> <p>age - age of tracke bird (AHY = after-hatch-year, HY = hatch-year)</p> <p> </p> <p> </p> <p><strong>Sod farm layer </strong><em>sodLayer_tgcp.tif</em><br>Raster layer representing sod farms in the Western Gulf Coastal Plain ecoregion of Texas. Resolution of 30m with values from 0 - 1, with 1 representing sod farms. Sod farms were identified via visual inspection, cross-referencing the U.S. Department of Agriculture's Cropland Data Layer with Google Earth high resolution imagery from 2022. This was a modification of preliminary data on sod farm distribution in the region compiled by B. Ortego, S. Gifford and J. Lyons.</p> <p> </p>
Table 1 in Patterns of Fasciola hepatica infection in Danish dairy cattle: implications for on-farm control of the parasite based on different diagnostic methods
<p><b>Table 1</b> Summary of data used as inclusion criteria for the 4 farms in the study</p><table><tbody><tr><th>Farm</th><th>Year</th><th>No. of heifers</th><th>No. of cows</th><th>Total no. of cattle</th><th>Liver condemnation (%)</th><th>BTM ELISA value (S/P%)a</th></tr></tbody><tbody><tr><th>C1</th><td>2011</td><td>72.5</td><td>176.5</td><td>314</td><td>6.2</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>65.5</td><td>184.5</td><td>303</td><td>21.3</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>65</td><td>187.5</td><td>315</td><td>30.0</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>63</td><td>183.5</td><td>312</td><td>18.6</td><td>179.3</td></tr><tr><th>C2</th><td>2011</td><td>103</td><td>135</td><td>292</td><td>8.3</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>98.5</td><td>145</td><td>300</td><td>11.9</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>105.5</td><td>144</td><td>314</td><td>16.1</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>111</td><td>149</td><td>331</td><td>19.4</td><td>181.2</td></tr><tr><th>O1</th><td>2011</td><td>145</td><td>172</td><td>367</td><td>2.6</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>141</td><td>168</td><td>362</td><td>7.6</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>124</td><td>174.5</td><td>354</td><td>33.3</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>172</td><td>183</td><td>425</td><td>23.3</td><td>221.4</td></tr><tr><th>O2</th><td>2011</td><td>90.5</td><td>113.5</td><td>251</td><td>18.1</td><td><i>–</i></td></tr><tr><th></th><td>2012</td><td>97.5</td><td>124</td><td>275</td><td>32.6</td><td><i>–</i></td></tr><tr><th></th><td>2013</td><td>111</td><td>131.5</td><td>282</td><td>27.7</td><td><i>–</i></td></tr><tr><th></th><td>2014</td><td>113</td><td>133.5</td><td>285</td><td>38.1</td><td>206.9</td></tr></tbody></table><p><sup>a</sup> by IDEXX ELISA test (cut-off is 30 and ≥ 150S/P% is considered high)</p>
Table 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
<p><b>Table 1:</b> Details of target genes (<i>hif-1α</i>, <i>hsp70</i>, <i>ras</i>, <i>mstn</i>, <i>acly</i>, <i>per-1</i>, <i>cry-1</i>, <i>ube3a</i> and <i>ogt</i>) and reference genes (<i>β- tubulin</i> and <i>β- actin</i>) primers.</p><table><tbody><tr><th><b>Gene</b></th><th><b>Length (bp)</b></th><th><b>R</b> <b>2</b></th><th><b>Efficiency (%)</b></th><th><b>Primers sequence (5ʹ-3ʹ) forward/reverse</b></th></tr></tbody><tbody><tr><th><i>tubulin</i> -F</th><td>20</td><td>0.99</td><td>109.5</td><td>GACGTGGTGCCCAAAGATGT</td></tr><tr><th><i>tubulin</i> -R</th><td>18</td><td>TGGATGGTGCGCTTGGT</td></tr><tr><th><i>β- actin</i> -F</th><td>21</td><td>0.99</td><td>100.5</td><td>GCTGTTTTCCCCTCCATTGTT</td></tr><tr><th><i>β- actin</i> -R</th><td>19</td><td>TCCCATGCCAACCATCACT</td></tr><tr><th><i>hif-1α</i> -F</th><td>20</td><td>0.99</td><td>105.2</td><td>CTTCTGAGCTCTGATGAGGC</td></tr><tr><th><i>hif-1α</i> -R</th><td>20</td><td>GAAAGCACCATCAGGAAGCC</td></tr><tr><th><i>hsp-70</i> -F</th><td>20</td><td>0.99</td><td>100.9</td><td>GCAAGGAGAACAAGATCACC</td></tr><tr><th><i>hsp-70</i> -R</th><td>19</td><td>CACTCCGTTGCACTTGTCC</td></tr><tr><th><i>mstn</i> -F</th><td>20</td><td>0.98</td><td>100.5</td><td>AATCCAAGCGAGGGAAAAGC</td></tr><tr><th><i>mstn</i> -R</th><td>22</td><td>CCTCCATCACCTGAAAGGTCTT</td></tr><tr><th><i>ras</i> -F</th><td>20</td><td>0.97</td><td>99.31</td><td>CCAGTACATGAGGACAGGAG</td></tr><tr><th><i>ras</i> -R</th><td>20</td><td>CAAGCACCATTGGCACATCG</td></tr><tr><th><i>acly</i> -F</th><td>19</td><td>0.99</td><td>100.7</td><td>ATCATCTCCCGCACTACAG</td></tr><tr><th><i>acly</i> -R</th><td>19</td><td>TACCTCCAATCTCTCCCAG</td></tr><tr><th><i>ube3a</i> -F</th><td>21</td><td>0.98</td><td>103.3</td><td>GCCATAAGCAAGCAGCACAAC</td></tr><tr><th><i>ube3a</i> -R</th><td>19</td><td>CCAGTCAGTCCGCACATCG</td></tr><tr><th><i>per-1</i> -F</th><td>20</td><td>0.98</td><td>104.1</td><td>TGTTGAAGTTTGTGCCCCAG</td></tr><tr><th><i>per-1</i> -R</th><td>18</td><td>CAGTCCAGATGCTCCTCC</td></tr><tr><th><i>cry-1</i> -F</th><td>19</td><td>0.99</td><td>103.6</td><td>GTCCAACAGCCCTCAAACT</td></tr><tr><th><i>cry-1</i> -R</th><td>18</td><td>TACGCCAAGCACTCCAGA</td></tr><tr><th><i>ogt</i> -F</th><td>19</td><td>0.99</td><td>104.1</td><td>CCTCCCTTTGCTGTGTTCC</td></tr><tr><th><i>ogt</i> -R</th><td>20</td><td>TGTCTGCTTTCCGCTTTCGC</td></tr></tbody></table>
Zabö Integrated Farming System
<p>This file includes the location boundary .shp file of the 3 GIAHS sites in India, AOI(Zabö) boundary .Shp file and the point location of the catchments.</p> <p> </p>
Supplementary Materials S1-S4 to Leipe et al., 2024. Human activities, early farming and natural environment in the north-western Kanto Plain (Central Japan) during the Final Jomon–Early Kofun period (990 cal BCE–330 cal CE) inferred from palynological and archaeobotanical records
<p>This dataset represents the Supplementary Materials S1-S4 to Leipe et al. (2024), <em>QEH</em> 2, 100030.</p> <p>Leipe et al., 2024. Human activities, early farming and natural environment in the north-western Kanto Plain (Central Japan) during the Final Jomon–Early Kofun period (990 cal BCE–330 cal CE) inferred from palynological and archaeobotanical records. <em>Quaternary Environments and Humans</em> 2, 100030 (doi: <a href="https://doi.org/10.1016/j.qeh.2024.100030">https://doi.org/10.1016/j.qeh.2024.100030</a>).</p> <p><strong>Supplementary Material S1</strong>: Pollen and non-pollen palynomorph records of a section (490.5-668.5 cm depth) of the MJ20 sediment core from Morinji Marsh, Tatebayashi City, Gunma Prefecture, Kanto region, Central Japan.</p> <p><strong>Supplementary Material S2</strong>. Sample-specific total counts of macrobotanical (seeds and fruits) and fungal remains and floated litres of sediment samples from ash pits (Yayoi period) and hearths (Kofun period) of the Ikegami archaeological site, Kumagaya City, Saitama Prefecture, Kanto region, Central Japan.</p> <p><strong>Supplementary Material S3</strong>. Photographs of charred remains of<em> Juglans ailantifolia</em> and <em>Aesculus turbinata</em> from the Middle Yayoi period ash pit samples at the Ikegami archaeological site, Kumagaya City, Saitama Prefecture, Kanto region, Central Japan.</p> <p><strong>Supplementary Material S4</strong>. Description of the As-B tephra in the MJ20 sediment core from Morinji Marsh, Tatebayashi City, Gunma Prefecture, Kanto region, Central Japan.</p>
Wind Value: Second Conference 2024 End of Life Choices for Wind Farms, Video
<p>Video of Slides written by Dorcas Mikindani presented by Peter Deeney. The topic is the use of real options in wind farm valuation as a guide for decisions at end of life.</p>
Pigs feeding behaviours from two different farms, including behaviours during a tail biting event
<p>These data are linked to the article from Ollagnier et al, 2021 (https://doi.org/10.1101/2021.05.11.443554)</p> <p><strong>Data desription</strong></p> <p>This data set comprises the feeding behaviours of two herds of grower-finisher pigs weighing between 25 and 100 kilograms. One data set originates from a testing boar station in Sweden and contains data collected from October 2004 to July 2007. The data set comes from a previous retrospective study that Wallenbeck and Keeling published in 2013. The second data set contains data from the experimental pig farm of Agroscope and comprises recordings from November 2018 to April 2020. As tail docking is prohibited in Sweden and in Switzerland, the data are from pigs with intact tails.</p> <p>The Swedish data set includes data from 42 pens (21 TB and 21 CTL ) of boars (purebred Yorkshire, Landrace or Hampshire) recorded 70 days before and after the TB date. Boars were housed in groups of 7 to 14 animals per pen. Each pen measured 15.7 m<sup>2 </sup>and had a slatted floor and plain resting area. All pigs had <em>ad libitum</em> access to the pelleted feed, which was optimised according to the Swedish nutrition norms for fattening pigs [25]. Water was provided <em>ad libitum</em> and straw was offered daily.</p> <p>The Swiss data set consisted of 23 pens (six TB and 17 CTL) of females and castrated male pigs (Swiss Large White), recorded 100 days before and after the TB date. Twenty pens (18 m<sup>2</sup>) contained 11 to 15 pigs each and were equipped with two automatic feeders; three pens (78 m<sup>2</sup>) were equipped with eight automatic feeders for 31 to 55 pigs each. All pens had straw in racks and sawdust on the floor. Water was available <em>ad libitum</em> through nipple drinkers. The pelleted finisher diet was formulated to have 20% lower dietary crude protein and essential amino acids compared to a standard diet formulated according to the Swiss feeding recommendations for pigs.</p> <p><strong>Data structure:</strong></p> <p>Three observations were considered to describe the feeding behaviours of pigs. </p> <p><strong>DFV: </strong>Number of visits to the feeder (from 0:00 to 23:59:59 that date), unit=n, </p> <p><strong>DFC: </strong>Total feed consumption (from 0:00 to 23:59:59 that date), unit=g</p> <p><strong>StdFC</strong>: Daily standard deviation of the feed consumption at each visit, unit=g</p> <p>The data set also contains the following information:</p> <p><strong>Farm:</strong> origin of the feeding behavior data (Swiss or Swedish farm)</p> <p><strong>ID: </strong>unique identification of the pig</p> <p><strong>date:</strong> date at which the feeding behavior is recorded, numerical format.</p> <p><strong>TBSTART:</strong> date at which the tail biting event started in tail biting pens. An arbitrary date has been taken for control pens, numerical format.</p> <p><strong>PenID: </strong>unique identification of the pen.</p> <p><strong>PenType: </strong>control (K) or tail biting (TB). A pen was assigned to the TB category if at least one pig had to be treated for tail damages.</p> <p> </p> <p> </p>
On farm field experiments testing TOMRES rootstocks under water stress
<p>This experiment aims to test EDYPRO biostimulants on farm applicatioin. The commercial variety Elpida F1 was either treated with biostimulants or not and cultivated under 2 water/nutritional regimens (standard water/nutrient supply vs 20% irrigation reduction/no nutrient supply).</p>
On farm field experiments testing TOMRES rootstocks under water stress (Season 2019-2020)
<p>This experiment aims to test EDYPRO biostimulants on farm application. The commercial variety Elpida F1 was either treated with biostimulants or not and cultivated under 2 water/nutritional regimens (standard water/nutrient supply vs 20% irrigation reduction/no nutrient supply).</p>
On farm field experiments testing TOMRES rootstocks under water stress
<p>This experiment aims to test two of the most performing Tomres used as rootstocks in the commercial variety (Elpida F1) cultivated in the region. More specifically, 2 tomato Tomres lines (TOMRES- 149, Bil-6191 and TOMRES 162, M82) x 2 water/nutritional regimens (standard water/nutrient supply vs 20% irrigation reduction/no nutrient supply). Greenhouse will also have non grafted plants on Armstrong rootstock cultivated under standard water/nutrient supply and 20% irrigation reduction/no nutrient supply</p>
Emissions from energy used in agriculture(on-farm)
<p>The FAOSTAT domain “Energy Use’’ disseminates information on activity data, emission factors and GHG emissions from fossils fuels and electricity. Information on fossil fuels used is sourced from UNSD and IEA (see methods), covering: Gas-Diesel Oil, Coal, Natural gas (including LNG), Motor Gasoline, Fuel Oil, Liquefied Petroleum Gas (LPG); and electricity.</p>
Effects of released farmed mallards on species richness of breeding waterbirds and amphibians in natural, restored, and constructed wetlands
<p>Common practices in current game management are wetland restoration and creation, as well as releases of quarry species. We studied the impact of releases of mallard ducklings on species richness of wild waterbirds and amphibians on three types of wetlands: natural, constructed and restored. Data on species richness, macrophyte cover and water characteristics (total phosphorous and pH) were collected at 32 sites in an agricultural landscape in southern Sweden. In total, 14 species of waterbirds were recorded, ranging from zero to seven per wetland and survey. Amphibians were present in 24 of the 32 wetlands; in total five species were found, ranging from zero to three per wetland. By using generalized linear modelling we found that wetland type best predicted waterbird species richness. Constructed wetlands had significantly more waterbird species, regardless of whether they were used for mallard releases or not. There were breeding amphibians in 62% of natural, 100% of restored and 77% of constructed wetlands. Breeding amphibians were present in 84% of wetlands without, and in 62% of wetlands with releases. However, included variables did not explain amphibian species richness in the wetlands. Releasing large numbers of mallards on a wetland and providing food ad libitum is likely to affect water quality, nutrient availability and predation pressure. Indeed, phosphorous levels were significantly higher in release wetlands, but no differences were found between wetland types.This means that mallard releases may increase nutrient loads in environments that are already eutrophied. However, in our study system releases did not influence species richness of waterbirds and amphibians locally. Constructing wetlands for mallard releases can thus have positive local effects on species richness.</p>
Figure 1 in Mite species (Acari) on blackberry cultivars in organic and conventional farms in Florida and Georgia, USA
Figure 1 Sampled farms in Florida and Georgia, USA.
Figure 1 in Cryptic speciation at organic-rich marine habitats: a new bacteriovore annelid from whale-fall and fish farms in the North-East Atlantic
Figure 1. Map showing collection sites in Sweden and Norway for the new species.
Dataset and Code for "A case study of space-time performance comparison of wind turbines on a wind farm"
<p>This is the computer code and partial dataset for producing the results in the paper, Ding, Kumar, Prakash, Kio, Liu, Liu, and Li, 2021, “A case study of space-time performance comparison of wind turbines on a wind farm,” <em>Renewable Energy</em>, Vol. 171, pp. 735-746 .</p> <p>[<strong>Note 1</strong>: In the Reproducibility Report, The table numbers are off by one, namely that Table 2 should be Table 3, and Table 3 should be Table 4.]</p> <p>[<strong>Note 2:</strong> The results in Table 4 included in the paper were produced by the DSWE version 1.3.4. Those results are still reproducible if using the same version of DSWE. Since then, DSWE went through some changes and updates. When using DSWE 1.5.1 version (the latest version as of May 10, 2022) and setting the optimization method to 'L-BFGS-B' (because version 1.3.4’s default optimization setting was 'L-BFGS-B'), the results corresponding to the second row in Table 4 are somewhat different. For the specific results using DSWE 1.5.1, please see the note section at the end of the <a href="https://aml.engr.tamu.edu/wp-content/uploads/sites/164/2022/05/J77_Reproducibility_Report_v2.pdf">updated Reproducibility Report</a>.]</p>
Dataset for "Power curve estimation with multivariate environmental factors for inland and offshore wind farms"
<p>This is the dataset used in the paper, Lee, Ding, Genton, and Xie, 2015, “Power curve estimation with multivariate environmental factors for inland and offshore wind farms,” <em>Journal of the American Statistical Association</em>, Vol. 110, pp. 56-67.</p>
Dataset of impacts of weather and climate services on farming in Burkina Faso 2019 and 2020
<p>The dataset contains the results of two surveys realized in 2019 and 2020 in the Municipality of Tenado, Burkina Faso. The surveys were conducted in October 2019 and in November 2020 using structured questionnaires translated and administered in the local language by trained enumerators. Moreover, yield plots were placed in farmers’ fields and yield was measured on site. The sample at the Tenado site consisted for each survey in approximately 300 farmers (150 treated and 150 control) in 12 villages for each year. The treatment is the training in the use of Weather and Climate Services in agriculture. The control group is random and represents the entire population. The treatment group is also random, from the participants in training. Observed variables are: </p> <p>N. seedings</p> <p>Quantity of seeds (kg/ha)</p> <p>N. fertilizations</p> <p>Quantity fertilizers (kg/ha)</p> <p>Working days</p> <p>Yield (kg/ha)</p> <p>Costs (FCFA/ha)</p> <ul> <li>Seeds cost (FCFA/ha)</li> </ul> <ul> <li>Fertilizers cost (FCFA/ha)</li> </ul> <ul> <li>Labor cost (FCFA/ha)</li> </ul> <p>Income (FCFA/ha)</p> <p>Gross benefit (FCFA/ha)</p> <p>The market price of sorghum and the cost of seeds, fertilizer and labor were considered stable over the 2 years and in particular the sorghum price was 200 FCFA per kg, while the costs were 350 FCFA for 1 kg of seeds, and 450 FCFA for 1 kg of fertilizer. Labor cost is accounted as the opportunity costs of working time considering that 1,500 FCFA is the daily wage in agriculture. Income was computed multiplying yield by the price of sorghum and the gross benefit is the income minus the costs.</p>
SMARTEOLE Wind Farm Control open dataset
<p><strong>Introduction</strong></p> <p>This dataset is issued from the third and final field campaign of the French national project SMARTEOLE. It consists in data from 7 wind turbines of a single wind farm (Sole du Moulin Vieux, located in France) for which Wind Farm Control field tests were performed to evaluate the performance of a wake steering strategy for improving the power production.</p> <p>The wind farm consists of 7x Senvion MM82 wind turbines (rotor diameter of 82m, nominal power of 2.05 MW).</p> <p> </p> <p><strong>Description</strong></p> <p>The tests were realized between 17 February – 25 May 2020, with wake steering implemented on turbine SMV6. This dataset covers this full period, and it has been pre-processed to facilitate the analysis of the Wind Farm Control experiment. All timesteps when at least one turbine was stopped were removed, and SCADA nacelle position and wind direction signals have been corrected to remove any north alignment issues. Finally, the time resolution has been standardized at 1-min from the raw data recorded at higher frequencies from the different sensors. For more details about the development of the field campaign and the pre-processing steps followed in the data analysis, please consult the related publication : <a href="https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html">https://wes.copernicus.org/articles/6/1427/2021/wes-6-1427-2021.html</a>. Some information can also be found in the related <a href="https://ieawindtask44.tudelft.nl/index.php?title=SMARTEOLE_Field_Test_3">IEA task 44 wiki page</a>.</p> <p>The following files can be found in the dataset :</p> <ul> <li>SMARTEOLE_WakeSteering_SCADA_1minData.csv : the Supervisory Control and Data Acquisition (SCADA) data from the 7 turbines.</li> <li>SMARTEOLE_WakeSteering_ControlLog_1minData.csv : logs from the control system located on turbine SMV6, responsible for the application of the wake steering. The applied yaw offset on the turbine at each timestep can be found here.</li> <li>SMARTEOLE_WakeSteering_WindCube_1minData.csv : data from the ground based WindCube profiler lidar, located between SMV2 and SMV3. This can be used to assess the ambient environmental wind conditions at the farm.</li> <li>SMARTEOLE_WakeSteering_Coordinates_staticData.csv : file listing the coordinates of the wind turbines in the farm and WindCube location in traditional Latitude / Longitude system (<a href="https://epsg.io/4326">WGS84</a>) and XY metric system (<a href="https://epsg.io/2154">French Lambert 93</a>).</li> <li>SMARTEOLE_WakeSteering_Map.pdf : the map of the farm showing the location of wind turbines and WindCube. This is the exact same map as the one seen in the paper indicated above.</li> <li>SMARTEOLE_WakeSteering_NTF_SMV6_staticData.csv : the transfer function used in the paper to correct the wind speed measured by SMV6 to better match the freestream wind speed at 150m upstream (i.e. approximately 1.8 diameters), derived using WindCube nacelle lidar installed on top of the turbine.</li> <li>SMARTEOLE_WakeSteering_correction_factors_SMV1237_staticData.csv : the transfer function used in the paper to derive and correct the reference power and wind speed signals —defined as the mean values of the power and wind speeds from SMV1, SMV2, SMV3, and SMV7— to remove biases from the values at SMV6 as a function of wind direction and wind speed. These corrected reference signals are used for quantifying the impact of the wake steering.</li> <li>SMARTEOLE_WakeSteering_GuaranteedPowerCurve_staticData.csv : the warranted power and thrust curves for the standard mode (Mode 0) of the MM82 wind turbine.</li> <li>SMARTEOLE_WakeSteering_ReadMe.xlsx : read me file indicating for each dataset the signification of the different variables.</li> </ul> <p>Unfortunately, the WindCube nacelle lidar data on top of SMV6 could not be shared, instead the transfer functions derived thanks to this sensor can be used to correct the SCADA channels. The Wind Energy Science publication describes how these transfer functions were obtained.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p>The creation of this dataset was realized in the scope of French national project SMARTEOLE, supported by the <em>Agence Nationale de la Recherche</em> (grant no. ANR-14-CE05-0034).</p> <p>Furthermore, we would like to thank ENGIE Green for allowing us to make this dataset publicly available.</p> <p> </p> <p><strong>How to cite this dataset</strong></p> <p>When using this dataset in future research, please add the following sentence in the Ackowledgement section of your publication :</p> <p>"The dataset used in this research has been obtained by ENGIE Green in the scope of French national project SMARTEOLE (grant no. ANR-14-CE05-0034)".</p> <p>When citing the dataset in the core text of a paper, the reference to <a href="https://wes.copernicus.org/articles/6/1427/2021/">Simley et al.</a> can simply be used.</p> <p> </p> <p><strong>Related datasets and publications</strong></p> <p>Several field test campaigns were realized in the scope of SMARTEOLE project. Although these data are not made publicly available by default, they can be shared in a per-project basis and under the protection of a dedicated NDA. Please refer to the following publications listed below to get an idea of the content of the different datasets.</p> <p><em>SMARTEOLE Field Test 1</em></p> <ul> <li>Ahmad T. et al., Field Implementation and Trial of Coordinated Control of WIND Farms, <em>IEEE Transactions on Sustainable Energy</em>, 9(3), 2018, 10.1109/TSTE.2017.2774508.</li> <li>Duc T., Optimization of wind farm power production using innovative control strategies, Master’s thesis, DTU Wind Energy-M-0161, 2017.</li> <li>Duc T. et al., Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm, <em>Wind Energy Science</em>, 4(2), 2019, 10.5194/wes-4-287-2019.</li> <li>Torres Garcia E. et al., Statistical characteristics of interacting wind turbine wakes from a 7-month LiDAR measurement campaign, <em>Renewable Energy</em>, 130, 2019, 10.1016/j.renene.2018.06.030.</li> <li>Hegazy A. et al., LiDAR and SCADA data processing for interacting wind turbine wakes with comparison to analytical wake models, <em>Renewable Energy</em>, 181, 2022, 10.1016/j.renene.2021.09.019.</li> </ul> <p><em>SMARTEOLE Field Test 2</em></p> <ul> <li>Tagliatti F., Investigation of Wind Turbine Fatigue Loads under Wind Farm Control: Analysis of Field Measurements, Master’s thesis, DTU Wind Energy-M-0302, 2019.</li> <li>Göçmen T. et al., FarmConners wind farm flow control benchmark – Part 1: Blind test results, <em>Wind Energy Science</em>, 7(5), 2022, 10.5194/wes-7-1791-2022.</li> </ul> <p><em>SMARTEOLE Field Test 3</em></p> <ul> <li>Simley E. et al., Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, <em>Wind Energy Science</em>, 6(6) 2021, 10.5194/wes-6-1427-2021.</li> </ul> <p> </p> <p><strong>Release Notes</strong></p> <ul> <li>v1.0 (2022-11-24) : first version of the dataset.</li> </ul>
Data for: Nitrous oxide emissions from groundnut and millets farms in semi-arid peninsular India
<p>Nitrous oxide (N<sub>2</sub>O) emissions response curves for crops grown outside temperate regions have been rare and have thus far arrived at conflicting conclusions. Most studies reporting N<sub>2</sub>O emissions from tropical cropping systems have examined only one or two nitrogen fertilizer application rate(s) which precludes the possibility of discovering nonlinear changes in emission factors (EF, % of added N converted to N<sub>2</sub>O-N) with increasing fertilizer-N rates. To examine the relationship between N rates and N<sub>2</sub>O fluxes in a tropical region, we compared farming practices with three or four N rates for their yield-scaled impacts from three crops in peninsular India. We measured N<sub>2</sub>O fluxes during nine seasons between 2012 and 2015, with N application rates ranging between 0 and 70, 0 and 90, and 0 and 480 kg-N ha<sup>-1</sup> for foxtail-millet (<em>Setaria italica</em> L., locally called korra), groundnut (<em>Arachis hypogaea</em> L., also called peanut) and finger-millet (<em>Eleusine coracana</em> L., locally called ragi), respectively. In two cases, the highest N application rate greatly exceeded crop-N needs. Potential climate smart farming agricultural practices (with low/optimized N rates) led to a 50-150% reduction in N<sub>2</sub>O emissions intensity (per unit yield) along with a reduction of 0.2-0.75 tCO2e ha<sup>-1</sup> season<sup>-1</sup> as compared to high N conventional applications. We found a non-linear increase in N<sub>2</sub>O flux in response to increasing applied N for both N-fixing and non N-fixing crops and the extent of super-linearity for non N-fixing crops was much higher than what has been reported earlier. If a linear fit is imposed on our datasets, the emission factors (EFs) for finger-millet and groundnut were ~3.5% and ~1.8%, respectively. Our data shows that for low-N tropical cropping systems, even when they have low soil carbon content, increase in N use to levels just above crop needs to enhance productivity might lead to relatively small increase in N<sub>2</sub>O emissions as compared to the impact of equivalent changes in fertilizer-N use in systems fertilized far beyond crop N needs.</p>
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