Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2

What? A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021. Why? Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders. How? Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo.  What is 'new' or corrected in version 2.2?  Edited/amended: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster.  Added: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file.  Omitted: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell. CRediT: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K Acknowledgements: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance.

opencc-by-nc-sa-4.0Sep 2020View details →

Food and Agriculture Biomass Input–Output (FABIO) database

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.  The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on <a href="https://github.com/fineprint-global/fabio">GitHub</a>. For more information please refer to <a href="https://fabio.fineprint.global">https://fabio.fineprint.global</a>. The database consists of the following main components, in compressed .rds format: <ul> <li>Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.</li> <li>Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.</li> <li>X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.</li> <li>L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).</li> <li>E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).</li> <li>mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).</li> <li>mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).</li> </ul> A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see <a href="http://doi.org/10.1021/acs.est.9b03554">Bruckner et al. 2019</a>). Product, process and country classifications can be found in the file fabio_classifications.xlsx. Footprint results are not contained in the database but can be calculated, e.g. by using this script: <a href="https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R">https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R</a>   How to cite: To cite FABIO work please refer to this paper: Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: <a href="https://doi.org/10.1021/acs.est.9b03554">10.1021/acs.est.9b03554</a>   License: This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at <a href="mailto:martin.bruckner@wu.ac.at">martin.bruckner@wu.ac.at</a>.   Known issues: The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

opencc-by-4.0Nov 2021View details →

Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"

Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis

opencc-by-4.0Jun 2022View details →

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification Across EU (Italy)

In order to shortlist possible options from a pool of 2700 crops, a crop-climate-soil matching ex-ercise was performed across Italian territory and crops with more than 70% suitability where chosen for further analysis. In the second phase, a multicriteria ranking index was employed to assign ranks to chosen crops of 4 main types; (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/ tubers and (iv) vegetables. In order to provide a comprehensive analysis, major crops that are grown in the region where also included in the analysis. The results of evaluation of 4 major criteria (a) calorie and nutrition demand b) functions and uses c) availability and acces-sibility to their genomic material d) possession of adaptive traits, and e) physiological traits) re-vealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd and Watercress.

opencc-by-4.0Apr 2024View details →

Agricultural plastic uses in Europe

For each European country, it is considered waste produced mainly from plastic used for: films for crop protection; nets; low tunnel films; soil mulching, solarization and direct covering; bags and containers for pesticides, fertilizers and other agrochemicals; silage bags; irrigation pipes; ropes and strings. Descriptions of different plastic application is given in this dataset.

opencc-by-4.0Feb 2019View details →

Agricultural areas (Comune di Napoli)

Urban Atlas based data subset, where every element with CODE 21000,22000,23000,24000 and 25000 was extracted as an agricultural area with the next information: gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint This data is an input for local effects calculation.

opencc-by-4.0Jun 2017View details →

An Information Ecology for Sustainable Agriculture

Feeding 10 billion people by 2050 will require transformative changes to our food production systems1, 2. Climate change3-6, water scarcity and urban demand7, herbicide-resistant weeds8, and declining soil9 and water quality10,11 are increasing crop production risks, lowering yields, and negatively impacting the environment. The increased use of sustainable agricultural practices  such as reduced-tillage12, diversified crop rotations13, and integrated weed management especially through incorporation of cover crops14, are necessary to achieve this goal. However, farmers repeatedly cite management complexity and a need for site- and system-specific information to overcome the barriers to adoption15, 16. Sustainable agriculture thus demands precision tools to account for genetic and environmental nuances in complex, adaptive, agricultural systems, while simultaneously responding to the social, technological, and economic contexts of farming. Precision Sustainable Agriculture17 uses a data-driven and human-centered approach to the research and development of on-farm monitoring tools, cloud-based, information management tools for large-scale agricultural research projects, decision support tools for agriculture data stakeholders, and modeling and analysis tools for use in sustainable agriculture. We are laying the foundation of an information ecology18 for sustainable agriculture: a system of tools, data, methods, and actors to maximize farm productivity, profitability, and sustainability. References: <ol> <li>Liu J, Folberth C, Yang H, Röckström J, Abbaspour K, Zehnder AJB. A Global and Spatially Explicit Assessment of Climate Change Impacts on Crop Production and Consumptive Water Use. PLOS ONE. 2013 Feb 27;8(2):e57750.</li> <li>Schmidhuber J, Tubiello FN. Global food security under climate change. PNAS. 2007 Dec 11;104(50):19703–8.</li> <li>Allan RP, Soden BJ. Atmospheric Warming and the Amplification of Precipitation Extremes. Science. 2008 Sep 12;321(5895):1481–4.</li> <li>Gornall J, Betts R, Burke E, Clark R, Camp J, Willett K, et al. Implications of climate change for agricultural productivity in the early twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences. 2010 Sep 27;365(1554):2973–89.</li> <li>Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA. 2014 Mar 4;111(9):3268–73.</li> <li>Trenberth KE, Dai A, Schrier G van der, Jones PD, Barichivich J, Briffa KR, et al. Global warming and changes in drought. Nature Climate Change. 2014 Jan;4(1):17–22.</li> <li>Flörke M, Kynast E, Bärlund I, Eisner S, Wimmer F, Alcamo J. Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study. Global Environmental Change. 2013 Feb 1;23(1):144–56.</li> <li>Heap I. Global perspective of herbicide-resistant weeds. Pest Management Science. 2014 Sep 1;70(9):1306–15</li> <li>Williams A, Hunter MC, Kammerer M, Kane DA, Jordan NR, Mortensen DA, et al. Soil Water Holding Capacity Mitigates Downside Risk and Volatility in US Rainfed Maize: Time to Invest in Soil Organic Matter? PLoS ONE. 2016;11(8):e0160974.</li> <li>Goolsby DA, Battaglin WA, Lawrence GB, Artz RS, Aulenbach BT, Hooper RP, et al. Flux and sources of nutrients in the Mississippi-Atchafalya river Basin topic 3 report. :156.</li> <li>Boyer EW, Goodale CL, Jaworski NA, Howarth RW. Anthropogenic nitrogen sources and relationships to riverine nitrogen export in the northeastern U.S.A. Biogeochemistry. 2002 Apr 1;57(1):137–69.</li> <li>Zibilske LM, Bradford JM. Soil Aggregation, Aggregate Carbon and Nitrogen, and Moisture Retention Induced by Conservation Tillage. Soil Science Society of America Journal. 2007 May 1;71(3):793–802.</li> <li>Cox HW, Kelly RM, Strong WM. Pulse crops in rotation with cereals can be a profitable alternative to nitrogen fertiliser in central Queensland. Crop Pasture Sci. 2010 Sep 30;61(9):752–62.</li> <li>Mortensen DA, Egan JF, Maxwell BD, Ryan MR, Smith RG. Navigating a Critical Juncture for Sustainable Weed Management. BioScience. 2012 Jan 1;62(1):75–84.</li> <li>Dunn M, Ulrich-Schad JD, Prokopy LS, Myers RL, Watts CR, Scanlon K. Perceptions and use of cover crops among early adopters: Findings from a national survey. Journal of Soil and Water Conservation. 2016 Jan 1;71(1):29–40.</li> <li>Myers R, Watts C. Progress and perspectives with cover crops: Interpreting three years of farmer surveys on cover crops. Journal of Soil and Water Conservation. 2015 Nov 1;70(6):125A-129A.</li> <li>Mirsky, S, Reberg-Horton, C, Raturi, A. Precision Sustainable Agriculture. Available: <a href="http://precisionsustainableag.org">http://precisionsustainableag.org</a></li> <li>Nardi B, O’ Day V. Information ecologies: Using technology with heart. MIT Press; 1999.</li> </ol>

opencc-by-4.0Jun 2024View details →

Generated WSP: Validation of a water-sensitive paper-based method for the characterization of agricultural spray droplets

Synthetic images were generated in a Python environment using the OpenCV library to replicate the distribution of droplets in WSP. The images display droplet stains represented by blue circles (255,0,0) on a yellow background (0,255,255) to enhance contrast and enable more precise analysis. The synthetic images were created in two distinct resolutions, namely 640x480 and 2560x1440 pixels, with the aim of reproducing the output of two specific digital microscopes: the Jiusion 640x480 and the Jiusion HD 2560x1440 (Shenzen, China). The resolution is chosen based on the expected practical application, ensuring that any image analysis algorithm developed can effectively process images with similar characteristics to those obtained under real conditions by these microscopes. Each pixel in this configuration corresponds to a physical size of 18.125 µm in images with a resolution of 640x480, and a size of 6.875 µm in images with a resolution of 2560x1440. Multiple patterns were created to simulate various configurations of droplet stains in WSP. The sizes of single droplet stains varied between 100 and 600 µm, with spacings of either 1000 µm or 2000 µm between drops (see attached figure). Furthermore, the same size range was utilised to generate patterns with double and overlaid droplet stains, with a consistent spacing of 2800 µm between each stain (see attached figure). The implementation of this systematic method guarantees the accurate calibration and application of image analysis algorithms in real-world situations. This allows for the representation of precise measurements and spacing that would be encountered in actual experimental conditions.

opencc-by-4.0Jun 2021View details →

Masks for ISIMIP3 Agriculture (GGCMI phase 3) model runs

This dataset consists of a netCDF file with a number of layers at half-degree global resolution. Each layer is a binary map representing whether each gridcell is included (1 if yes, 0 if no) in one or more input datasets used in the ISIMIP3 Agriculture (GGCMI phase 3) model runs. Individual-dataset masks: <ul> <li>has_soil indicates inclusion in the <a href="https://data.isimip.org/10.48364/ISIMIP.942125">ISIMIP3 soil input dataset (Volkholz & Müller, 2020)</a> (ignoring "gravel," which has some missing cells).</li> <li>has_cropcals indicates inclusion in the <a href="https://zenodo.org/record/5062513">Jägermeyr et al. (publication in prep.)</a> crop calendar dataset (ignoring second-season rice, which is not grown in all gridcells).</li> <li>has_lu indicates inclusion in all 15 area maps in the historical land use area dataset <a href="https://protocol.isimip.org/protocol/ISIMIP3b/index.html#socioeconomic-forcing">prepared for ISIMIP3</a> (landuse-totals_histsoc_annual_1850_2014.nc).</li> <li>has_crops indicates inclusion in all 15 area maps in the historical 15-crop dataset <a href="https://protocol.isimip.org/protocol/ISIMIP3b/index.html#socioeconomic-forcing">prepared for ISIMIP3</a> (landuse-15crops_histsoc_annual_1850_2014.nc). Note that there are two gridcells that are missing from this </li> <li>has_fertilizer indicates inclusion in every fertilizer_application_histsoc*.nc file in the <a href="http://doi.org/10.5281/zenodo.4954582">fertilizer and manure dataset prepared by Heinke et al. (2021) for GGCMI3</a>.</li> <li>has_all is a composite mask indicating inclusion in all of the above.</li> </ul> Also included are a figure showing the masks and the MATLAB script used to generate the data and figure. <ul> </ul>

opencc-by-4.0Oct 2018View details →

Spectral transmittance of solar radiation by screens and nets used in horticulture and agriculture

We present a dataset of measurement of the spectral transmittance of 197 horticultural nets and screens from five companies. These materials span a range of uses from shading and reducing the heat load on plants to blocking pests such as birds and insects. Routinely, these materials are used in greenhouses and polytunnels to reduce the sunlight received by plants, however their spectral transmittance is not routinely measured. The spectral irradiance that plants receive can affect plant growth and photomorphogenesis, hence this information is of value when selecting the most appropriate material for a given purpose. The spectral transmittance of the materials was measured outdoors close to solar noon using an array spectrometer calibrated for the range 290-900 nm and compared directly with the ambient solar spectral irradiance. The measured spectrum encompasses those regions perceived by plants through known photoreceptors and used by plants in photosynthesis: ultraviolet (UV); photosynthetically active radiation (PAR), and near infra red (far red – FR). The solar spectral photon irradiance (μmol m-2 s-1) transmitted by screens and nets from several manufacturers was measured with an array spectroradiometer. Our measurements and analyses are focused on the differences in spectral irradiance, created when employing these screens and nets, in order to address the lack of detailed studies of these light environments, rather than the physiochemical properties of materials or their cost-effectiveness. The measurements of spectral irradiance under climate screens, and shade and insect nets, were made on clear days in sunny conditions close to solar noon (between 10 a.m. to 2 p.m local time) at NC State University campus (35.78°N, -78.67°W) in late July and early August 2017, and in Viikki Field Plots at the University of Helsinki (60.22°N, 25.01°E, 55 m asl) in July and August 2018. The methods for measurements at North Carolina State University follow the protocol described below and published in <a href="https://doi.org/10.1371/journal.pone.0199628">Kotilainen et al., (2018)</a>, where a comprehensive assessment of the results of this subset of screens/nets and their meaning is also given. The measurements were performed in an open field with no surrounding structures or buildings within 20 m. Repeated measurements of each different sample were made in a randomised order, thus ensuring comparability among measurements. Measurements were made on a tripod 0.7 m above the ground and the sample was secured to a wooden plate 3 cm above the diffusor. A test, comparing four larger (1 x 1 m) samples against those of the standard dimensions that we used, found that the area of screen/net measured did not affect the results at this distance between the screen/net and diffusor. Thus, there was no evidence that unfiltered diffuse or scattered radiation interfered with measurements despite the relatively small dimensions of the sample. Measurements under each screen/net sample in 2017 (Svensson 13 x 19 cm, Mallas Textiles 8 x 10 cm) were made twice to account for any possible effect of sample placement over the cosine diffuser and change in the sun angle during a set of measurements.  Given that no significant differences were evidence, the 2018 screen/net samples (Criado y Lopez 8 x 12 cm, Howitec 15 x 25 cm, Huachang yarns 25 x 30 cm, and Jiangsu Huachang Yarns and Fabrics 8 x 12 cm) were only measurement once. A recording of spectral irradiance without the screen/net of filtered sunlight was made directly before and after each filter measurement (called “Open”). The spectrometer used had been calibrated for measurements of UV and visible solar radiation (Maya2000 Pro Ocean Optics, Dunedin, FL, USA; D7-H-SMA cosine diffuser, Bentham Instruments Ltd, Reading, UK - see <a href="https://doi.org/10.1002/ece3.4496">Hartikainen et al., 2018</a> for details of the measurement protocol). Briefly, each measurement of irradiance transmitted beneath a screen or net was followed by sequence of measurements in the dark and with a polycarbonate filter attenuating all UV radiation. These controls accounted for the dark noise and stray light in the UV waveband. Both a correction for the shape of the slit function and for stray light were included in the post-processing of the spectra (<a href="http://uv4plants.org/methods/how-to-check-an-array-spectrometer/">Aphalo et al., 2016</a>). Bracketing was performed by taking a measurement of the UV region and splicing this together this the entire spectrum. All measurements were processed using the Photobiology packages in R. Measurements of solar spectral irradiance in the wavelength range from 290 nm to 900 nm were processed in R, using the photobiology packages developed for spectral analysis (<a href="https://doi.org/10.19232/uv4pb.2015.1.14">Aphalo, 2015</a>). We present spectral photon irradiance (μmol m-2 s-1) and spectral energy irradiance (W m-2). Plants absorbs photons producing a chemical change (Grotthus Law) thus photon irradiance is more easily applicable understanding to biological processes in plants. The spectral transmittance of the screens/nets are the most useful data presented. Essentially the patterns of spectral attenuation will be consistent, irrespective of whether spectra are expressed as photon or energy irradiance. Utilizing predefined functions available in the photobiology packages, we calculated the integrals and photon ratios of these integrals as follows: UVB:PAR 280–315 nm/400-700 nm, UVA:PAR 315–400 nm/400-700 nm, blue:green (B:G) 420–490 nm/500-570 nm, blue:red (B:R) 420–490 nm/620-680 nm. Red and far-red for the calculation of R:FR ratio are 655–665 nm and 725–735 nm, respectively. UVB radiation and UVA radiation are defined according to ISO, blue, green and red according to <a href="https://doi.org/10.1104/pp.110.160820">Sellaro et al. (2010)</a>, and R:FR according to <a href="https://doi.org/10.1146/annurev.pp.33.060182.002405">Smith(1982)</a>. The same definitions of the UV-waveband are maintained for both spectral integrals and their ratios throughout, i.e. according to ISO, (<a href="http://doi:%2010.21273/HORTTECH03648-16">Both et al., 2017</a>). This is because the UVB and UVA wavebands of solar radiation follow distinct daily patterns of variation; UVB irradiance is highest during the four hours around solar noon, whereas the UVA region of solar radiation remains a similar proportion of total irradiance throughout the day. These differences also imply that UVA and UVB radiation follow different diurnal and seasonal patterns of variation (<a href="https://doi.org/10.1111/j.1751-1097.2007.00216.x">Seckmeyer et al., 2007</a>). Data Files Available DataBaseScreensNets.zip Graphs (.jpg files) of actual measured (1) spectral energy irradiance, (2) spectral photon irradiance, and (3) proportion transmittance of solar radiation, for each screen and net.  (1) Energy Irradiance figures (suffix _EI.) and (2) Photon Irradiance figures (suffix _PI.) are plot of the measured values of irradiance under the filter (screen/net) and corresponding measurements without the screen or net (“open” measurement) for comparison (290-898 nm wavelength range).  The proportion transmittance under each screen or net is calculated from comparison of the open and measured spectrum (suffix _Trans). The low-wavelength tail end of the spectrum is trimmed (<310 nm) in each plots since % transmittance are inflated by low signal to noise ratio in the UV-B region where irradiance values are very low. The database screens and net are identified by the name of the company “_” name of the screen/net for all 197 materials. These figures can be reproduced from the file “ScreensNets_irrad_trans.txt” using the R code “Plotting_DataBaseScreensNets.r” ImagesScreensNets.zip Image files (.jpg files) from photos and scans of each of the measured screens and nets. One image from each of the 197 filter materials (screens/nets) measured is stored in folders arranged according to the company for each filter type. The companies are: Criado y Lopez; HowiTech; Huanchang yarns; Jiangsu Huachang Yarns & Fabrics; Mallas_Textiles and Svensson. ScreensNets_irrad_trans.txt This is the main database file containing the measurements of spectral irradiance beneath each filter material (screen/net) from 290 nm – 898 nm and corresponding open reading, and calculated spectral transmittance. Data are in columns as follows: (A) Company – the Company name; (B) FilterName – the filter name as given by the company; (C) Serial - a serial number, effectively equivalent to the order in which the materials were measured; (D) wavelength – at intervals recorded by the array spectrometer running for each spectrum from 290.02 nm to 897.73 nm; (D) FilterEI - energy irradiance of transmitted solar radiation measured 3 cm beneath the filter material (screen/net) at each wavelength of the spectrum; (E) FilterPI – photon irradiance equivalent to the energy irradiance; (F) OpenEI – energy irradiance of solar radiation at the same location without the filter material (screen/net) (G) OpenPI – photon irradiance equivalent to the energy irradiance; (H) FilterFactor – the proportion of radiation transmitted by the filter material (screen/net) at each wavelength measured, a value between 0.0 and 1.0 (values out of range at low wavelengths in the UV-B region are replaced with 0.0 or 0.1). Processed spectra are given: processing of raw spectra was done with Photobiology packages in R. Full spectra were recorded with an integration time set manually to give maximum counts of just less than 60 000 at the wavelength corresponding to peak spectral irradiance. Bracketing was performed by recording a second spectrum (long spectrum) with ten-times longer integration time than this, to achieve greater accuracy of measurement in the UV region (< 400 nm). These two spectra were spliced together. Each filter measurement was accompanied by a dark measurement (to estimate dark noise) and a measurement under a polycarbonate filter (PC) to correct for stray light. In 2018, these two readings were performed immediately after the filter material (screen/net) was measured; both within 10 s total of the filter material measurement for both the full spectrum, and long spectrum. ScreensNets_irrad_trans.xlsx This Excel file contains the same information in columns as the file ScreensNets_irrad_trans.txt but with a second worksheet showing the trimming calculations for out-of-range readings at low UV-B wavelength and with an addition final column, the irradiance spectrum open29_irrad (described below). Open29_irrad.txt In order to obtain standardised BSWF files to comparison with each other, the calculated proportion spectral transmittance results for each filter material (screen/net) were applied to a “standard” solar-noon open-spectrum from Helsinki recorded on a date close to midsummer (Open29_irrad.txt). This spectrum was measured as described above. This spectrum was measured at Viikki Fields, Helsinki on Wed June 27th 2018 at 13:15:33 EEST (Integration Time, 110000 μsec; bracketting x10) in a completely open area. To apply the transmittance data to their own locations, database users should substitute the spectrum from their own location for Open29_irrad.txt to obtain spectral irradiance data for the effects of the filter materials (screens/net) at their site using the R code Calculating_Spectral_Integrals.r ScreensNets_spectral_integrals.txt The file gives a matrix of spectral integrals and ratios calculated with the Photobiology packages in R for each of the spectra presented in ScreensNets_irrad_trans.txt.  Column headings are the filter material ID, made up from the “Company name” “_” “filter name”. The first column contains row names identifying spectral integrals and ratios calculated – first as energy irradiance then as photon irradiance and finally as photon ratios. Calculations are made using the BSWF (Spectral_Integrals_Function.r) as follows: PAR_e; UVB_e; UVA_e; UVb350_e; UVa350_e; Blue_e; Green_e; Red_e; Far_red_e; GEN_G_e; GEN_T_e; PG_e; DNA_N_e; CIE_e; FLAV_e; Infra_red_e; PAR_q; UVB_q; UVA_q; UVb350_q; UVa350_q; Blue_q; Green_q; Red_q; Far_red_q; GEN_G_q; GEN_T_q; PG_q; DNA_N_q; CIE_q; FLAV_q; Infra_red_q; UVB_UVA; UVB_PAR; UVA_PAR; R_FR_Sellaro; R_FR_Smith10; R_FR_Smith20; B_G; B_R; PhyEqi. ScreensNets_spectral_integrals.xlsx This files contains the same data as ScreensNets_spectral_integrals.txt and shows on individual worksheets, processing of original, smoothed (in Photobiology package to improve the signal to noise in the UV-B tail of the spectr), and corrected (with values of transmittance greater than 1.0 or less than 0.0 replaced in the UV-B tail) data; and comparisons of the Original vs. Corrected, and Original vs. Smoothed data. The same BSWF calculations for the example open spectrum open29_irrad (used for standardisation) are given on their own worksheet, as is the corresponding “FilterFactor” (proportion spectral transmittance) for each spectral integral and spectral photon ratio. The final worksheet “Type” lists the filters and their expected function (i.e. shade, pest net, hale net, ground cover etc.). This “FilterFactor” information could be of practical use in situations where the spectral irradiance is unavailable for a given location, and comparisons among filters need to be made from only partial data (e.g. PAR PPDF).  These FilterFactors can be applied to the PAR PPDF for instance to calculate the daily light integral through the day for horticultural proposes.  Please note that differences in the shape of the solar spectrum at different locations will cause (small) deviations in the transmitted PAR PPFD calculated from the spectral integral compared with the more precise calculation from the spectral irradiance. Although for the purposes of comparison between filters these are likely to be of minor importance.  Plotting_DataBaseScreensNets.r This file gives the R code for plotting the graphs in DataBaseScreensNets.zip from the source file ScreensNets_irrad_trans.txt. Make sure that the required packages are loaded. The code was run in R version 3.4.3. Calculating_Spectral_Integrals.r The file gives the R code to calculate spectral integrals and to include an open measurement for standardisation (Open29_irrad) from the source file ScreensNets_irrad_trans.txt (as described above). The spectra in ScreensNets_irrad_trans.txt are converted to source.spct for use in the Photobiology packages. Spectral_Integrals_Function.r The file is a function requiring the Photobiology packages in R to run. It is needed to calculate the spectral integrals described above and can be amended to obtain whichever spectral integrals and photon ratios from the Photobiology packages are desired.

opencc-by-4.0Jul 2023View details →

Transparency in agricultural land lease by local government

In this research, the focus was on analysing transparency aspect of government and public administration, i.e. how transparent tenders for the allocation and disposition of state-owned agricultural land are conducted. The main objective of this work was to investigate and critically examine the practices of publishing tenders for the lease of state agricultural land in the local units of six selected counties in the Republic of Croatia.

opencc-by-4.0Apr 2022View details →

Stakeholders in Croatian Agriculture Open Data Ecosystem

The dataset shows the identified stakeholders in the Croatian agriculture open data ecosystem in the researched literature available on national portals Hrčak (the Portal of Croatian scientific and professional journals) and Dabar (Digital Academic Archives and Repositories). The complex query: "stakeholder" OR "persons" OR "actors" OR "agriculture" OR "agriculture business" OR "farms" OR "agriculture sector" OR "agriculture area" OR "agriculture field" AND "open data" was used for search of the national databases Hrčak and Dabar. The stakeholders identified in the query were classified and grouped into five key stakeholder groups: Agriculture producers/Farmers, Suppliers, Management and Support Organizations, Consumer Organizations/Consumers, Researches and Scientists, and Others.

openCC (other)Aug 2018View details →

Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data

This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.

Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Soil-Adjusted Vegetation Index (SAVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Soil-Adjusted Vegetation Index (SAVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Soil-Adjusted Vegetation Index (SAVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.