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1,609 results for “crops”
Trace Gas Fluxes on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1991 to 2019)
Dataset Abstract Trace gases (nitrous oxide, methane, and carbon dioxide) have been measured on the LTER Main Site since 1991 and on Successional and Forest sites since 1993. Trace gas fluxes are measured twice monthly or monthly until the ground freezes using permanently-installed, in-situ static chambers. CH4 and N2O are analyzed with gas-chromatography and CO2 with an infrared gas analyzer. Soil moisture and temperature are measured during sampling. original data source http://lter.kbs.msu.edu/datasets/16
Soil Moisture on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2019)
Dataset Abstract Measurements of soil moisture began in 1989 for all treatments on the LTER main site and in 1993 on the successional and forest sites. Soil moisture is analyzed on the baseline soil samplings which are collected twice monthly or monthly during the growing season. The percent gravimetric moisture content is calculated on a dry weight basis. Other datasets from the baseline soil samplings include Inorganic nitrogen and Total N and Total C. original data source http://lter.kbs.msu.edu/datasets/18
Annual Net Primary Production on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1990 to 2018)
Dataset Abstract Aboveground annual net primary production (ANPP) has been measured on the LTER main site since 1990 and on the successional and forested sites since 1993. ANPP is measured at peak biomass for a given treatment. In some systems with multiple harvests or complex communities that have peaks occurring at different times of the year, measurements are taken at multiple times per year. Additional ANPP measurements are made where appropriate using leaf litter traps, estimates of diameter from tree basal diameter and for the poplar treatment occasional destructive harvests. See the ANPP protocol for descriptions of the sampling and measurement methods for each of the treatments. original data source http://lter.kbs.msu.edu/datasets/22
Soil Inorganic Nitrogen on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2018)
Dataset AbstractMeasurement of soil inorganic nitrogen began in 1989 for all treatments on the LTER Main Site and 1993 on the Successional and Forest sites. Ammonium and nitrate are analyzed twice monthly or monthly during the growing season on baseline soil samplings. Additional datasets from the Baseline Soil Samplings include soil moisture, total N and total C.original data source http://lter.kbs.msu.edu/datasets/24
Insect Population Dynamics on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2019)
Dataset Abstract Plant dwelling insect occurrence in the LTER main site (all treatments) of the KBS-LTER has been recorded since 1989 and in the successional and forest sites since 1993. The effort has focused on characterizing the temporal and spatial abundance and diversity of a set of insects representative of a higher order insect trophic level, the herbivore predators. The insect database contains more than 400,000 records and consists of counts of adult insects of fourteen species of Coccinellidae, one species of Chrysopidae, and one species of Lampyridae from 30 sample sites in each of the seven treatments in the LTER Main Site. The standard method used to measure these organisms is a yellow sticky trap. Sampling is conducted weekly during the growing season as described in the sampling protocol. original data source http://lter.kbs.msu.edu/datasets/26
Main Cropping System Experiment Field Logs and treatment descriptions at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2020)
Dataset Abstract This dataset includes information about the LTER main site treatments, agronomic practices carried out on the treatments and approved site use requests. Most long-term hypotheses associated with the KBS LTER site are being tested within the context of the main cropping systems study. This study was established on a 48 ha area on which a series of 7 different cropping systems were established in spring 1988, each replicated in one of 6 ha blocks. An eighth never-tilled successional treatment, is located 200 m off-site, replicated as four 0.06 ha plots. Cropping systems include the following treatments: T1. Conventional: standard chemical input corn/soybean/wheat rotation conventionally tilled (corn/soybean prior to 1992) T2. No-till: standard chemical input corn/soybean/wheat rotation no-tilled (corn/soybean prior to 1992) T3. Reduced input: low chemical input corn/soybean/wheat rotation conventionally tilled (ridge till prior to 1994) T4. Biologically based: zero chemical input corn/soybean wheat rotation conventionally tilled (ridge till prior to 1994) T5. Poplar: Populus clones on short-rotation (6-7 year) harvest cycle T6. Alfalfa: continuous alfalfa, replanted every 6-7 years (converted to switchgrass in 2018) T7. Early successional community: historically tilled soil T8. Mown grassland community: never-tilled soil. For specific crops in a given year see the Annual Crops Summary Table. In 1993 a series of forest sites were added to the main cropping system study to provide long-term reference points and to allow hypotheses related to substrate diversity to be tested. These include: TCF. Coniferous forest: three conifer plantations, 40-60 years old TDF. Decidious forest: three deciduous forest stands, two old-growth and one 40-60 years post-cutting TSF. Mid-successional forest: three old-field (mid-successional) sites 40+ years post-abandonment. All share a soil series with the main cropping system treatments, and are within 5 km of all other sit
Data for 'Local food crop production can fulfil demand for less than one-third of the population'
<p><strong>This dataset is supplement to the following publication (<em>please cite that when using the data</em>):</strong></p> <p>Kinnunen et al. 2020. Local food crop production can fulfil demand for less than one-third of the population. Nature Food 1: 229–237. http://doi.org/10.1038/s43016-020-0060-7</p> <p> </p> <p><strong>Data description</strong></p> <p><strong><em>Distance to food:</em></strong> Globally optimized distance between crop production and consumption. The optimization creates a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. Data is in two formats: NetCDF (dist_food_netcdf.zip) and multi-band geotiff (dist_food_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (dist_food_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (dist_food_halfLoss.nc / .tif)</li> <li>halving the yield gap (dist_food_halfYieldGap.nc / .tif)</li> <li>both of these measures together (dist_food_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: km</p> <p> </p> <p><em><strong>Foodsheds:</strong></em> The data contains global foodsheds which are areas that are connected by food flows between raster cells. The food flows are from a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. In addition to normal foodsheds (values>0), there are two special cases: ridge-cells (value: -99) and unconnected single cells (value: -50). Ridge-cells are raster cells connected to multiple foodsheds, while being able to satisfy their own demand locally. Unconnected single cells are not connected to any other foodshed. Each positivie value is a crop specific id, signifying a connected foodshed area. </p> <p>Data is in two formats: NetCDF (foodsheds_netcdf.zip) and multi-band geotiff (foodsheds_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (foodsheds_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (foodsheds_halfLoss.nc / .tif)</li> <li>halving the yield gap (foodsheds_halfYieldGap.nc / .tif)</li> <li>both of these measures together (foodsheds_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: -</p> <p> </p>
Supplementary dataset to "Draft genome assembly of the biofuel grass crop Miscanthus sacchariflorus"
<p><em>Miscanthus sacchariflorus</em> (Maxim.) Hack. is a C4 perennial rhizomatous biofuel grass crop. <em>M. sacchariflorus</em> is among the most widely distributed species within the genus, particularly at cold northern latitudes, and one of the progenitor species of the main biomass commercial crop <em>M. × giganteus</em>. We generated a 2.54 Gbps whole-genome assembly of the diploid <em>M. sacchariflorus</em> “Robustus 297” genotype, which represented ~59% of the expected genome size. We later anchored this assembly in the chromosomal-scale <em>M. sinensis</em> genome to improve its contiguity. We annotated 86,767 and 69,049 protein-coding genes in the unanchored and anchored, respectively. We estimated our assemblies include ~85% of the <em>M. sacchariflorus</em> genes based on homology, core markers and RNA-seq alignments stats. Raw data and further metadata are available under Bioproject PRJNA435476.</p> <ul> <li>Msac_v2.fasta: Unanchored whole-genome assembly (WGA) of M. sacchariflorus in FASTA format.</li> <li>Msac_v3.fasta: The previous WGA re-scaffolded with the M. sinensis public reference.</li> <li>Msac_v3.agp: Chromosomal position in the M. sinensis reference of the previous scaffolds in Msac_v3.fasta</li> <li>Msac_v2.gff3: Gene annotation of the unanchored WGA in GFF3 format, which contains 86,767 coding genes</li> <li>Msac_v3.gff3: Gene annotation of the anchored WGA in GFF3 format, which contains 69,049 coding genes</li> <li>Msac_v2.func_annot.tsv: Text table containing the functional annotation of the 86,767 coding genes in Msac_v2.gff3</li> <li>Msac_v2.repeats_annotation.gff3: Repeats annotation (Repeatmasker) of the unanchored reference.</li> <li>Msac_v2.masked.fasta.gz: Repeats-masked version (Repeatmasker) of Msac_v2.fasta</li> <li>all.satsuma.blocks_Msac_v2-vs-Msin.gz: Every alignment from scaffolds in Msac_v3.fasta into M. sinensis reference</li> <li>Msac_v2.orthology_Msin.tsv: Ortologous between Msac_v2 and M. sinensis</li> <li>Msac_v3-vs-Msin.tsv: Ortologous between Msac_v3 and M. sinensis</li> </ul>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations
<p>This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations
<p>This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Data for "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany"
<p>This dataset is corresponding to an open-access article named "Crop Diversification in Viticulture with Aromatic Plants: Effects of Intercropping on Grapevine Productivity in a Steep-Slope Vineyard in the Mosel Area, Germany" published in Agriculture (https://www.mdpi.com/2077-0472/11/2/95; <a href="https://doi.org/10.3390/agriculture11020095">https://doi.org/10.3390/agriculture11020095</a>), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003]. </p>
Contrasting effects of landscape composition on crop yield mediated by specialist herbivores
<p>Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e. meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits.</p>
Seed mass data for crop species and wild progenitors
<p>Data supporting a comparison of seed masses in crop species and their wild progenitors.</p> <p>The data files which fed into the analysis reported in the paper are:</p> <ul> <li>Grass_crops_raw.csv</li> <li>Legume_crops_raw.csv</li> <li>Veg crops combined.csv</li> <li>Beet_seeds_dissected.csv - mass of true seeds of beet; the data in the main 'Veg crops' file are for beet seed capsules, which are easier to collect and weigh.</li> <li>Cassava_EMBRAPA_*.csv - extra seed mass data from cassava, analysed separately from the main data, and described in supplementary material 2.</li> </ul> <p>Scripts used in processing the data (*.py) and a Makefile controlling some processing steps are included.</p> <p>There is also some extra data collected on other species of vegetable crops: <em>Brassica </em>spp., Chicory & Endive (<em>Cichorium </em>spp.), Leek (<em>Allium ampeloprasum</em>), and on fibre crops: Kenaf & Roselle (<em>Hibiscus </em>spp.), Jute (<em>Corchorus olitorius</em>), Hemp (<em>Linum</em> spp.). The data available for these was not sufficient to include them in our analysis, but they are provided here anyway.</p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
qPCR raw datasets on the assessments of cover crop monocultures and mixtures improving the rhizosphere bacterial abundance and functionality through rerooting
<p>Quantitative PCR (qPCR) raw data includes the source data that corresponds to the the counts of 16S rRNA gene copies per gram of soil for different variations, as discussed in the research article - "Cover crop monocultures and mixtures improve the rhizosphere bacterial abundance and functionality through rerooting". The copy number of the 16S rRNA gene per gram of soil was quantified by SYBR® Green-based qPCR using a 7500 Fast Real-Time PCR System (Applied Biosystems™, Thermo Fisher Scientific, Waltham, MA, USA). Aliquots of the same DNA extract utilized in amplicon sequencing were used in qPCR. Dilutions of template DNA were used to compensate for the effect of PCR inhibitors in the samples. Each sample was analyzed in triplicate. A PCR amplicon of the <i>Escherichia coli</i> V3 region was used as standard. Each reaction of 20 µL contained 1 µL of template DNA, the forward primer 341F (Muyzer et al., 1993), the reverse primer 518R (Muyzer et al., 1993), and Luna® Universal qPCR Master Mix (NEB). Reaction conditions were an initial denaturation for 1 min at 95 °C, followed by 40 cycles of denaturation at 95 °C for 15 s and extension at 60 °C for 30 s. The melting curve was recorded in the temperature range of 60 °C to 95 °C. The 16S rRNA gene copy numbers per gram of soil were calculated using the standard curve method and then normalized against the standard (Adelowo et al., 2018). The average efficiency value was 100.77 ± 3.15 %. The absolute copy numbers for each bacterial phylum were calculated by multiplying the qPCR values by the relative abundance values in percent obtained from the 16S rRNA gene sequencing analyses.</p>
Database to: Cover crops affect pool specific soil organic carbon in cropland – A meta‐analysis
<p>Database to a meta-analysis studying the effects of cover crops on the mineral-associated organic carbon pool (MAOC), the particulate organic carbon pool (POC) and the microbial biomass carbon pool (MBC). Consists of:<br>1. information on the database<br>2. legend<br>3. list of included studies, all extracted data necessary for response ratio calculation and moderator analysis, and additional information</p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
<p>Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our <a href="https://www.mdpi.com/2072-4292/13/21/4302">original</a> publication for more details.</p> <p>0: Uncertain</p> <p>1: No Data</p> <p>2: Background class</p> <p>3: Unchanged coconut plantation</p> <p>4: Damaged coconut plantation</p> <p>5: New coconut plantation</p> <p> </p> <p> </p> <p> </p>
Dataset of crop yield and management in CS02 from Diverfarming project
<p>Auxiliary data related to crop management and data of different crop yields in mandarin monoculture and mandarin diversified treatments during three crop cycles in case study 02 from Diverfarming project</p>
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