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506 results for “crop data”
Data and code from: A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate, a greenhouse experiment at Dickinson College in Carlisle, PA, USA, 2021.
Data and R code associated with a greenhouse study investigating the influence of water stress on growth, root traits, and biomass of rye and crimson clover seedlings grown separately or together. Data were collected in the Dr. Inge P. Stafford Greenhouse of Dickinson College (Carlisle PA, USA) in June 2021.
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
Data from: Earthworms do not increase greenhouse gas emissions (CO2 and N2O) in an ecotron experiment simulating a realistic three-crop rotation system
<p><span>Earthworms are known to stimulate soil greenhouse gas (GHG) emissions, but the majority of previous studies have used simplified model systems or lacked continuous high-frequency measurements. To address this, we conducted a two-year study using large lysimeters (</span><span>5 m<sup>2</sup> area and 1.5 m soil depth) </span><span>in an ecotron facility, continuously measuring ecosystem-level CO<sub>2</sub>, N<sub>2</sub>O, and H<sub>2</sub>O fluxes. We investigated the impact of endogeic and anecic earthworms on GHG emissions and ecosystem water use efficiency (WUE) in a simulated agricultural setting. Although we observed transient stimulations of carbon fluxes in the presence of earthworms, cumulative fluxes over the study indicated no significant increase in CO<sub>2</sub> emissions. Endogeic earthworms reduced N<sub>2</sub>O emissions during the wheat culture (-44.6%), but this effect was not sustained throughout the experiment. No consistent effects on ecosystem evapotranspiration or WUE were found. Our study suggests that earthworms do not significantly contribute to GHG emissions over a two-year period in experimental conditions that mimic an agricultural setting. These findings highlight the need for realistic experiments and continuous GHG measurements.</span></p>
Data from: Winter game crop plots for gamebirds retain hedgerow breeding songbirds in an improved grassland landscape
<p>The cause of recent population declines in some farmland / hedgerow breeding bird species in the UK is related to a lack of cover and food resources in winter. In improved grassland areas some of those declines have been particularly acute and some have been shown to be related to the availability of grass and weed seed in winter. The provision of seed-bearing crops as part of AES options has been shown to benefit some of these birds. Game crop plots sown on shooting estates for holding and driving gamebirds in autumn and winter have been shown to hold relatively high densities of farmland and wood-edge birds during the winter.</p> <p>We studied breeding songbirds in hedges in a primarily improved grassland area in the SW of England where there are some large shooting estates that sow relatively large game crop plots (1 - 5 ha) in the landscape. In this study we found that otherwise similar hedges in terms of size and density near to those winter game crop plots, had between 1.5 and 2 times as many breeding resident songbirds per unit length the following spring compared to hedges further away from game crop plots. This was despite game management in these plots being wound down during February and in many cases, the crops themselves being removed by mid-March. Hedges within approximately 350m from game crop plots had more breeding birds. We discuss possible mechanisms and suggest that some passerines preferentially establish breeding territories in hedges near to game crops in late winter. We suggest how to distribute game crop plots to maximise any benefit in an improved grassland landscape.</p>
Data from: Carry-over effect of leguminous winter cover crops and living mulches on winter wheat as a second main crop following white cabbage
<p><strong>Background: </strong>In trials on two strategies for the integration of legumes in a vegetable crop rotation (leguminous winter cover crops and living mulches), data were collected on the two subsequent crops white cabbage and winter wheat. The data on biomass and soil mineral nitrogen content are made publicly available here. </p> <p> </p> <p><strong>Abstract:</strong> <span>The direct effect of winter cover crops (WCC) or living mulches (LM) on a first vegetable crop has already been investigated. However, little is known about the effect on growth and yield of a second cash crop. </span><span>The aim of the study was to assess the carry-over effect of legumes grown as WCC or LM on winter wheat as a second crop after cabbage measured in yield and nitrogen release.</span><span> Two field trials were carried out in Germany between 2019 and 2022. In the WCC trial rye, rye with vetch, vetch, pea and faba bean were used as WCC and compared to bare soil. The WCC biomass was incorporated before cabbage planting in late spring. For the LM trial, perennial ryegrass or white clover were used as LM during cabbage cultivation and compared to bare soil. The LM biomass was incorporated together with the cabbage residues (STU/STT) and compared to an early incorporation of LM biomass before cabbage planting (RT). Winter wheat in both trials was seeded as the second main crop in the rotation in the fall.</span></p>
Data for : Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops
<p>Data file for article: Effects of electrokinetic and ultrasonication pre-treatment and two-step anaerobic digestion of biowastes on the nitrogen fertiliser value by injection or surface banding to cereal crops (https://doi.org/10.1016/j.jenvman.2022.116699).</p>
Data to support the publication "The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment" https://doi.org/10.3390/land10090964
<p>Underlying data of soil measurements and analysis by TUC team for the publication “The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment” <a href="https://doi.org/10.3390/land10090964">https://doi.org/10.3390/land10090964</a> from the SoilCare project study sites in Crete. </p> <p>Abstract:</p> <p>The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management–or with some omissions–, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.</p>
Crop Classification Data-set
<p>The dataset consists of crop type training and testing data of more than 10 classes, collected using Ground Truth Surveys, in Harichand region of Khyber Pakhtoonkhwa, Pakistan. </p> <p>The dataset also contains 2 tiff files having Planet-Scope and Sentinel-2 raster data. </p> <p>https://drive.google.com/drive/folders/1SweabTezj78btq9wd3PRZYWrR_4gUuC4</p>
Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects
<p>Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.</p>
CN-N (version 1): a crop-specific, 1 km-resolution nitrogen rate data product in China over 2004–2016
<p>CN-N version 1: A dataset of crop-specific, 1 km resolution nitrogen rate for rice, wheat and maize in China over 2004-2016 </p>
Рис. 1. Àинамика посевных пΛощаΔей сои в Приморском крае в 1996–2018 гг. (по Δанным Àепартамента сеΛьского хозяйства и проΔовоΛьствия Приморского края) Fig. 1. The dynamic of soybean crop area at Primorsky Region in 1996–2018 (based on data from the Department of Agriculture and provision of the Primorsky Region) in Reproductive potential of Soybean Cyst Nematode Heterodera glycines - quarantine pest of soybean - in Primorsky Region conditions
Рис. 1. Àинамика посевных пΛощаΔей сои в Приморском крае в 1996–2018 гг. (по Δанным Àепартамента сеΛьского хозяйства и проΔовоΛьствия Приморского края) Fig. 1. The dynamic of soybean crop area at Primorsky Region in 1996–2018 (based on data from the Department of Agriculture and provision of the Primorsky Region)
Data from: Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species
<p>The sweet potato whitefly (Bemisia tabaci) is a damaging insect pest that feeds on hundreds of crop plants. Oviposition rate is a useful metric to screen plants for whitefly resistance. Whitefly eggs are small and translucent, and can therefore be hard to count on the leaves of some crops. In this research, we tested a selective egg staining process on five crop species to determine if egg staining can improve the visualization and quantification of whitefly eggs. By comparing the egg counts before and after staining using two-sample Wilcoxon signed-rank tests (a non-parametric test for paired analyses). Two individuals counted the eggs, and for both these counters we found a significant increase in the number of visible eggs after staining on melon, tomato, and cowpea. This method could be applied to improve phenotyping for whitefly resistance in plant breeding applications.</p>
Data from a greenhouse with and without 'Stringless Blue Lake' bean crop
<p>Data are presented from an environment managed by a microcontroller, which contains a set of sensors and actuators for its operation, data are collected between the months of June and September 2021 belonging to: a prototype greenhouse where Stringless Blue Lake beans are grown and another prototype greenhouse with the same physical characteristics where the variables are sensed without any type of crop and therefore without any control action.</p>
Data on Crop Yield, Nutrient Content of Barley (Hordeum vulgare L.) and Weather of a Vertical Agrivoltaic System in Sweden
<p>The dataset location is latitude 59.55° N and longitude 16.76° E in Kärrbo Prästgård, Sweden. </p> <p>Crop data:</p> <p>Raw data of barley related to yield kernels and straws (kg DM/ha), nitrogen content in kernels (%), crude protein in kernels (%), kernels yield (kg DM/ha), straws yield (kg DM/ha), starch content in kernels (%), and thousand kernel weight (%) from the harvest on September 12<sup>th</sup>, 2023, at the agrivoltaics research site in Kärrbo Prästgård, Sweden. Fifty squared samples (each 0.25 m<sup>2</sup>) distributed in 5 groups (A, B, C, D, E) were collected according to the layout presented in the corresponding publication. Groups A, B and C are based on the spatial location in the crop area between the three vertical rows of PV modules: west side (A), center side (B) and east side (C). Group R corresponds to the reference control plot conditions. Group D represent the crops that are growing in the space between the rows of the conventional ground-mounted PV system with 30° tilt. </p> <p> </p> <p>Weather data:</p> <p>1-hour timeseries averaged data measurements at local time, raw data, not quality controlled from the barley growing season at Kärrbo Prästgård, Sweden from May 7<sup>th</sup> to September 12<sup>th</sup>,2023.</p> <p>Temperature of air (°C), relative humidity (%), relative air pressure (hPa), wind speed (m/s), and precipitation (mm/h) are measured with a Lufft WS600-UMB Smart Weather Sensor located on-site on a 5 m height mast.</p> <p>Global and diffuse horizontal irradiance (W/m<sup>2</sup>) are measured with a Delta-T SPN1 Sunshine Pyranometer.</p> <p>Photosynthetically active radiation (µmol/m<sup>2</sup>/s) is measured with an Apogee PAR Quantum sensor SQ-500.</p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
data sets for the article Short‑term impact of crop diversifcation on soil carbon fuxes and balance in rainfed and irrigated woody cropping systems under semiarid Mediterranean conditions
<p>Diversifcation practices such as intercropping in woody cropping systems have recently been proposed as a promising management strategy for addressing problems related to soil degradation, climate change mitigation and food security. In this study, we assess the impact of several diversifcation practices in diferent management regimes on the main carbon fuxes regulating the soil carbon balance under semiarid Mediterranean conditions.</p>
Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale
<p>This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts.</p> <p>For more information about the dataset and the study, see the original article:</p> <p>Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy</p>
Global data on crop nutrient concentration and harvest indices
<div> <div> <div> <p>Estimates of crop nutrient removal (as crop products and crop residues) are an important component of crop nutrient balances. Crop nutrient removal can be estimated through multiplication of the quantity of crop products or crop residues (removed) by the nutrient concentration of those crop products and crop residue components respectively. Data for quantities of crop products removed at a country level are available through FAOSTAT (<a href="https://www.fao.org/faostat/en/">https://www.fao.org/faostat/en/</a>), but equivalent data for quantities of crop residues are not available at a global level. However, quantities of crop residues can be estimated if the relationship between quantity of crop residues and crop products is known. Harvest index (HI) provides one such indication of the relationship between quantity of crop products and crop residues. HI is the proportion of above-ground biomass as crop products and can be used to estimate quantity of crop residues based on quantity of crop products. Previously, meta-analyses or surveys have been performed to estimate nutrient concentrations of crop products and crop residues and harvest indices (collectively known as crop coefficients). The challenges for using these coefficients in global nutrient balances include the representativeness of world regions or countries. Moreover, it may be unclear which countries or crop types are actually represented in the analyses of data. In addition, units used among studies differ which makes comparisons challenging. To overcome these challenges, data from meta-analyses and surveys were collated in one dataset with standardised units and referrals to the original region and crop names used by the sources of data. Original region and crop names were converted into internationally recognised names, and crop coefficients were summarised into two Tiers of data, representing the world (Tier 1, with single coefficient values for the world) and specific regions or countries of the world (Tier 2, with single coefficient values for each country). This dataset will aid both global and regional analyses for crop nutrient balances. </p> </div> </div> </div>
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Remote sensing data for crop yield in CONUS
<p><strong>I) SUMMARY</strong></p> <p>This database contains harmonized time series for the study of crop yields using remote sensing data and meteorological data. We collected information on soybean, corn, and wheat yields (t/ha) over the CONUS (continuous US) from <a href="http://quickstats.nass.usda.gov/USDA-NASS">USDA-NASS</a> for years 2015–2018 at a county level, and collocated time series for the following variables:</p> <ul> <li>Enhanced Vegetation Index (EVI) from <a href="https://lpdaac.usgs.gov">MODIS</a> satellite (MOD13C1 v6 product)</li> <li>Soil Moisture (SM) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Vegetation Optical Depth (VOD) from SMAP satellite through <a href="https://zenodo.org/record/5619583#.Y2OkiXbMKUl">MT-DCA algorithm</a></li> <li>Maximum temperature (TMAX) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> <li>Precipitation (PRCP) from <a href="https://daac.ornl.gov">Daymet</a> v3</li> </ul> <p><strong>II) CONTACT</strong></p> <p>For questions, please email Laura Martínez-Ferrer at <a href="mailto:laura.martinez-ferrer@uv.es">laura.martinez-ferrer@uv.es</a></p> <p><strong>III) DATABASE</strong></p> <p>For each crop type, we provided CSV files containing the time series of the variables and yield described above. Furthermore, additional information for spatial and temporal identification such as a county identifier and a year are included. Lastly, country-shapefiles (.shp) are added for geospatial representation. Further details in readme.txt file.</p> <p><strong>IV) CITE</strong></p> <p>We kindly encourage to cite the following works if this database is used</p> <p>L. Martínez-Ferrer, M. Piles, G. Camps-Valls, Crop Yield Estimation and Interpretability With Gaussian Processes, IEEE Geoscience and Remote Sensing Letters, 2020, vol. 18, no 12, p. 2043-2047, DOI: <a href="https://doi.org/10.1109/LGRS.2020.3016140">10.1109/LGRS.2020.3016140</a> </p> <p>A. Mateo-Sanchis, J. E. Adsuara, M. Piles, J. Muñoz-Marí, A. Pérez-Suay and G. Camps-Valls, "Interpretable Long-Short Term Memory Networks for Crop Yield Estimation," in IEEE Geoscience and Remote Sensing Letters, DOI: <a href="https://ieeexplore.ieee.org/document/10041987">10.1109/LGRS.2023.3244064</a></p>
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