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129 results for “crop yield”
Figure 2 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 2. Soybean and Amoronthus polmeri (AMAPA) height (averaged across distance from the crop) at 0, 1, 2, 4, 6, and 8 wk after soybean emergence (WAE) (i.e., AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8, respectively). Vertical bars represent ± standard error of the mean from the analysis for comparisons within each sampling date (i.e., n = 12 for 0 WAE, 24 for 1 WAE, etc.).
Figure 9 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 9. Effects of weed establishment time on soybean yield averaged across Amoronthus polmeri distances from the crop row. Dashed lines indicate the confidence intervals at 95% confidence level (sample size n = 72). WAE, weeks after soybean emergence.
Figure 10 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 10. Effects of Amoronthus polmeri distance from the soybean row on crop yield averaged across A. polmeri establishment times. Vertical bars represent ± standard error of the mean (SE2014 = 337.45; SE2015 = 207.14) from the analysis for comparisons between A. polmeri distances from the crop with sample size n = 72.
Figure 6 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 6. Effects of weed establishment time on Amoronthus polmeri (AMAPA) flowering (averaged across distance from the crop) at various sampling occasions for 0, 1, 2, 4, 6, and 8 wk after soybean emergence (WAE) (i.e., AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8 respectively) in 2014 and 2015. Vertical bars represent ± standard error of the mean (i.e., flowering of the entire A. polmeri population was evaluated at each sampling occasion) from the analysis for comparisons within each sampling date (i.e., n = 12 plots for 0 WAE, 24 plots for 1 WAE, 36 plots for 2 WAE, etc.).
Figure 5 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 5. Effects of the interaction of weed establishment time and distance from the crop row on Amoronthus polmeri seed production before soybean harvest. Vertical bars represent ± standard error of the mean (SE2014 = 2,530.27; SE2015 = 1,008.30) from the analysis for comparisons between weed establishment times with sample size n = 72. WAE, weeks after soybean emergence.
Figure 8 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 8. Relationship between ground cover and extinction coefficient for each sampling date (n = 12 plots) throughout the 2015 growing season. WAE, weeks after soybean emergence
Figure 1 in Effects of Palmer Amaranth (Amoronthus polmeri) Establishment Time and Distance from the Crop Row on Biological and Phenological Characteristics of the Weed: Implications on Soybean Yield
Figure 1. Schematic representation of the experimental setup depicting the distance of Amoronthus polmeri (AMAPA) from the crop (i.e., 0, 24, and 48 cm from the soybean row) and the sequence of A. polmeri establishment time (i.e., 0, 1, 2, 4, 6, and 8 wk after soybean emergence [WAE] or AMAPA-0, AMAPA-1, AMAPA-2, AMAPA-4, AMAPA-6, and AMAPA-8, respectively). Each treatment combination (i.e., establishment time × distance from the crop) was applied only to one randomly selected experimental plot per replication.
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>
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
A large-ensemble simulation of yields and meteorological drivers to evaluate spatial compounding crop failures in Europe
<p>The dataset consists of a subset from Vogel et al. (2021), comprising large-ensemble simulations of winter wheat yields aggregated at the country level for 20 European countries. The winter wheat yields were simulated by the APSIM-Wheat model (version 7.10) (Zhang et al. 2014) driven by meteorological data from the EC-Earth global climate model (Hazeleger et al., 2010; Van der Wiel et al., 2019). To investigate meteorological drivers of crop failure, the dataset also includes monthly means of daily precipitation, vapour pressure deficit, and maximum temperature fields, of two leading European producers, i.e. France and Germany. For more details, see the description in Vogel et al. (2021).</p> <p>By using this data, you also agree to cite the reference below:</p> <p>Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., Zscheischler, J. (2021). Identifying meteorological drivers of extreme impacts: an application to simulated crop yields. Earth System Dynamics,12(1),151-172.</p>
PML-V2 China staple crop (maize, wheat, rice) evapotranspiration, gross primary production and yield over 2003-2018
<p>This dataset provides the yearly evapotranspiration (ET), gross primary production (GPP) and yield of the three staple crops (i.e., maize, wheat and rice) of China from 2003 to 2018. ET and GPP are estimated by the Penman-Monteith-Leuning version 2 model (PML-V2 model), and crop yield is the product of GPP and harvest index. The units of ET, GPP and yield are mm year<sup>-1</sup>, g C m<sup>-2</sup> year<sup>-1</sup> and kg ha<sup>-1</sup>, respectively.</p> <p>The PML-V2 model is calibrated and validated against the observed ET and GPP at EC sites for each crop type. The PML_V2 model uses CMFD meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 crop evapotranspiration, gross primary production datasets.</p> <p>The data format is tiff, the spatiotemporal resolution is yearly and 0.0125°, and the time span is 2003-2018. The file name is "crop_variable_year.tif". For example, a file named Maize_ETsum_2003.tif corresponds to the total ET of maize in 2003.</p>
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>
Model inputs and outputs for: Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
<p>Files used in initial submission of manuscript to <em>Geoscientific Model Development</em>. Files with names beginning sdates and gdds were used as model inputs for some experiments. File with name beginning hdates was used in postprocessing. ZIP archives are model experimental outputs.</p>
Data and scripts for the analysis of the influence of crop pollinator dependence and growth form on yield decline
<p>Marcelo A. Aizen, Gabriela R. Gleiser, Thomas Kitzberger, Ruben Milla. <strong>Being a tree crop increases the odds of experiencing yield declines irrespective of pollinator dependence </strong>(to be submitted to PCI)</p> <p> </p> <p>Data and R scripts to reproduce the analyses and the figures shown in the paper. All analyses were performed using R 4.0.2.</p> <p> </p> <p><strong>Data</strong></p> <p>1. FAOdata_21-12-2021.csv</p> <p>This file includes yearly data (1961-2020, column 8) on yield and cultivated area (columns 6 and 10) at the country, sub-regional, and regional levels (column 2) for each crop (column 4) drawn from the United Nations Food and Agriculture Organization database (data available at <a href="http://www.fao.org/faostat/en">http://www.fao.org/faostat/en</a>; accessed July 21-12-2021). [Used in Script 1 to generate the synthesis dataset]</p> <p>2. countries.csv</p> <p>This file provides information on the region (column 2) to which each country (column 1) belongs. [Used in Script 1 to generate the synthesis dataset]</p> <p>3. dependence.csv</p> <p>This file provides information on the pollinator dependence category (column 2) of each crop (column 1).</p> <p>4. traits.csv</p> <p>This file provides information on the traits of each crop other than pollinator dependence, including, besides the crop name (column1), the variables type of harvested organ (column 5) and growth form (column 6). [Used in Script 1 to generate the synthesis dataset]</p> <p>5. dataset.csv</p> <p>The synthesis dataset generated by Script 1.</p> <p>6. growth.csv</p> <p>The yield growth dataset generated by Script 1 and used as input by Scripts 2 and 3.</p> <p>7. phylonames.csv</p> <p>This file lists all the crops (column 1) and their equivalent tip names in the crop phylogeny (column 2). [Used in Script 2 for the phylogenetically-controlled analyses]</p> <p>8.phylo137.tre</p> <p>File containing the phylogenetic tree.</p> <p> </p> <p><strong>Scripts</strong></p> <p>1. dataset</p> <p>This R script curates and merges all the individual datasets mentioned above into a single dataset, estimating and adding to this single dataset the growth rate for each crop and country, and the (log) cumulative harvested area per crop and country over the period 1961-2020.</p> <p>2. analyses</p> <p>This R script includes all the analyses described in the article’s main text.</p> <p>3. figures</p> <p>This R script creates all the main and supplementary figures of this article.</p> <p>4. lme4_phylo_setup</p> <p>R function written by Li and Bolker (2019) to carry out phylogenetically-controlled generalized linear mixed-effects models as described in the main text of the article.</p> <p> </p> <p><strong>References</strong></p> <p>Li, M., and B. Bolker. 2019. wzmli/phyloglmm: First release of phylogenetic comparative analysis in lme4- verse. Zenodo. https://doi.org/10.5281/zenodo.2639887.</p>
Influence of crop field size on pest densities, pesticide use, and crop yield
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Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects
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Field experimental data of crop yields, nutrient concentrations, and harvest indices from around the world
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Data for: Quantifying direct yield benefits of soil carbon increases from cover cropping
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Cover crop application on dredged sediments increases corn yield through microorganism-associated enzyme-driven nutrient mineralization.
Common strategies to mitigate soil degradation of agricultural soils include cover crop application and soil amendment addition. Applying dredged sediments as a soil amendment is gaining popularity since they often provide benefits other amendments lack; however, their use with a cover crop is largely unexplored. To understand how cover crop use changes the restorative properties of dredged sediments, we assessed soil physical and chemical properties, enzymatic activities, and corn yield for plots of dredged sediments with and without a cover crop. Cover crop application on dredged sediments increased corn yields by ~24% when compared to dredged sediments alone. Increases in corn yield were driven by changes in nutrient mineralization, specifically within the nitrogen cycle. The physical and chemical properties of dredged sediments remained unchanged regardless of cover crop application. Our results suggest that when cover crops are applied to dredged sediments, crop yield increased through microorganism-driven nutrient mineralization. However, the physical and chemical environment remained optimal for corn growth within dredged sediments, regardless of cover crop application. This research is a vital step into understanding the use of dredged sediments in agricultural soil systems.
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Allen Brain Atlas
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.