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1,253 results for “yield”
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
WSC - Yield and water table depth shapefiles from Wibu field site
Yield data from the Wibu field site combined with a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages). It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
Effects of Long-Term Soil Warming on Microbial Yield, Acquisition, and Stress Traits at Harvard Forest 2014
Soil microbial traits drive ecosystem functions. This relationship can explain why microbial functional diversity is typically positively correlated with ecosystem function. However, microbial adaptation to climate change related warming stress can shift microbial traits with direct implications for carbon cycling in the soil. Here, we investigated how long-term warming affects the relationship between microbial trait diversity and ecosystem function. Soils were sampled after 24 years of +5\degree C warming alongside unheated control soils from the Harvard Forest Long-Term Ecological Research site. Ecosystem function was estimated from six different enzyme activities and microbial biomass. This data was coupled with metatranscriptomics sequencing, where reads were assigned to yield, acquisition, or stress trait categories. We found that in organic horizon soils, warming decreased the richness of acquisition-related traits. In the mineral soils, we observed that heated soils exhibited a negative relationship with the richness of acquisition related traits. These results suggest that the microbial communities exposed to long-term warming is shifting away from a resource acquisition life history strategy.
Production, biomass, and yield estimates for walleye populations in the Ceded Territory of Wisconsin from 1990-2017
Recreational fisheries are valued at $190B globally and constitute the predominant use of wild fish stocks in developed countries, with inland systems contributing the dominant fraction of recreational fisheries. Although inland recreational fisheries are thought to be highly resilient and self-regulating, the rapid pace of environmental change is increasing the vulnerability of these fisheries to overharvest and collapse. We evaluate an approach for detecting hidden overharvest of inland recreational fisheries based on empirical comparisons of harvest and biomass production. Using an extensive 28-year dataset of the walleye fisheries in Northern Wisconsin, USA, we compare empirical biomass harvest (Y) and calculated production (P) and biomass (B) for 390 lake-year combinations. Overharvest occurs when harvest exceeds production in that year. Biomass and biomass turnover (P/B) both declined by about 30% and about 20% over time while biomass harvest did not change, causing overharvest to increase. Our analysis revealed 40% of populations were production-overharvested, a rate about 10x higher than current estimates based on numerical harvest used by fisheries managers. Our study highlights the need for novel approaches to evaluate and conserve inland fisheries in the face of global change.
Evaluating rose yield responses to compost treatments: Data from an 18-month field study in Kenya
<p>This dataset and these scripts supports the manuscript 'Modelling cut rose yield after compost amendment over an 18-month period using repeated sigmoidal Gompertz curve fitting' by Evy de Nijs, Roland Bol, Albert Tietema & Emiel van Loon. </p> <p>Roses are an important crop for the floricultural sector of Kenya. Roses are a perennial crop and under continuous production for six to ten years. To optimize rose production, it is essential to understand how different management practices impact yield over time. This dataset contains a detailed record of rose yield data collected in an 18-month large-scale field experiment. The aim of this experiment was to evaluate the effect of pre-planting compost amendment on the yield and quality of cut roses. It was conducted in a polythene greenhouse near lake Naivasha, Kenya. Yield data included the number of stems harvested per day per flowering bed. Data presented here offer a comprehensive view of the impacts of different compost treatments on the yield of cut roses. Combined with the offered scripts, this is the framework presented in the aforementioned manuscript. This approach allows to use repeated growth curves to analyze yields compared to a baseline General Additive Model. </p> <p> </p> <p>de Nijs, E. A., Tietema, A., Bol, R., & van Loon, E. E. (2025). Modeling Cut Rose Yield Over an 18‐Month Period After Compost Amendment Using Repeated Sigmoidal Gompertz Curve Fitting. <em>Plant‐Environment Interactions</em>, <em>6</em>(3), e70049.</p>
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
Sample data for "A weakly supervised framework for high resolution crop yield forecasts"
<p>This dataset includes sample data for the United States to run the weakly supervised framework as described in the paper titled <em>A weakly supervised framework for high resolution crop yield forecasts</em>, accessible at </p> <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2205.09016">https://doi.org/10.48550/arXiv.2205.09016</a></td> </tr> </tbody> </table> <p> </p> <p>The updated paper (including results from the US) is published in Environmental Research Letters:</p> <p><a href="https://doi.org/10.1088/1748-9326/acf50e">https://doi.org/10.1088/1748-9326/acf50e</a></p> <p> </p> <p>The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting/tree/weaksup.</p> <p> </p> <p>Data</p> <p>1. County data (county-data.zip) for county-level strongly supervised models:</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p> <p>* CSSF_COUNTY_US.csv: Crop productivity indicators including total above-ground production (kg ha<sup>-1</sup>), total weight of storage organs (kg ha<sup>-1</sup>), development stage (0-2). Source: de Wit et al. (2022).</p> <p>* METEO_COUNTY_US.csv: Meteo data including maximum, minimum, average daily air temperature (℃); sum of daily precipitation (PREC) (mm); sum of daily evapotranspiration of short vegetation (ET0) (Penman-Monteith, Allen et al., (1998)) (mm); climate water balance = (PREC - ET0) (mm). Source: Boogaard et al. (2022).</p> <p>* REMOTE_SENSING_COUNTY_US.csv: Fraction of Absorbed Photosynthetically Active Radiation (Smoothed) (FAPAR). Source: Copernicus GLS (2020).</p> <p>* SOIL_COUNTY_US.csv: Soil water holding capacity. Source: WISE Soil Property Database (Batjes, 2016).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p> </p> <p>2. 10-km grid data (grid-data.zip) for grid-level strongly supervised models:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level (similar to county data above).</p> <p>* METEO_GRIDs_US.csv: Meteo data at 10km grid level (similar to county data above).</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level (similar to county data above).</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level (similar to county data above).</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021), Lobell et al. (2020).</p> <p> </p> <p>3. County labels and 10-km grid inputs (dscale-US.zip) for weak supervision:</p> <p>* COUNTY_GRIDS_US.csv: Mapping between counties and grids.</p> <p>* CSSF_GRIDS_US.csv: Crop productivity indicators at 10km grid level.</p> <p>* METEO_GRIDs_US.csv: Meteo indicators at 10km grid level.</p> <p>* REMOTE_SENSING_GRIDS_US.csv: FAPAR at 10km grid level.</p> <p>* SOIL_GRIDS_US.csv: Soil water holding capacity at 10km grid level.</p> <p>* YIELD_GRIDS_US.csv: Grid-level modeled yields (t ha<sup>-1</sup>). Source: Deines et al. (2021).</p> <p>* YIELD_COUNTY_US.csv: County yield statistics (bushels/acre). Source: NASS (USDA-NASS, 2022).</p> <p>* CROP_AREA_COUNTY_US.csv: County crop production area statistics (acres). Source: NASS (USDA-NASS, 2022).</p>
Hubbard Brook Experimental Forest: Sediment Yield in Weir Basins, 1956 - ongoing
Each year the sediment that collects in the stilling basin behind the v-notch weir is measured, excavated, sampled, dried, and weighed for Watersheds 1 through 8 at the Hubbard Brook Experimental Forest. Oven-dry weights are then calculated for all the sediment removed from the basin and extrapolated back over the watershed as mass of soil material lost per unit area. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Agronomic Yields in Row Crop Agriculture at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2021)
Dataset AbstractThis data set contains information about agronomic yields for the Main Cropping System Experiment which include treatments 1-4 (corn – wheat – soybean rotations) and after 1994 treatment 6 (alfalfa). Agronomic yields are measured during normal crop harvest; yields are determined by machine harvesters appropriate to each crop as described in the Agronomic protocol.original data source http://lter.kbs.msu.edu/datasets/23
Agronomic Yields on the Biodiversity Gradient Experiment at the Kellogg Biological Station, Hickory Corners, MI (2000 to 2020)
Dataset AbstractAgronomic yields have been measured on the Biodiversity Gradient Experiment since 2000. Samples are collected from the corn, soy and winter wheat plots before harvest by harvesting a subplot with a plot combine. Initial soil moisture from the Biodiversity Gradient baseline sampling is also available for corn, soy and wheat plots.original data source http://lter.kbs.msu.edu/datasets/35
Annual nutrient loading and yield to Plum Island Estuary, as measured at the Ipswich and Parker Dams
Nutrient concentrations for various forms of N, P, C, as well as suspended sediments, are determined from monthly grab samples taken at the Ipswich and Parker dams. These nutrient concentrations are then used in conjunction with USGS discharge data (recorded at gages in the Parker River at Byfield, MA and the Ipswich River at Ipswich, MA) to calculate annual nutrient loading to the Estuary, coming over each dam. Annual yield is also calculated for both dams.
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Yield determinants of Kappaphycus alvarezii seaweed in South Sulawesi, Indonesia
<p>Experimental data for the paper "Yield determinants of Kappaphycus alvarezii seaweed in South Sulawesi, Indonesia" by van Oort et al.:</p> <ul> <li>seaweed biomasss monitored bi-weekly in 5 cycles of 6 weeks (42 days) in 2023 - 2024 in two locations in South Sulawesi, Indonesia</li> <li>era5 oceanographic data for the two locations in South Sulawesi, monthly, 2015 - 2024</li> <li>water quality data for the two locations in South Sulawesi, bi-weekly, cycles 4 & 5 in 2024</li> <li>temperature data for the two locations in South Sulawesi, hourly, cycles 4 & 5 in 2024</li> <li>location data (kml files)</li> </ul> <p>Plus r-scripts for visualisation and some metadata</p>
Crop-specific global fertilizer application rates from "Closing yield gaps through nutrient and water management"
<p>Crop-specific global maps of N, P2O5, and K2O fertilizer application rates circa the year 2000 from the following paper:</p> <p>Mueller, ND, JS Gerber, M Johnston, DK Ray, N Ramankutty, and JA Foley. 2012. Closing yield gaps through nutrient and water management. <em>Nature</em> <strong>490</strong>: 254–257</p> <p>Data are provided at five arc-minute resolution and are saved as netcdf files. Fertilizer application rates are estimated from reconciling various national and subnational data sources. See the Supplementary Information from the 2012 paper for a full description of data sources and methods. Data quality for each grid cell is described in a map layer. Files containing the text "totalcons" sum nutrient consumption across crops per grid cell, using crop harvested areas from Monfreda et al. 2008 Global Biogeochemical Cycles. For maize, wheat, and soybean N application rates, additional maps and csv files (containing the text "politboundaries") identify the political units around the world containing unique information. Crops and crop group categories are consistent with those utilized in Monfreda et al. 2008 Global Biogeochemical Cycles.</p>
Experimental determination of the gadolinium L subshells fluorescence yields and Coster-Kronig transition probabilities
<p>Data associated with the two main tables of the publication "Experimental determination of the gadolinium L subshells fluorescence yields and Coster-Kronig transition probabilities". There are two .txt files containing tabulator-separated values:</p> <p><em>tabl01_ck.txt: </em>Data associated with Table 1 of the publication. This file contains the L subshell Coster-Kronig (CK) factors and their respective uncertainties.</p> <p><em>tabl02_fy.txt: </em>Data associated with Table 2 of the publication. This file contains the experimentally determined Gd L subshell fluorescence yields in comparison to available literature sources and their respective uncertainties.</p> <p>For more details see the original Open Access publication:</p> <p>Kayser, Y., Hönicke, P., Wansleben, M., Wählisch, A., Beckhoff, B., <em>X-Ray Spectrom</em> 2022, 1. <a href="https://doi.org/10.1002/xrs.3313">https://doi.org/10.1002/xrs.3313</a></p>
Quantum yield of Photosystem II of Eriophorum vaginatum leaves in the reciprocal transplant gardens at Toolik Lake, Coldfoot, and Sagwon- Alaska in 2016
Quantum yield of Photosystem II estimated from chlorophyll fluorescence of Eriophorum vaginatum leaves from tussocks in the reciprocal transplant gardons at Toolik Lake, Coldfoot, and Sagwon in 2016. A single transplant tussock per plot was repeatedly measured through the season.
KBS GLBRC Agronomic Yields at the Kellogg Biological Station, Hickory Corners, MI (2008 to 2021)
Dataset Abstract Agronomic yield record for Switchgrass Fertility Gradient at the KBS Great Lakes Biofuels Research Center (GLBRC) Intensive site and the GLBRC scale-up fields. This is a reduced dataset including the 0, 56 and 196 kg/ha N levels. Suggested Acknowledgement wording: "Support for this research was provided by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (Award DE-SC0018409), by the National Science Foundation Long-term Ecological Research Program (DEB 2224712) at the Kellogg Biological Station, and by Michigan State University AgBioResearch" original data source http://lter.kbs.msu.edu/datasets/65
Tree measurements and summaries of the field plots used to develop Rojo and Montero (1996) yield tables for Pinus sylvestris L. in central Spain
<p>Tree measurements for principal trees and trees marked for thinning and summaries of the Pinus silvestris L. plots measured for the construction of Rojo and Montero (1996) Pinus sylvestris L. yield tables for central Spain. PRM_Functions.R contains R functions implementing parameter recovery methods to transform Rojo and Montero (1996) Pinus sylvestris L. yield tables into a diameter distribution model.</p> <p><strong>Trees.csv: </strong>Comma separated file with headers in the first row. Each record represents a measured tree. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Type": Code indicating if the tree was marked for thinning.</li> <li>"ID_tree" Tree_Identifier</li> <li>"DBH1": First Diameter at breast height measurement for the tree.(mm)</li> <li> "DBH2" Second diameter at breast height measurement for the tree. The second measurement was taken in the direction perpendicular to the first measurement. (mm)</li> <li>"DBHmean": Mean of DBH 1 and DBH 2 <strong>and converted to cm</strong> (cm)</li> </ul> <p><strong>Plot_summaries.csv: </strong>Comma separated file with headers in the first row. Data digitized from Annex II of Rojo and Montero (1996). Each record contains different forest attributes of the plot. Fields:</p> <ul> <li>"PlotID": Identifier of the plot where the tree was measured</li> <li>"Age": Age of the plot determined from tree cores (Years)</li> <li>"Ho" Assman Dominant height for the plot (meters)</li> <li>"SiteIndex": Site index for the plot in meters. Site index is defined as the dominant height in meters measured or expected for the plot for an Age of 100 years.</li> <li>"MeanH" Mean tree height (m)</li> <li>"Dg" Quadratic mean diameter (cm)</li> <li>"Do" Dominant diameter. Mean diameter of the 100 largest trees of a hectare (cm)</li> <li>"N" Stand density (trees per hectare)</li> <li>"G" Plot basal area (m<sup>2</sup>/ha)</li> <li>"V" Total plot volume per unit area (m<sup>3</sup>/ha)</li> <li>"DeltaV" Periodic increment of merchantable volume (m<sup>3</sup>/ha)</li> <li>"Bark" Average percentage of total volume that is Bark. (%)</li> </ul> <p><strong>PRM_Functions.R: </strong>R functions to solve parameter recovery systems of equations based on mean and quadratic mean diameter and dominant diameter, quadratic mean diameter and stand density. Details provided as comments.</p> <p><strong>References</strong></p> <p>Rojo Alberto, Montero G (1996) El pino silvestre en la Sierra de Guadarrama: historia y selvicultura de los Pinares de Cercedilla, Navacerrada y Valsain. Ministerio de Agricultura, Pesca y Alimentación, Secretaria General Tecnica, Centro de Publicaciones, Madrid</p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
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