This dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa. Following the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following. Stover harvest rate at three levels: No, moderate (3.5 ± 1.1 Mg ha-1 yr-1), or high (5.0 ± 1.7 Mg ha-1 yr-1) stover harvest rates. No-till versus chisel-plow tillage. Originally, the 3 stover harvest rates were evaluated in a complete factorial design with tillage system. However, the no-till, no-harvest system performed poorly in continuous corn and was discontinued in 2012 due to lack of producer interest. Cropping sequence. In addition to evaluating continuous corn for all stover harvest rates and tillage systems, a corn-alfalfa rotation, and a corn-soybean-wheat rotation with winter cover crops were evaluated in a subset of the tillage and stover harvest rate treatments. One-time additions of biochar in 2013 at rates of either 9 Mg/ha or 30 Mg/ha were evaluated in a continuous corn cropping system. The dataset includes: 1) Crop biomass and yields for all crop phases in every year. 2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth. 3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017. 4) Soil strength and compaction was assessed to 60 cm depth in April 2021. These data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018). Resources in this dataset:Resource Title: R Script for Phillips et al. 2022. File Name: Field 70-71 Analysis Script_AgDataCommons.RResource Description: This R script includes analysis and figures for Phillips et al. "Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa". It focuses primarily on the soil compaction and strength data found in "Field 70-71 ConeIndex_BulkDensityDepths_2021". It also includes analysis of corn yields from "Field 70-71 CornYield_2008-2021" and weather conditions from "PRISM_MayTemps" and "Rainfall_AEA".Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/ Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021. File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csvResource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).Resource Title: Field 70-71 CornYield_2008-2021. File Name: Field 70-71 CornYield_2008-2021_ForR.csvResource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file "Field 70-71 Crop Yield File 2008-2020".Resource Title: PRISM_MayTemps. File Name: PRISM_MayTemps.csvResource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.Resource Title: Rainfall_AEA. File Name: Rainfall_AEA.csvResource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021. File Name: Field 70-71 Plot Status 2007-2021.xlsxResource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.Resource Title: Field 70-71 Deep Soil Cores 2012-2017. File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsxResource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.Resource Title: Field 70-71 Baseline Deep Soil Cores 2005. File Name: Field 70-71 Baseline Deep Soil Cores 2005.csvResource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.Resource Title: Field 70-71 Crop Yield File 2008-2020. File Name: Field 70-71 Crop Yield File 2008-2020.xlsxResource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.Resource Title: Field 70-71 Surface Soil Test Data 2007-2021. File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsxResource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. Resource Title: Iowa Stover Harvest Trial Data Dictionary. File Name: Field 70-71 Data Dictionary.xlsxResource Description: Data dictionary for all data files.

Global Corn Production Share by Country (Thousand Metric Tons), 2023 Discover more data with ReportLinker!

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Corn productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Corn - Operations with SalesCorn - Sales, Measured in US Dollars ($)Corn, Grain - Acres HarvestedCorn, Grain - Operations with Area Harvested - Area Harvested: (1.0 to 24.9 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (25.0 to 99.9 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (100 to 249 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (250 to 499 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (500 to 999 Acres)Corn, Grain - Operations with Area Harvested - Area Harvested: (1,000 or More Acres)Corn, Grain - Operations with Area HarvestedCorn, Grain - Production, Measured in BushelsCorn, Grain, Irrigated - Acres HarvestedCorn, Grain, Irrigated - Operations with Area HarvestedCorn, Silage - Acres HarvestedCorn, Silage - Operations with Area Harvested - Area Harvested: (1.0 to 24.9 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (25.0 to 99.9 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (100 to 249 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (250 to 499 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (500 to 999 Acres)Corn, Silage - Operations with Area Harvested - Area Harvested: (1,000 or More Acres)Corn, Silage - Operations with Area HarvestedCorn, Silage - Production, Measured in TonsCorn, Silage, Irrigated - Acres HarvestedCorn, Silage, Irrigated - Operations with Area HarvestedCorn, Traditional or Indian - Acres HarvestedCorn, Traditional or Indian - Operations with Area HarvestedCorn, Traditional or Indian - Production, Measured in lbsCorn, Traditional or Indian, Irrigated - Acres HarvestedCorn, Traditional or Indian, Irrigated - Operations with Area Harvested Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The Relationship between Crop Production and Climate Change

Explore the Relationship between Crop Production and Climate Change over time

About this dataset

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. Crop yields are the harvested production per unit of harvested area for crop products. In most cases yield data are not recorded but are obtained by dividing the production data by the data on the area harvested. The actual yield that is captured on a farm depends on several factors such as the crop's genetic potential, the amount of sunlight, water, and nutrients absorbed by the crop, the presence of weeds and pests. This indicator is presented for wheat, maize, rice, and soybean. Crop production is measured in tonnes per hectare.

This dataset includes information on crop production from 2010-2016

How to use the dataset

https://www.kaggle.com/usda/crop-production

Crop production is an important economic activity that affects commodity prices and macroeconomic uncertainty. This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare, in thousand hectares, and in thousand tonnes.

This dataset can be used to examine the effect of different crops on climate change and to compare yields between different climates

Research Ideas

• Determining how various factors affect crop production and yields
• Comparing crop yields between different types of crops
• Examining the impact of climate change on crop production

Acknowledgements

This dataset provides data on crop yields, harvested areas, and production quantities for wheat, maize, rice, and soybeans. The data are presented in tonnes per hectare

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: crop_production.csv | Column name | Description | |:---------------|:------------------------------------------------------------| | LOCATION | The country or region where the crop is grown. (String) | | INDICATOR | The indicator used to measure the crop production. (String) | | SUBJECT | The subject of the indicator. (String) | | MEASURE | The measure of the indicator. (String) | | FREQUENCY | The frequency of the data. (String) | | TIME | The time period of the data. (String) | | Value | The value of the indicator. (Float) | | Flag Codes | The flag codes of the data. (String) |

Retiriment Notice: This item is in mature support as of April 2025 and will be retired in December 2026. New data is available for your use directly from the Authoritative Provider. Esri recommends accessing the data from the source provider as soon as possible as our service will not longer be available after December 2026. Maize (Zea mays), also known as corn, is a crop of world wide importance. Originally domesticated in what is now Mexico, its tolerance of diverse climates has lead to its widespread cultivation. Globally, it is tied with rice as the second most widely grown crop. Only wheat is more widely grown. In Africa it is grown throughout the agricultural regions of the continent from the Nile Delta in the north to the country of South Africa in the south. In sub-Saharan Africa it is relied on as a staple crop for 50% of the population. Dataset Summary This layer provides access to a5 arc-minute(approximately 10 km at the equator)cell-sized raster of the 1999-2001 annual average area ofmaize harvested in Africa. The data are in units of hectares/grid cell. TheSPAM 2000 v3.0.6 data used to create this layerwere produced by the International Food Policy Research Institute in 2012.This dataset was created by spatially disaggregating national and sub-national harvest datausing the Spatial Production Allocation Model. Link to source metadata For more information about this dataset and the importance of maize as a staple food see the Harvest Choice webpage. The source data for this layer are available here.

This raster dataset depicts the average fractional proportion of a gridcell for green corn crops that were harvested circa 2000. Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

This raster dataset represents the agricultural census data quality for maize crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

Here are a few use cases for this project:

1. Precision Agriculture: The "corn" computer vision model can help farmers and agronomists monitor corn growth, detect diseases or nutrient deficiencies early, and optimize their farming practices by identifying different corn classes and health conditions in fields.

2. Crop Yield Prediction: By analyzing images of cornfields, researchers and agricultural experts can use the model to predict corn yields more accurately by considering the quantity and health of different corn classes, enabling informed decision making for farmers and supply chain stakeholders.

3. Automated Harvesting and Planting: The "corn" model can assist in the development of autonomous machinery for planting and harvesting, enhancing efficiency and reducing labor costs. The machinery can use the model to identify various corn classes and accurately navigate through the fields.

4. Drought Monitoring and Irrigation Management: The "corn" computer vision model can help monitor and detect signs of drought-induced stress in cornfields, allowing farmers to adjust irrigation systems more effectively and conserve water resources by applying targeted irrigation to specific corn classes or affected areas.

5. Biodiversity and Plant Breeding Research: By identifying different corn classes, the "corn" model can aid researchers in assessing and maintaining genetic diversity in cornfields, supporting plant breeding programs, and increasing crop resilience to pests, diseases, and environmental changes.

Context

Living in the Midwest United States, corn and soybeans are the staple crops that always fascinated me.

Content

The data contain annual numbers for acres planted, acres harvested, and the value of the crop production. Some data is not reported, which makes it even more fun to work with!

Acknowledgements

The data was culled from the National Agricultural Statistics Service (NASS) offers Quick Stats, an on-line database containing official published aggregate estimates related to U.S. agricultural production. NASS develops these estimates from data collected through:

1. hundreds of sample surveys conducted each year covering virtually every aspect of U.S. agriculture
2. the Census of Agriculture conducted every five years providing state- and county-level aggregates

Inspiration

Using this relatively small dataset, I am trying to include other sources (e.g. weather/climate data) to provide explanation for both productive and unproductive crop years.

USDA-ARS Colorado Maize Water Productivity

Harnessing Agroecological Insights for Optimal Crop Yields

By US Open Data Portal, data.gov [source]

About this dataset

The USDA-Agricultural Research Service carried out a remarkable experiment on the water productivity of maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in Colorado. This dataset includes data from two consecutive years, whose complete treatments involved various levels of water availability targeted at specific growth-stages. Different analyses have been conducted to provide insight into canopy growth and development (canopy height, cover, LAI), periodic irrigation and soil water measurements, estimates of crop evapotranspiration with hourly weather data from the CoAgMET network in Colorado's weather conditions, seasonal measurement of crop water use coupled with harvest index and yield. Additional soil data is also available for further validation and refining making this dataset an invaluable resource for studying maize crop models. With such wealth of information wrapped up so neatly, this USDA-ARS Maize Water Productivity Dataset 2012-2013 is primed to be your go-to maize cultivation study companion

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (Zea mays L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado. This dataset includes data from two years, with twelve treatments involved, providing information about canopy growth and development, as well as crop water use, harvest index and yield. You can use this dataset to better understand the effects of irrigation on water productivity for maize cultivars grown under different conditions.

• Understanding your Data: There are eight tabs within this Excel document, each tab containing information related to one aspect of measuring water productivity in maize cultivars over two years. The first sheet provides a list of descriptions for each element or value being measured listed at the top right corner including name, units, description and values that should be expected when using particular elements or values measured e g; canopy height vs estimated root depth etcetera). Below this list is another sheet which contains hourly weather data from Coagmet station GLY04 excluding precipitation which is provided once daily per treatment throughout both seasons within its individual sheet alongside LAI measurements also taken four times over each season plus crop growth stage plant height estimated root depth interpolated canopy cover ET coefficients deep percolation evaporation soil water deficits etcetera).

• Obtaining useful insights from your dataset : You can obtain valuable insights about how irrigation impacts different maize cultivar yields through analyses such as correlations between different environmental variables like soil moisture evapotranspiration etc.; plotting histograms depicting frequency distributions along certain columns like stand density compared against total above ground biomass so that decision makers can effectively plan interventions reducing potential losses due to extreme climate events while still maintaining precision agriculture principles designed minimize resource expenditure maintain maximum efficiency use subsistence/industrial agriculture

Research Ideas

• Modeling the response of maize crops to different irrigation and soil water availability regimes.
• Evaluating the influence of canopy growth, LAI, and evapotranspiration on crop yields.
• Developing an algorithm that uses weather data to estimate optimal irrigation schedules for maize crops in Colorado

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: comma-separated-values-file-3.csv | Column name | Description | |:----------------------------------|:------------------------------------------------------------------------| | Spreadsheet tab | The nam...

This dataset includes daily two-band Enhanced Vegetation Index (EVI2) at 30-m resolution over a Landsat scene (path 26 and row 31) in central Iowa. Fourteen years of daily EVI2 from 2001 to 2015 (except 2012) were generated through fusing and interpolating Landsat-MODIS data.Landsat surface reflectances were order and used in this study. Mostly clear Landsat images from each year were chosen to pair with MODIS images acquired from the same day to generate daily Landsat-MODIS surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Partially clear Landsat images were also used in generating the smoothed and gap-filled daily VI time-series. All available Landsat data including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) were used in this study.The MODIS data products were downloaded and processed. These include the daily surface reflectance at both 250m (MOD09GQ) and 500m (MOD09GA) resolution, the MODIS Bidirectional Reflectance Distribution Function (BRDF) parameters at 500m resolution, and the MODIS land cover types at 500m resolution (MCD12Q1). They were used to generated daily nadir BRDF-adjusted reflectance (NBAR) at 250m resolution for fusing with Landsat.The Landsat-MODIS data fusion results for 2001-2014 were generated from a previous study (Gao et al, 2017; doi: 10.1016/j.rse.2016.11.004). Data fusion results for 2015 were generated using Landsat 8 OLI images from day 194, 226, 258 and 338 in this study. Cloud masks were extracted from Landsat and MODIS QA layers and were used to exclude cloud, cloud shadow and snow pixels. Since Landsat 5 TM operational imaging ended in November 2011 and Landsat 8 OLI has not been launched until February 2013, Landsat 7 ETM+ Scan Line Corrector (SLC)-off images are the only available Landsat data. For this reason, 2012 was not included.Due to the cloud contamination in the Landsat and MODIS images, the fused Landsat-MODIS results still have invalid values or gaps. To fill these gaps, a modified Savitzky-Golay (SG) filter approach was built and applied to smooth and gap-fill EVI2. The SG filter is a moving fitting approach. Each point is smoothed using the value computed from the polynomial function fit to the observations within the moving window. The program removes spike points if the fitting errors are larger than the predefined threshold (default 3 standard deviation). The modified SG filter allows us to retain small variations but also fill large gaps in an unevenly distributed time-series EVI2.Daily EVI2 files are saved in one tar file per year. Each tar file contains a binary image file and a text header file that can be displayed in the ENVI software. The binary image file has the dimension of 7201 lines by 8061 samples by 365 days and is saved in BIP (band interleaved by pixel) format. EVI2 data are saved in 4-byte float number. The text header file contains necessary information including projection and geolocation. Daily EVI2 file is named as "flexfit_evi2.026031.yyyy.bin", where "026031" refers to the Landsat path and row, and yyyy represents year and ranges from 2001-2015.Resources in this dataset:Resource Title: Daily EVI2 Data Packages .File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node22870/These Daily EVI2 data packages are grouped by year. Each package includes a plain binary file that saves daily EVI2, and a ENVI header file (in text) that contains metadata and geolocation information. Contents are as follows: dailyVI.026031.2000.tar.gz dailyVI.026031.2001.tar.gz dailyVI.026031.2002.tar.gz dailyVI.026031.2003.tar.gz dailyVI.026031.2004.tar.gz dailyVI.026031.2005.tar.gz dailyVI.026031.2006.tar.gz dailyVI.026031.2007.tar.gz dailyVI.026031.2008.tar.gz dailyVI.026031.2009.tar.gz dailyVI.026031.2010.tar.gz dailyVI.026031.2011.tar.gz dailyVI.026031.2013.tar.gz dailyVI.026031.2014.tar.gz dailyVI.026031.2015.tar.gzSCINet users: The .tar.gz files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node22870/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users.For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers.This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

This raster dataset depicts the average number of hectares per land-area of a gridcell for maize crops. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003. EarthStat.org serves geographic data sets with the purpose of solving the grand challenge of feeding a growing global population while reducing agriculture’s impact on the environment. The data sets on EarthStat allow users to map the distribution of crops globally, analyze the impact of climate change on crop yields, understand the impacts of fertilizer and manure use and much more.

Southern rust of maize. Southern corn rust is one of the most common diseases in tropical and subtropical corn growing areas. Fast epidemic speed has a serious impact on yield. It is more dangerous than common corn rust. The pathogen of the disease overwinters in the winter corn growing areas along the southern coast. During a growing season, summer spores spread from south to north along with warm and humid air flow over a long distance. Hainan and Taiwan provinces in China are distributed, but in recent years, large-scale occurrence has occurred in some parts of the north, with a trend of spreading northward. The pathogen produces summer spores, which spread with the wind and rain, and is harmful to rolling. The pathogen can not live without the host plant for a long time. Symptoms are similar to those of common rust, but the color of summer spore heap of common rust is rust yellow, and that of summer spore heap of Southern rust is orange yellow. After the pathogen infects, the small green spots on the leaves become yellowish-brown protuberant blisters, i.e. the summer spore heap of the pathogen. Different from the common rust, the main symptoms of summer spores are that summer spores pile up on the front of leaves, which are abundant and densely distributed, and rarely on the back of leaves. Sometimes a small amount of summer spore piles appeared on the back of leaves, but only in the midrib and its vicinity. Summer spores are round and oval, smaller and lighter in color than those of common rust. The dehiscence of the epidermis covering the summer spore heap was slow but not obvious. In the late stage of onset, the winter spore heap was scattered near the summer spore heap. Winter spore heaps are dark brown to black, often with dark halos around them. The epidermis of the winter spore heap does not break. [Control methods]. [Planting disease-resistant varieties] The resistance of different maize varieties to rust is quite different, and using disease-resistant varieties is an effective way to control maize rust. [Scientific field management] timely sowing; appropriate reduction of nitrogen fertilizer, increased application of phosphorus and potassium fertilizer, timely spraying of foliar nutrients to improve disease resistance of maize plants; rational control of density, improve permeability. Early removal of plant debris in and around the field before planting, if found in the growing period should be timely pulled out and centralized destruction, maize harvest should also be timely removal of residual plants, stems and leaves, centralized burning or fertilization. Rotation and non-gramineous crop rotation can reduce the accumulation of pathogens. For sporadic maize, the diseased plants and residual disease bodies should be pulled out at any time. [Scientific field management] timely sowing; appropriate reduction of nitrogen fertilizer, increased application of phosphorus and potassium fertilizer, timely spraying of foliar nutrients to improve disease resistance of maize plants; rational control of density, improve permeability. Early removal of plant debris in and around the field before planting, if found in the growing period should be timely pulled out and centralized destruction, maize harvest should also be timely removal of residual plants, stems and leaves, centralized burning or fertilization. Rotation and non-gramineous crop rotation can reduce the accumulation of pathogens. For sporadic maize, the diseased plants and residual disease bodies should be pulled out at any time. [Pharmaceutical control, prevention mainly] Spraying agents containing difenoconazole, tebuconazole, triazolone, propiconazole, pyrimethyl ester, ethermycin ester and pyrazole ether ester can effectively alleviate the occurrence of Southern rust in late stage of maize trumpet-silking. Prevention plan: in order to prevent the occurrence of Southern rust, farmers can use 25% powder, 20%, three zolone pesticide spray control. If there is no prevention in the early stage, spraying in the early stage of rust can also control the incidence of rust and reduce the impact on production to a certain extent.

Fertilizer Use and Price

1960-2012 data on fertilizer consumption in the United States by plant nutrient

By Agriculture [source]

About this dataset

This product brings together an in-depth analysis of fertilizer consumption and prices in the United States across more than half a century (1960-2012). We provide valuable insights into how fertilizer use per crop area and for specific nutrients varies between major producing states, as well as offer data on mixed fertilizers, secondary nutrients, and micronutrients. Furthermore, our dataset includes farm prices for fertilizers, indices of wholesale fertilizer price through 2013 that allows us to compare changes in fertilizer costs. With this data set you can get a better understanding of how fertilizer use has evolved over time, what crops are being benefited from its availability the most and at what point does cost becomes a deciding factor. Get ready to explore U.S Fertilizer Consumption and Price Trends!

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How to use the dataset

This dataset is a comprehensive resource for investigating US Fertilizer Consumption and Price patterns between 1960 and 2013. In the dataset, you'll find data on fertilizer consumption in the United States by plant nutrient and major selected product, as well as consumption of mixed fertilizers, secondary nutrients, and micronutrients. There is also information about fertilizer use per receiving acre for several states that produce corn, cotton, soybeans, or wheat. The dataset provides information about farm prices for fertilizer along with indices of wholesale fertilizer price since 2013

Using this dataset can be useful to identify market trends in the US Fertilizer Market over the past half-decade. Additionally it can be used to identify state-level differences in production or usage that could provide insight into regional agricultural strategies.

To get started using this dataset: - Read up on its contents: Start by thoroughly reading up on what exactly this data contains so you are familiar with things like nutrient types mentioned or state producing crops mentioned in order to better understand how to interpret/use them correctly when analyzing these data points
- Identify key elements: As you read through each column of the data set think about which columns are most relevant to your research question/interest
- Organize & Analyze Data: now that you have identified key elements begin organizing them accordingly (separate out columns not needed) then start analyzing whatever questions/themes have presented themselves while doing research

• After following these steps your research process will be much more streamlined & organized making analysis simpler & results more accurate when interpreting this particular Unlocking US Fertilizer Consumption and Price Patterns Kaggle Dataset from 1960-2013

Research Ideas

• Analyzing the effect of fertilizer use on crop yields and prices over time to inform environmental policy decisions.
• Investigating regional differences in fertilizer prices, consumption and crop yield, to gain more insight into agriculture output variability across the US.
• Comparing fertilizer use per acre planted with crop yields to evaluate farmers’ return on investment by region and nutrient type used

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: fertilizeruse.csv | Column name | Description | |:--------------|:-----------------------------------| | Year | Year of the data sample. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Agriculture.

Spatial data on soils, land use, and topography, combined with knowledge of conservation effectiveness can be used to identify alternatives to reduce nutrient discharge from small watersheds. This database was developed to be used in conjunction with the Agricultural Conservation Planning Framework Toolkit. Data comprise soil survey information and land use. Soil characterization data were extracted from the Natural Resources Conservation Service (NRCS) Web Soil Survey (Soil Survey Staff, 2013). Land use coverages were developed to represent agricultural fields and the types and rotations of agricultural crops and other land cover types. Land use boundaries were produced by editing a publicly available USDA field boundaries dataset (pre-2008), with all ownership and county-level attributes removed. To ensure these field polygons were consistent with recent land use, the 2009 Cropland Data Layer (USDA-NASS, 2013) was examined for all fields larger than 16 ha. For those fields with multiple cover types, 2009 National Agricultural Imagery Program (NAIP) aerial photography was used as a basis to manually edit field boundaries. A field was considered to have multiple cover types and was edited if the dominant cover occupied <75% of the field, as indicated by the 2009 Cropland Data Layer. Updated field boundaries were then overlaid with data from USDA-National Agricultural Statistics Service (2013) Cropland Data Layer for 2000 – 2014, and each field was classified to represent crop rotations and land cover using the most recent six-year (2009-2014) sequence of land cover. Six-year land-cover strings (e.g., corn-corn-soybean-corn-soybean-corn) generated for each field were classified to represent major crop rotations, which were dominantly comprised of corn (Zea mays L.) and soybean (Glycine max (L.) Merr) annual row crops. The database does not include high-resolution digital elevation models (DEMs) derived from LiDAR (light detection and ranging) survey data, although these are needed by the Agricultural Conservation Planning Framework Toolkit and must be obtained independently. Database is scheduled to become available on October 1, 2015. Resources in this dataset:Resource Title: Land Use and Soils data, viewing and downloading page. File Name: Web Page, url: https://www.nrrig.mwa.ars.usda.gov/st40_huc/dwnldACPF.html Recent land use, field boundary, and soil survey information for individual HUC12 watersheds in Iowa, Illinois, and southern Minnesota. With this land use viewer web page, users may navigate to individual HUC12 watersheds, view land-use maps, and download land use and soils data that can be directly used as input data for the ACPF toolbox. Before developing information on conservation priorities and opportunities using the ACPF toolbox, users will need to obtain elevation data for their watershed, which is usually available from your state government.

Global Maize Yield by Country, 2023 Discover more data with ReportLinker!

Note: Pasture and hay budgets added on 2023/02/09 Enterprise budgets contained in this database were developed as part of the Floridian Aquifer Collaborative Engagement for Sustainability (FACETS), a large-scale, multi-institutional, multi-disciplinary project funded by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA). The goal of this project is to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds. As part of the larger FACETS project, budgets were developed for various cropping and forest systems in North Florida and South Georgia. This dataset includes budgets developed for pine plantations, corn and peanut crops, and hay and pasture production in the Lower Suwannee River Basin, Florida. Preliminary budgets were created based on information obtained from semi-structured interviews with producers, suppliers, local businesses, and area extension professionals. Following the construction of the preliminary budget, the project team conducted a review, along with a project advisory committee of stakeholders from agricultural, environmental, regulatory, and scientific partner organizations to confirm and/or adjust any data reported. Any specific information that was derived outside of, or in addition to, the process noted above is shown in summary on the second worksheet in this workbook, titled "Meta Data."
Upon finalization of the preliminary budget, enterprise-level models were developed to predict the impacts of Best Management Practice (BMP) adoption on the financial viability of enterprises under various alternative scenarios (Management Systems). The budgets for these Management Systems represent adjustments to management practices. Summary descriptions of each of these management systems are located on the worksheet titled "Management System Descriptions."
See file list for descriptions of each data file. Resources in this dataset:Resource Title: FACETS Corn enterprise budgets. File Name: FACETS_CornBudget.xlsxResource Title: FACETS Forest enterprise budgets. File Name: FACETS_ForestBudget.xlsxResource Title: FACETS Peanut enterprise budgets. File Name: FACETS_PeanutBudget.xlsxResource Title: FACETS Hay enterprise budgets. File Name: FACETS_HayBudget.xlsxResource Title: FACETS Pasture enterprise budgets. File Name: FACETS_PastureBudgets.xlsxResource Title: README file list. File Name: file_list_FACETS_budgets.txt

This is digital research metadata corresponding to a published manuscript in Energies (MDPI) entitled "Biochar stability in a highly weathered sandy soil under four years of continuous corn production", Volume 14, Issue 19, 6157. Dataset may be accessed via the included link at the Dryad data repository. Biochar is being considered a climate change mitigation tool by increasing soil organic carbon contents (SOC), however, questions remain concerning its longevity in soil. We applied 30,000 kg ha−1 of biochars to plots containing a Goldsboro sandy loam (Fine-loamy, siliceous, sub-active, thermic Aquic Paleudults) and then physically disked all plots. Thereafter, the plots were agronomically managed under 4 years (Y) of continuous corn (Zea mays, L.) planting. Annually, incremental soil along with corresponding bulk density samples were collected and SOC concentrations were measured in topsoil (down to 23-cm). The biochars were produced from Lodgepole pine (Pinus contorta) chip (PC) and Poultry litter (PL) feedstocks. An untreated Goldsboro soil (0 biochar) served as a control. After four years, SOC contents in the biochar treated plots were highest in the top 0–5 and 5–10 cm depth suggesting minimal deeper movement. Declines in SOC contents varied with depth and biochar type. After correction for SOC declines in controls, PL biochar treated soil had a similar decline in SOC (7.9 to 10.3%) contents. In contrast, the largest % SOC content decline (20.2%) occurred in 0–5 cm deep topsoil treated with PC biochar. Our results suggest that PC biochar had less stability in the Goldsboro soil than PL biochar after 4 years of corn grain production. Methods are described in the manuscript: https://doi.org/10.3390/en14196157. Descriptions corresponding to each figure and table in the manuscript are placed on separate tabs in the Excel file to clarify abbreviations and summarize the data headings and units. Resources in this dataset:Resource Title: Digital research data for Biochar stability in a highly weathered sandy soil under four years of continuous corn production. File Name: Web Page, url: https://doi.org/10.5061/dryad.xpnvx0kh2 Novak, Jeffrey et al. (2021), Digital research data from: Biochar stability in a highly weathered sandy soil under four years of continuous corn production, Dryad, Dataset, https://doi.org/10.5061/dryad.xpnvx0kh2

European Maize Production by Country, 2022 Discover more data with ReportLinker!