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.

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.

This dataset consists of growth and yield data for each year when maize (Zea mays, L., also known as corn in the United States) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data.Resources in this dataset:Resource Title: 1989 Bushland, TX, east maize growth and yield data. File Name: 1989_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: This dataset consists of growth and yield data for one of the seasons when maize was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. The growth and yield data include plant population density, height, plant row width, leaf area index, growth stage, total above-ground biomass, leaf and stem biomass, ear mass (when present), kernel number, and final yield. Data are from replicate samples in the field and non-destructive (except for final harvest) measurements on the weighing lysimeters. In most cases yield data are available from both manual sampling on replicate plots in each field and from machine harvest. There are separate spreadsheets for the east (NE and SE) lysimeters and fields, and for the west (NW and SW) lysimeters and fields. The spreadsheets contain tabs for data and corresponding tabs for data dictionaries. Typically there are separate data tabs and corresponding dictionaries for plant growth during the season, crop growth stage, plant population, manual harvest from replicate plots in each field and from lysimeter surfaces, and machine (combine) harvest, An Introduction tab explains the tab names and contents, lists the authors, explains conventions, and lists some relevant references.Resource Title: 1990 Bushland, TX, east maize growth and yield data. File Name: 1990_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1990 East.Resource Title: 1994 Bushland, TX, east maize growth and yield data. File Name: 1994_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 East.Resource Title: 1994 Bushland, TX, west maize growth and yield data. File Name: 1994_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 1994 West.Resource Title: 2013 Bushland, TX, west maize growth and yield data. File Name: 2013_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 West.Resource Title: 2016 Bushland, TX, east maize growth and yield data. File Name: 2016_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 East.Resource Title: 2016 Bushland, TX, west maize growth and yield data. File Name: 2016_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2016 West.Resource Title: 2018 Bushland, TX, west maize growth and yield data. File Name: 2018_West_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 West.Resource Title: 2013 Bushland, TX, east maize growth and yield data. File Name: 2013_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2013 East.Resource Title: 2018 Bushland, TX, east maize growth and yield data. File Name: 2018_East_Maize_Growth_and_Yield(ADC).xlsx. Resource Description: As above for 2018 East.

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.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

This raster dataset represents the average yield for green corn crops in tons per hectare. 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.

High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for operational crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022. We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1% to 10%, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels across all counties were 14% and 16%, respectively, illustrating the substantial benefits of 10-m maps over 30-m maps. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.

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.

This data comprises processed weather, soil, yield, and cultivation area for corn yield prediction in Sub-Sahara Africa, with emphasis on Nigeria. The data was collected to design a corn yield prediction model to help smallholder farmers make smart farming decisions. However, the data can serve several other purposes through analysis and interpretation.

The reference study region in Africa is Nigeria. The focuses on corn crop because there are over 211.4 million people, of which a large percentage of the population are smallholder farmers. Nigeria [9.0820° N, 8.6753° E] is within an arable land area of 34 million hectares located on the west coast of Africa. The region comprises of 36 states with the most and least number of districts being 214 and 10, respectively. For each state, the environment data are collected as follows.

Grid map climate data – This data spans spatial resolutions between ~1 km2 to ~340 km2 from the high spatial resolution WorldClim global climate database22. Each grid point on the map is monthly data from January to December between 1970 and 2000 years and records 8 climate variables. The variables are average temperature C0, minimum temperature C0, maximum temperature C0, precipitation (mm), solar radiation (kJ m^(-2) day(-1), wind speed (m s(-1)), and water vapor (kPa) taken at 30 seconds (s), 2.5 minutes m, 5 m, and 10 m.

Grid map soil data – This data is obtained from 250 minutes of spatial resolution AfSIS soil data23 from year 1960 to 2012. The variables are wet soil bulk density, dry bulk density (kg dm-3), clay percentage of plant available water content, hydraulic conductivity, the upper limit of plant available water content, the lower limit of, organic matter percentage, pH, sand percentage (g 100 g-1), silt percentage (g 100 g-1) and, clay percentage (g 100 g-1), and saturated volumetric water content variables measured at depths 0–5, 5–10, 10–15, 15–30, 30–45, 45–60, 60–80, 80–100, and 100–120 measured in centimeters (cm).

Corn yield data – This data is available on Kneoma Corporation website24. It ranged from years 1995 to 2006 and consisted of a corn yield of 1000 metric tonnes and a cultivation area of 1000 hectares.

Geolocation coordinates (latitude and longitude) – The geolocation of each of the 36 states with their districts is sampled from Google Maps. The output feds into the Esri-ArcGIS 2.5, a professional geographical software, for extracting the point-cloud values of each environmental variable (weather and soil) at specific geolocation of the 36 states of Nigeria.

Other Descriptions: Data type - Continous and Categorical Dataset Characteristics - Tabular Associated Tasks - Regression Feature Type - Real Number of Instances - 1828 Number of Features: 12

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.

The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

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 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.

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 forage 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.

Corn fell to 422.07 USd/BU on October 20, 2025, down 0.10% from the previous day. Over the past month, Corn's price has risen 0.08%, and is up 3.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on October of 2025.

This raster dataset depicts the average number of hectares per land-area of a gridcell for green corn 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.

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting

Overview

CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.

* Crops : Wheat & Maize
* Spatial Coverage : Wheat (29 countries), Maize (38).
See CY-Bench paper appendix for the list of countries.
* Temporal Coverage : Varies. See country-specific data

Data

Data format

The benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.

Data content

All data files are provided as .csv.

Folder structure

1. cybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**.
   ```
   CY-Bench
   │
   └─── maize
   │ │
   │ └─── AO
   │ │ -- crop_calendar_maize_AO.csv
   │ │ -- fpar_maize_AO.csv
   │ │ -- meteo_maize_AO.csv
   │ │ -- ndvi_maize_AO.csv
   │ │ -- soil_maize_AO.csv
   │ │ -- soil_moisture_maize_AO.csv
   │ │ -- yield_maize_AO.csv
   │ │
   │ └─── AR
   │ -- crop_calendar_maize_AR.csv
   │ -- fpar_maize_AR.csv
   │ -- ...
   │
   └─── wheat
   │ │
   │ └─── AR
   │ │ -- crop_calendar_wheat_AR.csv
   │ │ -- fpar_wheat_AR.csv
   │ │ ...
   ```

   Example : CSV data content for maize in country X

   ```
   X
   └─── crop_calendar_maize_X.csv
   │ -- crop_name (name of the crop)
   │ -- adm_id (unique identifier for a subnational unit)
   │ -- sos (start of crop season)
   │ -- eos (end of crop season)
   │
   └─── fpar_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- fpar
   │
   └─── meteo_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)

   │ -- tmin (minimum temperature)
   │ -- tmax (maximum temperature)
   │ -- prec (precipitation)
   │ -- rad (radiation)
   │ -- tavg (average temperature)
   │ -- et0 (evapotranspiration)
   │ -- cwb (crop water balance)
   │
   └─── ndvi_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- ndvi
   │
   └─── soil_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- awc (available water capacity)
   │ -- bulk_density
   │ -- drainage_class
   │
   └─── soil_moisture_maize_X.csv
   │ -- crop_name
   │ -- adm_id
   │ -- date (in the format YYYYMMdd)
   │ -- ssm (surface soil moisture)
   │ -- rsm ()
   │
   └─── yield_maize_X.csv
   │ -- crop_name
   │ -- country_code
   │ -- adm_id
   │ -- harvest_year
   │ -- yield
   │ -- harvest_area
   │ -- production

2. centroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows:

   centroids

   │ └─── AO
   │ │ -- AO.cpg
   │ │ -- AO.dbf
   │ │ -- AO.prj
   │ │ -- AO.shp
   │ │ -- AO.shx
   │ └─── AR
   │ │ -- AR.cpg
   │ │ -- AR.dbf
   │ │ -- AR.prj
   │ │ -- AR.shp
   │ │ -- AR.shx

   ...

   polygons

   │ └─── AO
   │ │ -- AO.cpg
   │ │ -- AO.dbf
   │ │ -- AO.prj
   │ │ -- AO.shp
   │ │ -- AO.shx
   │ └─── AR
   │ │ -- AR.cpg
   │ │ -- AR.dbf
   │ │ -- AR.prj
   │ │ -- AR.shp
   │ │ -- AR.shx

   ...

Data access

The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library

License and citation

We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.

This dataset consists of agronomic calendars for each growing season (year) when maize (Zea mays, L., also known as corn in the United States) was grown for grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Maize was grown for grain on four large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The four square fields are themselves arranged in a larger square with the fields in four adjacent quadrants of the larger square. Fields and lysimeters within each field are thus designated northeast (NE), southeast (SE), northwest (NW), and southwest (SW). Irrigation was by linear move sprinkler system in 1989, 1990, and 1994. In 2013, 2016, and 2018, two lysimeters and their respective fields (NE and SE) were irrigated using subsurface drip irrigation (SDI), and two lysimeters and their respective fields (NW and SW) were irrigated by a linear move sprinkler system. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. A crop calendar for each season lists by date the pertinent agronomic and maintenance operations (e.g., planting, thinning, fertilization, pesticide application, lysimeter maintenance, harvest). For each year there is a crop calendar for the two east lysimeters (NE and SE) and another calendar for the two west lysimeters (NW and SW). These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on maize ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield and have been used by the Agricultural Model Intercomparison and Improvement Project (AgMIP), by OPENET, and by many others for testing, and calibrating models of ET that use satellite and/or weather data.Resources in this dataset:Resource Title: 1989 Bushland, TX, east maize agronomic calendar. File Name: 1989_East_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 1990 Bushland, TX, east maize agronomic calendar. File Name: 1990_East_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 1994 Bushland, TX, east maize agronomic calendar. File Name: 1994_East_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 1994 Bushland, TX, west maize agronomic calendar. File Name: 1994_West_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2013 Bushland, TX, east maize agronomic calendar. File Name: 2013_East_Maize-Calendar.xlsx. Resource Description: This agronomic calendar lists agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2013 Bushland, TX, west maize agronomic calendar. File Name: 2013_West_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2018 Bushland, TX, west maize agronomic calendar. File Name: 2018_West_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2018 Bushland, TX, east maize agronomic calendar. File Name: 2018_East_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2016 Bushland, TX, west maize agronomic calendar. File Name: 2016_West_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.Resource Title: 2016 Bushland, TX, east maize agronomic calendar. File Name: 2016_East_Maize_Calendar.xlsx. Resource Description: This agronomic calendar lists by date the agronomic operations on the Bushland, TX, large weighing lysimeters and surrounding fields, including tillage, planting, fertilization, pesticide application, furrow diking, irrigations, etc., and also sensor installation, sensor reading that might disturb lysimeter operation (neutron probe readings), maintenance operations such as emptying drainage tanks, adjusting lysimeter scale counterweights, electronic and electrical maintenance, etc. Amounts and kinds of fertilizer and pesticide applications are given with proper chemical names and SI units.

This data set contains a single data file (.csv format) that provides gridded values of net primary productivity (NPP) for cropland in eight counties in the central United States for the year 1992 and estimates of interannual cropland NPP in Iowa for years from 1982 through 1996. The data file also includes climate, soil texture, and land cover data for each 0.5 degree grid cell. The magnitude and interannual variation in NPP was estimated using crop area and yield data from the U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). The major harvested commodities were corn, soybean, sorghum, sunflower, oats, barley, wheat, and hay. Total NPP estimates include both above- and below-ground components. County-level NPP in 1992 ranged from 195 to 760 gC/m2/year. The area of highest NPP, ranging from 650 to 760 gC/m2/year, was found in a band extending across Iowa, through northern Illinois, Indiana, and southwestern Ohio. Areas of moderate NPP, from 550 to 650 gC/m2/year, occurred mostly in Michigan and Wisconsin, while large areas of low NPP, from 200 to 550 gC/m2/year, occurred in North Dakota, southern Illinois, and Minnesota. The area of highest production was also the area with the largest proportion of land sown with corn and soybean. NPP for counties in Iowa varied among years (1982-1996) by a factor of 2, with the lowest NPP in 1983 (which had an unusually wet spring), in 1988 (which was a drought year), and in 1993 (which experienced floods). Revision Notes: The documentation for this data set has been modified, and the data files have been reformatted. The data files have been checked for accuracy and the contents are identical to those originally published in 2001.