This dataset encompasses agricultural data for multiple crops cultivated across various states in India from the year 1997 till 2020. The dataset provides crucial features related to crop yield prediction, including crop types, crop years, cropping seasons, states, areas under cultivation, production quantities, annual rainfall, fertilizer usage, pesticide usage, and calculated yields.

Columns Description:

1. Crop: The name of the crop cultivated.
2. Crop_Year: The year in which the crop was grown.
3. Season: The specific cropping season (e.g., Kharif, Rabi, Whole Year).
4. State: The Indian state where the crop was cultivated.
5. Area: The total land area (in hectares) under cultivation for the specific crop.
6. Production: The quantity of crop production (in metric tons).
7. Annual_Rainfall: The annual rainfall received in the crop-growing region (in mm).
8. Fertilizer: The total amount of fertilizer used for the crop (in kilograms).
9. Pesticide: The total amount of pesticide used for the crop (in kilograms).
10. Yield: The calculated crop yield (production per unit area).

Use Cases:

This comprehensive dataset is valuable for agricultural analysts, researchers, and data scientists interested in crop yield prediction and agricultural analysis. It offers insights into the relationship between various agronomic factors (e.g., rainfall, fertilizer, pesticide usage) and crop productivity across different states and crop types. Researchers can utilize this data to develop robust machine learning models for crop yield prediction and identify trends in agricultural production.

Note:

Given the diversity of crops, states, and years covered in this dataset, users are encouraged to exercise caution when drawing generalizations or making predictions for specific regions or timeframes outside the scope of the dataset. They are further advised to apply various feature engineering and feature selection techniques on this dataset, so as to make the dataset more robust and suitable for the ML model.

National Agricultural Statistics Service account, Iteration 2, Fiscal year 2022

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

This chart shows annual citations to papers affiliated with National Agricultural Statistics Service, grouped by publication year.

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

Brochure Theme: S5 - Statistical data - Agriculture Under Theme: S510.A1 - Agricultural statistics

This dataset contains agricultural data for 1,000,000 samples aimed at predicting crop yield (in tons per hectare) based on various factors. The dataset can be used for regression tasks in machine learning, especially for predicting crop productivity.

• Region: The geographical region where the crop is grown (North, East, South, West).
• Soil_Type: The type of soil in which the crop is planted (Clay, Sandy, Loam, Silt, Peaty, Chalky).
• Crop: The type of crop grown (Wheat, Rice, Maize, Barley, Soybean, Cotton).
• Rainfall_mm: The amount of rainfall received in millimeters during the crop growth period.
• Temperature_Celsius: The average temperature during the crop growth period, measured in degrees Celsius.
• Fertilizer_Used: Indicates whether fertilizer was applied (True = Yes, False = No).
• Irrigation_Used: Indicates whether irrigation was used during the crop growth period (True = Yes, False = No).
• Weather_Condition: The predominant weather condition during the growing season (Sunny, Rainy, Cloudy).
• Days_to_Harvest: The number of days taken for the crop to be harvested after planting.
• Yield_tons_per_hectare: The total crop yield produced, measured in tons per hectare.

Comprehensive collection of 412 African agricultural statistics covering crop diseases, mobile technology, economics, climate, and social dynamics

This publication has been discontinued, as it has been superseded by and comes under Agriculture in the UK now. This publication provides an easy-to-reference statistics on UK Agriculture, complementing its more comprehensive sister publication Agriculture in the UK. Designation: Official Statistics Alternative title: Agricultural Statistics in your Pocket

Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (RSQ) and normalized root mean square error (nRMSE). Overall, the most robustly performing ensemble learning technique was majority voting yielding RSQ and nRMSE values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached RSQ and nRMSE ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at 1×1km spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.

Apportionment file 11201667 retrieved from OMB public records

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA's National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context. Resources in this dataset:Resource Title: Ag Census Web Maps. File Name: Web Page, url: https://www.nass.usda.gov/Publications/AgCensus/2012/Online_Resources/Ag_Census_Web_Maps/Overview/index.php/ The interactive map application assembles maps and statistics from the 2012 Census of Agriculture in five broad categories:

Crops and Plants – Data on harvested acreage for major field crops, hay, and other forage crops, as well as acreage data for vegetables, fruits, tree nuts, and berries. Economics – Data on agriculture sales, farm income, government payments from conservation and farm programs, amounts received from loans, a broad range of production expenses, and value of buildings and equipment. Farms – Information on farm size, ownership, and Internet access, as well as data on total land in farms, land use, irrigation, fertilized cropland, and enrollment in crop insurance programs. Livestock and Animals – Statistics on cattle and calves, cows and heifers, milk cows, and other cattle, as well as hogs, sheep, goats, horses, and broilers. Operators – Statistics on hired farm labor, tenure, land rented or leased, primary occupation of farm operator, and demographic characteristics such as age, sex, race/ethnicity, and residence location.

The Ag Census Web Maps application allows you to:

Select a map to display from a the above five general categories and associated subcategories. Zoom and pan to a specific area; use the inset buttons to center the map on the continental United States; zoom to a specific state; and show the state mask to fade areas surrounding the state. Create and print maps showing the variation in a single data item across the United States (for example, average value of agricultural products sold per farm). Select a county and view and download the county’s data for a general category. Download the U.S. county-level dataset of mapped values for all categories in Microsoft ® Excel format.

A long term series of all the main agriculture census items collected in the June census. Source agency: Scottish Government Designation: National Statistics Language: English Alternative title: Abstract of Scottish Agricultural Statistics

This site provides interactive access to data from NASS, as part of a cooperative effort among USDA, the USDA Regional Pest Management Centers and the NSF Center for Integrated Pest Management (CIPM). All data available have been previously published by NASS and have been consolidated at the state level. Commodity acreages and active ingredient agricultural chemical use (% acres treated, ai/acre/treatment, average number of treatments, ai/acre, total ai used) data are available. All data can be searched by commodity, year, state and active ingredient. For more details on methodology, please see NASS website. Search results can be obtained in web format and as downloadable Excel files. For each individual active ingredient, commodity, year and statistic, dynamic U.S. maps of each use statistic can be generated. Agricultural chemical usage statistic data can also be seen in a graphical format. Currently, this site contains the data from 1990. We will continue to update the database annually. As this site is enhanced, we will also provide means and totals of the statistics over years, states, and commodities. This project is funded by USDA, The Cooperative State Research, Education, and Extension Service (CSREES), project award No. 2001-34366-10324. Resources in this dataset:Resource Title: Agricultural Chemical Use Program Data. File Name: Web Page, url: https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Chemical_Use/#data Since 2009, the release of chemical use surveys is available through Quick Stats. The following materials are available for each survey: highlights fact sheet, a methodology paper, and a set of data tables featuring commonly requested information.

A direct internet link to Solomon Island's agriculture statistics at a glance and other related information.

Results whole France including DOM Crop production: Areas, yields, harvested or marketed production Animal production: Livestock on farms, finished animals produced. (production, average weight, product weight), production and use of milk on the farm. Distribution of territory in (annual agricultural statistics): arable land, permanent crops, utilised agricultural area, total area.

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. The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishing. 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: Agricultural commoditiesGeographic 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: Almonds Animal Totals Barley, Cattle Chickens Corn Cotton Crop TotalsFarm Operations Government Programs Grain Grapes Hay Hogs Labor Machinery Totals Milk Producers Rice Sorghum Soybean Tractors Trucks Turkeys Wheat Winter WheatGeography 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.

Here we provide information for the DESIS and PRISMA Derived Spectral Library of Agricultural Crops in California which was developed using DESIS and PRISMA hyperspectral data acquired for 2020. The DESIS images used for this dataset are available through the German Aerospace Center and Teledyne Brown (2022). PRISMA images are available through the Italian Space Agency (ASI) (2022). The crop type data and confidence layer for the year 2020 can be accessed through the USDA National Agricultural Statistics Service (2022). The DESIS and PRISMA Derived Spectral Library of Agricultural Crops dataset characteristics are described below, with DESIS and PRISMA data provided in two separate CSV files. Related Primary Publication: Aneece, I.P., and Thenkabail, P.S., 2022, New generation hyperspectral sensor (DESIS and PRISMA) performances in agriculture.