The dataset you've provided appears to capture agricultural data for Karnataka, specifically focusing on crop yields in Mangalore. Key features include the year of production, geographic details, and environmental conditions such as rainfall (measured in mm), temperature (in degrees Celsius), and humidity (as a percentage). Soil type, irrigation method, and crop type are also recorded, along with crop yields, market price, and season of growth (e.g., Kharif).

The dataset includes several columns related to crop production conditions and outcomes. For example, coconut crop data reveals a pattern of yields over different area sizes, showing how factors like rainfall, temperature, and irrigation influence production. Prices also vary, offering insights into the economic aspects of agriculture in the region. This information could be used to study the impact of environmental conditions and farming techniques on crop productivity, assisting in the development of optimized agricultural practices tailored for specific soil types, climates, and crop needs.

These files from Statistics Canada present Census of Agriculture data allocated by standard census geographic polygons: Provinces and Territories (PR), Census Agricultural Regions (CAR), Census Divisions (CD) and Census Consolidated Subdivisions (CCS). Five datasets are provided: 1. Agricultural operation characteristics: includes information on farm type, operating arrangements, paid agricultural work and financial characteristics of the agricultural operation. 2. Land tenure and management practices: includes information on land use, land tenure, agricultural practices, land inputs, technologies used on the operation and the renewable energy production on the operation. 3. Crops: includes information on hay and field crops, vegetables (excluding greenhouse vegetables), fruits, berries, nuts, greenhouse productions and other crops. 4. Livestock, poultry and bees: includes information on livestock, poultry and bees. 5. Characteristics of farm operators: includes information on age, sex and the hours of works of farm operators. Note: For all the datasets, confidential values have been assigned a value of -1. Correction notice: On January 18, 2023, selected estimates have been corrected for selected variables in the following 2021 Census of Agriculture domains: Direct sales of agricultural products to consumers (Agricultural operations category), Succession plan for the agricultural operation (Agricultural operators category), and Renewable energy production (Use, tenure and practices category).

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete 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.

NASS USDA estimates the irrigated croplands at county level every five years. But this estimation does not provide the geospatial information of the irrigated croplands. To provide a comprehensive, consistent, and timely geospatially detailed information about irrigated cropland conterminous U.S. (CONUS), the “Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US)” product was produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center with funding from several USGS programs (National Land Imaging and National Water-Quality Assessment). A primary objective was to identify, and map irrigated agricultural areas to factor into water quality studies and drought monitoring investigations. This product uses three primary data inputs, (a) USDA county-level irrigation area statistics for 2002, (b) annual peak eMODIS Normalized Difference Vegetation Index (NDVI), and (c) a land cover mask for agricultural lands derived from NLCD to map the spatial distribution of irrigated lands across the conterminous United States. The MIrAD Version 4 offers the datasets for the years 2002, 2007, 2012, and 2017 at 250-m and 1-km spatial resolutions. The validation of MIrAD-US is a challenge because no other single-source current datasets are available at a national scale for comparison. Thus, this dataset should be considered provisional until a formal accuracy assessment can be completed. The product update is planned for every 5 years, synchronized with the update of the Census of Agriculture by the U.S Department of Agriculture (USDA) but contingent upon availability of Collection 6 (C6) Aqua eMODIS data and funding.

IOT Agriculture

## Overview

IOT Agriculture is a dataset for object detection tasks - it contains Insect Pest annotations for 547 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Salman Sajid

Contents

Precision Agriculture

## Overview

Precision Agriculture is a dataset for semantic segmentation tasks - it contains Precise Maps annotations for 4,121 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

season

Demographic, occupational, and economic information for over 21,000 rural households in the northern United States in 1860 are presented in this dataset. The data were obtained from the manuscript agricultural and population schedules of the 1860 United States Census and are provided for all households in a single township from each of 102 randomly-selected counties in sixteen northern states. Variables in the dataset include farm values, livestock, and crop production figures for the households which owned or operated farms (over half the households sampled), as well as value of real and personal estate, color, sex, age, literacy, school attendance, occupation, place of birth, and parents' nationality of all individuals residing in the sampled townships.

The total collected data consists of 3,156 images categorized into 10 different types of pests and diseases on rice. Additionally, this data has been analyzed and evaluated by from Cuu Long Delta Rice Research Institute

A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

The primary greenhouse gas (GHG) sources for agriculture are nitrous oxide (N2O) emissions from cropped and grazed soils, methane (CH4) emissions from ruminant livestock production and rice cultivation, and CH4 and N2O emissions from managed livestock waste. The management of cropped, grazed, and forestland has helped offset GHG emissions by promoting the biological uptake of carbon dioxide (CO2) through the incorporation of carbon into biomass, wood products, and soils, yielding a U.S. net emissions of 5,903 MMT CO2 eq (million metric tonnes of carbon dioxide equivalents). Net emissions equate to total greenhouse gas emissions minus CO2 sequestration in growing forests, wood products, and soils. The report 'U.S. Agriculture and Forestry Greenhouse Gas Inventory: 1990-2018' serves to estimate U.S. GHG emissions for the agricultural sector, to quantify uncertainty in emission estimates, and to estimate the potential of agriculture to mitigate U.S. GHG emissions. This dataset contains tabulated data from the figures and tables presented in Chapter 5, Energy Use in Agriculture, of the report. Data are presented for carbon dioxide emissions from on-farm energy use. Please refer to the report for full descriptions of and notes on the data. Resources in this dataset:Resource Title: Table 5-1. File Name: Table5_1.csvResource Description: Energy Use and Carbon Dioxide Emissions by Fuel Source on U.S. Farms, 2018. Energy consumed is shown in the table as QBTU (quadrillion British thermal units). Carbon content is displayed as MMT C/QBTU (million metric tons carbon per quadrillion British thermal units). Emissions are shown as Tg CO2 eq. (teragrams carbon dioxide equivalent). Resource Title: Data for Figure 5-1. File Name: Figure5_1.csvResource Description: CO2 Emissions From Energy Use in Agriculture, by State, 2018 in MMT CO2 eq. (million metric tons carbon dioxide equivalent).Resource Title: Data for Figure 5-2. File Name: Figure5_2.csvResource Description: Energy use in agriculture, by source, 1965–2018 in QBTU (quadrillion British thermal units).Resource Title: Data for Figure 5-3. File Name: Figure5_3.csvResource Description: CO2 Emissions from Energy Use in Agriculture, by Fuel Source, 2001, 2005, 2008, 2013, and 2018 in MMT CO2 eq. (million metric tons carbon dioxide equivalent).Resource Title: Chapter 5 tables and figures. File Name: Chapter 5 data.zip

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

The STRIVE project, funded by USAID's Displaced Children and Orphans Fund (DCOF) and managed by FHI 360, used market-led economic strengthening initiatives to improve the well-being of vulnerable children. Through STRIVE, ACDI/VOCA implemented the Agriculture for Children’s Empowerment (ACE) Project in Liberia, which is founded on the premise that increased household economic security will stimulate more consistent investments in children’s well being via longer term social investments in education and nutrition. ACE’s primary focus was on the horticulture value chain (VC) — the production and marketing of vegetables by smallholder farmers in Montserrado, Bong, and Nimba counties of Liberia. ACE also strengthened smallholder rice farming to increase household food security using a market-sensitive approach to rice seed lending and cultivation. This dataset contains endline information about each plot the household owns.

Ghana Compact - Agriculture - Post-Harvest

Maize and soybean yield data set for Precision Zonal Management (PZM) project from 2012-2015. Project compared chisel plow tillage against ridge tillage (PZM) systems, with and without winter cereal rye cover crops. Experimental sites in four US states: IL, MI, MN and PA. Data set provides plot-level yield data (kg/ha) for each site-year and for both crops. File also contains data set of maize and soybean yield stability, with soil properties measured in 2015 (end of experimental period) and delta values (values in 2015 minus values prior to experiment establishment in 2011). Resources in this dataset:Resource Title: Data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_yields_stability_soil.xlsxResource Description: Data files combined into a single excel document.Resource Title: Data Dictionary. File Name: PZM_data_dictionary.csvResource Title: Yield data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_Yields.csvResource Title: Stable Soil data for: A regionally-adapted implementation of conservation agriculture delivers rapid improvements to soil properties associated with crop yield stability. File Name: PZM_stable_soil.csv

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.

shahram-ali/Agriculture dataset hosted on Hugging Face and contributed by the HF Datasets community

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