Longitude and latitude, state, address, name, and zip code of Farmers Markets in the United States

The USDA National Farmers Market Directory, maintained by AMS Marketing Services, is designed to provide members of the public with convenient access to information about U.S. farmers market locations, directions, operating times, product offerings, and accepted forms of payment. Market information included in the Directory is voluntary and self-reported to AMS by market managers, representatives from State farmers market agencies and associations, and other key market personnel.

Unique values and counts of metadata location fields.

In order to assess the impact of the Land Restoration Program, understanding what land restoration options work, where and for whom, there is need to identify the context-specific variables that may influence the performance of the restoration options as well as their uptake. In addition to monitoring the performance of the restoration option being implemented, a registration of the farmers involved in the project was conducted. A standard household survey was used, assessing both the socio-economic and biophysical characteristics of the households. The farmers were from four district of Ethiopia: Boset, Gursum, Samre and Tsaeda Emba. The present dataset includes socio-economical data about 173 households, including general information about the farms. Specific data about agricultural operations, crops, trees and the experimental plots developed inside the project, are part of a separated dataset. NOTE: The coordinates were removed from the dataset in May 2021, in order to comply with GDPR standards. The location details are available on request: please contact the author and explain the purpose of your research.

Domain Dataset Grower

This dataset was generated by distilabel as a domain specific dataset for the domain of farming. The dataset used this seed data to generate the samples. The seed data was define by a domain expert and the generated data can be reviewed in this Argilla space here: Argilla If you want to define a domain specific seed dataset for your own domain, you can use the distilabel tool to generate the dataset, and seed your dataset here

  farming-data… See the full description on the dataset page: https://huggingface.co/datasets/aidev08/farming-data.

United States Agricultural Price Index: Received by Farmers: All Farm Products data was reported at 180.000 1990-1992=100 in Dec 2013. This records a decrease from the previous number of 184.000 1990-1992=100 for Nov 2013. United States Agricultural Price Index: Received by Farmers: All Farm Products data is updated monthly, averaging 101.000 1990-1992=100 from Jan 1975 (Median) to Dec 2013, with 468 observations. The data reached an all-time high of 217.000 1990-1992=100 in Jan 2013 and a record low of 67.000 1990-1992=100 in Apr 1975. United States Agricultural Price Index: Received by Farmers: All Farm Products data remains active status in CEIC and is reported by US Department of Agriculture. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

Data set on Fadama Cassava Farmers Database in Edo State Showing The Locations,Name of FCAs and FUGs,No Of Male and Female involved in Cassava Production,Total No of Cassava Production and The No of Hectares Put into Production as at January 15th,2014.

Comprehensive dataset of 609 Farmers' markets in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

United States Agricultural Price Index: Paid by Farmers: Livestock data was reported at 107.500 2011=100 in Oct 2018. This records an increase from the previous number of 107.000 2011=100 for Sep 2018. United States Agricultural Price Index: Paid by Farmers: Livestock data is updated monthly, averaging 107.000 2011=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 118.000 2011=100 in Sep 2014 and a record low of 88.000 2011=100 in Aug 2010. United States Agricultural Price Index: Paid by Farmers: Livestock data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

According to Cognitive Market Research, the Global Farm Management Software Market is expected to have a market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

The North American region is expected to have the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The Europe region is the fastest growing region with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
Precision Farming has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period
Cloud Based segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The software segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

Market Dynamics

Key Drivers

An Increase in the Use of Artificial Intelligence, Machine Learning, and Learning for Real-Time Farm Data Management

The demand for real-time data for decision-making and the rise in agricultural activity has led to an expansion of the farm management software market. Artificial intelligence and machine learning are rapidly becoming popular in several farming applications, including fish farming, precision farming, sophisticated greenhouse techniques, and animal monitoring. The 'Saagu Baagu' pilot, for instance, was created in collaboration with the Telangana state government in its Khammam district, supported by the Bill and Melinda Gates Foundation, and carried out by Digital Green in India, is reportedly one of the most successful implementations of the AI4AI initiative, according to the World Economic Forum. For over 7,000 farmers, the project has significantly enhanced the value chain for chilies. Telangana's state government, which established the nation's first framework for Agri data management and exchange as well as other supportive policies, has been instrumental in this shift. Saagu Baagu has shown impressive outcomes during its initial period of operation. A 21% increase in chili yields per acre, a 9% decrease in pesticide use, a 5% decrease in fertilizer use, and an 8% increase in unit prices as a result of quality improvements were observed by farmers involved in the program. Farmers' revenues have increased by more than INR 66,000 (about 800 USD) per acre per crop cycle as a result of these changes, nearly doubling their income. To make farm management easier, farm management software controls the data flow between hardware and personnel. Understanding the environment through data analysis from several farm management instruments, such as GPS, satellite imagery, and in-field sensors, is the main objective of the farm management framework. Data management is essential since agricultural management decisions are based on real-time data analysis from farm activities. With the increasing prevalence of artificial intelligence and machine learning, data management functions like as planning, purchasing, feeding, harvesting, marketing, and inventory control can now be facilitated by real-time access to data. The collection of real-time data from farming operations facilitates analysis and decision-making, thereby favoring the market growth for the farm management software market. (Source- https://www.weforum.org/impact/ai-for-agriculture-in-india/)

The implementation of government policies

The implementation of policies by governments across various countries is expected to facilitate the adoption of advanced agricultural techniques, hence driving the global market for farm management software. Adoption of precision agriculture, research and development, instruction, and training are supported by federal authorities. USDA offers financing programs and financial aid to encourage the adoption of precision agriculture technologies. One such program pays farmers for adopting practices that have a positive impact on conservation. For the fiscal years 2017–2021, the National Science Foundation (NSF) and the USDA will jointly grant about $200 million for precision agricultural research and development. The two agencies' collaborations to assist artificial intelligence (AI) research institutes are part of this funding. Similarly, the Indian go...

Chicago's Farmers Markets bring more than 70 vendors selling fresh fruits, vegetables, plants and flowers to neighborhoods throughout the City of Chicago. Markets are held Tuesday, Wednesday, Thursday, Saturday and Sunday around the city.

United States Agricultural Price Index: Paid by Farmers: Crop data was reported at 111.000 2011=100 in Oct 2018. This records an increase from the previous number of 110.600 2011=100 for Sep 2018. United States Agricultural Price Index: Paid by Farmers: Crop data is updated monthly, averaging 105.500 2011=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 111.000 2011=100 in Oct 2018 and a record low of 90.000 2011=100 in Jul 2010. United States Agricultural Price Index: Paid by Farmers: Crop data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

Abstract

Beyond causing immediate hardship and triggering a large exodus of displaced people, Russia’s military invasion of Ukraine and the blockade of its Black Sea export routes have also led to sharp increases in grain prices and raised concern about global food security. To provide information to the government for developing policies and programs to support the agricultural sector in Ukraine, the World Bank launched a nationwide survey of post-invasion farmers in cooperation with the Ministry of Agricultural Policy and Food (MAPF), with financial support from the EU, in areas controlled by Ukraine from October to December of 2022. The survey objective is to obtain information on changes in welfare, production, and productivity in the small and medium farm sector between 2021 and 2022 and to identify ways on how farmers could be most effectively supported. Data was collected via phone by the Kyiv International Institute of Sociology (KIIS) under the monitoring of World Bank research team.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame consisted of 63,374 registered farms. The distribution of farms by size and program participation shows that most of the farms are small (85%) with farm size less than 50 ha (35,264 PSG non-applicants vs. 18,605 PSG applicants) followed by farms with 50-120 ha (7.5% with 1,634 PSG non-applicants and 3,107 PSG applicants) and farms that are not eligible for PSG participation with size greater than 120 ha (7.5% with 2,743 less than 500 ha and 2,021 greater than 500 ha).

The expected sample size for the phone survey was 2,500 farms with 10% each in the small size category from PSG applicants and non-applicants, 20% each in the farm category of 50-120 ha from PSG applicants and non-applicants, 20% from the farm size category of 120-500 ha and 20% from the farm size category of greater than 500 ha. Given the expected high non-response rate of phone interviews, all the farms with size greater than 50 ha were included in the sample and then 1,125 and 1,126 farms were randomly selected from PSG non-participants and participants in the less than 50 ha category. The final response rate was about 20% with the lowest in the greater than 500 ha category (15%) and the highest in the 50-120 ha PSG non-applicant category (28%). The survey initially targets 2, 500 farms, and eventually collected data for 2, 251 farms.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire comprised four sections namely: • Screener & Background • Household Roster • Agricultural Production • Property and Finance

Cleaning operations

Data have been collected electronically. Survey logic has been incorporated into the instrument. After data collection, mainly general data completeness and outliers have been checked. Also, all text responses to open-ended questions have been analyzed and coded if necessary.

The final data file contains data from 2,251 interviews. It was provided to the World Bank team in SPSS formats.

Response rate

The overall and cooperation response rates were 21.3% and 37.3% respectively.

Data from the ABIS(Agricultural Business Information System) database with 178,345 total registered farmers across Jamaica.

There is aggregated Livestock, Crop and Property data from across the island. Dataset provided by RADA

Office of Agriculture's listing of farmers markets in the County. Includes market managers' name and contact information, seasons of operation, operation times and accepted programs. This data will update annually.

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.

United States Agricultural Price Index: Paid by Farmers: All Items data was reported at 213.000 1990-1992=100 in Dec 2013. This records a decrease from the previous number of 214.000 1990-1992=100 for Nov 2013. United States Agricultural Price Index: Paid by Farmers: All Items data is updated monthly, averaging 142.000 1990-1992=100 from Jan 1997 (Median) to Dec 2013, with 204 observations. The data reached an all-time high of 220.000 1990-1992=100 in Mar 2013 and a record low of 113.000 1990-1992=100 in Sep 1998. United States Agricultural Price Index: Paid by Farmers: All Items data remains active status in CEIC and is reported by US Department of Agriculture. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

United States Agricultural Price Index: Paid by Farmers: Production data was reported at 106.200 2011=100 in Oct 2018. This records an increase from the previous number of 105.800 2011=100 for Sep 2018. United States Agricultural Price Index: Paid by Farmers: Production data is updated monthly, averaging 106.000 2011=100 from Jan 2010 (Median) to Oct 2018, with 106 observations. The data reached an all-time high of 116.000 2011=100 in Jun 2014 and a record low of 86.000 2011=100 in Mar 2010. United States Agricultural Price Index: Paid by Farmers: Production data remains active status in CEIC and is reported by National Agricultural Statistics Service. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

United States Agricultural Price Index: Paid by Farmers: Production Items data was reported at 224.000 1990-1992=100 in Dec 2013. This records a decrease from the previous number of 225.000 1990-1992=100 for Nov 2013. United States Agricultural Price Index: Paid by Farmers: Production Items data is updated monthly, averaging 140.000 1990-1992=100 from Jan 1997 (Median) to Dec 2013, with 204 observations. The data reached an all-time high of 234.000 1990-1992=100 in Mar 2013 and a record low of 110.000 1990-1992=100 in Aug 1999. United States Agricultural Price Index: Paid by Farmers: Production Items data remains active status in CEIC and is reported by US Department of Agriculture. The data is categorized under Global Database’s United States – Table US.I043: Agricultural Price Index.