Texas was by far the leading U.S. state in terms of total number of farms, with about 231 thousand farms by the end of 2024. Iowa was ranked second, among the leading ten states, with 86.7 thousand farms as of 2023. Farms classification In the United States farms are classified based on the farm income and government payments into six sales classes. According to the USDA, about half of all farms in the U.S. were classified in the 1,000 to 9,999 U.S. dollars sales class in 2023. Farming sector in the U.S. The total number of farms in the United States has decreased steadily since 2007. As of 2022, there were about 1.89 million farms in the U.S., down slightly from 1.9 million in the previous year. Contrastingly, the average farm acreage in the United States has increased in the past few years. The number of employees, including both part-time and full-time workers, in this sector was over 965 thousand as of 2019.

This statistic shows the states with the highest wheat production in the United States in 2023 and 2024. North Dakota was ranked as the first leading wheat production state with about 367.7 million bushels produced in 2024 and just under 307 million bushels in 2023. Wheat production Wheat is the second most important grain that is cultivated in the United States, following only corn. Wheat is a cereal crop that can be classified into five major classes. These five wheat classes include hard red winter, hard red spring, soft red winter, white, and durum wheat. Each class has a different end-use and the cultivation tends to be region-specific. Hard red winter wheat is mainly grown in the Great Plains area ranging from Montana to Texas. This type is primarily used for the production of bread flour. Hard red spring wheat is mostly cultivated in the Northern Plains area. Their wheat ears are mainly taken for protein blending purposes. Durum wheat, which is primarily grown in North Dakota and Montana, is known for their excellent qualities for producing pasta. The wheat class everyone knows from their breakfast cereal is named white wheat. Almost every U.S. state is involved in agricultural wheat production. The latest statistics show that North Dakota, Kansas and Montana were the leading wheat producing states among the United States.

Abstract

For 156 years (1840 - 1996), the U.S. Department of Commerce, Bureau of the Census was responsible for collecting census of agriculture data. The 1997 Appropriations Act contained a provision that transferred the responsibility for the census of agriculture from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture is the 27th Federal census of agriculture and the third conducted by NASS. The first agriculture census was taken in 1840 as part of the sixth decennial census of population. The agriculture census continued to be taken as part of the decennial census through 1950. A separate middecade census of agriculture was conducted in 1925, 1935, and 1945. From 1954 to 1974, the census was taken for the years ending in 4 and 9. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data reference year so that it coincided with other economic censuses. This adjustment in timing established the agriculture census on a 5-year cycle collecting data for years ending in 2 and 7. Agriculture census data are used to:

• Evaluate, change, promote, and formulate farm and rural policies and programs that help agricultural producers; • Study historical trends, assess current conditions, and plan for the future; • Formulate market strategies, provide more efficient production and distribution systems, and locate facilities for agricultural communities; • Make energy projections and forecast needs for agricultural producers and their communities; • Develop new and improved methods to increase agricultural production and profitability; • Allocate local and national funds for farm programs, e.g. extension service projects, agricultural research, soil conservation programs, and land-grant colleges and universities; • Plan for operations during drought and emergency outbreaks of diseases or infestations of pests. • Analyze and report on the current state of food, fuel, feed, and fiber production in the United States.

American Samoa is one of the territories collectively referred as the "US Outlying areas". The 2008 American Samoa Census of Agriculture was conducted by personal interviews of all farm operations on the list of commercial farms, and supplemented by an area sample of the remaining households. The purpose of the area sample was to efficiently accountfor farms not on the commercialfarmlist and provide an accurate measure of the agricultural activity in American Samoa.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit for the CA 2008 was the farm, an operating unit defined as any place from which USD 1 000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year.

Kind of data

Census/enumeration data [cen]

Sampling procedure

i. Methodological modality for conducting the census The classical approach was used in the CA 2008.

ii. sample design The design of the sample for the 2008 Census of Agriculture made use of materials and information available from the American Samoa Department of Commerce. These included detailed maps of all the islands in the territory, up-to-date map-spotting (location on a map) of all households in the territory, a system of numbering each household to provide it a unique identifier, and identification of householdswhich were on the list of commercial farms. The households that were on the list of commercial farms were excluded from the universe used to select the area sample. A random sample of the remaining households was selected, using the available maps with the household identification information. It was determined that a 20 percent sample would be optimal. A serpentine selection methodology, starting at a point determined by the generation of a random number, was used to select the area sample.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

One questionnaire was used which collected information on:

• Land owned
• Field crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Aquaculture
• Expenditure
• Production expenses
• Machinery, equipment and buildings
• Household characteristics

Cleaning operations

1. DATA PROCESSING AND ARCHIVING The completed forms were scanned and Optical Mark Recognition (OMR) was used to retrieve categorical responses and to identify the other answer zones in which some type of mark was present. The edit system determined the best value to impute for reported responses that were deemed unreasonable and for required responses that were absent. The complex edit ensured the full internal consistency of the record. After tabulation and review of the aggregates, a comprehensive disclosure review was conducted. Cell suppression was used to protect the cells that were determined to be sensitive to a disclosure of information.

2. CENSUS DATA QUALITY NASS conducted an extensive program to follow-up all non-response. NASS also used capture-recapture methodology to adjust for under-coverage, non-response, and misclassification. To implement capture-recapture methods, two independent surveys were required --the 2012 Census of Agriculture (based on the Census Mail List) and the 2012 June Agricultural Survey (based on the area frame). Historically, NASS has been careful to maintain the independence of these two surveys.

Data appraisal

The complete data series from the 2008 Census of Agriculture is available from the NASS website free of charge in multiple formats, including Quick Stats 2.0 - an online database to retrieve customized tables with Census data at the national, state and county levels. The 2012 Census of Agriculture provides information on a range of topics, including agricultural practices, conservation, organic production, as well as traditional and specialty crops.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, _domain-specific databases, and the top journals compare how much data is in institutional vs. _domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find _domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known _domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were _domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of _domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared _domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the _domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

United States AI in Agriculture Market was valued at USD 351.09 Million in 2023 and is anticipated to reach USD 705.74 Million in the forecast period with a CAGR of 12.30% through 2029.

This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024).

This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop.

A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion.

This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036).

California was the leading U.S. state in terms of the overall number of milk cows, with a total of over 1.7 million milk cows as of 2024. The total number of milk cows on farms in the United States shows that California holds a significant share of the total number of milk cows in the country. Unsurprisingly, California is also the leading milk producing state in the United States. Dairy industry in the U.S. According to the USDA, milk from U.S. farms is 90 percent water, with milk fat and skim solids making up the remaining 10 percent. Cow milk is a component of several dietary staples, such as cheese, butter, and yoghurt. Dairy is a very important industry in the United States, with this sector alone creating significant employment throughout the United States. The overall income of dairy farms in the U.S. amounted to about 51.3 billion U.S. dollars. Holtsein is the most popular breed of dairy cow farmed in the United States. Holstein have the highest milk production per cow in comparison to any other breed. Where is the U.S. positioned in the global dairy market? Topped only by the EU-27, the United States ranks as the second largest cow milk producer in the world, followed by India, Russia, and China. The United States also features among the top ten global milk exporters. The outlook for the future of the industry is also good, with milk production in the United States projected to steadily increase over the next years.

Discover the top 5 largest beef-producing states in the United States, their production capacity, and contributions to the agriculture industry. Texas, Nebraska, Kansas, California, and Oklahoma take the lead in beef production.

The US Agriculture Equipment Market report segments the industry into Tractors, Plowing And Cultivating Machinery (Ploughs, Harrows, Cultivators & Tillers), Planting Machinery (Seed Drills, Planters, Spreaders), Harvesting Machiney (Combine Harvesters, Forage Harvesters), Haying And Forage Machinery (Mowers, Balers), Irrigation Machinery (Sprinkler Irrigation, Drip Irrigation), and Other Agricultural Machinery.

This statistic shows the ten U.S. states with the highest amount of milk production from 2020 to 2023. California, is the leading producer, where over four million pounds of milk were produced in 2023. Milk productionDairy farming is an agricultural business which is engaged in the long-term milk production within the dairy industry. It is a large contributor to the overall economy in many states. California, Wisconsin, New York, Idaho and Pennsylvania had the highest milk supply.The number of U.S. dairy farms has sharply decreased in the last decades, while dairy operations have ever-larger numbers of cows concentrated on a single farm. These extensive dairy farming conditions with a large herd size and a high milk output are seen as a profitable way for the milk industry in order to provide milk at relatively low cost for the consumer. Due to its high milk volume, the main cow used for milk production is the Holstein-Friesian. However, with this intensification of milking cows there comes a corresponding concentration of manure production which causes problems and challenges for the environment such as the risk of elevated nitrogen levels or contaminated ground water.Due to these environmental impacts, many dairy operations in Wisconsin are now facing opposition regarding plans to expand their dairy herds.

Graph and download economic data for Government subsidies: Federal: Agricultural (L312041A027NBEA) from 1960 to 2023 about subsidies, agriculture, federal, government, GDP, and USA.

United States Imports from China was US$462.62 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China - data, historical chart and statistics - was last updated on July of 2025.

The U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) provides funding and technical assistance for the implementation of practices that enable people to conserve, maintain, or improve soil, water, and related natural resources. NRCS conservation practices, or best management practices, relate the conservation practices to conservation practice physical effects (CPPE). Physical effects are listed in the data and purpose and include effects such as nutrients and pesticides transported to surface and/or groundwater. The CPPE is published in the NRCS Field Office Technical Guide and is listed in the sources section. NRCS's CPPE includes a limited set of practices. All practices that do not have an NRCS-assigned CPPE were evaluated using NRCS practice standard criteria and required enhancements to determine the most comparable practice that with an assigned CPPE. The CPPE categories are ranked on a scale of -5 to +5. These rankings are categorical and indicate a range from substantial worsening (-5) to substantial improvement (+5) with zero indicating no effect. NRCS conservation practice data are available to USGS researchers via a data sharing agreement.

This raster file represents land within the Grandview-Bruneau Water Budget Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data. A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using top-of-atmosphere reflectance data from Landsat 5 and Landsat 7, NASA STRM Digital Elevation data, Height Above Nearest Drainage (HAND) data, and METRIC data for March-October 2000. Landsat 5, Landsat 7, NASA STRM Digital Elevation data, HAND data, and METRIC are at a 30-meter spatial resolution. The Cropland Data Layer (CDL) from the United States Department of Agriculture National Agricultural Statistics Service (USDA NASS), National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), and U.S Fish & Wildlife Service National Wetlands Inventory data were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. Wetlands areas identified by US Fish & Wildlife Service data were masked as non-irrigated if they had no overlapping irrigation POUs or were manually determined to have no potential irrigation to correct some of the riparian areas that were falsely classified by the model as irrigated. “Speckling”, or small areas of incorrectly classified pixels, was reduced by a Boundary Clean smoothing technique which uses a descending sort order by size. Zones with larger total areas have a higher priority to expand into zones with smaller total areas.

Farm supply stores have experienced significant growth over the five years to 2024, driven by rising agricultural prices and increased disposable incomes. The COVID-19 pandemic spurred interest in gardening and rural lifestyles, boosting sales of animal feed, gardening tools, seeds and small-scale farming equipment. This surge in demand helped offset the negative impacts faced by other retail sectors during the pandemic. Larger companies like Tractor Supply Co. expanded their market presence, contributing to a steady increase in industry revenue, which grew at a CAGR of 5.6% to $21.7 billion over the past five years. However, higher interest rates towards the end of this period have begun to curb private spending on non-essential home improvements and large-ticket items, negatively impacting sales. Profitability for farm supply stores benefited from increased consumer spending and elevated agricultural prices. The home improvement trend during the pandemic led to a significant boost in sales, especially for pet and animal feed, agricultural equipment and replacement parts. Larger companies with strong brand recognition capitalized the most, but smaller stores also saw substantial gains. Despite these positive trends, high interest rates and persistent inflation are starting to affect consumers' disposable incomes, impacting sales and profitability for both large and small retailers. Looking ahead to the next five years, farm supply stores are expected to face several challenges. Disinflation may reduce farmers' incomes, making them more price-sensitive and potentially decreasing their spending on farming supplies. However, this could be balanced by increased purchasing power among hobby farmers and households, who may invest more in high-quality tools and equipment as their disposable incomes rise. Anticipated lower interest rates will make financing big-ticket items easier, encouraging more significant investments. The focus on sustainability will continue to grow, driving demand for eco-friendly and energy-efficient products. Revenue is expected to rise at a CAGR of 1.3% to $23.2 billion over the next five years. Although the industry may not match the rapid growth of the previous period, it is positioned for steady development, supported by ongoing demand for essential farming supplies and the increasing popularity of sustainable farming practices.

The GCEW herbicide data were collected from 1991-2010, and are documented at plot, field, and watershed scales. Atrazine concentrations in Goodwater Creek Experimental Watershed (GCEW) were shown to be among the highest of any watershed in the United States based on comparisons using the national Watershed Regressions for Pesticides (WARP) model and by direct comparison with the 112 watersheds used in the development of WARP. This 20-yr-long effort was augmented with a spatially broad effort within the Central Mississippi River Basin encompassing 12 related claypan watersheds in the Salt River Basin, two cave streams on the fringe of the Central Claypan Areas in the Bonne Femme watershed, and 95 streams in northern Missouri and southern Iowa. The research effort on herbicide transport has highlighted the importance of restrictive soil layers with smectitic mineralogy to the risk of transport vulnerability. Near-surface soil features, such as claypans and argillic horizons, result in greater herbicide transport than soils with high saturated hydraulic conductivities and low smectitic clay content. The data set contains concentration, load, and daily discharge data for Devils Icebox Cave and Hunters Cave from 1999 to 2002. The data are available in Microsoft Excel 2010 format. Sheet 1 (Cave Streams Metadata) contains supporting information regarding the length of record, site locations, parameters measured, parameter units, method detection limits, describes the meaning of zero and blank cells, and briefly describes unit area load computations. Sheet 2 (Devils Icebox Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Devils Icebox site for 12 analytes and two computed nutrient parameters. Sheet 3 (Devils Icebox SS Conc Data) contains 15-minute suspended sediment (SS) concentrations estimated from turbidity sensor data for the Devils Icebox site. Sheet 4 (Devils Icebox Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Devils Icebox site. Sheet 5 (Hunters Cave Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Hunters Cave site for 12 analytes and two computed nutrient parameters. Sheet 6 (Hunters Cave SS Conc Data) contains 15-minute SS concentrations estimated from turbidity sensor data for the Hunters Cave site. Sheet 7 (Hunters Cave Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Hunters Cave site. [Note: To support automated data access and processing, each worksheet has been extracted as a separate, machine-readable CSV file; see Data Dictionary for descriptions of variables and their concentration units.] Resources in this dataset:Resource Title: README - Metadata. File Name: LTAR_GCEW_herbicidewater_qual.xlsxResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as rendered in the Excel file. For additional information including site information, method detection limits, and methods citations, see Metadata tab. For Definitions used in machine-readable CSV files, see Data Dictionary.Resource Title: Excel data spreadsheet. File Name: c3.jeq2013.12.0516.ds1_.xlsxResource Description: Multi-page data spreadsheet containing data as well as metadata from this study.

A direct download of the data spreadsheet can be found here: https://dl.sciencesocieties.org/publications/datasets/jeq/C3.JEQ2013.12.0516.ds1/downloadResource Title: Devils Icebox Concentration Data. File Name: DevilsIceboxConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data).Resource Title: Devils Icebox Load and Discharge Data. File Name: DevilsIceboxLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Devils Icebox Suspended Sediment Concentration Data. File Name: DevilsIceboxSSConcData.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Load and Discharge Data. File Name: HuntersCaveLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Suspended Sediment Concentration Data. File Name: HuntersCaveSSConc.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Data Dictionary for machine-readable CSV files. File Name: LTAR_GCEW_herbicidewater_qual.csvResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as implemented in the extracted machine-readable CSV files.Resource Title: Hunters Cave Concentration Data. File Name: HuntersCaveConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data)

The agricultural machinery manufacturing industry in the US is currently experiencing significant challenges due to high crop production and persistently high interest rates. While crop prices expanded machinery sales early in the current period, record-high crop production levels have since led to an oversupply, causing crop prices to plummet. Consequently, farmers' incomes have fallen, resulting in tightened budgets and deferred machinery purchases. Additionally, the market has been impacted by consistently high interest rates, making financing for new equipment less accessible. This financial strain has narrowed the profit for manufacturers, affecting smaller manufacturers more severely. Despite these obstacles, emerging markets in Southeast Asia, Africa and Latin America are providing new avenues for growth, with increased demand driven by the modernization of farming practices in these regions. Industry revenue has fallen at a CAGR of 3.4% over the current period to reach an estimated $38.1 billion after an increase of 1.0% in 2025. While agricultural price pressures loom large, significant transformative trends are occurring within the industry. Precision agriculture technologies are increasingly being adopted, with large farms leading the way due to their ability to absorb high upfront costs and achieve long-term savings. These technologies are helping to open new revenue streams and product lines for dominant companies like John Deere, which is investing heavily in research and development. Meanwhile, the rise of sustainability as a key industry driver encourages companies to develop eco-friendly and energy-efficient machinery. Manufacturers are focusing on electric and hybrid tractors to align with global climate goals and respond to consumer demand for sustainable products. The outlook for the agricultural machinery manufacturing industry isn’t that strong. Agricultural price declines are likely to persist through 2030, intensifying pressure on revenue growth. Climate change will demand increased adoption of precision agriculture technologies as farmers seek to optimize resource use amid erratic weather patterns, but it will also create additional volatility and crop failures, weakening this core customer base. Simultaneously, labor shortages in the agricultural sector will drive the uptake of autonomous machinery, presenting new growth opportunities for manufacturers that invest in AI-powered solutions. As sustainability becomes a cornerstone, innovation in electric and hybrid machinery will also become crucial to capturing market share. Companies that can adapt to these evolving conditions will be well-positioned to capture a larger share of the agricultural machinery manufacturing market. Industry revenue is forecast to continue its decline at a CAGR of 0.6% to reach $37.0 billion in 2030.

Abstract

The National Institute of Statistics and Geography (INEGI) carried out the National Agricultural Survey 2019 (ENA 2019) to offer statistics on the production of crops and livestock species that are characterized by being the ones that mostly participate in the Gross Domestic Product of the primary sector in Mexico and which, according to the Sustainable Rural Development Law, are those products for which the State seeks the supply, promoting their access to less favored social groups. Likewise, the Food and Agriculture Organization of the United Nations (FAO) considers them essential for food security, agricultural sustainability and rural development.

The ENA 2019 allows to continue obtaining basic and structural statistics of the agricultural and livestock sector, as it is the fourth version of a series of National Agricultural Surveys that INEGI carried out in the years 2012, 2014 and 2017. This survey, in addition to allowing to know The current characteristics of the agricultural production units has been enriched in terms of the results achieved, because, for some priority crops in the Federal Government programs, data was obtained from the small and medium-sized units that have the smallest area planted in the country.

Geographic coverage

National and by Federative Entity.

Analysis unit

For ENA 2019, the Observation Unit is defined as the economic unit made up of one or more pieces of land located in the same municipality, where at least some of them carry out agricultural or forestry activities, under the control of the same administration. If the administration has land located in another municipality, it is considered as another production unit; that is, there will be as many production units as municipalities occupying their land.

Universe

The universe selected for the ENA 2019 was 79,252 production-product units, equivalent to 69,124 production units from which information of interest was obtained. These units come from the Update of the 2016 Agricultural Census Framework (AMCA 2016) and updated with information from the 2017 National Agricultural Survey (ENA 2017). This universe was defined from the 28 products of national interest, 5 of these livestock products being of economic importance for the country.

The products selected for the conformation of the universe of work of the ENA 2019 are 29 products, 24 agricultural: Avocado, Alfalfa, Amaranth, Rice, Cocoa, Coffee, Pumpkin, Sugar Cane, Onion, Chile, Strawberry, Bean, Tomato (Tomato Red), Lemon, White Corn, Yellow Corn, Mango, Apple, Orange, Banana, Sorghum, Soya, Wheat and Grape; while the five species and livestock products were made up of Bovines, Porcine, Poultry, Milk and Egg.

Kind of data

Sample Survey Data [ssd]

Sampling procedure

SAMPLE DESIGN The elements considered for the definition and construction of the sampling scheme of the 2019 National Agricultural Survey (ENA 2019), help determine the size, selection and distribution of the sample; Necessary and substantial elements to define the precision of the information, as well as the analysis of the uptake for the evaluation of the final estimates, through calculations such as the variance and the coefficient of variation.

TARGET POPULATION It is defined by all production units captured in the 2016 Agricultural Census Framework Update (AMCA 2016), updated with information from the 2017 National Agricultural Survey (ENA 2017) for the part of agricultural products and for the part of livestock producers it is taken of the 2007 Agricultural, Livestock and Forestry Census updated with the 2017 ENA that reported, at that time, producing any of the products of interest, classified according to their importance of national and/or state interest.

GEOGRAPHICAL AND SECTOR COVERAGE The survey was designed to obtain information at the national level for the products of interest and for each of the states for their main products.

DOMAIN OF STUDY It refers to subsets of the population under study for which it is intended to obtain information and for which a sample is designed independently for each of them. In this regard, it is worth mentioning that of the 29 products of the ENA 2019 work universe, 26 had a stratified probabilistic design (for purposes of the sample design, corn counts as a single product regardless of whether it is white grain corn or yellow grain corn , reason for which there are 26 and not 27 products); while for poultry and egg products, a non-probabilistic design was considered. The subsets under study are presented below: A. NATIONAL DOMAIN. Each of the 26 products by producer size (large and small and medium producers), obtaining a total of 52 domains, the products considered (Avocado, Alfalfa, Amaranth, Rice, Cattle, Cocoa, Coffee, Pumpkin, Sugarcane, Onion, Chile, Strawberry, Bean, Tomato (Red tomato), Milk, Lemon, Corn, Mango, Apple, Orange, Banana, Pork, Sorghum, Soy, Wheat, Grape). B. PRODUCT-FEDERAL ENTITY DOMAIN. For the main federal entities by producer size, for this case 60 product-federal entity domains were considered. C. DOMAIN PRODUCT-FEDERAL ENTITY-SIZE OF PRODUCTION UNIT BY AREA. (For ten products, the federative entity domain-size of production unit per area is necessary) for this case, 384 domains were considered.

SAMPLING UNIT The observation unit is the Production Unit (UDP), defined as: The economic unit made up of one or more pieces of land located in the same municipality, where at least some of them carry out agricultural or forestry activities, under the control of the same administration. Under this context, the sampling unit is the production-product unit. If the production unit has more than one product or crop, it will be included in two or more study domains.

SAMPLING FRAME It was integrated from two different sources: A. AGRICULTURAL PRODUCTS: the framework derived from the AMCA 2016, updated with the results of the ENA 2017, was the input for determining the sampling framework of the ENA 2019. B. LIVESTOCK PRODUCTS: the 2007 Agricultural, Livestock and Forestry Census, updated with the results of the 2017 ENA.

STRATIFICATION For agricultural products, the variable of interest for stratification was the planted area in hectares (ha), depending on the characteristics of the crop, from four to six strata. The determination of the ranges of the strata is obtained by the Dalenius-Hodges method. According to William G. Cochran (1977), "for a single feature or variable, the best feature is, of course, the frequency distribution. The next best is probably the frequency distribution, given the number of strata, the equations for determining the best limits between them under Neyman proportional assignment, have been obtained by Dalenius (1957)". For livestock products, the number of heads variable was used.

SAMPLING SCHEME For the products of interest, both large and small and medium producers, the sampling design is stratified probabilistic with simple random selection within each study domain: A. PROBABILISTIC. The selection units had a known, non-zero probability of being selected. B. STRATIFIED. Sampling units with similar characteristics were grouped to form strata. The results of the sample are generalized to the entire population and it is possible to know the precision of the results.

SAMPLE SIZE Different sample sizes were calculated for: A. SAMPLE SIZE FOR DOMAINS AT THE NATIONAL LEVEL (PRODUCT). For products of national interest, the sample size obtained for these domains is 19,320 production-product units; 10,968 for large producers and 8,352 for small and medium producers. B. SAMPLE SIZES FOR DOMAINS AT THE PRODUCT-FEDERAL ENTITY LEVEL. For products of state interest, the sample size obtained for these domains is 19,320 production-product units; 10,968 for large producers and 8,352 for small and medium producers. C. SAMPLE SIZES FOR DOMAINS AT THE PRODUCT-FEDERAL ENTITY-SIZE OF PRODUCTION UNIT LEVEL BY AREA. In this case, the calculation differentiated by producer size was made, in such a way that the sample size for small and medium-sized producers was strengthened, according to the following considerations: Yo. DOMAIN OF LARGE PRODUCERS. The sample size obtained for these domains is 3,255 production-product units. ii. DOMAIN OF SMALL AND MEDIUM PRODUCERS. The sample size obtained for these domains is 7,355 production-product units. D. SAMPLE SIZES FOR LIVESTOCK PRODUCTS Yo. DOMAIN OF LARGE PRODUCERS. For the bovine product, a relative error of 14% was considered for the national design sample. ii. The sample size obtained for these domains is 10,554 units. The interest of bovines is both the number of stocks and milk production.

SAMPLE ALLOCATION. For the three large levels of interest, (National (product), Product-federative entity and Product-federative entity-size of production unit by area). The sample was assigned in each stratum by the Neyman method according to the planted area or number of heads. Except for small and medium-sized producers in the domains at the product-federative entity-size of production unit per area level.

SAMPLE SELECTION It is performed randomly and independently for each study domain. The sample selected for the design is 79,252 production-product units, equivalent to 69,124 production units in which information of interest is obtained.

CALCULATION OF EXPANSION FACTORS Three different types of expansion factors were calculated, which are: A. Production-product unit expansion factors (for each production-product unit) B. Production unit expansion factors (based on design expansion factors for production-product units) C. Producer expansion factors (for each producer, based on design expansion factors for production-product units)

ADJUSTMENT TO EXPANSION FACTORS A. Expansion

This statistic shows the top ten cheese producing U.S. states in 2023. In that year, Wisconsin was the market leader, where around 3.5 billion pounds of cheese were produced. In the U.S., natural cheese makes up the largest share of cheese sales, generating approximately 11.7 billion U.S. dollars in 2022. Cheese in the United States Cheese is mostly common in the Western cultural sphere, where it is one of the basic foodstuffs, and a major agricultural product. With some seven million metric tons, the European Union is the world’s leading producer of cheese. The United States follows, producing approximately 6.35 million metric tons. The top cheese producing U.S. states are Wisconsin and California. Wisconsin’s nickname as “America’s Dairyland” punctuates the state’s leading position within the U.S. dairy industry. More than three and a half billion pounds of cheese are produced in Wisconsin. California is the second largest producer. As expected, these states also count the highest number of milk cows among all U.S. states. Total consumption of all types of cheese in the United States stands slightly lower than the production volume. This means, that every American consumes over 40 kilograms of cheese annually. The most popular types of cheese among U.S. consumers are Italian-style varieties like, for example, Mozzarella and Parmesan.

Rates of poverty and economic inequality in rural Alabama are among the nation's highest and increasing agricultural productivity can provide a needed boost to these communities. The transition from rain-fed to irrigation-fed (RFtoIF) agriculture has significantly increased farm productivity and profitability elsewhere in the United States. Despite this potential to enhance stability and resilience in rural economies, irrigated cropland accounts for only 5% of Alabama's total cropland as numerous barriers remain to irrigation adoption. To encourage RFtoIF transition, it is imperative to identify the challenges faced by individual farmers at farm, community, and state levels. This study presents a multi-level mixed effects survival analysis to identify the physiographic, socioecological, and economic factors that influence the location and timing of irrigation adoption. We integrate spatiotemporal cropland and climatological data with field-verified locations of center-pivot irrigation systems, local physiographic characteristics, and parcel-level surface water access and average well depth. Access to surface water, costs to access groundwater, and soil characteristics were generally important influences in all regions, but regions were differentiated by the extent to which new irrigation was more responsive to social influences vs. precipitation and price trends. Our findings also highlighted the diversity of farming conditions across the state, which suggested that diverse policy tools are needed that acknowledge the varying motivations and constraints faced by Alabama's farmers.