The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management. This dataset encompasses a wide range of information, including farm location, land area, cow population, farm size, production dates, product details, brand information, quantities, pricing, shelf life, storage conditions, expiration dates, sales information, customer locations, sales channels, stock quantities, stock thresholds, and reorder quantities.

Features:

1. Location: The geographical location of the dairy farm.
2. Total Land Area (acres): The total land area occupied by the dairy farm.
3. Number of Cows: The number of cows present in the dairy farm.
4. Farm Size: The size of the dairy farm(in sq.km).
5. Date: The date of data recording.
6. Product ID: The unique identifier for each dairy product.
7. Product Name: The name of the dairy product.
8. Brand: The brand associated with the dairy product.
9. Quantity (liters/kg): The quantity of the dairy product available.
10. Price per Unit: The price per unit of the dairy product.
11. Total Value: The total value of the available quantity of the dairy product.
12. Shelf Life (days): The shelf life of the dairy product in days.
13. Storage Condition: The recommended storage condition for the dairy product.
14. Production Date: The date of production for the dairy product.
15. Expiration Date: The date of expiration for the dairy product.
16. Quantity Sold (liters/kg): The quantity of the dairy product sold.
17. Price per Unit (sold): The price per unit at which the dairy product was sold.
18. Approx. Total Revenue (INR): The approximate total revenue generated from the sale of the dairy product.
19. Customer Location: The location of the customer who purchased the dairy product.
20. Sales Channel: The channel through which the dairy product was sold (Retail, Wholesale, Online).
21. Quantity in Stock (liters/kg): The quantity of the dairy product remaining in stock.
22. Minimum Stock Threshold (liters/kg): The minimum stock threshold for the dairy product.
23. Reorder Quantity (liters/kg): The recommended quantity to reorder for the dairy product.

Potential Use-Case:

This dataset can be used by researchers, analysts, and businesses in the dairy industry for various purposes, such as:

1. Analyzing the performance of dairy farms based on location, land area, and cow population.
2. Understanding the sales and distribution patterns of different dairy products across various brands and regions.
3. Studying the impact of storage conditions and shelf life on the quality and availability of dairy products.
4. Analyzing customer preferences and buying behavior based on location and sales channels.
5. Optimizing inventory management by tracking stock quantities, minimum thresholds, and reorder quantities.
6. Conducting market research and trend analysis in the dairy industry.
7. Developing predictive models for demand forecasting and pricing strategies.

Note: This dataset includes data from the period between 2019 and 2022, and it specifically focuses on selected dairy brands operating in specific states and union territories of India. There is an intentional drift highlighted in the dataset's figures due to its opensource and creative license, currently !

These data are from several USDA agencies. They were previously included in the Meat Statistics page in the Livestock, Dairy, and Poultry Outlook tables and may contain revisions not included in previous releases of the LDP tables.

To assess the magnitude of greenhouse gas (GHG) fluxes, nutrient runoff and leaching from dairy barnyards and to characterize factors controlling these fluxes, nine barnyards were built at the U.S. Dairy Forage Research Center Farm in Prairie du Sac, WI (latitude 43.33N, longitude 89.71W). The barnyards were designed to simulate outdoor cattle-holding areas on commercial dairy farms in Wisconsin. Each barnyard was approximately 7m x 7m; areas of barnyards 1-9 were 51.91, 47.29, 50.97, 46.32, 45.64, 46.30, 48.93, 48.78, 46.73 square meters, respectively. Factors investigated included three different surface materials (bark, sand, soil) and timing of cattle corralling. Each barnyard included a gravity drainage system that allowed leachate to be pumped out and analyzed. Each soil-covered barnyard also included a system to intercept runoff at the perimeter and drain to a pumping port, similar to the leachate systems. From October 2010 to October 2015, dairy heifers were placed onto experimental barnyards for approximately 7-day periods four times per year, generally in mid-spring, late-spring / early summer, mid-to-late summer and early-to-mid autumn. Heifers were fed once per day from total mixed rations consisting mostly of corn (maize) and alfalfa silages. Feed offered and feed refused were both weighed and analyzed for total nitrogen (N), carbon (C), phosphorus (P) and cell wall components (neutral detergent fiber, NDF). Leachate was pumped out of plots frequently enough to prevent saturation of surface materials and potential anaerobic conditions. Leachate was also pumped out the day before any gas flux measurements. Leachate total volume and nitrogen species were measured, and from “soil” barnyards the runoff was also measured. The starting bulk density, pH, total carbon (C) and total N of barnyard surface materials were analyzed. Decomposed bark in barnyards was replaced with new bark in 2013, before the spring flux measurements. Please note: the data presented here includes observations made in 2015; the original paper included observations through 2014 only. Gas fluxes (carbon dioxide, CO2; methane, CH4; ammonia, NH3; and nitrous oxide, N2O) were measured during the two days before heifers were corralled in barnyards, and during the two days after heifers were moved off the barnyards. During the first day of each two-day measurement period, gas fluxes were measured at two randomly selected locations within each barnyard. Each location was sampled once in the morning and once in the afternoon. During the second day, this procedure was repeated with two new randomly selected locations in each barnyard. This experiment was partially funded by a project called “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP). The Dairy CAP is funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP is to improve understanding of the magnitudes and controlling factors over GHG emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP is improving life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities. Resources in this dataset:Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: BYD_Data_Dictionary.xlsxResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: DairyCAP_Barnyards. File Name: BYD_Project_Data.xlsxResource Description: This is the complete data from the paper: Powell, J. M. & Vadas, P. A. (2016). Gas emissions from dairy barnyards. Animal Production Science, 56, 355-361. Data are separated into separate spreadsheet tabs.Resource Software Recommended: Microsoft Excel 2016,url: https://products.office.com/en-us/excel Resource Title: Data_dictionary_DairyCAP_Barnyards. File Name: Data_Dictionary_BYD.csvResource Description: This is the data dictionary for the data from the paper "Gas emissions from dairy barnyards" by Mark Powell and Peter Vadas. Resource Title: GHG Data. File Name: BYD_GHG.csvResource Description: Greenhouse gas flux dataResource Title: Intake Data. File Name: BYD_Intake.csvResource Title: Leachate Data. File Name: BYD_Leachate.csvResource Title: Runoff Data. File Name: BYD_Runoff.csvResource Title: Surface Data. File Name: BYD_Surface.csvResource Title: TMR Data. File Name: BYD_TMR.csvResource Description: Total mixed ration data

The MilKey project aims at assessing the environmental, economic, and social sustainability of European dairy production systems, and at identifying ‘win-win’ farming practices for sustainable and greenhouse gas (GHG) optimised milk production. These data collection template were prepared to guide stakeholders wishing to conduct a sustainability assessment of dairy production systems. This template covers all the necessary data to assess the environmental, economic, and social sustainability dimensions of commercial dairy farms. Data requirements gathered in this template were deduced from the list of sustainability indicators presented in the DEXi-Dairy indicator handbook. The template is composed of 5 parts, i.e., Parts I-II-III for the environmental assessment, Part IV for the economic assessment, and Part V for the social assessment. The number of files to fill out depends on the number and nature of additional farming enterprises present on case study dairy farms. 1) Part I concerns the general information that must be collected on all commercial farms. 2) Part II focuses specifically on the dairy enterprise and must thus be completed for all commercial farms. 3) Part III records information about a potential beef enterprise and must thus be filled out for commercial farms that have an additional beef enterprise. 4) Part IV gathers all the economic data and must be filled out for all commercial case study farms. 5) Part V gathers all the social data and must be filled out for all commercial case study farms. Please refer to the guide for the collection of farm environmental and economic data for the detailed description of all variables included in Parts I-II-III-IV.

Representative dairy farms were modeled using the Integrated Farm System Model with 20 farms in each of 6 regions of the United States for the years of 1971 and 2020 to determine improvements made in reducing environmental impacts over the 50-year period. Important data and information describing these farms are documented in these tables. These data include the farm location, number of cows and heifers maintained, milk produced, feeds and nutrient contents fed, crop areas, crop yields, fertilizer and lime application rates, irrigation water applied, milking and housing facilities, manure collection, storage and application methods used, and soil characteristics. These data are published as supplementary information for the article “Fifty years of environmental progress for United States dairy farms” published in the Journal of Dairy Science.

United States Long Term Projections: Dairy: Milk Production & Marketings: Milk Per Cow data was reported at 26,630.000 lb in 2034. This records an increase from the previous number of 26,380.000 lb for 2033. United States Long Term Projections: Dairy: Milk Production & Marketings: Milk Per Cow data is updated yearly, averaging 25,170.000 lb from Dec 2022 (Median) to 2034, with 13 observations. The data reached an all-time high of 26,630.000 lb in 2034 and a record low of 24,087.000 lb in 2022. United States Long Term Projections: Dairy: Milk Production & Marketings: Milk Per Cow data remains active status in CEIC and is reported by U.S. Department of Agriculture. The data is categorized under Global Database’s United States – Table US.RI039: Agricultural Projections: Dairy.

In the Netherlands about 96 percent of all raw cow's milk from dairy farmers is delivered to dairy factories. The remaining 4 percent is kept by the dairy farmers themselves for their own use (to feed young cattle and/or manufacture dairy products). This table contains data about the volume of cow's milk delivered by dairy farmers and the products manufactured by the dairy industry in the Netherlands. The table contains monthly figures as well as yearly figures. The figures on raw cow's milk concern volume, protein content and fat content. Dairy products include butter, cheese, milk powder, concentrated milk and whey in powder or block form.

The data in this table are provided by the Netherlands Enterprise Agency (RVO) gathers data for two series on dairy products, namely monthly statistics and yearly statistics. As the monthly figures are based on about 98 percent of all cow's milk delivered to dairy factories, a provisional adjustment is done by the Agency. This adjustment only concerns collected milk and these monthly figures are revised when the yearly figures become available.

The table does not contain figures about fresh products such as drinking milk for consumption or acidified milk products such as yoghurts. Figures about these products are only available for the years 1995-1997.

Data available from: January 1995

Status of the figures: The monthly figures are provisional at first publication. Definite month and year figures are published in August/December of the following year.

Changes as of 18 November 2025: The provisional figures of September 2025 have been added. The provisional figures of June 2025, July 2025 and August 2025 have been updated.

When will new figures be published? Approximately six weeks after the month under review.

The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.

A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.

Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).

For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).

Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).

The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield[1] for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).

All variable processing from FADN data is shown in the Annex, as performed in Stata software.

[1] The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.

Abstract

The Smallholder Dairy Commercialization Programme (SDCP) was designed to address constraints in the smallholders’ milk sector in Kenya by increasing smallholders’ production, productivity and participation in milk markets. It pursued these objectives by training dairy groups, offering technical support for household dairy production and developing milk-marketing chains.

SDCP provided training to dairy farmers to build their enterprise, managerial and organisational skills. Aside from training, the programme also aimed to enhance dairy farming productivity and reduce production costs through demonstration, field days and grants. To strengthen market linkages, SDCP invested in improving road infrastructure and conducted additional training on milk-handling practices and value-added opportunities.

The programme identified three main areas where barriers to improving dairy income potentially operate: dairy group activities, household production and market intermediaries. Programme designers hypothesised that increasing net dairy income for smallholder farmers can occur through four primary contextual factors (1) increasing milk production; (2) increasing milk prices; (3) decreasing the costs of producing milk; and (4) decreasing the transaction costs of participation in input and output markets. They assumed that increased net income will lead to improved food security and increased participation by women and marginalised communities.

For more information, please click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-participatory-small-scale-irrigation-development-programme .

Geographic coverage

Districts/counties in the western region of Kenya.

Analysis unit

Households

Universe

Smallholder dairy farmers

Kind of data

Sample survey data [ssd]

Sampling procedure

The estimation of the project's impact was based on a comprehensive quantitative and qualitative survey. Eight study divisions were identified as valid controls, 95 treatment and 89 control dairy groups were chosen, and 1,297 beneficiary and 1,265 comparison dairy farmers were interviewed.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed to collect detailed data about milk production, cost, and sales to generate information on net milk income and milk sales, which are two primary outcomes of interest to assess project impact. The project also aimed to reduce seasonality of milk production, so that net incomes would be higher and less variable throughout the year. Thus, the questionnaire also collected data on practices, such as second-season fodder grass production, associated with less pronounced seasonality in milk production.While a full-scale consumption module was not included, a module to capture dietary diversity was. The questionnaire also included sections to recover information on the most important control variables at the household level, in order to improve precision of estimating project impact. These included basic household demographics and wealth variables; landholdings; and access to extension and other sources of information, density of social networks, etc.

Importantly, a dairy group questionnaire was also designed. The functioning of dairy groups (i.e., structure, conduct, and performance) is likely to have a strong impact on the ability of households to benefit from project activities, many of which were carried out through the dairy group leadership. Indicators of dairy group performance can serve as controls and can also provide valuable additional insights to feed into future project designs. The dairy group questionnaire also included a module on the history of presence of other development projects in addition to SDCP, which could prove to be useful control information, as well as basic information on community characteristics.

Note: some variables may have missing labels. Please, refer to the questionnaire for more details.

These are datasets given on excel spreadsheet. The data was used in Statistical Analysis Software (SAS 2011) analysis for a Masters dissertation titled 'Comparative performance of KiwiCross™, Holstein and Jersey dairy cattle on pasture herds in KwaZulu-Natal'. Data was extracted from the South African national milk recording database Integrated Registration and Genetic Information System (INTERGIS) and from farm records that were collected during the biological impact assesment trial for the introduction of the KiwiCross™ sire breed into South Africa. INTERGIS data consists of results from individual milk recording samples taken during the animal's first lactation in accordance with International Committee for Animal Recording (ICAR) rules to ensure representable lactation information. Farm data includes fertility data i.e. number of conceptions per pregnancy.

This EnviroAtlas dataset summarizes by county the number of farm operations with dairy cows and the number of heads they manage. The data come from the Census of Agriculture, which is administered every five years by the US Department of Agriculture (USDA), and include the years 2002, 2007, 2012, and 2017. The Census classifies cattle managed on operations as beef cows, dairy cows, or other cattle (which encompasses heifers, steers, bulls, and calves). Only data regarding dairy cows are displayed in this layer. Operations are categorized into small, medium, or large, based on how many heads they manage. For each county and Census year, the dataset reports the number of farm operations that manage dairy cows, the number of heads on their property at the end of the Census year, and a breakdown of the operations into small, medium, and large. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This carbon footprint study for fluid milk was commissioned in order to identify where the industry can innovate to reduce greenhouse gas (GHG) emissions across the supply chain. To proactively meet the needs of the marketplace, the U.S. dairy industry is working together to further improve environmental performance in a way that makes good business sense for the entire supply chain. In January 2009, the Innovation Center for U.S. Dairy -- which represents approximately 80% of the dairy industry -- endorsed a voluntary goal to reduce GHG emissions of fluid milk by 25% by 2020. Based on a preliminary assessment of GHG emissions, a portfolio of ten mitigation projects across the supply chain were launched in 2009. At the same time, the industry commissioned a greenhouse gas life cycle assessment, or carbon footprint study, for fluid milk in order to identify where the industry can innovate to reduce GHG emissions across the supply chain to achieve the greatest gains. The Innovation Center for U.S. Dairy selected the Applied Sustainability Center at the University of Arkansas to conduct the first U.S. national-level fluid milk carbon footprint study, and Michigan Technological University was chosen to assist. The study provides a benchmark to measure the industry’s progress toward achieving its voluntary reduction goal. The data will serve as the foundation for the creation of best practices and decision-support tools for producers, processors and others throughout the dairy supply chain. The data are being released through the USDA -National Agricultural Library's Life Cycle Assessment (LCA) Digital Commons to provide transparency in the project and allow LCA practitioners working in the dairy industry access to the data to use and build upon. This study was limited to GHG emissions in order to estimate a carbon footprint for U.S. dairy operations (fluid milk). The study follows International Organization for Standardization (ISO) protocols to provide credibility, transparency and objectivity of the methods, data, and results. Part of the ISO compliance is an external review by a panel of LCA and agricultural experts. Their full review is included as an appendix to the main report, which is included in the link below. Fully ISO-compliant life cycle assessments are required to include additional environmental impact areas such as water quality, air quality, and/or human health, for example; interpretation of the results presented in this document, and more importantly, actions taken in response to the reported results should be used with caution because GHG emissions represent only a single dimension of the environmental impacts of fluid milk production. The Innovation Center is commissioning further studies to expand this work to include other environmental impact categories. Similarly, the unit processes in the database released here were developed specifically to measure the GHG emissions of fluid milk produced in the United States. Practitioners should use caution if using the upstream processes, developed here, outside the context of U.S. fluid milk production. The upstream processes developed in this project were developed for a specific purpose and were developed using industry specific information. The data may not be applicable outside of the context of this project. The National Agricultural Library and the University of Arkansas are currently collaborating to release the new product flows that stand alone developed through this project individually in the LCA Digital Commons. The complete project data are available at the links below. Resources in this dataset:Resource Title: Dairy Innovation - OLCA. File Name: Dairy_Innovation_OLCA_1_3.zipResource Description: Data files that contain processes, systems, and style sheets.

Dairy Data Analytics Market Outlook

According to our latest research, the global Dairy Data Analytics market size in 2024 stood at USD 1.62 billion, reflecting the sectorÂ’s rapid digital transformation. The market is expected to expand at a Compound Annual Growth Rate (CAGR) of 18.4% from 2025 to 2033, reaching a projected value of USD 7.74 billion by 2033. This robust growth is primarily driven by the increasing adoption of advanced data-driven technologies across the dairy industry, the rising need for operational efficiency, and the growing demand for high-quality dairy products in both developed and emerging markets.

One of the key growth factors propelling the Dairy Data Analytics market is the escalating demand for real-time insights into milk production and supply chain operations. Dairy producers are increasingly leveraging analytics to monitor animal health, optimize feed and resource allocation, and enhance milk yield. The integration of Internet of Things (IoT) devices, sensors, and cloud-based platforms has made it possible for dairy farms to collect vast amounts of data, which can be analyzed to make informed decisions that directly impact productivity and profitability. As the dairy industry faces mounting pressures from fluctuating milk prices and changing consumer preferences, the ability to harness actionable insights from data analytics has become a crucial differentiator.

Another significant driver is the emphasis on quality control and traceability within the dairy supply chain. Regulatory bodies and consumers alike are demanding greater transparency regarding the origin, handling, and quality of dairy products. Dairy data analytics solutions enable stakeholders to track and trace products from farm to shelf, ensuring compliance with stringent food safety standards. This capability not only mitigates risks associated with recalls and contamination but also builds trust with consumers, thereby enhancing brand reputation and market competitiveness. Additionally, quality control analytics help identify inefficiencies and potential bottlenecks in processing and distribution, further improving operational outcomes.

The surge in digital transformation initiatives across the dairy sector is fostering innovation in farm management and financial analytics. Modern dairy farms are moving away from traditional practices and adopting sophisticated software and hardware solutions to automate processes such as feeding, milking, and health monitoring. These advancements are complemented by financial analytics tools that provide detailed insights into cost structures, revenue streams, and profitability metrics. As a result, dairy enterprises can make data-driven investments, optimize resource allocation, and enhance long-term sustainability. The growing availability of cloud-based solutions is also democratizing access to advanced analytics, enabling even small and medium-sized farms to benefit from cutting-edge technologies without significant upfront investments.

The introduction of Dairy Milk Recording Software is revolutionizing the way dairy farms manage and analyze milk production data. This software provides dairy producers with real-time access to detailed records of milk yield, quality, and composition, enabling them to make informed decisions that enhance productivity and profitability. By integrating seamlessly with existing farm management systems, Dairy Milk Recording Software allows for the efficient tracking of individual cow performance, helping farmers identify top producers and optimize breeding programs. Additionally, the software's ability to generate comprehensive reports and analytics supports strategic planning and resource allocation, ensuring that dairy operations remain competitive in a rapidly evolving market.

From a regional perspective, North America and Europe currently dominate the Dairy Data Analytics market, owing to their well-established dairy industries and early adoption of digital technologies. However, the Asia Pacific region is witnessing the fastest growth, driven by the rapid modernization of dairy farms, increasing government support for technology adoption, and rising consumer awareness regarding food safety. Countries such as India, China, and Australia are investing heavily in smart agriculture initiatives, which is expected to significan

Nathan A. Slaton, Rajveer Singh, Uzair Ahmad, Cheri Villines, Russell Delong, and Otis Robinson[Note: Updated for 2025 release]. The database contains select properties of 16,728 dairy, poultry, and swine manure samples submitted between 1 January 2005 and 31 December 2024 to the University of Arkansas Division of Agriculture Fayetteville Agricultural Diagnostic Laboratory (FADL). Most samples were submitted by clients with active animal production farms to determine manure properties for nutrient management planning. Most samples are from farms within Arkansas (4,862) followed by Tennessee (386), and Oklahoma (206). Many of the samples from 2005–2022 do not include a county and state of origin, but Arkansas is the primary state of origin for these samples in the database. Metadata describing the production system, manure collection and storage, age, and bedding was provided by clients and assumed to be reasonably accurate. Animal type, Bedding type, and Manure type metadata not provided by the client were listed as “Unknown”. Metadata for Sample age (days), State, County, and some analytes were sometimes missing and left as blank cells.We could not find a single literature source that describes all production systems and manure/litter types, but the information in Malone (1992), Key et al. (2011), and USDA-NRCS (2012), describe animal production systems, manure forms, and the factors that influence litter/manure production in animal production systems in the USA that may help understand the types of litter/manure forms included in this database.Poultry litter (Dry) SamplesThe database includes information for >14,000 poultry samples submitted from 1 January 2005 through 31 December 2024. Samples in the database represented Broiler, Hen, Pullet, Turkey, Cornish, Rooster, and Unknown (no animal-specific production system noted). An example manure submission form is shown in Figure 1. Manure types include Cake, Cleanout, Compost, Dead bird compost, Deep stack, Dry stack, Fresh litter, In-house, Lagoon liquid, Lagoon sludge, Loose, Pellets, Sludge, and Unknown. Bedding materials include Rice (Oryza sativa L.) hulls, Sawdust, Wood shavings, mixtures of Rice hulls and Sawdust, Rice hulls and Wood shavings, Wood shavings and Sawdust, Straw and Wood shavings, and Unknown.Arkansas clients usually deliver samples directly to the FADL or a local county Extension office where a sample submission form (Figure 1) is completed, and the sample is shipped to the laboratory. Samples from Oklahoma are often delivered directly to the FADL. When a sample arrives at the lab, the date received and the lab identification number are added to the sample’s submission form, which is filed for record-keeping. The lab identification numbers contain 5-6 digits, are numbered sequentially in the order received at the lab, and represent information including (from left to right): Letter M (Manure; note some samples include M and others do not because “M” was omitted when entered into the database); first or second number (1-10 or 20) stands for the year; and the last 4 numbers in the lab number are the order the sample was logged in at the FADL. The dataset also includes columns for the year and date received.Using a scoop or spatula, the bulk manure sample (as received) is split into two representative subsamples (~100 mL or cm3 each) and placed into plastic bags. The subsamples are refrigerated at 4°C until further analysis. One of the subsamples is homogenized and ground using a coffee bean grinder for pH, electrical conductivity, and total nutrient analysis. The second subsample remains unaltered (as-received) and is used for moisture determination and water-extractable phosphorus (WEP) analysis. A homogenized, ground subsample was initially used for WEP, but starting in 2009, the unaltered, “as-received” sample has been used for WEP analysis. The change was made because of speculation that homogenizing the subsample increased the WEP, and the research performed to develop the Arkansas P index used unaltered, “as-received” litter. Any remaining bulk sample is stored at room temperature until analysis is complete and the results are reported to the client. The FADL has participated in the Minnesota Manure Proficiency Program (https://www.mda.state.mn.us/pesticide-fertilizer/certified-testing-laboratories-manure-soil) as part of the quality assurance and control program since 2005.The database includes two columns for WEP data (i.e., Arkansas WEP and Universal WEP). Water-extractable P was originally performed using the 10:1 water/litter (v:w) ratio, identified as the Arkansas method (Wolf et al., 2009). The Universal WEP method (Spargo, 2022; Wolf et al., 2009) is now used to determine water-extractable nutrients in manure samples. The Arkansas WEP method was used on poultry litter samples through 2009 since this was required for samples submitted from the Eucha-Spavinaw watershed (Sharpley et al., 2009; 2010). Beginning in 2010, the laboratory switched WEP analyses to the Universal WEP method. The Universal water-extraction method (100:1) is the only method used for the determination of water-extractable potassium (WEK).The counties and states of sample origin were not recorded in the original poultry litter dataset but were added for samples submitted beginning 1 January 2023. The county and state details were added to random samples that were checked for accuracy of analytical information. Please note that even when the county of litter origin is provided, it may not be accurate since the county of Extension office that received the sample may not be consistent with the county of production. Information included in the column identified as “Clients” has two levels: “ESWMT” (Eucha-Spavinaw Watershed Management Team) and “Other”. Samples with the client identified as ESWMT were submitted from poultry farms located within the Eucha-Spavinaw watershed (DeLaune et al., 2006; Sharpley et al., 2009). The ESWMT label identified these samples for the analysis requirements set by the watershed regulations, requiring all poultry litter samples be analyzed for WEP (OCCWQD, 2007).Dairy and Swine Liquid Manure SamplesThe database includes dairy and swine manure properties and metadata for 678 dairy and 1934 swine samples submitted from 1 January 2007 through 31 December 2024. The dairy and swine data include samples of dry and liquid manure forms. Most samples include geographic origin metadata at the state and county levels. Metadata for dairy and swine sample manure types include Cleanout, Compost, Dry stack, Fresh from floor, Lagoon sludge, Lagoon liquid, Milk wash water, Pit, Holding Pond, Settling basin liquid, Settling basin sludge, Sludge, Tank, Wash water, and Unknown. Sample age metadata should be used with caution since some values are very low (e.g., 1-7 days) and may misrepresent the requested information.Clients are provided with 500 ml (16.9 oz; 73×164 mm D×H: 53 mm cap) leakproof bottles and shipping boxes (Figure 2). Upon delivery, samples are refrigerated until the analyses are completed. The analyses performed were based on client requests and include the percent solids for liquid samples or percent moisture for dry samples.References1. DeLaune, P.B., Haggard, B.E., Daniel, T.C., Chaubey, I., & Cochran, M.J. (2006). The Eucha/Spavinaw phosphorus index: A court mandated index for litter management. J. Soil Water Cons., 61(2), 96–105.2. Key, N., McBride, W.D., Ribaudo, M., & Sneeringer, S. (2011). Trends and developments in hog manure management: 1998-2009. EIB-81. USDA, Econ. Res. Serv., Washington, DC.3. Malone, G.W. (1992). Nutrient enrichment in integrated broiler production systems. Poult. Sci., 71(7), 1117–1122.4. Oklahoma Conservation Commission Water Quality Division (OCCWQD). (2007). Watershed based plan for the lake Eucha/lake Spavinaw watershed. Oklahoma Conservation Commission. https://conservation.ok.gov/wp-content/uploads/2021/07/Eucha_Spavinaw-Watershed-Based-Plan-2009.pdf5. Sharpley, A., Herron, S., West, C., & Daniel, T. (2009). Outcomes of phosphorus-based nutrient management in the Eucha-Spavinaw watershed. In A.J. Franzluebbers (Ed), Farming with grass: Achieving sustainable mixed agricultural landscapes (pp. 192–204). Soil and Water Conservation Society, Ankeny, IA.6. Sharpley, A., Moore, P., VanDavender, K., Daniels, M., Delp, W., Haggard, B., Daniel, T., & Baber, A. (2010). Arkansas phosphorus index. FSA-9531. University of Arkansas Coop. Ext. Serv. https://www.uaex.uada.edu/publications/PDF/FSA-9531.pdf7. Spargo, J.T. (2022). M-6.1 Water extractable phosphorus, 100:1 solution to solids ratio. In M.L. Wilson & S. Cortus (Eds.), Recommended Methods of Manure Analysis (2nd ed., pp. 83–86). University of Minnesota Libraries Publishing, Minneapolis, MN.8. United States Department of Agriculture, Natural Resources Conservation Service (USDA-NRCS). (2012). Chapter 4: Agricultural waste characteristics. In Part 651: Agricultural Waste Management Field Handbook. USDA, Soil Cons. Serv., Washington, DC.9. Wolf, A.M., Moore, P.A., Jr., Kleinman, P.J.A., & Sullivan, D.M. (2009). Water-extractable phosphorus in animal manure and biosolids. In J.L. Kovar & G.M. Pierzynski (Eds.), Methods of Phosphorus Analysis for Soils, Sediments, Residuals, and Waters

The statistical data generated through the administration of the Federal milk order program is recognized widely as one of the benefits of this program. These data provide comprehensive and accurate information on milk supplies, utilization, and sales, as well as class prices established under the orders and prices paid to dairy farmers (producers). The sources of this data are monthly reports of receipts and utilization, producer payroll reports, and reports of nonpool handlers filed by milk processors (handlers) subject to the provisions of the various milk orders. The local market administrator (MA) uses these reports to determine pool obligations under the order and to verify proper payments to producers. Auditors employed by the MA review handler records to assure the accuracy of reported information. Reporting errors are corrected; if necessary, pool obligations are revised. After the pool obligations have been determined the local market administrator summarizes the individual handler reports and submits a series of order summary reports to the Market Information Branch (MIB) in Dairy Programs. The MIB summarizes the individual order data and disseminates this information via monthly, bimonthly, and annual releases or publications. Since milk marketing order statistics are based on reports filed by the population of possible reporting firms and not a sample, these statistics are comprehensive. Also, since these individual firm reports are subject to audit and verification, these statistics are accurate. The Federal milk order statistics database contains historical information, beginning in January 2000, generated by the administration of the Federal milk order program. Most of the information in the database has been published previously by the Market Information Branch in Dairy Programs either on its web site or in the Dairy Market News Report. New users are encouraged to use the "User Guide" to learn how to navigate the search screens. If you are interested in a description of the Federal milk order statistics program, or want current data, in ready made table form, use the "Current Information" link.

This study was conducted to evaluate the productive performances of crossbred dairy cattle at Holetta agricultural research center’s dairy farm. A total of 6685 performance records were used and analyzed to determine the effect of period of calving, season of calving, parity and genetic group. Parameters used as indicator of productive performances were lactation milk yield (LMY), daily milk yield (DMY) and lactation length (LL). The GLM procedure of SAS 2004 was used for analysis. The overall least square means and standard errors for Lactation milk yield (LMY), daily milk yield (DMY) and lactation length (LL) were 2204.05 ± 21.12 kg, 6.88 ± 0.05 kg and 326.69 ± 2.03 days, respectively. Result of fixed effect analysis indicated that calving period, genetic group and parity were significantly (p<0.001) influenced all productive traits. LMY, DMY, and LL were sensitive to seasonal variation. Comparisons among the crosses revealed a clear-cut difference among the genetic groups. Milk production in the first generation crosses increased more compared to second generations. There were marked decline in performance among 50% F1 (Borena dam x Holstein Friesian sire), F2 (F1 dam x F1 sire) and F3 (F2 dam x F2 sire) from 2203kg of milk to 1697 and 1522 kg, respectively. The 75% first generation was superior LMY compared with other genetic groups and produced about 34.2 %, 74.3%, 94.3% and 45.9% more milk than 50% F1, F2, F3 and 75% second generations, respectively. The higher milk yield of 75% first generation and 50% F1 crosses from other genetic groups could be associated with higher heterosis effect in F1, higher milk gene in 75% and longer lactation length. Based on the productive performances evaluation, the results of LMY, DMY, and LL for high grade (75% first generation) in the present study revealed that performances were continued to increase with increasing proportion of exotic gene.

The Dairy Market report segments the industry into Category (Butter, Cheese, Cream, Dairy Desserts, Milk, Sour Milk Drinks, Yogurt), Distribution Channel (Off-Trade, On-Trade), and Region (Africa, Asia-Pacific, Europe, Middle East, North America, South America). Includes five years of historical data and forecasts for the next five years.

Milk Quality Prediction (Classification)

About dataset This dataset is manually collected from observations. It helps us to build machine learning models to predict the quality of milk. This dataset consists of 7 independent variables ie pH, Temperature, Taste, Odor, Fat, Turbidity, and Color. Generally, the Grade or Quality of the milk depends on these parameters. These parameters play a vital role in the predictive analysis of the milk.

Usage The target variable is nothing but the Grade of the milk. It can be

Target

Low (Bad)

Medium (Moderate)

High (Good)

If Taste, Odor, Fat, and Turbidity are satisfied with optimal conditions then they will assign 1 otherwise 0. Temperature and ph are given their actual values in the dataset.

We have to perform data preprocessing, and data augmentation techniques to build statistical and predictive models to predict the quality of the milk.

Inspiration To leverage the benefits of machine learning in the dairy industry.

Lithuania Exports of dairy products, eggs, honey, edible products to Latvia was US$90.79 Million during 2024, according to the United Nations COMTRADE database on international trade. Lithuania Exports of dairy products, eggs, honey, edible products to Latvia - data, historical chart and statistics - was last updated on December of 2025.