This links to a file called report.json which contains Agricultural Meteorology Data for the past 7 days for a number of synoptic weather stations. The file is updated daily. Notes on the table All data are averaged or summed over the 7 day period A blank entry means that data were not available Normal means 30 year means from 1981 to 2010 * Temp: Average Air Temperature and difference from normal in degrees C * Rain: Total rainfall in mm and % of normal * Sun: Total sunshine duration in hours and % of normal * Soil: Average 10cm soil temperature in degrees C and difference from normal * Wind: Average wind speed in knots and difference from normal * Radiation: Total solar radiation in Joules/cm2 and % of normal

Global Big Data Analytics In Agriculture market size is expected to reach $2.42 billion by 2029 at 13.2%, segmented as by solution, data management and storage solutions, data visualization tools, predictive analytics solutions

According to Cognitive Market Research, the global agriculture analytics market size is USD 1.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 13.1% from 2024 to 2031. Market Dynamics of Agriculture Analytics Market

Key Drivers for Agriculture Analytics Market

Expanding Government Schemes and Policies-Rising government initiatives around the world are bolstering agricultural modernization initiatives with subsidies, grants, and regulations. These are driving the adoption of analytics solutions for environmental monitoring and precision agriculture in response to rising consumer and regulatory demands for sustainable farming practices. The widespread adoption of big data in farming, which allows farmers to gain useful knowledge that will lead to improved crop yields, is driving the demand for agriculture analytics.

Key Restraints for Agriculture Analytics Market

Inadequate access to reliable and fast internet can impede the ability to gather and analyze data in real-time, which is essential for accurate agricultural analytics. This could slow market growth. Industry expansion is being impeded by factors such as the high cost of data collecting and processing. Introduction of the Agriculture Analytics Market

Agriculture analytics is the practice of analyzing data, using technology, and applying statistical methods to understand and manage resources, weather, crop yields, and market trends better. Improved sustainability, less risk, and maximum productivity can be achieved through well-informed decision-making by farmers and other stakeholders. Increasing government initiatives to implement new agricultural techniques is the main development factor for the agriculture analytics industry. More and more data is being generated in the agricultural industry at an exponential rate. Another factor fueling the expansion of agriculture analytics is the rising use of Internet of Things devices that gather data from linked agricultural machinery like smart tractors and drones. Moreover, there has been a strong push to improve farm offerings, and the result is a dramatic increase in investment and technical innovation in the agricultural sector around the world. Weather data analytics, crop growth monitoring, land preparation, and other analytics tools are becoming more popular among farmland owners as a means to optimize agricultural operations. Due to these, the demand for the agriculture analytics market will grow in the coming years.

The global Agricultural Big Data Services market is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period of 2025-2033. The market growth is primarily driven by the increasing adoption of smart farming techniques, growing demand for precision agriculture, and government initiatives to promote digital agriculture. The rising need for data-driven insights to optimize crop yield and improve farm efficiency further contributes to the market expansion. Key trends shaping the Agricultural Big Data Services market include the integration of artificial intelligence and machine learning algorithms for data analysis, the adoption of cloud-based platforms for data storage and processing, and the emergence of data visualization tools for easy interpretation of complex data. However, the high cost of implementation and the lack of skilled workforce in rural areas pose challenges to market growth. Regional analysis indicates that North America and Europe hold a dominant market share, owing to the early adoption of smart farming technologies. Asia Pacific is expected to witness significant growth in the coming years due to government initiatives and increasing agricultural production in the region.

Aggregate usage of Restricted Use Pesticides as reported through the Hawaii Agricultural Good Neighbor Program.

Big Data Analytics in Agriculture Market Outlook

The global big data analytics in agriculture market is anticipated to witness substantial growth from 2024 to 2032. In 2023, the market size was valued at approximately USD 2.5 billion, and it is projected to reach around USD 8.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 14.1%. Several factors are driving this impressive growth, including the increasing adoption of precision farming techniques and the heightened need for sustainable agricultural practices to meet the rising global food demand. As the agriculture industry shifts towards more data-driven methodologies, big data analytics emerges as a critical tool for enhancing productivity and efficiency.

One of the significant growth factors propelling the big data analytics in agriculture market is the rise in global population, which has resulted in an increased demand for food. To cope with this demand, farmers and agribusinesses are turning to technology-driven solutions such as big data analytics to optimize production processes and maximize yield. Big data analytics provides insights into various agricultural practices, helping to improve crop management and resource utilization. Additionally, the pressure to adopt environmentally friendly practices is encouraging the use of analytics to minimize waste and optimize resource usage, thereby supporting sustainable agriculture.

Technological advancements in data processing and analysis are also playing a crucial role in the market's expansion. The integration of the Internet of Things (IoT) with big data analytics allows for real-time data gathering from various agricultural equipment and sensors. This capability enables the precise monitoring of farm conditions, leading to data-driven decision-making processes that optimize crop growth, pest control, and harvesting schedules. Furthermore, advancements in machine learning and artificial intelligence are enhancing the predictive capabilities of big data analytics, allowing for better anticipation of weather patterns, disease outbreaks, and market trends, which are vital for strategic planning and risk management in agriculture.

Another significant growth factor is the increased investment in agricultural technology by both government and private sectors. Governments around the world are recognizing the importance of agricultural technology in ensuring food security and are therefore investing in research and development initiatives. Additionally, venture capitalists and private firms are funding startups that specialize in agricultural analytics, further propelling market growth. The collaboration between technology companies and agricultural stakeholders is resulting in the development of innovative solutions that are tailored to the specific needs of the agricultural sector, thereby enhancing the market uptake of big data analytics.

From a regional perspective, North America holds a significant share of the big data analytics in agriculture market due to the presence of advanced agricultural practices and the early adoption of technology. Meanwhile, the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period. This growth can be attributed to the increasing population in countries like China and India, which is driving the demand for food and pushing the agricultural sector to adopt advanced technologies. Additionally, government initiatives in these regions to support technological integration in agriculture are further aiding market growth. Europe is also witnessing steady growth, with an increasing focus on sustainable farming practices and the utilization of analytics to enhance productivity.

Component Analysis

The component segment of the big data analytics in agriculture market comprises software, hardware, and services, each playing a vital role in the effective deployment and utilization of data analytics in agriculture. Software solutions in this market are particularly critical, as they provide the platforms and applications necessary for data collection, analysis, and visualization. These software applications range from farm management systems to predictive analytics tools that help farmers make informed decisions about crop planting, pest control, and resource management. With advancements in cloud computing and AI, software solutions are becoming more sophisticated, offering enhanced functionalities and user-friendly interfaces that cater to the specific needs of the agricultural sector.

Hardware components, such as sensors, drones, and IoT devices, are essential for the col

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

This statistic shows the share of agricultural retailers in the United States offering yield monitor data analysis from 2005 to 2018. According to the report, the share of retailers offering yield data monitoring analysis will increase from ** percent in 2015 to ** percent in 2018.

Agriculture Analytics Market is Segmented by Component (Solutions and Services), Application (Farm Analytics, Livestock Analytics, and More), Deployment Model (On-Premise, Cloud-Centric SaaS, and Edge/Hybrid), Farm Size (Small Farms, Medium Farms, and Large Farms), Data Source (Satellite and Aerial Imagery, UAV/Drone Imagery, and More), Analytics Technique (Descriptive, Predictive (ML), and More), and Geography.

Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Agriculture Analytics Market Outlook

The global agriculture analytics market size is poised for substantial growth, with an estimated value of USD 1.2 billion in 2023, projected to reach USD 3.8 billion by 2032, reflecting a robust CAGR of 13.5% over the forecast period. This remarkable growth can be attributed to several factors, including the increasing need for data-driven decision-making in farming practices, the rising adoption of advanced technologies in agriculture, and the growing emphasis on sustainable farming practices. As the world’s population continues to rise, there is a greater demand for food production efficiency, which is driving the adoption of agriculture analytics to optimize crop yields, reduce waste, and enhance overall farm productivity.

One of the primary growth factors for the agriculture analytics market is the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in agriculture. These technologies enable farmers to monitor field conditions in real-time, predict weather patterns, and manage resources more efficiently. The utilization of precision farming techniques, aided by analytics, allows for precise application of water, fertilizers, and pesticides, thereby increasing yields and reducing environmental impact. Moreover, these technologies facilitate better risk management by providing predictive insights that help farmers make informed decisions, which is critical in the face of climate change and unpredictable weather conditions.

Another significant driver of growth in the agriculture analytics market is the government support and initiatives aimed at promoting smart farming practices. Many countries are investing in the agricultural sector to ensure food security and sustainable farming. Governments are offering subsidies and incentives for the adoption of advanced agricultural technologies and analytics solutions. Additionally, collaborations between governments, technology providers, and research institutions are being fostered to accelerate innovation and the implementation of agriculture analytics. This support is crucial for small and medium-sized farms that may lack the resources to invest in advanced technologies independently.

Moreover, the increasing consumer awareness and demand for sustainably grown food products are pushing agribusinesses to adopt analytics solutions. Consumers are becoming more conscious of food safety, quality, and environmental sustainability, leading to a shift towards organic and locally sourced products. Thus, agribusinesses are leveraging analytics to improve traceability, monitor environmental impact, and ensure compliance with regulations related to sustainable practices. This not only helps in achieving operational efficiency but also in building trust among consumers, thereby enhancing market competitiveness.

In terms of regional outlook, North America dominates the agriculture analytics market, accounting for a significant share due to the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate, driven by the increasing adoption of smart farming technologies in countries like India and China. The large agricultural base, coupled with government initiatives to modernize agriculture, is propelling the demand for analytics solutions in this region. Additionally, Europe is also showing considerable market growth, focusing on sustainable farming practices and regulatory compliance.

Component Analysis

The agriculture analytics market is segmented by component into software and services, each playing a crucial role in the adoption and implementation of analytics solutions in agriculture. Software solutions, which include predictive analytics, data management, and visualization tools, are increasingly being integrated into farming operations. These solutions offer real-time insights into various farm activities, enabling farmers to make data-driven decisions. The software segment is witnessing rapid growth due to continuous advancements in technology and the development of user-friendly applications that cater to the specific needs of farmers and agribusinesses.

Within the software segment, predictive analytics tools are gaining prominence as they allow farmers to anticipate potential challenges and optimize their operations accordingly. These tools use historical data and machine learning algorithms to predict weather patterns, pest outbreaks, and crop yields. By providing actionable insights, predictive analytics h

Agricultural Data Logger Market Outlook

The global market size for agricultural data loggers was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 2.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.5% from 2024 to 2032. This significant growth can be attributed to the increasing need for precision farming practices and advancements in IoT technologies. Governments and private entities are investing heavily in digital agriculture to enhance productivity and ensure sustainable farming, which is a major growth factor for this market.

One of the primary growth factors driving the agricultural data logger market is the rising adoption of precision farming techniques. Precision farming relies heavily on data collection and analysis to make informed decisions regarding crop management, soil health, and resource utilization. Data loggers play a crucial role in gathering and storing this data, making them indispensable tools for modern agriculture. The increasing awareness among farmers about the benefits of data-driven farming practices is leading to higher adoption rates, thereby fueling market growth.

Another significant factor contributing to the expansion of the agricultural data logger market is technological advancements. The integration of Internet of Things (IoT) and Artificial Intelligence (AI) in agriculture has revolutionized the way data is collected, analyzed, and utilized. Modern data loggers come equipped with advanced sensors and wireless connectivity, enabling real-time monitoring and data transmission. This technological leap not only enhances the efficiency of data collection but also allows for predictive analytics, which can preemptively address potential issues in crop management and soil conditions.

Environmental sustainability and regulatory compliance are also key drivers for the agricultural data logger market. As concerns about climate change and resource conservation grow, there is an increasing emphasis on sustainable farming practices. Data loggers help farmers monitor and manage their use of water, fertilizers, and pesticides more efficiently, leading to minimal environmental impact. Furthermore, regulatory bodies around the world are mandating data-driven approaches for compliance with environmental standards, thereby pushing the demand for agricultural data loggers.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the agricultural data logger market. This growth is driven by the large agricultural base, rapid technological adoption, and significant investments in digital agriculture initiatives in countries like India and China. North America and Europe, with their advanced agricultural practices and higher adoption rates of precision farming, are also significant markets. These regions are supported by strong governmental policies promoting sustainable agriculture and technological innovation.

Product Type Analysis

The agricultural data logger market is segmented by product type into Wireless Data Loggers, USB Data Loggers, Bluetooth Data Loggers, and Others. Wireless data loggers are gaining substantial traction due to their ability to provide real-time monitoring and data transfer. These devices are particularly useful in large farming operations where constant data flow is essential for efficient management. The convenience of remote access and control further adds to their popularity, making them a preferred choice for modern farmers and agribusinesses.

USB data loggers, on the other hand, continue to hold a significant share in the market due to their reliability and cost-effectiveness. These data loggers are generally preferred for smaller-scale operations where real-time monitoring may not be as crucial. USB data loggers are easy to use and require minimal technical expertise, making them accessible to a broader range of users. Their affordability also makes them an attractive option for farmers operating on a tighter budget.

Bluetooth data loggers offer a middle ground between wireless and USB data loggers. These devices provide the flexibility of wireless data transfer without the need for an extensive network setup. Bluetooth data loggers are particularly useful for mid-sized farms and research applications where mobility and ease of access to data are crucial. Their popularity is growing as more farmers and researchers look for convenient and efficient data logging solutions.

The "Others" category includes specialized data loggers

Agricultural Data Report. Published by Met Éireann. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This links to a file called report.json which contains Agricultural Meteorology Data for the past 7 days for a number of synoptic weather stations. The file is updated daily. Notes on the table All data are averaged or summed over the 7 day period A blank entry means that data were not available Normal means 30 year means from 1981 to 2010 • Temp: Average Air Temperature and difference from normal in degrees C • Rain: Total rainfall in mm and % of normal • Sun: Total sunshine duration in hours and % of normal • Soil: Average 10cm soil temperature in degrees C and difference from normal • Wind: Average wind speed in knots and difference from normal • Radiation: Total solar radiation in Joules/cm2 and % of normal...

This metadata report documents tabular data sets consisting of items from the Census of Agriculture. These data are a subset of items from county-level data (including state totals) for the conterminous United States covering the census reporting years (every five years, with adjustments for 1978 and 1982) beginning with the 1950 Census of Agriculture and ending with the 2012 Census of Agriculture. Historical (1950-1997) data were extracted from digital files obtained through the Intra-university Consortium on Political and Social Research (ICPSR). More current (1997-2012) data were extracted from the National Agriculture Statistical Service (NASS) Census Query Tool for the census years of 1997, 2002, 2007, and 2012. Most census reports contain item values from the prior census for comparison. At times these values are updated or reweighted by the reporting agency; the Census Bureau prior to 1997 or NASS from 1997 on. Where available, the updated or reweighted data were used; otherwise, the original reported values were used. Changes in census item definitions and reporting as well as changes to county areas and names over the time span required a degree of manipulation on the data and county codes to make the data as comparable as possible over time. Not all of the census items are present for the entire 1950-2012 time span as certain items have been added since 1950 and when possible the items were derived from other items by subtracting or combining sub items. Specific changes and calculations are documented in the processing steps sections of this report. Other missing data occurs at the state and (or) county level due to census non-disclosure rules where small numbers of farms reporting an item have acres and (or) production values withheld to prevent identification of individual farms. In general, caution should be exercised when comparing current (2012) data with values reported in earlier censuses. While the 1974-2012 data are comparable, data prior to 1974 will have inflated farm counts and slightly inflated production amounts due to the differences in collection methods, primarily, the definition of a farm. Further discussion on comparability can be found the comparability section of the Supplemental Information element of this metadata report. Excluded from the tabular data are the District of Columbia, Menominee County, Wisconsin, and the independent cities of Virginia with the exception of the three county-equivalent cities of Chesapeake City, Suffolk, and Virginia Beach. Data for independent cities of Virginia prior to 1959 have been included with their surrounding or adjacent county. Please refer to the Supplemental Information element for information on terminology, the Census of Agriculture, the Inter-university Consortium for Political and Social Research (ICPSR), table and variable structure, data comparability, all farms and economic class 1-5 farms, item calculations, increase of farms from 1974 to 1978, missing data and exclusion explanations, 1978 crop irregularities, pastureland irregularities, county alignment, definitions, and references. In addition to the metadata is an excel workbook (VariableKey.xlsx) with spreadsheets containing key spreadsheets for items and variables by category and a spreadsheet noting the presence or absence of entire variable data by year. Note: this dataset was updated on 2016-02-10 to populate omitted irrigation values for Miami-Dade County, Florida in 1997.

The global farm management software and data analytics market valued at $4,525.3 million in 2023 and will grow at a CAGR of 14.49%, reaching $19,831.7 Million by 2034.

The global farm data management system market is projected to reach a value of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growing adoption of precision farming practices, increasing awareness of the benefits of data-driven decision-making, and government initiatives to promote smart agriculture are key drivers of market growth. Moreover, the integration of IoT sensors and advanced analytics tools is further fueling market expansion. The market is segmented based on application (farmland & farms, agricultural cooperatives), type (software & service, hardware), and region (North America, Europe, Asia Pacific, Middle East & Africa, South America). North America holds a dominant market share due to the early adoption of precision farming technologies and the presence of key players such as BASF, Bayer-Monsanto, and Syngenta-ChemChina. Asia Pacific is expected to witness significant growth over the forecast period, driven by increasing agricultural productivity and government support for digital agriculture initiatives. Executive Summary Globally, the farm data management system market is valued at $1.2 billion, experiencing significant growth driven by technological advancements and increasing adoption. The report provides insights into the industry's competitive landscape, product offerings, regional trends, growth drivers, and emerging innovations.

Agricultural Land: Gujarat: Type of Use: Reporting Area for Land Utilisation Statistics data was reported at 18,810.000 ha th in 2022. This stayed constant from the previous number of 18,810.000 ha th for 2021. Agricultural Land: Gujarat: Type of Use: Reporting Area for Land Utilisation Statistics data is updated yearly, averaging 18,868.100 ha th from Mar 2003 (Median) to 2022, with 20 observations. The data reached an all-time high of 19,069.000 ha th in 2017 and a record low of 18,638.000 ha th in 2003. Agricultural Land: Gujarat: Type of Use: Reporting Area for Land Utilisation Statistics data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIJ013: Agricultural Land: Type of Use: Gujarat.

This data release provides estimates of annual agricultural use of pesticide compounds in counties of the conterminous United States, for years 2013-17, compiled by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). For all States except California, U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District-level pesticide-use data to estimate county-level pesticide use. Where Crop Reporting District data were not available or were incomplete, estimated pesticide-use values were calculated with two different methods, resulting in a low and a high estimate based on different assumptions about missing survey data (Thelin and Stone, 2013). Pesticide-use data for California for 2013-17 were obtained from the online Department of Pesticide Regulation Pesticide Use Reporting (DPR–PUR) database (California Department of Pesticide Regulation, variously dated). The California county data were appended after the estimation process was completed for the rest of the Nation. Estimates of annual agricultural pesticide use are provided as downloadable, tab-delimited files, organized by compound, year, state Federal Information Processing Standard (FIPS) code, county Federal Information Processing Standard code, and amount in kilograms (kg). This data release contains estimates of annual agricultural pesticide use for 2013-17 along with associated metadata. This data release is a continuation of the 1992-2009 pesticide-use estimates reported by Stone (2013) and the 2008-2012 pesticide-use estimates reported by Baker and Stone (2015). Tables of annual agricultural pesticide use estimates for 1992-2012 are available for download on the Pesticide National Synthesis Project (PNSP) webpage: https://doi.org/doi:10.5066/F7NP22KM. Beginning 2015, the provider of the surveyed pesticide data used to derive the county-level use estimates discontinued making estimates for seed treatment application of pesticides because of complexity and uncertainty. Pesticide-use estimates prior to 2015 include estimates with seed treatment application. Data for some compounds were missing from the version 1 estimates. Pesticide use estimates for compounds with missing data have been updated in version 2.0. First posted November 26, 2019 Revised May 27, 2020, ver. 2.0 References cited: Baker, N.T., and Stone, W.W., 2015, Estimated annual agricultural pesticide use for counties of the conterminous United States, 2008–12: U.S. Geological Survey Data Series 907, 9 p., accessed July 12, 2015, at https://doi.org/10.3133/ds907. California Department of Pesticide Regulation, variously dated, Pesticide use reporting (PUR): California Department of Pesticide Regulation Pesticide Use Reporting (PUR), Pesticide Use Report Data (PUR archives pur2013.zip, pur2014.zip, pur2015.zip, pur2016.zip, pur2017.zip), accessed June 26, 2019, at http://www.cdpr.ca.gov/docs/pur/purmain.htm. Stone, W.W., 2013, Estimated annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Data Series 752, 1-p. pamphlet, 14 tables, accessed July 12, 2015, at http://pubs.usgs.gov/ds/752/. Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013–5009, 54 p., accessed July 12, 2015, at http://pubs.usgs.gov/sir/2013/5009/.

Chesapeake Bay restoration solutions, Best Management Practices (BMPs), progress reporting by tributary basin. All units in acres unless otherwise noted.