Live Cattle rose to 232.95 USd/Lbs on July 30, 2025, up 1.40% from the previous day. Over the past month, Live Cattle's price has risen 10.53%, and is up 24.42% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Live Cattle - values, historical data, forecasts and news - updated on July of 2025.

China Livestock: Number: Cow data was reported at 105,085.102 Unit th in 2023. This records an increase from the previous number of 102,158.520 Unit th for 2022. China Livestock: Number: Cow data is updated yearly, averaging 103,974.569 Unit th from Dec 1989 (Median) to 2023, with 35 observations. The data reached an all-time high of 132,060.000 Unit th in 1995 and a record low of 88,344.899 Unit th in 2016. China Livestock: Number: Cow data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Number of Livestock: Large Animals: Cow .

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

A spatially disaggregated global livestock dataset containing information on biomass use, production, feed efficiency, excretion, and greenhouse gas emissions for 28 world regions, 8 livestock production systems, 4 animal species (cattle, small ruminants, pigs, and poultry), and 3 livestock products (milk, meat, and eggs) for the year 2000. The dataset highlights: (i) feed efficiency as a key driver of productivity, resource use, and greenhouse gas emission intensities, with vast differences between production systems and animal products; (ii) the importance of grasslands as a global resource, supplying almost 50% of biomass for animals while continuing to be at the epicentre of land conversion processes; and (iii) the importance of mixed crop–livestock systems, producing the greater part of animal production (over 60%) in both the developed and the developing world. These data provide critical information for developing targeted, sustainable solutions for the livestock sector and its widely ranging contribution to the global food system.

Lineage: A livestock systems classification updated by Robinson et al (2011) was used as the starting point. It is based on agro-ecological differentiation (arid, humid and temperate/tropical highland areas), which helps in establishing the composition of diets for animals in different regions and agro-agroecologies and in the future to elicit the impacts that climate change might have on feed resources and land use. We differentiated 8 different types of livestock systems in 28 geographical regions of the world for this study. Numbers of animals for each of these systems and regions were estimated using the data of Wint and Robinson (2007) for the year 2000.

For ruminants (cattle, sheep and goats), we disaggregated the dairy and beef cattle herds using livestock demographic data for total cattle, sheep and goats and the dairy females for each species, respectively, from FAOSTAT. We used herd dynamics models parameterised for each region and production system using reproduction and mortality rates obtained from extensive literature reviews to estimate herd composition. For monogastrics (pigs and poultry), we only differentiated two systems: smallholder and industrial production systems. The allocation of poultry, eggs and pork production was done on the basis of knowledge of the total product output from these two systems from national information from selected countries in the different regions, applied to the respective region.

Biomass consumption and productivity estimations from different species in each region and system followed a three stage process. First, feed availability of four main types of feeds (grass, crop residues, grains, occasional feeds) was estimated using hybrid maps of grassland productivity and EPIC model output (Havlik et al 2013) for humid and temperate regions of the world. Crop residue availability was estimated using the SPAM cropland layers (You et al 2014) and coefficients of stover use for animal feeding and harvest indexes for different parts of the world. Grain availability for animal production was taken from the FAO Commodity balance sheets and the availability of occasional feeds like cut and carry grasses and legumes was obtained from literature reviews.

The second step consisted of developing feasible diets for each species in each region and production system. The proportions of each feed in the diet of each species was obtained from extensive information available in the literature and from databases and feeding practice surveys at key research centres in the world (i.e. FAO, ILRI). Data on feed quality was obtained from the databases containing regional feed composition data for each feed (Herrero et al 2008). The third step consisted of estimating productivity. For ruminants, the information on the quantity and quality of the different feeds was then used to parameterise an IPCC tier 3 digestion and metabolism model (RUMINANT, Herrero et al 2002), as described in Herrero et al (2008) and Thornton and Herrero (2010). The model estimated productivity (milk, meat), methane emissions and manure and nitrogen excretion. For monogastrics, information on feed quality was used to estimate feed intake, productivity and feed use efficiency using standard nutrient requirements guidelines (NRC 2008). The estimation of methane and nitrous oxide emissions from manure, and of nitrous oxide from pastures followed an IPCC tier 2 approach, for each species, system and region. Further details are available in the Supplementary Information of Herrero et al. 2013.

All information on animal production (bovine milk, bovine meat, sheep and goat milk, sheep and goat meat, pork, poultry and eggs) and for grains as feed was harmonised with FAOSTAT’s commodity balance sheets at national level following an iterative procedure restricted to deviate +/- 20% from the statistical data in FAOSTAT.

The size of the collection is 1.32 GB, 192 zip files.

This dataset provides livestock data for US Counties within the contiguous US. Census data of cattle, poultry (fowl), hogs, horses and sheep are provided. These data are estimated counts for 1990 based on an average of 1987 and 1992 census data from US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

Beef rose to 294.50 BRL/15KG on July 29, 2025, up 0.63% from the previous day. Over the past month, Beef's price has fallen 7.21%, but it is still 26.75% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Beef - values, historical data, forecasts and news - updated on July of 2025.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,776 observations with 37 variables; Profiles Layer Field = 1,493 observations with 64 variables; Profiles Layer Lab= 1,386 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The BENEFIT-REALISE program implements its interventions in 60 PSNP weredas in four regions (Tigray, Amhara, Oromia, and SNNPR).Accordingly, in 2019, BENEFIT-REALISE along with the MoA initiated a wereda-wide soil resource characterization and mapping task at1:50,000 scale in 15 BENEFIT-REALISE intervention weredas: 3 of Tigray, 6 of Amhara, 3 of Oromia, and 3 of SNNPR. Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. 10.13140/RG.2.2.31759.41123. Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b.

TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Here are a few use cases for this project:

1. Livestock Management: Livestalk can be used by farmers and livestock owners to monitor and identify different cow classes, ensuring proper tracking and care for each animal. This can help in preventing misidentification, loss, theft, and improving overall herd health.

2. Animal Conservation & Research: Researchers and wildlife conservationists can use the Livestalk model to study and monitor cow populations in various ecosystems, assisting in efforts to preserve and protect native species and their habitats.

3. Smart Farming Technology: The Livestalk model can be integrated into smart farming systems, enabling automatic identification and sorting of different cow classes, enhancing efficiency, and reducing labor requirements in modern agriculture practices.

4. Veterinary Medicine & Diagnostics: Veterinarians can use the Livestalk model to assist in identifying specific cow classes, aiding in the diagnosis and treatment of illnesses related to certain species or breeds, improving overall animal health and well-being.

5. Educational & Public Awareness: The Livestalk computer vision model can be used in educational settings as a learning tool for students and the general public interested in understanding and identifying different cow classes, promoting public awareness about biodiversity, and encouraging the appreciation of the natural world.

World Religions Across Regions

Analyzing Adherence Across Regions, States and the Global System

By Correlates of War Project [source]

About this dataset

The World Religion Project (WRP) is an ambitious endeavor to conduct a comprehensive analysis of religious adherence throughout the world from 1945 to 2010. This cutting-edge project offers unparalleled insight into the religious behavior of people in different countries, regions, and continents during this time period. Its datasets provide important information about the numbers and percentages of adherents across a multitude of different religions, religion families, and non-religious affiliations.

The WRP consists of three distinct datasets: the national religion dataset, regional religion dataset, and global religion dataset. Each is focused on understanding individually specific realms for varied analysis approaches - from individual states to global systems. The national dataset provides data on number of adherents by state as well as percentage population practicing a given faith group in five-year increments; focusing attention to how this number evolves from nation to nation over time. Similarly, regional data is provided at five year intervals highlighting individual region designations with one modification – Pacific Ocean states have been reclassified into their own Oceania category according to Country Code Number 900 or above). Finally at a global level – all states are aggregated in order that we may understand a snapshot view at any five-year interval between 1945‐2010 regarding relationships between religions or religio‐families within one location or transnationally.

This project was developed in three stages: firstly forming a religions tree (a systematic classification), secondly collecting data such as this provided by WRP according to that classification structure – lastly cleaning the data so discrepancies may be reconciled and imported where needed with gaps selected when unknown values were encountered during collection process . We would encourage anyone wishing details undergoing more detailed reading/analysis relating various use applications for these rich datasets - please contact Zeev Maoz (University California Davis) & Errol A Henderson _(Pennsylvania State University)

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How to use the dataset

The World Religions Project (WRP) dataset offers a comprehensive look at religious adherence around the world within a single dataset. With this dataset, you can track global religious trends over a period of 65 years and explore how they’ve changed during that time. By exploring the WRP data set, you’ll gain insight into cross-regional and cross-time patterns in religious affiliation around the world.

Research Ideas

• Analyzing historical patterns of religious growth and decline across different regions
• Creating visualizations to compare religious adherence in various states, countries, or globally
• Studying the impact of governmental policies on religious participation over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: WRP regional data.csv | Column name | Description | |:-----------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | Year | Reference year for data collection. (Integer) | | Region | World region according to Correlates Of War (COW) Regional Systemizations with one modification (Oceania category for COW country code ...

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The crop response dataset (N=1,550 observations) is extracted, transformed, and uploaded into a harmonized template, consisting of 76 variables. Recent efforts by the Federal and Regional research centres in collaboration with the MoA, RBoA’s and ATA have shown that there was a significant potassium deficiency in significant agricultural lands of the country. Potassium deficiency was observed through soil fertility assessment surveys and crop response studies.Hence, the promotion of potassium fertilizer use in the agricultural system would be of great importance to increase the balanced fertilizer use system in the country.

In the year 2016/ 2017, a project known as “Large Scale Popularization of Potassium Fertilizer Use in Ethiopia” was implemented from October 2015 to March 2017 by Sasakawa Africa Association/Sasakawa Global 2000 in collaboration with the Ministry of Agriculture, ATA, AGRA and other stakeholders. To achieve the set goals and objectives of the project, in the 2016/2017 cropping season, 18,203 KCL demonstrations were implemented in the four project regions, Amhara, Oromia, SNNPRs and Tigray on five crops, Teff, wheat, Maize, Barley and Sesame. Accordingly, voluminous crop response to the fertilizer dataset was generated by this project.

Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. 10.13140/RG.2.2.31759.41123. Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Washington State Department of Agriculture regulates dairy farm compliance with state water quality and food safety law. This includes regular inspections of dairy production fields and facilities. The milking facilities, which generally represent the heart of the operation, are mapped for internal and public use.This dataset includes all active cow dairy milking facilities. The data are updated quarterly. The dataset includes information about the spatial distribution of dairies in Washington State and information about each business itself. Pursuant to WAC 16-06-210, some information is expressed in ranges to meet non-disclosure requirements.The following is a description of the attributes included with the WA Dairies dataset:

Field
Description


AG ID
The agency given identification number assigned at the initial licensing of the dairy.


Facility Size
This is a general summary of the farm size. For DNMP purposes, size is determined by mature (milking + dry) animal numbers; with a dairy herd of up to 199 animals being a Small, 200-699 being medium, and 700 or greater being Large.


Business Name
The name which appears on the milking license.


Site Address
The street address of the farm milking facility (not the business mailing address).


Site City
The city wherein lies the milking facility.


County
The county wherein lies the milking facility.


DNMP Region
The Dairy Nutrient Management Program Region wherein lies the milking facility.


CAFO Status
This field denotes whether or not the dairy milking license has an associated Confined Animal Feeding Operation (CAFO) permit.


CAFO ID
The permit identification number for the associated dairy.


Range Current Acres
The current and approximate acreage of land application or farming production land associated with the dairy.


Range Current Milking
The current and approximate number of milking animals currently in rotation.


Range Current Dry
The current and approximate number of mature dry animals currently in rotation.


Range Current Heifers
The current and approximate number of heifers (ages 6 months old to fresh) currently in rotation.


Range Current Calves
The current and approximate number of calves (ages 0 to 6 months) currently in rotation.


Latitude (WGS84)
Latitude Datum World Geodetic System 1984


Longitude (WGS84)
Longitude Datum World Geodetic System 1984


WRIA
The Water Resources Inventory Area (WRIA) wherein lies the milking facility.


Conservation District
The Conservation District serving the dairy business.


DNMA Status
Indicates whether the dairy is currently licensed and is regulated under food safety laws and dairy nutrient management act requirements.

Content

In this dataset, you will find information about the billions of religious believers and their population's growth over a 65 year time period from 1945 to 2010.

Acknowledgements

This dataset comes from https://data.world/cow/world-religion-data.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,842 observations with 37 variables; Profiles Layer Field = 6,365 observations with 64 variables; Profiles Layer Lab= 4,575 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template, adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Africa Soil Profile Database (Leenaars et al, 2014): The existing accessible compiled legacy soil profile database of Ethiopia prepared by the Africa soil profile database consisted of 1,842 legacy soil profile observations (Batjas et al., 2020; Leenaars et al., 2014).

Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. https://hdl.handle.net/10568/110868 Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b. TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 1,659 observations with 37 variables; Profiles Layer Field = 2,373 observations with 64 variables; Profiles Layer Lab= 2,373 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template , adapted from Batjes 2022; Leenaars et al, 2014, from the below source: Ministry of Agriculture (MOA) Sustainable Land Management (SLM) program watershed-based soil profile data. Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. https://hdl.handle.net/10568/110868 Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020.

Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014.

TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

This experiment was designed to determine if tannin concentration and nitrogen (N) content of field-applied dairy cow manure influences greenhouse gas (GHG) emissions from soil, soil N mineralization, and plant productivity. The study was conducted at the U.S. Dairy Forage Research Center farm in Prairie du Sac, WI. Field experiments were set up with six randomized complete blocks with two factors (tannin concentration at 3 levels and N fertilization at 2 levels), plus a control (no manure application) treatment in each block. To begin the experiment, three experimental diets with differing tannin concentrations (0, 0.4, and 1.8% tannin as dry matter intake), were fed to Holstein dairy cows for three months. Manure collected at the end of the three month period was field-applied at two N application rates (240 and 360 kg N ha-1). Manure was broadcast and disk-incorporated on May 15, 2014, in fields that were not irrigated and had no tile drainage. This was followed by tillage using a rototiller. Maize (corn for silage) was planted on May 20, 2014. Corn silage was harvested on September 17, 2014, leaving 20 cm-high residue (stubble). GHG flux measurements took place starting May 15, 2014, with GHG sampling every 7-21 days until harvest. Soil chamber dimensions for flux measurements were 15.24 cm (height), 76.2 cm (length), 42.3 cm (width). In May 2014, soil was collected for analysis of physical characteristics. Cores 90 cm deep and 10 cm diameter were collected using a Giddings probe and analyzed at the UW Soil and Forage Lab (complete methods at http://uwlab.webhosting.cals.wisc.edu/wp-content/uploads/sites/17/2015/09/rfs_front_18may2016-2.pdf). According to the USDA NRCS classification system, soils at the site are St. Charles Silt Loam, fine-silty, mixed, superactive, mesic Typic Hapludalfs, with more than 1.5 m depth to an impermeable layer. The data are presented as a spreadsheet file with seven sheets: (1) experimental design, (2) soil physical characteristics, (3) gas fluxes, (4) soil nitrogen at 0-10 cm depth, (5) soil nitrogen at 10-20 cm depth, (6), chemical characteristics of dairy manure, (7) crop yield and biomass characteristics. There is also a data dictionary spreadsheet file with a separate dictionary sheet for each data sheet. Weather data for the field site (not presented here) can be found at: http://www.wunderground.com/personal-weather-station/dashboard?ID=KWIMERRI3#history Resources in this dataset:Resource Title: Data dictionary for: Effects of tannin in dairy cow diets and land application of manure on soil gas fluxes and nitrogen dynamics. File Name: DairyCAP_tannin_data_dictionary_02012017.csvResource Software Recommended: Microsoft Excel 2013,url: https://products.office.com/en-us/excel Resource Title: Effects of tannin in dairy cow diets and land application of manure on soil gas fluxes and nitrogen dynamics. File Name: DairyCAP_tannin_02012017.xlsxResource Description: The data are presented as a spreadsheet file with seven sheets: (1) experimental design, (2) soil physical characteristics, (3) gas fluxes, (4) soil nitrogen at 0-10 cm depth, (5) soil nitrogen at 10-20 cm depth, (6), chemical characteristics of dairy manure, (7) crop yield and biomass characteristics.Resource Software Recommended: Microsoft Excel 2013,url: https://products.office.com/en-us/excel Resource Title: crop yield and biomass characteristics. File Name: DairyCAP_tannin_Crops.csvResource Description: CSV extract from the overall dataset to be used with data visualizations.Resource Title: experimental design. File Name: DairyCAP_tannin_Experiment.csvResource Description: CSV extract from the overall dataset to be used with data visualizations. Resource Title: gas fluxes. File Name: DairyCAP_tannin_GasFluxes.csvResource Description: CSV extract from the overall dataset to be used with data visualizations. Resource Title: chemical characteristics of dairy manure. File Name: DairyCAP_tannin_Manure.csvResource Description: CSV extract from the overall dataset to be used with data visualizations.Resource Title: soil physical characteristics. File Name: DairyCAP_tannin_Soils.csvResource Description: CSV extract from the overall dataset to be used with data visualizations. Resource Title: soil nitrogen at 0-10 cm depth. File Name: DairyCAP_tannin_UpperSoil1.csvResource Description: CSV extract from the overall dataset to be used with data visualizations.Resource Title: soil nitrogen at 10-20 cm depth. File Name: DairyCAP_tannin_LowerSoil.csvResource Description: CSV extract from the overall dataset to be used with data visualizations.

This dataset includes the scripts to reproduce the models presented in the paper. The cleaned data used for the analyses is also available. Abstract of the article: Precision farming technology, including GPS collars with biologging, has revolutionized remote livestock monitoring in extensive grazing systems. High resolution accelerometry can be used to infer the behavior of an animal. Previous behavioral classification studies using accelerometer data have focused on a few key behaviors and were mostly conducted in controlled situations. Here, we conducted behavioral observations of 38 beef cows (Hereford, Limousine, Charolais, Simmental/NRF/Hereford mix) free-ranging in rugged, forested areas, and fitted with a commercially available virtual fence collar (Nofence) containing a 10Hz tri-axial accelerometer. We used random forest models to calibrate data from the accelerometers on both commonly documented (e.g., feeding, resting, walking) and rarer (e.g., suckling calf, head butting, allogrooming) behaviors. Our goal was to assess pre-processing decisions including different running mean intervals (smoothing window of 1, 5, or 20 seconds), collar orientation and feature selection (orientation-dependent versus orientation-independent features). We identified the 10 most common behaviors exhibited by the cows. Models based only on orientation-independent features did not perform better than models based on orientation-dependent features, despite variation in how collars were attached (direction and tightness). Using a 20 seconds running mean and orientation-dependent features resulted in the highest model performance (model accuracy: 0.998, precision: 0.991, and recall: 0.989). We also used this model to add 11 rarer behaviors (each < 0.1% of the data; e.g. head butting, throwing head, self-grooming). These rarer behaviors were predicted with less accuracy because they were not observed at all for some individuals, but overall model performance remained high (accuracy, precision, recall >98%). Our study suggests that the accelerometers in the Nofence collars are suitable to identify the most common behaviors of free-ranging cattle. The results of this study could be used in future research for understanding cattle habitat selection in rugged forest ranges, herd dynamics, or responses to stressors such as carnivores, as well as to improve cattle management and welfare.

BackgroundBovine viral diarrhea is one of the diseases that cause huge economic losses in animal husbandry. Many countries or regions have successively introduced eradication plans, but BVDV still has a high prevalence in the world. This meta-analysis aims to investigate the prevalence and risk factors of BVDV in the world in recent 10 years, and is expected to provide some reference and theoretical basis for BVDV control plans in different regions.MethodRelevant articles published from 2010 to 2021 were mainly retrieved from NCBI, ScienceDirect, Chongqing VIP, Chinese web of knowledge (CNKI), web of science and Wanfang databases.Results128 data were used to analyze the prevalence of BVDV from 2010 to 2021. BVDV antigen prevalence rate is 15.74% (95% CI: 11.35–20.68), antibody prevalence rate is 42.77% (95% CI: 37.01–48.63). In the two databases of antigen and antibody, regions, sampling time, samples, detection methods, species, health status, age, sex, breeding mode, and seasonal subgroups were discussed and analyzed, respectively. In the antigen database, the prevalence of dairy cows in the breed subgroup, ELISA in the detection method subgroup, ear tissue in the sample subgroup, and extensive breeding in the breeding mode were the lowest, with significant differences. In the antibody database, the prevalence rate of dairy cows in the breed subgroup and intensive farming was the highest, with a significant difference. The subgroups in the remaining two databases were not significantly different.ConclusionThis meta-analysis determined the prevalence of BVDV in global cattle herds from 2010 to 2021. The prevalence of BVDV varies from region to region, and the situation is still not optimistic. In daily feeding, we should pay attention to the rigorous and comprehensive management to minimize the spread of virus. The government should enforce BVDV prevention and control, implement control or eradication policies according to local conditions, and adjust the policies in time.