Live Cattle rose to 237.80 USd/Lbs on August 20, 2025, up 0.63% from the previous day. Over the past month, Live Cattle's price has risen 5.58%, and is up 31.54% 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 August of 2025.

Feeder Cattle rose to 356.35 USd/Lbs on August 20, 2025, up 1.65% from the previous day. Over the past month, Feeder Cattle's price has risen 8.77%, and is up 49.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Feeder Cattle - values, historical data, forecasts and news - updated on August of 2025.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Slaughter Cattle (WPU0131) from Jan 1947 to Jul 2025 about slaughter, cattle, livestock, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Beef rose to 310.55 BRL/15KG on August 20, 2025, up 0.91% from the previous day. Over the past month, Beef's price has risen 5.67%, and is up 31.87% compared to the same time last year, 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 August of 2025.

The Livestock and Meat Trade Data Set contains monthly and annual data for imports and exports of live cattle, hogs, sheep, and goats, as well as beef and veal, pork, lamb and mutton, chicken meat, turkey meat, and eggs. The tables report physical quantities, not dollar values or unit prices. Data on beef and veal, pork, and lamb and mutton are on a carcass-weight-equivalent basis. Breakdowns by country are included.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ZIP file of CSV formatted data Web page with links to Excel files For complete information, please visit https://data.gov.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

CME feeder cattle futures

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness.Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (Ro 1.287), hence regarded as “slow” pathogen, and evaluate the effects of disease interventions.Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network.Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public–private partnerships, would serve to limit its spread.

Tick (Bids | Asks | Trades | Settle) sample data for Live Cattle (Day Pit) LC timestamped in Chicago time

Tick (trades only) sample data for Feeder Cattle (All Sessions) FCA timestamped in Chicago time

The US beef cattle production industry is currently marked by tight supply conditions and elevated prices. Over recent years, persistent drought conditions have led to significant herd liquidation, with beef cow numbers falling to historic lows. This contraction has created a bottleneck in calf production and feeder cattle availability, sustaining high cattle prices. In tandem, elevated feed costs have pressured prices upwards and profit down, driving revenue as cattle producers seek to pass on costs and prevent further profit declines. As herd rebuilding has remained slow, cattle supplies have remained low and kept prices high even as feed, energy and other key agricultural input costs have declined from their highs in 2022. Industry revenue has grown at a CAGR of 6.0% during the current period to reach an estimated $95.9 billion after declining by 2.4% in 2025 as reduced consumption and supplies limit sales. Consumer preferences are shifting in the beef cattle production industry. There is an increasing awareness of environmental and health-related concerns associated with beef consumption. Consequently, many consumers are reducing their intake of conventional beef, turning instead towards more sustainable options and alternatives that are perceived as healthier or higher quality, such as grass-fed and organic beef. This shift has spurred growth in these segments as consumers look for transparency and ethical farming practices. Retailers and restaurants have responded accordingly by offering more options that align with these consumer preferences. However, these trends also pose challenges, especially for smaller producers who face significant costs associated with transitioning to sustainable practices or achieving certifications like organic or "sustainably raised." Though opportunities for growth will continue to present themselves, the outlook for the industry as a whole does not look as positive in the next five years. Poultry, pork and plant-based proteins will threaten beef demand as they appeal to health-conscious customers, particularly as cattle prices are elevated. Climate change will also continue to introduce environmental pressures, demanding resilience and adaptability from producers. Periods of stable weather could facilitate herd rebuilding, leading to increased cattle supplies and dropping prices, but continued climatic fluctuations and extreme weather events could reduce the consistency of production and increase revenue volatility. Advancements in technology, such as drones and wearable sensors, promise to help optimize cattle management, improving operational efficiencies and animal welfare. These innovations, however, require investment and broader accessibility through government support to ensure equitable adoption across the industry. Additionally, while global trade disruptions remain a concern due to disease outbreaks and geopolitical tensions, US producers will have opportunities in niche market segments to differentiate themselves, counterbalancing some of these pressures. Overall, revenue for cattle producers is forecast to decline through 2030 at a CAGR of 0.4% to $94.0 billion.

This dataset provides values for FEEDER CATTLE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Learn about the factors that influence the beef cattle market, market segmentation, pricing and trading, and industry trends in this informative article. Discover how this complex and dynamic industry plays an important role in the global food supply chain.

This dataset provides a list of the tests undertaken by APHA testing laboratories on Cattle samples in 2015 paid for by international trade surveillance contracts. The dataset includes the following fields: Year; Species class; Species; Test code; test description; Number of tests (the volume of tests performed in the 12 month period). Attribution statement: ©Crown Copyright, APHA 2016

TSM04 - Exports of Cattle and Beef. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Exports of Cattle and Beef...

28545 Global import shipment records of Cattle with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Tick (trades only) sample data for Live Cattle (All Sessions) GLA timestamped in Chicago time

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions.

High prices have consistently elevated revenues for Canadian cattle producers over the current period, but also discouraged herd rebuilding and drained cattle supplies. Cattle prices have surged due to reduced herds in North America, influenced by persistent droughts impeding effective herd rebuilding. Although producers are generally inclined to rebuild, the volatility of high prices, along with the unpredictability of future drought impacts, has discouraged extensive retention practices. Profit has also been pressured by elevated input costs, particularly feed, but extreme cattle prices have allowed profit to recover and expand since its low in 2022. Compounding these challenges is the difficulty in passing increased costs onto consumers, who have shown a growing propensity to switch to alternative proteins. This, combined with the inherent volatility in agricultural outputs due to extreme weather events, continues to strain the financial health of producers despite elevated cattle prices. Overall, revenue has climbed at a CAGR of 4.4% since 2020, including an increase of 2.0% to reach an estimated $25.6 billion in 2025 as beef prices remain on the rise. Consumer behaviour around beef is being reshaped by health perceptions and sustainability concerns, as well as high beef prices. Persistent health advisories recommending reduced red meat consumption influence both domestic and global market demands, pushing consumers towards substitute proteins. Awareness around sustainability is intensifying interest in plant-based alternatives as environmentally friendly consumption gains traction. While inflation has moderated overall, beef prices continue to rise in response to supply-related constraints, making the protein more costly and steering some consumers toward more affordable options like pork and poultry. Industry associations and producers are focusing on marketing beef’s value, quality and affordability to retain consumer interest amid these shifts. The future outlook for the cattle industry will be strongly influenced by red meat prices, which will see initial short-term price increases and then are expected to ease over time, ultimately resulting in higher price levels in 2030 compared to 2025. These trends are driven by supply constraints and shifting global demands, while herd rebuilding efforts will gradually moderate the huge price increases of the current period. Concurrently, sustained pressures from consumer sustainability concerns are likely to continue spurring interest in alternative proteins, propelling producers toward adopting emission-reducing production methods. Nonetheless, rising disposable incomes, especially in emerging beef export markets, present opportunities for Canadian producers by increasing demand for premium beef products. Expanding into new markets will be particularly important for beef producers and the cattle farmers supplying them as US-Canada trade tensions and tariffs shake the stability of this major buyer. Additionally, anticipated global population growth will support heightened protein demand overall. Revenue is expected to climb at a CAGR of 0.1% to reach $25.8 billion over the five years to 2030.

In France, the cattle sector has been in surplus since 2015. Indeed, since that year, exports had exceeded imports by at least *** million euros. Peaks in the French cattle industry's surplus can be observed in 2017 and 2020.

Canadian international merchandise trade data for selected livestock and beef products. Products are presented by Harmonized System (HS) Code. The table details imports and exports, mode of transportation, quantity and value.