MAgPIE Model outputs and scripts for analysis of markups, based on MarkupsChen package version 1.2 available here: https://github.com/caviddhen/MarkupsChen/releases/tag/v1.2

Food Price Index in World increased to 130.10 Index Points in July from 128 Index Points in June of 2025. This dataset includes a chart with historical data for World Food Price Index.

This dataset is about books. It has 1 row and is filtered where the book is The future of farming and food prices in the Common Market. It features 7 columns including author, publication date, language, and book publisher.

This report displays the findings of a survey on whether food costs will get better or worse for consumers in the future in selected European countries as of September 2018. During the survey period, it was reported that ** percent of Polish respondents stated that they expected future food costs to improve.

According to a survey conducted in Canada in 2023, over 43 percent of respondents stated they would increase their focus on sales and promotions in the coming year to compensate for future grocery price fluctuations. Some 34.6 percent will chose to use coupons more often,while 33.6 percent with use loyalty programs more frequently.

This dataset contains the data and scripts required to reproduce the tables and figures in the study titled "Income, consumer preferences, and the future of livestock-derived food demand." R scripts were run using R version 4.0.5 on Windows 10 x64. All the data and script should be placed in one folder. Add a R project into the folder (for example, "project_ldfDemand.Rproj"). Open the R project before running the scripts. The scripts (extension .R) are ordered sequentially, and should be run sequentially for the first time. The script "22masterFile.R" is the master file that runs all scripts sequentially from start to finish. The study generated simulation results in GAMS. The GAMS code is not part of the scripts in this dataset. Please direct any questions on the GAMS code and input data to Adam Komarek.

The survey regarding consumer expectations of the future cost of food in the United States suggests that both in 2018 and 2021, attitudes towards future food prices were largely pessimistic. In 2018, ** percent of respondents indicated that they expected the cost of food to improve, while ** percent expected prices to worsen. In 2021, ** percent of people surveyed responded that prices would get better, while ** percent expected the cost of the food they eat to get worse.

Cost of food in European Union increased 3.90 percent in July of 2025 over the same month in the previous year. This dataset provides - European Union Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Seneca Foods (SENEA) Stock: Is the Future Bright for This Food Giant?

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

Cost of food in Canada increased 3.30 percent in July of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Canada Food Inflation - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

According to a survey carried out by ProdegeMR in Canada in August 2020, some ***** percent of consumers think food prices will increase in the future.

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.

BNP Paribas Global Agri TR Index: The Future of Food?

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

Overview

The report presents updated estimates of household food expenditure trends and examines further issues relating to Australia's household food expenditure. The analysis builds on a June 2017 ABARES report that examined recent trends in food demand in Australia and a range of food security issues.

Key Issues

Between 2009-10 and 2016-17, the key drivers of Australia's household food demand growth were, in order of importance, population growth, changes in tastes and preferences (including lifestyle choices), lower real food prices and real income growth. While population growth is important, increasing the number of people seeking to meet their energy and nutrition requirements, there has also been a broadly-based shift toward spending on meals out and fast foods, with the share of meals out and fast foods in household food expenditure in Australia increasing from 31 per cent in 2009-10 to 34 per cent in 2015-16. This increases food expenditure per person, all else constant.

Domestic household consumption is still the most important market for food producers (based on value), but food exports have recovered strongly in recent years, from $25 billion in 2009-10 to $39 billion in 2016-17 (in 2015-16 prices); the share of exports in Australia's indicative food production increased from a recent low of 25 per cent in 2009-10 to 33 per cent in 2016-17.

Two key questions posed in the report relate to food security across population sub-groups and economic opportunities for farmers and other food product and service providers. • Food security-based on average outcomes in population sub-groups in 2015-16 using HES data, the Australian Government's transfer system is important in ensuring a high level of food security across households in Australia; some households, such as those highly reliant on family support payments, may require complementary support, for example, from non-government organisations.

• Economic opportunities in the domestic food supply chain-future food demand growth in Australia will be underpinned by population and income growth. For people living in higher income and/or net worth households, there is a demonstrated willingness to pay a premium for quality attributes of food products and services, including convenience factors. Food labelling is a key approach to inform consumers about quality attributes that may earn a price premium.

A key challenge in the long-term trend toward increased demand for meals out and fast foods is to ensure people have information about food attributes such as nutrition content. Reliable and well understood food product and service labelling may enhance nutrition security in Australia, and allow consumers to make food choices that are more closely aligned with their tastes and preferences (including in relation to nutrition and health), and wider circumstances, as well as contributing to reducing food waste.

The Bolivian baby food market stood at $72M in 2024, surging by 10% against the previous year. The market value increased at an average annual rate of +1.9% over the period from 2012 to 2024; the trend pattern remained relatively stable, with somewhat noticeable fluctuations being observed in certain years. Baby food consumption peaked in 2024 and is expected to retain growth in the near future.

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.

S-Net ITG Agriculture USDindex: The Future of Food Security?

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

Cost of food in Nigeria increased 22.74 percent in July of 2025 over the same month in the previous year. This dataset provides - Nigeria Food Inflation - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Is iShares Emergent Food and AgTech Multisector ETF the Future of Food?

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

This data package contains information on adverse Food Events 2004 to 2018, ingredient database of dietary supplements, International Food Consumption Database and Nutrition Assistance Program Participation and Cost Database.

According to Cognitive Market Research, the global Food and Beverage market size is USD 6684.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 6.80% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 2673.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.0% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 2005.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1537.37million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.8% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 334.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 133.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.5% from 2024 to 2031.
The Breakfast Cereals held the highest Food and Beverage market revenue share in 2024.

Market Dynamics of Food and Beverage Market

Key Drivers of Food and Beverage Market

Rising Global Population to Increase the Demand Globally

The increasing number of people on the planet is driving up demand for food and drink, particularly in developing countries where disposable incomes are rising. There is a proportional increase in the demand for food and drink as more people enter the consumer market. The need for agricultural and food production systems to develop and adapt to satisfy growing demands is highlighted by this trend. Furthermore, it emphasizes how important sustainable practices are to ensuring food security over the long term and reducing environmental impacts. To address these issues and create resilient and equitable food systems that can meet the demands of an expanding population while preserving the planet's resources for future generations, governments, businesses, and communities must work together.

Urbanization and Busy Lifestyles to Propel Market Growth

Convenient, ready-to-eat food and beverages are in high demand due to urbanization and the spread of hectic lives. The need for easy and convenient food options has increased as more people live in cities and manage busy schedules. As a result of this trend, the availability of packaged foods, frozen dinners, and grab-and-go options has increased, appealing to consumers who want convenience without sacrificing flavor or nutrition. With urbanization driven by social and economic considerations, the portable food and beverage product market is expected to grow even further. In response to changing customer tastes, food producers and distributors are coming up with new and inventive ways to provide a wide range of easily accessible products that meet the needs of both busy lifestyles and urban residents.

Restraint Factors of Food and Beverage Market

Rising Food Prices to Limit the Sales

Increased food costs are frequently caused by changes in the price of agricultural commodities, which are made worse by supply chain interruptions and extreme weather. These dynamics, especially for vulnerable people, can substantially impact affordability and consumer purchasing. When staple foods rise in price, households might have to spend more of their income to cover their fundamental nutritional needs, leaving them with less money to spend on other necessities. Furthermore, rising food prices have the potential to worsen food insecurity, increasing the likelihood of poverty and malnourishment in impacted areas. Businesses, civil society, and governments must tackle these issues by strengthening the food systems' resilience, reducing price volatility, and guaranteeing that all societal segments have fair access to reasonably priced and nutrient-dense food.

Stringent Regulatory and Compliance Requirements 

The food and beverage sector faces a complicated array of safety, labeling, packaging, and environmental regulations that differ by area and nation. From the sourcing of ingredients to nutritional information and sustainability requirements, businesses must consistently adjust to changing legal norms. Managing these regulations can heighten operational complexity and compliance expenses, part...

For the seventh consecutive year, the Brazilian dog and cat food market recorded growth in sales value, which increased by 2.5% to $3.5B in 2024. The market value increased at an average annual rate of +1.4% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. Over the period under review, the market reached the peak level in 2024 and is likely to see steady growth in the near future.