Types and Examples of Secondary Data Sources

Every agribusiness decision – whether it’s entering a new market, planning a planting season, or forecasting commodity prices – needs data to back it up. But collecting that data from scratch every time would be enormously expensive and time-consuming. That’s where secondary data becomes indispensable. Secondary data refers to information that has already been collected, processed, and published by others – not for your specific research purpose, but for various other reasons that still make it highly applicable to your needs. In agribusiness research, knowing where to find reliable secondary data, and understanding its different types, is a fundamental skill.

What secondary data sources are and why they matter 🔗

Secondary data sources are any existing records, reports, databases, or publications that a researcher draws upon rather than generating through original fieldwork. According to the Food and Agriculture Organization (FAO), no research study should begin without first searching secondary sources – in many cases, they may be entirely sufficient to answer the research question, eliminating the need for costly primary data collection altogether. Beyond saving time and money, secondary sources can actually yield more accurate results in some situations, particularly when a government agency or international body has conducted a large-scale census or survey far beyond the reach of individual researchers.

Secondary data sources fall into two broad categories: internal sources and external sources. Both are valuable, and the best agribusiness research typically draws on both.

Internal sources of secondary data 🔗

Internal data sources are those that originate within an organization. This data was originally recorded for another purpose – such as accounting or operational management – but it becomes secondary data when applied to a new research question. Because it is exclusive to the organization, it gives researchers a competitive edge that external sources simply cannot offer.

Financial records 🔗

An agribusiness’s financial records – including income statements, balance sheets, and budget reports – are a rich source of data. They reveal spending patterns, investment decisions, and overall financial performance over time. Analyzing these records can help identify areas of efficiency or concern, and support forecasting for future operations.

Sales and transaction data 🔗

Sales records document how many units were sold, revenue generated, and which customer segments are purchasing what. Transaction histories provide insights into market trends, customer preferences, and seasonal variations. For example, a farm input supplier’s sales records might show exactly which fertilizers sell fastest before the planting season – a pattern that is immediately actionable.

Operational and production records 🔗

Data on crop yields, livestock performance, equipment usage, and maintenance schedules offers a clear window into operational productivity. Internal data should always be considered a first line of enquiry in any research project because it is typically the quickest, cheapest, and most convenient source available. Quality control reports and compliance records also fall under this category, helping researchers benchmark performance against industry standards.

External sources of secondary data 🔗

External sources come from outside the organization. They provide context about the broader industry, market conditions, and macroeconomic environment that internal data alone cannot supply. External secondary data has been gathered and published by government agencies, industry associations, research firms, and academic institutions, and is typically accessible through libraries, online portals, or subscription services.

Publications: books, journals, and magazines 🔗

Academic and professional publications are among the most reliable external sources. Books provide deep theoretical and applied coverage of agribusiness topics. Peer-reviewed journals publish original research, systematic reviews, and case studies that have been vetted for accuracy and methodology. Trade magazines, meanwhile, offer timely coverage of industry news, emerging trends, and expert commentary. Together, these publications support both foundational understanding and current awareness in any area of agribusiness research.

Government statistics 🔗

Government agencies are among the most prolific producers of secondary data relevant to agriculture. They regularly publish agricultural censuses, economic surveys, trade statistics, and production data covering a wide range of indicators. Government sources are often free, since the data collection has already been paid for through public funds. In the United States, for instance, the USDA’s Agricultural Marketing Service maintains extensive market research and pricing data on commodities, livestock, and food products. These statistics are invaluable for benchmarking, trend analysis, and policy evaluation.

Trade association reports 🔗

Trade associations – organizations that represent specific sectors of the agricultural industry – compile and publish data relevant to their members and the broader public. Their reports typically include market analysis, industry performance benchmarks, membership surveys, and annual summaries of sector-wide trends. Secondary market research uses outside information assembled by industry and trade associations, labour organizations, and chambers of commerce, often distributed through newsletters, trade publications, and official reports. For agribusiness researchers, these reports offer sector-specific insights that neither government statistics nor academic journals fully capture.

Commercial data from market research firms 🔗

Private market research firms collect and sell data on consumer behavior, commodity price trends, competitive landscapes, and demand forecasts. Commercial data sources may involve subscription or association fees, but they often provide more targeted and up-to-date intelligence than freely available public data. Firms specializing in agricultural market analysis produce detailed reports used by agribusinesses for product development, go-to-market strategy, and competitive positioning. These commercial datasets are particularly useful when an organization needs granular market data that government sources do not offer.

Databases from national and international institutions 🔗

Some of the most comprehensive secondary data in agriculture comes from large national and international institutions. The FAO is dedicated to collecting, analyzing, and disseminating food and agriculture statistics to inform decisions on hunger, rural poverty, food systems productivity, and sustainable resource use. Its flagship platform, FAOSTAT, provides free access to food and agriculture statistics for over 245 countries and territories, covering everything from crop production and trade to food security indicators and emissions data – with time-series records stretching back to 1961.

The World Bank’s data portal offers economic indicators, agricultural development project outcomes, and country-specific information critical for comparative and international research. The International Monetary Fund (IMF) contributes macroeconomic data on financial stability, global economic trends, and country-level fiscal indicators. For researchers at universities and research libraries, platforms like the UC Berkeley Agricultural and Resource Economics data guide curate access to dozens of these institutional databases, including OECD statistics, the International Household Survey Network, and the World Resources Institute’s environmental and food systems datasets.

How to evaluate and use secondary data effectively 🔗

Not all secondary data is equally reliable. There is a need to evaluate the quality of both the source of the data and the data itself before drawing conclusions. Key questions to ask include: Who collected this data, and for what purpose? What methodology was used? Is it current enough to be relevant? Are the definitions consistent with your research needs?

One practical challenge is that different organizations may define the same concept differently. What counts as “smallholder farming” in one government report may differ from the definition in an international agency’s dataset. Researchers must check for consistency across sources and, when needed, adjust for these definitional differences before making comparisons. Using multiple sources – rather than relying on just one – is always advisable. Whenever possible, marketing researchers ought to use multiple sources of secondary data to cross-check findings and reduce the risk of acting on incomplete or biased information.

It is also worth remembering that secondary data, while often highly useful, was not collected to answer your specific question. Variables that matter to your research may simply not be captured in existing datasets. In such cases, secondary sources serve best as context-builders and starting points – supplemented by primary data collection where gaps remain.

Internal vs. external sources: knowing when to use what 🔗

The choice between internal and external secondary sources often depends on the scope of the research question. If the question is about your own organization’s performance – say, whether a particular product line is profitable or whether a new market segment is buying more – internal records are the right first stop. They are exclusive, readily accessible, and directly relevant.

If the question reaches beyond your organization – into industry trends, competitor behavior, international markets, or macroeconomic conditions – external sources become essential. A cooperative trying to decide whether to expand exports, for example, would need trade statistics from government databases, commodity price data from market research firms, and perhaps development indicators from the World Bank to form a complete picture.

In practice, the most rigorous agribusiness research combines both. Internal data provides organizational specificity; external data provides the broader context that makes that specificity meaningful.

What do you think? When conducting agribusiness research, do you think internal data or external data is harder to access and use effectively – and what makes one more challenging than the other? If you were advising a smallholder farming cooperative on where to start gathering data for a market entry decision, which secondary sources would you point them to first, and why?

References

1. https://coresignal.com/blog/secondary-data/
2. https://www.fao.org/4/w3241e/w3241e03.htm
3. https://www.djsresearch.co.uk/glossary/item/Secondary-Market-Research
4. https://www.sganalytics.com/blog/primary-and-secondary-market-research/
5. https://kpu.pressbooks.pub/openimc/chapter/primary-data-v-s-secondary-data/
6. https://www.ams.usda.gov/services/market-research
7. https://www.fao.org/statistics/en/
8. https://www.fao.org/faostat/
9. https://guides.lib.berkeley.edu/ARE/finddata