From small to big agrifood data: A 100-year timeline

Source: http://bigdatatimeline.wageningenur.nl/en/

The Wageningen University and Research Centre (WUR) is a Dutch public university in Wageningen, Netherlands, which consists of the Wageningen University and the former agricultural research institutes (Dienst Landbouwkundig Onderzoek – DLO). WUR is one of the top institutes at a global level in the field of agri-food and environmental research.

As expected, such a large and active group of research institutes produces huge amounts of data – and WUR has developed the expertise throughout the years to make good use of big data in the agri-food research context. It has been almost one century (98 years, to be more precise) since WUR started collecting research data of various types, using various means and managing all this information and data so that it can be easily reused.

How has this data been collected through these years, for which purpose and how can it benefit future research? A beautiful timeline produced by WUR provides the answers to these questions.

The timeline is divided in four (4) major periods:

  1. 1918-1980: Institutional data collection. During this period, data collection was usually organized by the university or other institutes, such as the government – as a result, a high percentage of this data remained restricted within the specific institute. For example, the government researched data on land registry primarily for its own use. Data and information (positions) were recorded in an analogue form, usually in logbooks. Only in the late 60’s and 70’s Landbouw Economisch Instituut introduced the use of computers for data collection and management.
  2. 1980-2000: The computer goes mainstream. Starting with the 80’s, computers were more widely and frequently used for carrying out research and collecting/storing/managing data. Such data were fed to computer models for conducting experiments and simulations. e.g. for crop growth which led to relatively safe predictions related to agricultural modeling.
  3. 2000-present: Digital data as forecaster. Based on the experience gained during the previous period, models were improved and optimized while simulations have become more accurate and sophisticated. A vast amount of data is collected through satellites e.g. in the form of images that need to be described, organized and processed before they are stored.
  4. Present-2030: Open data and big data. Sources of data become more (in number) and more diverse; sensor data, satellite images, crowd-sourced data and information collected through social media etc. The need for understanding and combining these different types of data led to the rapid development of the data science and big data analytics. Big data becomes available at different levels of detail and domains, including (but not limited to) food safety, food sustainability, crop improvement, marketing and improving the food chain.

The timeline references GODAN (the Global Open Data for Agriculture and Nutrition) as an initiative advocating for opening up agri-food and nutrition data openly available for all stakeholders, such as governments, industry and research. At the same time, the timeline includes a number of use cases for each period, highlighting the collection, use and management of data in these different periods.

Apart from providing an interesting overview of the background of agri-food data collection for the past century, this timeline highlights the importance of big data in agriculture and its huge potential for the next years, in addressing issues in critical sectors like food safety, food security and food production. The timeline is available online while additional information on the work of WUR with big data in the agri-food sector is available at www.wageningenur.nl/bigdata.

Agroknow is working with UN FAO in the Societal Challenge 2: Food security, sustainable agriculture and forestry, marine and maritime and inland water research, and the Bioeconomy in the context of the BigDataEurope Horizon 2020 projectTaking into consideration the integration of the SemaGrow stack with the AGINFRA agri-food research e-infrastructure, which enhances the latter with big data processing capabilities, such news and views as the ones presented in the WUR big data timeline are of high interest to AGINFRA and our work in the BigDataEurope project.

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