Although the Earth provides humans with enough resources to feed our growing population, over 815 million people are living with chronic hunger. The current state of the food system sees insufficient crop yields, declining human health, unsustainable diets, and farmers struggling to earn a living.
the International Food Policy Research Institute (IFPRI) and the Centre for Agriculture and Bioscience International (CABI) have recently launched a new book, entitled “Agriculture for Improved Nutrition: Seizing the Momentum,” which explores the potential for agriculture to improve human nutrition. Data, and in particular big data, could help to broadly understand - and therefore predict - food systems, global land use, food choices, and consumer behaviour.
Over the course of the past ten years, “big data” as a term has adapted to the rapidly evolving global landscape. Many wonder if big data is a trend, hyped by the media, or if indeed it does have the power to “disrupt” agriculture and nutrition systems. Big data is often conflated with a slew of other concepts, such as open data and precision agriculture. The chapter “Big Data in Agriculture and Nutrition” in the IFPRI/CABI book outlines the basics of big data and its applicability to agriculture and nutrition, and defines common hurdles to maximising the benefits for big data for all within food systems.
What is big data?
The overarching components of big data that apply to most disciplines are nicknamed the Three Vs: Volume, Velocity, and Variety. With a Fourth V, Veracity, relevant to agriculture and nutrition.
Volume: How much data is collected. Collected data needs to be stored and curated. Of course, volume depends on the amount over time, which informs the next component.
Velocity: How fast data is collected. In agriculture and nutrition, an oft-mentioned benefit of big data is the opportunity for near-real-time analysis and decision-making.
Variety: What type of data is collected. Variety of data is potentially the component that makes big data applicable to agriculture and nutrition. With the onset of digital data collection, the internet, and smartphones, big data has changed what data “looks like”. Instead of numbers in a spreadsheet, data can include maps and GPS coordinates, photos, texts, relationships among other outputs.
Veracity: How reliable is the data source. Good data quality is essential for correct decision making.
Big Data in Agriculture and Nutrition
Data is collected from a variety of sources and in many different fashions, which is why applications of big data are can be applied in such a variety of ways across the food system. As mentioned before, in order for data to be “big”, there must be a large amount, collected quickly, that takes a wide variety of forms. There are many datasets within agriculture and nutrition that fulfill these criteria, including:
Big data is used to create software to predict and quickly determine where a problem is happening and tell the user the best way to overcome it. In order for that software to be written, large amounts of data, evidence and analysis, sourced from industry, academia, government and others to support the recommendations.
While big data informs software solutions, big data is also generated by the users through farm equipment, mobile phones, and social media. When someone uses the app, the information they input and their behavior while using the app then becomes big data for others to interpret and use. As the number of mobile phones and smartphones increase, the data that is generated also increases.
Two of the biggest challenges for farmers are risk anticipation and safety nets. Early warning systems and insurance help farmers overcome these risks, especially in the age of climate change. Big data is allowing for better early warning systems and insurance schemes than ever before using a combination of data types. Initiatives such as Famine Early Warning System Network (FEWS NET) can drastically improve evidence-based analysis for decision-making in the most vulnerable places. FEWS NET was established in 1985, and due to big data from satellites and research, they can publish reports and maps of food insecurity projections, as well as crisis alerts and specific data on weather, markets and nutrition, allowing governments to help citizens in a timely way (FEWS NET, 2018). The impact of FEWS NET could be observed further if research was collected on how governments used the early warning system and how lives of the affected people were improved.
Big data can also help with insurance and access to credit with a combination of big data types. India-based company Satsure analyses satellite data, market data, and weather data using machine learning and big data analytics to ensure that farmers in India that have suffered crop loss due to climatic shocks receive compensation quickly. Satsure is a relatively new company and the success the insurance programme is forthcoming (e-Agriculture, 2017).
You can learn much more about big data in agriculture and nutrition, along with ways that human nutrition may be improved through sustainable agricultural systems, in Chapter 14 entitled Big Data in Agriculture and Nutrition, from the joint IFPRI/CABI publication Agriculture for Improved Nutrition: Seizing the Momentum.