Major drivers of agricultural big data – implications for suppliers
Big data refers to the “generation of enormous amounts of data due to new technologies for measurement, collection and storage”1 that are being accumulated in such vast quantities that they are impossible to assess using conventional analysis techniques.
It is increasingly cited as one of the major drivers of agricultural development in the context of yield enhancement and cost management. It is also viewed as one of the key means permitting farmers to derive maximum value from inputs in ways that optimize the use of scarce resources and improve agricultural productivity to meet an ever-increasing global food demand. This white paper outlines the major drivers of agricultural big data to date, how these are likely to evolve and the possible implications for crop protection suppliers.
Major drivers of big data in farming
Precision agriculture – is defined as “a management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture, or yield, for optimum profitability, sustainability, and protection of the environment”2. Having emerged around 20 years ago, today it frequently produces key data elements necessary for big data applications thus making it a major driver. A key aspect of precision farming is that it deploys variable rate technology and real-time data to adapt to the growing conditions within a field to optimize input usage and associated outputs. Some experts believe that variable rate applications will become as commonplace as flat rate applications have been3, and therefore, precision agriculture will continue to be a major driver of the future growth of big data in farming.
Sensors – encompass both remote sensing (e.g. satellite technology, UAVs/drones etc.) and in-field sensors which monitor anything from seed applications to yield. Sensors are often seen as the building blocks for precision agriculture systems and a key means to collect the vast volumes of information used in big data analysis. According to a DigitalGlobe white paper4, technological advancements in remote sensing coupled with advances in IT, cloud computing, mobile technology, widespread adoption of GPS, and digital technologies have resulted in “the development of decision support systems that can integrate various remote observations with field measurements to provide actionable intelligence in the field”.
Internet of Things – Porter and Heppelmann (2014)5 state that this phrase was coined to reflect the growing number of smart, connected products and to highlight the new opportunities that they can represent. They also state that arrays of farm equipment types are becoming ever more inter-connected and linked with external data sources to facilitate farm equipment optimization. John Deere and AGCO, for example, are not only connecting farm machinery but irrigation and soil and nutrient sources with data on weather, crop prices, and commodity futures to optimize farm performance. Industry boundaries are being redefined and tractor manufacturing for example has expanded into farm equipment optimisation.
Smartphone usage – according to the New York Times6, the consumerization of IT is “moving once-expensive software onto smartphones that can connect to cheaper cloud systems. The ever-shrinking cost of semiconductors is making once exotic tech a commonplace on much farm equipment”. Based on a 2014 Syngenta survey7, an estimated 64% of UK farmers use a smartphone. This coupled with the vast array of agricultural apps available, means that smartphones serve as a critical platform to provide big data solutions to farmers whilst also being a major source from which big data insights can be gleaned. For example, machine-based data on seed plantings in individual fields combined with weather and disease data could be used by agricultural suppliers to notify farmers of pending threats and propose mitigation strategies. Smartphone usage will continue to exert a major influence on big data usage in agriculture in both developed and emerging economies in the coming years.
Cloud computing – According to Intel8, organizations should “look to cloud computing as the structure to support their big data projects” because it offers a cost-effective way to support big data technologies and the advanced analytics applications that can drive business value. It also claims that interest in applying big data analytics from sensors and intelligent systems is continuously increasing as companies seek to gain faster, richer insight more cost effectively than in the past. Having the capacity to deal with the increasingly vast volumes of data whilst generating actionable real-time insights that can be disseminated across the organisation is a major challenge for most agricultural companies which need to continue to focus on their core value propositions. Many of these companies will require support in terms of platforms or analytics solutions such as the new Agility platform launched by Proagrica.
Implications for agricultural suppliers
The aforementioned drivers will continue to play a key role in the development of agricultural big data in the years ahead. Also, if the costs of technologies such as sensors reflect cost trends in other technologies, lower-cost versions will emerge which will further contribute to the proliferation of agricultural data. This means that in addition to the volume of data, the velocity (i.e. the rates at which data is collected) and variety of data (e.g. the number of data sources such as plant genomics, machinery data etc.) will also expand exponentially. For agricultural suppliers, there are several implications including:
Business Intelligence (BI) platforms will need to be agile and user-friendly – research from Gartner9 suggests that BI platforms will need to “support organizational needs for greater accessibility, agility and analytical insight from a diverse range of data sources”. This in conjunction with the added pressures of resource scarcity as well as the need to feed a growing global population means that it will become imperative for agricultural suppliers to have access to, and the capability to process, the real-time insights available from big data. Agility is a great example of how organizations can leverage the largest, broadest and most accurate data in the industry to transform how they approach the market and to tailor product offerings to growers and customers in-season. Traditionally, suppliers have had to rely on surveys whose results are usually not available until the end of the growing season to analyse the market and introduce changes for the next season, by which time the market dynamics might have changed substantially.
Emergence of new competitive threats – a recent Harvard Business Review article10 points out that data-enabled disruption has the potential to overhaul the competitive environment of an industry vertical and cites examples such as smartphones and Uber. Similar threats could emerge in agriculture and it is critical for suppliers to identify the emergence of new competitors at an early stage so that they can adapt their strategies accordingly.
More evidence-based data required to substantiate product claims – Farmers and advisers are increasingly gaining access to advanced decision-support systems and will be better positioned to examine whether the claims agricultural suppliers make represent a compelling value proposition for their specific circumstances. As farmers come to expect this evidence from their suppliers as well as having access to high quality, relevant trend data themselves, companies can apply the insights derivable from platforms such as Agility to facilitate higher farmer engagement in the strategies selected for their specific farm.
- Russo, J. (2013), “Big Data & Precision Agriculture”.
- USDA (2007), “Precision Agriculture: NRCS Support for Emerging Technologies”.
- Grassi, M.J. (2015), “Outlook 2016: Banking On Big Data Progress”.
- DigitalGlobe (2015), “Remote Sensing Technology Trends and Agriculture”.
- Porter, M. and Heppelmann, J.E. (2014), How Smart Connected Products Are Transforming Companies, Harvard Business Review.
- New York Times (2014), “A Low-Cost Alternative to Pricy Big Data on the Farm”.
- Intel (2015), “Big Data in the Cloud: Converging Technologies”.
- Gartner (2015), “Technology Insight for Modern Business Intelligence and Analytics Platforms”.
- Wessel, M. (2016), “How Big Data Is Changing Disruptive Innovation”, Harvard Business Review.