How big data will change agriculture

 

In recent years, big data has become prominent across a variety of economic sectors and is now being increasingly applied to agriculture.

Russo (2013)1, states that big data refers to the “generation of enormous amounts of data due to new technologies for measurement, collection and storage” that are being accumulated in such vast quantities that they are impossible to assess using conventional analysis techniques. Within agriculture, these technologies include sensors, geospatial datasets, as well as information from smart-connected devices (e.g. machinery) linked to the Cloud via the Internet of Things. Big data also encompasses datasets collected for other purposes (e.g. farm compliance data) which would have remained in silos but whose potential can now be used in other contexts to deliver real-time actionable insights for farmers and agricultural suppliers.

Why is it crucial for suppliers in agriculture?

According to SAS2, it is not the amount of data that is important, it is what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions to drive competitive advantage. It therefore offers great opportunities in agriculture which include:

  • Vast potential to increase productivity and innovation: The McKinsey Global Institute3 states that big data has the potential to “transform economies, delivering a new wave of productivity”. In the process, it will change the basis for competition and presents substantial opportunities for those with the capabilities to exploit the potentially highly-valuable insights available. Within agriculture, as global food demand is projected to double by 2050 due to rising populations, farmers and agricultural suppliers will increasingly be expected to do more with less by increasing productivity from limited resources and inputs. Due to these pressures, innovative technologies such as Precision Farming will play a major role in the development of agriculture and will present a multitude of opportunities to farmers to adapt their practices and input applications to inter and intra-field variability in crops. For crop protection suppliers, this means that products could be applied in a multitude of dose rates and tank mixes within a single field. As a result the broad-brush approach of conventional analyses techniques (e.g. surveys undertaken post-application) will become increasingly redundant and unreliable.
  • Real-time insights to help performance optimization: advanced analytics can show how farmers are utilising their inputs and what adaptations are required to take account of emerging weather events or disease outbreaks. The key challenge will be to deliver such real-time insights clearly and concisely to enable effective decision-making. To achieve this, advanced algorithms are needed to swiftly unlock the highly valuable insights available from big data so that products are performing to expectations on an ongoing basis despite changing conditions.
  • The development of highly specific customer segmentations: to tailor product offerings to precisely meet customer needs as they evolve. For instance, if Black Grass becomes problematic in a given region, suppliers can deploy big data techniques such as real-time micro-segmentation of customers to target promotional and marketing activities, thus facilitating better utilization of marketing spend. Such analytics would also facilitate the development of more sophisticated pricing strategies that better match price and value at the segment level.
  • Data becoming a major source of competitive advantage: therefore, big data emerging as a key way for companies to gain competitive advantage over their competitors by unearthing valuable insights more quickly and by developing and adapting products that are better matched to meeting specific customer needs on an on-going basis. Competitors that fail to develop or gain access to sophisticated analytics expertise will be left behind.

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