Quantitative Benchmarks for the Agriculture Industry

As charted below, organisations in the agriculture sector evaluate Big Data solutions in terms of profit increases, even though margins are currently small. Increasing the precision of agriculture and yield predictions could boost profits and broaden margins. In having a precise view of production, farmers can optimise the use of land and seeds, pushing to the full the exploitation of their capabilities. For now, investments are mostly undertaken by larger agricultural organisations. But, with technology's dropping prices, even smaller agricultural concerns will be able to access these technologies.

The ability to plant seeds in an optimal/efficient way through precision agriculture (distancing seeds based on potential plant growth) – controlling the productivity of seeds and soil and forecasting it (yield monitoring and prediction) – and the application of predictive machinery maintenance will help organisations to organise activities better within the year (exact date to plant and harvest, the optimisation of crop rotation, maintenance schedules, etc.), improve revenue streams, and increase margins.

In this regard, the qualitative KPIs of product/service quality and time efficiency play relevant roles. Providing higher-quality and more nutritious produce on time will increase bargaining power when setting yield prices. Hence, farmers are not so interested in finding new sources of revenue or inventing completely new services, but they are expecting better value from data to enable them to change traditional/old-fashioned gut feeling-based business decisions. 

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