Data analytics in farming: still an infant industry?

In  smart farming, automation has always been a step ahead of data analytics – GPS-guided tractors or milking robots provide benefits in terms of comfort, time-saving, seeding or harvesting accuracy. When it comes to reaping the benefits of sensor data collection, however, things are more arduous. Data and sensors do not have value in themselves: they need to be aggregated and milled through data-processing algorithms before they can be turned into valuable information – mostly agronomic advice and other support for farm management. 

According to cropping systems economist Terry Griffin, the smart farming revolution is hampered by the widening gap between the volume of collected data – which experiences a boom as sensors spread across farms – and the actual usage of data, which remains undertapped. In order to boost the transformation of data into valuable assests, two transformations need to happen.

First, big data anlytics is characterised by strong network effects – the phenomenon where a service or product value is a function of how many people use it, much like in social networks or search engines. According to Griffin, the agricultural data anylitics sector is in need of restructuring, as too many data companies are sharing the farms’ data pie.

Second, farmers need to see the value of sharing their data with the agri-tech or ICT industry – in the form of more accurate agronomic advice, and, possibily, in the form of tangible assets in their balance sheet. It is only when proving that the data is worth a lot to the farmer that smart farming can come to fruition.

Read the following article for more insights on how to unlock usage and value from data generated by smart farming equipment. If you wish to learn more about how collected data can be leveraged by processing tools and transformed into valuable information, have a look at this article.

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