Advantages of the Big Data technology for Smart Farming


Advantages of the Big Data technology for Smart Farming

Gepubliceerd op
2 november 2016

FAO stated that ‘a more sustainable agriculture’ is one of the big challenges of global human population. Agriculture has to produce more food, feed, fuel, flowers, etc., with less use of natural resources and with less adverse side effects on the environment and society and the expectations are that precision agriculture and the acronym smart farming will be necessary to achieve this. More with less, is also a credo of Wageningen University & Research. Big data applications are likely to contribute to more efficient agro-food chains, and so likely to contribute to a more sustainable agriculture.

Internet of Things

Big Data use is changing the scope and organisation of farming through a pull-push mechanism. Global issues such as food security and safety, sustainability and as a result efficiency improvement are better addressed by Big Data analyses and applications. These issues make that the scope of Big Data applications extends far beyond farming alone, but covers the entire supply chain. The Internet of Things development, wirelessly connecting all kind of objects and devices in farming and the value chain, is producing many new data that are real-time accessible. Analytics is a key success factor to create value out of these data.

Foto bij rapport Big data SF.JPG

Applications of big data technology

Wageningen Research, TNO and NLR (Netherlands Aerospace Centre) worked together to explore the field of Big Data and concepts like Smart Farming. In this report (a literature review and dairy farm case study) they describe a study on the application of big data technology in the context of smart farming in agriculture. The promise of Big Data in agriculture is alluring, but several challenges have to be addressed for increased uptake of Big Data applications. Although there are certainly technical issues to be resolved the authors recommend to focus on the governance issues that they identified and design suitable business models because these are currently the most inhibiting factors. Additionally, the benefits of Big Data applications have to be demonstrated to end users by independent researchers.