Since the beginning of this century, a wide range of advanced digital technologies have been intensively used and daily updated, generating a massive amount of data ready to be analysed. In detail, large sets of heterogeneous data or currently known as “Big Data”, offer detailed information about populations and/or several variables, although the processing and managements of these sets of data demand alternative and advanced data management techniques.
Since the beginning of this century, a wide range of advanced digital technologies have been intensively used and daily updated, generating a massive amount of data ready to be analysed. In detail, large sets of heterogeneous data or currently known as “Big Data”, offer detailed information about populations and/or several variables, although the processing and managements of these sets of data demand alternative and advanced data management techniques.
The application of granular and cloud computing allows the extraction of the knowledge from the data, bringing out the hidden patterns that facilitate results comprehension by the potential end-users, including agriculture.
The Big Data Agriculture is a very recent topic, since it required an integral transformation and digitalisation of farms worldwide. The analysis of agriculture’s Big Data has a wide range of applications at different levels, for instance, high precision weather forecasting (microscale) or monitorization of farmers occupational health (macroscale), but the greatest achievement is the liaising of farmers, agri-food supply chain actors and policymakers in actions implementation based in real-time information without biases.
The sustainability of Big Data Agriculture has been assessed in order to understand their real environmental and socioeconomic impacts. Undoubtably, the connection of big data and technology provide tools for the estimation and reduction of environmental footprint in agriculture through the efficient utilization of natural resources and agrochemicals as well. However, several studies raised awareness of big data effectiveness in small-scale farming, since these farmers, mainly in rural areas, neither have access to these data nor results from those big data analyses. On the other hand, the economic value of agricultural big data can reduce input costs through efficient utilization of resources, while increasing the economic performance of farms enterprises.
Indeed, agricultural big data still has challenges that should be addressed, such as the quality and privacy data and its availability, therefore the liaise of agri-food actors is mandatory coupled to big data technologies implementation in value chain like blockchain technology. Notwithstanding, sustainable guidelines should be stated by different levels of governance, finding suitable actions according with each farming region requirements.