What’s stopping big data from transforming food and agriculture?
To meet the challenges of growing food demand and climate change, policymakers and industry leaders are seeking assistance from technology such as IoT (Internet of Things), big data, analytics, AI (Artificial Intelligence) and cloud computing. Whilst big data holds great promise for the future of agriculture, significant challenges remain. What are the issues surrounding big data and what do providers need to do to realise the full potential of the technology?
The promise of big data
Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, input use and more. Data allows farmers to make better decisions, such as what crops to plant for better profitability and when to harvest. Big data can also help achieve supply chain efficiencies by tracking and optimizing delivery routes and reducing waste.
Despite the promise of data to help farmers meet global challenges, there are still barriers holding the industry back. A study of US farmers found that 62% did not use farm-level data software in 2019 and that 46% store and manage their data primarily on paper records. The study also found that 70% of those who did use data software in 2019 are not having all their needs met by the software.
Privacy and trust issues
A key barrier to uptake is a lack of trust in data sharing. Farmers identify unequal financial gain from data sharing, where downstream organizations profit from farm-level data sharing at higher levels than farmers. A study of US farmers found that more than half said they do not trust the federal government or private companies with their data.
A study of Australian grain farmers found that issues related to trust and transparency are central concerns, alongside concerns about who will benefit from access to and use of “farmers’ data”. The EDF (Environmental Defense Fund) believes that many farmers are not investing in data technology due to a lack of formal standards in privacy and security.
By using big data, farmers may also feel that they risk becoming dependent on technology for decision-making. The same companies providing the data platforms are also providing fertilizers, seeds, and equipment and there is potential for conflicts of interest.
Investing in big data is expensive due to the volume, and variability of the data. Agriculture could learn from other sectors such as biomedicine that are further along in the adoption of big data. According to Dr. Jim Ostell, the Director of the National Center for Biotechnology Information (NCBI), investment from the private sector could enable the development of big data for agriculture. However, to gain public funding, the private sector must address issues such as data ownership to ensure the integrity of the data.
Funding issues for big data don’t just centre around the technology itself. The regions that could potentially benefit most from getting the best out of the land in developing countries are often lacking the necessary mobile networks, internet and social media, network devices and sensors, and satellite communication systems.
Resourcing big data in agriculture
Farmers have access to more data about their crops and livestock than ever before, however using it effectively is another matter. In a 2020 study of US farmers, 70% said they did not have the training or skills to use more data on their farms, and 75% said they did not have time to deal with it.
As agribusinesses become larger and more diverse, the volume of data collected, and its management becomes more complex. New sources of data include data from social media outlets and supplier network channels combined with sensor and machine data coming from farm equipment and in the farm fields.
How can big data be effectively used?
The right data means the potential to do more with less to meet sustainability goals, to use fewer inputs to achieve higher yields and greater profitability. But this requires looking beyond data collection, machine learning and innovative technology and seeing eye-to-eye with farmers.
Data providers need to provide clarity on what data is collected, why, and how it will ultimately be used. By meeting farmers’ needs and addressing their concerns, data collection and sharing can be scaled up and the positive impact of data-informed decisions realised.
Farrelly & Mitchell
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