As part of Defra’s plan to become ‘open by default’, the first datasets relating to milk production have just been released.... →
Humans began cultivating crops over 10,000 years ago. Since then, farming practices have evolved...
Humans began cultivating crops over 10,000 years ago. Since then, farming practices have evolved to incorporate irrigation, crop domestication and pest and weed control. To feed our growing global population, cope with a changing climate and reduce the environmental damage caused by farming, the next step in the evolution of agriculture must be found. The agricultural industry is turning to data science to provide it.
The use of big data to enable site-specific, field-specific or even hectare-specific crop management regimes is known as precision agriculture. This management concept is only possible because of the increasing accuracy of global positioning system (GPS) technologies. Combining accurate GPS information with data about the environment (topography, climate, soil characteristics and compaction) and the crop itself (leaf colour, water status, historical yield) allows creation of detailed maps of the farmed area.
Once this information is collected from records, on-farm sensors or even satellite images, it can be integrated and analysed. Finding correlations and patterns in this way shows farmers how to get the best out of their land by, for example, optimising irrigation, fertiliser and pesticide use, and planting depth.
This technology is available now. And it is improving rapidly. Data is already being gathered from numerous sources. In addition to adapting to the changing global food requirements, this data could substantially increase the traceability of food throughout the food chain. Increasing the efficiency of agriculture will improve the economics of food production and facilitate food security.
However, the technology capability is not the factor that limits overall uptake. The concerns around big data usage are common to other industries: fears around lack of data ownership, security risks, and the use of information from big data to slant the balance of commercial relationships between farmers, their suppliers and their customers. Farmers are worried that their data could be used to punish them for mistakes, that a computer error could lead to fines, or that their landowners could use their yield results against them. In addition, many of the systems are not compatible with one another, making the farmer dependent on one provider, and reducing their ability to share data with colleagues, suppliers or customers.
The Open Ag Data Alliance aims to eliminate some of these hurdles. The mission? To help farmers access and control their data. They are developing open data sharing standards and open source software libraries, and their alliance fosters relationships between developers, companies, farmers and academics that are using technology to improve farming efficiency. A key tenet of the Alliance to build trust is the promise that the project will never be run by one single company.
Improving farming practices is vital to creating an agricultural industry that can cope with change and continue to supply food for the world. Data analytics can help with this goal. Encouraging uptake of big data technology in this sector, like in all sectors, requires incentives to be aligned and value to be generated for every stakeholder along the value chain.