Big data needed for an agricultural boom
Big data in agriculture is instrumental not just for farmers who produce our food but for various stakeholders and along the agriculture value chain.
Photo by CIMMYT
With a dramatic rise in population and environmental issues, such as climate change, agriculture faces the dual challenge of feeding an increasing number of people while making efforts to conserve the environment.
To get through these challenges, inefficiencies prevalent in farming and uncertainties about weather, pests and diseases have to be resolved. For that, actors of food systems should embrace newer technologies that can make agriculture more productive, resilient and conservative.
Big data can be a big help in driving agriculture towards these attributes.
Things that were beyond human imagination a few years ago are now easily possible, thanks to a variety of information that is now more readily available. This huge amount of information, also known as big data, has revolutionized every aspect of our life, including agriculture.
Farmers are beginning to know this growing information by analyzing and processing a vast amount of data collected by smart devices running in their field. There is now the possibility to predict crop yield and other attributes before even making their way to the field.
Through the analysis of several years of crop data and weather, farmers can know their harvest beforehand.
Furthermore, the increased ability to collect and analyze a large number of data has aided in forecasting the weather accurately to help farmers take proactive actions to maximize their yield.
The use of big data doesn’t end with just farmers but stretches to agriculture scientists and researchers too.
Scientists are engaged in an effort to develop new varieties of crops that give a higher yield on limited land space and the colossal amount of years of plant data collected is enabling researchers to engineer plants that will do just that.
However, reaching higher heights of production alone is not enough to build a nutritionally secured world.
In addition to an efficient production system, the processing and distribution system should also be efficient enough to make such nutritionally rich food available to all.
Big data can be used to enhance distribution by informing suppliers and farmer about potential buyers; where they are and how they can be contacted. Furthermore, big data can be used to determine the quality of market processes and assist in removing market barriers.
Product marketing can also be enhanced by adding value to the field produce.
For that, different algorithms are already being used to create different products by mixing, cooking or processing whatever farmer has with him. Such added value has helped farmers generate a higher income with any kind of produce they have.
To my previous point, the current challenge of agriculture is not only to feed the planet but also to make the planet environmentally cleaner.
Big data is already in a wide use to assess the environmental risk possessed by several agro-based industries and agriculture itself.
For instance, Aqueduct, a water risk mapping tool, is used to monitor and calculate water risk anywhere in the world based on various parameters such as water quality.
Such assessment can help increase the awareness of farmers and industries about their potential impact on the environment which will ultimately reduce their environmental footprint.
Big data is also integrated with government policies and regulations for their better implementation.
For example, governments in several countries have implemented latest sensor technologies as HPLC to detect pesticide level that has helped in regulating pesticide contents in several crops.
Finally, big data carries a vast scope in agriculture. However, problems associated with this technology have limited its potentiality to develop agriculture to a higher echelon.
Many farmers in remote areas, for instance, are still maneuvering to get access to big data. Even if the farmer gets access to it, the vast volume of big data makes it difficult for them to decide which data to use and which to discard.
Problems in big data analytics and interoperability are major constraints of this technology.
Proper investment is needed to improve accessibility, interoperability, standard, and analytics, ensuring the right data goes to the right people at the right time and that it is analyzed in the right way.
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