(By Nathan Kung & Joshua Von Fange)
Food demand is expected to increase by 59%-98% by 2050, driven by growing world population and rising incomes in developing markets. However, crop yields have remained relatively flat in recent years amid a diminishing availability of arable land. If crop yields continue to grow at the current rates, the forecasted demand for food cannot be met. To tackle this problem, there is a growing trend to leverage big data tools in farming to help farmers be more effective and profitable. Investment is also pouring into digital farming, with billions of dollars currently funding agriculture technology startups.
How is big data used in farming? Drones and sensors are installed in the farms to gather real-time data, which is sent to a network of remote servers for cloud computing. Market data, including price of crops and fertilizer information, is also stored in the cloud computer network. With mobile devices, farmers can then access to the vast amount of information, including local weather forecast, GPS data, soil specifics, seed, fertilizer and crop protectant specifications, market price, etc. The information is structured to assist farmers to make good decisions, including where and what to plant, when to harvest, and other real-time operational issues, to improve production, cut operational costs, minimize environmental impact, and cope with climate change, and other threats that come their way.
Impact on value chain
This technology is expected to have huge impact on various stakeholders in agriculture value chain. Information brings more power to farmers, who now have visibility of crop pricing and performance of seeds and fertilizer. Better forecast tools reduce the weather risk faced by farmers. Leveraging information on demand side, farmers can adapt products that meet specific customer needs on an on-going basis with a more sophisticated pricing strategy. This also helps farmers track yields, monitor field conditions, and manage inputs precisely across fields, substantially increasing productivity and profitability.
However, the transparency of information may negatively impact distributor dealers, and large seed and chemical producers. As farmers minimize waste by using inputs more effectively and they have more information about quality of inputs, they can make a more informed decision on what and how much to purchase. Meanwhile, as they better understand demand of the market, they can negotiate a better selling price with the distributor dealers, cutting the margin of these intermediaries.
At the same time, as technologies and big data reduce information risk and improve operational efficiencies, more and more farms may consolidate to become mega-farms to further cut overhead and operational costs. This may shift even more power and influence from other stakeholders to the mega-farms.
Social and environmental performance
In the short run, as this technology reduces some uncertainty risks and makes crop yields more stable, the price of the final produce will likely drop gradually. At the same time, farmers will better cater to customers’ demand. For instance, they will produce more high-quality organic food, which is popular in developed countries. In the long run, food shortage may be relieved as the crop yield will climb thanks to the big data and cloud computing technology. However, when more and more farms consolidate, many farming jobs may be replaced by robotics that can work 24×7 to achieve efficiency and productivity.
In terms of environmental performance, farmers can utilize water and fertilizer more efficiently, reducing cost and waste, which is beneficial to the environment. Nitrogen fertilizer not soaked up by crops emits a powerful greenhouse gas, while runoff from excess fertilizer leads to toxic algae blooms that can pollute drinking water and create dead zones in the ocean. In the long run, as soil is used in a more effective and controlled manner, the availability of arable land may gradually increase, providing more greenery to the environment.
In summary, big data brings big opportunities in farming. In the past, it was difficult to use traditional methods to get accurate data, e.g. getting people to count the number of crops. Now the new technologies are solving the biggest challenge in agriculture: variation in weather, rainfall, soil quality and fertiliser. With more investments and startups coming into this area, we believe more and more technologies will be used in farming in the next decade.
(698 words)
- The future of food demand: understanding differences in global economic models
http://onlinelibrary.wiley.com/doi/10.1111/agec.12089/abstract
- Yield Trends Are Insufficient to Double Global Crop Production by 2050
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0066428
- Data Farming: How Big Data Is Revolutionizing Big Ag
http://ohiovalleyresource.org/2016/09/16/data-farming-big-data-revolutionizing-big-ag/
- Precision Farming And The Role Of Big Data
Having worked in the agriculture and retail sector in India briefly, the need for digitisation is very crucial to match supply and demand. However, the challenges are more of the social nature that financial in most developing economies. While big data can iron out a lot of anomalies in this industry, it is essential that the relevant education and training is undertaken to develop this into a norm. Companies should look into more than just providing the technology; they should shift more risk onto themselves by delivering the technology with the knowledge and tools to the customer to implement.
Precision farming is an interesting concept that is derived from the idea of better use of technology and big data to reallocate resources for better crop yields at lower costs than the current methods. I look forward to understanding how this technology can be scaled up, what infrastructure support might it need, and how regulation in addition to government intervention can assist in pushing it further.