How Big Data Analytics Are Impacting the Agriculture Industry
By: Kayla Matthews on August 30, 2019
Agriculture hasn't always been an industry people associate with big data analytics or data centers, but that's changing. From time immemorial, farmers have been quick to turn to new technologies that allow them to improve their crop yields and work more efficiently. As big data analytics have more widely available across industries, many farmers and companies in the agricultural sector are using those tools in an effort to become more productive and competitive.
How Agriculture Uses Big Data
1. Agriculture Data Analysis Can Improve Crop Management
Crop management has come a long way from when agricultural professionals typically relied on experience and suggestions from fellow farmers in the area to understand which crops grow best during certain times of year and other specifics. Then, much of agriculture was guesswork, and even the most experienced farmers sometimes had failed growing seasons that became prohibitively costly.
Now, it's possible to engage in something called bioprospecting, which involves identifying which plants produce molecules with potential active or functional benefits for specific markets. Following the identification process, biochemists and other professionals grow thousands of varieties of those plants through conventional breeding technologies.
Since today's analytics software can show trends in tremendous amounts of data, plant breeding efforts like those described above could happen with greater efficiency and fewer errors. As such, plant science and other technologies could advance at faster-than-expected rates.
During research at Iowa State University, scientists used machine learning to sort through seed varieties stored at gene banks all over the world and determine which would be most useful to breeders trying to produce superior versions. Yield predictions generated by data analysis were 76 percent accurate in the study, where scientists began with 962 accessions from a database and narrowed it down to 200 with help from data analytics.
2. Data Analytics in Agriculture Provide Better Risk Assessments
Another way data centers and analytics technology help the agriculture sector is by providing insights that could make farmers and other members of the agriculture supply chain more aware of risks.
A Brazilian company called AgroTools is leading the way in showing what's possible. It's a client of the Google Cloud platform and specializes in assessing huge databases of satellite images to monitor properties spread across an area of land that's as large as Italy, Denmark and France combined. One of the purposes of doing these daily checks is to confirm that the producers of raw materials in the supply chain depend on sustainable sources and products.
The company's intensive data requirements made Google Cloud a smart choice to meet its needs. That's especially true if AgroTools needs to scale up depending on an increase in informational feeds or other trends.
3. Big Data IoT Sensors Could Transform Livestock Care
Illnesses can spread quickly in a herd of hundreds or thousands of cows. In many instances, the sicknesses contracted by a few cows spread to dozens of others before farmers realize the problem. Thankfully, several IoT gadgets exist to prevent that issue and others.
Some of them monitor fertility, which could be specifically advantageous on properties where farmers depend heavily on successful breeding. Others notify farmers when cows are in periods of high milk production. Based on what the data says, farmworkers can do things like adding a type of grain that promotes lactation to an animal's feeding regimen.
Sensors collect data about behavioral abnormalities, too. Since those variations could be the first sign of a severe illness, the information helps farmers be proactive in curbing possible health issues by isolating cows that may be ill.
Since these sensors typically receive data continually and could be used on farms with thousands of cows, it's easy to understand why data centers are instrumental in helping agriculture professionals collect and retrieve information.
4. Data Analytics Can Improve Agricultural Supply Chain Management
Agricultural supply chains present a number of challenges for farmers and distributors. Unlike most goods, food products are perishable and can pose a health risk if they’re not handled properly during transport.
Data analytics can greatly improve the way these products make their way from the farmers’ fields to markets around the world. Distributors will be able to identify inefficiencies in their supply chains to help agricultural products get to their destination faster and more cost-effectively. Retailers can use sales and inventory data, as well as information they’ve gathered about customer behavior, to minimize waste and excess inventory while staying a step ahead of market demands.
5. Big Data Could Unlock the Potential of Urban Farming
Urban agriculture, both in terms of people growing food in community plots or in rooftop gardens, has become a popular trend around the world in recent decades. While the ability to provide locally-grown crops to urban populations shouldn’t be discounted, there are many benefits to urban farming that go beyond simply providing food to local communities. There is a positive environmental impact in terms of reducing runoff from heavy rainfall and improving air quality. The work that must go into planting and maintaining these plots also helps to strengthen the community’s social bonds.
Armed with big data analysis, urban farmers can improve efficiency and maximize the potential of the limited space available to them. Some researchers believe that those efforts could ultimately produce as much as 180 million metric tons of food each year, representing about ten percent of the global output of legumes, roots and tubers, and vegetable crops.
Farming and Agriculture Can Also Drive Data Center Efficiency
Waste is a fact of life for people who make their livings on farms of all sizes. When Apple started planning a data center in Denmark, it wanted to use biological waste from nearby farms in creative ways. The company partnered with Aarhus University for a multiyear agreement intended to figure out ways to convert biogas made from things like straw and manure into electricity.
One proposed idea involved feeding agricultural waste into a digester to make methane. This solution would reportedly power the data center, plus turn some of the waste into fertilizer farmers could use.
In another case, a data center operated by Google in Taiwan hopes to rely on local aquaculture professionals and compensate them for allowing it to mount solar panels on poles inside fish ponds. Aquaculture is closely related to agriculture, and since this arrangement aims to maximize land use efficiency while respecting local ecology, future agreements like this one could benefit agriculture, too.
Both these cases show how data center representatives can collaborate with farmers to determine ways to meet eco-friendly energy goals. If they both succeed over a long-term basis, people could see future examples of agriculture workers and data center operators benefitting from each other.
While big data analytics and agriculture might seem as if they belong in different centuries, recent trends suggest that they have a bright future together. As climate change continues to impact global food supplies, farmers will hopefully be able to turn to technology to overcome these challenges. Considering that agriculture has historically benefited from technological innovation, the adoption of big data analysis may one day be regarded as consequential as the mechanization of farming.
About Kayla Matthews
Kayla Matthews writes about data centers and big data for several industry publications, including The Data Center Journal, Data Center Frontier and insideBIGDATA. To read more posts from Kayla, you can follower her personal tech blog at ProductivityBytes.com.