Agriculture hasn't always been an industry people associate with data centers and analytics, but that's changing. Here are four ways data is positively impacting agriculture.
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.
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.
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.
AgroTools carries out more than 200,000 analyses annually on 1,151 layers of information. Due to advancements such as drones and Internet of Things (IoT) gadgets leading to the collection of more information, the total volume of AgroTools' data could reach 1,600 petabytes in a matter of years.
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.
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.
These examples prove how data centers are essential in helping agriculture professionals succeed. Sometimes, data centers also benefit from farmers beyond the monetary compensation agriculture companies provide as clients.
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.