For the past few years, scientists throughout the world have referenced an impending food shortage of global proportions. The prospect of feeding 9 billion people in the year 2050 is intimidating, motivating organizations to turn to advanced technology. If harnessed properly, Big Data could help agriculturalists and food companies find ways to supply a world population that’s increasing dramatically.
Moving into the 21st century
When the farming industry comes to mind, people often think of an archaic, anachronistic practice that lags behind when it comes to technological progression. Although every other sector seems to be adopting , advanced software solutions, and analytics programs, agriculture appears to have been left in the dust.
Even though such a perception may be widespread, there’s no denying the sector’s importance: “No farms, no food” is the way numerous bumper stickers read. Yet, it’s important to remember that big agriculture corporations like Monsanto consistently fund and launch highly sophisticated research and development projects aimed toward improving production rates and promoting sustainability.
TechRepublic reported that Monsanto uses data analytics tools to help farmers achieve greater crop yields, employ fewer chemicals, and reduce water usage, leading to wider profit margins and more sustainable farming practices. The news source noted that the company estimated increased use of algorithmic information scrutiny could potentially lead to a $20 billion per year increase in worldwide crop production.
Starting at the ground level
According to a study conducted in 2012 by PrecisionAg Institute, soybean growers that used data analysis applications reported average savings of 15 percent on expenses such as seed, fertilizer, fungicide, herbicide, and other chemicals. These deductions result in more affordable food products, enabling consumers of limited means to buy more.
TechRepublic cited Monsanto’s acquisition of two major companies as a driving force in improving analytics adoption in farming. The company acquired Precision Planting, a hardware and software developer that helps growers deduce seed space, depth, and root systems in fields, and Climate Corporation, a weather data analysis startup based in San Francisco, in 2012 and October 2013, respectively.
“We expect the precision agriculture space to continue to grow quickly as data becomes cheaper to store and easier to move from platform to platform,” said Monsanto President and COO Brett Begemann, as quoted by TechRepublic.
The source referenced a sales transaction between Climate Corporation and Indiana farmer Jeff McGee that helped the grower establish a platform to collect information from various sources (fertilizer cost, soil quality, weather conditions, etc.) and store it on a public where a data analysis program can provide actionable insight.
TechRepublic acknowledged that the company’s Climate Pro system, which distributes sensors throughout a user’s farmland, costs $15 per acre, but is estimated to increase profits by an average of $100 per acre.
Chris Jones, a 29-year-old Purdue graduate, uses the service to optimize his operations. He told the source that he frequently logs onto his private account on Climate Corporation’s website and adds hypothetical information into theservice to see how it will affect the price of his crop and how much yield he can expect.
Jones also noted that any problems he’s having with his equipment is automatically loaded into the system. For example, if one of his John Deere tractors is experiencing a minor issue that’s easy to overlook (but could result in serious issues if left unattended), Climate Corporation automatically notifies the nearest dealership of the problem, which then contacts Jones.
Preparing for the future
Ultimately, data analytics allows farmers to scrutinize specificity and incorporate complex science into their practices. To sustainably grow large amounts of produce, it’s imperative that agriculturalists outsource to that combine cloud environments with complex, useable analysis tools.
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