Microservices Matters

Sep 15 2015   6:19PM GMT

How big data could help prevent food shortages

Fred Churchville Fred Churchville Profile: Fred Churchville

Big Data
Big Data analytics
big data applications
Data Analytics

From Big Data Innovation 2015, Boston

In 2014, Randy Dowdy, a farmer from Georgia, set the highest yield ever in the National Corn Growers Association National Corn Yield Contest with 503 bushels per acre. What was the secret behind his record breaking yield? According to Erik Andrejko, head of Data Science at the Climate Corporation, it is a result of data science – using big data to support decision making processes that are crucial to production results.

And, believe it or not, big data could help prevent a potential famine in the future. According to current growing and consumption rates, agriculture experts expect a 60% shortfall in crop production by 2050. But by using technology that helps analyze and model big data, Andrejko believes farmers can prevent this from happening.

The use of technology in agriculture in order to increase crop yields is not new – farmers have been leveraging the use of self-driving tractors for years. The use of big data analytics in farming is still in its early stages, but exponential decreases in storage in compute costs, increases in mobile connectivity, and advances in IoT technology is opening the door for big data to make a big impact.

So how exactly can applying data science can impact farming efforts – or “farm to fork,” as Andrejko calls it? It starts with the installation of ubiquitous sensors in agricultural fields – 100 billion over the next four years. These sensors can detect key information, such as soil moisture, that will help ensure better care for hundreds of acres of crops. If these sensors can help farmers better regulate the application of nutrients like nitrogen – on which farmers spend almost $2.5 billion a year – it could revolutionize the business.

But there are still plenty of challenges in applying data science to agricultural needs: namely, a prominent amount of missing and sparse data. And even once you have the data, how do you determine which pieces of data actually indicate a potential impact on yield? In other words, how do you actually turn most of the data into useful information?

Andrejko says the answer to this is creating structural, usable models that explicitly lay out how each piece of data fits into the yield equation. By applying these models – and teaching farmers how to use them appropriately – he hopes they can use them to make higher yields a reality.

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