Written by M. Sean High – Staff Attorney
To create agricultural Big Data, relevant crop information is collected from individual farms and aggregated with similar information from other farms. The aggregated information is then combined with “highly detailed records of historic weather patterns, topography and crop performance,” to create models and simulations that attempt to predict future conditions and help farmers make decisions that will improve yields and productivity. As a result, instead of merely blanketing fields with arbitrary amounts of seed, water, and fertilizer, farmers are able to selectively apply these inputs to specifically targeted portions of the land.
Some agricultural Big Data companies have asserted that farmers utilizing agricultural Big Data can eventually increase their average corn harvest by an additional 40 bushels per acre. While the majority of farmers currently employing agricultural Big Data have only seen corn yields increase by 5-10 bushels per acre, agricultural Big Data companies maintain that the higher yields will eventually be realized once additional information is gathered from more farmers and pooled.
Though the interest in agricultural Big Data is a relatively recent phenomenon, much of the agricultural information currently being utilized was available to farmers in the mid-1990s. Lack of development of this agricultural information was primarily a result of underpowered computer processors and high data storage costs. Today, however, the average smartphone has considerably more processing power than the top-of-the-line computers in the mid-1990s, and fees associated with most types of data storage are relatively low.