In the News:
Potato Grower – by Greg Killian
In April 2019, Cornell and Purdue University announced a partnership establishing the first Feed the Future Innovation Lab for Food Safety, with a goal to raise awareness of and solve some of the world’s greatest challenges in food insecurity and agriculture. One year later, the shock of empty grocery store shelves made most Americans acutely aware of the importance of food security—and gave many a newfound appreciation for ranchers, farmers and truck drivers, among others, who struggled to keep the supply chain from breaking during an unprecedented pandemic. In other parts of the world, food insecurity has been a continuous battle, one which the United Nations (UN) has sought to address with its Sustainable Development Goals. Revealed five years ago, included among the 17 goals is ending hunger by 2030.
According to the UN, this ambitious goal now seems an impossibility, even if a profound change occurs in agricultural productivity and sustainable food production, given that 820 million people experienced food scarcity in 2019. Contrary to what many believe, food scarcity is not the result of a lack of food being produced; rather, it is a combination of challenges including but not limited to inadequate transportation, food contamination, spoilage and waste, which on average amounts to roughly 1.3 billion tons annually at a cost of $1 trillion. With global food demand projected to double in 30 years due to rising populations, farmers and agricultural suppliers will increasingly be expected to do more with less by improving productivity from limited resources and inputs.
To bolster the global agricultural industry’s sustainable growth, a number of farms have been utilizing tools such as machine learning, artificial intelligence (AI) and data analytics to increase crop yields. This approach, referred to as precision farming, uses technology to help farmers more accurately predict natural conditions and react to them in the quickest way possible, as well as to choose the best crops for specific growing areas via the use of data analytics.