Breaking Down World Food Production, Harvest, and Crop Yield Data
We’ve blogged before about international food consumption data in Wolfram|Alpha, and queries about this data have proved to be a favorite among our users, with good reason: it’s fascinating to explore the world’s food supply and to visualize trends in consumption. In an attempt to fill in a more complete picture of global agricultural trends, we’ve added more data from the FAO—this time covering food production, harvest, and crop yields around the world.
Try asking Wolfram|Alpha about “coffee production in the Americas“, for example, and you’ll see that Brazil is the clear leader in this region, producing more than 2.5 times as much as #2-ranked Colombia. On a per capita basis, however, Honduras comes out on top, producing more than 60 pounds of coffee per person in 2009.
Or ask about the corn harvest area in the United States and China, and you’ll see how the gap between the two countries has shrunk from some 20 million acres in 1961 to virtually nothing in 2009. But interestingly, the US has an average yield per acre nearly twice that of China.
(Another surprising fact: the United States may be colloquially known as “the land of apple pie”, but China surpassed the US in production of the key ingredient some 20 years ago.)
Simple queries for data about one type of food in one country or in a small number of countries will return a “Comparisons” pod showing available data for consumption, production, harvest area, and yield of that particular product, as well as a larger pod showing data for many other types of food—all perfect for getting a sense of which crops or other agricultural products contribute most to a nation’s economy (and perhaps its collective waistline).
Be sure to try all the different types of queries Wolfram|Alpha supports around this data, including questions about specific agricultural products in a particular region, comparisons of different products within a given country, or questions about different aspects of a single product, as well as the multi-country comparisons highlighted above. As always, tell us if you come across an interesting trend or comparison that could be a Wolfram Fun Fact and let us know what other international statistics you’d like to explore in Wolfram|Alpha.
May I have the herd (cattle) and flock (sheep) numbers in Australia for the past 15 years correlated to rainfall? Thx ^RW
@ Roger –
Thanks for the comment. Our livestock numbers are at a national level while our weather data is at a local level. Right now we don’t compute aggregate/average rainfall for an entire country for example.
How cool, great for my senior class analysis of trends in Ag and Hort. Thank you!!