Canada Markets

Ag Makes the List of Statistics Canada's 2015 Highlights

Cliff Jamieson
By  Cliff Jamieson , Canadian Grains Analyst
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Statistics Canada's December 22 blog titled "A year in stats" looks at a number of 2015 releases which made news headlines across the country. As stated by the blog writer "we do not decide what information will be picked up by the media, but we notice when our data become part of the national conversation." Of the 13 topics listed, which include Canada's aging population, Canada's rising household debt, weak energy data among others, one section pertained to technological advancements in the presentation of agriculture data in 2015.

Titled "Who is watching my soybeans grow?" this section of the blog discusses Statistics Canada's collaboration with Agriculture and Agri-Food Canada to use remote sensing to forecast crop yields. Statistics Canada issued its first release of official model-based yield estimates on Sept. 17, which included estimates based on satellite data obtained from Statistics Canada's Crop Condition Assessment Program, data from Statistics Canada's previous surveys along with agroclimatic data.

This initiative is viewed to be the first in the world for a statistical agency and will eventually lead to the agency's goal of both reducing costs and reducing the amount of direct surveys with producers. A Western Producer interview with Frederic Bedard, senior analyst with Statistics Canada in September, suggested that if all goes well, this methodology will replace the September estimates, while today's blog suggests that farmers will no longer be asked to respond to the farm survey which is normally conducted in September. As Bedard stated to the Western Producer, "The less contact we get with farmers makes everyone happier."

What about accuracy? Today's blog suggests that the new yield model will provide accurate crop production information for Canadian farmers, while Cindy Carter, senior analyst for Statistics Canada's crops unit told back in September that "The report isn't more or less accurate than StatsCan's previous crop report; it's just a different way of collecting the information."

Released Sept. 17, the first model-based report slightly improved prospects since the release of the July estimates released Aug. 21, while one media report at the time viewed this report as confirmation that 2015 output would be decreased from the volume achieved in 2014. The model-based analysis suggested the total volume of major Canadian grains, oilseeds, pulses and special crops would reach 74.704 million metric tons.

On Oct. 2, Statistics Canada released the September estimates based on Sept. 3 to 13 interviews, with overall production boosted slightly to 76.060 mmt. The survey method saw spring wheat and winter wheat production to be estimated approximately 760,000 metric tons higher than reported in the model-based analysis, while barley production was estimated to be close to 600,000 mt higher, lentils close to 270,000 mt higher, peas 290,000 mt higher and canola production was estimated 141,800 mt lower.

The final November estimates, released Dec. 4, saw production estimates tip the scales to indicate the second largest crop ever in this country, with production of major grains estimated at 83.297 mmt. Total production is approximately 13.9% above the original July estimates, 11.5% higher than the model-based estimates and 9.5% higher than the September estimates based on survey results. Instead of being a smaller crop than 2014, overall production was higher.

As discussed previously in this blog, the largest swing in estimated production over the growing season was seen in canola. The original July estimate was 13.3 mmt, the model-based estimate was 14.439 mmt, the September estimate based on surveys was 14.3 mmt and the final November estimate was 17.2 mmt.

While this new technology may be cost effective and save a number of irritating calls from data collectors in harvest time during what used to be the survey period, the game of estimating production may have changed little.

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