Satellites Are Great, But They Don’t Beat U.S. Crop Forecasters

Alex Whitebrook/ April 13, 2018/ News/ 0 comments

For decades, government satellites have been taking detailed photographs of crops around the world that are now being tapped by traders like Cargill Inc. to gain an edge in global grain markets.

But the U.S. Department of Agriculture — the benchmark in forecasting domestic crops — says the images by themselves still can’t be relied upon to predict annual corn, wheat or soybean harvests. Instead, the government’s main source of information remains farmer surveys and random field samples.

“Satellites are not advanced enough to differentiate crop acres yet, so there is a loss of precision,” said Seth Meyer, the chairman of the World Outlook Board, the USDA agency responsible for world crop forecasts. “This technology is going to get better, but right now it’s just one tool in our forecasting toolbox.”

Getting accurate assessments of major U.S. crops valued at more than $100 billion last year is a recurring challenge for traders, consumers and farmers. Crop conditions can change with the weather over the long growing season, so any early forecasts may be far off the mark when harvest rolls around.

Some scientists expected satellite images to eventually make the job easier. The U.S. has been taking pictures from space since the 1970s to track everything from the weather to troop movements. But it wasn’t until the last few years that advances in digital technology and computing power made those billions of images more useful in crop forecasting.

Making Switch

Statistics Canada, the government agency that produces the country’s monthly crop forecasts, already is using the technology in key crop assessments. In 2016, StatsCan switched to using only satellite- and weather-based models for a the monthly production report published in September, saving about C$150,000 ($95,000) in farm and field surveys for that month. Other reports during the year still rely on the surveys.

“There’s been a lot research put into this model to verify its robustness,” said Gordon Reichert, head of remote sensing analysis at Statistics Canada. “Feedback from the grain companies in Canada has been favorable.”

Satellites scan thousands of square miles of agricultural land and record daily changes in areas as small as two dining tables, mostly by analyzing how green the fields are from planting to harvest. Machine-learning algorithms then match those characteristics with historical data and production results to make forecasts.

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