Using Stan to improve rice yields

Matt Espe writes:

Here is a new paper citing Stan and the rstanarm package.

Yield gap analysis of US rice production systems shows opportunities for improvement. Matthew B. Espe, Kenneth G. Cassman, Haishun Yang, Nicolas Guilpart, Patricio Grassini, Justin Van Wart, Merle Anders, Donn Beighley, Dustin Harrell, Steve Linscombe, Kent McKenzie, Randall Mutters, Lloyd T. Wilson, Bruce A. Linquist. Field Crops Research. Volume 196, September 2016, Pages 276–283.

Many thanks to everyone on the development team for some excellent tools!

I’ve not read the paper, but, hey, if Stan can improve U.S. rice yields by a factor of 1.5, that’s cool. Then all our research will have been worth it.

5 thoughts on “Using Stan to improve rice yields

    • I had the same thought, but wasn’t sure I wanted to express it. I’d put it a bit differently, however. Shouldn’t the 50% increase seem as unbelievable as some of the claims that we’ve been critical of from the psychological literature?

      • Depends on what we know about rice yields and farming interventions. I say “we” because I know nothing at all about rice! The paper cites a 15% rise in productivity between 2009–2011 and 2012–2015, but I don’t know how those periods were chosen and what’s involved in measurement or what might be a factor in the change (the authors also question the measurement right after they cite the data).

    • Thanks for your interest – not sure that my work is worth front page exposure!

      Regarding the factor of 1.5, I am not sure I ever put it in those terms, but I believe this is the theoretical gain possible in the lowest performing areas. These areas currently yield around 6-7 t/ha, whereas the potential production is estimated to be around 12-14 t/ha. This estimate is not so unbelievable given prior knowledge of the system in question; record yields are on the order of 14 t/ha, and the physiological maximum potential is estimated to be around 22 t/ha.

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