An empirical, Bayesian approach to modelling crop yield: Maize in USA
We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an
Shirley, Raphael et al.
Fecha de publicación:
2
2020