Science Daily May 3, 2019
An international team of researchers (Australia, Spain. Germany USA – University of M Minnesota, Switzerland) analyzed the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using yield data and applying a machine-learning algorithm. They found that growing season climate factors explain 20%–49% of the variance of yield anomalies, with 18%–43% of the explained variance attributable to climate extremes. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors. The hotspot regions that are critical for global production include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production…read more. Open Access TECHNICAL ARTICLEÂ