Advancing agricultural research using machine learning algorithms

Authors

Spyridon Mourtzinis, Agstat Consulting
Paul D. Esker, Pennsylvania State University
James E. Specht, University of Nebraska - Lincoln
Shawn P. Conley, University of Wisconsin - Madison

Date of this Version

9-9-2021

Comments

CC-BY OPEN ACCESS

Abstract

Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.