Agronomy and Horticulture, Department of
Document Type
Article
Date of this Version
9-9-2021
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.
Included in
Agricultural Science Commons, Agriculture Commons, Agronomy and Crop Sciences Commons, Botany Commons, Horticulture Commons, Other Plant Sciences Commons, Plant Biology Commons
Comments
CC-BY OPEN ACCESS