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.
Highlights
Rising global population and climate change realities dictate that agricultural productivity must be accelerated
It is assumed that background management practices are optimal or at least relevant to what most farmers use in the region, which may not be realistic for many farmers
Given all the well-known deficiencies of current agricultural research methods, we argue that a method that allows environment-specific identification of unique cropping systems with the greatest yield potential is essential to meet future food demand
Summary
Rising global population and climate change realities dictate that agricultural productivity must be accelerated. 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. A sustainable solution to this challenge is to increase crop yield without massive cropland area expansion This can be achieved by identifying and adopting best management practices. Seed genetics (G) and crop management decisions (M), interact with the effect of environment (E: soil and in-season weather conditions), thereby resulting in a near infinite number of combinations of G × E × M that can impact crop yield. Multi-year-site performance trials that account for large environmental and background management variability is another common practice in agricultural research. Such trials usually estimate an average effect across environments and background cropping systems. It has been shown that management is an important source of uncertainty in process-based models, which can lead to substantial and varying degree of bias in yield estimates across the US, even when using harmonized p arameters[13]
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