Dr Patricio Grassini1
University of Nebraska -Lincoln
Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. The most common approach for evaluating yield-increasing practices involves conducting controlled research trials in which researchers experimentally evaluate various input levels or management practices to identify whether a particular input or practice improve yield, and if the degree of yield improvement justifies input costs. However, it is difficult and costly to run field experiments to evaluate each potential factor that might limit producer yields. Likewise, it is problematic to extrapolate results from these localized experiments to far-flung producer fields, especially if there is lack of an appropriate description of the biophysical environment (e.g., climate, soil) where these experiments are conducted. Finally, even with a large number of site-year experiments, management × environment (M × E) interactions are difficult to interpret without a rational understanding of what the word “environment” means beyond “site” and “year”. We argue here that having a database containing yield and management data from producer fields across multiple regions and years, properly contextualized relative to the biophysical environment, can be considered equivalent to running hundreds of field experiments to capture both major management effects and M x E interactions. Such analysis of large-scale producer data can provide a focus as to what treatments are the most promising to evaluate in more cost-effective agronomic field trial evaluations. We present here a novel approach that combines producer-reported data and a spatial framework to identify explanatory causes for yield variation over large geographic regions with diversity of climate, soils, and water regimes and we discussed how it can help inform and strategize research and extension programs at both local and regional levels.