Core Ideas The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this work. The purpose of this article is to describe how the research was undertaken, reasons for the research methods, and the project's potential value. The project generated a valuable dataset across a wide array of weather and soils that allows evaluation of N decision tools. Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public‐domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project's anticipated value. The project was initiated in a partnership between eight U.S. Midwest land‐grant universities, USDA‐ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools.

The North American Project to Evaluate Soil Health Measurements was initiated with the objective to identify widely applicable soil health measurements for evaluation of agricultural management practices intended to improve soil health. More than 20 indicators were chosen for assessment across 120 long‐term agricultural research sites spanning from north‐central Canada to southern Mexico. The indicators being evaluated include common standard measures of soil, but also newer techniques of visible and near‐infrared reflectance spectroscopy, a smart phone app, and metagenomics. The aim of using consistent sampling and analytical protocols across selected sites was to provide a database of soil health indicator results that can be used to better understand how land use and management has affected the condition of soil ecosystem provisioning for agricultural biomass production and water resources, as well as nutrient and C cycling. The objective of this paper is to provide documentation of the overall design, and methods being employed to identify soil health indicators sensitive across agricultural management practices, pedologies, and geographies.

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Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45-270 kg N ha -1 on 45 kg ha -1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha -1 of EONR > 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha -1 with no N applied at planting and 74 to 118 kg N ha -1 with 45 kg of N ha -1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed.