Winter cover crops (WCCs) are effective at reducing N losses from temperate agroecosystems. Although extensive research on WCCs has demonstrated numerous benefits, overall adoption rates in the Midwest U.S. remain low. We evaluated an alternative to WCC’s ability to reduce nitrate (NO3−)-N leaching; that is, adding an inexpensive, easy-to-apply, form of labile carbon (C) as a soil amendment intended to immobilize N and mitigate leaching. In the autumn in a typical maize–soybean rotation, we added crude glycerol (a C-rich, biodiesel byproduct) and hypothesized that glycerol carbon (Cglyc) would immobilize N and have no effect on crop growth. More specifically, Cglyc was broadcast applied at three rates (0, 216, and 866 kg C ha−1 y−1) and combined factorially with six spring-applied fertilizer N rates (0, 56, 112, 168, 224, and 280 kg N ha−1) at two sites. In response, we measured: soil profile NO3−-N, leached NO3−-N, crop health (via SPAD), yield, and maize agronomic optimum N rate (AONR). Cglyc reduced spring soil profile NO3−-N by 14–24% across site-years, but had highly variable and non-significant effects on NO3−-N leaching. Cglyc had an inconsistent impact on crop SPAD and yield, with Cglyc increasing AONR by ~63 kg N ha−1 (or 31–40%) at one of two sites. Our results show promise for using labile C as a “liquid cover crop” soil amendment. Future studies should explore greater labile C application rates and alternate application timing in order to fine-tune the balance between environmental benefits and crop productivity.

Fertilizing maize at an optimum nitrogen rate is imperative to maximize productivity and sustainability. Using a combination of long-term (n = 379) and short-term (n = 176) experiments, we show that the economic optimum nitrogen rate for US maize production has increased by 2.7 kg N ha−1 yr−1 from 1991 to 2021 (1.2% per year) simultaneously with grain yields and nitrogen losses. By accounting for societal cost estimates for nitrogen losses, we estimate an environmental optimum rate, which has also increased over time but at a lower rate than the economic optimum nitrogen rate. Furthermore, we provide evidence that reducing rates from the economic to environmental optimum nitrogen rate could reduce US maize productivity by 6% while slightly reducing nitrogen losses. We call for enhanced assessments and predictability of the economic and environmental optimum nitrogen rate to meet rising maize production while avoiding unnecessary nitrogen losses. Maize production is dependent on Nitrogen fertilizer input. Here, the authors use long-term and short-term experiments to demonstrate that economic and environmental optimum nitrogen fertilization rates have increased between 1991 and 2021.

This protocol was used by a collaborative research team referred to as the “NutriNet Project” in corn-based cropping systems of the U.S. Midwest and Canada. Plant measurements were collected from field-based experiments to determine aboveground biomass and nutrient concentration at different crop development stages. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) are the cash crops and cereal rye (Secale cereale L.) is the cover crop described in this protocol. The total amount of nutrient uptake is calculated based on the plant biomass and nutrient concentration measurements. Nutrient analysis was conducted using dried, ground plant samples, separated into grain and non-grain components when applicable. Ancillary measurements, such as chlorophyll meter readings and corn stalk nitrate concentrations, are also described as these provide important context for informing nutrient management decisions. Corn was the dominant crop and focus of the project team when collecting measurements; this is evident by the detail describing corn in this protocol.

The economically optimal nitrogen rate (EONR), while an accepted standard as the “right rate” for corn ( Zea mays L.) fertilization, does not directly account for environmental impacts. This study evaluated the effects of nitrogen (N) fertilizer application rate and timing on crop N use and N loss potential, using residual soil nitrate‐N (RSN; 0‐ to 0.9‐m depth) relative to EONR. The evaluation was conducted using 49 N response trials from eight US Midwest states from 2014 to 2016. Nitrogen rates were applied as ammonium nitrate, either all at planting or split between at planting (45 kg N ha −1 ) and the remainder at the ∼V9 growth stage. At EONR, RSN was 42 kg N ha −1 for at‐plant applications and 62 kg N ha −1 for split applications. However, unaccounted for N at the end of the growing season was greater for at‐plant (46 kg N ha −1 ) than for split applications (21 kg N ha −1 ). This suggests a higher susceptibility of N loss during the early season for at‐planting applications and after the season for split applications. Differences in RSN at the EONR between N timings were not explained by differences in total aboveground N uptake at R6. Residual soil nitrate did not substantially increase until N application rates exceeded the EONR by 30 kg N ha −1 . These findings support using EONR, at an N:corn price ratio of 5.6, as an N application sustainability standard that balances profitability and environmental concerns.

AbstractWinter cereal rye (Secale cereale L.), a commonly used cover crop in corn (Zea mays L.) systems, has potential to scavenge soil NO3–N through a fibrous root system. This study aimed to quantify root and shoot biomass, carbon (C), and nitrogen (N) partitioning in rye cover crop at the time of termination in spring. This was a 1‐year study conducted at a site with a no‐till corn–soybean [Glycine max (L.) Merr.] rotation, rye drilled following grain crop harvest, and three N rates applied to corn (0, 135, and 225 kg N ha−1, respectively). Rye root biomass to 60‐cm depth following corn and 30‐cm depth following soybean was estimated using ingrowth tubes installed in the fall after rye seeding and removed at the time of rye termination in the spring. For rye, 48% and 62% of the total root biomass were present in the top 15‐cm depth, following corn and soybean, respectively. Overall, the shoot biomass, C, and N were significantly greater than for roots, with approximately two times more shoot than root material and only 33%–36% of total plant C and 17%–18% of total plant N in the root biomass. The C:N ratio of root biomass was consistently high (47–52) and at least double that of the shoot (16–23). With high C, low N, and high C:N ratio of the rye roots, inorganic‐N from soil or degrading shoot biomass could be immobilized with root degradation and reduce potential N recycling.

The application of nitrogen (N) fertilizer both underpins high productivity of agricultural systems and contributes to multiple environmental harms. The search for ways that farmers can optimize the N fertilizer applications to their crops is of global significance. A common concept in developing recommendations for N fertilizer applications is the “mass balance paradigm” – that is, bigger crops need more N, and smaller less – despite several studies showing that the crop yield at the optimum N rate (N opt ) is poorly related to N opt . In this study we simulated two contrasting field experiments where crops were grown for 5 and 16 consecutive years under uniform management, but in which yield at N opt was poorly correlated to N opt . We found that N lost to the environment relative to yields (i.e., kg N t -1 ) varied +/- 124 and 164 % of the mean in the simulations of the experiments. Conversely, N exported in harvested produce (kg N t -1 ) was +/- 11 and 48 % of the mean. Given the experiments were uniformly managed across time, the variations result from crop-to-crop climatic differences. These results provide, for the first time, a quantitative example of the importance of climatic causes of the poor correlation between yield at N opt and N opt . An implication of this result is that, even if yield of the coming crop could be accurately predicted it would be of little use in determining the amount of N fertilizer farmers need to apply because of the variability in environmental N losses and/or crop N uptake. These results, in addition to previous empirical evidence that yield at N opt and N opt are poorly correlated, may help industry and farmers move to more credible systems of N fertilizer management.

Ranking the contribution of genotype, environment, and management (G x E x M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G x E x M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G x M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G x E x M factors influenced the yield response to N fertilizer in different ways. Soil-related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G x M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions. Genotype, management, and environment explained 27%, 31%, and 41% of the economic optimum N rate (EONR) variability. Weather variability, crop radiation use efficiency, and inorganic nitrogen were the most influential factors on the EONR. Soil nitrogen-related factors mostly affected the grain yield at low N-fertilizer rates. Increases in plant density and changes in genetics synergistically increased EONR.

Context or problem: Nitrogen (N) fertilizer is among the costliest inputs to maize (Zea mays L.) production, and the most challenging input to predict the optimum application for enhanced productivity while preventing loss to the environment. Objective: This study aimed to determine if late spring maize stalk sap nitrate-N concentrations measured during vegetative growth stages can be used to guide in-season N fertilizer input decisions. Methods: Maize stalk sap nitrate-N concentrations were measured at the seven to nine-leaf (V7-V9) developmental stage across eight sites (location-crop rotation-year) in Iowa. Each site received four to eight pre-plant N fertilizer rates. Results: At each site, the stalk nitrate-N concentration consistently increased with the N fertilization rate. Relative grain yield was positively related to sap nitrate concentration. In addition, there was a positive relationship between sap nitrate concentration, tissue total N concentration, late spring soil nitrate test, and end-of-season maize stalk nitrate test. Overall, across all sites, the sap nitrate-N concentration that indicated N sufficiency (i.e., N supply sufficient to achieve the highest relative yield) spanned a relatively narrow range (715-893 mg N L-1 sap) compared to the full observed range (22-1478 mg N L-1 sap). Conclusion: Observations from this multi-site-year study suggest that stalk sap nitrate concentration has the potential to aid in-season N fertilizer application recommendations. Thus, it deserves further study considering other environmental and management factors that potentially affect the sap nitrate N concentrations. Implications or significance: A stalk sap nitrate test along with rapid, reliable, and low-cost nitrate sensors can create unprecedented databases to optimize soil fertility and plant nutrition.

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

CONTEXT: Process-based models are increasingly used to explain and predict crop yields and long-term changes in soil organic matter (SOM) and hence should be regularly evaluated for their accuracy. Currently, there is a knowledge gap of how well process-based models can estimate the economic optimum nitrogen rate (EONR) across environments and years.OBJECTIVES: We evaluated the Agricultural Production Systems sIMulator (APSIM) software ability to simulate corn yield response to nitrogen (N) input and crop rotation in long term experiments. Furthermore, we explored causes for over/under prediction of the EONR. METHODS: Measurements included crop yields from 14 long-term (N) fertilizer rate experiments representing major production regions in the U.S. Corn Belt and SOM distribution from seven long-term experiments. Corn yield response to N rate was analyzed with statistical models to estimate the EONR (386 N rate trials).RESULTS And CONCLUSIONS: The model successfully captured spatiotemporal patterns in observed crop yields across N rates in the soybean-corn (SC) system, but overpredicted crop yield by 5-25% at high N rates in the corn-corn (CC) system. This overprediction was partially resolved by adding algorithms in APSIM to account for the well-known continuous corn yield penalty. The improved model simulated yield response to N and the EONR with a model agreement of 0.93 and 0.66, respectively, across rotations. The lower accuracy in predicting EONR compared to crop yields was attributed to 1) inherent model error in simulating yields: for example, a 10% error in yield simulation of a single point can result in a 34% error in EONR; and 2) the inability of the APSIM model to fully generate the quadratic nature of corn yield response to N rate, which resulted in the linear-plateau model being selected in most cases. Forcing use of only a quadratic plateau or quadratic regression model to fit the simulated yields, which is the expected biophysical yield response to N, improved EONR prediction by 23% while the accuracy of the statistical model fit was minimally decreased (<1%). The APSIM model simulated SOM distributions after 20 years of cropping with an agreement index of 0.93. We believe that APSIM is well suited to supplement N research in the U.S. Corn Belt, recognizing identified limitations between yield estimation and N response determination. SIGNIFICANCE: This research provided a solution for process-based models to cope with the continuous corn yield penalty, thoroughly evaluated APSIM, and identified research priorities towards increasing EONR prediction.

Improving corn (Zea mays L.) nitrogen (N) rate fertilizer recommendation tools can improve farmers' profits and mitigate N pollution. Numerous approaches have been tested to improve these tools, but to date improvements for predicting economically optimum N rate (EONR) have been modest. This work's objective was to use ensemble learning to improve our estimation of EONR (for a single at-planting and split N application timing) by combining multiple corn N recommendation tools. The evaluation was conducted using 49 corn N response trials from eight states in the US Corn Belt and three growing seasons (2014-2016). Elastic net and decision tree approaches regressed EONR against three unique tools for each N application timing. Tools used in various combinations included a yield goal method, two soil nitrate tests (pre-plant and late season), a computer simulation crop model (Maize-N), and canopy reflectance sensing. Any combination of two or three N recommendation tools improved or maintained performance metrics (R-2, root-mean square error , and number of sites close to EONR). The best results for a single at-planting recommendation occurred when combining the three at-planting N recommendation tools (including interactions) with an elastic net regression model. This combined recommendation tool had a significant linear relationship with EONR (R-2 = 0.46), an increase of 0.27 over the best tool evaluated alone. Combining multiple tools increased the implementation cost, but it did not reduce profitability and, sometimes, improved profitability. These results show tools can be combined to better match EONR, and thus could aid farmers in improving N management.

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R2 = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest.

BackgroundLabile carbon (C-labile) limits soil microbial growth and is critical for soil functions like nitrogen (N) immobilization. Most experiments evaluating C-labile additions use laboratory incubations. We need to field-apply C-labile to fully understand its fate and effects on soils, especially at depth, but high cost and logistical difficulties hinder this approach. AimsHere, we evaluated the impact of adding an in situ pulse of an inexpensive and C-13-depleted source of C-labile-crude glycerol carbon (C-glyc), a by-product from biodiesel production-to agricultural soils under typical crop rotations in Iowa, USA. MethodsWe broadcast-applied C-glyc at three rates (0, 216, and 866 kg C ha(-1)) in autumn after soybean harvest, tracked its fate, and measured its impact on soil C and N dynamics to four depths (0-5, 5-15, 15-30, and 30-45 cm). Nineteen days later, we measured C-glyc in microbial biomass carbon (MBC), salt-extractable organic C, and potentially mineralizable C pools. We paired these measurements with nitrate N (NO3--N) and potential net N mineralization to examine short-term effects on N cycling. ResultsC(glyc) was found to at least 45-cm depth with the majority in MBC (18%-23% of total C-glyc added). The delta C-13 values of the other measured C pools were too variable to accurately track the C-labile fate. NO3--N was decreased by 13%-57% with the 216 and 866 kg C ha(-1) rates, respectively, and was strongly related to greater microbial uptake of C-glyc (i.e., immobilization via microbial biomass). Crude glycerol application had minor effects on soil pH-the greatest rate decreased pH 0.18 units compared to the control. ConclusionsOverall, glycerol is an inexpensive and effective way to measure in situ, C-labile dynamics with soil depth-analogous to how mobile, dissolved organic C might behave in soils-and can be applied to rapidly immobilize NO3--N.

Sulfur (S) is an important nutrient for plant growth and crop yield. In the northern U.S. Corn Belt, a decrease in atmospheric S deposition and an increase in grain harvest have increased the frequency of S deficiency in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.]. Nevertheless, S deficiency remains difficult to predict owing to complex interactions between the production of plant-available sulfate-S from soil organic matter (SOM) mineralization and sulfate-S losses to leaching. Our objective was to measure corn and soybean yield response to four S fertilizers (ammonium sulfate, elemental S, gypsum, and polyhalite) in the 1st year following fertilizer application and in the 2nd (residual) year following fertilizer application in Iowa. Across 4 site-years, none of the S fertilizer products increased soybean yield. Across 12 site-years in the 1st year following application, sulfate-S based fertilizers increased corn yield in 4 site-years while elemental S had no effect. Across 6 site-years in the second residual year following application, elemental S, polyhalite, and gypsum increased corn yield in 1 site-year while ammonium sulfate had no effect. Laboratory incubations confirmed that there was slower production of plant-available S from elemental compared with sulfate-based fertilizers, potentially explaining the lack of elemental S effect in the 1st year following application. In contrast to our expectations, there was a weak, but positive relationship between corn yield response to S fertilizer and SOM concentration. Overall, the kinetics of S fertilizer mineralization and solubility appear to affect the magnitude and timing of crop response to S fertilizer.

Loss of nitrate-nitrogen (NO3--N) from Midwestern U.S. agricultural fields can impair water quality and be an economic loss to farmers. Winter cover crops have shown promise as a remedy, but low adoption illustrates the need for alternatives. Here, we tested whether adding a carbon (C)-rich soil amendment (i.e., crude glycerol, a biodiesel byproduct) can increase soil microbial biomass (MB) and promote N immobilisation under various conditions and then determined whether and when immobilised N would be released. We conducted a laboratory incubation with a full factorial combination of four glycerol rates (0, +117, +468 and 1872 mg C kg(-1) soil), three supplemental NO3--N rates (0, +10 and +40 mg N kg(-1)) and two soils (Clarion clay loam and Sparta loamy sand). Soil inorganic N (NH4+-N and NO3--N) and MB were measured at seven and three time points, respectively, across the 98 days incubation period. Across all treatments, glycerol increased MBN in both short term (7 days; 4%-1137% compared to no glycerol addition) and long term (98 days; 10%-169%) and decreased NO3--N with increasing rate of glycerol. Adding glycerol caused net N immobilisation of 21%-61% (+-117 mg C kg(-1) addition) and similar to 100% (+ 468 and 1872 mg C kg(-1) addition) compared to the control. Some of that immobilised inorganic N was likely released through MB turnover, but timing and rate of release depended on the soil and added N rate. Adding 40 mg N kg(-1) with no glycerol showed nearly twice the net N mineralisation rate than with the low or no applied N - providing evidence for soil N priming. Overall, glycerol has the potential for use as a soil amendment to increase MB and temporarily immobilise NO3-N and then make some of that N crop available through MB turnover.

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

Based on a seminal paper published by Stanford in 1973, many land grant universities adopted N rate guidelines that used expected yield times a factor (such as 1.2 lb N/bu), with adjustments for previous crop, to formulate N rate recommendations for corn. However, discrepancies between yield‐based N rate recommendations and recent N response data led to the development of an empirical approach, using N response data to generate optimum N rates. The idea of combining crop N response data, dubbed the Maximum Return to Nitrogen (MRTN) approach, was initiated in the mid‐2000s and is today used in seven Corn Belt states. Earn 1.5 CEUs in Nutrient Management by taking the quiz for the article at https://bit.ly/35Fy683. View all CEUs at https://web.sciencesocieties.org/Learning‐Center/Courses.

In the U.S. Midwest, nitrate in subsurface tile drainage from corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] systems is detrimental to water quality at local and national scales. The objective of this replicated plot study in northwest Iowa, performed in 2015-2020, was to investigate the influence of nitrogen (N) fertilizer timing on crop production and NO3 load in subsurface (tile) drainage discharge. Four treatments applied to corn included fall anhydrous ammonia with a nitrification inhibitor (F), spring anhydrous ammonia (S), split-banded urea at planting and mid-vegetative growth (SS), and no N fertilizer (0N). Across crops and years, NO3 -N concentration in subsurface drainage discharge was the same at 11.7 mg L-1 for F and S applied anhydrous ammonia (AA). The NO3 -N concentration was statistically lower with SS urea (10 mg L-1 ) than F and S, and 0N was lower than SS at 8.3 mg L-1 . Average annual NO3 -N loads were not different between any treatments due to plot variability in drainage discharge. Corn responded to N application, with overall mean yield the same for F, S, and SS. There were no agronomic or water quality benefits for applying AA in spring compared with fall, where the F included a nitrification inhibitor and was applied to cold soils. Split-applied urea had a small positive water quality impact but no crop yield enhancement. This study shows that there were improvements to NO3 -N concentration in subsurface drainage discharge, but more nutrient reduction practices are needed than fertilizer N management alone to reduce nitrate load to surface water systems.

1 USDA-ARS Cropping Systems and Water Quality Research Unit, 243 Agric. Eng. Bldg., Columbia, MO 65211, USA 2 Iowa State Univ., 3208 Agronomy Hall, Ames, IA 50011, USA 3 Purdue Univ., Lilly 3-365, West Lafayette, IN 47907, USA 4 Independent Agronomist, 13801 Summit Dr., Clive, IA 50325, USA 5 Univ. of Nebraska, Keim 367, Lincoln, NE 68583, USA 6 Univ. of Minnesota, 1991 Upper Buford Circle, St. Paul, MN 55108, USA 7 North Dakota State Univ., PO Box 6050, Fargo, ND 58108, USA 8 University of Wisconsin-Madison, 1525 Observatory Dr., Madison, WI 53706, USA 9 Corteva Agriscience, 8325 NW 62nd Ave., Johnston, IA 50131, USA 10 Univ. of Illinois, W-301 Turner Hall, 1102 S. Goodwin, Urbana, IL 61801, USA 11 Soil Health Institute, 6807 Ridge Rd, Lincoln, NE 68512, USA

Improving corn (Zeamays L.) N managementis pertinent to economic andenvironmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014-2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset.