Mitigating nitrogen (N) losses from agriculture without negatively impacting crop productivity is a pressing environmental and economic challenge. Reductions in N fertilizer rate are often highlighted as a solution, yet the degree to which crop yields and economic returns may be impacted at the field-level remains unclear, in part due to limited data availability. Farmers are risk averse and potential yield losses may limit the success of voluntary N loss mitigation protocols, thus understanding field-level yield tradeoffs is critical to inform policy development. Using a case study of soil N2O mitigation in the US Midwest, we conducted an ex-post assessment of two economic and two environmental N rate reduction strategies to identify promising practices for maintaining maize yields and economic returns while reducing N2O emissions per unit yield (i.e. yield-scaled emissions) compared to an assumed baseline N input level. Maize yield response data from 201 on-farm N rate experiments were combined with an empirical equation predicting N2O emissions as a function of N rate. Results indicate that the economic strategy aimed at maximizing returns to N (MRTN) led to moderate but consistent reductions in yield-scaled N2O emissions with small negative impacts on yield and slight increases in median returns. The economic optimum N rate strategy reduced yield-scaled N2O emissions in 75% of cases but increased them otherwise, challenging the assumption that this strategy will automatically reduce environmental impacts per unit production. Both environmental strategies, one designed to increase N recovery efficiency and one to balance N inputs with grain N removal, further reduced yield-scaled N2O emissions but were also associated with negative yield penalties and decreased returns. These results highlight the inherent tension between achieving agronomic and economic goals while reducing environmental impacts which is often overlooked in policy discussions. To enable the development of more scalable environmental N loss mitigation strategies, yield tradeoffs occurring at the critical point of adoption (i.e. the farm-level) should be considered.

Core Ideas DSSAT was calibrated for simulating soil N concentration during the maize growing season. Two-step validation included research experiments and 49 commercial maize fields. Model performance was fair in predicting SOM mineralization and N management impacts. The model captured effects of early season rainfall on soil N variability across sites. Farmers have limited knowledge of inorganic soil nitrogen (N) concentration during maize (Zea mays L.) growth in the US Midwest, particularly after periods of wet spring weather. The objectives of this study were to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model for predicting inorganic soil N concentration using data from three field experiments in Illinois, to evaluate model performance against three independent sites and additional data from 49 commercial maize fields, and to assess the impacts of rainfall variability on the predicted decrease in soil N concentration early in the growing season. Model calibration included adjustments to soil organic matter (SOM) decomposition parameters based on predicted soil organic carbon concentration (obtained from gSSURGO) and soil drainage rates. Model performance was considered “fair” in predicting SOM mineralization dynamics and the effects of fall vs. spring N fertilizer application across the validation datasets (normalized RMSE, 21.2–25.7%). The model also captured the variability in soil N concentration across 49 commercial fields (R2 = 0.68–0.88; slope, 0.99–1.24), with higher cumulative rainfall from January to July (>800 mm) reducing predicted soil N availability compared with fields receiving less rainfall (500–600 mm). Results suggest that DSSAT has the potential to estimate soil N availability across variable weather patterns, soil properties, and fertilizer management scenarios in Illinois. However, future work is needed to further improve model accuracy, especially if it is to be used as a decision support tool for farmers.

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

Relationships between greenhouse gas emissions, yields, and soil properties are not well known. Utilizing two datasets from long-term cropping systems in Illinois, USA, our we aim to address these knowledge gaps. The objective of this study was to explore the relationships between the physical and chemical properties and greenhouse gas (GHG) emissions of soil, and cash crop yields over a four-year time-period and following 15 years of treatment implementation in Illinois, USA. The experimental layout was a split-plot arrangement involving rotation and tillage treatments in a randomized complete block design with four replications. The studied crop rotations were continuous corn [Zea mays L.] (CCC), corn-soybean [Glycine max (L.) Merr.] (CS), continuous soybean (SSS), and corn-soybean-wheat [Triticum aestivum L.] (CSW), with each phase being present for every year. The tillage options were chisel tillage (T) and no-tillage (NT). We used an array of multivariate approaches to analyze both of our datasets that included 31 soil properties, GHG emissions (N2 O, CO2, and CH4 ) and cash crop yields. The results from our analyses indicate that N2 O emissions are associated with a low soil pH, an increased Al concentration, the presence of soil nitrate throughout the growing season, an increase in plant available water (PAW) and an increased soil C concentration. Likewise, soil CO2 respiration was correlated with low pH, elevated Al concentrations, low Ca, increased PAW, higher levels of microbial biomass carbon (MBC), and lower water aggregate stability (WAS). Emissions of CH4 were associated with increased levels of MBC. Lastly, the yield index (YdI) was correlated with lower levels of soil Ca and available P and lower values of WAS. The association between high YdI and lower WAS can be attributed to tillage, as tillage lowers WAS, but increases yields in highly productive cropping systems in the Midwest.

Two of the most important agricultural practices aimed at improving soil properties are crop rotations and no-tillage, yet relatively few studies have assessed their long-term impacts on crop yields and soil greenhouse gas (GHG) emissions. The objective of this study was to determine the influence of tillage and crop rotation on soil GHG emissions and yields following 15 years of treatment implementation in a long-term cropping systems experiment in Illinois, USA. The experimental design was a split-plot RCBD with crop rotation as the main plot: (continuous corn [Zea mays L.] (CCC), corn-soybean [Glycine max (L.) Merr.] (CS), continuous soybean (SSS), and corn-soybean-wheat [Triticum aestivum L.] (CSW); with each phase of each crop rotation present every year) and tillage as the subplot: chisel tillage (T) and no-tillage (NT). Tillage increased the yields of corn and soybean. Tillage and crop rotation had no effect on methane (CH₄) emissions (p = 0.4738 and p = 0.8494 respectively) and only rotation had an effect on cumulative carbon dioxide (CO₂) (p = 0.0137). However, their interaction affected cumulative nitrous oxide (N₂O) emissions significantly (p = 0.0960); N₂O emissions from tilled CCC were the greatest at 6.9 kg-N ha⁻¹-yr⁻¹; while emissions from NT CCC (4.0 kg-N ha⁻¹-yr⁻¹) were not different than both T CS or NT CS (3.6 and 3.3 kg-N ha⁻¹⁻yr⁻¹, respectively). Utilizing just a CS crop rotation increased corn yields by around 20% while reducing N₂O emissions by around 35%; soybean yields were 7% greater and N₂O emissions were not affected. Therefore results from this long-term study indicate that a CS rotation has the ability to increase yields and reduce GHG emissions compared to either CCC or SSS alone, yet moving to a CSW rotation did not further increase yields or reduce N₂O emissions.

The environmental consequences of residue removal practices to support cellulosic biofuel production remain poorly understood. In the U.S. Midwest, corn (Zea mays L.) stover removal combined with no-till practices may increase or decrease soil N2O emissions by influencing soil moisture, temperature, and nutrient dynamics, yet empirical evidence from long-term field experiments is inconsistent. We investigated the effects of residue management (residue retained or removed) and tillage (chisel-till or no-till) on cumulative soil nitrous oxide (N2O) emissions, grain yield, and yield-scaled N2O emissions in a 3-year study initiated 10 years after treatment implementation in a long-term, continuous corn experiment in Illinois, United States. Crop yields were affected by treatment in only one of three study years, with the combination of residue removal and no-till reducing yields compared to both chisel-till treatments. Cumulative N2O emissions, soil inorganic N concentrations, and yield-scaled N2O emissions differed over the 3-year period and were significantly affected by tillage, with no response to residue management. In 2 years, no-till decreased cumulative N2O emissions and yield-scaled N2O emissions by an average of 64% and 60%, respectively. Correlations between daily N2O fluxes and soil moisture, temperature, and inorganic N concentrations suggested that the relative importance of these variables changed depending on year and treatment. While more research across a range of sites and management practices is needed, our findings support previous studies which have challenged IPCC methodology assumptions regarding the effects of residue removal on N2O emissions. We conclude there is inherent difficulty in predicting the impacts of residue removal due to the complexity of soil processes underlying N2O emissions coupled with inter-annual weather variability in this rainfed continuous corn system. Future efforts to evaluate the net greenhouse gas emissions of cellulosic biofuel production may benefit from accounting for this uncertainty.

Nutrient loss reduction strategies have recently been developed in the U.S. Midwest to decrease the environmental footprint associated with nitrogen (N) fertilizer use. Although these strategies generally suggest decreasing N rates and shifting the timing of N application from fall to spring, the spatiotemporal impacts of these practices on maize yield and fertilizer N use efficiency (NUE, kg grain yield increase per kg N applied) have not been assessed at the watershed scale using crop simulation models. We simulated the effects of N fertilizer rate (0, 168, 190, 224 kg N ha-1) and application timing [fall-applied N (FN): 100% N applied on 1 December; spring-applied N (SN): 100% N applied 10 days before planting; split N: 66% N applied on 1 December + 34% N applied 10 days before planting] on maize grain yield (GY) across 3042 points in Illinois during 2011-2015 using the DSSAT-CERES-Maize model. When simulations were scaled up to the watershed level, results suggest that increases in average maize GY for SN compared to FN occurred in years with higher than average winter rainfall (2011, 2013), whereas yields were similar (+/- 4%) in 2012, 2014, and 2015. Accordingly, differences in NUE for SN compared to FN were small (0.0-1.4 kg GY/kg N) when cumulative winter rainfall was < 300 mm, but increased to 0.1-9.2 kg GY/kg N when winter rainfall was > 500 mm at both 168 kg N ha-1 and 224 kg N ha-1. The combined practice of reducing N fertilizer amounts from 224 kg N ha-1 to 190 kg N ha-1 and shifting from FN to SN resulted in a wide range of yield responses during 2011-2015, with the probability of increasing yields varying from <10% to >70% of simulation points within a watershed. Positive impacts on both GY and NUE occurred in only 60% of simulations for this scenario, highlighting the challenge of simultaneously improving yield and NUE with a 15% N rate reduction in this region.