The agricultural sector is responsible for substantial amounts of greenhouse gas emissions that exacerbate climate change. Such greenhouse gas emissions from upland crops are difficult to abate because they are dominated by nitrous oxide (N₂O) production from soil processes. Strategies to reduce these emissions focus on N fertilizer management, and there is a widespread assumption that legume crops, which do not receive N fertilizer, emit little N₂O. Here we show that this assumption is incorrect; approximately 40% of N₂O emissions from the most extensive cropping system in North America—the maize–soybean rotation—occur during the soybean phase. Yet, due to the lack of N fertilizer input, opportunities for emissions abatement from the soybean phase are unclear. Using models of cropping systems, we developed a strategy that combines cover-crop management and earlier planting of extended growth soybean varieties to reduce emissions from soybean production by 33%. These practices, which complement N fertilizer management in maize, are widely accessible and represent an immediate, climate-smart strategy to reduce nitrous oxide emissions from soybean production, thus not only contributing to climate-change mitigation but also maintaining productivity while adapting to changing weather patterns.

Harvesting a winter rye energy cover crop (Secale cereale L.) could help sustain growing food and energy demand, provide new revenue streams, and enhance ecosystem services without inducing land-use change. A two-year field experiment with three planting methods and three N fertilization rates (0, 60, and 120 kg N ha⁻¹) that produced >5.0 Mg ha⁻¹ yr⁻¹ of biomass was evaluated for (1) fresh and anaerobically digested rye forage quality; (2) revenue potential from renewable bioenergy, carbon markets, and digestate feed protein; and (3) potential greenhouse gas (GHG) offsets. We showed that rye can be harvested as forage for animals or anaerobically digested to produce renewable natural gas (RNG), with the residue after digestion (digestate) still available as a feed protein concentrate. Anaerobically digesting rye improved forage quality indicators. Digestion significantly decreased acid- and neutral-detergent fiber (ADF and NDF) by 5.2% and 17.8%, respectively, while significantly increasing crude protein (CP) (33.6%), total digestible nutrients (TDN) (2.0%), relative feed value (RFV) (23.6%), net energy for lactation (8.3%), maintenance (7.5%), and gain (20.0%). Using market prices for RNG, high protein feed, and GHG mitigation, potential on-farm revenue ranged from USD 307 Mg⁻¹ and USD 502 Mg⁻¹ dry matter with an average of USD 402 Mg⁻¹. However, there are substantial costs associated with RNG and the revenue potential does not represent the profitability of this system. Evaluation of the integrated system showed GHG emissions associated with rye fertilization were more than offset by the benefits of increasing yield in the 60 kg N ha⁻¹ treatment. The overall carbon footprint of the integrated system was strongly carbon negative, confirming the potential of this strategy to sustainably intensify land use in the Midwestern United States.

Core Ideas: Fertilizing winter rye increased estimated revenue and harvestable biomass. Fertilizing winter rye increased net energy production. Harvesting fertilized winter rye reduced simulated drainage N loss. Rye revenue in response to fertilizer rate plateaued at approximately 120 kg N ha⁻¹. Field studies are needed to evaluate fertilized/harvested rye cover crop. Harvesting fertilized rye (Secale cereale L.) cover crop has been suggested as a method to increase producer revenue and biofuel feedstock production, but drainage N loss impacts are currently unknown. Using the tested Root Zone Water Quality Model (RZWQM) across several N rates, spring application of 120 kg N ha^‐1 prior to winter rye harvest reduced drainage N loss by 54% compared with no cover crop and by 18% compared with planted rye that was neither fertilized nor harvested. Estimates of producer revenue and net energy were also positive, with 8.3 Mg ha^‐1 of harvested rye biomass. If confirmed by field studies, these results suggest that double‐cropping fertilized rye is a promising strategy to increase producer revenue, increase net energy production, and reduce drainage N loss.

An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.