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Cover crops are known to promote many aspects of soil and water quality, yet estimates find that in 2012 only 2.3% of the total agricultural lands in the Midwestern USA were using cover crops. Focus groups were conducted across the Corn Belt state of Iowa to better understand how farmers confront barriers to cover crop adoption in highly intensive agricultural production systems. Although much prior research has focused on analyzing factors that help predict cover crop use on farms, there is limited research on how farmers navigate and overcome field-level (e.g. proper planting of a cover crop) and structural barriers (e.g. market forces) associated with the use of cover crops. The results from the analysis of these conversations suggest that there is a complex dialectical relationship between farmers' individual management decisions and the broader agricultural context in the region that constrains their decisions. Farmers in these focus groups shared how they navigate complex management decisions within a generally homogenized agricultural and economic landscape that makes cover crop integration challenging. Many who joined the focus groups have found ways to overcome barriers and successfully integrate cover crops into their cropping systems. This is illustrated through farmers' descriptions of their ‘whole system’ approach to cover crops management, where they described how they prioritize the success of their cover crops by focusing on multiple aspects of management, including changes they have made to nutrient application and modifications to equipment. These producers also engage with farmer networks to gain strategies for overcoming management challenges associated with cover crops. Although many participants had successfully planted cover crops, they tended to believe that greater economic incentives and/or more diverse crop and livestock markets would be needed to spur more widespread adoption of the practice. Our results further illustrate how structural and field-level barriers constrain individual actions, as it is not simply the basic agronomic considerations (such as seeding and terminating cover crops) that pose a challenge to their use, but also the broader economic and market drivers that exist in agriculturally intensive systems. Our study provides evidence that reducing structural barriers to adoption may be necessary to increase the use of this conservation practice to reduce environmental impacts associated with intensive agricultural production.

Nitrous oxide (N₂O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N₂O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N₂O mixing ratios from a very tall tower within the US Corn Belt—one of the most intensive agricultural regions of the world—combined with inverse modeling, shows large interannual variability in N₂O emissions (316 Gg N₂O-N·y−1 to 585 Gg N₂O-N·y−1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N₂O emissions that will exceed 600 Gg N₂O-N·y−1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N₂O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N₂O emission mitigation efforts to achieve its goals.

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

In the US Corn Belt, a recent doubling in commodity prices has created incentives for landowners to convert grassland to corn and soybean cropping. Here, we use land cover data from the National Agricultural Statistics Service Cropland Data Layer to assess grassland conversion from 2006 to 2011 in the Western Corn Belt (WCB): five states including North Dakota, South Dakota, Nebraska, Minnesota, and Iowa. Our analysis identifies areas with elevated rates of grass-to-corn/soy conversion (1.0–5.4% annually). Across the WCB, we found a net decline in grass-dominated land cover totaling nearly 530,000 ha. With respect to agronomic attributes of lands undergoing grassland conversion, corn/soy production is expanding onto marginal lands characterized by high erosion risk and vulnerability to drought. Grassland conversion is also concentrated in close proximity to wetlands, posing a threat to waterfowl breeding in the Prairie Pothole Region. Longer-term land cover trends from North Dakota and Iowa indicate that recent grassland conversion represents a persistent shift in land use rather than short-term variability in crop rotation patterns. Our results show that the WCB is rapidly moving down a pathway of increased corn and soybean cultivation. As a result, the window of opportunity for realizing the benefits of a biofuel industry based on perennial bioenergy crops, rather than corn ethanol and soy biodiesel, may be closing in the WCB.

Yield loss due to water deficit is ubiquitous in maize (Zea mays L.) production environments in the United States. The impact of water deficits on yield depends on the cropping system management and physiological characteristics of the hybrid. Genotypic diversity among maize hybrids in the transpiration response to vapor pressure deficit (VPD) indicates that a limited‐transpiration trait may contribute to improved drought tolerance and yield in maize. By limiting transpiration at VPD above a VPD threshold, this trait can increase both daily transpiration efficiency and water availability for late‐season use. Reduced water use, however, may compromise yield potential. The complexity associated with genotype × environment × management interactions can be explored in a quantitative assessment using a simulation model. A simulation study was conducted to assess the likely effect of genotypic variation in limited‐transpiration rate on yield performance of maize at a regional scale in the United States. We demonstrated that the limited‐transpiration trait can result in improved maize performance in drought‐prone environments and that the impact of the trait on maize productivity varies with geography, environment type, expression of the trait, and plant density. The largest average yield increase was simulated for drought‐prone environments (135 g m−2), while a small yield penalty was simulated for environments where water was not limiting (–33 g m−2). Outcomes from this simulation study help interpret the ubiquitous nature of variation for the limited‐transpiration trait in maize germplasm and provide insights into the plausible role of the trait in past and future maize genetic improvement.

Determining which corn (Zea mays L.) N fertilizer rate recommendation tools best predict crop N need would be valuable for maximizing profits and minimizing environmental consequences. Simultaneous comparisons of multiple tools across various environmental conditions have been limited. The objectives of this research were to evaluate the performance of publicly‐available N fertilizer recommendation tools across diverse soil and weather conditions for: (i) prescribing N rates for planting and split‐fertilizer applications, and (ii) economic and environmental effects. Corn N‐response trials using standardized methods were conducted at 49 sites, spanning eight US Midwest states and three growing seasons. Nitrogen applications included eight rates in 45 kg N ha−1 increments all at‐planting and matching rates with 45 kg N ha−1 at‐planting plus at the V9 development stage. Tool performances were compared to the economically optimal N rate (EONR). Over this large geographic region, only 10 of 31 recommendation tools (mainly soil nitrate tests) produced N rate recommendations that weakly correlated to EONR (P ≤ .10; r2 ≤ .20). With other metrics of performance, the Maximum Return to N (MRTN) soil nitrate tests, and canopy reflectance sensing came close to matching EONR. Economically, all tools but the Maize‐N crop growth model had similar returns compared to EONR. Environmentally, yield goal based tools resulted in the highest environmental costs. Results show that no tool was universally reliable over this study's diverse growing environments, suggesting that additional tool development is needed to better represent N inputs and crop utilization at a larger regional level.

Recent increases in corn (Zea mays L.) production in the U.S. Corn Belt have necessitated the conversion of rotations to continuous corn, and an increase in the frequency of tillage. The objective of this study was to assess the effect of rotation and tillage on soil physical and chemical properties in soils typical of Illinois. Sequences of continuous corn (CCC), 2‐yr corn–soybean [Glycine max (L.) Merr.] (CS) rotation, 3‐yr corn–soybean–wheat (Triticum aestivum L.) (CSW) rotation, and continuous soybean (SSS) were split into conventional tillage (CT) and no‐till (NT) subplots at two Illinois sites. After 15 yr, bulk density (BD) under NT was 2.4% greater than under CT. Water aggregate stability (WAS) was 0.84 kg kg−1 under NT compared to 0.81 kg kg−1 under CT. Similarly, soil organic carbon (SOC) and total nitrogen (TN) were greater under NT than under CT with SOC values for 0 to 60 cm of 96.0 and 91.0 Mg ha−1 and TN values of 8.87 and 8.40 Mg ha−1 for NT and CT, respectively. Rotations affected WAS, TN, and K levels with WAS being greatest for the CSW rotation at 0.87 kg kg−1, decreasing with more soybean years (CS, 0.82 kg kg−1 and SSS, 0.79 kg kg−1). A similar pattern was detected for TN and exchangeable K. Results indicated that while the use of NT improved soil quality, long‐term implementation of continuous corn had similar soil quality parameters to those found under a corn–soybean rotation.

N₂O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N₂O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N₂O emissions, implying that EFs may be biased low. Here, we measured N₂O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N₂O fluxes from headwater streams often exceeded 29 nmol N₂O-N m⁻²·s⁻¹ and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N₂O emissions by 40%. We show that accounting for zero-order streams as potential N₂O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5runderestimation explains the large differences observed between top-down and bottom-up emission estimates.

Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R2 = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions.

Core Ideas A Machine Learning approach was innovatively used to predict corn EONR. Two features were created to approximate hydrological conditions for modeling EONR. Soil hydrology conditions were found essential in successful modeling in‐season EONR. Determination of in‐season N requirement for corn (Zea mays L.) is challenging due to interactions of genotype, environment, and management. Machine learning (ML), with its predictive power to tackle complex systems, may solve this barrier in the development of locally based N recommendations. The objective of this study was to explore application of ML methodologies to predict economic optimum nitrogen rate (EONR) for corn using data from 47 experiments across the US Corn Belt. Two features, a water table adjusted available water capacity (AWCwt) and a ratio of in‐season rainfall to AWCwt (RAWCwt), were created to capture the impact of soil hydrology on N dynamics. Four ML models—linear regression (LR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO) regression, and gradient boost regression trees (GBRT)—were assessed and validated using “leave‐one‐location‐out” (LOLO) and “leave‐one‐year‐out” (LOYO) approaches. Generally, RR outperformed other models in predicting both at planting and split EONR times. Among the 47 tested sites, for 33 sites the predicted split EONR using RR fell within the 95% confidence interval, suggesting the chance of using the RR model to make an acceptable prediction of split EONR is ∼70%. When RR was used to test split EONR prediction with input weather features surrogated with 10 yr of historical weather data, the model demonstrated robustness (MAE, 33.6 kg ha−1; R2 = 0.46). Incorporating mechanistically derived hydrological features significantly enhanced the ability of the ML procedures to model EONR. Improvement in estimating in‐season soil hydrological status seems essential for success in modeling N demand.