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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 EF5r underestimation explains the large differences observed between top-down and bottom-up emission estimates. [ABSTRACT FROM AUTHOR]

Agriculture must increase production for a growing population while simultaneously reducing its environmental impacts. These goals need not be in tension with one another. Here we outline a vision for improving both the productivity and environmental performance of agriculture in the U.S. Midwest, also known as the Corn Belt. Mean annual precipitation has increased throughout the region over the past 50 years, consistent with climate models that attribute the increase to a warming troposphere. Stream gauge data indicate that higher precipitation has been matched or exceeded by higher stream flows, contributing to flooding, soil loss, and excessive nutrient flux to the Gulf of Mexico. We propose increasing landscape hydrologie storage through construction of ponds and restoration of wetlands to retain water for supplemental irrigation while also reducing flood risks. Primary productivity is proportional to transpiration, and analysis shows that in the U.S. Midwest both can be sustainably increased with supplemental irrigation. The proposed strategy should reduce interannual yield variability by limiting losses due to transient drought, while facilitating adoption of cropping systems that "perennialize" the landscape to take advantage of the full potential growing season. When implemented in concert, these practices should reduce the riverine nitrogen export that is a primary cause of hypoxia in the Gulf of Mexico. Erosive sediment losses should also be reduced through the combination of enhanced hydrologie storage and increased vegetative cover. Successful implementation would require watershed-scale coordination among producers and landowners. An obvious mechanism to encourage this is governmental farm policy. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N 2 O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N 2 O budget. To date, studies on local surface N 2 O emissions and the atmospheric N 2 O budget have commonly used Lagrangian models. In the present study, we used an Eulerian model - Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N 2 O emissions in the Corn Belt and observed atmospheric N 2 O mixing ratios. We derived a simple equation to relate the emission strengths to atmospheric N 2 O mixing ratios, and used the derived equation and hourly atmospheric N 2 O measurements at the KCMP tall tower in Minnesota to constrain agricultural N 2 O emissions. The modeled spatial patterns of atmospheric N 2 O were evaluated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N 2 O mixing ratios monitored at the KCMP tower. Agricultural N 2 O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emissions in the Corn Belt for June 1–20, 2010 - a peak emission period. Optimized mean N 2 O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m − 2 s − 1 for June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N 2 O mixing ratios after optimization were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns suggest that the underestimate of emissions using IPCC (Inter-governmental Panel on Climate Change) inventory methodology is not dependent on tower measurement location. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to clarify the influence of crop plants on atmospheric methane (CH4) in an agriculture-dominated landscape in the Upper Midwest of the United States. Measurements were carried out at two contrasting scales. At the plant scale, CH4 fluxes from soybean and corn plants were measured with a laser-based plant chamber system. At the landscape scale, the land surface flux was estimated with a modified Bowen ratio technique using measurements made on a tall tower. The chamber data revealed a diurnal pattern for the plant CH4 flux: it was positive (an emission rate of 0.4±0.1 nmol m(-2) s(-1), average of soybean and corn, in reference to the unit ground area) during the day, and negative (an uptake rate of -0.8±0.8 nmol m(-2) s(-1)) during the night. At the landscape scale, the flux was estimated to be 14.8 nmol m(-2) s(-1) at night and highly uncertain during the day, but the available references and the flux estimates from the equilibrium methods suggested that the CH4 flux during the entire observation period was similar to the estimated nighttime flux. Thus, soybean and corn plants have a negligible role in the landscape-scale CH4 budget.