Biogeochemical models simulate soil nitrogen (N) turnover and are often used to assess N losses through denitrification. Though models simulate a complete N budget, only specific N pools/fluxes (i.e. N2O, NO3-, NH3, NOx) are usually published, because the full budget cannot be validated with measured data. Field studies rarely include full N balances, especially N2 fluxes, which are difficult to quantify. Limiting publication of modeling results based on available field data is a missed opportunity to improve the understanding of modeled processes. We suggest that the modeler community support publication of all simulated N pools and processes in future studies.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their vulnerability to permafrost thawing induced by climate change. An ensemble of six terrestrial biosphere models suggests NHL soil N2O emissions doubled since the preindustrial 1860s, increasing on average by 2.0±1.0 Gg N yr-1 (p<0.01). This trend reversed after the 1980s because of reduced nitrogen fertilizer application in non-permafrost regions and increased plant growth due to CO2 fertilization suppressed emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions.

We highlight a mechanism for the co-production of research with local communities as a means of elevating the social relevance of the geosciences, increasing the potential for broader and more diverse participation. We outline the concept of an “equitable exchange” as an ethical framework guiding these interactions. This principled research model emphasizes that “currencies”- the rewards and value from participating in research - may differ between local communities and geoscientists. For those engaged in this work, an equitable exchange emboldens boundary spanning geoscientists to bring their whole selves to the work, providing a means for inclusive climates and rewarding cultural competency.

We synthesized N2O emissions over North America using 17 bottom-up (BU) estimates from 1980-2016 and five top-down (TD) estimates from 1998-2016. The BU-based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007-2016, North American N2O emissions are estimated at 1.7 (1.0-3.0) Tg N yr-1 (BU) and 1.3 (0.9-1.5) Tg N yr-1 (TD). Anthropogenic emissions were twice larger than natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process-based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories based on tier 1 approaches.

Managing Nitrogen (N) is fundamental, yet challenging, for sustainable development: N is essential for producing crops and livestock and consequently important for food security. However, the rising N inputs from human activities to the biosphere has gone beyond a “planetary boundary for a safe operating space”, leading to severe environmental issues, ranging from local water and air pollution to global climate change. To address the N challenge, more N-efficient technologies in crop or livestock production alone are insufficient. A broader understanding of the complex trade-offs and responses of markets, governments, and consumers is needed. Here we present a hierarchical framework encompassing the complex cycles of N in human activities and natural systems, yet simple enough to guide policies and day-to-day actions: The Cropping system is nested within Animal-crop system, which is nested within the Food system, and finally nested within watershed Ecosystem (CAFE).

Nitrous oxide (N2O) is a potent greenhouse gas and stratospheric ozone-depleting substance. More than half of anthropogenic N2O emissions result from agricultural activities. A broad objective of this on-farm research in eastern Maryland was to investigate whether drainage water management, which reduces nitrate export, would increase greenhouse gas emissions, but here we focus upon comparing chamber and tower measurements of N2O fluxes from a single field. Chamber methods usually suffer from poor spatial and temporal resolution. Automating chambers using in situ fast response analyzers improves temporal but not spatial resolution. Tower-based micrometeorological methods improve both temporal and spatial resolution, but require a high-frequency, high-sensitivity laser instrument. We compared auto-chamber and micrometeorological gradient methods for N2O flux measurement during a period early in the 2019 corn-growing season. A 3 m tall tower was deployed to allow for near-continuous gradient flux measurements using an Aerodyne Quantum Cascade Laser. Four Eosense closed dynamic automated chambers (eocAC) and a multiplexer (eosMX) were installed near the tower and connected to a Picarro G2308 gas analyzer. Both methods captured strong pulses of N2O fluxes after rainfall and fertilization events, demonstrating these major drivers of large emissions. Fluxes from the two methods were linearly correlated (R2 = 0.54), but the slope (1.29 ± 0.08) and y-intercept (48.3 ± 19.2) indicate that the chambers generally estimated higher fluxes. Aggregating over the measurement period, the automated chamber estimate was 2.5 kg N2O-N/ha in 19 days, whereas the tower-based gradient estimate was 1.3 kg N2O-N/ha in 19 days. The tower footprint includes some area (4%) covered by ditches and could extend beyond the field at times, but this is unlikely the only explanation. The small number of chambers may have sampled an area of above average flux, or there could be unknown measurement bias or interpolation error in one or both methods. To our knowledge, this is the first such methodological comparison of N2O fluxes since these sensitive, fast response instruments have become available, and our results demonstrate that additional work is needed to gain more confidence in reported fluxes by either method.

Excess nutrient loading to downstream waters has been a persistent environmental concern, especially in agricultural settings. Drainage water management (DWM) is a best management practice intended to reduce nitrogen export from fertilized lands by increasing groundwater levels, slowing the loss of nutrient-rich water and increasing its time in contact with the soil, thus creating greater opportunity for denitrification. This BMP has shown to be effective at reducing dissolved nitrate (TDN) export, but a question remains about potential unintended pollution swapping. The concern is that denitrification could result in nitrous oxide (N2O) emissions and that higher soil moisture could also create suitable conditions for methanogenesis and methane (CH4) emissions. Here we report on two years of monthly static soil gas chamber fluxes and hydrologic nutrient fluxes during a full corn/soybean rotation cycle on the Eastern Shore of Maryland. For N2O, there were significant interactions between season, crop type, and treatment, such as higher fluxes during the fertilization period in the corn year in the DWM treatment, which was consistent with our concern about pollution swapping. However, this brief additional pulse of N2O did not result in a statistically significant increase at an annual scale, nor was there an increase in annual CH4 emissions. At the same time, annual TDN load was significantly lower in the DWM ditches compared to the control. With no significant treatment effect on soil gas fluxes and a significant treatment effect on TDN export, we conclude that pollution swapping of nitrate reduction for greenhouse gases did not occur significantly in this application of DWM to a corn/soybean system. We did, however, find evidence of pollution swapping of phosphorus and nitrogen, as total phosphorus load was higher in the DWM. With more water in the field, the reduced conditions appear to cause a release of soil bound phosphorus. While greenhouse gas production may not be as much of a concern, increased phosphorus export represents a form of pollution swapping that must be accounted for in determining the value of this BMP.

Reducing nitrous oxide (N2O) emissions from agriculture is critical to limiting future global warming. In response, a growing number of food retailers and manufacturers have committed to reducing N2O emissions from their vast networks of farmer suppliers by providing technical assistance and financial incentives. A key challenge for such companies is demonstrating that their efforts are leading to meaningful progress towards their climate mitigation commitments. We show that a simplified version of soil surface nitrogen (N) balance, the difference between N inputs to and outputs from a farm field (e.g., fertilizer N minus crop N), is a robust indicator of N2O emissions. Furthermore, we present a generalized environmental model which will allow food-supply-chain companies to translate aggregated and anonymized changes in average N balance across their supplying farms into aggregated changes in N2O emissions. This research is an important first step, based on currently available science, in helping companies demonstrate the impact of their sustainability efforts.

The ratification of Sustainable Development Goals (SDGs) by all member countries of the United Nations demonstrates the determination of the international community in moving towards a sustainable future. To enable and encourage accountability, independent and transparent measurements of national sustainability efforts are essential. Among all sectors, agriculture is fundamental to all three pillars of sustainability, namely environment, society, and economy. However, the definition of a sustainable agriculture and the feasibility of measuring it remain elusive, in part because it encompasses both biophysical and socio-economic components that are still poorly integrated. Therefore, we have been developing a Sustainable Agriculture Matrix (SAM) on a national scale in order to measure country-level performance in agriculture. First proposed by Swaminathan for agricultural research and policy in 1990s, SAM is a collection of indicators measuring sustainable agriculture from environmental, social, and economic dimensions (Table 1). Specifically, from an Environment perspective, sustainable agriculture reduces unsustainable use of water resources for agricultural production, further loss of biodiversity from converting native habits to agriculture, production of forms of pollution that affect local and regional water and air quality, and emissions of greenhouse gases, and it maintains or improves soil health and fertility. From an Economic perspective, sustainable agriculture improves the economic viability of the agricultural sector by enhancing agricultural productivity and profitability, advancing agricultural innovation capacity, providing farmers access to market and credits, reducing farmers’ risks. From a Social perspective, sustainable agriculture improves farmers’ wellbeing, respects farmers’ rights, promotes equitable opportunities, and benefits the whole society with enhanced system resilience and improved health and nutrition. Translating the illustrative concepts into measurable indicators will not only provide an independent and transparent measurement of national performance in the sustainability of agriculture production, which is at the center of Water-Energy-Food nexus, but also provide timely information to help guide evolving national policies regarding agriculture, trade, environment, and national security.

Applying Phosphorus (P) to global cropland supports crop growth and helps to address the increasing global food demand. However, poor management of P application leads to nutrient loss and environmental pollution in many countries, while some countries (e.g., India and Vietnam) are also facing the depletion of national phosphate rock reserves. One critical strategy to address these challenges is to improve phosphorus use efficiency (PUE) in crop production. The success of this strategy depends on improving regional PUE with advanced technologies and effective management strategies, and an understanding of relevant socio-economic and agronomic drivers influencing regional and global PUE. However, low-efficiency regions and the key drivers remain unclear, and no studies have quantified the impacts of PUE improvement on addressing P challenges. This study developed a unique database of P budget and PUE by country and crop type over 50 years, and examines the temporal and spatial patterns, and makes projection of future P budget under three scenarios with different PUE improvement levels. By studying the historical data, we found that PUE has been significantly affected by a country’s development stage, crop portfolios, nitrogen use efficiency (NUE), fertilizer to crop price ratio, and average farm size. By improving the global PUE in crop production from the current 60% to around 69-82% by 2050, we could decrease the global P surplus from 8.8 in 2010 to about 4.5-9 Tg P yr-1 by 2050. Improvement of some countries (e.g., China and India) and some crop types (e.g., fruit and vegetable) should be prioritized, as they currently have relatively lower PUE.

The agricultural Sustainable Nitrogen Management Index (SNMI) is defined to provide a more comprehensive measurement of the environmental performance of the agricultural production. Here, the SNMI is defined based on two important efficiency terms in crop production, namely Nitrogen Use Efficiency and land use efficiency. As more data become available, the SNMI could be reviewed and improved by including more efficiency terms in crop production, such as water use efficiency.