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Farmers, food supply-chain entities, and policymakers need a simple but robust indicator to demonstrate progress toward reducing nitrogen pollution associated with food production. We show that nitrogen balance—the difference between nitrogen inputs and nitrogen outputs in an agricultural production system—is a robust measure of nitrogen losses that is simple to calculate, easily understood, and based on readily available farm data. Nitrogen balance provides farmers with a means of demonstrating to an increasingly concerned public that they are succeeding in reducing nitrogen losses while also improving the overall sustainability of their farming operation. Likewise, supply-chain companies and policymakers can use nitrogen balance to track progress toward sustainability goals. We describe the value of nitrogen balance in translating environmental targets into actionable goals for farmers and illustrate the potential roles of science, policy, and agricultural support networks in helping farmers achieve them.

N itrogen (N) is an essential nutrient for crop production, and the manufacture of synthetic N fertilizer has facilitated impressive advances in food production that have assisted in the avoidance of widespread hunger despite a rapidly growing twentieth century global population. However, this nutrient also causes major environmental concerns, including nitrate (NO3−) contamination of groundwater in agricultural regions and hypoxia and dead zones in estuaries. Also, gaseous forms of N contribute to air quality issues. Nitrous oxide (N2O), a potent greenhouse gas, contributes to climate change, and ammonia (NH3) emissions contribute to fine particulate air pollution, a cause of respiratory diseases (Pinder et al. 2007). Anthropogenic N pollution, therefore, has large social costs, with estimated potential ecosystem and health damages of US$157 billion annually (Sobota et al. 2015). Despite many mitigation efforts, the N loss from agricultural systems continues to be a major concern, and in 2017 the hypoxic zone in the Gulf of Mexico reached the greatest extent ever recorded (NOAA 2017). There is a pressing need to improve the management of agricultural N, and the 4R concept has been proposed as a conceptual path toward more efficient N management (Snyder et al. 2009; TFI 2017). This approach promotes field…

Here we study the soilscape (soil-landscape) evolution of a field site in the semiarid zone of Israel. This region, like similar regions around the world, was subject to intensive loess accumulation during the Pleistocene and early Holocene. Today, hillslopes in this region are dominated by exposed bedrock with deep loess depositions in the valleys and floodplains. The drivers and mechanism that led to this soilscape are unclear. Within this context, we use a soilscape evolution model (mARM5D) to study the potential mechanisms that led to this soilscape. We focus on advancing our conceptual understanding of the processes at the core of this soilscape evolution by studying the effects of fluvial and diffusive sediment transport mechanisms, and the potential effects of climatic and anthropogenic drivers. Our results show that, in our field site, dominated by aeolian soil development, hillslope fluvial sediment transport (e.g., surface wash and gullies) led to downslope thinning in soil, while diffusive transport (e.g., soil creep) led to deeper and more localized soil features at the lower sections of the hillslopes. The results suggest that, in this semiarid, aeolian-dominated and soil-depleted landscape, the top section of the hillslopes is dominated by diffusive transport and the bottom by fluvial transport. Temporal variability in environmental drivers had a considerable effect on soilscape evolution. Short but intensive changes during the late Holocene, imitating anthropogenic land use alterations, rapidly changed the site's soil distribution. This leads us to assume that this region's soil-depleted hillslopes are, at least in part, the result of anthropogenic drivers.

Environmental nitrogen (N) losses (e.g., nitrate leaching, denitrification, and ammonia volatilization) frequently occur in maize ( L.) agroecosystems. Decision support systems, designed to optimize the application of N fertilizer in these systems, have been developed using physically based models such as the Precision Nitrogen Management (PNM) model of soil and crop processes, which is an integral component of Adapt-N, a decision support tool providing N fertilizer recommendations for maize production. Such models can also be used to estimate N losses associated with particular management practices and over a range of current climates and future climate projections. The objectives of this study were to update the PNM model to include an option for simulating soil-water processes in artificially drained soils, and to calibrate the revised PNM model and test it against multiyear field studies in New York and Minnesota with different soils and management practices. Minimal calibration was required for the model. Denitrification rate constants were calibrated by minimizing the error between simulated and observed nitrate leaching for each study site. The normalized root mean squared error of cumulative daily drainage for the validation sets ranged from 10 to 23%. For cumulative daily nitrate leaching, the normalized root mean squared error ranged from 11 to 28% for the validation sets. The minimal calibration required and relatively simple data inputs make the PNM model a broadly applicable tool for simulating water and N flows in maize systems.

The US Midwest is the largest and most intensive corn (Zea mays, L.) production region in the world. However, N losses from corn systems cause serious environmental impacts including dead zones in coastal waters, groundwater pollution, particulate air pollution, and global warming. New approaches to reducing N losses are urgently needed. N surplus is gaining attention as such an approach for multiple cropping systems. We combined experimental data from 127 on-farm field trials conducted in seven US states during the 2011–2016 growing seasons with biochemical simulations using the PNM model to quantify the benefits of a dynamic location-adapted management approach to reduce N surplus. We found that this approach allowed large reductions in N rate (32%) and N surplus (36%) compared to existing static approaches, without reducing yield and substantially reducing yield-scaled N losses (11%). Across all sites, yield-scaled N losses increased linearly with N surplus values above ~48 kg ha−1. Using the dynamic model-based N management approach enabled growers to get much closer to this target than using existing static methods, while maintaining yield. Therefore, this approach can substantially reduce N surplus and N pollution potential compared to static N management.