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.

Maize (Zea mays L.) production accounts for the largest share of crop land area in the United States and is the largest consumer of nitrogen (N) fertilizers. Routine application of N fertilizer in excess of crop demand has led to well-documented environmental problems and social costs. Current N rate recommendation tools are highly generalized over space and time and therefore do not allow for precision N management through adaptive and site-specifi c approaches. Adapt-N is a computational tool that combines soil, crop, and management information with nearreal-time weather data to estimate optimum N application rates for maize. We evaluated this precision nutrient management tool during four growing seasons (2011 through 2014) with 113 on-farm strip trials in Iowa and New York. Each trial included yield results from replicated fi eld-scale plots involving two sidedress N rate treatments: Adapt-N-estimated and grower-selected (conventional). Adapt-N rates were on average 53 and 31 kg ha -1 lower than Grower rates for New York and Iowa, respectively (-34% overall), with no statistically signifi cant diff erence in yields. On average, Adapt-N rates increased grower profi ts by $65 ha -1 and reduced simulated environmental N losses by 28 kg ha -1 (38%). Profi ts from Adapt-N rates were noticeably higher under wet early-season conditions when higher N rate recommendations than the Grower rates prevented yield losses from N defi ciencies. In conclusion, Adapt-N recommendations resulted in both increased grower profi ts and decreased environmental N losses by accounting for variable site and weather conditions.

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over‐apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses. Static Stanford‐type N recommendation tools are typically promoted in the United States, but new dynamic model‐based decision tools allow for highly adaptive N recommendations that account for specific production environments and conditions. This study compares the Corn N Calculator (CNC), a static N recommendation tool for New York, to Adapt‐N, a dynamic simulation tool that combines soil, crop, and management information with real‐time weather data to estimate optimum N application rates for maize. The efficiency of the two tools in predicting the Economically Optimum N Rate (EONR) is compared using field data from 14 multiple N‐rate trials conducted in New York during the years 2011 through 2015. The CNC tool was used with both realistic grower‐estimated potential yields and those extracted from the CNC default database, which were found to be unrealistically low when compared with field data. By accounting for weather and site‐specific conditions, the Adapt‐N tool was found to increase the farmer profits and significantly improve the prediction of the EONR (RMSE = 34 kg ha−1). Furthermore, using a dynamic instead of a static approach led to reduced N application rates, which in turn resulted in substantially lower simulated environmental N losses. This study shows that better N management through a dynamic decision tool such as Adapt‐N can help reduce environmental impacts while sustaining farm economic viability. Core Ideas Dynamic N recommendation tool reduces environmental impacts over static approach. Dynamic N recommendation tool accounts for different production environments. Dynamic N recommendation tool is successful in estimating field‐measured EONR.

Abstract:The Phosphorus (P) Index concept is used in many states to help develop nutrient management plans for livestock agriculture to protect water quality. Although many P indices conceptually incorporate variable source area (VSA) runoff processes, they generally assume proximity to a water course is an adequate proxy of runoff risk. Here we propose a VSA-based transport factor that uses the topographic index concept to indicate runoff risk. We compared both transport factors based on the current New York State P Index and our VSA-based P Index to field measures of runoff probability across an abandoned agricultural site in upstate New York. We also compared transport factors and P indices using the current and VSAbased approaches on a farm in the Catskill Mountains of New York to evaluate differences at whole-field and farm scales. Field runoff-risk measurements were better correlated with VSA-based transport factor (r 2 = 0.63, α = 0.05) than with the current dissolved-P transport factor (r 2 = 0.40, α = 0.05). Although these point-scale differences in transport factor values translated into field-scale differences in transport factor, the net differences at the farm scale and in P Index were not very large. On a field-by-field basis, 12 out of 21 fields had different transport factor categories with the VSA method. However, the total land area classified as high risk changed very little between the two methods. There was more land classified as moderate risk using the VSA-based approach than using the current methods, due to some low risk areas being classified as higher risk and some high-risk areas being classified as lower risk. The VSA approach allows managers and producers to more easily manage farm units (e.g., fields) at finer resolutions both spatially and temporally, which will increase the options for managing nutrients on fields. These types of more rigorous links between management tools and physical hydrology provide valuable, more scientifically defensible information for improving our ability to control nonpoint source pollution. Key words: nutrient management-Phosphorus Index-variable source areasThis paper proposes a variable source area (VSA) based transport factor for incorporation into an existing Phosphorus (P) Index to give farmers and nutrient management planners improved information about the relative risks of runoff from different parts of a farm. Phosphorus in agricultural runoff often contributes to eutrophication of streams and other fresh water systems (Carpenter et al. 1998). The P Index is a widely used tool for managing phosphorus (P) from nonpoint sources (NPS), which helps identify fields or parts of the landscape that are particularly susceptible to P loss, including P-enriched runoff (Sharpley et al. 2003). The information contained in the P Index is used in farm nutrient management plans to strategize manure spreading, fertilization, and other management decisions to reduce risks of NPS P transport to streams.The New York State (NYS) P runoff index was developed by a m...

Riparian buffers are commonly promoted to protect stream water quality. A common conceptual assumption is that buffers "intercept" and treat upland runoff. As a shift in paradigm, it is proposed instead that riparian buffers should be recognized as the parts of the landscape that most frequently generate storm runoff. Thus, water quality can be protected from contaminated storm runoff by disassociating riparian buffers from potentially polluting activities. This paper reviews and synthesizes some simple engineering approaches that can be used to delineate riparian buffers for rural watersheds based on risk of generating runoff. Although reference is made to specific future research that may improve the proposed methods for delineating riparian buffers, the approaches described here provide planners and engineers with a set of currently available scientifically defensible tools. It is recommended that planners and engineers use available rainfall and stream discharge data to parameterize the buffer-sizing equations and use variable-width buffers, based on a topographic index, to achieve a realistic representation of runoff generating areas.