A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

Soil health (Sh) refers to the ability of a soil to function and provide ecosystem services. The Comprehensive Assessment of Soil health (CASh) is an approach that measures 15 physical, biological, and chemical soil indicators, which are interpreted through scoring functions. This study reports on the Sh status of 5767 samples from the mid-Atlantic, midwest, and northeast regions of the uSA as evaluated using CASh. descriptive statistics and AnOvAs of subdatasets by region and soil textural group for Sh indicators, in addition to correlation coefficients, principal component (pC) analysis, and best subsets regression (BSr) were performed. from these analyses, new CASh scoring functions were developed. Separate scoring functions by textural group (fine, medium, coarse) were necessary for Wet Aggregate Stability (WAS), Available Water Capacity (AWS), Organic matter (Om), Active Carbon (AC), and protein. differences existed among regions, especially for WAS, Om, protein, and respiration (resp), where the midwest had relatively lower mean values compared to the mid-Atlantic and northeast. Biological properties and WAS showed moderately strong correlations (r = 0.58-0.78) and the highest loadings for the first two pCs. BSr results using the overall soil quality index as the response variable indicated that AC accounts for 45% of the variation, with additional predictability from penetration resistance, resp, and WAS (68%). These four indicators are suggested for simplified Sh tests. We conclude that the CASh approach can be successfully applied to evaluate the health status of soils with differing pedogenetic histories. C onceptually, soil health (SH) represents the emerging understanding of soil quality. Both terms refer to the ability of a soil to function and provide ecosystem services based on its inherent characteristics (e.g., texture, mineralogy) and environmental conditions (Karlen et al., 1997;Andrews et al., 2004;Idowu et al., 2009). A soil's health status, within the context of land use and management goals, however, is consistent with the understanding of soils as a dynamic, complex, and living system (Doran and Zeiss, 2000). Intensive agriculture and poor land management practices have led to widespread soil degradation associated with increasing topsoil erosion, nutrient depletion, pollution, compaction, and loss of organic matter (Matson et al., 1997). A sustainable future with an ever-growing global population depends on healthy, well-functioning soils, which increase water and air quality, sup- Core Ideas• Summary statistics were developed from a robust multiregional soil health (Sh) dataset.• Active carbon, organic matter, and penetration resistance were most useful soil health indicators.• midwestern soils had relatively lower mean values for measured biological properties than northeast or mid-Atlantic soils.

Rising concerns about greenhouse gases, increased fuel prices, and the potential for new high value agricultural products have raised interest in the use of maize stover for bioenergy production. However, residue harvest must be weighed against potential negative impacts on soil quality. This study, conducted in Chazy, NY, evaluated the long‐term effects of 32 yr of maize (Zea mays L.) stover harvest vs. stover return on soil quality in the surface layer (5–66 mm) under plow till (PT) and no‐till (NT) systems on a Raynham silt loam (coarse‐silty, mixed, active, nonacid, mesic Aeric Epiaquept) using physical, chemical, and biological soil properties as soil quality indicators. Twenty‐five soil properties were measured, including standard chemical soil tests, aggregate stability (WSA), bulk density, (ρb) penetration resistance (PR), saturated hydraulic conductivity (Ks), infiltrability (Infilt), several porosity indicators (aeration pores(PO > 1000), soil water potential = Ψ > −0.36 kPa; air‐filled pores at field capacity (PO > 30), Ψ > −10kPa; available water capacity (AWC), −1500 < Ψ < −10 kPa), total organic matter (OM), parasitic (Nemparasitic) and beneficial nematode (Nem beneficial) populations, decomposition rate (Decomp), potentially mineralizable N (PMN) and easily extractable (EEG) and total glomalin (TG). Only eight indicators were adversely affected by stover harvest, and most of these effects were significant only under NT. Almost all indicators affected by stover removal were affected equally or more adversely by tillage. A total of 15 indicators were adversely affected by tillage. Results of this study suggest that, on a silt loam soil in a temperate climate, long‐term stover harvest had lower adverse impacts on soil quality than long‐term tillage. Stover harvest appears to be sustainable when practiced under NT management.

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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.