The Nitrogen Balancing Act: Tracking the Environmental Performance of Food Production.

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.

Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach.

Large temporal and spatial variability in soil nitrogen (N) availability leads many farmers across the United States to over-apply N fertilizers in maize ( 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). 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.

Assessment of the quality of the Harran Plain soils under long-term cultivation.

Soil quality refers to the ability of soils to perform their functions well. The soils of the Harran Plain, Turkey, have been put into intensive crop production with the introduction of an irrigation scheme and become increasingly degraded due to unsustainable management and cropping systems that resulted in the loss of production potential. The goal of this study was to quantify the quality of common soil series in the Plain using soil quality indexes (SQI) and to compare SQIs of two long-term crop rotations, cotton and wheat-corn cultivation, and different soil types. Over 400 samples were collected at a 0- to 30-cm depth and analyzed for 31 soil variables. The best representative soil quality variables forming a minimum data set (MDS) were selected using principal component analysis (PCA), and soil quality scores were obtained using both linear and non-linear scoring functions. The MDS included three physical (hydraulic conductivity, bulk density, and plant available water content), two biological (soil organic matter and catalase enzyme activity), and nine chemical soil quality indicators (CEC, pH, plant available Cu and Fe, exchangeable Na and K, soluble Ca, Mg, and Na). Because of the low level of SOM, soil qualities were overall low with indexes obtained using two scoring functions ranging from 38.0/100 to 48.7/100. Correlations between SQI obtained using two approaches (linear vs. non-linear; r > 0.61) and using two data sets (all data vs. MDS; r > 0.79) were high. Non-linear scoring functions were more sensitive to management impacts. ANOVA models testing the individual impacts of soil types and crop management on soil quality were statistically different (p < 0.01), but the models including interactions were not. Overall, the fields under cotton cultivation were generally associated with higher clay contents and had the lowest SQIs as a result of intensive cultivation.

Drainage and Nitrate Leaching from Artificially Drained Maize Fields Simulated by the Precision Nitrogen Management Model.

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.

Losses of Ammonia and Nitrate from Agriculture and Their Effect on Nitrogen Recovery in the European Union and the United States between 1900 and 2050.

Historical trends and levels of nitrogen (N) budgets and emissions to air and water in the European Union and the United States are markedly different. Agro-environmental policy approaches also differ, with emphasis on voluntary or incentive-based schemes in the United States versus a more regulatory approach in the European Union. This paper explores the implications of these differences for attaining long-term policy targets for air and water quality. Nutrient surplus problems were more severe in the European Union than in the United States during the 1970s and 1980s. The EU Nitrates and National Emission Ceilings directives contributed to decreases in fertilizer use, N surplus, and ammonia (NH) emissions, whereas in the United States they stabilized, although NH emissions are still increasing. These differences were analyzed using statistical data for 1900-2005 and the global IMAGE model. IMAGE could reproduce NH emissions and soil N surpluses at different scales (European Union and United States, country and state) and N loads in the Rhine and Mississippi. The regulation-driven changes during the past 25 yr in the European Union have reduced public concerns and have brought agricultural N loads to the aquatic environment closer to US levels. Despite differences in agro-environmental policies and agricultural structure (more N-fixing soybean and more spatially separated feed and livestock production in the United States than in the European Union), current N use efficiency in US and EU crop production is similar. IMAGE projections for the IAASTD-baseline scenario indicate that N loading to the environment in 2050 will be similar to current levels. In the United States, environmental N loads will remain substantially smaller than in the European Union, whereas agricultural production in 2050 in the United States will increase by 30% relative to 2005, as compared with an increase of 8% in the European Union. However, in the United States, even rigorous mitigation with maximum recycling of manure N and a 25% reduction in fertilizer use will not achieve the policy target to halve the N export to the Gulf of Mexico.

Ecological insights into soil health according to the genomic traits and environment-wide associations of bacteria in agricultural soils.

Soil microbiomes are sensitive to current and previous soil conditions, and bacterial 'bioindicators' of biological, physical, and chemical soil properties have considerable potential for soil health assessment. However, the lack of ecological or physiological information for most soil microorganisms limits our ability to interpret the associations of bioindicators and, thus, their utility for guiding management. We identified bioindicators of tillage intensity and twelve soil properties used to rate soil health using a 16S rRNA gene-based survey of farmland across North America. We then inferred the genomic traits of bioindicators and evaluated their environment-wide associations (EWAS) with respect to agricultural management practice, disturbance, and plant associations with 89 studies from agroecosystems. Most bioindicators were either positively correlated with biological properties (e.g., organic matter) or negatively correlated with physical and chemical properties. Higher soil health ratings corresponded with smaller genome size and higher coding density, while lower ratings corresponded with larger genomes and higher rrn copy number. Community-weighted genome size explained most variation in health ratings. EWAS linked prominent bioindicators with the impacts of environmental disturbances. Our findings provide ecological insights into bioindicators of soil properties relevant to soil health management, illustrating the tight coupling of microbiome and soil function.

Effect of manure application timing, crop, and soil type on nitrate leaching.

Timing of manure application affects N leaching. This 3-yr study quantified N losses from liquid manure application on two soils, a Muskellunge clay loam and a Stafford loamy sand, as affected by cropping system and timing of application. Dairy manure was applied at an annual rate of 93 800 L ha(-1) on replicated drained plots under continuous maize (Zea mays L.) in early fall, late fall, early spring, and as a split application in early and late spring. Variable rates of supplemental sidedress N fertilizer were applied as needed. Manure was applied on orchardgrass (Dactylis glomerata L.) in split applications in early fall and late spring, and early and late spring, with supplemental N fertilizer topdressed as NH4NO3 in early spring at 75 kg N ha(-1). Drain water was sampled at least weekly when lines were flowing. Three-year FWM (flow-weighted mean) NO3-N concentrations on loamy sand soil averaged 2.5 times higher (12.7 mg L(-1)) than those on clay loam plots (5.2 mg L(-1)), and those for fall applications on maize-cropped land averaged >10 mg L(-1) on the clay loam and >20 mg L(-1) on the loamy sand. Nitrate-N concentrations among application seasons followed the pattern early fall > late fall > early spring = early + late spring. For grass, average NO3-N concentrations from manure application remained well below 10 mg L(-1). Fall manure applications on maize show high NO3-N leaching risks, especially on sandy soils, and manure applications on grass pose minimal leaching concern.

Pesticide management for water quality: principles and practices

It intends to explain the fates of pesticidesafter they have been introduced into the environment, in addition, processes which affect pesticide movement and management practices which reduce their losses are discussed. It presents a table of pesticides with their Health Advisory Levels (HAL).