John A. Lory

Phosphorus indices: why we need to take stock of how we are doing.

Many states have invested significant resources to identify components of their Phosphorus (P) Index that reliably estimate the relative risk of P loss and incentivize conservation management. However, differences in management recommendations and manure application guidelines for similar field conditions among state P Indices, coupled with minimal reductions in the extent of P-impaired surface waters and soil test P (STP) levels, led the U.S. Natural Resources Conservation Service (NRCS) to revise the 590 Nutrient Management Standard. In preparation for this revision, NRCS requested that a review of the scientific underpinnings and accuracy of current P Indices be undertaken. They also sought to standardize the interpretation and management implications of P Indices, including establishment of ratings above which P applications should be curtailed. Although some states have initiated STP thresholds above which no application of P is allowed, STP alone cannot define a sites risk of P loss. Phosphorus Indices are intended to account for all of the major factors leading to P loss. A rigorous evaluation of P Indices is needed to determine if they are directionally and magnitudinally correct. Although use of observed P loss data under various management scenarios is ideal, such data are spatially and temporally limited. Alternatively, the use of a locally validated water quality model that has been shown to provide accurate estimates of P loss may be the most expedient option to conduct Index assessments in the short time required by the newly revised 590 Standard.

Using a Phosphorus Loss Model to Evaluate and Improve Phosphorus Indices

In most states, the phosphorus (P) index (PI) is the adopted strategy for assessing a fields vulnerability to P loss; however, many state PIs have not been rigorously evaluated against measured P loss data to determine how well the PI assigns P loss risk-a major reason being the lack of field data available for such an analysis. Given the lack of P loss data available for PI evaluation, our goal was to demonstrate how a P loss model can be used to evaluate and revise a PI using the Pennsylvania (PA) PI as an example. Our first objective was to compare two different formulations-multiplicative and component-for calculating a PI. Our second objective was to evaluate whether output from a P loss model can be used to improve PI weighting by calculating weights for modified versions of the PA PI from model-generated P loss data. Our results indicate that several potential limitations exist with the original multiplicative index formulation and that a component formulation is more consistent with how P loss is calculated with P loss models and generally provides more accurate estimates of P loss. Moreover, using the PI weights calculated from the model-generated data noticeably improved the correlation between PI values and a large and diverse measured P loss data set. The approach we use here can be used with any P loss model and PI and thus can serve as a guide to assist states in evaluating and modifying their PI.

Effects of observability and complexity on farmers’ adoption of environmental practices

The ability of both regulators and farmers themselves to monitor the impact of environmental practices may be an issue with nonpoint source pollution. Effects that can be perceived via direct sensory evidence provide information at low cost. Results from a survey of livestock farmers suggest that a practice that has more obvious effects on water quality, manure application setbacks, is more likely to be adopted than a more complicated one with less visible effects, manure testing. Farmers’ perceptions of the profitability of the two practices were similar. The importance of observability and complexity has implications for educational programs.

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with > 0.50, Nash-Sutcliffe efficiency > 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices.

Effects of long-term poultry litter application on phosphorus soil chemistry and runoff water quality.

Continuous application of poultry litter (PL) significantly changes many soil properties, including soil test P (STP); Al, Fe, and Ca concentrations; and pH, which can affect the potential for P transport in surface runoff water. We conducted rainfall simulations on three historically acidic silt loam soils in Arkansas, Missouri, and Virginia to establish if long-term PL applications would affect soil inorganic P fractions and the resulting dissolved reactive P (DRP) in runoff water. Soil samples (0-5 cm depth) were taken to find sites ranging in Mehlich-3 STP from 20 to 1154 mg P kg. Simulated rainfall events were conducted on 3-m plots at 6.7 cm h, and runoff was collected for 30 min. Correlation between Mehlich-3 and runoff DRP indicated a linear relationship to 833 mg Mehlich-3 P kg. As Mehlich-3 STP increased, a concomitant increase in soil pH and Ca occurred on all soils. Soil P fractionation demonstrated that, as Mehlich-3 STP generally increased above 450 mg P kg (from high to very high), the easily soluble and loosely bound P fractions decreased by 3 to 10%. Water-insoluble complexes of P bound to Al and Ca were the main drivers in the reduction of DRP in runoff, accounting for up to 43 and 38% of total P, respectively. Basing runoff DRP concentration projections solely on Mehlich-3 STP may overestimate runoff P losses from soils receiving long-term PL applications due to dissolution of water-insoluble Ca-P compounds.

Multisite Evaluation of APEX for Water Quality: II. Regional Parameterization

Phosphorus (P) Index assessment requires independent estimates of long-term average annual P loss from fields, representing multiple climatic scenarios, management practices, and landscape positions. Because currently available measured data are insufficient to evaluate P Index performance, calibrated and validated process-based models have been proposed as tools to generate the required data. The objectives of this research were to develop a regional parameterization for the Agricultural Policy Environmental eXtender (APEX) model to estimate edge-of-field runoff, sediment, and P losses in restricted-layer soils of Missouri and Kansas and to assess the performance of this parameterization using monitoring data from multiple sites in this region. Five site-specific calibrated models (SSCM) from within the region were used to develop a regionally calibrated model (RCM), which was further calibrated and validated with measured data. Performance of the RCM was similar to that of the SSCMs for runoff simulation and had Nash-Sutcliffe efficiency (NSE) > 0.72 and absolute percent bias (|PBIAS|) < 18% for both calibration and validation. The RCM could not simulate sediment loss (NSE < 0, |PBIAS| > 90%) and was particularly ineffective at simulating sediment loss from locations with small sediment loads. The RCM had acceptable performance for simulation of total P loss (NSE > 0.74, |PBIAS| < 30%) but underperformed the SSCMs. Total P-loss estimates should be used with caution due to poor simulation of sediment loss. Although we did not attain our goal of a robust regional parameterization of APEX for estimating sediment and total P losses, runoff estimates with the RCM were acceptable for P Index evaluation.

Increased access to soil testing databases through the world wide web: opportunities and issues

Soil testing laboratories throughout the United States have assisted agricultural producers to more efficiently manage soil nutrient amendments since the 1940s. Information generated during soil testing, such as client and sample information, and results of soil analyses and nutrient recommendations, are maintained in records referred to collectively as the soil testing database. The purpose of this paper is to discuss current and potential uses of soil testing databases and issues associated with increased access to this environmental information through electronic media such as the World Wide Web. Current uses of soil testing databases include determining general trends in nutrient levels on county, state or regional scales, evaluating the performance of the soil testing laboratory and testing for relationships among soil properties. Typically, public access to soil testing databases is restricted to annual summaries which protect the confidentiality of individual soil test clients. However, in an effort to improve services for clients, soil testing laboratories have begun to use the World-Wide Web to provide clients and other authorized individuals password-protected access to soil testing results. Possible additional benefits of web-based access are the opportunity to facilitate client feedback and to provide decision support programs and information for clients. Other potential uses of the soil testing database could be progressively more intrusive on the privacy rights of soil test clients, including commercial marketing of fertilizer products and evaluation of compliance with environmental regulations. Issues of privacy and ownership of environmental information first may need to be addressed, possibly through legislation, before soil-testing databases can be fully utilized without inhibiting public participation in soil testing programs. We envision that once appropriate policies and safeguards are developed to protect the rights of clients, soil testing databases could be part of an interactive and centralized geographically referenced database that integrates available information, promotes more rapid communication, and provides low-cost and customized client decision support in environmental and agricultural management.

Multisite Evaluation of APEX for Water Quality: I. Best Professional Judgment Parameterization

The Agricultural Policy Environmental eXtender (APEX) model is capable of estimating edge-of-field water, nutrient, and sediment transport and is used to assess the environmental impacts of management practices. The current practice is to fully calibrate the model for each site simulation, a task that requires resources and data not always available. The objective of this study was to compare model performance for flow, sediment, and phosphorus transport under two parameterization schemes: a best professional judgment (BPJ) parameterization based on readily available data and a fully calibrated parameterization based on site-specific soil, weather, event flow, and water quality data. The analysis was conducted using 12 datasets at four locations representing poorly drained soils and row-crop production under different tillage systems. Model performance was based on the Nash-Sutcliffe efficiency (NSE), the coefficient of determination () and the regression slope between simulated and measured annualized loads across all site years. Although the BPJ model performance for flow was acceptable (NSE = 0.7) at the annual time step, calibration improved it (NSE = 0.9). Acceptable simulation of sediment and total phosphorus transport (NSE = 0.5 and 0.9, respectively) was obtained only after full calibration at each site. Given the unacceptable performance of the BPJ approach, uncalibrated use of APEX for planning or management purposes may be misleading. Model calibration with water quality data prior to using APEX for simulating sediment and total phosphorus loss is essential.

Nitrogen response of no-till corn in first and second years following Conservation Reserve Program.

Abstract A considerable amount of land enrolled in the Conservation Reserve Program (CRP) has been and will be returned to row crop production. It is difficult to predict how to manage nitrogen (N) fertilizer for these row crops, since there are plausible reasons to expect either substantial N immobilization or substantial N mineralization due to the effects of CRP enrollment. Our objective was to characterize corn (Zea mays L.) yield response to N following CRP in order to develop N management recommendations. Corn was planted either directly into killed CRP sod (CRP‐corn) or following soybean [Glycine max (L.) Merr.] that had been planted into killed CRP sod (CRP‐SB‐corn)‐ We applied a range of N rates and determined the economically optimum N rate from the yield response data. In both years of the study, the optimum N rate for CRP‐corn was much higher (181 and 230 lb N acre‐1 in 1996 and 1997, respectively) than theoptimum N rate for CRP‐SB‐corn(108 and 113 lb Nacre‐1 in 1996 and 1997, respectively). CRP‐corn with no N fertilizer appeared extremely N deficient for the first half of the season. We observed a large flush of inorganic soil N in late summer of the first year out of CRP, but this N was apparently too late for optimum corn production that season. We recommend soybean as the first choice row crop to plant immediately following CRP. If corn is to be planted immediately following CRP, we recommend higher‐than‐normal N rates to optimize production.

Short-term Forecasting Tools for Agricultural Nutrient Management

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsins Runoff Risk Advisory Forecast (RRAF) System, (ii) New Yorks Hydrologically Sensitive Area Prediction Tool, (iii) Virginias Saturated Area Forecast Model, (iv) Pennsylvanias Fertilizer Forecaster, (v) Washingtons Application Risk Management (ARM) System, and (vi) Missouris Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure.