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.

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 (r 2 ) 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. (P) and sediment loss from agricultural fields continues to degrade fresh water quality despite decades of efforts to understand loss processes and implement management practices to reduce nonpoint pollution (e.g., Sharpley et al., 1994Sharpley et al., , 2015Sims and Kleinman, 2005;Jarvie et al., 2013). Failure of conservation practices to produce expected improvements in water quality has renewed appreciation for the complexity of P movement within landscapes and to waterbodies Sharpley et al., 2013). Processbased watershed-and field-scale models offer the prospect of integrating knowledge to assess and quantify impacts of conservation and management practices.The P Index was developed in the mid-1990s to assess risk of P loss from agricultural land (Lemunyon and Gilbert, 1993) and is now an integral part of the NRCS 590 Nutrient Management Standard and other state and federal programs (Sharpley et al., 2003(Sharpley et al., , 2017. However, the diversity in P Index ratings and P management recommendations for similar conditions led and Osmond et al. (2006) to emphasize the need for science-based assessment and improvement of existing P Indices.Extensive testing of P Indices requires water quality data from field-scale watersheds from a broad range of soils and management scenarios with a sufficient number of monitoring years to estimate long-term average annual losses. Such extensive datasets are rare. Hence, computer models must ...

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 edgeof-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. T he Phosphorus (P) Index was developed as a tool to assess the risk of P loss from agricultural fields. Although this tool has been used to encourage the adoption of conservation practices and develop nutrient management plans, excess P losses from agricultural fields and associated water quality degradation persist ( Jarvie et al., 2013;Sharpley et al., 2015;USEPA, 2016). Due to the lack of water quality improvement and the disparity among state P Indices (Osmond et al., 2006), it has been proposed that the P Indices undergo evaluation to ensure accuracy in P-loss risk assessment (Sharpley et al., 2012).A wide variety of methods have been used to assess P Indices (Nelson and Shober, 2012); however, the ideal assessment would include comparison of P Index results to independently obtained quantitative estimates of long-term average annual P loss across a wide range of soils, topography, and management practices . This type of quantitative independent assessment requires long-term average annual estimates of P loss because the P Index is a generalized assessment of the average risk of P loss across an extended period, as opposed to an assessment of P loss for a specific year or weather sequence. Because measured edge-of-field P-loss data are highly dependent on the weather patterns during the years of data col...

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.

Yield loss due to natural disasters, such as storms with high-speed winds and rainfall, can significantly damage standing corn (Zea mays L.) plants and yield. Using a geospatial approach, the study aimed to estimate green snap wind damage to corn and assess potential yield and economic loss in the Mississippi Delta. Midseason corn (V12–V14) snapping occurred on 8 June 2022. We recorded green snap damage in 13 fields [1.0 to 2.0 hectares (ha−1)] with low (224 kg ha−1) and high (336 kg ha−1) N rates and two different row orientations (north–south and east–west) after the damage. The results indicated no nitrogen rates or row orientation effect on green snap damage. The average yield loss could be ~29.25 kg ha−1, with every 1% increase in green snap wind damage causing significant economic loss to producers. Research methods can help scientists to estimate potential green snap yield loss due to severe winds in the larger fields. Research results can also help estimate potential yield and economic loss to assist producers and other stakeholders in decision-making to prepare for changing weather patterns and unprecedented severe windstorms in the future.

Winter manure application contributes substantial nutrient loss during snowmelt and influences water quality. The goal of this study is to develop best management practices (BMPs) for winter manure management. We compared nutrient concentrations in snowmelt runoff from three dates of feedlot solid beef manure application (November, January, and March) at 18 tons ha−1 on untilled and fall-tilled plots. The manure was applied at a single rate. Sixteen 4 m2 steel frames were installed in the fall to define individual plots. Treatments were randomly assigned so that each tillage area had two control plots, two that received manure during November, two in January, and two in March. Snowmelt runoff from each individual plot was collected in March and analyzed for runoff volume (RO), ammonium-nitrogen (NH4-N), nitrate-nitrogen (NO3-N), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), total phosphorus (TP), and total dissolved phosphorus (TDP). Snowmelt runoff concentrations and loads of NH4-N, TKN, TP, and TDP were significantly higher in runoff from manure application treatments compared to control. The concentration of NH4-N and loads of NH4-N and TDP were significantly (p = 0.05) greater (42%, 51%, and 47%, respectively) from untilled compared to fall-tilled plots. The November application significantly increased RO, NH4-N, and TDP concentrations and loads in the snowmelt runoff compared to January and March applications. Results showed that nutrient losses in snowmelt runoff were reduced from manure applications on snow compared to non-snow applications. The fall tillage before winter manure application decreased nutrient losses compared to untilled fields.

Abstract.State-of-the-art model parameterization consists of calibrating and validating the model using monitored data. When data are not available, many studies have relied on alternate strategies, including parameter sets obtained for eco-hydrologically similar watersheds and best professional judgment (BPJ). The objectives of this study were to (1) test the accuracy of four APEX parameterization strategies to predict runoff quantity and quality in a small agricultural watershed and (2) compare the effects of the different parameter sets on relative and absolute water quality outcomes for different conservation practice scenarios. A BPJ and three parameter sets obtained on nearby and more distant sites, including two fully calibrated parameter sets, i.e., for runoff, sediment, and total phosphorus (TP), and one partially calibrated parameter set, i.e., for runoff and TP only, were evaluated based on edge-of-field runoff, sediment loss, and TP loss, as well as for long-term annual predictions and relative changes for six conservation practice scenarios. Only the parameter sets that were fully calibrated met the model performance criteria on the test watershed and produced similar 30-year average annual predictions for the conservation practices. Relative changes in conservation outcomes were similar for the fully and partially calibrated parameter sets. They were different for the BPJ parameter set for at least one conservation practice. In the absence of site-specific data, the best parameterization strategy was to use parameter sets from a model calibrated for runoff, sediment, and nutrient losses from an eco-hydrologically similar site. Partial calibration of the model was sufficient to obtain consistent relative effects of the conservation practices. Keywords: Conservation practices, Hydrologic modeling, Model parameterization, Phosphorus, Sediment.

Cover crops can be effective in minimizing nutrient losses from agricultural fields. The objective of this study was to determine the impact of cover crop (rye, Secale cereale L.) and winter manure application on nutrient loss in simulated rainfall runoff. A split block design study with manure (as vertical block) and cover crops (as horizontal block) was established in 2009. Two rain simulations (the first defined as “dry” and the second “wet”) using sixteen 4 m2 steel frames were conducted in May 2010. The runoff volume collected from each plot was analyzed for nitrate–nitrogen (NO3–N), total suspended solids, total Kjeldahl nitrogen, total phosphorus, and total dissolved phosphorus. In the dry run, the concentration and load of NO3–N were significantly lower (p = 0.05) in runoff with the cover crop than in no‐cover crop treatment. Overall, cover crops reduced nutrient loss in concentration by 6%–48% in the dry and 8%–40% in the wet run than with no‐cover crops. The concentration and load of NO3–N were significantly higher under manure treatments in both “dry” and “wet” runoff runs compared to no‐manure application. Manure application increased nutrient loss in concentration by 6%–58% in the dry and 10%–69% in the wet run than with no‐manure application. This study helps us to understand the complexity of winter manure application with cover crops and potential risks of nutrient loss to surface runoff during spring in the Northern Great plains of the Dakotas.