Nutrient tracking tool -
Descriptions of these equations were reviewed in Gassman et al. Equations that represent all aspects of the hydrologic cycle, erosion, plant growth, aspects of livestock management, nutrient cycles, and routing are used and represented within the model Gassman et al. Management practices are also incorporated and modify the model output.
These equations have an influence over the temporal discretization used by the model resulting in daily calculations. Although the APEX output files are available to download, the NTT interface summarizes this output and has it available for download as annual averages. Just like the base version of APEX, the NTT is designed to evaluate the effectiveness of several conservation practices, including reforestation, on nutrient export from fields to surface waters Saleh et al.
Four main scenarios were set up:. Tillage, fertilization, planting, and harvesting practices were incorporated into these scenarios as described in Keller and Fox Tillage consisted of chisel plowing in the spring, field cultivating prior to planting, and disking post-harvest.
Fertilizer was applied in the spring; planting occurred in early May for corn and mid-May for soybeans with harvests set for October Online Resource Table S1. The tiled fields used the same scenarios with the addition of tiling specified for each field in NTT.
One downside of NTT version 20—2 used in this study is that red pine plantation is the only included forest type, which may not be suitable for all locations Saleh et al. A subset of forested land within the WLCWR watershed was simulated with the NTT to simulate the conversion of forested land to row crop agriculture using the scenarios listed above.
The simulation results from the WLCWR are labeled as low priority regardless of scoring criteria because the NLCD dataset classifies the land cover as forested, shrubby, or wetlands.
We used the NTT to simulate annual field export of total nitrogen TN , total phosphorus TP , and sediment over a year period — NTT model results break the TN into runoff N, tile drain N, organic N, and subsurface N components. TP is separated into organic, soluble reactive P orthophosphate , and tile drain P loads.
Each of the individual components makes up the total N or P values. Sediment export is defined as surface erosion and not broken down into other components. Sensitivity testing was conducted on P export results from the NTT among the fields in the PC watershed. Mid-priority areas were included in order to complete the watershed-scale evaluation described below and were added to the sensitivity analysis after completing that evaluation.
A mid-priority area is one that may have a relatively steep slope or low infiltration capacity that resulted in a score of 4 or 5 but did not represent combination of both low infiltration and steep slopes such as areas ranked as high priority scores of 6 or greater.
The soluble P runoff coefficient and the P enrichment ratio coefficient for routing were left at the default values of 20 0. Other parameters that were included in the sensitivity analysis were the soluble P runoff exponent, the P enrichment ratio exponent for routing, and the soluble P leaching equilibrium dissociation constant value.
These parameters are used in the NTT model equations to generate the nutrient export results. Parameters were adjusted to maximize P export from NTT simulations at the field scale. Using the changed parameter values, the NTT simulated new export values for the chosen fields that were compared to the original values.
The sensitivity analysis was used to check if unadjusted parameter settings caused the large discrepancy between the high and mid-priority aggregated modeled NTT exports and the sub-watershed data used during the watershed-scale evaluation described below.
Nitrogen was excluded from this exercise because TN watershed export data is unavailable for PC. Verifying the modeled results with export data at both the field and watershed scale acts as a proof of concept that any modeled reductions from revegetation mitigation using the NTT may be an effective mitigation strategy for the FRW.
The comparison between NTT modeled field exports and published watershed loads additionally checks the magnitude of the NTT model results against the measured data.
Field-scale data was recently published Komiskey et al. Geological Survey a , b. The data collection period for these studies is significantly shorter than the average annual sediment and nutrient exports estimated from the NTT over the year simulation period.
The published watershed export values, or loads, from the LER and PC were compared against the manually aggregated NTT year-simulated average annual sediment and P total load results in the tiled and high priority areas under the corn—soybean rotation scenario business as usual with the addition of mid-priority areas scores of 4 and 5 , which represented the remainder of the agricultural area in row crops located near a stream channel or drainage network.
Changing the high priority, mid-priority, and tiled unit area loads into total loads and aggregating across the watershed area makes the values comparable to the published measured loads for each watershed.
For the field-scale evaluation, NTT parameters were adjusted along with the cropping system to better match the field-scale characteristics and management practices Online Resource Table S2. The comparison was completed on a single edge-of-field monitoring site ESW3 that had both surface and tile drain datasets that were collected from to Komiskey et al.
The NTT was run separately for the surface and subsurface export from the field because the tiled portion drains approximately 10 ha and the surface drains approximately 2.
The parameters were largely set based on previous work using the ArcApex model Kalk and involved adjusting settings to resemble the cropping and soil systems found in the field along with incorporating a 5-year training or model warm up period prior to the to simulation.
Model predictions improved with the increased temporal range. The NTT was run with the parameter values used for the watershed-scale evaluation of the NTT output and collated but the model was run with the 5-year training period and for to so that it could be compared to Kalk in addition to the measured data.
After completing this initial evaluation, two additional steps were attempted to improve results. First, instead of having the initial soil P estimated by the model, the soil P was changed to the measured value Second, a longer model run of 35 years was simulated with the original parameter settings.
Nash Sutcliffe efficiency NSE was calculated for the discharge and phosphorus values based on annual variation between modeled and observed values. A NSE value of 0. The m grid cell score based on slope and hydrologic drainage class were computed for the FRW Fig. In total, 30 tiled fields and high priority fields were simulated with the NTT.
Tiled fields in a corn—soybean rotation showed an average annual loss of High priority fields showed the greatest nutrient and sediment export ranges compared to the tiled fields Fig. The conversion of tiled lands to a red pine plantation decreased loads the greatest.
The forest conversion resulted in The untiled high priority fields showed a similar trend with forest conversion corresponding to Buffer strips worked better at reducing sediment compared to nutrients but did not provide as great of reductions as the forest conversion scenario.
The buffer strips in high priority untiled fields reduced TN, TP, and sediment by 8. Tiled systems reduced TN, TP, and sediment by 0. Nutrient tracking tool NTT modeling results for sediment, total nitrogen TN , and total phosphorus TP in the Lower East River LER , Plum Creek PC , and White Lake Creek—Wolf River WLCWR for all, high priority, low priority, mid-priority, and tiled fields.
The values represent the average annual unit area export of all fields in the respective watersheds simulated for 35 years. The LER had 30 tiled and high priority fields; PC had 47 tiled, high priority, and mid-priority fields; WLCWR had 5 low priority fields evaluated. The NTT was used to simulate 47 tiled, high priority, and mid-priority fields.
The corn—soybean rotation in tiled fields were projected to yield The corn—soybean rotation in the high priority fields resulted in Similar to the LER, tile removal and conversion to forest resulted in the greatest nutrient and sediment reductions with Forest conversion in the non-tiled high priority areas showed similar results with Tile drainage that circumvents buffer strips increased TN and TP loads by Sediment export was reduced by Nutrient tracking tool NTT modeling results for sediment, total nitrogen TN , and total phosphorus TP using corn soybean, forest conversion, and corn soybean with m buffer management scenarios in the Lower East River LER , Plum Creek PC , and White Lake Creek—Wolf River WLCWR for all high priority, low priority, mid-priority, and tiled fields.
The distribution of values represents the average annual results of each field simulated for 35 years. Only five of these low priority fields were simulated with the NTT, but the 5 fields were spatially comparable to the other sub-watersheds. The modeled forested landscapes in the WLCWR result in less nutrient and sediment losses to streams compared to conversion to row crop agriculture.
Overall, the NTT produced sediment exports that fell within observed ranges at the watershed scale, but TP was underestimated in the LER using the year simulation with default parameter values and the corn—soybean management scenario.
According to the NTT simulations, the LER tiled and high priority fields exported 2. Total nitrogen was not measured at the watershed outlet, so it was excluded from the evaluation.
From to , the measured annual TP export reported at the LER USGS gaging station gauge number averaged Geological Survey a. Geological Survey b. Comparison of the TP values indicates the NTT year average annual field export is well under the TP yield measured at the watershed outlet, with the NTT field export prediction approximately one order of magnitude lower than the annual average measured at the watershed outlet over — In PC, the tiled, high priority, and mid-priority fields combined were predicted to annually export 6.
Both the TP and sediment modeled field export fell within the ranges of observed exports at the PC gauge from to USGS gauge : 6. The measured surface and tile drain discharge, TN, TP, and sediment export from the edge-of-field ESW3 location were checked against the NTT model output using the NSE value.
Using NTT model parameter values from the watershed wide comparison referred to as default values , the average annual surface runoff was estimated at 3, and 3, m 3 using the adjusted parameter values from Kalk When the model training period was increased from 5 to 14 years using the default parameters the NSE increased by 0.
The average annual tile drainage discharge using the adjusted parameter values was 17, m 3 and 14, m 3 using the default parameters. Although both the adjusted parameter value and default parameter discharges were close to the observed average annual discharge, the negative NSE values resulted from calibration difficulties to capture the annual tile discharge variation.
Total nitrogen surface and tile exports were undervalued when using the default and updated parameter changes and parameters need further refinement. Total phosphorus showed similar trends as TN.
The NSE results were also improved when the soil P value was manually adjusted based on soil P test results Kalk Although TP results were poor in terms of the NSE, using default parameter settings with a long training period resulted in a NSE of 0.
Values below 0 for the NSE suggest that the observed mean does a better job of predicting discharge or export than the model. Adjusting model parameters to maximize TP export and limit the discrepancy with the measured watershed data resulted in minimal changes within the high priority and mid-priority sites in PC.
Despite these parameter changes and the resultant increases, the nutrient and sediment export were within the original simulation parameter setting margin of error Online Resource Table S3 to S7.
The field-scale results were poor compared to the observed USGS edge-of-field location at ESW3, based on the NSE values and other work using ArcAPEX at this site. Kalk used three edge-of-field locations for model calibration including ESW3 and found a NSE value of 0. Despite that success when using the same parameter values in this study, the NSE value for discharge was significantly lower NSE 0.
The U. Geological Survey dataset was collected on an event basis following edge-of-field sampling protocols Komiskey et al. Having adjusted for differences in timescales, a key difference between the NTT and ArcAPEX research was that the NTT used PRISM data for the weather modeling whereas the work using ArcAPEX used rain gauge data collected at the field location.
The PRISM precipitation estimates are derived from weather station data that informs m grid cells that may vary from local data collected at the ESW3 location and could have resulted in some of the discrepancies in modeled water volumes.
Precipitation can vary significantly from what was observed at a weather station and a field a few kilometers away. If hydrologic differences exist or are inaccurate, then the nutrient and sediment export cannot be calibrated appropriately within the NTT model.
This lack of partitioning has dramatic effects on DP and P lost through tile drains. Across all six locations evaluated from within the Lower FRW in Kalk , they found DP and tile drained P were quite different than measured values despite calibrating the model to the surface and tile hydrologic volumes.
Similarly, tile drained P was essentially estimated at zero by the NTT in this study which differed from the U. Geological Survey field measurements. One change that did make TP export more accurate in both this study and Kalk was the inclusion of measured Bray P from field sediment samples.
The NTT will automatically set this value based on the soil characteristics and farming management scenarios and, in the case of Kalk , the soil P was estimated at roughly half the value as the measured P at the site.
Even with the default soil P settings, the TP values had better NSE scores when using a longer model training period and should be utilized in future modeling efforts in the FRW. Updating the underlying equations could be one remedy to the undervalued P exports.
The P equations are based on partitioning and mineralization research from the s Knisel ; Jones et al. However, this change is outside the control of the average NTT and APEX user.
One other large challenge in the calibration is how to handle the presence of tiling and snowmelt in the NTT and underlying APEX system. The surface drainage area in the observed field was approximately 2. Without explicit knowledge of tile networks, the accurate simulation of agricultural fields with tile drainage poses a challenge due to unknown hydrologic connectivity which is critical in systems that experience snowmelt.
Kalk noted that the APEX model had a difficult time predicting the influence of snowmelt during their calibration because APEX uses solar radiation to predict snowmelt which is a simplification of the melting process. Relying solely on solar radiation to simulate snowmelt may not work well in the upper Midwest where the largest nutrient and sediment loads typically occur during the spring snowmelt period Huisman et al.
Overall, APEX and the NTT need further refinement to accurately model field-scale exports within the Lower FRW, including but not limited to field data such as tile networks, nutrient levels from soil tests, fertilization practices, management choices, and the ability to choose specific vegetation for buffers and forested areas to inform model calibration and assess model output.
At the watershed scale, aggregating undervalued field-scale results could compound into the large discrepancies observed for TP at the LER and PC Table 2. If APEX is undervaluing the DP and tile drain export, aggregating those results to the watershed scale could drastically reduce the estimated nutrient loads at the watershed outlets.
However, without field data from other locations for verification, it is impossible to know if this undervaluation of loads is field-specific. Incorrect soil P data may also contribute to the discrepancies between the observed and simulated TP export values.
Additionally, when aggregating to an entire watershed one shortcoming of using field-scale models is the lack of accounting for storage and transport processes within the stream channel.
Phosphorus in particular can be held in varying sediment fractions with differing capacities to release SRP to the water column Lannergård et al. Associations with iron and manganese oxides may be reduced by microbiota in anoxic conditions and release P. In systems with high calcium concentrations, secondary mineral formation of apatite which binds calcium and P in a mineral structure may essentially eliminate any possible SRP release because the mineral has low solubility.
Additionally, biological processes paired with hydrologic fluxes may lengthen the time both P and sediment are stored within the stream channel Dolph et al. However, aggregated results for sediment were within observed ranges at the watershed scale despite having NSE values below zero an indication that the observed mean predicted sediment export better than the modeled results at the field scale , indicating that modeled values were close to actual field-scale exports but not a replacement for actual field data.
These discrepancies between the field- and watershed-scale results emphasizes tradeoffs between field-scale accuracy and the accuracy needed for a generalized watershed-scale aggregation when a model cannot be finely tuned to every field within a watershed due to lack of site knowledge and resources.
Despite these results, the NTT and the underlying APEX model have been found to be effective models in agricultural systems at the field scale Gassman et al.
The more a model is calibrated to an individual farming system, the more likely it is to accurately predict nutrient and sediment losses.
Shortcomings can arise when model parameters are applied across varying sites and when applying a field-scale model at the larger watershed spatial scale.
The NTT was not designed to account for hydrologic, sediment, and nutrient dynamics beyond the field or at a high temporal resolution. While some daily data can be extracted from the underlying APEX files, most of the nutrient and sediment export data is extractable as an average monthly or annual value.
This lack of temporal resolution should be taken into consideration and further validated before using the NTT because of the mounting evidence that extreme events account for most of the annual nutrient and sediment export within watersheds Dolph et al. Channel processes and hydrogeological routing are also absent from the NTT and could be a source of disparities between aggregated field-scale model results and watershed outlet data particularly in systems with built up P reservoirs.
Other models that include better subsurface hydrologic routing, nutrient and sediment storage parameters, channel processes, and the ability to incorporate land uses other than agriculture may be more effective at modeling nutrient and sediment export at larger scales and in more diverse landscapes.
The USEPA summarizes 30 different models ranging from simple to difficult and two modeling systems that can be used for watershed protection USEPA Some of these models, such as the NTT, address the field scale and require moderate knowledge of the field system, whereas others only work at the watershed scale e.
Other models are capable of modeling at both scales such as SWAT Soil and Water Assessment Tool. The greatest benefit of the NTT is the intuitive and easy-to-use interface.
Even though limitations exist, it is a more accessible model to a variety of users e. Model choice should also take the availability of data for validation into consideration.
Data found for this study encompassed a spatial extent beyond what is found at the field scale. Although the aggregated data comparison for the LER and PC fell within measured ranges observed at USGS gage locations for sediment, in-channel and watershed-scale processes are absent from the modeling effort and could be a source of error in the LER, PC, and other sub-watersheds.
These potential sources of error bring to question the suitability of using the NTT, or other field-scale planning tools, in future work to model nutrient and sediment export at the watershed or larger spatial scale. Despite a lack of field-scale data, the goal to assess potential benefits of revegetation as a mitigation strategy was able to be accomplished using the NTT and existing datasets.
As a field-scale model, the NTT is an excellent tool for understanding the interplay between agriculture and conservation practices that can be tailored to individual farming systems.
To aggregate data over a broad spatial extent, such as a watershed, meant that individual fields were simplified Keller and Fox by making assumptions about the cropping systems to complete the analysis because specific management characteristics were unknown.
Future work with the NTT could be improved by diversifying farm management practices and adding complexity e. Tailoring model parameters would likely reduce disparities between results and field-scale observational data.
The addition of buffers decreased the modeled N, P, and sediment loads from the fields versus the typical corn—soybean rotation. Our ability to modify buffers based on individual field physical characteristics such as topography and landscape setting is another limitation of the NTT but could provide further load reductions.
Within the NTT, a buffer width may be specified but the user interface does not allow for the modification, the addition of multiple buffers on a field, or specific site selection. Although not used in this study, one way to work around this limitation is to divide a field into smaller sub-areas.
One or multiple subareas could be designated as a buffer with full vegetative coverage. Users could then use the NTT routing function to have the cropped sub-areas flow into the buffered sub-areas to assess buffer placement on the nutrient and sediment exports.
Buffer design that varies buffers with topography and drainage characteristics such that areas where the majority of nutrients and sediment are being exported from the field surface i. Total buffer area can also be spread out into multiple strips within a field and still yield reductions as long as the design allows for the greatest contact between the buffer and the flow bearing nutrients and sediment from the field Hernandez-Santana et al.
Buffers are a commonly used best management practice, so field-scale designs are paramount to their success. If landowners are unwilling to segment a field using vegetated strips, conservation practices may still be optimized by placing vegetated features near the channel Hansen et al.
Placing vegetation between the fields and the stream would further delay routing and be pivotal in reducing nutrient and sediment loads entering the stream channel Kreiling et al. Nutrient and sediment storm event data from PC shows hysteresis patterns where transport to streams is delayed in the upper portion of the watershed suggesting mobilization from fields Rose and Karwan and provides evidence that vegetation could further delay and largely reduce delivery to the stream channel.
Despite many positives, buffer effectiveness varies spatially, temporally, and by species composition Schulte et al.
Increased buffer thickness at concentrated flow points can drastically improve the nutrient and sediment retention Dosskey et al.
Buffers can act as nutrient sources especially in cold climates Vanrobaeys et al. Deep-rooted perennial plants have been shown to increase labile P previously immobilized in soils Stutter et al.
Harvesting buffers could help offset these negatives by physically removing nutrients tied up in biomass. Removal of one kind of vegetation within a buffer at a time e.
Being able to assess different vegetation communities for both buffer and forest conversion management scenarios would be an improvement to the NTT model version 20—2 framework. Vegetation choices for forest conversion and buffers were unavailable for this research and would be a useful addition to future versions of the NTT software.
Nutrient losses following agricultural land conversion to forest or other regionally appropriate vegetation could be improved with a broader variety of forest and endemic vegetation cover types.
Specifically, the conversion of agricultural land to red pine plantation may not be as beneficial to nutrient and sediment export as other forest types or forested wetlands historically found in the area.
In many locations within the Great Lakes region, red pine may not be a suitable species. Within the NTT, the fields are assumed to be managed for agricultural products and the NTT was not designed to simulate land conversion between agriculture and specific forest or wetland vegetation communities.
In these forested systems, the NTT might not fully parameterize the nutrient and water quality tradeoffs under the full range of other forest and wetland vegetation types. Incorporating vegetation suitability from soil survey databases into the scoring system may be one alternative to work around these limitations in the future.
If the forest conversion scenario were expanded to include open or forested wetlands, the field-scale results have the potential to yield more realistic results at the watershed scale from a mitigation standpoint.
Both upland forests and forested wetlands retain nutrients, which may provide a means for economic income through tree harvesting, and these landscape features dampen the effects of extreme hydrologic events by lowering the magnitude and increasing the duration of elevated discharge observed in stream networks Kovacic et al.
Future work assessing the strategic placement of vegetated areas could have a dramatic effect on nutrient and sediment loads observed in the stream channel.
An example of vegetated systems strategically placed on a large scale are the Everglades Stormwater Treatment Areas STAs which were created to intercept and slow the nutrient and sediment loads flowing from agricultural areas to the Everglades.
These wetlands have succeeded in reducing nutrients and sediment in addition to providing flood mitigation Moustafa et al. A similar scenario could work as a long-term mitigation strategy within the FRW if perennial upland forest, prairie, or wetland vegetation is implemented at a larger scale.
The NTT does allow for the simultaneous incorporation of other management practices despite limitations on complexity such as with buffer design. The scenarios we tested were buffer installation and land conversion to a forested plantation system.
Buffer installation combined with other conservation practices such as implementation of no-till or the use of cover crops could further reduce nutrient and sediment loads and increase buffer effectiveness. These practices behave relatively similar to buffers where carbon storage, water infiltration, water storage, nutrient retention, and erosion protection are field characteristics that are generally improved with adoption along with improvements to overall soil health and biodiversity Kaspar and Singer ; Poeplau and Don ; Myers et al.
Cover crops in particular reduce farm costs when combined with other cropping practices and provide many economic benefits to the landowner Myers et al.
On the other side of the spectrum, the conversion of cropped fields to forests or wetland vegetation, albeit an effective way to reduce nutrient and sediment loads, is unlikely to be implemented as a management option without further economic incentives.
In spite of the simplification to not incorporate other practices to compare aggregated modeled results to the loads observed at the LER and PC watersheds, future work incorporating more specific cropping systems will likely improve model results and close the gap between the observed P export and the modeled export.
One benefit of working in the LER and PC watersheds was the inclusion of tile drainage in the management scenarios for the Lower FRW.
Future work in larger watersheds will need to overcome data limitations regarding tile drainage extent. No large-scale records exist of tile networks because they are implemented on private property. However, those increases were within the margin of error for the scenario outcomes using the original model parameter values.
Geological Survey b , but LER is still much lower than measured U. Despite the dissimilarity between the gauge data and the model, the model results show that the inclusion of tile drainage reduces the effects of vegetation as a nutrient and sediment mitigation strategy and increases nutrient export which is in general agreement with the literature Royer et al.
Tile drains tend to increase dissolved N and P losses from fields, but in comparison to undrained fields increased surface losses may have a net zero effect on TP export depending on the agricultural management practices used in the field Royer et al.
The presence of tile drains allows for targeted mitigation of the drainage systems Schilling et al. Terminating tile drains prior to reaching buffers or rerouting drainage so that tile effluent must flow through vegetated buffers, wetlands, or bioreactors are common mitigation strategies that yield nutrient and sediment reductions despite the presence of subsurface drainage Jaynes and Isenhart ; Carstensen et al.
The NTT was used to successfully simulate nutrient and sediment export from three sub-watersheds within the FRW. By using slope and hydrologic drainage class, conservation measures in high priority areas were shown to have greater sediment, TN, and TP reductions than mid-priority scoring areas within the PC watershed when buffer and forest conversion scenarios were compared to the corn—soybean rotation.
Future work will be needed to build datasets to further validate NTT results at both the field and watershed scales to meet load reduction requirements of sub-watersheds in the FRW. Modeled field hydrologic discharge along with nutrient and sediment export were not accurate compared to measured field-scale means using generalized parameter settings.
The field-scale modeling exercise showed that field-scale data is pivotal to accurately modeling field exports with the NTT. Detailed management practices and inputting Bray P measurements increased the field-scale accuracy.
When scaling up to watershed exports using the generalized parameter settings worked well for estimating sediment loads. The aggregated values of TP were undervalued but sediment fell within range of observed annual fluxes at the sampling locations.
Although modeled results fell within observed ranges for sediment export at the watershed scale, the NTT and its user-friendly interface are likely better suited for use on an individual field basis rather than at a larger aggregated watershed scale. The NTT was designed to focus on individual fields and could prove to be a powerful tool to help agricultural producers implement changes and model scenarios to reduce nutrient and sediment loads into downstream waterbodies by playing to its strengths.
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Accessed 8 Jan Dodds WK, Bouska WW, Eitzmann JL et al Eutrophication of U. freshwaters: analysis of potential economic damages. Dolph CL, Boardman E, Danesh-Yazdi M et al Phosphorus transport in intensively managed watersheds.
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USDA For Serv - Gen Tech Rep PNW-GTR 1— Hadley DW, Pelham J Glacial deposits of Wisconsin: Sand and gravel resource potential. In: Wisconsin Geological and Natural History Survey. Hanrahan BR, King KW, Williams MR Controls on subsurface nitrate and dissolved reactive phosphorus losses from agricultural fields during precipitation-driven events.
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Jones CA, Cole CV, Sharpley AN, Williams JR A simplified soil and plant phosphorus model. Soil Sci Soc Am J — Kalk FS Evaluation of the APEX model to simulate runoff, sediment, and phosphorus loss from agricultural fields in Northeast Wisconsin [Masters Thesis, University of Wisconsin-Green Bay].
Kaspar TC, Singer JW The use of cover crops to manage soil. Keller AA, Fox J Giving credit to reforestation for water quality benefits. PLoS ONE — Klaiber LB, Kramer SR, Young EO Impacts of tile drainage on phosphorus losses from edge-of-field plots in the Lake Champlain Basin of New York.
Water switzerland — Knisel WG CREAMS, A field scale model for chemicals, runoff, and erosion from agricultural management systems. Department of Agriculture, Science and Education Administration, conservation research report number Komiskey MJ, Stuntebeck TD, Loken LC et al Nutrient and sediment concentrations, loads, yields, and rainfall characteristics at USGS surface and subsurface-tile edge-of-field agricultural monitoring sites in Great Lakes States ver.
Geological Survey data release. Kovacic DA, Twait RM, Wallace MP, Bowling JM Use of created wetlands to improve water quality in the Midwest-Lake Bloomington case study. Ecol Eng — Kreiling RM, Bartsch LA, Perner PM et al Riparian forest cover modulates phosphorus storage and nitrogen cycling in agricultural stream sediments.
Environ Manage — Lannergård EE, Agstam-Norlin O, Huser BJ et al New insights into legacy phosphorus from fractionation of streambed sediment. J Geophys Res Biogeosciences In both calibration and validation, the Cohen's D and PBIAS for annual crop yields, tile discharge, surface runoff, DRP, particulate P, and nitrate-N showed that the average simulated results were similar to the average observed values for each variable.
The calibrated model simulated well the annual averages of crop yields, flows, and nutrient losses across fields. The tile drainage and phosphorus transport subroutines in the Nutrient Tracking Tool should be further improved to better simulate the dynamics of discharge and phosphorus transport through subsurface drainage.
Stakeholders can use the verified model to evaluate the effectiveness of conservation practices in improving the water quality across the Western Lake Erie Basin. Keywords: Best management practices; Crop yields; Dissolved reactive phosphorus; Nutrient load reductions; Subsurface drainage systems.
Abstract Agricultural field- and watershed-scale water quality models are used to assess the potential impact of management practices to reduce nutrient and sediment exports.
Cedar Nootropic for Sleep and Relaxation is Maximize glycogen replenishment northeast of Springfield in Lane County, Tooll and flows into Ulcer prevention through exercise Maximize glycogen replenishment River. While tfacking to sensitive species, including spring Chinook, native trout, Western pond turtles and American beaver, the toil quality of the basin has been negatively impacted by farming and gravel extraction. Agriculture can disturb soil and often requires fertilizers or pesticides. When it rains, runoff carries nutrients and soil off the land and into nearby waterways. An overabundance of nutrients promotes excessive plant and algae growth, reducing water quality and harming native species. Excess sediment can decrease water clarity and fill in the streambed substrate that salmon use to build their redds. Nutrient and sediment pollution of Online fitness coaching waters remains a rtacking challenge Nutrirnt improving water quality. This study Herbal remedies for digestive problems Nutrient tracking tool user-friendly Maximize glycogen replenishment tool and assesses its Nutdient to model at both the field and watershed scale tarcking the Maximize glycogen replenishment River Watershed FRWWisconsin, USA, along with assessing how targeted vegetation implementation could attenuate nutrient and sediment exports. To assess potential load reductions, the nutrient tracking tool NTT was used with a scoring system to identify areas where vegetation mitigation could be implemented within three selected FRW sub-watersheds. A corn soybean rotation, an implementation of a m-vegetated buffer, a full forest conversion, and tiling were modeled and assessed. The corn—soybean results were aggregated and compared to watershed level gauge data in two sub-watersheds.
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