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Potential use of a new nitrogen trading tool to assess nitrogen management practices to protect groundwater quality
Computers and Electronics in Agriculture 169 (2020) 105195
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Potential use of a new nitrogen trading tool to assess nitrogen management practices to protect groundwater quality
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Jorge A. Delgado , James C. Ascough II, Nathan Lighthart, Donna Neer ⁎
USDA, Agricultural Research Service, 2150 Centre Avenue, Building D, Suite 100, Fort Collins, CO 80526, United States
ARTICLE INFO
ABSTRACT
Keywords: Air quality trading Best management practices Cover crops Fertilizer Groundwater Water quality trading
The Nitrogen Loss and Environmental Assessment Package (NLEAP) with Geographic Information Systems (GIS), version 5.0, with a Nitrogen Trading Tool (NTT) is a model that can be used to integrate site-specific information about soils, weather, and management to conduct quick assessments of nitrogen (N) management practices to increase N use efficiencies while minimizing N losses. This tool is a valuable resource that can be used to conduct quick assessments of risky combinations of management practices and landscapes across several fields to identify hot spot areas that are susceptible to nitrogen losses. The NLEAP GIS 5.0 NTT can quickly assess the temporal and spatial N losses of a given scenario simulated across all the fields and subtract them from the baseline scenario. This allows the user to compare scenarios and use the tool as a nitrogen trading tool to assess the potential reductions in nitrate leaching or emissions of nitrous oxide that may be achieved with a given management practice. The tool can be used to quickly assess the potential to use management practices to reduce the potential losses of reactive nitrogen to groundwater and the atmosphere, as well as the potential to trade these reductions in reactive nitrogen in water or air quality markets. It can quickly calculate the potential savings (reductions in) direct and indirect N2O emissions (and the carbon sequestration equivalent) achieved due to improvements in N management practices. The carbon sequestration equivalent could also be traded in air quality markets. There is potential to use the tool to assess which areas are more sensitive to nitrate leaching, which can impact groundwater quality, and to assess what would be the best management practices to implement in these areas for protection of groundwater quality.
1. Introduction N use has been reported to significantly impact surface and groundwater quality (Delgado and Follett, 2010). One of the main mechanisms for nitrogen losses is nitrate leaching, which can impact water quality, and management is essential to reducing the impacts of nitrate losses from the root zone (Meisinger and Delgado, 2002). These losses of reactive nitrogen can significantly impact the environment and contribute to lower water quality and/or to effects from the nitrogen cascade, where reactive nitrogen (e.g., nitrate leaching) contributes to additional losses of reactive nitrogen (e.g., N2O emissions due to denitrification) (Galloway et al., 2003; Cowling et al., 2002). Several studies have reported that N losses from agricultural fields are one of the sources that contribute to negative impacts on surface water quality such as the hypoxic zone in the Gulf of Mexico (Goolsby et al., 2001; Robertson et al., 2009). Nitrate leaching from tile systems in the Midwest has been reported as a main pathway contributing to the transport of nitrogen from agricultural fields to surface waters (Goolsby
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et al., 2001; Turner and Rabalais, 2003). An example of another system that can lose nitrogen at a high rate is a system of shallow-rooted crops grown in coarse-textured sandy soils (Delgado, 1998, 2001; Delgado et al., 2001a). However, even with the potentially higher losses of reactive N to the environment associated with N application, N fertilizer is key for global stability and sustainability. N inputs are needed to increase yields of cultivated systems, and this key agricultural input contributes to food security. The majority of soil-crop-landscape combinations throughout the world respond to N inputs with higher yields. There have been reports that groundwater resources from southcentral Colorado have been significantly impacted by N inputs that have contributed to higher levels of groundwater NO3-N than the nitrate-N concentration levels considered safe for drinking water by the EPA (Edelmann and Buckles, 1984; Austin, 1993; USEPA, 1989). Many reports have shown that the use of best management practices across this region has contributed to significant reduction (and even mining of) nitrate, potentially contributing to reclamation of groundwater resources (Delgado, 2001, 1998; Delgado et al., 2001a; 2007). Using the
Corresponding author. E-mail address: [email protected] (J.A. Delgado).
https://doi.org/10.1016/j.compag.2019.105195 Received 11 August 2019; Received in revised form 20 December 2019; Accepted 27 December 2019 0168-1699/ Published by Elsevier B.V.
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J.A. Delgado, et al.
Fig. 1. Residual soil nitrate nitrogen predicted by NLEAP versus observed residual soil nitrate nitrogen from combined results for 205 site-years of validation testing of NLEAP under irrigated and non-irrigated agriculture in the USA and other countries (Argentina and China).
Fig. 3. NLEAP GIS 5.0 showing a graph of a monthly simulation of nitrate leaching under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
results from these studies, we estimate that the use of cover crops, rotating deep-rooted crops with shallow-rooted crops, and other best management practices have contributed to the mining of 11.1 metric tons of NO3-N from groundwater since 2001, with a monetary value equivalent to $19.8 million, and higher potato yields, with an estimated monetary impact of $89 million for farmers since 2001 (Delgado, 1998, 2001; Delgado et al., 1999, 2000, 2001a,b, 2007). Despite these positive impacts on groundwater resources, farmers do not get any
additional economic benefits for improving and/or protecting water quality beyond higher N use efficiencies and higher yields. The NLEAP model has been successfully used to assess the effects of management practices on N dynamics across different cropping systems
Fig. 2. NLEAP GIS 5.0 showing a graph of a monthly simulation of nitrate leaching under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 2
Computers and Electronics in Agriculture 169 (2020) 105195
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Fig. 4. NLEAP GIS 5.0 showing a graph of a simulation of total nitrate leaching over a 24-year period under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of a low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of the USA (Fig. 1; Delgado and Follett, 2010). Recently the NLEAP model was used to assess the impact of N fertilizers on groundwater nitrate concentrations in a region of Utah with similar irrigated systems to those in south-central Colorado (Reuben and Sorensen, 2014). For this study in Utah, there was a positive correlation between the leaching predicted by NLEAP and the groundwater concentrations, with higher nitrate levels in those areas associated with higher nitrate leaching potential (Reuben and Sorensen, 2014). Delgado et al. (2008; 2010) discussed the potential to use the NTT to assess the effects of best management practices in reducing nitrogen losses and how we can use robust tools such as the NTT to assess the quantity of these reductions, which could be traded as commodities in environmental markets. Water quality and air quality trading markets have been proposed by several authors (Ribaudo et al., 1999, 2008). Delgado et al. (2008; 2010) proposed a new, holistic NTT approach for trading N where assessments of the benefits from changes in management practices account for the potential for trading reductions in reactive nitrogen losses that could be traded in air quality markets (e.g. direct and indirect N2O emissions, NH3), as well as the potential for trading reductions in nitrate losses (which could be traded in water quality markets). Delgado et al. (2008, 2010) reported that we can use computer tools such as the NLEAP-GIS 4.2 to assess the potential benefits of management practices in reducing nitrogen losses. A new, more powerful NTT was developed that can rapidly integrate the effects of management practices across the landscape. This new NTT is embedded in the new NLEAP GIS 5.0 that was published by Ascough and Delgado (Ascough and Delgado, 2015) and is available for download at: https://data.nal. usda.gov/dataset/nleap-gis-50. This paper is the first to present the
capabilities of the new and improved NLEAP GIS 5.0 Nitrogen Trading Tool (Fig. 2). There are other nutrient trading tools that can be used to assess the potential for N trading such as the Nutrient Tracking Tool (Saleh et al., 2011), NTRADER (Cox et al., 2013), and the World Resources Institute (WRI) (http://pdf.wri.org/annual_report_full.pdf) NutrientNet with its WRI spreadsheets. The NLEAP GIS 5.0 NTT can be used to calculate the differences (Δ) in reactive N losses (NTT-DNLreac), which is equal to the sum of the ΔNO3-N (difference in nitrate leaching), ΔN2O-N (difference in emissions of the trace gas nitrous oxide), ΔNH3-N (difference in ammonia volatilization), ΔNst (difference in off-site surface N transport not connected to soil erosion), and ΔNer (difference in N transported off-site with soil erosion). Delgado et al. (2008) reported that, analogously to a bank account, in the NTT a positive number indicates that there are savings available to trade, while a negative one indicates that there are no savings to trade. 2. Approach Data was collected at 26 cooperators’ farms from south-central Colorado to assess crop nitrogen uptake under commercial field operations for different varieties of wheat, barley, potato, lettuce, carrot, and cover crops (e.g., rye) (from 1992 to 1999 over 70 site-years). Information on irrigation amount, date of application, date of planting and harvesting, and agricultural operations such as tillage was collected. Additionally, information was collected on the fertilizer sources used, the amount, and time and method of application. Other data, such as the site-specific yields at each farm, were also collected. Weather data from the nearest weather station located in central Colorado were
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Fig. 5. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of high nitrogen input to a potato and barley rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the high nitrogen input, potato-barley management scenario with a gradient from a minimum monthly nitrate leaching loss of zero to a maximum of about 120 lbs NO3-N/acre (134 kg NO3-N/ha).
also used to calculate evapotranspiration. Results from these studies have been presented in several publications (Delgado, 1998, 2001; Delgado et al., 1999, 2000, 2001a,b, 2007). The NLEAP model was able to simulate the effects of management practices on nitrogen dynamics, transport of nitrate in the soil profile, recovery of nitrate from deep within the soil profile by winter cover crops, nitrogen uptake, residual soil nitrate in the soil profile, and soil water content. Using the collected data we were able to develop a series of management scenarios for different crop rotations, identify the areas in the landscape most sensitive to nitrate leaching, and identify the N management practices that contributed to higher rates of reactive N losses. We tested the new tool with a high nitrogen application rate scenario; an improved, low nitrogen application rate scenario; and a scenario with an improved, low nitrogen application rate and the use of a summer cover crop (Figs. 2 and 3). The high nitrogen rate scenario had high nitrogen input to a potato and barley rotation that received 280 and 134 kg N ha−1, respectively. The improved, low nitrogen rate scenario for the potato and barley rotation received 190 and 67 kg N ha−1, respectively. The scenario with a potato-summer cover crop rotation that contributed to the mining of nitrate from groundwater and increased potato tuber yield and quality received 190 and 0 kg N ha−1 for the potato and the summer cover crop, respectively.
(Fig. 4). The temporal graph in Fig. 3 shows that the month with the highest nitrate leaching losses had losses close to 65 lbs NO3-N acre-1 (73 kg NO3-N ha−1). Although the simulation analysis was conducted for ninety-four fields across this area of south-central Colorado (Fig. 2), the graph is only showing the nitrate leaching for field #1 area which had a Gunbarrel loamy sand (top northwest corner of the simulated field; Field MUs-1–1,Gn,Gn,Gn). The NLEAP GIS 5.0 can quickly show the temporal variability across all these fields in daily, monthly, yearly, or total NO3-N time scale intervals. It can quickly graph the different soil types for any of the simulated fields (Fig. 3, Field MUs1–1,Gn,Gn,Gn). Nitrate leaching losses were significantly higher with the shallowerrooted potato crop than with the small grain barley or summer cover crop (Fig. 4, Field MUs-1–1,Gn,Gn,Gn). Both the malting barley and summer cover crop served as a scavenger crop and had significantly lower nitrate leaching than the shallow-rooted potato crop (Fig. 4). The leaching for the potato crop under an improved nitrogen management (low nitrogen input) scenario and under the scenario with improved nitrogen management and a summer cover crop in the rotation (low nitrogen input with summer cover crop scenario) was about one fifth of the nitrate leaching of the high nitrogen input scenario. This shows how sensitive these irrigated shallow-rooted crop systems grown under sandy, coarse-textured soils are to nitrate leaching, especially when the fertilizer nitrogen input is over-applied. The potato yields with the potato-summer cover crop rotation were higher, and yet the potatosummer cover crop rotation had the lowest nitrate leaching losses. This is in agreement with the Delgado et al. (2007) findings that summer cover crops significantly contribute to increased potato tuber quality and yields while reducing nitrate leaching losses (Fig. 4).
3. The new NLEAP GIS 5.0 NTT The monthly data for the 24-year evaluation of the (i) high nitrogen, (ii) low nitrogen, and (iii) low-nitrogen-with-a-summer-cover-crop scenarios show that the higher nitrate leaching losses occurred in the scenario with high nitrogen input to the potato and barley rotation
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Fig. 6. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of low nitrogen input for a potato and barley rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the low nitrogen input, potato barley management scenario with a gradient from a minimum monthly nitrate leaching loss of zero to a maximum of about 33 lbs NO3-N/acre (37 kg NO3-N/ha).
The NLEAP GIS 5.0 can rapidly calculate the total nitrate leaching losses spatially for any combination of landscape and management across the evaluated area. For example, for field #1 (Field MUs1–1,Gn,Gn,Gn) there was a total nitrate leaching loss over the 24 simulated years of 1,259 lbs NO3-N /acre (1,410 kg NO3-N ha−1) (Fig. 4), which is about 4.5 times the 282 lbs NO3-N /acre nitrate (316 kg NO3-N ha−1) leaching that this section of the field would have if the nitrogen management is improved. The nitrate leaching losses over 24 years can even be reduced further if a summer cover crop is added to the potato rotation, with a lower nitrate leaching of 230 lbs NO3-N /acre (258 kg NO3-N ha−1) over the 24-year evaluated period. The spatial distribution of nitrate leaching across the simulated area is shown in Fig. 5. The temporal graph in the bottom right part of Fig. 5 shows the average simulated monthly nitrate leaching losses for the 24year period for all of the fields. The NLEAP GIS 5.0 can show a color ramp (gradient) from the lowest value of zero nitrate leaching to the highest monthly value observed of 118 lbs NO3-N/acre (132 kg NO3-N ha−1). The graph shows that for July 1990 there was a gradient ranging from areas as low as about 47 lbs NO3-N/acre (53 kg NO3-N ha−1) to the highest amount of 118 lbs NO3-N per acre (132 kg NO3-N ha−1). This color ramp allows quick identification of the areas of the evaluated fields that are more sensitive to leaching losses (Fig. 5). This will allow the farmer to make a decision about what management practices to implement to reduce these losses and assess the quantity of reductions in nitrate leaching that could be traded in a water quality market. Both the scenario with the low nitrogen input for the potato and barley rotation, and the scenario with the low nitrogen input for the potato and summer cover crop rotation, show that the nitrate leaching losses were significantly lower (spatially and temporally) across the
simulated area. The maximum loss for the scenario with the low input in the potato barley rotation during these 24 years was 33 lb NO3-N acre-1 for June 1974 (37 kg NO3-N ha−1) (Fig. 6). The maximum loss for the scenario with the potato-summer cover crop rotation across all these fields and different soil types during these 24 years was even lower at 30 lb NO3-N acre-1 also for June 1974 (34 kg NO3-N ha−1) (Fig. 7). These values can be used to estimate the potential savings in nitrate leaching that could be traded over a 24-year period if the farmers improve the management practices and reduce the losses of nitrate to groundwater. The NLEAP GIS 5.0 NTT rapidly sums for the baseline scenario the nitrogen losses across the 94 center pivots with their respective spatial variability, covering a total area of 12,123.5 acres (4,906 ha) (Fig. 8). Besides summing the total nitrogen losses and individual nitrogen losses for each of the simulated nitrogen loss pathways, it also rapidly calculates the reductions or increases in nitrogen losses from an alternative management practices that are simulated, relative to the baseline scenario. This allows the user to evaluate a given management practice in comparison to a baseline scenario. The total reduction in total N losses across the 94 center pivots over the 24-year period in the low-nitrogen input, potato-barley management scenario was 15,890,000 lbs N (17,800,000 kg N) when compared to the baseline scenario (Fig. 8). With the low-nitrogen input potatosummer cover crop rotation, the total savings in total N losses was higher, at 16,920,000 lbs of N (18,950,000 kg N). For the high-nitrogen input potato-barley scenario, the nitrate leaching over the 24-year period was 15,220,000 lbs of NO3-N (17,050,000 kg NO3-N), which was much higher than the 3,058,000 lbs of NO3-N (3,425,000 kg NO3-N) with the low-nitrogen input, potato-barley scenario, and higher than
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Fig. 7. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of low nitrogen input for a potato and summer cover crop rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the low input potato-summer cover crop management scenario with a gradient from a minimum of zero monthly nitrate leaching to a maximum of about 30 lbs NO3-N/acre (34 kg NO3-N/ha).
the 2,331,000 lbs of NO3-N (2,611,000 kg NO3-N) with the low-nitrogen input, potato and summer cover crop scenario. Compared to the baseline scenario, the low-N input potato-barley scenario had a total NO3-N savings (reduction in NO3-N losses) of 12,170,000 lbs (13,630,000 kg N), and the low-N input potato-summer cover crop scenario had a total NO3-N savings of 12,890,000 lbs (14,440,000 kg N). Similarly, the same quick calculations were done for the N in runoff (Nst-N), N volatilized (NH3-N), N denitrified (N2-N) and N emitted (N2O-N). Compared to the baseline scenario, the low-N input potato-barley scenario had a total reactive N savings of 12,470,000 (13,960,000 kg N per year) and the low-N input potato-summer cover crop scenario had a total reactive N savings of 12,990,000 (14,550,000 kg N per year) over the 24-year period. The average savings in direct N2O emissions was 1,672,000 and 1,680,000 lbs of C sequestration equivalent per year across the 94 center pivots for the low-nitrogen input, potato-barley scenario and the low-nitrogen input, potato-summer cover crop scenario, respectively (1,872,000 and 1,881,000 kg C per year, respectively). The total direct and indirect savings in N2O emissions by implementing better nitrogen management with the low input potato-barley rotation or a low-input potato-summer cover crop rotation across the 94 center pivots during the 24-year period were 52,240,000 and 52,870,000 lbs of C (58,510,000 and 59,220,000 kg of C) in carbon sequestration equivalent, respectively.
management practices for a crop rotation, weather, soil properties, and hydrology to assess their spatial and temporal effects on nitrogen dynamics and the potential for nitrogen losses to groundwater via nitrate leaching or to the atmosphere via ammonia volatilization and nitrous oxide gas emissions. It can quickly assess the spatial nitrogen losses across the simulated area by field and soil type, and temporal losses across the simulated time (e.g., 24 years). The NLEAP GIS 5.0 NTT can quickly sum all these losses across the simulated area, including nitrate leaching losses and direct and indirect emissions of N2O.
4. Conclusions
Declaration of Competing Interest
The NLEAP GIS 5.0 NTT is a quick and powerful tool that can provide a series of robust calculations about the impact of current nitrogen management practices across a large number of fields. The tool can quickly integrate information on nitrogen management,
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CRediT authorship contribution statement Jorge A. Delgado: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Supervision. James C. Ascough: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization, Project administration, Supervision. Nathan Lighthart: Methodology, Software, Validation, Data curation, Writing - review & editing, Visualization. Donna Neer: Software, Validation, Investigation, Data curation, Writing - review & editing, Visualization.
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Fig. 8. NLEAP GIS 5.0 NTT showing output from the generated results, assessing the total nitrogen losses across the whole area and via different nitrogen loss pathways over a 24-year period. The NLEAP GIS 5.0 NTT also calculates the differences between the baseline scenario and the alternative scenarios. The NLEAP GIS 5.0 NTT also calculates the potential for savings in direct and indirect N2O emissions expressed in equivalent carbon sequestration due to improved nitrogen management scenarios and reductions of reactive nitrogen losses to the environment.
Appendix A. Supplementary material
M.J., Riggenbach, R.R., Sparks, R.T., Thompson, A., Kawanabe, L.M., Kunugi, A., 2001b. Use of innovative tools to increase nitrogen use efficiency and protect environmental quality in crop rotations. Commun. Soil Sci. Plant Anal. 32, 1321–1354. Delgado, J.A., Sparks, R.T., Follett, R.F., Sharkoff, J.L., Riggenbach, R.R., 1999. Use of winter cover crops to conserve water and water quality in the San Luis Valley of south central Colorado. In: Lal, R. (Ed.), Soil Quality and Soil Erosion. Lewis Publishers, Boca Raton, FL, pp. 125–142. Delgado, J.A., Dillon, M.A., Sparks, R.T., Essah, S.Y.C., 2007. A decade of advances in cover crops: cover crops with limited irrigation can increase yields, crop quality, and nutrient and water use efficiencies while protecting the environment. J. Soil Water Conserv. 62, 110A–117A. Delgado, J.A., Follett, R.F. (Eds.), 2010. Advances in Nitrogen Management for Water Quality. SWCS, Ankeny, IA. Delgado, J.A., Follett, R.F., Shaffer, M.J., 2000. Simulation of NO3-N dynamics for cropping systems with different rooting depths. J. Soil Sci. Soc. Am. 64, 1050–1054. Delgado, J.A., Riggenbach, R.R., Sparks, R.T., Dillon, M.A., Kawanabe, L.M., Ristau, R.J., 2001b. Evaluation of nitrate-nitrogen transport in a potato-barley rotation. Soil Sci. Soc. Am. J. 65, 878–883. Edelmann, P., Buckles, D.R., 1984. Quality of Ground Water in Agricultural Areas of the San Luis Valley, South-Central Colorado. Water-Resources Investigations. Report 834281. U.S. Geological Survey, Lakewood, CO. Galloway, J.N., Aber, J.D., Erisman, J.W., Seitzinger, S.P., Howarth, R.W., Cowling, E.B., Cosby, B.J., 2003. The nitrogen cascade. BioScience 53, 341–356. Goolsby, D.A., Battaglin, W.A., Aulenbach, B.T., Hooper, R.P., 2001. Nitrogen input to the gulf of Mexico. J. Environ. Qual. 30, 329–336. Meisinger, J.J., Delgado, J.A., 2002. Principles for managing nitrogen leaching. J. Soil Water Conserv. 57, 485–498. Reuben, T.N., Sorensen, D.L., 2014. Estimated nitrate loadings from lawns, irrigated cropland, and on-site wastewater to an aquifer in Ogden Valley. Utah. J. Soil Water Conserv. 69, 243–253. Ribaudo, M., Hansen, L., Hellerstein, D., Greene, C., 2008. The Use of Markets to Increase Private Investment in Environmental Stewardship. Economic Research Report 64, USDA, Economic Research Service, Washington, D.C. Ribaudo, M., Horan, R.D., Smith, M.E. 1999. Economics of Water Quality Protection from Nonpoint Sources: Theory and Practice. Agricultural Economic Report 782, USDA, Economic Research Service, Washington, D.C.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.compag.2019.105195. References Ascough II, J.C., Delgado, J.A., 2015. Modeling landscape-scale nitrogen management for conservation. In: Mueller, T., Sassenrath, G.F. (Eds.), GIS Applications for Agriculture: Conservation Planning. CRC Press, Boca Raton, FL, pp. 101–118. Austin, B., 1993. Report to the Commissioner of Agriculture, Colorado Department of Agriculture, Groundwater Monitoring Activities San Luis Valley Unconfined Aquifer. Colorado Department of Public Health and Environment, Denver, CO. Cowling, E., Galloway, J., Furiness, C., Erisman, J.W., 2002. Optimizing nitrogen management and energy production and environmental protection: Report from the Second International Nitrogen Conference. Bolger Center, Potomac, MD, 14-18 Oct. 2001. Cox, T.J., Rutherford, J.C., Kerr, S.C., Smeaton, D.C., Palliser, C.C., 2013. An integrated model for simulating nitrogen trading in an agricultural catchment with complex hydrogeology. J. Environ. Manage. 127, 268–277. Delgado, J.A., 1998. Sequential NLEAP simulations to examine effect of early and late planted winter cover crops on nitrogen dynamics. J. Soil Water Conserv. 53, 241–244. Delgado, J.A., 2001. Use of simulations for evaluation of best management practices on irrigated cropping systems. In: Shaffer, M.J., Ma, L., Hansen, S. (Eds.), Modeling Carbon and Nitrogen Dynamics for Soil Management. Lewis Publishers, Boca Raton, FL, pp. 355–381. Delgado, J.A., Gross, C.M., Lal, H., Cover, H., Gagliardi, P., McKinney, S.P., Hesketh, E., Shaffer, M.J., 2010. A new GIS nitrogen trading tool concept for conservation and reduction of reactive nitrogen losses to the environment. Adv. Agron. 105, 117–171. Delgado, J.A., Shaffer, M.J., Lal, H., McKinney, S., Gross, C.M., Cover, H., 2008. Assessment of nitrogen losses to the environment with a Nitrogen Trading Tool (NTT). Comput. Electron. Agric. 63, 193–206. Delgado, J.A., Ristau, R.J., Dillon, M.A., Duke, H.R., Stuebe, A., Follett, R.F., Shaffer,
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J.A. Delgado, et al. Robertson, D.M., Schwarz, G.E., Saad, D.A., Alexander, R.B., 2009. Incorporating uncertainty into the ranking of SPARROW model nutrient yields from Mississippi/ Atchafalaya River Basin watersheds. J. Am. Water Resour. Assoc. 45 (2), 535–549. Saleh, A., Gallego, O., Osei, E., Lal, H., Gross, C., McKinney, S., Cover, H., 2011. Nutrient Tracking Tool—A user friendly tool for calculating nutrient reductions for water
quality trading. J. Soil Water Conserv. 66, 400–410. Turner, R.E., Rabalais, N.N., 2003. Linking landscape and water quality in the Mississippi River Basin for 200 years. BioScience 53 (6), 563–572. USEPA. 1989. Federal Register. 54 FR22062, 22May. USEPA, Washington, D.C.
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Potential use of a new nitrogen trading tool to assess nitrogen management practices to protect groundwater quality
T
Jorge A. Delgado , James C. Ascough II, Nathan Lighthart, Donna Neer ⁎
USDA, Agricultural Research Service, 2150 Centre Avenue, Building D, Suite 100, Fort Collins, CO 80526, United States
ARTICLE INFO
ABSTRACT
Keywords: Air quality trading Best management practices Cover crops Fertilizer Groundwater Water quality trading
The Nitrogen Loss and Environmental Assessment Package (NLEAP) with Geographic Information Systems (GIS), version 5.0, with a Nitrogen Trading Tool (NTT) is a model that can be used to integrate site-specific information about soils, weather, and management to conduct quick assessments of nitrogen (N) management practices to increase N use efficiencies while minimizing N losses. This tool is a valuable resource that can be used to conduct quick assessments of risky combinations of management practices and landscapes across several fields to identify hot spot areas that are susceptible to nitrogen losses. The NLEAP GIS 5.0 NTT can quickly assess the temporal and spatial N losses of a given scenario simulated across all the fields and subtract them from the baseline scenario. This allows the user to compare scenarios and use the tool as a nitrogen trading tool to assess the potential reductions in nitrate leaching or emissions of nitrous oxide that may be achieved with a given management practice. The tool can be used to quickly assess the potential to use management practices to reduce the potential losses of reactive nitrogen to groundwater and the atmosphere, as well as the potential to trade these reductions in reactive nitrogen in water or air quality markets. It can quickly calculate the potential savings (reductions in) direct and indirect N2O emissions (and the carbon sequestration equivalent) achieved due to improvements in N management practices. The carbon sequestration equivalent could also be traded in air quality markets. There is potential to use the tool to assess which areas are more sensitive to nitrate leaching, which can impact groundwater quality, and to assess what would be the best management practices to implement in these areas for protection of groundwater quality.
1. Introduction N use has been reported to significantly impact surface and groundwater quality (Delgado and Follett, 2010). One of the main mechanisms for nitrogen losses is nitrate leaching, which can impact water quality, and management is essential to reducing the impacts of nitrate losses from the root zone (Meisinger and Delgado, 2002). These losses of reactive nitrogen can significantly impact the environment and contribute to lower water quality and/or to effects from the nitrogen cascade, where reactive nitrogen (e.g., nitrate leaching) contributes to additional losses of reactive nitrogen (e.g., N2O emissions due to denitrification) (Galloway et al., 2003; Cowling et al., 2002). Several studies have reported that N losses from agricultural fields are one of the sources that contribute to negative impacts on surface water quality such as the hypoxic zone in the Gulf of Mexico (Goolsby et al., 2001; Robertson et al., 2009). Nitrate leaching from tile systems in the Midwest has been reported as a main pathway contributing to the transport of nitrogen from agricultural fields to surface waters (Goolsby
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et al., 2001; Turner and Rabalais, 2003). An example of another system that can lose nitrogen at a high rate is a system of shallow-rooted crops grown in coarse-textured sandy soils (Delgado, 1998, 2001; Delgado et al., 2001a). However, even with the potentially higher losses of reactive N to the environment associated with N application, N fertilizer is key for global stability and sustainability. N inputs are needed to increase yields of cultivated systems, and this key agricultural input contributes to food security. The majority of soil-crop-landscape combinations throughout the world respond to N inputs with higher yields. There have been reports that groundwater resources from southcentral Colorado have been significantly impacted by N inputs that have contributed to higher levels of groundwater NO3-N than the nitrate-N concentration levels considered safe for drinking water by the EPA (Edelmann and Buckles, 1984; Austin, 1993; USEPA, 1989). Many reports have shown that the use of best management practices across this region has contributed to significant reduction (and even mining of) nitrate, potentially contributing to reclamation of groundwater resources (Delgado, 2001, 1998; Delgado et al., 2001a; 2007). Using the
Corresponding author. E-mail address: [email protected] (J.A. Delgado).
https://doi.org/10.1016/j.compag.2019.105195 Received 11 August 2019; Received in revised form 20 December 2019; Accepted 27 December 2019 0168-1699/ Published by Elsevier B.V.
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Fig. 1. Residual soil nitrate nitrogen predicted by NLEAP versus observed residual soil nitrate nitrogen from combined results for 205 site-years of validation testing of NLEAP under irrigated and non-irrigated agriculture in the USA and other countries (Argentina and China).
Fig. 3. NLEAP GIS 5.0 showing a graph of a monthly simulation of nitrate leaching under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
results from these studies, we estimate that the use of cover crops, rotating deep-rooted crops with shallow-rooted crops, and other best management practices have contributed to the mining of 11.1 metric tons of NO3-N from groundwater since 2001, with a monetary value equivalent to $19.8 million, and higher potato yields, with an estimated monetary impact of $89 million for farmers since 2001 (Delgado, 1998, 2001; Delgado et al., 1999, 2000, 2001a,b, 2007). Despite these positive impacts on groundwater resources, farmers do not get any
additional economic benefits for improving and/or protecting water quality beyond higher N use efficiencies and higher yields. The NLEAP model has been successfully used to assess the effects of management practices on N dynamics across different cropping systems
Fig. 2. NLEAP GIS 5.0 showing a graph of a monthly simulation of nitrate leaching under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 2
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Fig. 4. NLEAP GIS 5.0 showing a graph of a simulation of total nitrate leaching over a 24-year period under a scenario of high nitrogen input for a potato and barley rotation; a scenario of low nitrogen input for a potato and barley rotation; and a scenario of a low nitrogen input for a potato and summer cover crop rotation. The simulation of these three scenarios was conducted for an area of field #1 (most northwestern field being evaluated) with a sandy loam Gun barrel soil, indicated with a red border. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of the USA (Fig. 1; Delgado and Follett, 2010). Recently the NLEAP model was used to assess the impact of N fertilizers on groundwater nitrate concentrations in a region of Utah with similar irrigated systems to those in south-central Colorado (Reuben and Sorensen, 2014). For this study in Utah, there was a positive correlation between the leaching predicted by NLEAP and the groundwater concentrations, with higher nitrate levels in those areas associated with higher nitrate leaching potential (Reuben and Sorensen, 2014). Delgado et al. (2008; 2010) discussed the potential to use the NTT to assess the effects of best management practices in reducing nitrogen losses and how we can use robust tools such as the NTT to assess the quantity of these reductions, which could be traded as commodities in environmental markets. Water quality and air quality trading markets have been proposed by several authors (Ribaudo et al., 1999, 2008). Delgado et al. (2008; 2010) proposed a new, holistic NTT approach for trading N where assessments of the benefits from changes in management practices account for the potential for trading reductions in reactive nitrogen losses that could be traded in air quality markets (e.g. direct and indirect N2O emissions, NH3), as well as the potential for trading reductions in nitrate losses (which could be traded in water quality markets). Delgado et al. (2008, 2010) reported that we can use computer tools such as the NLEAP-GIS 4.2 to assess the potential benefits of management practices in reducing nitrogen losses. A new, more powerful NTT was developed that can rapidly integrate the effects of management practices across the landscape. This new NTT is embedded in the new NLEAP GIS 5.0 that was published by Ascough and Delgado (Ascough and Delgado, 2015) and is available for download at: https://data.nal. usda.gov/dataset/nleap-gis-50. This paper is the first to present the
capabilities of the new and improved NLEAP GIS 5.0 Nitrogen Trading Tool (Fig. 2). There are other nutrient trading tools that can be used to assess the potential for N trading such as the Nutrient Tracking Tool (Saleh et al., 2011), NTRADER (Cox et al., 2013), and the World Resources Institute (WRI) (http://pdf.wri.org/annual_report_full.pdf) NutrientNet with its WRI spreadsheets. The NLEAP GIS 5.0 NTT can be used to calculate the differences (Δ) in reactive N losses (NTT-DNLreac), which is equal to the sum of the ΔNO3-N (difference in nitrate leaching), ΔN2O-N (difference in emissions of the trace gas nitrous oxide), ΔNH3-N (difference in ammonia volatilization), ΔNst (difference in off-site surface N transport not connected to soil erosion), and ΔNer (difference in N transported off-site with soil erosion). Delgado et al. (2008) reported that, analogously to a bank account, in the NTT a positive number indicates that there are savings available to trade, while a negative one indicates that there are no savings to trade. 2. Approach Data was collected at 26 cooperators’ farms from south-central Colorado to assess crop nitrogen uptake under commercial field operations for different varieties of wheat, barley, potato, lettuce, carrot, and cover crops (e.g., rye) (from 1992 to 1999 over 70 site-years). Information on irrigation amount, date of application, date of planting and harvesting, and agricultural operations such as tillage was collected. Additionally, information was collected on the fertilizer sources used, the amount, and time and method of application. Other data, such as the site-specific yields at each farm, were also collected. Weather data from the nearest weather station located in central Colorado were
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Fig. 5. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of high nitrogen input to a potato and barley rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the high nitrogen input, potato-barley management scenario with a gradient from a minimum monthly nitrate leaching loss of zero to a maximum of about 120 lbs NO3-N/acre (134 kg NO3-N/ha).
also used to calculate evapotranspiration. Results from these studies have been presented in several publications (Delgado, 1998, 2001; Delgado et al., 1999, 2000, 2001a,b, 2007). The NLEAP model was able to simulate the effects of management practices on nitrogen dynamics, transport of nitrate in the soil profile, recovery of nitrate from deep within the soil profile by winter cover crops, nitrogen uptake, residual soil nitrate in the soil profile, and soil water content. Using the collected data we were able to develop a series of management scenarios for different crop rotations, identify the areas in the landscape most sensitive to nitrate leaching, and identify the N management practices that contributed to higher rates of reactive N losses. We tested the new tool with a high nitrogen application rate scenario; an improved, low nitrogen application rate scenario; and a scenario with an improved, low nitrogen application rate and the use of a summer cover crop (Figs. 2 and 3). The high nitrogen rate scenario had high nitrogen input to a potato and barley rotation that received 280 and 134 kg N ha−1, respectively. The improved, low nitrogen rate scenario for the potato and barley rotation received 190 and 67 kg N ha−1, respectively. The scenario with a potato-summer cover crop rotation that contributed to the mining of nitrate from groundwater and increased potato tuber yield and quality received 190 and 0 kg N ha−1 for the potato and the summer cover crop, respectively.
(Fig. 4). The temporal graph in Fig. 3 shows that the month with the highest nitrate leaching losses had losses close to 65 lbs NO3-N acre-1 (73 kg NO3-N ha−1). Although the simulation analysis was conducted for ninety-four fields across this area of south-central Colorado (Fig. 2), the graph is only showing the nitrate leaching for field #1 area which had a Gunbarrel loamy sand (top northwest corner of the simulated field; Field MUs-1–1,Gn,Gn,Gn). The NLEAP GIS 5.0 can quickly show the temporal variability across all these fields in daily, monthly, yearly, or total NO3-N time scale intervals. It can quickly graph the different soil types for any of the simulated fields (Fig. 3, Field MUs1–1,Gn,Gn,Gn). Nitrate leaching losses were significantly higher with the shallowerrooted potato crop than with the small grain barley or summer cover crop (Fig. 4, Field MUs-1–1,Gn,Gn,Gn). Both the malting barley and summer cover crop served as a scavenger crop and had significantly lower nitrate leaching than the shallow-rooted potato crop (Fig. 4). The leaching for the potato crop under an improved nitrogen management (low nitrogen input) scenario and under the scenario with improved nitrogen management and a summer cover crop in the rotation (low nitrogen input with summer cover crop scenario) was about one fifth of the nitrate leaching of the high nitrogen input scenario. This shows how sensitive these irrigated shallow-rooted crop systems grown under sandy, coarse-textured soils are to nitrate leaching, especially when the fertilizer nitrogen input is over-applied. The potato yields with the potato-summer cover crop rotation were higher, and yet the potatosummer cover crop rotation had the lowest nitrate leaching losses. This is in agreement with the Delgado et al. (2007) findings that summer cover crops significantly contribute to increased potato tuber quality and yields while reducing nitrate leaching losses (Fig. 4).
3. The new NLEAP GIS 5.0 NTT The monthly data for the 24-year evaluation of the (i) high nitrogen, (ii) low nitrogen, and (iii) low-nitrogen-with-a-summer-cover-crop scenarios show that the higher nitrate leaching losses occurred in the scenario with high nitrogen input to the potato and barley rotation
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Fig. 6. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of low nitrogen input for a potato and barley rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the low nitrogen input, potato barley management scenario with a gradient from a minimum monthly nitrate leaching loss of zero to a maximum of about 33 lbs NO3-N/acre (37 kg NO3-N/ha).
The NLEAP GIS 5.0 can rapidly calculate the total nitrate leaching losses spatially for any combination of landscape and management across the evaluated area. For example, for field #1 (Field MUs1–1,Gn,Gn,Gn) there was a total nitrate leaching loss over the 24 simulated years of 1,259 lbs NO3-N /acre (1,410 kg NO3-N ha−1) (Fig. 4), which is about 4.5 times the 282 lbs NO3-N /acre nitrate (316 kg NO3-N ha−1) leaching that this section of the field would have if the nitrogen management is improved. The nitrate leaching losses over 24 years can even be reduced further if a summer cover crop is added to the potato rotation, with a lower nitrate leaching of 230 lbs NO3-N /acre (258 kg NO3-N ha−1) over the 24-year evaluated period. The spatial distribution of nitrate leaching across the simulated area is shown in Fig. 5. The temporal graph in the bottom right part of Fig. 5 shows the average simulated monthly nitrate leaching losses for the 24year period for all of the fields. The NLEAP GIS 5.0 can show a color ramp (gradient) from the lowest value of zero nitrate leaching to the highest monthly value observed of 118 lbs NO3-N/acre (132 kg NO3-N ha−1). The graph shows that for July 1990 there was a gradient ranging from areas as low as about 47 lbs NO3-N/acre (53 kg NO3-N ha−1) to the highest amount of 118 lbs NO3-N per acre (132 kg NO3-N ha−1). This color ramp allows quick identification of the areas of the evaluated fields that are more sensitive to leaching losses (Fig. 5). This will allow the farmer to make a decision about what management practices to implement to reduce these losses and assess the quantity of reductions in nitrate leaching that could be traded in a water quality market. Both the scenario with the low nitrogen input for the potato and barley rotation, and the scenario with the low nitrogen input for the potato and summer cover crop rotation, show that the nitrate leaching losses were significantly lower (spatially and temporally) across the
simulated area. The maximum loss for the scenario with the low input in the potato barley rotation during these 24 years was 33 lb NO3-N acre-1 for June 1974 (37 kg NO3-N ha−1) (Fig. 6). The maximum loss for the scenario with the potato-summer cover crop rotation across all these fields and different soil types during these 24 years was even lower at 30 lb NO3-N acre-1 also for June 1974 (34 kg NO3-N ha−1) (Fig. 7). These values can be used to estimate the potential savings in nitrate leaching that could be traded over a 24-year period if the farmers improve the management practices and reduce the losses of nitrate to groundwater. The NLEAP GIS 5.0 NTT rapidly sums for the baseline scenario the nitrogen losses across the 94 center pivots with their respective spatial variability, covering a total area of 12,123.5 acres (4,906 ha) (Fig. 8). Besides summing the total nitrogen losses and individual nitrogen losses for each of the simulated nitrogen loss pathways, it also rapidly calculates the reductions or increases in nitrogen losses from an alternative management practices that are simulated, relative to the baseline scenario. This allows the user to evaluate a given management practice in comparison to a baseline scenario. The total reduction in total N losses across the 94 center pivots over the 24-year period in the low-nitrogen input, potato-barley management scenario was 15,890,000 lbs N (17,800,000 kg N) when compared to the baseline scenario (Fig. 8). With the low-nitrogen input potatosummer cover crop rotation, the total savings in total N losses was higher, at 16,920,000 lbs of N (18,950,000 kg N). For the high-nitrogen input potato-barley scenario, the nitrate leaching over the 24-year period was 15,220,000 lbs of NO3-N (17,050,000 kg NO3-N), which was much higher than the 3,058,000 lbs of NO3-N (3,425,000 kg NO3-N) with the low-nitrogen input, potato-barley scenario, and higher than
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Fig. 7. NLEAP GIS 5.0 showing a monthly graphic simulation of the average nitrate leaching over a 24-year period under a scenario of low nitrogen input for a potato and summer cover crop rotation. The model shows the spatial distribution of the nitrate leaching across all fields under the low input potato-summer cover crop management scenario with a gradient from a minimum of zero monthly nitrate leaching to a maximum of about 30 lbs NO3-N/acre (34 kg NO3-N/ha).
the 2,331,000 lbs of NO3-N (2,611,000 kg NO3-N) with the low-nitrogen input, potato and summer cover crop scenario. Compared to the baseline scenario, the low-N input potato-barley scenario had a total NO3-N savings (reduction in NO3-N losses) of 12,170,000 lbs (13,630,000 kg N), and the low-N input potato-summer cover crop scenario had a total NO3-N savings of 12,890,000 lbs (14,440,000 kg N). Similarly, the same quick calculations were done for the N in runoff (Nst-N), N volatilized (NH3-N), N denitrified (N2-N) and N emitted (N2O-N). Compared to the baseline scenario, the low-N input potato-barley scenario had a total reactive N savings of 12,470,000 (13,960,000 kg N per year) and the low-N input potato-summer cover crop scenario had a total reactive N savings of 12,990,000 (14,550,000 kg N per year) over the 24-year period. The average savings in direct N2O emissions was 1,672,000 and 1,680,000 lbs of C sequestration equivalent per year across the 94 center pivots for the low-nitrogen input, potato-barley scenario and the low-nitrogen input, potato-summer cover crop scenario, respectively (1,872,000 and 1,881,000 kg C per year, respectively). The total direct and indirect savings in N2O emissions by implementing better nitrogen management with the low input potato-barley rotation or a low-input potato-summer cover crop rotation across the 94 center pivots during the 24-year period were 52,240,000 and 52,870,000 lbs of C (58,510,000 and 59,220,000 kg of C) in carbon sequestration equivalent, respectively.
management practices for a crop rotation, weather, soil properties, and hydrology to assess their spatial and temporal effects on nitrogen dynamics and the potential for nitrogen losses to groundwater via nitrate leaching or to the atmosphere via ammonia volatilization and nitrous oxide gas emissions. It can quickly assess the spatial nitrogen losses across the simulated area by field and soil type, and temporal losses across the simulated time (e.g., 24 years). The NLEAP GIS 5.0 NTT can quickly sum all these losses across the simulated area, including nitrate leaching losses and direct and indirect emissions of N2O.
4. Conclusions
Declaration of Competing Interest
The NLEAP GIS 5.0 NTT is a quick and powerful tool that can provide a series of robust calculations about the impact of current nitrogen management practices across a large number of fields. The tool can quickly integrate information on nitrogen management,
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CRediT authorship contribution statement Jorge A. Delgado: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration, Supervision. James C. Ascough: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization, Project administration, Supervision. Nathan Lighthart: Methodology, Software, Validation, Data curation, Writing - review & editing, Visualization. Donna Neer: Software, Validation, Investigation, Data curation, Writing - review & editing, Visualization.
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Fig. 8. NLEAP GIS 5.0 NTT showing output from the generated results, assessing the total nitrogen losses across the whole area and via different nitrogen loss pathways over a 24-year period. The NLEAP GIS 5.0 NTT also calculates the differences between the baseline scenario and the alternative scenarios. The NLEAP GIS 5.0 NTT also calculates the potential for savings in direct and indirect N2O emissions expressed in equivalent carbon sequestration due to improved nitrogen management scenarios and reductions of reactive nitrogen losses to the environment.
Appendix A. Supplementary material
M.J., Riggenbach, R.R., Sparks, R.T., Thompson, A., Kawanabe, L.M., Kunugi, A., 2001b. Use of innovative tools to increase nitrogen use efficiency and protect environmental quality in crop rotations. Commun. Soil Sci. Plant Anal. 32, 1321–1354. Delgado, J.A., Sparks, R.T., Follett, R.F., Sharkoff, J.L., Riggenbach, R.R., 1999. Use of winter cover crops to conserve water and water quality in the San Luis Valley of south central Colorado. In: Lal, R. (Ed.), Soil Quality and Soil Erosion. Lewis Publishers, Boca Raton, FL, pp. 125–142. Delgado, J.A., Dillon, M.A., Sparks, R.T., Essah, S.Y.C., 2007. A decade of advances in cover crops: cover crops with limited irrigation can increase yields, crop quality, and nutrient and water use efficiencies while protecting the environment. J. Soil Water Conserv. 62, 110A–117A. Delgado, J.A., Follett, R.F. (Eds.), 2010. Advances in Nitrogen Management for Water Quality. SWCS, Ankeny, IA. Delgado, J.A., Follett, R.F., Shaffer, M.J., 2000. Simulation of NO3-N dynamics for cropping systems with different rooting depths. J. Soil Sci. Soc. Am. 64, 1050–1054. Delgado, J.A., Riggenbach, R.R., Sparks, R.T., Dillon, M.A., Kawanabe, L.M., Ristau, R.J., 2001b. Evaluation of nitrate-nitrogen transport in a potato-barley rotation. Soil Sci. Soc. Am. J. 65, 878–883. Edelmann, P., Buckles, D.R., 1984. Quality of Ground Water in Agricultural Areas of the San Luis Valley, South-Central Colorado. Water-Resources Investigations. Report 834281. U.S. Geological Survey, Lakewood, CO. Galloway, J.N., Aber, J.D., Erisman, J.W., Seitzinger, S.P., Howarth, R.W., Cowling, E.B., Cosby, B.J., 2003. The nitrogen cascade. BioScience 53, 341–356. Goolsby, D.A., Battaglin, W.A., Aulenbach, B.T., Hooper, R.P., 2001. Nitrogen input to the gulf of Mexico. J. Environ. Qual. 30, 329–336. Meisinger, J.J., Delgado, J.A., 2002. Principles for managing nitrogen leaching. J. Soil Water Conserv. 57, 485–498. Reuben, T.N., Sorensen, D.L., 2014. Estimated nitrate loadings from lawns, irrigated cropland, and on-site wastewater to an aquifer in Ogden Valley. Utah. J. Soil Water Conserv. 69, 243–253. Ribaudo, M., Hansen, L., Hellerstein, D., Greene, C., 2008. The Use of Markets to Increase Private Investment in Environmental Stewardship. Economic Research Report 64, USDA, Economic Research Service, Washington, D.C. Ribaudo, M., Horan, R.D., Smith, M.E. 1999. Economics of Water Quality Protection from Nonpoint Sources: Theory and Practice. Agricultural Economic Report 782, USDA, Economic Research Service, Washington, D.C.
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