Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2)
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
Finnish Meteorological Institute, Helsinki, Finland
Peter Hess
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
Jeff Melkonian
Section of Soil and Crop Sciences, Cornell University, Ithaca, NY, USA
William R. Wieder
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA
Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4219, https://doi.org/10.5194/egusphere-2025-4219, 2025
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We modelled different water table scenarios in drained agricultural peatlands to investigate the impact of water management on greenhouse gas emissions. Our results show that raising the water table reduces emissions, even in fields with thinner peat layers and conservative water management practices. Carbon dioxide emissions were more affected than nitrous oxide emissions. This study sheds light on the role of peatlands in mitigating emissions. Simulations were run using a process-based model.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
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Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
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Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
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We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
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Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
Julius Vira, Peter Hess, Money Ossohou, and Corinne Galy-Lacaux
Atmos. Chem. Phys., 22, 1883–1904, https://doi.org/10.5194/acp-22-1883-2022, https://doi.org/10.5194/acp-22-1883-2022, 2022
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Ammonia is one of the main components of nitrogen deposition. Here we use a new model to assess the ammonia emissions from agriculture, the largest anthropogenic source of ammonia. The model results are consistent with earlier estimates over industrialized regions in agreement with observations. However, the model predicts much higher emissions over sub-Saharan Africa compared to earlier estimates. Available observations from surface stations and satellites support these higher emissions.
Henri Kajasilta, Stephanie Gerin, Milla Niiranen, Miika Läpikivi, Maarit Liimatainen, David Kraus, Henriikka Vekuri, Mika Korkiakoski, Liisa Kulmala, Jari Liski, and Julius Vira
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This preprint is open for discussion and under review for Biogeosciences (BG).
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We modelled different water table scenarios in drained agricultural peatlands to investigate the impact of water management on greenhouse gas emissions. Our results show that raising the water table reduces emissions, even in fields with thinner peat layers and conservative water management practices. Carbon dioxide emissions were more affected than nitrous oxide emissions. This study sheds light on the role of peatlands in mitigating emissions. Simulations were run using a process-based model.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, P. Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankararaman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Johann Engelbrecht, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbigniew Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gómez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal L. Weagle, and Xi Zhao
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Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Here, we present a new compilation of PM2.5 and PM10 aerosol observations, focusing on the spatial variability across different observational stations, including composition, and demonstrate a method for comparing the data sets to model output.
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Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankarararman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Hannele Hakola, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbiginiw Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gomez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal Weagle, and Xi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
Preprint withdrawn
Short summary
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Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
Brooke A. Eastman, William R. Wieder, Melannie D. Hartman, Edward R. Brzostek, and William T. Peterjohn
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We compared soil model performance to data from a long-term nitrogen addition experiment in a forested ecosystem. We found that in order for soil carbon models to accurately predict future forest carbon sequestration, two key processes must respond dynamically to nitrogen availability: (1) plant allocation of carbon to wood versus roots and (2) rates of soil organic matter decomposition. Long-term experiments can help improve our predictions of the land carbon sink and its climate impact.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
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The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Ye Wang, Natalie Mahowald, Peter Hess, Wenxiu Sun, and Gang Chen
Atmos. Chem. Phys., 22, 7575–7592, https://doi.org/10.5194/acp-22-7575-2022, https://doi.org/10.5194/acp-22-7575-2022, 2022
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PM2.5 is positively related to anticyclonic wave activity (AWA) changes close to the observing sites. Changes between current and future climates in AWA can explain up to 75 % of PM2.5 variability at some stations using a linear regression model. Our analysis indicates that higher PM2.5 concentrations occur when a positive AWA anomaly is prominent, which could be critical for understanding how pollutants respond to changing atmospheric circulation and for developing robust pollution projections.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
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We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
Short summary
Short summary
We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Olli Nevalainen, Olli Niemitalo, Istem Fer, Antti Juntunen, Tuomas Mattila, Olli Koskela, Joni Kukkamäki, Layla Höckerstedt, Laura Mäkelä, Pieta Jarva, Laura Heimsch, Henriikka Vekuri, Liisa Kulmala, Åsa Stam, Otto Kuusela, Stephanie Gerin, Toni Viskari, Julius Vira, Jari Hyväluoma, Juha-Pekka Tuovinen, Annalea Lohila, Tuomas Laurila, Jussi Heinonsalo, Tuula Aalto, Iivari Kunttu, and Jari Liski
Geosci. Instrum. Method. Data Syst., 11, 93–109, https://doi.org/10.5194/gi-11-93-2022, https://doi.org/10.5194/gi-11-93-2022, 2022
Short summary
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Better monitoring of soil carbon sequestration is needed to understand the best carbon farming practices in different soils and climate conditions. We, the Field Observatory Network (FiON), have therefore established a methodology for monitoring and forecasting agricultural carbon sequestration by combining offline and near-real-time field measurements, weather data, satellite imagery, and modeling. To disseminate our work, we built a website called the Field Observatory (fieldobservatory.org).
Julius Vira, Peter Hess, Money Ossohou, and Corinne Galy-Lacaux
Atmos. Chem. Phys., 22, 1883–1904, https://doi.org/10.5194/acp-22-1883-2022, https://doi.org/10.5194/acp-22-1883-2022, 2022
Short summary
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Ammonia is one of the main components of nitrogen deposition. Here we use a new model to assess the ammonia emissions from agriculture, the largest anthropogenic source of ammonia. The model results are consistent with earlier estimates over industrialized regions in agreement with observations. However, the model predicts much higher emissions over sub-Saharan Africa compared to earlier estimates. Available observations from surface stations and satellites support these higher emissions.
Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
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A large ensemble of simulations with 100 members has been conducted with the state-of-the-art CESM2 Earth system model, using historical and SSP3-7.0 forcing. Our main finding is that there are significant changes in the variance of the Earth system in response to anthropogenic forcing, with these changes spanning a broad range of variables important to impacts for human populations and ecosystems.
William R. Wieder, Derek Pierson, Stevan Earl, Kate Lajtha, Sara G. Baer, Ford Ballantyne, Asmeret Asefaw Berhe, Sharon A. Billings, Laurel M. Brigham, Stephany S. Chacon, Jennifer Fraterrigo, Serita D. Frey, Katerina Georgiou, Marie-Anne de Graaff, A. Stuart Grandy, Melannie D. Hartman, Sarah E. Hobbie, Chris Johnson, Jason Kaye, Emily Kyker-Snowman, Marcy E. Litvak, Michelle C. Mack, Avni Malhotra, Jessica A. M. Moore, Knute Nadelhoffer, Craig Rasmussen, Whendee L. Silver, Benjamin N. Sulman, Xanthe Walker, and Samantha Weintraub
Earth Syst. Sci. Data, 13, 1843–1854, https://doi.org/10.5194/essd-13-1843-2021, https://doi.org/10.5194/essd-13-1843-2021, 2021
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Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Here we present the SOils DAta Harmonization database (SoDaH), a flexible database designed to harmonize diverse SOM datasets from multiple research networks.
Emily Kyker-Snowman, William R. Wieder, Serita D. Frey, and A. Stuart Grandy
Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, https://doi.org/10.5194/gmd-13-4413-2020, 2020
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Microbes drive carbon (C) and nitrogen (N) transformations in soil, and soil models have started to include explicit microbial physiology and functioning to try to reduce uncertainty in soil–climate feedbacks. Here, we add N cycling to a microbially explicit soil C model and reproduce C and N dynamics in soil during litter decomposition across a range of sites. We discuss model-generated hypotheses about soil C and N cycling and highlight the need for landscape-scale model evaluation data.
Cited articles
Agehara, S. and Warncke, D. D.:
Soil Moisture and Temperature Effects on Nitrogen Release from Organic Nitrogen Sources,
Soil Sci. Soc. Am. J.,
69, 1844–1855, https://doi.org/10.2136/sssaj2004.0361, 2005. a, b, c
Aneja, V. P., Schlesinger, W. H., Erisman, J. W., Behera, S. N., Sharma, M., and Battye, W.:
Reactive nitrogen emissions from crop and livestock farming in India,
Atmos. Environ.,
47, 92–103, https://doi.org/10.1016/j.atmosenv.2011.11.026, 2012. a, b
Badger, A. M. and Dirmeyer, P. A.: Climate response to Amazon forest replacement by heterogeneous crop cover, Hydrol. Earth Syst. Sci., 19, 4547–4557, https://doi.org/10.5194/hess-19-4547-2015, 2015. a
Bash, J. O., Cooter, E. J., Dennis, R. L., Walker, J. T., and Pleim, J. E.: Evaluation of a regional air-quality model with bidirectional NH3 exchange coupled to an agroecosystem model, Biogeosciences, 10, 1635–1645, https://doi.org/10.5194/bg-10-1635-2013, 2013. a
Battye, W., Aneja, V. P., and Schlesinger, W. H.:
Is nitrogen the next carbon?,
Earths Future,
5, 894–904, https://doi.org/10.1002/2017EF000592, 2017. a
Bear, J. and Verruijt, A.: Modeling groundwater flow and pollution, D. Reidel Publishing Company, Dordrecht, 1987. a
Bell, M., Flechard, C., Fauvel, Y., Häni, C., Sintermann, J., Jocher, M., Menzi, H., Hensen, A., and Neftel, A.: Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling, Atmos. Meas. Tech., 10, 1875–1892, https://doi.org/10.5194/amt-10-1875-2017, 2017. a, b
Beusen, A. H., Bouwman, A. F., Heuberger, P. S., Van Drecht, G., and Van Der Hoek, K. W.:
Bottom-up uncertainty estimates of global ammonia emissions from global agricultural production systems,
Atmos. Environ.,
42, 6067–6077, https://doi.org/10.1016/j.atmosenv.2008.03.044, 2008. a, b, c, d, e, f, g, h
Bittman, S., Van Vliet, L. J., Kowalenko, C. G., McGinn, S., Hunt, D. E., and Bounaix, F.:
Surface-banding liquid manure over aeration slots: A new low-disturbance method for reducing ammonia emissions and improving yield of perennial grasses,
Agron. J.,
97, 1304–1313, https://doi.org/10.2134/agronj2004.0277, 2005. a, b
Black, A. S., Sherlock, R. R., Smith, N. P., Cameron, K. C., and Goh, K. M.:
Effects of form of nitrogen, season, and urea application rate on ammonia volatilisation from pastures,
New Zeal. J. Agr. Res.,
28, 469–474, https://doi.org/10.1080/00288233.1985.10417992, 1985. a, b, c, d
Black, A. S., Sherlock, R. R., Smith, N. P., and Cameron, K. C.:
Ammonia volatilisation from urea broadcast in spring on to autumn-sown wheat,
New Zeal. J. Crop Hort.,
17, 175–182, https://doi.org/10.1080/01140671.1989.10428028, 1989. a, b
Bouwman, A. F., Lee, D. S., Asman, W. A., Dentener, F. J., Van Der Hoek, K. W., and Olivier, J. G.:
A global high-resolution emission inventory for ammonia,
Global Biogeochem. Cy.,
11, 561–587, https://doi.org/10.1029/97GB02266, 1997. a, b
Bouwman, A. F., Van Der Hoek, K. W., Eickhout, B., and Soenario, I.:
Exploring changes in world ruminant production systems,
Agr. Syst.,
84, 121–153, https://doi.org/10.1016/j.agsy.2004.05.006, 2005. a
Bussink, D. W., Huijsmans, J. F. M., and Ketelaars, J. J. M. H.:
Ammonia volatilization from nitric-acid-treated cattle slurry surface applied to grassland,
Netherlands Journal of Agricultural Science,
42, https://doi.org/10.18174/njas.v42i4.590, 1994. a
Cai, G. X., Chen, D. L., Ding, H., Pacholski, A., Fan, X. H., and Zhu, Z. L.:
Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain,
Nutr. Cycl. Agroecosys.,
63, 187–195, https://doi.org/10.1023/A:1021198724250, 2002. a
Castesana, P. S., Dawidowski, L. E., Finster, L., Gómez, D. R., and Taboada, M. A.:
Ammonia emissions from the agriculture sector in Argentina; 2000–2012,
Atmos. Environ.,
178, 293–304, https://doi.org/10.1016/j.atmosenv.2018.02.003, 2018. a, b, c, d
Cooter, E. J., Bash, J. O., Walker, J. T., Jones, M. R., and Robarge, W.:
Estimation of NH3 bi-directional flux from managed agricultural soils,
Atmos. Environ.,
44, 2107–2115, https://doi.org/10.1016/j.atmosenv.2010.02.044, 2010. a
Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013, https://doi.org/10.5194/essd-10-1987-2018, 2018. a, b
Dell, C. J., Kleinman, P. J., Schmidt, J. P., and Beegle, D. B.:
Low-Disturbance Manure Incorporation Effects on Ammonia and Nitrate Loss,
J. Environ. Qual.,
41, 928–937, https://doi.org/10.2134/jeq2011.0327, 2012. a, b
Delon, C., Galy-Lacaux, C., Boone, A., Liousse, C., Serça, D., Adon, M., Diop, B., Akpo, A., Lavenu, F., Mougin, E., and Timouk, F.: Atmospheric nitrogen budget in Sahelian dry savannas, Atmos. Chem. Phys., 10, 2691–2708, https://doi.org/10.5194/acp-10-2691-2010, 2010. a
Dentener, F., Drevet, J., Lamarque, J. F., Bey, I., Eickhout, B., Fiore, A. M., Hauglustaine, D., Horowitz, L. W., Krol, M., Kulshrestha, U. C., Lawrence, M., Galy-Lacaux, C., Rast, S., Shindell, D., Stevenson, D., Van Noije, T., Atherton, C., Bell, N., Bergman, D., Butler, T., Cofala, J., Collins, B., Doherty, R., Ellingsen, K., Galloway, J., Gauss, M., Montanaro, V., Müller, J. F., Pitari, G., Rodriguez, J., Sanderson, M., Solmon, F., Strahan, S., Schultz, M., Sudo, K., Szopa, S., and Wild, O.:
Nitrogen and sulfur deposition on regional and global scales: A multimodel evaluation,
Global Biogeochem. Cy.,
20, GB4003, https://doi.org/10.1029/2005GB002672, 2006. a
Duprè, C., Stevens, C. J., Ranke, T., Bleekers, A., Peppler-Lisbach, C., Gowing, D. J. G., Dise, N. B., E, D., Bobbink, R., and Diekmann, M.:
Changes in species richness and composition in European acidic grasslands over the past 70 years: the contribution of cumulative atmospheric nitrogen deposition,
Glob. Change Biol.,
16, 344–357, https://doi.org/10.1111/j.1365-2486.2009.01982.x, 2010. a
EEA:
EMEP/EEA air pollutant emission inventory guidebook 2016,
Tech. rep.,
European Environmental Agency, Publications Office of the European Union, Luxembourg, 2016. a
FAO/IIASA/ISCRIC/IIS-CAS/JRC:
Harmonized World Soil Database (version 1.1),
FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2009.
Fuller, E. N., Schettler, P. D., and Giddings, J. C.:
A new method for prediction of binary gas-phase diffusion coefficients,
Ind. Eng. Chem.,
58, 18–27, https://doi.org/10.1021/ie50677a007, 1966. a
Genermont, S. and Cellier, P.:
A mechanistic model for estimating amnmonia volatilization from slurry applied to bare soil,
Agr. Forest Meteorol.,
88, 145–167, 1997. a
Gilmour, J. T., Cogger, C. G., Jacobs, L. W., Evanylo, G. K., and Sullivan, D. M.:
Decomposition and plant-available nitrogen in biosolids,
J. Environ. Qual.,
32, 1498–1507, 2003. a
Giltrap, D., Saggar, S., Rodriguez, J., and Bishop, P.:
Modelling NH3 volatilisation within a urine patch using NZ-DNDC,
Nutr. Cycl. Agroecosys.,
108, 267–277, https://doi.org/10.1007/s10705-017-9854-x, 2017. a
Gyldenkærne, S., Skjøth, C. A., Hertel, O., and Ellermann, T.:
A dynamical ammonia emission parameterization for use in air pollution models,
J. Geophys. Res.-Atmos.,
110, 1–14, https://doi.org/10.1029/2004JD005459, 2005. a, b, c, d
Hafner, S. D., Pacholski, A., Bittman, S., Burchill, W., Bussink, W., Chantigny, M., Carozzi, M., Génermont, S., Häni, C., Hansen, M. N., Huijsmans, J., Hunt, D., Kupper, T., Lanigan, G., Loubet, B., Misselbrook, T., Meisinger, J. J., Neftel, A., Nyord, T., Pedersen, S. V., Sintermann, J., Thompson, R. B., Vermeulen, B., Vestergaard, A. V., Voylokov, P., Williams, J. R., and Sommer, S. G.:
The ALFAM2 database on ammonia emission from field-applied manure: Description and illustrative analysis,
Agr. Forest Meteorol.,
258, 66–79, https://doi.org/10.1016/j.agrformet.2017.11.027, 2018. a
Hamaoui-Laguel, L., Meleux, F., Beekmann, M., Bessagnet, B., Génermont, S., and Celier, P.:
Modelling agricultural ammonia emissions : impact on particulate matter formation,
Conference “Nitrogen & global change: key findings and future challenges”, pp. 3–4, 11–15 April 2011, Edingbourgh, UK, 2011. a
Harper, L. A.:
Ammonia: Measurement Issues,
in: Micrometeorology in Agricultural Systems, Agronomy Monograph no. 47,
edited by: Hatfield, J. L. and Baker, J. M.,
American Society of Agronomy, Inc., Crop Science Society of America, Inc, and Soil Science Society of America, Inc. Madison, Wisconsin, USA, pp. 345–379, https://doi.org/10.2134/agronmonogr47.c15, 2005. a
Heald, C. L., Collett Jr., J. L., Lee, T., Benedict, K. B., Schwandner, F. M., Li, Y., Clarisse, L., Hurtmans, D. R., Van Damme, M., Clerbaux, C., Coheur, P.-F., Philip, S., Martin, R. V., and Pye, H. O. T.: Atmospheric ammonia and particulate inorganic nitrogen over the United States, Atmos. Chem. Phys., 12, 10295–10312, https://doi.org/10.5194/acp-12-10295-2012, 2012. a
Holcomb, J. C., Sullivan, D. M., Horneck, D. A., and Clough, G. H.:
Effect of Irrigation Rate on Ammonia Volatilization,
Soil Sci. Soc. Am. J.,
75, 2341–2347, https://doi.org/10.2136/sssaj2010.0446, 2011. a, b, c
Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and Zhang, H.:
A high-resolution ammonia emission inventory in China,
Global Biogeochem. Cy.,
26, 1–14, https://doi.org/10.1029/2011GB004161, 2012. a
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.:
Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands,
Climatic Change,
109, 117–161, https://doi.org/10.1007/s10584-011-0153-2, 2011. a, b
IAEA:
Guidelines for sustainable manure management in Asian livestock production systems,
Tech. Rep. IAEA-TECDOC-1582,
IAEA, Vienna, 2008. a
IPCC:
2006 IPCC Guidelines for National Greenhouse Gas Inventories,
IGES, Hayama, Kanagawa, Japan, Japan, 2006. a
Jarvis, S. C., Sherwood, M., and Steenvoorden, J.:
Nitrogen losses from animal manures: from grazed pastures and from applied slurry,
in: Animal Manure on Grassland and Fodder Crops. Fertilizer or Waste?,
edited by: Van Der Meer, H. G., Unwin, R. J., Van Dijk, T. A., and Ennik, G. C.,
Martinus Nijhoff Publishers, Dordrecht, pp. 195–212, 1987. a
Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang, Q., Shao, M., and Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058, https://doi.org/10.5194/acp-16-2043-2016, 2016. a
Klimont, Z. and Brink, C.:
Modeling of emissions of air pollutants and greenhouse gases from agricultural sources in Europe,
Tech. rep.,
International Institute for Applied Systems Analysis, Laxenburg, Austria, 2004. a
Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058, https://doi.org/10.5194/acp-13-11019-2013, 2013. a, b
Lamarque, J.-F., Emmons, L. K., Hess, P. G., Kinnison, D. E., Tilmes, S., Vitt, F., Heald, C. L., Holland, E. A., Lauritzen, P. H., Neu, J., Orlando, J. J., Rasch, P. J., and Tyndall, G. K.: CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model, Geosci. Model Dev., 5, 369–411, https://doi.org/10.5194/gmd-5-369-2012, 2012. a, b
Laubach, J., Taghizadeh-Toosi, A., Sherlock, R. R., and Kelliher, F. M.:
Measuring and modelling ammonia emissions from a regular pattern of cattle urine patches,
Agr. Forest Meteorol.,
156, 1–17, https://doi.org/10.1016/j.agrformet.2011.12.007, 2012. a, b, c
Laubach, J., Taghizadeh-Toosi, A., Gibbs, S. J., Sherlock, R. R., Kelliher, F. M., and Grover, S. P. P.: Ammonia emissions from cattle urine and dung excreted on pasture, Biogeosciences, 10, 327–338, https://doi.org/10.5194/bg-10-327-2013, 2013. a, b
Lawrence, D. M., Hurtt, G. C., Arneth, A., Brovkin, V., Calvin, K. V., Jones, A. D., Jones, C. D., Lawrence, P. J., de Noblet-Ducoudré, N., Pongratz, J., Seneviratne, S. I., and Shevliakova, E.: The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design, Geosci. Model Dev., 9, 2973–2998, https://doi.org/10.5194/gmd-9-2973-2016, 2016. a
Lawrence, D. M., Fisher, R. M., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G.,
Ghimere, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H.,
Lombardozzi, D. L., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R.,
Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns,
S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner,
M., Fox, A. M., Gentine, P. G., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S.,
Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket,
J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang,
J., Thomas, R. Q., Val Martin, M., and Zeng, X.: Community Land Model version 5:
Description of new features, benchmarking, and impact of forcing uncertainty, J.
Adv. Model. Earth Syst., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a, b, c, d, e
Levis, S., Bonan, G. B., Kluzek, E., Thornton, P. E., Jones, A., Sacks, W. J., and Kucharik, C. J.:
Interactive crop management in the Community Earth System Model (CESM1): Seasonal influences on land–atmosphere fluxes,
J. Climate,
25, 4839–4859, 2012. a
Li, C., Salas, W., Zhang, R., Krauter, C., Rotz, A., and Mitloehner, F.:
Manure-DNDC: A biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems,
Nutr. Cycl. Agroecosys.,
93, 163–200, https://doi.org/10.1007/s10705-012-9507-z, 2012. a
Li, J., Yang, H., Zhou, F., Zhang, X., Luo, J., Li, Y., Lindsey, S., Shi, Y., He, H., and Zhang, X.:
Effects of maize residue return rate on nitrogen transformations and gaseous losses in an arable soil,
Agr. Water Manage.,
211, 132–141, https://doi.org/10.1016/j.agwat.2018.09.049, 2019. a
Lombardozzi, D. L., Lu, Y., Lawrence, P. J., Lawrence, D. M., Swenson, S., Oleson, K. W.,
Wieder, W. R., and Ainsworth, E. A.: Simulating Agriculture in the Community Land
Model Version 5, J. Geophys. Res.-Biogeo., 125,
1–19, https://doi.org/10.1029/2019jg005529, 2020. a, b
Lorimor, J., Powers, W., and Sutton, A.:
Manure Characteristics,
in: Manure Management Systems Series,
Midwest Plan Service, Ames, Iowa, pp. 1–23, 2001. a
Manzoni, S. and Porporato, A.:
Soil carbon and nitrogen mineralization: Theory and models across scales,
Soil Biol. Biochem.,
41, 1355–1379, https://doi.org/10.1016/j.soilbio.2009.02.031, 2009. a
Martínez-Lagos, J., Salazar, F., Alfaro, M., and Misselbrook, T.:
Ammonia volatilization following dairy slurry application to a permanent grassland on a volcanic soil,
Atmos. Environ.,
80, 226–231, https://doi.org/10.1016/j.atmosenv.2013.08.005, 2013. a
Meisinger, J. J. and Jokela, W. E.:
Ammonia volatilization from dairy and poultry manure,
in: Proceedings from managing nutrients and pathogens from animal agriculture,
Camp Hill, Pennsylvania, 28–30 March 2000, pp. 334–354, 2000. a
Misselbrook, T., Misselbrook, T., Scholefield, D., and Parkinson, R.:
Using time domain reflectometry to characterize cattle and pig slurry infiltration into soil,
Soil Use and Manage.,
21, 167–172, https://doi.org/10.1111/j.1475-2743.2005.tb00121.x, 2005a. a
Misselbrook, T. H., Nicholson, F. A., and Chambers, B. J.:
Predicting ammonia losses following the application of livestock manure to land,
Bioresource Technol.,
96, 159–168, https://doi.org/10.1016/j.biortech.2004.05.004, 2005b. a, b, c
Mohini, M., Mondal, G., Thakur, S. S., and Gupta, S.:
Trends in methane emission from Indian livestock,
in: Proceedings of XVI Biennial Animal Nutrition Conference on Innovative Approaches for Animal Feeding and Nutritional Research, NDRI, 6–8 February 2016, Karnal, 2016. a
Móring, A., Vieno, M., Doherty, R. M., Laubach, J., Taghizadeh-Toosi, A., and Sutton, M. A.: A process-based model for ammonia emission from urine patches, GAG (Generation of Ammonia from Grazing): description and sensitivity analysis, Biogeosciences, 13, 1837–1861, https://doi.org/10.5194/bg-13-1837-2016, 2016. a, b
Muñoz, E., Navia, R., Zaror, C., and Alfaro, M.:
Ammonia emissions from livestock production in Chile: an inventory and uncertainty analysis,
J. Soil Sci. Plant Nut.,
16, 60–75, https://doi.org/10.4067/S0718-95162016005000005, 2016. a, b
Ndambi, O. A., Pelster, D. E., Owino, J. O., de Buisonjé, F., and Vellinga, T.:
Manure Management Practices and Policies in Sub-Saharan Africa: Implications on Manure Quality as a Fertilizer,
Frontiers in Sustainable Food Systems,
3, 29, https://doi.org/10.3389/fsufs.2019.00029, 2019. a
Ni, K., Pacholski, A., and Kage, H.:
Ammonia volatilization after application of urea to winter wheat over 3 years affected by novel urease and nitrification inhibitors,
Agr. Ecosyst. Environ.,
197, 184–194, https://doi.org/10.1016/j.agee.2014.08.007, 2014. a
Pain, B. F., Phillips, V. R., Clarkson, C. R., and Klarenbeek, J. V.:
Loss of nitrogen through ammonia volatilisation during and following the application of pig or cattle slurry to grassland,
J. Sci. Food Agr.,
47, 1–12, https://doi.org/10.1002/jsfa.2740470102, 1989. a
Pan, B., Lam, S. K., Mosier, A., Luo, Y., and Chen, D.:
Ammonia volatilization from synthetic fertilizers and its mitigation strategies: A global synthesis,
Agr. Ecosyst. Environ.,
232, 283–289, https://doi.org/10.1016/j.agee.2016.08.019, 2016. a
Pang, P. C., Hedlin, R. A., and Cho, C. M.:
Transformation and movement of band-applied urea, ammonium sulfate, and ammonium hydroxide during incubation in several manitoba soils,
Can. J. Soil Sci.,
53, 331–341, 1973. a
Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., and Henze, D. K.:
Ammonia emissions in the United States, european union, and China derived by high-resolution inversion of ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_NH3),
J. Geophys. Res.,
119, 4343–4364, https://doi.org/10.1002/2013JD021130, 2014. a, b
Paulot, F., Ginoux, P., Cooke, W. F., Donner, L. J., Fan, S., Lin, M.-Y., Mao, J., Naik, V., and Horowitz, L. W.: Sensitivity of nitrate aerosols to ammonia emissions and to nitrate chemistry: implications for present and future nitrate optical depth, Atmos. Chem. Phys., 16, 1459–1477, https://doi.org/10.5194/acp-16-1459-2016, 2016. a
Payne, R. J., Dise, N. B., Field, C. D., Dore, A. J., Caporn, S. J., and Stevens, C. J.:
Nitrogen deposition and plant biodiversity: past, present, and future,
Front. Ecol. Environ.,
15, 431–436, https://doi.org/10.1002/fee.1528, 2017. a
Petersen, S. O. and Andersen, M. N.:
Influence of soil water potential and slurry type on denitrification activity,
Soil Biol. Biochem.,
28, 977–980, https://doi.org/10.1016/0038-0717(96)00067-3, 1996. a
Pinder, R. W., Pekney, N. J., Davidson, C. I., and Adams, P. J.:
A process-based model of ammonia emissions from dairy cows: Improved temporal and spatial resolution,
Atmos. Environ.,
38, 1357–1365, https://doi.org/10.1016/j.atmosenv.2003.11.024, 2004. a
Pleim, J. E., Ran, L., Appel, W., Shephard, M. W., and Cady-Pereira, K.:
New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model,
J. Adv. Model. Earth Sy.,
11, 2934–2957, https://doi.org/10.1029/2019ms001728, 2019. a
Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D.:
Characterizing the spatial patterns of global fertilizer application and manure production,
Earth Interact., 14, 1–22, https://doi.org/10.1175/2009EI288.1, 2010. a
Prasad, C. S., Gowda, N. K. S., Anandan, S., Sharma, K., and Mohini, M.: Reactive
Nitrogen in Environment vis-à-vis Livestock Production System: Possible Remedies, in: The Indian Nitrogen Assessment, edited by: Abrol,
Y. P., Adhya, T. K., Aneja, V. P., Raghuram, N., Pathak, H., Kulshrestha, U., Sharma, C.,
and Singh, B., Elsevier,
235–247, https://doi.org/10.1016/B978-0-12-811836-8.00016-1, 2017. a, b
Rachhpal-Singh and Nye, P.:
A model of ammonia volatilization from applied urea. I. development of the model,
J. Soil Sci.,
37, 9–20, https://doi.org/10.1111/j.1365-2389.1986.tb00002.x, 1986. a
Riddick, S., Ward, D., Hess, P., Mahowald, N., Massad, R., and Holland, E.: Estimate of changes in agricultural terrestrial nitrogen pathways and ammonia emissions from 1850 to present in the Community Earth System Model, Biogeosciences, 13, 3397–3426, https://doi.org/10.5194/bg-13-3397-2016, 2016. a, b, c, d, e, f, g
Rienecker, M. M., Suarez,
M. J., Gelaro,
R., Todling,
R., Bacmeister,
J., Liu,
E., Bosilovich,
M. G., Schubert,
S. D., Takacs,
L., Kim,
G.-K., Bloom,
S., Chen,
J., Collins,
D., Conaty,
A., da Silva,
A., Gu,
W., Joiner,
J., Koster,
R. D., Lucchesi,
R., Molod,
A., Owens,
T., Pawson,
S., Pegion,
P., Redder,
C. R., Reichle,
R., Robertson,
F. R., Ruddick,
A. G., Sienkiewicz,
M., and Woollen,
J.:
MERRA: NASA's modern-era retrospective analysis for research and applications,
J. Climate,
24, 3624–3648, 2011. a
Robinson, T. P., Thornton, P. K., Franceschini, G., Kruska, R. L., Chiozza, F., Notenbaert,
A., Cecchi, G., Herrero, M., Epprecht, M., Fritz, S., You, L., Conchedda, G., and See, L.:
Global livestock production systems,
FAO and ILRI, Rome, 2011. a
Robinson, T. P., Wint, G. R. W., Conchedda, G., Van Boeckel, T. P., Ercoli, V., Palamara, E., Cinardi, G., D'Aietti, L., Hay, S. I., and Gilbert, M.:
Mapping the global distribution of livestock,
PloS ONE,
9, e96084, https://doi.org/10.1371/journal.pone.0096084, 2014. a, b
Rochette, P., Angers, D. A., Chantigny, M. H., Gasser, M.-O., MacDonald, J. D., Pelster, D. E., and Bertrand, N.:
Ammonia Volatilization and Nitrogen Retention: How Deep to Incorporate Urea?,
J. Environ. Qual.,
42, 1635, https://doi.org/10.2134/jeq2013.05.0192, 2013. a, b
Ryden, J., Whitehead, D., Lockyer, D., Thompson, R., Skinner, J., and Garwood, E.:
Ammonia emission from grassland and livestock production systems in the UK,
Environ. Pollut.,
48, 173–184, https://doi.org/10.1016/0269-7491(87)90032-7, 1987. a
Saarijärvi, K., Mattila, P. K., and Virkajärvi, P.:
Ammonia volatilization from artificial dung and urine patches measured by the equilibrium concentration technique (JTI method),
Atmos. Environ.,
40, 5137–5145, https://doi.org/10.1016/j.atmosenv.2006.03.052, 2006. a
Sadeghi, A. M., Kissel, D. E., and Cabrera, M. L.:
Estimating molecular diffusion coefficients of urea in unsaturated soil,
Soil Sci. Soc. Am. J.,
53, 15–18, 1989. a
Seré, C., Steinfeld, H., and Groenewold, J.:
World livestock production systems,
Food and Agriculture Organization of the United Nations, 1996. a
Sherlock, R. and Goh, K.:
Dynamics of ammonia volatilization from simulated urine patchese and aqueous urea applied to pasture. I. Field Experiments,
Fert. Res.,
5, 181–195, https://doi.org/10.1007/BF01052715, 1984. a, b
Sherlock, R. R., Sommer, S. G., Khan, R. Z., Wood, C. W., Guertal, E. A., Freney, J. R., Dawson, C. O., and Cameron, K. C.:
Ammonia, Methane and Nitrous Oxide Emission from Pig Slurry Applied to a Pasture in New Zealand,
J. Environ. Qual.,
31, 1491–1501, 2002. a
Sintermann, J., Ammann, C., Kuhn, U., Spirig, C., Hirschberger, R., Gärtner, A., and Neftel, A.: Determination of field scale ammonia emissions for common slurry spreading practice with two independent methods, Atmos. Meas. Tech., 4, 1821–1840, https://doi.org/10.5194/amt-4-1821-2011, 2011. a, b, c, d
Sintermann, J., Neftel, A., Ammann, C., Häni, C., Hensen, A., Loubet, B., and Flechard, C. R.: Are ammonia emissions from field-applied slurry substantially over-estimated in European emission inventories?, Biogeosciences, 9, 1611–1632, https://doi.org/10.5194/bg-9-1611-2012, 2012. a
Smith, K. A., Jackson, D. R., and Pepper, T. J.:
Nutrient losses by surface run-off following the application of organic manures to arable land. 1. Nitrogen,
Environ. Pollut.,
112, 41–51, 2001. a
Sommer, S. G. and Jacobsen, O. H.:
Infiltration of slurry liquid and volatilization of ammonia from surface applied pig slurry as affected by soil water content,
J. Agr. Sci.,
132, 297–303, https://doi.org/10.1017/S0021859698006261, 1999. a
Sommer, S. G., Friis, E., Bach, A., and Schørring, J. K.:
Ammonia volatilization from pig slurry applied with trail hoses or broadspread to winter wheat: effects of crop developmental stage, microclimate, and leaf ammonia adsorption,
J. Environ. Qual.,
26, 1153–1160, https://doi.org/10.1002/0470848944, 1997. a, b
Sommer, S. G., Génermont, S., Cellier, P., Hutchings, N. J., Olesen, J. E., and Morvan, T.:
Processes controlling ammonia emission from livestock slurry in the field,
Eur. J. Agron.,
19, 465–486, https://doi.org/10.1016/S1161-0301(03)00037-6, 2003. a, b
Sommer, S. G., Jensen, L. S., Clausen, S. B., and Søgaard, H. T.:
Ammonia volatilization from surface-applied livestock slurry as affected by slurry composition and slurry infiltration depth,
J. Agr. Sci.,
144, 229–235, https://doi.org/10.1017/S0021859606006022, 2006. a, b
Spirig, C., Flechard, C. R., Ammann, C., and Neftel, A.: The annual ammonia budget of fertilised cut grassland – Part 1: Micrometeorological flux measurements and emissions after slurry application, Biogeosciences, 7, 521–536, https://doi.org/10.5194/bg-7-521-2010, 2010. a, b, c, d
Spurway, C. H.:
Soil reaction (pH) preferences of plants.,
Special Bulletin,
Michigan Agricultural Experiment Station, East Lansing, 306, 1941. a
Stange, C. F. and Neue, H.-U.: Measuring and modelling seasonal variation of gross nitrification rates in response to long-term fertilisation, Biogeosciences, 6, 2181–2192, https://doi.org/10.5194/bg-6-2181-2009, 2009. a, b, c
Strokal, M., Ma, L., Bai, Z., Luan, S., Kroeze, C., Oenema, O., Velthof, G., and Zhang, F.:
Alarming nutrient pollution of Chinese rivers as a result of agricultural transitions,
Environ. Res. Lett.,
11, 024014, https://doi.org/10.1088/1748-9326/11/2/024014, 2016. a
Sutton, M. A., Reis, S., Riddick, S. N., Dragosits, U., Nemitz, E., Theobald, M. R., Tang, Y. S., Braban, C. F., Vieno, M., Dore, A. J., Mitchell, R. F., Wanless, S., Daunt, F., Fowler, D., Blackall, T. D., Milford, C., Flechard, C. R., Loubet, B., Massad, R., Cellier, P., Personne, E., Coheur, P. F., Clarisse, L., Van Damme, M., Ngadi, Y., Clerbaux, C., Skjøth, C. A., Geels, C., Hertel, O., Kruit, R. J., Pinder, R. W., Bash, J. O., Walker, J. T., Simpson, D., Horváth, L., Misselbrook, T. H., Bleeker, A., Dentener, F., and de Vries, W.:
Towards a climate-dependent paradigm of ammonia emission and deposition,
Philos. T. R. Soc. B,,
368, 20130166, https://doi.org/10.1098/rstb.2013.0166, 2013. a, b, c, d
Tang, J. Y. and Riley, W. J.: Technical Note: Simple formulations and solutions of the dual-phase diffusive transport for biogeochemical modeling, Biogeosciences, 11, 3721–3728, https://doi.org/10.5194/bg-11-3721-2014, 2014. a
Thompson, R. B. and Meisinger, J. J.:
Gaseous nitrogen losses and ammonia volatilization measurement following land application of cattle slurry in the mid-Atlantic region of the USA,
Plant Soil,
266, 231–246, https://doi.org/10.1007/s11104-005-1361-1, 2004. a, b
Turner, D. A., Edis, R. B., Chen, D., Freney, J. R., Denmead, O. T., and Christie, R.:
Determination and mitigation of ammonia loss from urea applied to winter wheat with N-(n-butyl) thiophosphorictriamide,
Agr. Ecosyst. Environ.,
137, 261–266, https://doi.org/10.1016/j.agee.2010.02.011, 2010. a
Vaio, N., Calvert, V. H., Rema, J. A., Cabrera, M. L., Kissel, D., and Newsome, J. F.:
Ammonia Volatilization from Urea-Based Fertilizers Applied to Tall Fescue Pastures in Georgia, USA,
Soil Sci. Soc. Am. J.,
72, 1665–1671, https://doi.org/10.2136/sssaj2007.0300, 2008. a
Van Der Molen, J., Beljaars, A. C. M., Chardon, W. J., Jury, W. A., and Van Faassen, H. G.:
Ammonia volatilization from arable land after surface application or incorporation of dairy cattle slurry. 2. Derivation of a transfer model,
Netherlands Journal of Agricultural Science,
38, 239–254, 1990a. a
Van Der Molen, J., Van Faassen, H. G., Leclerc, M. Y., Vriesma, R., and Chardon, W. J.:
Ammonia volatilization from arable land after surface application or incorporation of dairy cattle slurry. 1. Field estimates,
Neth. J. Agr. Sci.,
38, 145–158, 1990b. a
Vandre, R., Clemens, J., Goldbach, H., and Kaupenjohann, M.:
NH3 and N2O Emissions after Landspreading of Slurry as Influenced by Application Technique and Dry Matter-Reduction. I. NH3 Emissions,
J. Plant Nutr. Soil Sc.,
160, 303–307, 1997. a
Vet, R., Artz, R. S., Carou, S., Shaw, M., Ro, C. U., Aas, W., Baker, A., Bowersox, V. C., Dentener, F., Galy-Lacaux, C., Hou, A., Pienaar, J. J., Gillett, R., Forti, M. C., Gromov, S., Hara, H., Khodzher, T., Mahowald, N. M., Nickovic, S., Rao, P. S., and Reid, N. W.:
A global assessment of precipitation chemistry and deposition of sulfur, nitrogen, sea salt, base cations, organic acids, acidity and pH, and phosphorus,
Atmos. Environ.,
93, 3–100, https://doi.org/10.1016/j.atmosenv.2013.10.060, 2014. a
Viovy, N.:
CRUNCEP Version 7 – Atmospheric Forcing Data for the Community Land Model,
Research Data Archive at the National Center for Atmospheric Research, Computational
and Information Systems Laboratory, Boulder, Colorado, https://doi.org/10.5065/PZ8F-F017, 2018. a, b
Vira, J., Hess, P., Melkonian, J., and Wieder, W. R.: Flow of Agricultural Nitrogen, version 2 (FANv2) (Version May 2020), Zenodo, https://doi.org/10.5281/zenodo.3841776, 2019. a
Vira, J., Hess, P., Melkonian, J., and Wieder, W.: Flow of Agricultural Nitrogen, version 2 (FANv2): Model input and output data (Version Revised May 2020) [Data set], Zenodo, https://doi.org/10.5281/zenodo.3841723, 2020.
a
Wang, H., Zhang, D., Zhang, Y., Zhai, L., Yin, B., Zhou, F., Geng, Y., Pan, J., Luo, J., Gu, B., and Liu, H.:
Ammonia emissions from paddy fields are underestimated in China,
Environ. Pollut.,
235, 482–488, https://doi.org/10.1016/j.envpol.2017.12.103, 2018. a
Wesely, M. L.:
Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models,
Atmos. Environ.,
23, 1293–1304, 1989. a
Whitehead, D. C. and Raistrick, N.:
Effects of plant material on ammonia volatilization from simulated livestock urine applied to soil,
Biol. Fert. Soils,
13, 92–95, 1992. a
Wint, W. and Robinson, T.:
Gridded livestock of the world 2007,
FAO, Roma (Italia), 2007. a
Xu, R., Tian, H., Pan, S., Prior, S. A., Feng, Y., Batchelor, W. D., Chen, J., and Yang, J.:
Global ammonia emissions from synthetic nitrogen fertilizer applications in agricultural systems: Empirical and process-based estimates and uncertainty,
Glob. Change Biol.,
25, 314–326, https://doi.org/10.1111/gcb.14499, 2019. a, b, c
Xu, R. T., Pan, S. F., Chen, J., Chen, G. S., Yang, J., Dangal, S. R. S., Shepard, J. P., and Tian, H. Q.:
Half-Century Ammonia Emissions From Agricultural Systems in Southern Asia: Magnitude, Spatiotemporal Patterns, and Implications for Human Health,
GeoHealth,
2, 40–53, https://doi.org/10.1002/2017GH000098, 2018. a, b, c, d
Zaehle, S. and Dalmonech, D.:
Carbon-nitrogen interactions on land at global scales: Current understanding in modelling climate biosphere feedbacks,
Curr. Opin. Env. Sust.,
3, 311–320, https://doi.org/10.1016/j.cosust.2011.08.008, 2011. a
Zhang, B., Tian, H., Lu, C., Dangal, S. R. S., Yang, J., and Pan, S.: Global manure nitrogen production and application in cropland during 1860–2014: a 5 arcmin gridded global dataset for Earth system modeling, Earth Syst. Sci. Data, 9, 667–678, https://doi.org/10.5194/essd-9-667-2017, 2017. a
Zhang, L., Chen, Y., Zhao, Y., Henze, D. K., Zhu, L., Song, Y., Paulot, F., Liu, X., Pan, Y., Lin, Y., and Huang, B.: Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates, Atmos. Chem. Phys., 18, 339–355, https://doi.org/10.5194/acp-18-339-2018, 2018. a, b, c, d, e, f
Short summary
Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric chemistry. We developed a model to simulate how ammonia emissions respond to changes in temperature and soil moisture, and we evaluated agricultural ammonia emissions globally. The simulated emissions agree with earlier estimates over many regions, but the results highlight the variability of ammonia emissions and suggest that emissions in warm climates may be higher than previously thought.
Mostly emitted by the agricultural sector, ammonia has an important role in atmospheric...