Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions
"Nitrous oxide (N2O) is a potent greenhouse gas emitted during soil nitrogen cycling. Excess nitrogen fertilization leads to increased N2O emissions, which is a waste of applied nitrogen. Optimized nitrogen fertilizer management (4R nutrient management: right product, right rate, right time, right...
| Autores principales: | , , , , , , , , |
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| Formato: | Resumen |
| Lenguaje: | Inglés |
| Publicado: |
International Livestock Research Institute
2025
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| Acceso en línea: | https://hdl.handle.net/10568/174607 |
| _version_ | 1855519386251558912 |
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| author | Harris, E. Barthel, M. Leitner, Sonja Ouma, T. Agredazywczuk, P. Otinga, A. Njoroge, R. Oduor, Collins Oluoch, K. C. |
| author_browse | Agredazywczuk, P. Barthel, M. Harris, E. Leitner, Sonja Njoroge, R. Oduor, Collins Oluoch, K. C. Otinga, A. Ouma, T. |
| author_facet | Harris, E. Barthel, M. Leitner, Sonja Ouma, T. Agredazywczuk, P. Otinga, A. Njoroge, R. Oduor, Collins Oluoch, K. C. |
| author_sort | Harris, E. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | "Nitrous oxide (N2O) is a potent greenhouse gas emitted during soil nitrogen cycling. Excess nitrogen fertilization leads to increased N2O emissions, which is a waste of applied nitrogen. Optimized nitrogen fertilizer management (4R nutrient management: right product, right rate, right time, right method/place) can enhance nitrogen use efficiency and reduce N2O emissions without reducing crop yields, mitigating the climate impact of agriculture. This is particularly relevant in developing regions like sub-Saharan Africa where fertilizer use is expected to increase over coming decades. Effective fertilizer management offers multiple benefits: Boosting food security while safeguarding the environment and minimizing input costs for farmers.
Quantifying N2O emissions at the field and farm level is challenging. Therefore, N2O is often not included in agroecosystem assessments, which may focus on variables such as the CO2 budget or soil carbon balance. Typical methods to quantify N2O fluxes – such as automated chamber measurements and eddy covariance – are expensive and require advanced knowledge and infrastructure. Moreover, N2O emissions are highly heterogeneous in space and time, thus many measurements are needed to quantify emissions. Novel measurements, models and machine learning can be used in combination with existing techniques to understand drivers, increase spatial coverage, and extrapolate to new locations.
Measurement innovations focusing on low-cost sensing of N2O will provide much needed data in remote and developing regions. Low-cost sensing is particularly suited in direct soil gas measurements, where N2O concentrations and variability are much higher than in free air. Specialised algorithms are needed to estimate fluxes based on soil gas measurements. Machine learning and process modelling approaches can furthermore be used to understand drivers and create simple simulations of N2O emissions, to extrapolate in space and time based on existing (sparse) measurements. These approaches can also leverage proxies, such as isotopic composition, to estimate emissions. Measurement campaigns in data-poor regions should prioritise calibration, collection of ancillary data (such as soil moisture, temperature and nitrogen content), robust metadata reporting, and open data sharing, to maximise the impact of measurements and facilitate data-driven analyses. Development of these tools and approaches will allow N2O emissions to be estimated for different sites and scenarios, opening the way for simple emission accounting and the inclusion of N2O in agroecosystem assessments." |
| format | Abstract |
| id | CGSpace174607 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Livestock Research Institute |
| publisherStr | International Livestock Research Institute |
| record_format | dspace |
| spelling | CGSpace1746072025-06-20T11:35:58Z Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions Harris, E. Barthel, M. Leitner, Sonja Ouma, T. Agredazywczuk, P. Otinga, A. Njoroge, R. Oduor, Collins Oluoch, K. C. nitrous oxide greenhouse gases greenhouse gas emissions climate change mitigation agriculture "Nitrous oxide (N2O) is a potent greenhouse gas emitted during soil nitrogen cycling. Excess nitrogen fertilization leads to increased N2O emissions, which is a waste of applied nitrogen. Optimized nitrogen fertilizer management (4R nutrient management: right product, right rate, right time, right method/place) can enhance nitrogen use efficiency and reduce N2O emissions without reducing crop yields, mitigating the climate impact of agriculture. This is particularly relevant in developing regions like sub-Saharan Africa where fertilizer use is expected to increase over coming decades. Effective fertilizer management offers multiple benefits: Boosting food security while safeguarding the environment and minimizing input costs for farmers. Quantifying N2O emissions at the field and farm level is challenging. Therefore, N2O is often not included in agroecosystem assessments, which may focus on variables such as the CO2 budget or soil carbon balance. Typical methods to quantify N2O fluxes – such as automated chamber measurements and eddy covariance – are expensive and require advanced knowledge and infrastructure. Moreover, N2O emissions are highly heterogeneous in space and time, thus many measurements are needed to quantify emissions. Novel measurements, models and machine learning can be used in combination with existing techniques to understand drivers, increase spatial coverage, and extrapolate to new locations. Measurement innovations focusing on low-cost sensing of N2O will provide much needed data in remote and developing regions. Low-cost sensing is particularly suited in direct soil gas measurements, where N2O concentrations and variability are much higher than in free air. Specialised algorithms are needed to estimate fluxes based on soil gas measurements. Machine learning and process modelling approaches can furthermore be used to understand drivers and create simple simulations of N2O emissions, to extrapolate in space and time based on existing (sparse) measurements. These approaches can also leverage proxies, such as isotopic composition, to estimate emissions. Measurement campaigns in data-poor regions should prioritise calibration, collection of ancillary data (such as soil moisture, temperature and nitrogen content), robust metadata reporting, and open data sharing, to maximise the impact of measurements and facilitate data-driven analyses. Development of these tools and approaches will allow N2O emissions to be estimated for different sites and scenarios, opening the way for simple emission accounting and the inclusion of N2O in agroecosystem assessments." 2025-03-14 2025-05-15T08:05:38Z 2025-05-15T08:05:38Z Abstract https://hdl.handle.net/10568/174607 en Open Access application/pdf International Livestock Research Institute Harris, E., Barthel, M., Leitner, S., Ouma, T., Agredazywczuk, P., Otinga, A., Njoroge, R., Oduor, C., Oluoch, K.C. and Six, J. 2025. Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions, EGU General Assembly 2025, Vienna, Austria, 27 April–2 May 2025. EGU25-5654 https://doi.org/10.5194/egusphere-egu25-5654 |
| spellingShingle | nitrous oxide greenhouse gases greenhouse gas emissions climate change mitigation agriculture Harris, E. Barthel, M. Leitner, Sonja Ouma, T. Agredazywczuk, P. Otinga, A. Njoroge, R. Oduor, Collins Oluoch, K. C. Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title | Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title_full | Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title_fullStr | Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title_full_unstemmed | Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title_short | Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions |
| title_sort | bringing together measurements and data science for better nitrous oxide emission accounting in data poor regions |
| topic | nitrous oxide greenhouse gases greenhouse gas emissions climate change mitigation agriculture |
| url | https://hdl.handle.net/10568/174607 |
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