IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v472y2022ics0304380022002125.html
   My bibliography  Save this article

Modelling direct field nitrogen emissions using a semi-mechanistic leaching model newly implemented in Indigo-N v3

Author

Listed:
  • Bockstaller, Christian
  • Galland, Victor
  • Avadí, Angel

Abstract

Nitrogen plays a major role in agroecosystems as the key nutrient in agricultural production as well as a source of different emissions, which exceed currently planetary boundaries. N losses, conditioned by both pedoclimatic conditions and agricultural strategies (e.g. rotations, fertilisation), predominantly take the form of ammonia (NH3) volatilisation, nitrate (NO3) leaching, nitrification-driven nitric oxide (NOx) emission to air, and denitrification-driven nitrous oxide (NOx and N2O) emissions to air. The multiplication of initiatives and studies on nitrogen modelling resulted in a broad offer of complex simulation models (Tier 3) on one extreme of the gradient between feasibility and integration of processes. On the other side, a multiplication of initiatives has led to a broad offer of causal indicators in the form of proxies and considering one or a few input variables (Tier 1). A relevant compromise between those extremes lies in the development of operational models using a restricted number of parameters and input variables (Tier 2). Here, we propose a new semi-mechanistic operational model for the estimation of direct field N emissions (NH3, NO3, NOx and N2O) from contrasting agricultural situations: the Indigo-N v3 (I-N3) model. The gaseous emissions are based on Tier 1 (NOx) and Tier 2 (NH3, N2O) methods taken from the literature, with some enhancements, while we developed a totally new semi-mechanistic approach for nitrate leaching. A comparison of I-N3 outputs was performed with measurements of nitrate leaching in three countries (15 arable fields in France, 3 sugar cane fields at Reunion Inland, and 5 cropped fields in Kenya) and showed a reasonable predictive quality for temperate arable fields, and for some of the tropical fields (1 in Reunion and 3 in Kenya). It also performed better than the previous version of Indigo-N (IN-2) and the SALCA/SQCB models. In comparison with previous Tier 2 models, the newly developed Indigo-N v3 presents an original position on the gradient between integration of processes and feasibility of the simulation of processes. Another novelty of I-N3 lies in its broad scope, designed to be valid for temperate and non-temperate crops, including annual field crops, short-cycle vegetables, temporary grasslands and perennial grasses (such as sugarcane, miscanthus or switchgrass). Parameterisation and validation should be continued for further crops, such as associations and short cycle vegetables.

Suggested Citation

  • Bockstaller, Christian & Galland, Victor & Avadí, Angel, 2022. "Modelling direct field nitrogen emissions using a semi-mechanistic leaching model newly implemented in Indigo-N v3," Ecological Modelling, Elsevier, vol. 472(C).
  • Handle: RePEc:eee:ecomod:v:472:y:2022:i:c:s0304380022002125
    DOI: 10.1016/j.ecolmodel.2022.110109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022002125
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110109?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Keating, Brian A., 2020. "Crop, soil and farm systems models – science, engineering or snake oil revisited," Agricultural Systems, Elsevier, vol. 184(C).
    2. Mark A. Sutton & Oene Oenema & Jan Willem Erisman & Adrian Leip & Hans van Grinsven & Wilfried Winiwarter, 2011. "Too much of a good thing," Nature, Nature, vol. 472(7342), pages 159-161, April.
    3. Yang, J. & Greenwood, D. J. & Rowell, D. L. & Wadsworth, G. A. & Burns, I. G., 2000. "Statistical methods for evaluating a crop nitrogen simulation model, N_ABLE," Agricultural Systems, Elsevier, vol. 64(1), pages 37-53, April.
    4. Xiao, Sinan & Lu, Zhenzhou & Xu, Liyang, 2017. "Multivariate sensitivity analysis based on the direction of eigen space through principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 1-10.
    5. Benoit, Marie & Garnier, Josette & Beaudoin, Nicolas & Billen, Gilles, 2016. "A participative network of organic and conventional crop farms in the Seine Basin (France) for evaluating nitrate leaching and yield performance," Agricultural Systems, Elsevier, vol. 148(C), pages 105-113.
    6. Zheng Shi & Sean Crowell & Yiqi Luo & Berrien Moore, 2018. "Model structures amplify uncertainty in predicted soil carbon responses to climate change," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Diaz-Gonzalez, Freddy A. & Vuelvas, Jose. & Vallejo, Victoria E. & Patino, D., 2023. "Fertilization rate optimization model for potato crops to maximize yield while reducing polluting nitrogen emissions," Ecological Modelling, Elsevier, vol. 485(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lamboni, Matieyendou, 2019. "Multivariate sensitivity analysis: Minimum variance unbiased estimators of the first-order and total-effect covariance matrices," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 67-92.
    2. Meyer-Aurich, Andreas & Karatay, Yusuf Nadi, 2019. "Effects of uncertainty and farmers' risk aversion on optimal N fertilizer supply in wheat production in Germany," Agricultural Systems, Elsevier, vol. 173(C), pages 130-139.
    3. Taingaun Sourn & Sophak Pok & Phanith Chou & Nareth Nut & Dyna Theng & Phanna Rath & Manuel R. Reyes & P.V. Vara Prasad, 2021. "Evaluation of Land Use and Land Cover Change and Its Drivers in Battambang Province, Cambodia from 1998 to 2018," Sustainability, MDPI, vol. 13(20), pages 1-22, October.
    4. Simon Anastasiadis & Marie-Laure Nauleau & Suzi Kerr & Tim Cox & Kit Rutherford, 2011. "Does Complex Hydrology Require Complex Water Quality Policy? NManager Simulations for Lake Rotorua," Working Papers 11_14, Motu Economic and Public Policy Research.
    5. Tong-Hui Wu & Yu-Fu Hu & Yan-Yan Zhang & Xiang-Yang Shu & Ze-Peng Yang & Wei Zhou & Cheng-Yi Huang & Jie Li & Zhi Li & Jia He & Ying Yu, 2022. "Changes in soil organic carbon and its fractions under grassland reclamation in alpine-cold soils, China," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 17(4), pages 211-221.
    6. Ledgard, Stewart F. & Wei, Sha & Wang, Xiaoqin & Falconer, Shelley & Zhang, Nannan & Zhang, Xiying & Ma, Lin, 2019. "Nitrogen and carbon footprints of dairy farm systems in China and New Zealand, as influenced by productivity, feed sources and mitigations," Agricultural Water Management, Elsevier, vol. 213(C), pages 155-163.
    7. Charné Viljoen & Janke van der Colf & Pieter Andreas Swanepoel, 2020. "Benefits Are Limited with High Nitrogen Fertiliser Rates in Kikuyu-Ryegrass Pasture Systems," Land, MDPI, vol. 9(6), pages 1-20, May.
    8. Lamboni, Matieyendou, 2021. "Derivative-based integral equalities and inequality: A proxy-measure for sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 137-161.
    9. Cathal Buckley & Paul Murphy & David Wall, 2013. "Farm-gate N and P balances and use efficiencies across specialist dairy farms in the Republic Ireland," Working Papers 1302, Rural Economy and Development Programme,Teagasc.
    10. Andreas Meyer-Aurich & Jørgen Olesen & Annette Prochnow & Reiner Brunsch, 2013. "Greenhouse gas mitigation with scarce land: The potential contribution of increased nitrogen input," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 18(7), pages 921-932, October.
    11. Chen, Xin & Molina-Cristóbal, Arturo & Guenov, Marin D. & Riaz, Atif, 2019. "Efficient method for variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 97-115.
    12. Yusuf Nadi Karatay & Andreas Meyer-Aurich, 2018. "A Model Approach for Yield-Zone-Specific Cost Estimation of Greenhouse Gas Mitigation by Nitrogen Fertilizer Reduction," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    13. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).
    14. Cecilia Bellora & Élodie Blanc & Jean-Marc Bourgeon & Eric Strobl, 2018. "Estimating the Impact of Crop Diversity on Agricultural Productivity in South Africa," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 185-215, National Bureau of Economic Research, Inc.
    15. Pringle, M.J. & Marchant, B.P. & Lark, R.M., 2008. "Analysis of two variants of a spatially distributed crop model, using wavelet transforms and geostatistics," Agricultural Systems, Elsevier, vol. 98(2), pages 135-146, September.
    16. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    17. Ahmmed Md Motasim & Abd Wahid Samsuri & Arina Shairah Abdul Sukor & Amin Mohd Adibah, 2021. "Gaseous Nitrogen Losses from Tropical Soils with Liquid or Granular Urea Fertilizer Application," Sustainability, MDPI, vol. 13(6), pages 1-11, March.
    18. Sandra Lage & Zivan Gojkovic & Christiane Funk & Francesco G. Gentili, 2018. "Algal Biomass from Wastewater and Flue Gases as a Source of Bioenergy," Energies, MDPI, vol. 11(3), pages 1-30, March.
    19. Zhaoxia Wang & Jing Zhao, 2018. "Optimization of Passive Envelop Energy Efficient Measures for Office Buildings in Different Climate Regions of China Based on Modified Sensitivity Analysis," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    20. Stirling, Sofía & Fariña, Santiago & Pacheco, David & Vibart, Ronaldo, 2021. "Whole-farm modelling of grazing dairy systems in Uruguay," Agricultural Systems, Elsevier, vol. 193(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:472:y:2022:i:c:s0304380022002125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.