IDEAS home Printed from https://ideas.repec.org/a/eee/ecolec/v204y2023ipbs0921800922003299.html
   My bibliography  Save this article

Economic and commercial analysis of reusing dam reservoir sediments

Author

Listed:
  • Nikafkar, Nasrin
  • Alroaia, Younos Vakil
  • Heydariyeh, Seyyed Abdollah
  • Schleiss, Anton J.

Abstract

Improper use of chemical fertilizers has led to land degradation. Organic crops, which are farmed using organic fertilizers to reduce the negative environmental effects, are considered an alternative solution to develop the ecosystem health and improve the soil. There are different sources of organic fertilizers. This research aimed to study the feasibility of reusing the sediments of the Latian Dam reservoir in Iran as an organic fertilizer to revitalize agricultural soil on a commercial scale. The correlation of elements to sediments was first demonstrated using the regression method. The Mann-Kendall trend test was then used to examine the data trend. The use of a time series method to predict the five-year sediment amount and its NPK (nitrogen, phosphorus, and potassium) content allowed for the creation of a sample year for future research. Finally, the economic value of the elements was calculated using the replacement cost method, and cost-benefit analyses were also carried out. The results indicated that the reuse of dam reservoir sediments not only leads to considerable profits but also makes it possible to save foreign exchange by restricting imports and increasing the inflow of foreign exchange through the export of organic fertilizers.

Suggested Citation

  • Nikafkar, Nasrin & Alroaia, Younos Vakil & Heydariyeh, Seyyed Abdollah & Schleiss, Anton J., 2023. "Economic and commercial analysis of reusing dam reservoir sediments," Ecological Economics, Elsevier, vol. 204(PB).
    • Handle: RePEc:eee:ecolec:v:204:y:2023:i:pb:s0921800922003299
    DOI: 10.1016/j.ecolecon.2022.107668
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921800922003299
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolecon.2022.107668?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. Chojnacka, K. & Witek-Krowiak, A. & Moustakas, K. & Skrzypczak, D. & Mikula, K. & Loizidou, M., 2020. "A transition from conventional irrigation to fertigation with reclaimed wastewater: Prospects and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Kieslich, Marcus & Salles, Jean-Michel, 2021. "Implementation context and science-policy interfaces: Implications for the economic valuation of ecosystem services," Ecological Economics, Elsevier, vol. 179(C).
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Sheehan, C. & Harrington, J. & Murphy, J.D., 2010. "A technical assessment of topsoil production from dredged material," Resources, Conservation & Recycling, Elsevier, vol. 54(12), pages 1377-1385.
    5. Gezahegn Weldu Woldemariam & Anteneh Derribew Iguala & Solomon Tekalign & Ramireddy Uttama Reddy, 2018. "Spatial Modeling of Soil Erosion Risk and Its Implication for Conservation Planning: the Case of the Gobele Watershed, East Hararghe Zone, Ethiopia," Land, MDPI, vol. 7(1), pages 1-25, February.
    6. Giancarlo Renella, 2021. "Recycling and Reuse of Sediments in Agriculture: Where Is the Problem?," Sustainability, MDPI, vol. 13(4), pages 1-12, February.
    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. Mazhar Hussain & Daniel Levacher & Nathalie Leblanc & Hafida Zmamou & Irini Djeran-Maigre & Andry Razakamanantsoa, 2023. "Testing the Feasibility of Usumacinta River Sediments as a Renewable Resource for Landscaping and Agronomy," Sustainability, MDPI, vol. 15(22), pages 1-11, November.

    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. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    2. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
    3. Bolinches, Antonio & Blanco-Gutiérrez, Irene & Zubelzu, Sergio & Esteve, Paloma & Gómez-Ramos, Almudena, 2022. "A method for the prioritization of water reuse projects in agriculture irrigation," Agricultural Water Management, Elsevier, vol. 263(C).
    4. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    5. Man Li & Tao Ye & Peijun Shi & Jian Fang, 2015. "Impacts of the global economic crisis and Tohoku earthquake on Sino–Japan trade: a comparative perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 541-556, January.
    6. Caglar, Abdullah Emre & Daştan, Muhammet & Avci, Salih Bortecine, 2024. "Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach," Applied Energy, Elsevier, vol. 365(C).
    7. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.
    8. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    9. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
    10. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    11. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Paul Doukhan & Gabriel Lang & Anne Leucht & Michael H. Neumann, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 290-314, May.
    12. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
    13. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    14. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
    15. Mergani A. Khairalla & Xu Ning & Nashat T. AL-Jallad & Musaab O. El-Faroug, 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model," Energies, MDPI, vol. 11(6), pages 1-21, June.
    16. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.
    17. Salas-Molina, Francisco & Martin, Francisco J. & Rodríguez-Aguilar, Juan A. & Serrà, Joan & Arcos, Josep Ll., 2017. "Empowering cash managers to achieve cost savings by improving predictive accuracy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 403-415.
    18. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    19. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    20. Riezebos, Jan & Zhu, Stuart X., 2020. "Inventory control with seasonality of lead times," Omega, Elsevier, vol. 92(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:ecolec:v:204:y:2023:i:pb:s0921800922003299. 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.elsevier.com/locate/ecolecon .

    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.