IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v243y2021ics037837741931546x.html
   My bibliography  Save this article

The contribution of percolation to water balances in water-seeded rice systems

Author

Listed:
  • LaHue, Gabriel T.
  • Linquist, Bruce A.

Abstract

Rice (Oryza sativa) has one of highest applied water footprints of any crop, due in many cases to high percolation and lateral seepage rates in flooded rice fields. Better understanding the magnitude and variability of these subsurface water flows and their contribution to the rice field water balance is critical for efforts to reduce the water footprint of rice production and to limit the transport of pollutants to surface water and groundwater. Percolation was directly measured in eight direct-seeded California rice fields that ranged from 20 to 61% clay and a complete water balance was developed for three of these fields. For these latter fields, directly measured percolation rates were compared to percolation calculated with Darcy’s law, and combined percolation and lateral seepage calculated as the residual of a water balance was compared to directly measured values. Across eight fields, cumulative percolation over the growing season ranged from 0.04 to 6.95 cm season−1. The mean cumulative percolation for the three water balance fields was 2.1 cm based on direct measurements compared to 3.2 cm based on Darcy’s law calculations. Combined percolation and lateral seepage calculated as the residual term of a water balance for the three fields was 17.1 cm, compared to 4.4 cm based on direct measurements, corresponding to 13.2 % and 3.4 % of water inputs, respectively (inputs were 98–99 % from irrigation). Based on these results, water management strategies that remove floodwater (e.g. alternate wetting and drying) would have limited potential to reduce water inputs in California rice production (0.7–2.7 cm season-1). Furthermore, using the most conservative (largest) estimates for each component of the water balance, we conclude that the average water requirement for California rice fields is approximately 108 cm season-1.

Suggested Citation

  • LaHue, Gabriel T. & Linquist, Bruce A., 2021. "The contribution of percolation to water balances in water-seeded rice systems," Agricultural Water Management, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:agiwat:v:243:y:2021:i:c:s037837741931546x
    DOI: 10.1016/j.agwat.2020.106445
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037837741931546X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2020.106445?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. Marcos, Mathias & Sharifi, Hussain & Grattan, Stephen R. & Linquist, Bruce A., 2018. "Spatio-temporal salinity dynamics and yield response of rice in water-seeded rice fields," Agricultural Water Management, Elsevier, vol. 195(C), pages 37-46.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Wopereis, M. C. S. & Bouman, B. A. M. & Kropff, M. J. & ten Berge, H. F. M. & Maligaya, A. R., 1994. "Water use efficiency of flooded rice fields I. Validation of the soil-water balance model SAWAH," Agricultural Water Management, Elsevier, vol. 26(4), pages 277-289, December.
    4. Darzi-Naftchali, Abdullah & Karandish, Fatemeh & Šimůnek, Jiří, 2018. "Numerical modeling of soil water dynamics in subsurface drained paddies with midseason drainage or alternate wetting and drying management," Agricultural Water Management, Elsevier, vol. 197(C), pages 67-78.
    5. Singh, K. B. & Gajri, P. R. & Arora, V. K., 2001. "Modelling the effects of soil and water management practices on the water balance and performance of rice," Agricultural Water Management, Elsevier, vol. 49(2), pages 77-95, July.
    6. Cesari de Maria, Sandra & Rienzner, Michele & Facchi, Arianna & Chiaradia, Enrico Antonio & Romani, Marco & Gandolfi, Claudio, 2016. "Water balance implications of switching from continuous submergence to flush irrigation in a rice-growing district," Agricultural Water Management, Elsevier, vol. 171(C), pages 108-119.
    Full references (including those not matched with items on IDEAS)

    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. Belder, P. & Bouman, B. A.M. & Spiertz, J.H.J., 2007. "Exploring options for water savings in lowland rice using a modelling approach," Agricultural Systems, Elsevier, vol. 92(1-3), pages 91-114, January.
    2. Xu, Baoli & Shao, Dongguo & Fang, Longzhang & Yang, Xia & Chen, Shu & Gu, Wenquan, 2019. "Modelling percolation and lateral seepage in a paddy field-bund landscape with a shallow groundwater table," Agricultural Water Management, Elsevier, vol. 214(C), pages 87-96.
    3. Antonopoulos, Vassilis Z., 2010. "Modelling of water and nitrogen balances in the ponded water and soil profile of rice fields in Northern Greece," Agricultural Water Management, Elsevier, vol. 98(2), pages 321-330, December.
    4. Masseroni, Daniele & Moller, Peter & Tyrell, Reece & Romani, Marco & Lasagna, Alberto & Sali, Guido & Facchi, Arianna & Gandolfi, Claudio, 2018. "Evaluating performances of the first automatic system for paddy irrigation in Europe," Agricultural Water Management, Elsevier, vol. 201(C), pages 58-69.
    5. de Silva, C. Shanthi & Rushton, K.R., 2008. "Representation of rainfed valley ricefields using a soil-water balance model," Agricultural Water Management, Elsevier, vol. 95(3), pages 271-282, March.
    6. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    7. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    8. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    9. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    10. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    11. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    12. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    13. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    14. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    15. S Ariane Christie & Amanda S Conroy & Rachael A Callcut & Alan E Hubbard & Mitchell J Cohen, 2019. "Dynamic multi-outcome prediction after injury: Applying adaptive machine learning for precision medicine in trauma," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-13, April.
    16. Zhu Wang, 2022. "MM for penalized estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 54-75, March.
    17. Ida Kubiszewski & Kenneth Mulder & Diane Jarvis & Robert Costanza, 2022. "Toward better measurement of sustainable development and wellbeing: A small number of SDG indicators reliably predict life satisfaction," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(1), pages 139-148, February.
    18. Gustavo A. Alonso-Silverio & Víctor Francisco-García & Iris P. Guzmán-Guzmán & Elías Ventura-Molina & Antonio Alarcón-Paredes, 2021. "Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance," Mathematics, MDPI, vol. 9(20), pages 1-13, October.
    19. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    20. Naimoli, Antonio, 2022. "Modelling the persistence of Covid-19 positivity rate in Italy," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).

    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:agiwat:v:243:y:2021:i:c:s037837741931546x. 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/agwat .

    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.