IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v179y2017icp390-400.html
   My bibliography  Save this article

An irrigation schedule testing model for optimization of the Smartirrigation avocado app

Author

Listed:
  • Mbabazi, Deanroy
  • Migliaccio, Kati W.
  • Crane, Jonathan H.
  • Fraisse, Clyde
  • Zotarelli, Lincoln
  • Morgan, Kelly T.
  • Kiggundu, Nicholas

Abstract

A series of mobile irrigation apps have been developed on the basis that irrigation schedules can be estimated using an average of the previous five days of crop evapotranspiration (ETc). The application of this average ETc methodology for developing an irrigation schedule has not been fully evaluated for the suite of apps, including the avocado app. Thus, an irrigation testing model was developed and is presented here that simulates irrigation depths based on the Smartirrigation avocado app operation technique and uses a soil water balance to simulate drainage, soil water content, runoff and plant water stress. The objectives were to identify an optimum number of previous days average ETc needed for estimating of an app irrigation schedule and to evaluate seasonal influences (wet and dry seasons) on the irrigation schedule through development and application of an irrigation testing model. Four different methods (3, 4, 6 and 7 previous days ETc) were tested and compared to the current methodology (that uses the average of the previous 5days ETc) for developing irrigation schedules for five years from 2010 to 2014. In general, no significant differences (p≤0.05) were observed for irrigation depths, drainage depths, runoff and soil water content simulated among the app scheduling methods. Sixty two to 67% water savings were predicted with the app irrigation scheduling methods compared to a time based irrigation method (38mmweek−1). Average irrigation and drainage depths simulated were 15 to 41% and 160 to 512% greater in wet seasons compared to dry seasons, respectively. Ninety seven to 100% of the drainage was simulated during rainfall events. The current app methodology (previous 5days ETc) was found to capture weather variability for irrigation schedule development. Use of previous 6 and 7days ETc was also sufficient. Addition of a wireless connection allowing users to modify the irrigation schedule using the smartphone app is recommended to reduce water losses during wet seasons and/or when rainfall events are predicted or occur.

Suggested Citation

  • Mbabazi, Deanroy & Migliaccio, Kati W. & Crane, Jonathan H. & Fraisse, Clyde & Zotarelli, Lincoln & Morgan, Kelly T. & Kiggundu, Nicholas, 2017. "An irrigation schedule testing model for optimization of the Smartirrigation avocado app," Agricultural Water Management, Elsevier, vol. 179(C), pages 390-400.
    • Handle: RePEc:eee:agiwat:v:179:y:2017:i:c:p:390-400
    DOI: 10.1016/j.agwat.2016.09.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377416303365
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2016.09.006?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. Migliaccio, Kati W. & Schaffer, Bruce & Crane, Jonathan H. & Davies, Frederick S., 2010. "Plant response to evapotranspiration and soil water sensor irrigation scheduling methods for papaya production in south Florida," Agricultural Water Management, Elsevier, vol. 97(10), pages 1452-1460, October.
    2. Romero, Consuelo C. & Dukes, Michael D. & Baigorria, Guillermo A. & Cohen, Ron, 2009. "Comparing theoretical irrigation requirement and actual irrigation for citrus in Florida," Agricultural Water Management, Elsevier, vol. 96(3), pages 473-483, March.
    3. Davis, S.L. & Dukes, M.D., 2010. "Irrigation scheduling performance by evapotranspiration-based controllers," Agricultural Water Management, Elsevier, vol. 98(1), pages 19-28, December.
    4. Odegard, I.Y.R. & van der Voet, E., 2014. "The future of food — Scenarios and the effect on natural resource use in agriculture in 2050," Ecological Economics, Elsevier, vol. 97(C), pages 51-59.
    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. Erazo-Mesa, Edwin & Gómez, Edgar Hincapié & Sánchez, Andrés Echeverri, 2022. "Surface soil water content as an indicator of Hass avocado irrigation scheduling," Agricultural Water Management, Elsevier, vol. 273(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. Marjan Aziz & Madeeha Khan & Naveeda Anjum & Muhammad Sultan & Redmond R. Shamshiri & Sobhy M. Ibrahim & Siva K. Balasundram & Muhammad Aleem, 2022. "Scientific Irrigation Scheduling for Sustainable Production in Olive Groves," Agriculture, MDPI, vol. 12(4), pages 1-14, April.
    2. Doreen Fedrigo-Fazio & Jean-Pierre Schweitzer & Patrick Ten Brink & Leonardo Mazza & Alison Ratliff & Emma Watkins, 2016. "Evidence of Absolute Decoupling from Real World Policy Mixes in Europe," Sustainability, MDPI, vol. 8(6), pages 1-22, May.
    3. Distefano, Tiziano & Chiarotti, Guido & Laio, Francesco & Ridolfi, Luca, 2019. "Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness," Ecological Economics, Elsevier, vol. 159(C), pages 122-132.
    4. Helin, Janne & Weikard, Hans-Peter, 2019. "A model for estimating phosphorus requirements of world food production," Agricultural Systems, Elsevier, vol. 176(C).
    5. Dorin, Bruno & Joly, Pierre-Benoît, 2020. "Modelling world agriculture as a learning machine? From mainstream models to Agribiom 1.0," Land Use Policy, Elsevier, vol. 96(C).
    6. Fabio Boschetti & Elizabeth A. Fulton & Nicola J. Grigg, 2014. "Citizens’ Views of Australia’s Future to 2050," Sustainability, MDPI, vol. 7(1), pages 1-26, December.
    7. Grundy, Michael J. & Bryan, Brett A. & Nolan, Martin & Battaglia, Michael & Hatfield-Dodds, Steve & Connor, Jeffery D. & Keating, Brian A., 2016. "Scenarios for Australian agricultural production and land use to 2050," Agricultural Systems, Elsevier, vol. 142(C), pages 70-83.
    8. Shi, Jianchu & Wu, Xun & Wang, Xiaoyu & Zhang, Mo & Han, Le & Zhang, Wenjing & Liu, Wen & Zuo, Qiang & Wu, Xiaoguang & Zhang, Hongfei & Ben-Gal, Alon, 2020. "Determining threshold values for root-soil water weighted plant water deficit index based smart irrigation," Agricultural Water Management, Elsevier, vol. 230(C).
    9. Li, Shengping & Tan, Deshui & Wu, Xueping & Degré, Aurore & Long, Huaiyu & Zhang, Shuxiang & Lu, Jinjing & Gao, Lili & Zheng, Fengjun & Liu, Xiaotong & Liang, Guopeng, 2021. "Negative pressure irrigation increases vegetable water productivity and nitrogen use efficiency by improving soil water and NO3–-N distributions," Agricultural Water Management, Elsevier, vol. 251(C).
    10. Kassaye, Kassu Tadesse & Boulange, Julien & Lam, Van Thinh & Saito, Hirotaka & Watanabe, Hirozumi, 2020. "Monitoring soil water content for decision supporting in agricultural water management based on critical threshold values adopted for Andosol in the temperate monsoon climate," Agricultural Water Management, Elsevier, vol. 229(C).
    11. Rodolfo Garza-Torres & Enrique Troyo-Diéguez & Alejandra Nieto-Garibay & Gregorio Lucero-Vega & Francisco Javier Magallón-Barajas & Emilio García-Galindo & Yenitze Fimbres-Acedo & Bernardo Murillo-Ama, 2020. "Environmental and Management Considerations for Adopting the Halophyte Salicornia bigelovii Torr. as a Sustainable Seawater-Irrigated Crop," Sustainability, MDPI, vol. 12(2), pages 1-14, January.
    12. Hossain, Mokter, 2018. "Grassroots innovation: The state of the art and future perspectives," Technology in Society, Elsevier, vol. 55(C), pages 63-69.
    13. Wanjiru, Evan M. & Xia, Xiaohua, 2015. "Energy-water optimization model incorporating rooftop water harvesting for lawn irrigation," Applied Energy, Elsevier, vol. 160(C), pages 521-531.
    14. Said A. Hamido & Kelly T. Morgan, 2021. "The Effect of Irrigation Rate on the Water Relations of Young Citrus Trees in High-Density Planting," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    15. Sara Komenda & Martha C. Monroe, 2023. "Clues in the Data: The Role of Education in Adopting Technology That Enhances Sustainable Lifestyle Choices," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    16. Wang, Qunyan & Jia, Yifan & Pang, Zhongjun & Zhou, Jianbin & Scriber, Kevin Emmanuel & Liang, Bin & Chen, Zhujun, 2024. "Intelligent fertigation improves tomato yield and quality and water and nutrient use efficiency in solar greenhouse production," Agricultural Water Management, Elsevier, vol. 298(C).
    17. Stacy Slobodiuk & Caitlin Niven & Greer Arthur & Siddhartha Thakur & Ayse Ercumen, 2021. "Does Irrigation with Treated and Untreated Wastewater Increase Antimicrobial Resistance in Soil and Water: A Systematic Review," IJERPH, MDPI, vol. 18(21), pages 1-19, October.
    18. Bowles, Nicholas & Alexander, Samuel & Hadjikakou, Michalis, 2019. "The livestock sector and planetary boundaries: A ‘limits to growth’ perspective with dietary implications," Ecological Economics, Elsevier, vol. 160(C), pages 128-136.
    19. Haghverdi, Amir & Singh, Amninder & Sapkota, Anish & Reiter, Maggie & Ghodsi, Somayeh, 2021. "Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California," Agricultural Water Management, Elsevier, vol. 245(C).
    20. Teshome, Fitsum T. & Bayabil, Haimanote K. & Schaffer, Bruce & Ampatzidis, Yiannis & Hoogenboom, Gerrit & Singh, Aditya, 2023. "Exploring deficit irrigation as a water conservation strategy: Insights from field experiments and model simulation," Agricultural Water Management, Elsevier, vol. 289(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:agiwat:v:179:y:2017:i:c:p:390-400. 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.