IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i4p630-d96009.html
   My bibliography  Save this article

Identifying Irrigation Strategies for Improved Agricultural Water Productivity in Irrigated Maize Production through Crop Simulation Modelling

Author

Listed:
  • Geneille E. Greaves

    (Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, 1 Shuefu Rd., Pingtung 912, Taiwan)

  • Yu-Min Wang

    (Department of Civil Engineering, National Pingtung University of Science and Technology, 1 Shuefu Rd., Pingtung 912, Taiwan)

Abstract

Identifying irrigation strategies that improve agricultural water use efficiency (WUE) have a pivotal role to play in sustainable water development. In this study, the AquaCrop model was used to examine the impact of different irrigation scheduling options on yields to identify viable strategies to enhance WUE for irrigated maize. Two scheduling scenarios at water application depths ranging from 20 to 50 mm were investigated: schedules based on allowable depletion of total available water (TAW) in the root zone and interval schedules based on irrigating at predefined daily intervals. For both scenarios, simulated yields, seasonal water applied and percent percolation loss were within the range of 9.16 to 10.22 ton/ha, 180 to 950 mm and 0–61%, respectively. The WUE in terms of water applied (WUE Irr ) and crop evapotranspiration (WUE ET ) ranged from 1.07 to 5.48 kg/m 3 and 2.42 to 4.42 kg/m 3 , respectively. The results revealed that depletion levels of 40–50% TAW at water depths of 20–40 mm could be used to obtain high WUE without significant yield penalty. Moreover, a good balance between yield, improved WUE ET and percolation reduction was observed at water depths of 30–40 mm for daily intervals with water applied during the vegetative-reproductive stage of 7–5, 10–5 and 10–7. The identified strategies can contribute to the development of best management practices for water conservation.

Suggested Citation

  • Geneille E. Greaves & Yu-Min Wang, 2017. "Identifying Irrigation Strategies for Improved Agricultural Water Productivity in Irrigated Maize Production through Crop Simulation Modelling," Sustainability, MDPI, vol. 9(4), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:630-:d:96009
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/4/630/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/4/630/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geerts, S. & Raes, D. & Garcia, M. & Taboada, C. & Miranda, R. & Cusicanqui, J. & Mhizha, T. & Vacher, J., 2009. "Modeling the potential for closing quinoa yield gaps under varying water availability in the Bolivian Altiplano," Agricultural Water Management, Elsevier, vol. 96(11), pages 1652-1658, November.
    2. Levidow, Les & Zaccaria, Daniele & Maia, Rodrigo & Vivas, Eduardo & Todorovic, Mladen & Scardigno, Alessandra, 2014. "Improving water-efficient irrigation: Prospects and difficulties of innovative practices," Agricultural Water Management, Elsevier, vol. 146(C), pages 84-94.
    3. Abi Saab, Marie Therese & Todorovic, Mladen & Albrizio, Rossella, 2015. "Comparing AquaCrop and CropSyst models in simulating barley growth and yield under different water and nitrogen regimes. Does calibration year influence the performance of crop growth models?," Agricultural Water Management, Elsevier, vol. 147(C), pages 21-33.
    4. Sakthivadivel,R. & Aloysius, N. & Matsuno,Y., 2001. "Assessment of performance and impact of irrigation and water resources systems in Taiwan and Sri Lanka," IWMI Working Papers H028854, International Water Management Institute.
    5. Geerts, S. & Raes, D. & Garcia, M., 2010. "Using AquaCrop to derive deficit irrigation schedules," Agricultural Water Management, Elsevier, vol. 98(1), pages 213-216, December.
    6. Farre, Imma & Faci, Jose Maria, 2006. "Comparative response of maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) to deficit irrigation in a Mediterranean environment," Agricultural Water Management, Elsevier, vol. 83(1-2), pages 135-143, May.
    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. Himanshu, Sushil Kumar & Ale, Srinivasulu & Bordovsky, James & Darapuneni, Murali, 2019. "Evaluation of crop-growth-stage-based deficit irrigation strategies for cotton production in the Southern High Plains," Agricultural Water Management, Elsevier, vol. 225(C).
    2. Sarker, Khokan Kumer & Hossain, Akbar & Timsina, Jagadish & Biswas, Sujit Kumar & Malone, Sparkle L. & Alam, Md. Khairul & Loescher, Henry W. & Bazzaz, Mahfuz, 2020. "Alternate furrow irrigation can maintain grain yield and nutrient content, and increase crop water productivity in dry season maize in sub-tropical climate of South Asia," Agricultural Water Management, Elsevier, vol. 238(C).
    3. Himanshu, Sushil K. & Ale, Srinivasulu & Bell, Jourdan & Fan, Yubing & Samanta, Sayantan & Bordovsky, James P. & Gitz III, Dennis C. & Lascano, Robert J. & Brauer, David K., 2023. "Evaluation of growth-stage-based variable deficit irrigation strategies for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 280(C).
    4. Liu, Qi & Niu, Jun & Wood, Jeffrey D. & Kang, Shaozhong, 2022. "Spatial optimization of cropping pattern in the upper-middle reaches of the Heihe River basin, Northwest China," Agricultural Water Management, Elsevier, vol. 264(C).
    5. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).
    6. Alex Zizinga & Jackson Gilbert Majaliwa Mwanjalolo & Britta Tietjen & Bobe Bedadi & Ramon Amaro de Sales & Dennis Beesigamukama, 2022. "Simulating Maize Productivity under Selected Climate Smart Agriculture Practices Using AquaCrop Model in a Sub-humid Environment," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    7. Himanshu, Sushil Kumar & Fan, Yubing & Ale, Srinivasulu & Bordovsky, James, 2021. "Simulated efficient growth-stage-based deficit irrigation strategies for maximizing cotton yield, crop water productivity and net returns," Agricultural Water Management, Elsevier, vol. 250(C).
    8. Chen, Xiaoping & Dong, Haibo & Qi, Zhiming & Gui, Dongwei & Ma, Liwang & Thorp, Kelly R. & Malone, Robert & Wu, Hao & Liu, Bo & Feng, Shaoyuan, 2025. "Potential deficit irrigation adaptation strategies under climate change for sustaining cotton production in hyper–arid areas," Agricultural Water Management, Elsevier, vol. 312(C).
    9. Ratchaneewan Chuchird & Nophea Sasaki & Issei Abe, 2017. "Influencing Factors of the Adoption of Agricultural Irrigation Technologies and the Economic Returns: A Case Study in Chaiyaphum Province, Thailand," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    10. Aizhi Yu & Entai Cai & Min Yang & Zhishan Li, 2022. "An Analysis of Water Use Efficiency of Staple Grain Productions in China: Based on the Crop Water Footprints at Provincial Level," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    11. Fan, Yubing & Himanshu, Sushil K. & Ale, Srinivasulu & DeLaune, Paul B. & Zhang, Tian & Park, Seong C. & Colaizzi, Paul D. & Evett, Steven R. & Baumhardt, R. Louis, 2022. "The synergy between water conservation and economic profitability of adopting alternative irrigation systems for cotton production in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 262(C).
    12. Nyathi, M.K. & van Halsema, G.E. & Annandale, J.G. & Struik, P.C., 2018. "Calibration and validation of the AquaCrop model for repeatedly harvested leafy vegetables grown under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 208(C), pages 107-119.
    13. Miguel Ángel Pardo Picazo & Juan Manzano Juárez & Diego García-Márquez, 2018. "Energy Consumption Optimization in Irrigation Networks Supplied by a Standalone Direct Pumping Photovoltaic System," Sustainability, MDPI, vol. 10(11), pages 1-17, 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. López-Urrea, R. & Domínguez, A. & Pardo, J.J. & Montoya, F. & García-Vila, M. & Martínez-Romero, A., 2020. "Parameterization and comparison of the AquaCrop and MOPECO models for a high-yielding barley cultivar under different irrigation levels," Agricultural Water Management, Elsevier, vol. 230(C).
    2. Ignacio Lorite & Margarita García-Vila & María-Ascensión Carmona & Cristina Santos & María-Auxiliadora Soriano, 2012. "Assessment of the Irrigation Advisory Services’ Recommendations and Farmers’ Irrigation Management: A Case Study in Southern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2397-2419, June.
    3. Ward, Frank A. & Potter, Nicholas A. & Hrozencik, R. Aaron, 2025. "Managing agricultural water use in the Western United States: A search for efficient climate adaptation institutions," Agricultural Water Management, Elsevier, vol. 308(C).
    4. 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).
    5. Tiecheng Bai & Nannan Zhang & Youqi Chen & Benoit Mercatoris, 2019. "Assessing the Performance of the WOFOST Model in Simulating Jujube Fruit Tree Growth under Different Irrigation Regimes," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    6. Kögler, F. & Söffker, D., 2017. "Water (stress) models and deficit irrigation: System-theoretical description and causality mapping," Ecological Modelling, Elsevier, vol. 361(C), pages 135-156.
    7. Gheysari, Mahdi & Mirlatifi, Seyed Majid & Bannayan, Mohammad & Homaee, Mehdi & Hoogenboom, Gerrit, 2009. "Interaction of water and nitrogen on maize grown for silage," Agricultural Water Management, Elsevier, vol. 96(5), pages 809-821, May.
    8. Giorgio Baiamonte & Mario Minacapilli & Giuseppina Crescimanno, 2020. "Effects of Biochar on Irrigation Management and Water Use Efficiency for Three Different Crops in a Desert Sandy Soil," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    9. 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.
    10. Geerts, S. & Raes, D. & Garcia, M., 2010. "Using AquaCrop to derive deficit irrigation schedules," Agricultural Water Management, Elsevier, vol. 98(1), pages 213-216, December.
    11. Peragón, Juan M. & Pérez-Latorre, Francisco J. & Delgado, Antonio & Tóth, Tibor, 2018. "Best management irrigation practices assessed by a GIS-based decision tool for reducing salinization risks in olive orchards," Agricultural Water Management, Elsevier, vol. 202(C), pages 33-41.
    12. Ireneusz Cymes & Ewa Dragańska & Zbigniew Brodziński, 2022. "Potential Possibilities of Using Groundwater for Crop Irrigation in the Context of Climate Change," Agriculture, MDPI, vol. 12(6), pages 1-14, May.
    13. Kaur, Lovepreet & Kaur, Anureet & Brar, A.S., 2021. "Water use efficiency of green gram (Vigna radiata L.) impacted by paddy straw mulch and irrigation regimes in north-western India," Agricultural Water Management, Elsevier, vol. 258(C).
    14. Zou, Haiyang & Fan, Junliang & Zhang, Fucang & Xiang, Youzhen & Wu, Lifeng & Yan, Shicheng, 2020. "Optimization of drip irrigation and fertilization regimes for high grain yield, crop water productivity and economic benefits of spring maize in Northwest China," Agricultural Water Management, Elsevier, vol. 230(C).
    15. Tomaz, Alexandra & Palma, José Ferro & Ramos, Tiago & Costa, Maria Natividade & Rosa, Elizabete & Santos, Marta & Boteta, Luís & Dôres, José & Patanita, Manuel, 2021. "Yield, technological quality and water footprints of wheat under Mediterranean climate conditions: A field experiment to evaluate the effects of irrigation and nitrogen fertilization strategies," Agricultural Water Management, Elsevier, vol. 258(C).
    16. Luxon Nhamo & James Magidi & Adolph Nyamugama & Alistair D. Clulow & Mbulisi Sibanda & Vimbayi G. P. Chimonyo & Tafadzwanashe Mabhaudhi, 2020. "Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
    17. Gheysari, Mahdi & Pirnajmedin, Fatemeh & Movahedrad, Hamid & Majidi, Mohammad Mahdi & Zareian, Mohammad Javad, 2021. "Crop yield and irrigation water productivity of silage maize under two water stress strategies in semi-arid environment: Two different pot and field experiments," Agricultural Water Management, Elsevier, vol. 255(C).
    18. Neal, J.S. & Fulkerson, W.J. & Hacker, R.B., 2011. "Differences in water use efficiency among annual forages used by the dairy industry under optimum and deficit irrigation," Agricultural Water Management, Elsevier, vol. 98(5), pages 759-774, March.
    19. Ma, Xiaochi & Sanguinet, Karen A. & Jacoby, Pete W., 2020. "Direct root-zone irrigation outperforms surface drip irrigation for grape yield and crop water use efficiency while restricting root growth," Agricultural Water Management, Elsevier, vol. 231(C).
    20. Mabhaudhi, T. & Mpandeli, S. & Nhamo, Luxon & Chimonyo, V. G. P. & Nhemachena, Charles & Senzanje, A. & Naidoo, D. & Modi, A. T., 2018. "Prospects for improving irrigated agriculture in Southern Africa: linking water, energy and food," Papers published in Journals (Open Access), International Water Management Institute, pages 10(12):1-16.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jsusta:v:9:y:2017:i:4:p:630-:d:96009. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.