IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v150y2015icp18-34.html
   My bibliography  Save this article

Modification of maize simulation model for predicting growth and yield of winter wheat under different applied water and nitrogen

Author

Listed:
  • Mahbod, Mehdi
  • Zand-Parsa, Shahrokh
  • Sepaskhah, Ali Reza

Abstract

Model WSM (Wheat Simulation Model) was developed based on the previous model (MSM, Maize Simulation Model). The planted cultivar was Shiraz with five irrigation treatments (1.2, 1.0, 0.8, and 0.5 ratios of the potential irrigation requirement under surface irrigation system and rain-fed) and four applied nitrogen treatments (0, 46, 92, 136kgNha−1). The irrigation water requirements were estimated by measuring the differences between soil field capacity and measured soil water content at root depth in the full irrigation treatment before irrigation. In the WSM model, dynamic flow of water, nitrogen, and heat through the soil were simulated numerically in an unsteady state condition at soil profile. Water and nitrogen transfer in the soil are governed by the Richard's equation and the diffusion convection equation, respectively. Emergence time of seed after sowing was simulated using soil water content, temperature, sowing depth, and soil particle diameters using beta function. Plant growth stages were simulated considering photoperiod, vernalization and air temperature. Hourly simulation of actual evaporation from soil surface and transpiration were simulated using the Penman–Monteith method based on atmospheric conditions and soil water content at root depth. Nitrogen uptake was simulated through mass flow and diffusion processes during the growing season. Produced dry matter was simulated as a function of hourly corrected intercepted radiation (based on air temperature) by plant leaves, maximum and minimum plant top N concentration and the amount of N uptake. Wheat grain yield was simulated by the ratio of grain N uptake and grain N concentration that were estimated by an empirical equation as a function of simulated top N uptake. Obtained experimental data in 2009–2010 were used to calibrate the model. The experimental results from 2010 to 2011 validated favorably the proposed model.

Suggested Citation

  • Mahbod, Mehdi & Zand-Parsa, Shahrokh & Sepaskhah, Ali Reza, 2015. "Modification of maize simulation model for predicting growth and yield of winter wheat under different applied water and nitrogen," Agricultural Water Management, Elsevier, vol. 150(C), pages 18-34.
    • Handle: RePEc:eee:agiwat:v:150:y:2015:i:c:p:18-34
    DOI: 10.1016/j.agwat.2014.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377414003709
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2014.11.009?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. Zand-Parsa, Sh. & Sepaskhah, A.R. & Ronaghi, A., 2006. "Development and evaluation of integrated water and nitrogen model for maize," Agricultural Water Management, Elsevier, vol. 81(3), pages 227-256, March.
    2. Bert, Federico E. & Laciana, Carlos E. & Podesta, Guillermo P. & Satorre, Emilio H. & Menendez, Angel N., 2007. "Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation," Agricultural Systems, Elsevier, vol. 94(2), pages 141-150, May.
    3. Wang, Enli & Engel, Thomas, 1998. "Simulation of phenological development of wheat crops," Agricultural Systems, Elsevier, vol. 58(1), pages 1-24, September.
    4. Kiniry, James R. & Williams, J. R. & Gassman, Philip W. & Debacke, P., 1992. "General, Process-Oriented Model for Two Competing Plant Species (A)," Staff General Research Papers Archive 483, Iowa State University, Department of Economics.
    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. Mosaffa, Hamid Reza & Sepaskhah, Ali Reza, 2019. "Performance of irrigation regimes and water salinity on winter wheat as influenced by planting methods," Agricultural Water Management, Elsevier, vol. 216(C), pages 444-456.

    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. Garcia y Garcia, Axel & Guerra, Larry C. & Hoogenboom, Gerrit, 2008. "Impact of generated solar radiation on simulated crop growth and yield," Ecological Modelling, Elsevier, vol. 210(3), pages 312-326.
    2. Xie, Yun & Kiniry, James R. & Williams, Jimmy R., 2003. "The ALMANAC model's sensitivity to input variables," Agricultural Systems, Elsevier, vol. 78(1), pages 1-16, October.
    3. Paff, K. & Timlin, D. & Fleisher, D.H., 2023. "A comparison of wheat leaf-appearance rate submodules for DSSAT CROPSIM-CERES (CSCER)," Ecological Modelling, Elsevier, vol. 482(C).
    4. Čerkasova, Natalja & White, Michael & Arnold, Jeffrey & Bieger, Katrin & Allen, Peter & Gao, Jungang & Gambone, Marilyn & Meki, Manyowa & Kiniry, James & Gassman, Philip W., 2023. "Field scale SWAT+ modeling of corn and soybean yields for the contiguous United States: National Agroecosystem Model Development," Agricultural Systems, Elsevier, vol. 210(C).
    5. W. Yan & Y. Zhong & Z. Shangguan, 2015. "The relationships and sensibility of wheat C:N:P stoichiometry and water use efficiency under nitrogen fertilization," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 61(5), pages 201-207.
    6. DeJonge, Kendall C. & Ascough, James C. & Ahmadi, Mehdi & Andales, Allan A. & Arabi, Mazdak, 2012. "Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments," Ecological Modelling, Elsevier, vol. 231(C), pages 113-125.
    7. Sardorbek Musayev & Jonathan Mellor & Tara Walsh & Emmanouil Anagnostou, 2022. "Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia," Forecasting, MDPI, vol. 4(1), pages 1-22, March.
    8. Kiniry, James R. & Bean, Brent & Xie, Yun & Chen, Pei-yu, 2004. "Maize yield potential: critical processes and simulation modeling in a high-yielding environment," Agricultural Systems, Elsevier, vol. 82(1), pages 45-56, October.
    9. Iqbal, M. Anjum & Shen, Yanjun & Stricevic, Ruzica & Pei, Hongwei & Sun, Hongyoung & Amiri, Ebrahim & Penas, Angel & del Rio, Sara, 2014. "Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation," Agricultural Water Management, Elsevier, vol. 135(C), pages 61-72.
    10. Sepaskhah, Ali Reza & Fahandezh-Saadi, Saghar & Zand-Parsa, Shahrokh, 2011. "Logistic model application for prediction of maize yield under water and nitrogen management," Agricultural Water Management, Elsevier, vol. 99(1), pages 51-57.
    11. Stricevic, Ruzica & Cosic, Marija & Djurovic, Nevenka & Pejic, Borivoj & Maksimovic, Livija, 2011. "Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower," Agricultural Water Management, Elsevier, vol. 98(10), pages 1615-1621, August.
    12. Talebizadeh, Mansour & Moriasi, Daniel & Gowda, Prasanna & Steiner, Jean L. & Tadesse, Haile K. & Nelson, Amanda M. & Starks, Patrick, 2018. "Simultaneous calibration of evapotranspiration and crop yield in agronomic system modeling using the APEX model," Agricultural Water Management, Elsevier, vol. 208(C), pages 299-306.
    13. Ascough II, J.C. & Andales, A.A. & Sherrod, L.A. & McMaster, G.S. & Hansen, N.C. & DeJonge, K.C. & Fathelrahman, E.M. & Ahuja, L.R. & Peterson, G.A. & Hoag, D.L., 2010. "Simulating landscape catena effects in no-till dryland agroecosystems using GPFARM," Agricultural Systems, Elsevier, vol. 103(8), pages 569-584, October.
    14. Zhang, Yan & Ma, Qian & Liu, Donghua & Sun, Lefeng & Ren, Xiaolong & Ali, Shahzad & Zhang, Peng & Jia, Zhikuan, 2018. "Effects of different fertilizer strategies on soil water utilization and maize yield in the ridge and furrow rainfall harvesting system in semiarid regions of China," Agricultural Water Management, Elsevier, vol. 208(C), pages 414-421.
    15. Napoli, Marco & Orlandini, Simone, 2015. "Evaluating the Arc-SWAT2009 in predicting runoff, sediment, and nutrient yields from a vineyard and an olive orchard in Central Italy," Agricultural Water Management, Elsevier, vol. 153(C), pages 51-62.
    16. Żyromski, Andrzej & Szulczewski, Wiesław & Biniak-Pieróg, Małgorzata & Jakubowski, Wojciech, 2016. "The estimation of basket willow (Salix viminalis) yield – New approach. Part I: Background and statistical description," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1118-1126.
    17. Mabhaudhi, Tafadzwanashe & Dirwai, Tinashe Lindel & Taguta, Cuthbert & Sikka, Alok & Lautze, Jonathan, 2023. "Mapping Decision Support Tools (DSTs) on agricultural water productivity: A global systematic scoping review," Agricultural Water Management, Elsevier, vol. 290(C).
    18. Gao, Haihe & Yan, Changrong & Liu, Qin & Li, Zhen & Yang, Xiao & Qi, Ruimin, 2019. "Exploring optimal soil mulching to enhance yield and water use efficiency in maize cropping in China: A meta-analysis," Agricultural Water Management, Elsevier, vol. 225(C).
    19. Colas, Floriane & Gauchi, Jean-Pierre & Villerd, Jean & Colbach, Nathalie, 2021. "Simplifying a complex computer model: Sensitivity analysis and metamodelling of an 3D individual-based crop-weed canopy model," Ecological Modelling, Elsevier, vol. 454(C).
    20. Larson, James A. & English, Burton C. & He, Lixia, 2008. "Risk and Return for Bioenergy Crops under Alternative Contracting Arrangements," 2008 Annual Meeting, February 2-6, 2008, Dallas, Texas 6842, Southern Agricultural Economics Association.

    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:150:y:2015:i:c:p:18-34. 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.