اعتبارسنجی و صحت‌سنجی مدل AquaCrop در شبیه‌سازی میزان عملکرد، ماده خشک و بهره-وری آب ذرت دانه‌ای (Zea mays L.) تحت روش‌های مختلف آبیاری و سطوح کود نیتروژنه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه تولید و ژنتیک گیاهی، دانشکده علوم و مهندسی کشاورزی، دانشگاه رازی، کرمانشاه، ایران

2 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه، ایران

چکیده

هدف از این مطالعه، ارزیابی مدل AquaCrop از نظر شبیه‌سازی عملکرد دانه و زیست‌توده ذرت دانه‌ای تحت دو روش آبیاری (فتیله‌ای و سطحی) و کوددهی نیتروژن (کود سرک و کودآبیاری) بود. این آزمایش در دو سال 1399 و 1400 در دانشکده کشاورزی دانشگاه رازی انجام شد. تیمارهای آزمایشی شامل آبیاری فتیله‌ای و آبیاری سطحی (در سه سطح 100،70 و 50 درصد نیاز آبی گیاه) و دو سطح کود نیتروژنه 100 و 50 درصد نیاز کودی گیاه بود. نتایج نشان داد که مقدار ریشه میانگین مربعات خطا (RMSE) در شبیه­سازی روند توسعه پوشش گیاهی در تیمارهای مختلف آبیاری و کودی در مرحله واسنجی (1399) بین 5/1 تا 1/6 درصد و در مرحله صحت­سنجی (1400) بین 2 و 4/6 درصد به‌دست آمد. مقدار ضریب کارآیی مدل (EF) در شبیه­سازی پوشش گیاهی، در مرحله واسنجی بین 91/0 و 99/0 و در مرحله صحت­سنجی بین 93/0 و 99/0 بود. مقدار RMSE در شبیه­سازی ماده خشک برای مرحله واسنجی بین 07/1 تا 1/2 تن در هکتار و در مرحله صحت­سنجی بین 4/1 تا 9/2 تن در هکتار به‌دست آمد. همچنین نتایج شبیه­سازی مدل AquaCrop نشان داد که این مدل توانست شبیه­سازی بهره­وری آب را بهتر از عملکرد دانه و ماده خشک کل انجام دهد، به­طوری‌که ضریب تبیین (R2) مربوط به بهره­وری آب، عملکرد دانه و ماده خشک کل ذرت به‌ترتیب برابر با 82/0، 52/0 و 54/0 به‌دست آمد. در مجموع، AquaCrop به‌خوبی برای منطقه مورد مطالعه انتخاب شد و توانایی AquaCrop در زمینه ارائه یک مدل کم‌آبیاری مانند روش فتیله­ای مناسب است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Calibration and Verification of the AquaCrop Model in Simulating the Yield, Dry Matter and Water Productivity of Grain Corn (Zea mays L.) under Different Irrigation Methods and Nitrogen Fertilizer Levels

نویسندگان [English]

  • zhaleh zarei 1
  • Hassan Heidari 1
  • Saeid Jalali Honarmand 1
  • Ali Bafkar 2
1 Department of Plant Production and Genetics Engineering, Faculty of Agricultural Science and Engineering, Razi University, Kermanshah, Iran
2 Department of Water Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran
چکیده [English]

Introduction
The decrease in rainfall in recent decades and the occurrence of drought in Iran, which is one of the arid and semi-arid regions, as well as the significant wastage of water in the agricultural sector, have caused many researchers to look for new irrigation methods that lead to improve water consumption. Therefore, the importance of conducting this experiment is to reduce surface water evaporation, save water, reduce leaching and use of nitrogen fertilizer in maize cultivation. Nowadays, due to the decrease in rainfall and the excessive water consumption in the agricultural sector, the importance of water resources management has received more attention from researchers. Therefore, simulation models an effective role in evaluating irrigation management methods to improve water consumption in the agricultural sector. This study aimed to assess the AquaCrop model in terms of simulating the yield and biomass of maize under two methods of irrigation (wick and surface) and nitrogen fertilization (topdressing and fertigation).
Materials and Methods
This experiment was carried out in 2020 and 2021 in the Faculty of Agriculture of Razi University. Experimental treatments included wick irrigation and surface irrigation (at three levels of 100, 70, and 50%) and two levels of nitrogen fertilizer 100 and 50% of the plant's fertilizer requirement. The statistical design was factorial in the form of randomized complete blocks and was implemented in three replications. In the wick irrigation method, fertilizer was provided to the plant through tanks and in solution. In the surface irrigation method, fertilizer was topdressing on the soil surface. Nitrogen fertilizer was applied in three stages. In each watering, the amount of water used will be measured and recorded through the meter. At the end of the experiment, the data measured in the first and second years were used for calibration and validation, respectively. For both stages of calibration and validation of the model, the measured (observed) and simulated values of yield, biomass, canopy cover, and water productivity were compared and statistically analyzed. For statistical evaluation, root mean square error (RMSE), efficiency coefficient of the Nash-Sutcliffe model (EF), and Wilmot agreement index (d) were used.
Results and Discussion
The results indicated that the root mean square error (RMSE) for simulating canopy cover development under various irrigation and fertilizer treatments ranged from 1.5% to 6.1% during the calibration stage (2020) and from 2% to 6.4% during the verification stage (2021). The model's efficiency factor (EF) for canopy cover simulation was between 0.91 and 0.99 during the calibration stage and between 0.93 and 0.99 during the validation stage. For biomass simulation, the RMSE values ranged from 1.07 to 2.1 tons/ha in the calibration stage and from 1.4 to 2.9 tons/ha in the validation stage. The model's EF for biomass simulation ranged from 0.87 to 0.98 in the calibration stage and from 0.92 to 0.99 in the validation stage. These results demonstrate the model's high accuracy and reliability in simulating both canopy cover and biomass development across different treatments. AquaCrop simulation results showed that the model was able to simulate water productivity better than yield and total biomass so the coefficient of determination (R2) related to water productivity, yield, and biomass of maize was obtained as 0.82, 0.52, and 0.54 respectively.
Conclusion
The simulation of yield, dry matter, and water productivity in the calibration and verification stage was lower than the actual value in all treatments. The biggest difference between the observed and simulated values of yield, biomass and water productivity was observed in the wick treatment (first and second year). According to the obtained results, it can be concluded that AquaCrop was chosen well and within the expectations for the studied area and the ability of AquaCrop in the field to provide a low irrigation model like the wick method is suitable and practical.

کلیدواژه‌ها [English]

  • Calibration
  • Irrigation management
  • Modeling
  • Urea fertilizer
  • Verification

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Abedinpour, M. (2016). Testing of AquaCrop model for maize under different water and nitrogen managements. Journal of Agroecology and Natural Resource Management, 3(1), 6-9.
  2. Abedinpour, M. (2020). Evaluation of AquaCrop model in soybean cultivation under different planting dates and deficit irrigation treatments. Iran Agricultural Research, 39(2), 37-46. https://doi.org/10.22099/IAR.2020.36152.1391
  3. Abedinpour, M. (2021). The comparison of DSSAT-CERES and AquaCrop models for wheat under water–nitrogen interactions. Communications in Soil Science and Plant Analysis, 52(17), 2002-2017. https://doi.org/10.1080/00103624.2021.1908323
  4. Adeboye, O.B., Schultz, B., Adeboye, A.P., Adekalu, K.O., & Osunbitan, J.A. (2021). Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Information Processing in Agriculture, 8(3), 419–436. https://doi.org/10.1016/j.inpa.2020.10.002
  5. Alishiri, R., Paknejad, F., & Aghayari, F. (2014). Simulation of sugar beet growth under different water regimes and nitrogen levels by aqua crop. International Journal of Biosciences, 4(4), 1-9. https://doi.org/10.12692/ijb/4.4.1-9
  6. Amiri, E. (2016). Calibration and testing of the Aquacrop model for rice under water and nitrogen management. Communications in Soil Science and Plant Analysis, 47(3), 387-403. https://doi.org/10.1080/00103624.2015.1123719
  7. Azizi Mobaser, J.A., Ramazani Moghadam, J., & Asghari, A. (2022). Evaluating the efficiency of AquaCrop model for corn plant underwater and fertilizer management (Case study: Shush city). Advanced Technologies in Water Efficiency, 2(1), 67-84. (In Persian with English abstract). https://doi.org/10.22126/ATWE.2022.7483.1014
  8. Cui, Z., Effah, Z., Yan, B., Gao, Y., Wu, B., Wang, Y., Xu, P., Wang, H., Zhao, B., & Wang, Y. (2023). Water and nitrogen coupling increased the water-nitrogen use efficiency of oilseed flax. Plants, 12, 51. https://doi.org/10.3390/plants12010051
  9. Dercas, N., Dalezios, N.R., Stamatiadis, S., Evangelou, E., Glampedakis, A., Mantonanakis, G., & Tserlikakis, N. (2022). AquaCrop simulation of winter wheat under different N management practices. Hydrology, 9(4), 1-20. https://doi.org/10.3390/hydrology9040056
  10. Foster, T., Brozović, N., Butler, A.P., Neale, C.M.U., Raes, D., Steduto, P., Fereres, E., & Hsiao, T.C. (2017). AquaCrop-OS: An open source version of FAO’s crop water productivity model. Agricultural Water Management, 181, 18-22. https://doi.org/10.1016/j.agwat.2016.11.015
  11. Greaves, G.E., & Wang, Y.M., (2016). Assessment of FAO AquaCrop model for simulating maize growth and productivity under deficit irrigation in a tropical environment. Water, 8(12), 1-18. https://doi.org/10.3390/w8120557
  12. Heng, L.K., Hsiao, T., Evett, S., Howell, T., & Steduto, P. (2009). Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal, 101(3), 488-498. https://doi.org/10.2134/agronj2008.0029xs
  13. Iqbal, M.A., Shen, Y., Stricevic, R., Pei, H., Sun, H., Amiri, E., Penas, A., & del Rio, S. (2014). Evaluation of the FAO AquaCrop model for winter wheat on the NorthChina plain under deficit irrigation from field experiment to regionalyield simulation. Agricultural Water Management, 135, 61-72. https://doi.org/10.1016/j.agwat.2013.12.012
  14. 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, 230, 1-14.
  15. Nazari, B., Liaghat, A., Akbari, M.R., & Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7–18. https://doi.org/10.1016/j.agwat.2018.06.003
  16. Pawar, G.S., Kale, M.U., & Lokhande, J.N. (2017). Response of AquaCrop model to different irrigation schedules for irrigated cabbage. Agricultural Research, 6(1), 73–81. https://doi.org/10.1007/s40003-016-0238-2
  17. Raes, D., Steduto, P., Hsiao, T.C., & Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: ii. main algorithms and software description. Aronomy Journal, 101(3), 438-447. https://doi.org/10.2134/agronj2008.0140s
  18. Rahimikhoob, H., Sohrabi, T., & Delshad, M. (2021). Simulating crop response to nitrogen-deficiency stress using the critical nitrogen concentration concept and the AquaCrop. Scientia Horticulturae, 285(110194), 1-10. https://doi.org/10.1016/j.scienta.2021.110194
  19. Rezaverdinejad, V., Khorsand, A., & Shahidi, A. (2014). Evaluation and comparison of aquacrop and FAO models for yield prediction of winter wheat under environmental stresses. Journal of Biodiversity and Environmental Sciences, (JBES) 4(6), 438-449.
  20. Shirazi, S.Z., Mei, X., Liu, B., & Liu, Y. (2021). Assessment of the AquaCrop model under different irrigation scenarios in the North China plain. Agricultural Water Management, 257, 1-17. https://doi.org/10.1016/j.agwat.2021.107120
  21. Steduto, P., Hsiao, T.C., Fereres, E., & Raes, D. (2012). Crop yield response to water. FAOIrrigation and Drainage Paper No. 66. Food and Agriculture Organization of theUnited Nations, Rome, Italy. https://www.fao.org/4/i2800e/i2800e00.htm
  22. Steduto, P., Raes, D., Hsiao, T.C., Fereres, E., Heng, L.K., Howell, T.A., Evett, S.R., Rojas-Lara, B.A., Farahani, H.J., Izzi, G., Oweis, T.Y., Wani, S.P., Hoogeveen, J., & Geerts, S. (2009). Concepts and applications of Aquacrop: The Fao crop water productivity model. Crop Modeling and Decision Support, Springer, Berlin, Heidelberg. pp. 175–191. https://doi.org/10.1007/978-3-642-01132-0_19
  23. Umesh, B., Reddy, K.S., Polisgowdar, B.S., Maruthi, V., Satishkumar, U., Ayyanagoudar, M.S., Rao, S., & Veeresh, H. (2022). Assessment of climate change impact on maize (Zea mays) through aquacrop model in semi-arid alfisol of southern Telangana. Agricultural Water Management, 274, 1-9. https://doi.org/10.1016/j.agwat.2022.107950
  24. Vanuytrecht, E., Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., Heng, L.K., Vila, M.G., & Moreno, P.M. (2014). AquaCrop: FAO's crop water productivity and yield response model. Environmental Modelling and Software, 62, 351-360. https://doi.org/10.1016/j.envsoft.2014.08.005
  25. Willmott, C.J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63(1), 1309–1313. https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2
  26. Wu, H., Yue, Q., Guo, P., Xu, X., & Huang, X. (2022). Improving the AquaCrop model to achieve direct simulation of evapotranspiration under nitrogen stress and joint simulation-optimization of irrigation and fertilizer schedules. Agricultural Water Management, 266, 107599. https://doi.org/10.1016/j.agwat.2022.107599
  27. Zhang, C., Xie, Z., Wang, Q., Tang, M., Feng, S., & Cai, H. (2022). AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity. Agricultural Water Management, 266, 107580. https://doi.org/10.1016/j.agwat.2022.107580

 

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