bulukazari, S., Babazadeh, H., Ebrahimipak, N., Mousavi-Jahromi, S., Ramezani_etedali, H. (2021). Development of Production Functions for Important Cereals of Qazvin Plain under Water Shortage and Salinity Stress Using AquaCrop Model and Artificial Neural Network. , 35(2), 123-137. doi: 10.22092/jwra.2021.354490.867
Sara bulukazari; Hossein Babazadeh; Nyazali Ebrahimipak; Seyed Habib Mousavi-Jahromi; Hadi Ramezani_etedali. "Development of Production Functions for Important Cereals of Qazvin Plain under Water Shortage and Salinity Stress Using AquaCrop Model and Artificial Neural Network". , 35, 2, 2021, 123-137. doi: 10.22092/jwra.2021.354490.867
bulukazari, S., Babazadeh, H., Ebrahimipak, N., Mousavi-Jahromi, S., Ramezani_etedali, H. (2021). 'Development of Production Functions for Important Cereals of Qazvin Plain under Water Shortage and Salinity Stress Using AquaCrop Model and Artificial Neural Network', , 35(2), pp. 123-137. doi: 10.22092/jwra.2021.354490.867
bulukazari, S., Babazadeh, H., Ebrahimipak, N., Mousavi-Jahromi, S., Ramezani_etedali, H. Development of Production Functions for Important Cereals of Qazvin Plain under Water Shortage and Salinity Stress Using AquaCrop Model and Artificial Neural Network. , 2021; 35(2): 123-137. doi: 10.22092/jwra.2021.354490.867

Development of Production Functions for Important Cereals of Qazvin Plain under Water Shortage and Salinity Stress Using AquaCrop Model and Artificial Neural Network

پژوهش آب در کشاورزی
Article 3, Volume 35, Issue 2, September 2021, Pages 123-137    PDF (1.03 M)
Document Type: Research Paper
DOI: 10.22092/jwra.2021.354490.867
Authors
Sara bulukazari1; Hossein Babazadeh* 2; Nyazali Ebrahimipak3; Seyed Habib Mousavi-Jahromi4; Hadi Ramezani_etedali5
1Dept. of Agricultural Systems Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Water Science and Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Department of Irrigation, Soil and Water Research Institute, Agricultural Research, Education and Promotion Organization, Karaj, Iran
4Department of Civil Engineering, Islamic Azad Uni.(IAU), Shahr-e-Qods Branch, Tehran, Iran
5Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran
Abstract
In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated by considering 4 soil and water salinity levels and 4 deficit irrigation levels for the major cereal crops including wheat, barley, and corn in Qazvin Plain. The results showed that the coefficients of determination for wheat, barley, and corn yield were 0.97, 0.86 and 0.91, respectively. Therefore, the model can evaluate the performance in salinity and deficit irrigation conditions with a good approximation. To determine the optimal production functions of each crop, the results of the plant model were compared with three models of linear and nonlinear regression, and artificial neural network. The neural network model was able to estimate the performance compared to the AquaCrop model with lower error and higher correlation (0.99). These values in the linear function for wheat, barley, and corn were 0.98, 0.95, and 0.78 and in the nonlinear function as 0.92, 0.86 and 0.81, respectively. Also, the error calculated in the neural network method for wheat, barley, and maize was 40.16, 62.09, and 57.08 kg, respectively, which were less than the linear model by 75 %, 70 %, and 95 %; and less than the exponential model by 90 %, 85 %, and 93%, respectively. The best trained network for determining the water-salt production function for barley and wheat 5 Nero and for corn 7 Nero was introduced in the single layer structure. Sensitivity analysis on wheat and barley showed that this model had low sensitivity to irrigation and salinity parameters and only corn plant showed a moderate range sensitivity to salinity parameter.
Keywords
Sensitivity Analysis; Wheat; Barley; Corn; Irrigation management
References
  1. پیری، ح.، انصاری، ح. و پارسا، م. 1397. ﺗﻌﯿﯿﻦ ﺗﺎﺑﻊ ﺗﻮﻟﯿﺪ آب- ﺷﻮری- ﻋﻤﻠﮑﺮد ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ زﻣﺎن ﺑﺮداﺷﺖ ﻋﻠﻮﻓﻪ و ارزﯾﺎﺑﯽ ﺷﺎﺧﺺ­ﻫﺎی ﺗﻮﻟﯿﺪ در ذرت ﺧﻮﺷﻪای. ﻣﺠله‏ی ﻣﻬﻨﺪﺳﯽ ﻣﻨﺎﺑﻊ آب، 11: 26-15.
  2. تافته، آ.، بابازاده، ح.، ابراهیمی پاک، ن.ع. و کاوه، ف. 1393. بهینه سازی تخصیص آب ماهانه برای بیشینه­سازی کارایی مصرف آب بر اساس تابع تولید محصول.(مطالعه موردی دشت قزوین). رساله دکتری دانشکده کشاورزی و منابع طبیعی دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران.
  3. حیدری نیا، م.، برومندنسب، س. و ناصری، ع. 1396. ارزیابی مدلAquaCrop در تخمین عملکرد ذرت و شوری خاک تحت شرایط مدیریت­های مختلف زراعی و آبیاری با آب شور. تحقیقات آب و خاک ایران، 48(1): 49-61.‎
  4. خرسند، ا.، رضاوردی نژاد، و. شهیدی، ع. 1393.ارزیابی عملکرد مدل AquaCropدر پیش­بینی عملکرد گندم، رطوبت و شوری نیمرخ خاک تحت تنش­های شوری و کم آبی. مدیریت آب آبیاری، 4(1): 104-89.
  5. رمضانی اعتدالی، ه.، لیاقت، ع.، پارسی نژاد، م. و توکلی، ع. 1395. واسنجی و اعتبارسنجی مدل AquaCropدر مدیریت آبیاری غلات مهم. نشریه آبیاری و زهکشی ایران. 3(10): 397-389.
  6. شیرمحمدی، ع. اکبرخانی، ز. انصاری، ح. علیزاده، ا. کافی، م. 1392. ارزیابی توابع تولید آب شوری عملکرد در ذرت علوفه­ای در استان خراسان رضوی نشریه آبیاری زهکشی ایران، 4(7): 543-535.
  7. صفری، ف.، رمضانی اعتدالی، ه.، کاویانی، ع.، آبابایی، ب.، 1398.اﻣﻜﺎنﺳﻨﺠﻲ ﺷﺒﻜﻪ آﻣﻮزش­ﻫﺎی ﻋﺼﺒﻲ ﻣﺼﻨﻮﻋﻲ ﻣﺪل ﺑﺎ اﺳﺘﻔﺎده از مدلﻫﺎی ﮔﻴﺎﻫﻲ ﺑﺮای ﭘﻴﺶﺑﻴﻨﻲ ﻋﻤﻠﻜﺮد و ﻃﻮل دورهﻫﺎی رﺷﺪ ﮔﻨﺪم . مجله علمی و ترویجی نیوار، 104-105: 101-112.
  8. کریمی اورگانی، ح.، رحیمی خوب، ع. و نظری فر، م.ح. 1396. ارزیابی مدل AquaCropدر شبیه‏سازی زیست توده جو در شرایط کم‏آبیاری. نشریه پژوهش آب در کشاورزی، 31(3): 353-341.
  9. محمدی، م.، داوری، ک.، قهرمان، انصاری، ح. و حق‏وردی. ا. 1394.واسنجی و صحت‌سنجی مدل AquaCrop برای شبیه‌سازی عملکرد گندم بهاره تحت تنش همزمان شوری و خشکی، پژوهش آب در کشاورزی، 29(3): 295-277.
  10. مهرآذر، آ.، سلطانی، ج. رحمتی، ا. 1394. ارزیابی مدل AquaCropدر شبیه‏سازی عملکرد ذرت تحت شرایط تنش شوری، نشریه آب و خاک (علوم و صنایع کشاورزی). 30(5): 1439- 1426.
  11. یزدانی، و. داوری، ک. قهرمان، ب. و کافی، م. 1393. ارزیابی علمکرد کلزا به‏صورت تابعی از ارتفاع آب آبیاری و شوری در منطقه مشهد. فصلنامه علوم پژوهشی علوم آبیاری و آب، 5(18): 53-32.
  12. Alvarez, A. 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. Eur J. Agron, 30: 70-77.
  13. Brumbelow, K. and Georgakakos, A. 2007. Determining crop-water production functions using yield–irrigation gradient algorithms. Agricultural Water Management 87: 151–161.
  14. Choopan, Y. and Emami, S. 2019. Estimation of barley yield under irrigation with wastewater using RBF and GFF models of artificial neural network. Journal of Applied Research in Water and Wastewater, 6(1): 73-79.
  15. Farhangian Kashani, S. and Monem, R. 2010. Effect of salt stress on seed germination characters of ten St. John’s wort (hypericum perforatum l.) genotypes. Journal of Crop Production Research (Environmental Stresses in Plant Sciences), 2(1): 75-81.
  16. Foster, T. and Brozović, N. 2018. Simulating crop-water production functions using crop growth models to support water policy assessments. Ecological Economics, 152: 9-21.
  17. Gandhi, N. and Armstrong, L. J. 2016. Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques. In 2016 IEEE International Conference on Advances in Computer Applications (ICACA) (pp. 357-363). IEEE.
  18. García-Vilaa, M. and Fereresa, E. 2012. Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. Europ. J. Agronomy 36: 21– 31.
  19. Hsiao, T. C., Heng, L. K., Steduto, P., Rojas-Lara, B., Raes, D. and Fereres, E. 2009. AquaCrop-the FAO crop model to simulate yield response to water, III: Parameterization and testing for maize. Agronomy Journal, 101: 448-459.
  20. Hussain, S. and Hatibaruah, D. 2015. Modeling for prediction of tomato yield and its deviation using artificial neural network. International Journal of Engineering Trends and Technology, 29: 102-108.
  21. Jiang, Y., Xu, X., Huang, Q., Huo, Z. and Huang, G. 2016. Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model. Agricultural water management, 178: 76-88.
  22. Kaul, M., Hill, R. L. and Walthall, C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1): 1-18.
  23. Kim, D. and Kaluarachchi, J. 2015. Validating FAO AquaCrop using Landsat images and regional crop information. Agricultural Water Management, 149 143–155.
  24. Liu, H. F., Genard, M., Guichard, S. and Bertin, N. 2007. Model-assisted analysis of tomato fruit growth in relation to carbon and water fluxes, Experimental Botany, 58 (13): 3567-3580.
  25. López-Aguilar, K., Benavides-Mendoza, A., González-Morales, S., Juárez-Maldonado, A., Chiñas-Sánchez, P. and Morelos-Moreno, A. 2020. Artificial neural network modeling of greenhouse tomato yield and aerial dry matter. Agriculture, 10(4): 97.
  26. Mohammadi, M., Ghahraman, B., Davary, K., Ansari, H. Shahidi, A. and Bannayan, M. 2016. Nested Validation of AquaCrop Model for Simulation of Winter Wheat Grain Yield, Soil Moisture and Salinity Profiles under Simultaneous Salinity and Water Stress. Irrigation and Drainage, 65: 112–128.
  27. Noori, N., Kalin, L. and Isik, S. 2020. Water quality prediction using SWAT-ANN coupled approach. Journal of Hydrology, 590: 125220.
  28. Singh A. K., Tripathy, R. and Chopra, U. K. 2008. Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop. Agricultural Water Management, 95: 776-
  29. Xin, H., Peiling, Y., Shumei, R., Yunkai, L., Guangyu, J. and Lianhao, L. 2016. Quantitative response of oil sunflower yield to evapotranspiration and soil salinity with saline water irrigation. International Journal of Agricultural and Biological Engineering, 9(2): 63-73.
  30. Zhang, B., Feng, G., Ahuja, L. R., Kong, X., Ouyang, Y., Adeli, A. and Jenkins, J. N. 2018. Soybean crop-water production functions in a humid region across years and soils determined with APEX model. Agricultural Water Management, 204: 180-191.
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