Evaluation of Irrigation Management and the Possibility of Improving It in Wheat Fields in Sistan Dam Irrigation District Using SWAP Model

Document Type : Research Paper

Authors

1 Department of Water, Faculty of Water and Soil, Zabol University, Zabol, Iran

2 Associate Professor/ university of zabol

3 Irrigation Systems Research Department Agricultural Engineering Research Institute

4 Scientific staff/University of Zabol

Abstract

In this research, the status of water productivity for wheat production and strategies to increase water productivity in different quantitative conditions of water was determined and analyzed in the irrigation district of Sistan Dam. In this regard, the SWAP simulation model was calibrated and validated by considering the current water resources operation, various quantities of irrigation water, and use of field information. Water production functions were used to determine the irrigation schedule (time and depth) for wheat crop. The results of field measurements in the crop year 2016-2017 in the irrigation district showed that farmers irrigate wheat on average four times in the current conditions. Farmers' average crop yield and water productivity were about 1450 kg/ha and 0.41 kg/m3, respectively. These results show that available water is not appropriately used and should be addressed with practical solutions to improve water productivity. The validation and calibration results of the SWAP model also showed the high accuracy of the model in the case study. The results of different management scenarios of eliminating some irrigation shifts compared to the existing conditions indicated that, although there was no significant difference in water productivity, crop yield decreases about 37%. The results of evaluating the scenarios of reducing the depth and frequency of irrigation (using 640 mm per season and applying 40 mm at each shift) showed that, with reliable and timely water supply and more frequent irrigation, water productivity could be increased by 30% compared to the baseline scenario; and crop yield can be doubled. In these scenarios, the presence of adequate moisture in the plant's root zone increases the yield, and the amount of deep percolation is greatly reduced. The amount of water used by farmers is excessive for various reasons. Therefore, it is recommended to train farmers on how to improve irrigation and crop management according to the water available in the irrigation district, so that they can distribute water according to the real needs of the plant and irrigate at the right time and sufficient quantity.

Keywords

References

  1. اکبری م، دهقانی سانیچ ح و میرلطیفی م، 1387. تأثیر اصلاح تقویم آبیاری در بهره­وری آب در کشاورزی. صفحه­های 23 تا 25. سومین کنفرانس مدیریت منابع آب در ایران. مهرماه، دانشکده عمران، دانشگاه تبریز.
  2. اکبری م، 1390. بیلان آب خاک و عملکرد محصول گندم با استفاده از مدل شبیه­سازی AquaCrop (مطالعه موردی در شبکه آبیاری آبشار اصفهان). مجله تحقیقات مهندسی کشاورزی، 12 (4): 34-19.
  3. بادیه­نشین ع ر، نوری ح و وظیفه­دوست م، 1393. بهبود برآورد عملکرد محصول در مدل شبیه­سازی SWAP با استفاده از داده­های ماهواره­ای. تحقیقات آب و خاک ایران، 45(4): 379-388.
  4. دهقان ه، علیزاده ا، انصاری ح و نادریان­فر م، 1390. ارزیابی روش­های افزایش عملکرد و بهره­وری آب گندم با استفاده از گزینه­های مدل SWAP(مطالعه موردی: منطقه نیشابور). چهارم کنفرانس مدیریت منابع آب ایران، 13-14 اردیبهشت، دانشگاه صنعتی امیرکبیر، تهران.
  5. طاوسی ت، شجاع ف و عسگری 1، 1398. بازنگری پهنه­های اقلیمی شمال شرق ایران بر پایة کاربرد تلفیقی تغییر شاخص خشکی. نشریه مدیریت بیابان، 13: 117-134.
  6. محمدی ا، دلبری م، ابول­پور ب، افراسیاب پ و محمدرضاپور ا، 1399. بررسی عوامل مبهم در تخصیص آب شبکه آبیاری سد سیستان. نشریه علمی پژوهشی مهندسی آبیاری و آب ایران، 10 (39): 174-159.
  7. مختاری، شیدا، 1390. توسعه و کاربرد یک مدل ساده (VSM) جهت تخمین منطقه‌ای عملکرد برنج با بهره‌گیری از داده‌های ماهواره‌ای. پایان­نامه کارشناسی ارشد، دانشکده کشاورزی، دانشگاه گیلان.
  8. وردی­نژاد و، سهرابی ت، حیدری ن، عراقی­نژاد ش و فیضی م، 1389. تعیین عمق بهینه آبیاری محصولات زراعی در شرایط شوری با استفاده از مدل SWAP. نشریه آب و خاک، 24 (3): 463-475.
  9. وردی­نژاد و، سهرابی ت، فیضی م، حیدری ن و عراقی­نژاد ش، 1389. الگوبندی عملکرد محصولات مختلف در شرایط شوری آب آبیاری با استفاده از مدل SWAP. مجله دانش آب و خاک، 20 (4): 97-111.
  10. Ahmadi A, Moridi A and Han D, 2015. Uncertainty assessment in environmental risk through Bayesian networks. Journal of Environmental Informatics, 25(1): 46–59.
  11. Ahmadvand, MR and Najafpur ZA, 2010. Investigation of cultivation surface, production and supportive policies of wheat during the first to fourth development plans. 2Quarterly Journal of Economic Research and Policies, 18(53): 59-76.
  12. Aggarwal PK, Kalra N, Singh AK and Sinha SK, 1994. Analyzing the limitations set by climatic factors, geotype, and water and nitrogen availability on productivity of wheat I. The model description, parameterization and validation, Field Crops Research. 38(12), 73-91.
  13. Amiri E, 2016. Assessment of CERES-Wheat Model in simulation of varieties of wheat yield under different irrigation treatments. Journal of Soil and Water Resources Conservation, 5(3): 73-85
  14. Bonfante A, Basile A, Acutis M, De Mascellis R, Manna P, Perego A and Terribile F, 2010. SWAP, CropSyst and MACRO comparison in two contrasting soils cropped with maize in Northern Italy. Agricultural Water Management. 97, 1051–1062.
  15. Chai T and Draxler RR, 2014. Root mean square error (RMSE) or mean absolute error (MAE) Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7 (3), 1247–1250.
  16. Diepen CA, Van Wolf J and van Keulen H, 1989. WOFOST: a simulation model of crop production. Soil Use Manage. 5(1), 16–24.
  17. Droogers P, Torabi M, Akbari M and Pazira E, 2001, Field-scale modeling to explore salinity problems in irrigated agriculture. Irrig. Drain. 50, 77-90
  18. Droogers P and Torabi M, 2002. Field scale scenarios for water and salinity management by simulation modeling in the Zayandeh Rud basin. Esfahan Province. Iran. IAERI-IWMI Research Reports 12
  19. Farahani HJ, Izzi G, Steduto P and Oweis TY, 2009. Parameterization and evaluation of AquaCrop for full and deficit irrigated cotton. Agron. J. 101, 469-476.
  20. Feddes RA, Kowalik PJ and Zarandy H, 1978. Simulation of Field Water Use and Crop Yield. Pudoc, Wageningen.
  21. Garcia-Vila M, Fereres E, Mateos L, Orgaz F and Steduto P, 2009. Deficit irrigation optimization of cotton with AquaCrop. Agron. J. 101, 477-487.
  22. Godwin DC and Jones CA, 1991. Nitrogen dynamics in soil–plant systems, In: R. J. Hanks, J. T. Ritchie (Eds.). Modelling plant and soil systems, Agron. Monog. 31, ASA, CSSA, SSSA, Madison, WI, USA.
  23. Hamada K, Inoue H, Mochizuki H, Asakura M, Shimizu Y and Takemura T, 2020. Evaluating Maize Drought and Wet Stress in a Converted Japanese Paddy Field Using a SWAP Model. Water, 12(5): 1-15.
  24. Hassanli M, Ebrahimian H, Mohammadi E, Rahimi A and Shokouhi A, 2016. Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models. Agric. Water Manage, 176:91-99.
  25. Jonubi R, Rezaverdinejad V and Salemi H, 2018. Enhancing field scale water productivity for several rice cultivars under limited water supply. Paddy and water environment, 16(1):125-141.
  26. Kroes J G and Van Dam JC, 2003. Reference Manual SWAP version 3.03. (Ed). Alterra- report: Alterra Green WorldResearch (Vol. 773). (pp.1-211).MI: Wageningen University and Research Centre.
  27. Kumar P, Sarangi A, Singh DK, Parihar SS and Sahoo RN, 2015. Simulation of salt dynamics in the root zone and yield of wheat crop under irrigated saline regimes using SWAP model. Agric. Water Manage. 148, 72–83
  28. Minasny B, McBratney AB, 2007. Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma 140, 324–336.
  29. Mo X and Liu S, 2001. Simulating evapotranspiration and photosynthesis of winter wheat over the growing season. Agricultural and Forest Meteorology, 109(10), 203–222.
  30. Molden D, 2007. Water for food, water for life: A comprehensive assessment of water management in agriculture. Earthscan. London.
  31. Mostafazadeh-fard B, Mansouri H, Mousavi SF and Feyzi M, 2009. Effects of different levels of irrigation water salinity and leaching on yield and yield components of wheat in an arid region. Journal of Irrigation and Drainage Engineering. 135(1):32-38.
  32. Pan Y, Yuan C and Jing S, 2020. Simulation and optimization of irrigation schedule for summer maize based on SWAP model in saline region. International Journal of Agricultural and Biological Engineering, 13(3):117-122.
  33. Pandi H, Asadi Kapourchal S, Vazifedoust M and Rezaei M, 2020. Simulation of Rice Yield and its Components Using SWAP Model and Remote Sensing Technology for Optimal Use of Water and Soil. Environment and Water Engineering, 6(4):374-387.
  34. Raes D, Steduto P, Hsiao TC and Fereres E, 2009. AquaCrop. The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 101, 438-447.
  35. Ruiz ME and Utset A, 2003. Models for predicting water use and crop yields. Report of college on soil Physics. 323-328.
  36. Singh R, Van Dam JC and Feddes RA, 2006. Water Productivity analysis of irrigated crops in Sirsa district, India. Agriculture Water Management. 82:253-278.
  37. Singh UK, Ren L and Kang S, 2010. Simulation of soil water in space and time using an agro hydrological model and remote sensing techniques, Agricultural Water Management, 97 (8): 1210- 1220.
  38. Steduto P, Hsiao TC and Fereres E, 2007. On the conservative behavior of biomass water productivity. Irrig. Sci. 25, 189-207.
  39. Steduto P, Hsiao TC, Raes D and Fereres E, 2009. AquaCropThe FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101, 426-437.
  40. Utset A, Velicia H, Delrio B, Morillo R, Centenio JA and Martinez JC, 2007. Calibrating and validating an agrohidlogical model to simulate sugar beet water use under Mediterranean conditions. Agricultural Water Management, 94(3): 11-21.
  41. Vazifedoust M, van Dam JC, Feddes RA and Feizi M, 2008. Increasing water productivity of irrigated crops under limited water supply at field scale. Agricultural Water Management, 95(2): 89-102.
Journal of Water Research in Agriculture
Volume 35, Issue 1
June 2021
Pages 1-17

Files

Share

How to cite

Statistics