برنامه‌ریزی آبیاری به منظور ارتقاء بهره‌وری مصرف آب در زراعت گندم با استفاده از مدل AquaCrop

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه خاکشناسی، واحد علوم و تحقیقات خوزستان، دانشگاه آزاد اسلامی، اهواز، ایران؛ دانشجوی دکتری گروه خاکشناسی، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.

2 عضو هیات علمی گروه خاکشناسی، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی، تهران، ایران.

3 عضو هیات علمی گروه خاکشناسی، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

4 بخش تحقیقات اصلاح و تهیه نهال و بذر، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز، ایران .

5 عضو هیات علمی گروه خاکشناسی، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.

چکیده

برنامه‌ریزی آبیاری به‌عنوان یکی از روش‌های مدیریت آب در مزرعه نقش کلیدی در ارتقای بهره‌وری مصرف آب در مناطق خشک و نیمه‌خشک ایفا می‌نماید. آبیاری مازاد و فاصله طولانی بین آبیاری‌ها از نقاط ضعف در برنامه‌ریزی آبیاری رایج در مزارع گندم خوزستان به‌شمار می‌آیند. لذا این تحقیق با هدف برنامه‌ریزی آبیاری به‌منظور افزایش عملکرد گندم و بهره‌وری مصرف آب در دو منطقه از جنوب استان اجرا گردید. در این تحقیق به کمک مدل آکواکراپ، 10 سناریوی برنامه‌ریزی آبیاری از پنج نوبت آبیاری به میزان 400 میلی‌متر تا هفت نوبت به میزان 650 میلی‌متر، برای یک دوره 12 ساله شبیه‌سازی و اثرات هر سناریو بر عملکرد دانه و بهره‌وری آب با یکدیگر مقایسه گردیدند. نتایج نشان داد که بیشترین میانگین عملکرد (4900 کیلوگرم در هکتار) در سال‌های شبیه‌سازی به سناریوهای نه و 10 (هفت نوبت آبیاری با 600 تا 650 میلی‌متر آب مصرفی) تعلق داشته و در مقابل کمترین مقدار (4200 کیلوگرم در هکتار) به سناریوی پنج (با شش نوبت آبیاری و 500 میلی‌متر آب) اختصاص داشت. بالاترین نتایج بهره‌وری مصرف آب نیز متعلق به سناریوهای نه و 10 (16/1 کیلوگرم بر مترمکعب) و پایین‌ترین نتایج (04/1 کیلوگرم بر مترمکعب) در سناریوی پنج حاصل گردید. همچنین شاخص‌های ارزیابی ضریب پیرسون، خطای میانگین مربعات ریشه (نرمال شده) و شاخص سازگاری ویلموت به‌ترتیب برای مقدار آب خاک 82/0، 5 و 90/0 درصد، پوشش سایه‌انداز 98/0، 13 و 99/0، و بیوماس 99/0، 4/11 و 99/0 بودند.  لذا، نتایج ارزیابی نشان از توانایی قابل قبول مدل در شبیه‌سازی متغیرهای اندازه‌گیری‌شده دارد.            

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Irrigation Scheduling to Increase Water Productivity Using AquaCrop Model

نویسندگان [English]

  • Mohiaddin Goosheh 1
  • Ebrahim Pazira 2
  • Ali Gholami 3
  • Bahram Andarzian 4
  • Ebrahim Panahpour 5
1 Department of Soil Science, Khuzestan Science and Research Branch, Islamic Azad University, Ahvaz, Iran; Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
2 Department of Soil Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran .
3 Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
4 Seed and Plant Improvement Department, Research and Education Center of Agriculture and Natural Resources of Khuzestan, Agricultural Research, Education and Extension Organization, Ahvaz, Iran.
5 Department of Soil Science, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
چکیده [English]

Introduction
Khuzestan plain as one of the fertile regions in Iran is suffering from some problems such as soil salinity and water deficit. The most important irrigated crop in Khuzestan is wheat and its average yield in the southern parts of Khuzestan reaches 2 to 3 t ha-1,. Irrigation management and optimal conditions, however, should be provided to reduce both water and salinity stresses in the crop yield in the region. To introduce the best irrigation schedule for wheat in the study area, we applied the AquaCrop model to simulate the irrigation scheduling for the crop. The aims were to (1) calibrate and validate the model, (2) determine the appropriate irrigation scheduling for wheat to improve water productivity and increase grain yield, and (3) also evaluate the performance of the model.    
 
Methodology
     To achieve the aims of the research, the Elhai region was selected in almost the provincial center with the coordinates of 31° 38' N and 48° 37' E. The AquaCrop model was, then, used for simulating grain yield and water productivity. This model required daily climate data, phonological and agronomic data, soil characteristics, irrigation water, and groundwater data to be able to simulate the plant and soil parameters. A field experiment was conducted in the Elhai area for collecting the data as was mentioned above (as model inputs) during the wheat growth season (2014-2015). Two farms with different soil characteristics were selected for this purpose. However, in order to calibrate and validate the model, more data was needed. Therefore, two other field experiments were carried out in the site of Veys. Consequently, one farm was used for calibration and three farms were, in turn, used for the validation of the model. Sampling from soil profile (1.2 m) was carried out in the growing season. Water and ground water samples were, then, taken in each irrigation event. In order to be able to assess the irrigation scheduling scenarios accurately, it was necessary to consider a wide range of events, times and amounts of water in simulating scenarios. In this case, ten scenarios were run for simulating the grain yield and water productivity for a 12-year period (2003-2014) in farm 1 of Elhai (Table 1).

کلیدواژه‌ها [English]

  • Arid and semi-arid regions
  • Irrigation
  • Simulation models
  • Water productivity
  • Wheat
1- Abi Saab, M.T., Todorovic, M., and Albrizio, R., 2014. 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, 147, pp. 21-33.
 
2- Andarzian, ‌., Bannayan, M., Steduto, P., Mazraeh, H., Barati, M.E., Barati, M.A. and Rahnama, A., 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management100(1), pp.1-8.
 
3- Araya, A., Kisekka, I., and Holman, J., 2016. Evaluating deficit irrigation management strategies for grain sorghum using AquaCrop. Irrigation Science, 34, pp. 465-481.
 
4- Benabdelouahab, T., Balaghi, R., Hadria, R., Lionboui, H., Djaby, B., and Tychon, B., 2016. Testing AquaCrop to simulation a semi-arid irrigated perimeter in Morocco. Irrigation and Drainage, 65, pp. 631-643.
 
5- Bitri, M., Grazhdani, S., and Ahmeti, A., 2014. Validation of the AquaCrop model for full and deficit irrigated potato production in environmental condition of Korca zone, south-eastern Albania. International Journal of Innovative Research in Science, Engineering and Technology, 3(5), pp. 12013-12020.
 
6- Dominguez, A., Tarjuelo, J.M., de Juan, J.A., Lopez-Mata, E., Breidy, J., and Karam, F., 2011. Deficit irrigation under water stress and salinity conditions: The MOPECO-Salt model. Agricultural Water Management, 98, pp. 1451-1461.
 
7- Doorenbos, J., and kassam, A.H., 1979. Yield response to water. Irrigation and Drainage, FAO paper no. 33,Rome, Italy.
 
8- El-Mesiry, T., Abdallh, E.F., Gaballah, M.S., and Ouda, S.A., 2007. Using yield-stress model in irrigation management for wheat grown under saline conditions. Australian Journal of Basic and Applied Science, 1(4), pp. 600-609.
 
9- Ferjani, N., Daghari, H., and Hammami, M., 2013. Assessment of actual irrigation management in Kalaat El Andalous District (Tunisia): Impact on soil salinity and water table level. Journal of Agricultural Science, 5, pp. 46-56.
 
10- Fernandez-Cirelli, A., Arumi, J.L., Rivera, D., and Boochs, P.W., 2009. Environmental effects of irrigation in arid and semi-arid regions. Chilean Journal of Agricultural Research, 69 (Suppl.1), pp. 27-40.
 
11- Gebreselassie, Y., Ayana, M., and Tadele, K., 2015. Field experimentation based simulation of yield response of maize crop to deficit irrigation using AquaCrop model, Arba Minch, Ethiopia. African Journal of Agricultural Research, 10(4), pp. 269-280.
 
12- Jefferies, M., and Been, K., 2016. Soil variability and characteristic states, In: Soil Liquefaction: A Critical State Approach. CRC Press, Second Edition, pp. 203-224.
 
13- Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J., and Ritchie, J.T., 2003. The DSSAT cropping system model. European Journal of Agronomy, 18, pp.  235-265.
 
14- Kama, A.A.L., and Tomini, A., 2013. Water conservation versus soil salinity control. Environmental Model Assessment, 18, pp. 647-660.
 
15- Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., Mclean, G., Verburg, K., Snow, V., Dimes, M., Silburn, E.,  Wang, S., Brown, K.L., Bristow, S., Asseng, S., Chapman, R.L., McCown, J.P., Freebairn, D.M., and Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, pp. 267-288.
 
16- Liu, T., Liu, L., Luo, Y., and Lai, J., 2015. Simulation of groundwater evaporation and groundwater depth using SWAT in the irrigation district with shallow water table. Environmental Earth Science, 74, pp. 315-324.
 
17- Liu, J., and Pattey, E., 2010. Green Crop Tracker v.1.0. Agriculture and Agri-Food CanadaGreenCropTracker@agr.gc.ca
 
18- Mohammadi, M., Ghahramani, 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, pp. 112-128.
 
19- Mondal, M.S., Saleh, A.F.M., Akanda, A.R., Biswas, S.K., Moslehuddin, A.Z., Zaman, S., Lazar, A.N., and Clarke, D., 2015. Simulating yield response of rice to salinity stress with the AquaCrop model. Environmental Sciences: Processes Impacts, 17, pp. 1118-1126.
 
20- Nain, A.S., and Kersebaum, K.C., 2007. Calibration and validation of CERES model for simulating water and nutrients in Germany, in: Kersebaum K.C., et al. (eds), Modeling water and nutrient dynamics in soil-crop systems, pp. 161-181.
 
21- Qadir, M., Qureshi, A.S., and Cheraghi, S.A.M., 2007. Extent and characterization of salt-affected soils in Iran and strategies for their amelioration and management. Land Degradation & Development, 19, pp. 214-227.
 
22- Qureshi, A.S., Ahmad, W., and Ahmad, A.A., 2013. Optimum groundwater table and irrigation schedules for controlling soil salinity in Central Iraq. Irrigation and Drainage, 62, pp. 414-424.
 
23- Raes, D., Steduto, P., Hsiao, T.C., and Fereres, E., 2015. AquaCrop new features and updates version 5.0. FAO land and water division, Rome, Italy.
 
24- Raes, D., 2012. The ET0 Calculator v.3.2. FAO, http://www.fao.org/nr/water/ ET0.html
 
25- Raes, D., Willems, P., and Gbaguidi, F., 2006. RAINBOW a software package for analyzing climatological/ hydrological data frequency analysis- test of homogeneity ver.2.2. K.U. Leuven University, Leuven, Belgium.
 
26- Rajabi, R., Poostini, K., Gahanipoor P., Ahmadi, A., 2000. Effects of salinity on yield decreasing and some physiological properties of 30 wheat cultivar. Agricultural Sciences Journal, 11 (2), pp. 153-163 (in Persian).
 
27- Seeboonruang, U., 2013. Relationship between groundwater properties and soil salinity at the Lower Nam Kam River Basin in Thailand. Environmental Earth Science69, pp. 1803-1812.
 
28- Smedema, L.K., 2007. Revisiting currently applied pipe drain depths for waterlogging and salinity control of irrigated land in the (semi) arid zone. Irrigation and Drainage, 56, pp. 379-387.
 
29- Tavakoli, A.R., Moghadam, M.M., and Sepaskhah, A.R., 2015. Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran. Agricultural Water Management, 161, pp. 136-146.
 
30- Trombetta, A., Iacobellis, V., Tarantino, E., and Gentile, F., 2016. Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, 164, pp. 304-316.
 
31- Wang, X., Yang, J., Liu, G., Yao, R., and Yu, S., 2015. Impact of irrigation volume and water salinity on winter wheat productivity and soil salinity distribution. Agricultural Water Management, 149, pp. 44-54.
 
32- Zeleke, K.T., Luckett, D., and Cowley, R., 2011. Calibration and testing of the FAO AquaCrop model for canola. Agronomy Journal, 103, pp. 1610-1618.
 
33- Zhang, H., and Oweis, T., 1999. Water-yield relations and optimal irrigation scheduling of wheat in the Mediterranean region. Agricultural Water Management, 38, pp. 195-211.