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ارزیابی مدل CERES-Maize برای شبیه‌سازی گیاه ذرت تحت سناریوهای مختلف مدیریت آبیاری و کود نیتروژن | ||
| تحقیقات آب و خاک ایران | ||
| دوره 53، شماره 10، دی 1401، صفحه 2295-2310 اصل مقاله (1.32 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2022.344865.669300 | ||
| نویسندگان | ||
| کریم نیسی1؛ اصلان اگدرنژاد* 2؛ فریبرز عباسی*3 | ||
| 1دانشجوی کارشناسی ارشد آبیاری و زهکشی، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 2استادیار، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 3استاد پژوهش، مؤسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران | ||
| چکیده | ||
| مدل‌سازی گیاهی روشی ارزان، سریع و توانمند برای دستیابی به نتایج اثر عوامل مختلف بر رشد گیاهان زراعی است. از این رو، مدل‌های گیاهی مانند مدل CERES-Maize برای شبیه‌سازی عملکرد گیاهان بسط داده شده است. با توجه به اینکه مقدار آب آبیاری و کود نیتروژن دو عامل بسیار مهم برای بهبود عملکرد ذرت هستند؛ اطلاع از دقت و خطای مدل CERES-Maize برای شبیه‌سازی عملکرد این گیاه زراعی تحت تیمارهای اشاره شده اهمیت دارد. از این رو، تحقیق حاضر در مزرعه 500 هکتاری موسسه تحقیقات اصلاح و تهیه نهال و بذر در طول جغرافیایی 58/50 درجه شرقی و عرض جغرافیایی 56/35 انجام شد. در این پژوهش دو رقم ذرت دبل کراس 370 و سینگل کراس 260 مورد مطالعه قرار گرفتند. در رقم دبل کراس 370، دو عامل مقدار آب آبیاری در چهار سطح (W1: 120، W2: 100، WI3: 80 و W4: 60 درصد نیاز آبی) و کود نیتروژن در چهار مقدار (N1: 100، N2: 80، N3:60 و N4: صفر درصد نیاز نیتروژن خالص) و در رقم سینگل کراس 260 عامل سطوح کودی در چهار سطح (N1: 100، N2: 80، N3:60 و N4: 50 درصد نیاز نیتروژن خالص) تحت مطالعه قرار گرفتند. نتایج برای هر دو رقم نشان داد که مدل CERES-Maize دچار خطای کم‌برآوردی (0 ≥ MBE) شد. خطای این مدل برای شبیه‌سازی عملکرد رقم دبل کراس 370 برابر با 24/1 تن در هکتار و برای رقم سینگل کراس 260 برابر با 44/0 تن در هکتار بود. دقت مدل CERES-Maize برای شبیه‌سازی این دو رقم به ترتیب در دسته‌ی خوب (13/0 = NRMSE) و عالی (06/0 = NRMSE) قرار داشت. خطای مدل CERES-Maize برای تیمارهای آبی در رقم دبل کراس 370 بین 65/1-89/0 تن در هکتار و برای تیمارهای کودی در این رقم بین 9/1-43/0 تن در هکتار بود. در حالی که تفاوتی بین خطای مدل در دو حالت تقسیط کود مشاهده نشد. بنابراین، مدل نسبت به تقسیط کود حساسیتی نداشت در حالی که تغییرات آب آبیاری و مقدار کود بر دقت آن اثر زیادی داشت. براساس کلیه نتایج، مدل CERES-Maize برای شبیه‌سازی هر دو رقم ذرت پیشنهاد می‌شود گرچه دقت آن در رقم سینگل کراس 260 بیشتر بود. | ||
| کلیدواژه‌ها | ||
| تنش کودی؛ تقسیط کود؛ مدل‌سازی گیاهی؛ مدل CERES-Maize | ||
| عنوان مقاله [English] | ||
| Evaluation of ceres-maize model for simulation of maize under different scenarios of irrigation and nitrogen fertilizer management | ||
| نویسندگان [English] | ||
| Karim Neysi1؛ Aslan Egdernezhad2؛ Fariborz Abbasi3 | ||
| 1M.Sc. Student of Irrigation and drainage, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 2Assistant Professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 3Professor of Irrigation and Drainage Engineering, Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. | ||
| چکیده [English] | ||
| Crop modeling is a cheap, fast and powerful method to achieve the results of the effect of various factors on crop growth. Hence, crop models such as CERES-Maize have been developed to simulate plant performance. Given that the amounts of irrigation water and nitrogen fertilizer are two very important factors to improve corn yield; it is important to know the accuracy and error of the CERES-Maize model to simulate the yield of this crop under the mentioned treatments. Therefore, the present study was conducted at a 500-hectare farm of the Seed and Plant Breeding Research Institute located at 50.58° East longitude and 35.56° latitude on two corn cultivars (double cross 370 and single cross 260). For double cross 370, two factors including the amount of irrigation water at four levels (W1: 120, W2: 100, WI3: 80 and W4: 60 percent of water requirement) and nitrogen fertilizer at four levels (N1: 100, N2: 80, N3: 60 and N4: zero percent of nitrogen requirement) were considered. For single cross 260, four fertilizer levels (N1: 100, N2: 80 and N3: 60 and N4: 50 percent of nitrogen requirement) were studied. The results for both cultivars showed that the CERES-Maize model underestimated crop yield (0 ≥ MBE). The amount of error for simulating yield of double cross cultivar 370 and single cross cultivar 260 was 1.24 and 0.44 tons per hectare, respectively. The accuracy of CERES-Maize model for simulating these two cultivars was in the category of good (NRMSE = 0.13) and excellent (NRMSE = 0.06), respectively. The error of CERES-Maize model for double cross cultivar 370 and for irrigation treatments was in the range of 0.89-1.65 t/ha and for fertilizer treatments was in the range of 0.43-9.9 t/ha. No difference was observed between the model errors in two fertilizer applications. Therefore, the model was not sensitive to fertilizer apportionment, while changes in irrigation water and fertilizer amount had a great effect on its accuracy. Based on all the results, the CERES-Maize model is recommended for simulation of both corn cultivars, although its accuracy was higher for the single cross 260 cultivar. | ||
| کلیدواژه‌ها [English] | ||
| Fertilizer Stress, Fertilizer Splitting, Crop Modeling, CERES-Maize | ||
| مراجع | ||
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Abbasi F, Chogan R., Gheibi, M (2015). Investigating the possibility of reducing nitrogen losses in corn under furrow fertigation. Final Report of the Research Project. (in Persian). Abbasi F, Chogan, R., Alizadeh H. A, Moeini R, Noori R, Aghaei A, Shafiei P (2011). Study on surface fertigation effect on maize water use efficiency, yield and yield criteria in Karaj. Final Report of the Research Project. (in Persian). Ahmadee M., Ghanbarpouri M, Egdernezhad A. (2021). Applied Irrigation Water of Wheat using Sensitivity Analysis and Evaluation of Aqua Crop. Water Management in Agriculture. 8(1): 15-30. (in Persian). Alishiri R, Paknejad F, Aghayari F. (2014). Simulation of sugar beet growth under different water regimes and nitrogen levels by AquaCrop. Bioscience. 4(4): 1-9. Asadi M E, Clemente R S (2003). Evaluation of CERES-Maize of DSSAT model to simulate nitrate leaching, yield and soil moisture content under tropical conditions. Journal of Food, Agriculture and Environment, l (3&4): 270-276. (in Persian). Barker A V, Pilbeam D. V. (2007). Handbook of Plant Nutrition, 1st Ed. CRC Press, Boca Raton. Bert F E, Laciana C E, Podestá G. P, Satorre E H, Menéndez A. N. (2007). Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation. Agricultural Systems. 94, 141-150. Boogaard H. L., Van Diepen C. A., Rotter R. P., Cabrera J. M. C. A. Van Laar H H (1998). WOFOST 7.1; user's guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center 1.5 (No. 52). SC-DLO. Cavero J, Playan E, Zapata N, Faci J. M. (2001). Simulation of maize yield variability within a surface irrigated field. Agronomy Journal, 93: 773-782. Dokoohaki, H., Gheysari, M., Mousavi, S. F., Zand-Parsa, Sh., Miguez, F. E., Archontoulis, S. V., H00genboom, G. (2016). Coupling and testing a new soil water module in DSSAT CERES-Maize model for maize production under semi-arid condition. Agricultural Water Management. 163: 90-99. Ebrahimipak N, Ahmadee M, Egdernezhad A, Khashei Siuki A. (2018). Evaluation of AquaCrop to simulate saffron (crocus sativus L.) yield under different water management scenarios and zeolite amount, Journal of Water and Soil Resources Conservation, 8(1): 117-132. (in Persian). Egdernezhad A, Ebrahimipak N, Tafteh A, Ahmadee M. (2019). Canola Irrigation Scheduling using AquaCrop Model in Qazvin Plain, Water Management in Agriculture, 5(2): 53-64. (in Persian). Esmaeilian Y. (2014). Simulation the response of maize varieties to irrigation and nitrogen management under different climatic conditions. PhD Thesis. University of Zabol. (in Persian). FAO (2014). Statistical Database of the Food and Agriculture Organization of the United Nations. FAO, Rome. Fu Ch, Wang J, Gong Sh, Zhang Y, Wang Ch, Mo Y (2020). Optimization of irrigation and fertilization of drip-irrigated corn in the chernozem area of north-east China based on the CERES-Maize model. Irrigation and Drainage. 1-18. Geerts S, Raes D, Garcia M, Miranda R, Cusicanqui JA. (2009). Simulating yield response to water of quinoa (Chenopodium quinoaWilld.) with FAO-AquaCrop. Agronomy. 101: 499-508. Gerik T J., Rosenthal W D and Duncan R R.(1988). Simulating grain yield and plant development of ratoon grain sorghum over diverse environments. Field Crop Research.19(1): 63–74. Ghannoum O, Evans J. R, Chow W. S, Andrews T. J, Conroy J. P, von Caemmerer S. (2005). Faster rubisco is the key to superior nitrogen-use efficiency in NADP-malic enzyme relative to NAD-malic enzyme C 4 grasses. Plant Physiology. 137: 638-650. Gungula D, Kling J, Togun A. (2003). CERES-Maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agronomy Journal. 95, 892-899. Hoogenboom G, C.H. Porter K.J., Boote V, Shelia P.W, Wilkens U, Singh J.W, White S, Asseng J.I. Lizaso, L.P. Moreno, W. Pavan, R. Ogoshi, L.A. Hunt, G.Y. Tsuji, J.W. Jones. (2019). The DSSAT crop modeling ecosystem. In: p.173-216 [K.J. Boote, editor] Advances in Crop Modeling for a Sustainable Agriculture. Burleigh Dodds Science Publishing, Cambridge, United Kingdom (http://dx.doi.org/10.19103/AS.2019.0061.10). Hoogenboom, G., C.H. Porter, V. Shelia, K.J. Boote, U. Singh, J.W. White, W. Pavan, F.A.A. Oliveira, L.P. Moreno-Cadena, J.I. Lizaso, S. Asseng, D.N.L. Pequeno, B.A. Kimball, P.D. Alderman, K.R. Thorp, M.R. Jones, S.V. Cuadra, M.S. Vianna, F.J. Villalobos, T.B. Ferreira, W.D. Batchelor, J. Koo, L.A. Hunt, J.W. Jones. (2021). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.8 (www.DSSAT.net). DSSAT Foundation. Gainesville, Florida, USA. Hopkins W. G. (2004). Introduction to Plant Physiology. 3rd Ed. John Wiely and Sons, New York. Jones J, Keating B, Porter C. (2001). Approaches to modular model development. Agricultural Systems70, 421-443. Liu W, Botner D, Sakamoto C. (1989). Application of CERES-Maize model to yield prediction of a Brazilian maize hybrid. Agricultural and Forest Meteorology. 45, 299-312. López-Cedrón F.X, Boote K.J, Pineiro J, Sau F. (2008). Improving the CERES-Maize model ability to simulate water deficit impact on maize production and yield components. Agronomy Journal. 100: 296-307. Miao Y, Mulla D J, Batchelor W. D, Paz J. O, Robert P. C. Wiebers M. (2006). Evaluating management zone optimal nitrogen rates with a crop growth model. Agronomy Journal, 98: 545-553. Mohamadzade, F., Gheysari, M., Eshgizadeh, H., Tabatabaei, M. S., Hoogenboom, G. (2022). The effect of water and nitrogen on drip tape irrigated silage maize grown under arid conditions: Experimental and simulations. Agricultural Water Management. 271. 107821. Mortvedt J J, Westfall D. G. Shanahan J. F. (2001). Fertilizing spring-seeded small grains. http:// www. colostate. Edu/Depts/Coop Ext. Mubeen M., Ahmad A., Wajid A, Khaliq T, Bakhsh A. (2013). Evaluating CSM-CERES-Maize model for irrigation scheduling in semi-arid conditions of Punjab. Pakistan. International Joural of Agricultural and Biology. 15: 1-10. Namihira T, Shinzato N., Akamine H, Nakamura I, Maekawa H, Kawamoto Y. Matsui T. (2011). The effect of nitrogen fertilization to the sward on guineagrass (Panicum maximum Jacq cv. Gatton) silage fermentation. Asian-Aust. J. Anim. Sci. 24: 358-363. Nouna B., Katerji N. Mastrorilli M. (2000). Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance. Eur. J. Agron. 13: 309-322. Osmond D. L, Riha S. J. (1990). Nitrogen fertilizer requirements for maize produced in the tropics: A comparison of three computer-based recommendation systems. Agr. Syst. 50:37-50. Paknejad, F., Moayeri Por, Sh., Aghayari, F., Nabi Ilkaei, M. (2017). Simulation of maize yield with different levels of nitrogen by using dssat model. Journal of Crop Ecophysiology. 11(3): 503-518. Rabie, M., Ghesari, M., Mirlatifi, S. M. (2013). Evaluation of DSSAT model for nitrate leaching under different water and nitrogen rates in maize field. J. Sci. & Technol. Agric. & Natur. Resour., Water and Soil Sci. 17(63): 71-80. (in Persian). Rahmani M. (2018). Simulating the effect of planting date and population of B73 female parent inbredline on seed production of hybrid maize KSC704 in Karaj by DSSAT- CSM-CERES-Maize model. PhD Thesis. Guilan University. (in Persian). Saseendran S. A, Ma L, Nielsen D, Vigil M, Ahuja L. (2005). Simulating planting date effects on corn production using RZWQM and CERES-Maize models. Agronomy Journal. 97, 58-71 Sinclair T R, Seligman N. a. G. (1996). Crop modeling: from infancy to maturity. Agronomy Journal88, 698-704. Teh C. B. (2006). Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. BrownWalker Press Boca Raton, Florida USA. 526 pp.. Van Dam JC, Huygen J, Wesseling JG, Feddes RA, Kabat P, Van Walsum PEV, Groenendijk P, Van Diepen CA. (1997). Theory of SWAP Version 2.0, Report #71. Department Water Resources. Wageningen Agricultural University. 167 pp. Xu Z. Z, Yu Z. W, Wang D. Zhang Y. L. (2005). Nitrogen accumulation and translocation for winter wheat under different irrigation regimes. J. Agron. Crop Sci. 191: 439-449. | ||
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