شبیه‌سازی اثر کود نیتروژن بر تولید ذرت (Zea maize) توسط مدل CERES-Maize تحت شرایط اقلیمی کرمانشاه

نوع مقاله : مقالات پژوهشی

نویسنده

دانشگاه رازی

چکیده

به منظور واسنجی و ارزیابی مدل CERES-Maize در شبیه‌سازی تولید ذرت تحت شرایط کاربرد مقادیر مختلف کود نیتروژن آزمایشی به صورت کرت‏های خرد شده در قالب طرح پایه بلوک‌های کامل تصادفی با 4 تکرار در کرمانشاه در سال 1393 اجرا شد. تیمارها شامل کود نیتروژن (صفر، 138، 238، 350 و 483 کیلوگرم اوره در هکتار) به‌عنوان کرت‏های اصلی و ارقام ذرت 704SC-، 678BC- و سیمون به‌عنوان کرت‏های فرعی بودند. ضرایب ژنتیکی ارقام توسط بخش محاسبه ضرایب ژنتیکی برای تیمار 350 کیلوگرم اوره در هکتار محاسبه شد. نتایج واسنجی نشان داد، مدل قادر است با حداقل اختلاف، ویژگی‌های رشد و نمو را برای ارقام ذرت شبیه‌سازی کند که بیانگر دقت بالای ضرایب ژنتیکی محاسبه شده بود. نتایج ارزیابی‌های مدل نشان داد که میزان nRMSE وزن خشک کل در ارقام 704SC-، 678BC- و سیمون به ترتیب، 2/6، 2/8 و 8/5 درصد میانگین مشاهده‌ها بود. میزان nRMSE عملکرد دانه نیز برای ارقام 704SC-، 678BC- و سیمون به ترتیب، 3/4، 4/11 و 1/8 درصد میانگین مشاهده‌ها بود. هم در شرایط شبیه-سازی و هم در شرایط مزرعه با افزایش میزان کود نیتروژن از صفر به 138، 238، 350 و 483 کیلوگرم اوره در هکتار شاخص سطح برگ، عملکرد وزن خشک کل و عملکرد دانه ارقام ذرت افزایش یافت. رقم سیمون در مقایسه با سایر ارقام از عملکرد دانه بیشتری برخوردار بود. بطورکلی نتایج نشان داد که مدل CERES-Maize قادر بود واکنش ارقام ذرت نسبت به تغییرات نیتروژن را با دقت بالایی پیش‌بینی کند.

کلیدواژه‌ها


عنوان مقاله [English]

Simulation of Nitrogen Fertilizer Effect on Maize (Zea maize) Production by CERES-Maize Model under Kermanshah Climate Condition

نویسنده [English]

  • Farzad Mondani
Razi University
چکیده [English]

Introduction: The DSSAT consists of a series of popular and widely used process-based crop growth simulation models. The models have been used worldwide to simulate crop biomass and yield, and soil nitrogen leaching under different management practices and various climatic conditions. The DSSAT has also proven to be a useful tool for selecting improved agricultural practices. Among all management agronomic factors, nitrogen fertilizer and crop species are major effective aspects impacting crops production. Although limited use of nitrogen fertilizer seems likely to reduce crop yield, high application rates of nitrogen causes serious adverse environmental effects. Thus, management of nitrogen fertilizer consumption decreases production costs and environmental pollution in agroecosystems. Therefore, the objectives of the present study were: (1) to determine the genetic coefficients and calibrate the CERES-Maize model (2) to evaluate the performances of the CERES-Maize model in simulating maize cultivars growth, development and grain yield for different fertilizer nitrogen application rates under Kermanshah climate condition.
Materials and Methods: This experiment was carried out in a split-plot design with 5 levels of nitrogen fertilizer application (0, 138, 238, 350 and 483 kg ha-1 urea) as main plots, 3 current maize cultivars SC-704, BC-678 and Simon as sub plots, and 4 replications in 2014. The required model inputs describe field management, daily weather condition, soil profile characteristics, and cultivar characteristics. The cultivar coefficients were obtained under optimum conditions (i.e., minimum stress in weather and nutrients). The genetic coefficients (P1, P2, P5, G2, G3 and PHINT) of the maize cultivars i.e. SC-704, BC-78 and Simon were determined using the GenCal software of DSSAT v 4.6 for 350 kg Urea ha-1 treatment (optimum nitrogen fertilizer amount based on the results of soil library). Model performance was evaluated by comparing simulated and measured values of maize cultivars phonological development stages (DVS), leaf area index, total dry weight and grain yield for independent nitrogen fertilizer treatment (0, 138, 238 and 483 kg Urea ha-1) by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d).
Results and Discussion: The coefficients of P1, P2, P5, G2, G3 and PHINT ranged between 275 to 286 °C day, 0.576 to 1.80 days hr-1, 910 to 950 °C day, 940 to 990 number per plant, 7.0 to 7.9 mg day-1 and 51.70 to 51.97 °C day , respectively, for all cultivars. The CERES-Maize model was able to accurately simulate growth, development stages and yield for maize cultivars. For both simulated and measured conditions, leaf area index, total dry weight and grain yield were improved by increasing the application of nitrogen fertilizer. Simon cultivar had higher simulated (9925 kg.ha-1) and measured (10467 kg.ha-1) grain yield in respect to other cultivars. The validation results also indicated that the CERES-Maize model gave a reliable estimate of growth, development stages and grain yield for maize cultivars in the different fertilizer nitrogen application rates. The value of RMSE and nRMSE for leaf area index of SC-704, BC-78 and Simon cultivars were 0.56, 0.46 and 0.36 and 25.5%, 21.8% and 16.3%, respectively. The index of agreement (d) for leaf area index ranged from 0.94 to 0.98. The RMSE and nRMSE magnitudes for total dry weight of SC-704, BC-78 and Simon cultivars were 440.1, 569.6 and 419.7 and 6.2%, 8.2% and 5.8%, respectively. The index of agreement (d) for total dry weight ranged from 0.94 to 0.95. The RMSE and nRMSE values for SC-704, BC-78 and Simon grain yield were equal to 163.7, 345.2 and 314.4 and 4.3%, 11.4% and 8.1%, respectively. The index of agreement (d) for grain yield ranged from 0.93 to 0.98.
Conclusion: The results indicated that the CERES-Maize was parameterized reliably for three maize cultivars under Kermanshah climate conditions. The results of validation also showed that the CERES-Maize model was able to give an accurate simulation of all studied traits of maize cultivars except leaf area index in different fertilizer nitrogen application rates.

کلیدواژه‌ها [English]

  • Development stages
  • grain yield
  • Model calibration
  • Model validation
  • Total dry weight
1- Abdian A., Rahimzade Khoei F., Anvari Savojbolagh K., and Rahimzada S. 2010. Evaluation of yield and yield components of early maturing maize in the second cropping. 11th Agronomy and Crop Breeding Congress. Tehran's Shahid Beheshti University, Iran. (in Persian with English abstract).
2- Amiri E., Rezaei M., Bannayan M., and Soufizadeh S. 2013. Calibration and Evaluation of CERES Rice Model under Different Nitrogen- and Water-Management Options in Semi-Mediterranean Climate Condition. Communications in Soil Science and Plant Analysis, 44: 1814-1830.
3- Bannayan M., Crout N.M.J., and Hoogenboom G. 2003. Application of the CERES-Wheat model for within-season prediction of winter wheat yield in theUnited Kingdom. Agronomy Journal, 95:114-125.
4- Cassman K.G., Dobermann A., Walters D.T., and Yang H. 2003. Meeting cereal demand while protecting natural resources and improving environmental quality. Annual Review of Environment and Resources, 28: 315-358.
5- Chisanga C.B., Phiri E., Shepande C., and Sichingabula H. 2015. Evaluating CERES-Maize Model Using Planting Dates and Nitrogen Fertilizer in Zambia. Journal of Agricultural Science, 7: 79-97.
6- Dehghanpoor Z. 2014. Technical instructions planting and harvesting of corn (grain and forage). Agricultural Training Press.
7- FaridH.U., Bakhsh A., Mahmood-Khan Z., Ahmad N., and Ahmad A. 2015. Calibration and Validation of CERES-Wheat (Triticum Aestivum) Model for Simulating Fertilizer Application Rates in Management Zones. Journal of Agricultural Science, 7: 115-127.
8- Gulser F. 2005. Effect of ammonium sulphate and urea on NO3 and NO2 accumulation nutrient contents and yield criteria in spinach. Scientia Horticulture, 106: 330-340.
9- Hoogenboom G., Jones J.W.P., Wilkens W., Porter C.H., Boote K.J., Hunt L.A., Singh U., Lizaso J.I., White J.W., Uryasev O., Ogoshi R., Koo J., Shelia V., and Tsuji G.Y. 2015. Decision Support System for Agrotechnology Transfer (DSSAT) Version4.6 (http://dssat.net). DSSAT Foundation, Prosser, Washington.
10- Jones C.A., and Kiniry J.R. 1986. CERES-Maize: A Simulation Model of Maize Growth and Development Texas A&M University Press, College Station, TX, USA.
11- 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. DSSAT Cropping System Model. European Journal of Agronomy, 18: 235‐265.
12- Liu S., Yang J.Y., Zhang X.Y., Drury C.F., Reynolds W.D., and Hoogenboom G. 2013. Modelling crop yield, soilwater content and soil temperature for a soybean–maize rotation under conventional and conservation tillage systems in Northeast China.Agricultural Water Management, 123: 32-44.
13- Liu H.L., Yang J.Y., Drury C.F., Reynolds W.D., Tan C.S., and Bai Y.L., et al., 2011. Usingthe DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling infields under long-term continuous maize production. Nutrient Cycling in Agroecosystems, 89: 313-328.
14- Lopez-Cedron F.X., Boote K.J., Piñeiro J., and Sau F. 2008. Improving the CERES-Maize modelability to simulate water deficit impact on maizeproduction and yield components. Agronomy Journal, 100: 296-307.
15- Mahru A.H., Soltani A., Galeshi S., and Kalate-Arabi M. 2010. Estimates of genetic coefficients and evaluation of model DSSAT for Golestan province.Elecronic Journal of Crop Production, 3: 229-253. (in Persian with English abstract).
16- Nouna B.B., Katerji N., and Mastrorilli M. 2003. Using the CERES-Maize model in a semi-arid Mediterranean environment: New modeling of leaf area and water stress functions. European Journal of Agronomy, 19: 115-123.
17- Nouna B.B., Katerji N., and Mastrorilli M. 2000. Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance. European Journal of Agronomy, 13: 309-322.
18- Rabie M., Mirlatifi S.M., and Gheysari M. 2012. Calibration and Evaluation of the CSM-CERES-MAIZE Model for Maize Hybrid 704 Single-Cross in Varamin. Journal of Water and Soil, 26: 290-299. (in Persian with English abstract).
19- Ritchie J.T., Singh U., Godwin D.C., and Bowen W.T. 1998. Cereal growth, development, and yield.p. 79-98. InTsujiG.Y. et al. (ed.). Understanding Options for Agricultural Production. Kluwer Academic Publishing, Dordrecht, The Netherlands.
20- Soltani A., Robertson M.J., Mohammad-Nejad Y., and Rahemi-Karizaki A.2006. Modeling chickpea growth and development: leaf production andsenescence. Field Crop Research, 99: 14-23.
21- Soltani A., and Hoogenboom G. 2007. Assessing crop management options with cropsimulation models based on generated weather data. Field Crop Research, 103: 198-207.
22- Ting L.Z., Yang J.Y., Drury C.F., and Hoogenboom G. 2015. Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China. Agricultural Systems, 135: 90-104.
23- Tojo Soler C.M., Sentelhas P.C., and Hoogenboom G. 2007. Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy, 27: 165-177.
24- White J.W. 2003. Modeling Temperature Response in Wheat and Maize: Proceedings of aWorkshop, CIMMYT, El Batan, Mexico, 23-25 April 2001. NRG-GIS Series 03-01. Mexico, D.F.: CIMMYT.
25- Willmott C.J. 1982. Some comments on the evaluation of model performance. Bulletin American Meteorological Society, 63: 1309-1313.
26- Yang Z., Wilarkson G.G., Buol G.S., Bowman D.T., and Heiniger R.W. 2009. Estimating Genetic Coefficients for the CSM-CERES-Maize model in North Carolina environments. Agronomy Journal, 101: 1276-1285.
27- Yang J.M., Yang J.Y., Dou S., Yang X.M., and Hoogenboom G. 2013. Simulating the effect of long-term fertilization on maize yield and soil C/N dynamics in northeastern China using DSSAT and CENTURY-based soil model. Nutrient Cycling in Agroecosystems, 95: 287-303.
28- Yang J.M., Yang J.Y., Liu S., and Hoogenboom G. 2014. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems, 127: 81-89.