Simulation of soybean growth and yield using iLegume_Soybean model in Mazandaran

Subject Areas : Journal of Plant Ecophysiology

Ali Rahemi Karizaki ¹ , Morteza Nouralizadeh ²

1 - Dept. of Agronomy Gonbade-Kavous University of Agriculture Sciences and Natural Resources
2 - Gonbad university

Received: 2018-05-20 Accepted : 2018-12-01 Published : 2020-03-20

Keywords: Modeling, Grain yield, harvest index,

Abstract :

soybean is one of the most important oily plants in the world. More than 50 percent of the world's oilseed production is concentrated on soybean. The purpose of this study was to evaluate and apply a simple model of soybean growth and yield in eastern Mazandaran provinc. Different aspects of plant growth in the model are as follows including phonological development, leaf area changes, and the production and distribution of dry matter. Grain yield, biological yield and harvest index were simulated using different scenarios. The results showed that the regression coefficients of the observed grain yield and harvest index versus simulated values based on 95% confidence intervals were not significantly different with the coefficients of line 1: 1 (a = 0 and b = 1). The values of R2 for grain yield and harvest index were 0.96 and 0.80 respectively. Despite R2 of linear regressed line between observed yield biological versus predicted values was 0.96 but bias frome the 1:1 line was high. Therefore, it can be said that the model has the ability to predict grain yield and harvest index in the environmental conditions of Mazandaran, but it can not be suitable for soybean biological yield in Mazandaran.

References:

باقری، و.، و ترابی، ب. 1394. مدلی ساده برای شبیه‌سازی رشد، نمو و عملکرد گیاه باقلا در استان گلستان. نشریه تولید گیاهان زراعی. جلد هشتم. شماره 2. صفحه 133-152.

رسام، ق.، و سلطانی،ا. 1393. ساخت و ارزیابی مدلی ساده برای شبیه‌سازی رشد و عملکرد سویا. نشریه پژوهش‌های تولید گیاهی. جلد 21. شماره 2. صفحه 87-105.

سرائی تبریزی، م.، م. پارسی‌نژاد.، و بابازاده، ح. 1393. ارزیابی کارائی مدل CROPWAT 8.0 در پیش‌بینی و برآورد مصرف آب و میزان عمل کرد محصول سویا با استفاده از داده‌های مزرعه‌ای در منطقه کرج. نشریه زراعت (پژوهش و سازندگی). شماره 102. صفحه 161-170.

سلطانی، ا. 1388. مدلسازی گیاهان زراعی. انتشارات جهاد دانشگاهی مشهد. 176 صفحه.

فراهانی‌پاد، پ.، پاک‌نژاد، ف.، ایلیکایی، م.، حبیبی، د.، و داوودی فرد، م. 1391. شبیه سازی عملکرد و اجزای عملکرد سویا (رقم ویلیامز) در چهار تاریخ کاشت با استفاده از مدل CROPGRO-Soybean  در منطقه کرج. مجله زراعت و اصلاح نباتات. جلد 8. شماره 4. صفحه 31-41.

نه‌بندانی، ع.، ا. سلطانی.، ا. زینلی.، س. رئیسی و رجبی. ر. 1394. پارامتریابی و ارزیابی مدل SSM-iLegume برای پیش‌بینی رشد و عملکرد سویا در شرایط گرگان. نشریه پژوهش‌های تولید گیاهی. جلد 23 شماره 3. صفحه 1-26.

Angus, J.F., Cunningham, R.B., Moncur, M.W., and Mackenzie, D.H. 1980. Phasic development in field crops. I. Thermal response in the seedling phase Field Crops Res. 3: 365-78.

Anonymus. 2018. www. FAO.org/faostat/en/data.

Boote, K.J., and Pickering, N.B. 1994. Modeling photosynthesis of row crop canopies. Hort. Sci. 29: 1423-1434.

Geoffrey, E.O., Wietse, H.P., Iwan, S., Omondi, P., and Ronald W.A. 2018. Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts. Agricultural and Forest Meteorology 250: 243–261.

Goudriaan, J., and van Laar, H.H. 1994. Modelling potential crop growth processes. Kluwer Academic Pub. Dordrecht, 238p.

Hammer, G.L., and Muchow, R.C. 1994. Assessing climatic risk to sorghum production in water-limited subtropical environments: I. Development and testing of a simulation model. Field Crops Res. 36: 221-234.

Hunt, L.A., and Pararajasingham, S. 1995. CROPSIM-WHEAT: A model describing the growth and development of wheat. Can. J. Plant Sci. 75: 619-632.

Kobayashi, K., and Salam, M.U. 2000. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92: 345-352.

Liu, X., Jin, J., Herbert, S.J., Zhang, Q., and Wang, G. 2004. Yield components, dry matter, LAI and LAD of soybeans in Northeast China. Field Crops Res. 90: 125-140.

Lizaso, J.I., Batchelor, W.D., Boote, K.J., and Westgate, M.E. 2005. Development of a leaf level canopy assimilation model for CERES-Maize. Agron. J. 97: 722-733.

Mercau, J.L., Dardanelli, J.L., Collino, D.J., Andriani, J.M., Irigoyen, A., and Satorre, E.H. 2007. Predicting on-farm soybean yields in the pampas using CROPGRO-soybean. Field Crops Res. 100: 200-209.

Muchow, R.C., and Davis, R. 1988. Effect of nitrogen supply on the comparative productivity of maize and sorghum in a semi-arid tropical environment. II. Radiation interception and biomass accumulation. Field Crops Res. 18: 17-30.

OECD/FAO 2015. Oilseeds and oilseed products. in OECD-FAO Agricultural Outlook 2015, OECD Publishing, Paris.

O'Leary, G.J., and Connor, D.J. 1998. A simulation study of wheat crop response to water supply, nitrogen nutrition, stubble retention and tillage. Aust J. Agric. Res. 49: 11-19.

Rafael B., Paulo C. S.,and Kenneth J. B. 2017. Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research. 200: 28–37

Soltani, A., Torabi, B., and Zarei, H. 2005. Modeling crop yield using a modified harvest index-based approach: Application in chickpea. Field Crops Res. 91: 273-285.

Soltani, A., Hammer, G.L., Torabi, B., Robertson, M.J., and Zeinali, E. 2006. Modeling chickpea growth and development: Phonological development. Field Crops Res. 99: 1-13.

Soltani, A., and Sinclair, T.R. 2011. A simple model for chickpea development, growth and yield. Field Crops Res. 124: 252-260.

Steel, R.G.D., and Torrie, J.H. 1985. Principles and Procedures of Statistics –A Biometrical Approach. McGraw-Hill Book Co., Mexico, 622p.

Stockle, C. O. 2007. The cropSyst model: A brief description. Biological systems engineering dep., Washington state University, Pullman, Wa, USA.

Van Ittersum, M.K., Leffelaar, P.A., Van Keulen, H., Kropff, M.J., Bastiaans L., and Goudriaan, J. 2003. On approaches and applications of the Wageningen crop models. Eur. J. Agro. 18:3. 201-234.

Villalobos, F.J., Hall, A.J., Ritchie, J.T., and Orgaz, F. 1996. OILCROP-SUN: A development, growth and yield model of the sunflower crop. Agron. J. 88: 403-415.

Williams, J.R., Jones, C.A., Kiniry, J.R., and Spanel, D.A. 1989. The EPIC crop growth model. Trans. ASAE. 32: 497-510.

Willmott, C.J., Akleson, G.S., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., Odonnell, J., and Rowe, C.M. 1985. Statistics for the evaluation and comparison of models. J. Geophys. Res. 90: 8995-9005.

Xiao, H., Guorui, H., Chaoqing, Y., Shaoqiang, N., and Le, Y. 2017. A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging. Field Crops Research 211: 114–124.

Xiao, G., Zhang, Q., Zhang, F., Ma, F., Wang, J., Huang, J., Luo, C., He, X., and Qiu, Z. 2016. Warming influences the yield and water use efficiency of winter wheat in the semiarid regions of Northwest China. Field Crop Res. 199, 129–135.

Zheng, W., Paula, P., Yu, L., Wei, W.,and  Luis S. P. 2015. Modelling transpiration, soil evaporation and yield prediction ofsoybean in North China Plain. Agricultural Water Management 147: 43–53.