Parameterization and evaluation of SSM_iCrop2 model to simulate the growth and yield of bean (Phaseolus vulgaris L.) in Iran

پژوهش‌های حبوبات ایران

Volume 13, Issue 1 - Serial Number 25, June 2022, Pages 37-54   

Document Type: Original Article

DOI: 10.22067/ijpr.v13i1.2103-1004

Authors

Samaneh Mohammadi^* ; Ebrahim Zeinali; Afshin Soltani; Benjamin Torabi

Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

Abstract

Introduction
Common bean (Phaseolus vulgaris L.) with 20-25% protein and 50-56% carbohydrate content, has a crucial role in supplying the required proteins and maintenance of food security of the community. Among the Asian countries, China, Iran, Japan and Turkey are the major producers of common bean. According to the figures provided by the Ministry of Agriculture, cultivation area and production of bean in Iran in 2016 were 114593 ha and 222705 tones, respectively. In recent years, due to the increase in the population and in order to rapidly meet the demand for more food as well as decision making at micro and macro-levels, simulation of crop growth and yield using the models has gained attention due to rapid preparation of the results, lowering the execution costs and the possibility of simulation under various climatic and management conditions. In order to model the growth stages and yield of bean using the figures of Iranian meteorology organization (minimum and maximum temperatures, radiation and rainfall), a study was conducted at Gorgan University of Agricultural Sciences and Natural Resources. The simple SSM_iCrop2 model was used for this study. This model has been tested and proved for a wide range of plant species. This model requires easily available and limited input information. The aim of this study was to parameterize and evaluate the SSM_iCrop2 model for simulation of growth and yield of common bean in order to investigate the effect of climatic, soil and crop management factors as well as determination of genetic coefficients under Iran conditions using the sub-models associated with phenology, dry matter production and distribution and the changes in leaf area.
 
Materials and Methods
SSM_iCrop2 model was used as the base of this study. Observed and simulated yield and days to maturity values were compared for parameterization and evaluation of the model. For this purpose, a series of experimental data (data associated with the growth and production of bean and reports from the published and unpublished papers) in major bean cultivation areas of the country were used. First, parameters related to phenology, leaf area, dry matter production, yield formation and water relations were estimated. Then, the model was evaluated using a series of data which were independent from the experimental data used for parameterization. Crop management inputs were also entered according to the experiment reports. For statistical analysis and investigation of model precision in comparison of the data recorded in the previous studies with the data simulated by the model, correlation coefficient (r), root mean square error (RMSE) and coefficient of variation (CV) were calculated and 1:1 diagram was also drawn.
 
Results and Discussion
In parameterization of SSM_iCrop2 model for bean, the comparison of observed and simulated days to maturity with RMSE, CV and r values of respectively 14 days, 13 percent and 0.76 and comparison of observed and simulated grain yield with RMSE, CV and r values of 62 g m^-2, 20 percent and 0.84 indicated the accuracy of the used parameters. Furthermore, in the evaluation stage, RMSE, CV and r values for days to maturity were 8 days, 8 percent and 0.74 and for grain yield were 53 g m^-2, 19 percent and 0.77, respectively, which confirms the precision of the model simulation. The model simulated the evapotranspiration of been in a good manner. The values for RMSE, CV and r for the comparison of the observed and simulated evapotranspiration were 63 mm, 11 percent and 0.85, respectively. Application of SSM_iCrop2 model is simple and acceptably precise simulation is possible with minimal parameters and inputs. This model was able to simulate the growth period and yield of bean cultivars in a good manner despite high variations using thermal unit parameters form sowing to harvest, maximum leaf area and maximum harvest index.
 
Conclusion
Growth and yield of bean was successfully simulated using SSM_iCrop2 model using minimal and available parameters despite different growth habits and high phenotypic and genotypic variations among the cultivars. The results of the model evaluation performed using RMSE, r and CV showed that this model is able to simulate maturity time and grain yield of bean sown in various dates under Iran climatic conditions with a high precision. Thus, due to suitable precision of SSM_iCrop2 model in simulation of bean phenology and yield, it may be used as a suitable tool for investigation of crop systems and interpretation of results under various environmental and management conditions for planning and improving the management of bean fields in the country.

Keywords

Bean; Evapotranspiration; Grain yield; Leaf area; Phenology

References

1. Aiking, H. 2011. Future protein supply. Trends in Food Science & Technology 22: 112-120.
2. Amir, , and Sinclair, T.R. 1991. A model of water limitation on spring wheat growth and yield. Field Crops Research 28(1-2): 59-69.
3. Assady, B., Dorri, H.R., and Ghadiri, A. Evaluation of chitti bean genotyps to drought stress using stress tolerance indices. Seed and Plant Improvement Journal 27-1(4): 615-630. (In Persian with English Summary).
4. Beebe, S.E., Rao, I.M., Blair, M.W., and Acosta-Gallegos, J.A. 2013. Phenotyping common beans for adaptation to drought. Frontiers in Physiology 4:
5. Dadrasi, A., Torabi, B., Rahimi, A., Soltani, A., and Zeinali, E. 2020. Parameterization and evaluation of a simple simulation model (SSM_iCrop2) for potato (Solanum tuberosum) growth and yield in Iran. Potato Research 63: 545-563.
6. Ghanbari Motlaq, M., Rastgoo, M., Pouryousef, M., and Saba, J. 2012. The effect of sowing date and weed interference on growth indices of different red bean (Phaseolus vulgaris) cultivars. Journal of Plant Protection 25(4): 378-390. (In Persian with English Summary).
7. Ghanbari, A.A. 2015. Developmental stages and phenology of common bean genotypes under normal irrigation and water deficit conditions. Agronomy Journal 28(107): 190-199. (In Persian with English Summary).
8. Ghanbari, A.A., Mousavi, S.H., Keshavarz, S., and Abbasian, A. 2014. Assessment of variation in physiological growth indices in common bean genotypes under water deficit condition. Seed and Plant Production 30(2): 199-222. (In Persian with English Summary).
9. Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, L., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., and Toulmin, C. 2010. Food Security: the challenge of feeding 9 billion people. Science 327: 812-
10. Hoogenboom, G., Jones, J.W., and Wilkens, P.W. 2004. Decision Support System for Agrotechnology Transfer Version 4.0. Honolulu, HI: Univ. Hawaii.
11. Hydari, S., Sajedi, N.A., and Madani, M.J. 2015. The effects of integrated management on yield, yield components and weed control of bean. Iranian Journal of Pulses Research 6(2): 139-150. (In Persian with English Summary).
12. Jamieson, P.D., Semenov, M.A., Brooking, I.R., and Francis, G.S. 1998. Sirius: a mechanistic model of wheat response to environmental variation. European Journal of Agronomy 8: 161-
13. Jamshidi, M., Danesh-Shahraki, A., and Hashemi-Jazi, M. 2016. Effect of foliar application of manganese and zinc on grain yield and yield components of red bean (Phaseolus vulgaris) in drought conditions. Iranian Journal of Pulses Research 7(2): 164-174. (In Persian with English Summary).
14. Karimzadeh, H., Nezami, A., Kafi, M., and Tadayon, M.R. 2017. Effects of deficit irrigation on yield and yield components of pinto bean genotypes in Shahrekord. Iranian Journal of Pulses Research 8(1): 113-126. (In Persian with English Summary).
15. Koo, J., and Dimes, J. 2013. HC27 Generic Soil Profile Database. http://hdl.handle.net/1902.1/20299, Harvard Data verse, V4.
16. Lobell, D.B., Cassman, K.G., and Field, C.B. 2009. Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment and Resources 34: 179-204.
17. Majnoon Hosseini, N. 2008. Agriculture and Production of L Tehran University Jahad Publications. (In Persian)
18. Marrou, H., Sinclair, T.R., and Metral, R. 2014. Assessment of irrigation scenarios to improve performances of Lingot bean (Phaseolus vulgaris) in southwest France. European Journal of Agronomy 59: 22-28.
19. Mehrpouyan, M., Faramarzi, A., Jaefari, A., and Siyami, K. 2010. The effect of different methods and different dates of sowing on yield and yield components in two cultivars of common bean (Phaseolus vulgaris). Iranian Journal of Pulses Research 1(1): 9-17. (In Persian with English Summary).
20. Mehrpoyan, M., and Shirani Rad, A.H. 2011. Comparing the biological nitrogen fixation efficiency, in native and non-native strains of Rhizobium leguminosarum; bv. phaseoli in common bean. Iranian Journal of Pulses Research 2(2): 109-120. (In Persian with English Summary).
21. Ministry of Agriculture Jahad. 2016. Agricultural Statistics. [WWW Document], n.d. URL https://www.maj.ir/ (Accessed 6/29/20).
22. Mirhashemi Aghdam, R.S., Tadyon, M.S., and Zade Bagheri, M. 2014. Evaluation of competitiveness of different bean cultivars and pigweed as affected by nitrogen. Plant Ecophysiology 5(15): 49-62. (In Persian with English Summary).
23. Mohajerani, S.S., Alavi Fazel, M., Madani, H., Lack, S., and Modhej, A. 2016. Yield and physiological response of red bean genotypes (Phaseolus vulgaris) to cutting irrigation off at different growth stages. Journal of Crop Ecophysiology 10(1): 213-224. (In Persian with English Summary).
24. Nehbandani, A., Soltani, A., Nourbakhsh, F., and Dadrasi, A. 2020. Estimating crop model parameters for simulating soybean production in Iran conditions. Oilseeds and Fats, Crops and Lipids 27:
25. Nehbandani, A., Soltani, A., Zeinali, E., Raisi, S., and Rajabi, R. 2016. Parameterization and evaluation of SSM-soybean model for prediction of growth and yield of soybean in Gorgan. Journal of Plant Production Research 22(3): 1-26. (In Persian with English Summary).
26. Noorhosseini, A., Soltani, A., and Ajamnoroozi, H. 2018. Simulating peanut (Arachis hypogaea) growth and yield with the use of the Simple Simulation Model (SSM). Computers and Electronics in Agriculture 145: 63-75.
27. Omidi, F., and Sepehri, A. 2015. Effect of Sodium Nitroprusside application on leaf area, growth and water use efficiency of Kidney bean under water deficit stress. Journal of Crops Improvement 6(4): 871-885. (In Persian with English Summary).
28. Parvizi, S., Amirnia, R., Bernosy, I., Paseban Islam, B., Hasanzadeh Gorttapeh, A., and Raeii, Y. 2011. Evaluation of different plant densities effects on rate and process of grain filling, yield and yield components in varieties of dry bean. Journal of Plant Production 18(1): 69-86. (In Persian with English Summary).
29. Rahmani, T., Heidari Sharifabad, H., and Madani, H. 2012. Effect of planting date and comparing yield between red bean cultivars in Ali-Ggoudarz , Lorestan, Iran. New Finding in Agriculture 6(4): 321-335. (In Persian with English Summary).
30. Rucell, G., Javis, P.G., and Monteith, J.L. 1989. Absorption of radiation by canopies and stand growth. 21-39 In: G. Russell, B. Marshall and P.G. Jarvis (Eds.). Plant Canopies: Their Growth, Form and Function. Cambridge University Press.
31. Sadeghipour, O., Ghaffari Khaligh, H., and Monem, R. 2005. Effect of plant density on yield and yield components of common bean determinate and indeterminate cultivars. Journal of Agricultural Sciences 11(1): 149-160. (In Persian with English Summary).
32. Sadeghipur, O., and Ghafarikhaligh, H. 2002. Effects of weeding and different herbicides on weed control in common bean (Phaseolus vulgaris). Iranian Journal of Crop Sciences 4(4): 277-282. (In Persian with English Summary).
33. Safapour, M., Khaghani, Sh., and Teymoori, M. 2012. Comparison of drought tolerance index on morphological and agronomical traits in black bean (Phaseolus vulgaris). New Finding in Agriculture 6(4): 337-349. (In Persian with English Summary).
34. Salehi, F. 2015. Principles of bean cultivation. Agricultural Education and Natural Resources Research Publications p. 2. (In Persian).
35. Salehi, M., Akbari, R., and Khorshidi Benam, M.B. 2008. A study on response of yield and seed yield components of red bean (Phaseolus vulgaris) genotypes to delay in planting in Miyaneh region. Journal of Water and Soil Science 12(43): 105-115. (In Persian with English Summary).
36. Sinclair, T.R. 2006. A reminder of the limitations in using Beer's law to estimate daily radiation interception by vegetation. Crop Science 46: 2343-2347.
37. Sinclair, T.R., Kitani, S., Hinson, K., Bruniard, J., and Horie, T. 1991. Soybean flowering date: linear and logistic models based on temperature and photoperiod. Crop Science 31: 786-790.
38. Sinclair, TR., Soltani, A., Marrou, H., Ghanem, M., and Vadez, V. 2020. Geospatial assessment for crop physiological and management improvements with examples using the simple simulation model. Crop Science 60: 700-
39. Singh, S.P. 1999. Developments in plant breeding: Common Bean Improvement in the Twenty-First C Kluwer Academic Publishers, the Netherlands. 409 p.
40. Soltani, A. 2009. Mathematical Modeling of the C Mashhad University Jahad Publications, p. 175. (In Persian)
41. Soltani, A., and Hoogenboom, G. 2007. Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research 103: 198-
42. Soltani, A., and Sinclair, T. 2015. A comparison of four wheat models with respect to robustness and transparency: Simulation in a temperate, sub-humid environment. Field Crops Research 175: 37-
43. Soltani, A., and Sinclair, T.R. 2012. Modeling Physiology of Crop Development, Growth and Y Cabi, P. 322.
44. Soltani, A., Alimagham, M., Nehbandani, A., Zand, E., Bagheri, A.R., Rahimian, H., Fattah-Taleghani, D., and Ahmadi, K. 2016. Preparing a model for simulating the country's agricultural policy and evaluating food security with it by 2050. Agricultural Research Education and Extension Organization. (In Persian).
45. Soltani, A., Alimagham, S., Nehbandani, A., Torabi, B., Zeinali, E., and Dadrasi, A. 2020a. SSM_iCrop2: A simple model for diverse crop species over large areas. Agricultural Systems 182: 102855.
46. Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Vadez, V., van Loon, M.P., and van Ittersum, M.K. 2020b. Future food self-sufficiency in Iran: A model-based analysis. Global Food Security 24: 100351.
47. Soltani, A., Alimagham, S.M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Ghassemi, S., Vadez, V., Sinclair, T.R., and van Ittersum, M.K. 2020c. Modeling plant production at country level as affected by availability and productivity of land and water. Agricultural Systems 183: 102859.
48. Soltani, A., and Sinclair, T.R. 2011. A simple model for chickpea development, growth and yield. Field Crops Research 124: 252-260.
49. Soltani, A., Gholipoor, M., and Hajizadeh, H. 2005. SBEET: A simple model to simulate growthand yield of sugar beet. Agricultural Science and Technology 19: 11-26.
50. Torabi, B., Ebrahimi, N., Soltani, A., and Zeinali, E. 2020. Parameterization and evaluation of SSM-iCrop model for prediction of growth and development of faba bean in climatic conditions of Gorgan. Journal of Crops Improvement 22(4): 531-542. (In Persian with English Summary).
51. Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P., and Hochman, Z. 2013. Yield gap analysis with local to global relevance. A R Field Crops Research 143: 4-17.
52. Williams, J.R., Jones, C.A., Kiniry, J.R., and Spanel, D.A. 1989. The EPIC crop growth model. Transactions of ASAE 32: 497-510.
53. Zhang, H., Tao, F., and Zhou, G. 2019. Potential yields, yield gaps, and optimal agronomic management practices for rice production systems in different regions of China. Agricultural Systems 171: 100-