Simulation of rice irrigation water productivity under different irrigation and nitrogen fertilizer managements

Document Type : Research Paper

Authors

1 M.Sc. Student, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

2 Associate Professor, Department of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran; and Department of Water Engineering and Environment, Caspian Sea Basin Research Center, Rasht, Iran

3 Research Assistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran

Abstract

Introduction
Increasing irrigation water productivity is one of the key topics in food production in different countries of the world, especially in water-scarce countries such as Iran. Plant growth models, in addition to predicting yield, are capable of evaluating diversity and risks of different management scenarios. Plants modeling can lead to a reduction in the use of production resources by finding optimal management scenarios. The objective of this study was to simulate the physical water productivity, leaf area index and evapotranspiration of three rice genotypes under different irrigation and nitrogen fertilizer managements using the CERES-Rice model.

Materials and methods
This experiment was conducted with 36 treatments in a split-plot design based on randomized complete block design with three replications in the Rice Research Institute of Iran, Rasht, Guilan province, Iran, during two cropping years, 2017 and 2018. Irrigation management at four levels including permanent flood irrigation and intermittent irrigation with irrigation intervals of 7, 14, and 21 days was considered as the main factor, rice genotypes at three levels including the certified local variety Hashemi, line M5 and M12 line as the sub-factor, and nitrogen fertilizer at three levels including 60, 80 and, 100 kg/ha net nitrogen fertilizer as the sub-sub-factor. After harvest, grain yield was measured in kg/ha and then irrigation water productivity was calculated from the ratio of grain yield to water volume used. In this study, the plant growth model of CERES-Rice version 4.7.5 was used for modeling, and data from 2017 and 2018 were used to validate and calibrate the model, respectively. Graphical comparative methods and statistical indicators including root mean square error (RMSE), normalized root mean square error (NRMSE) and model efficiency (EF) were also used to evaluate the model performance.

Research findings
The results of this study showed that the predicted yields of the CERES-Rice model had a similar trend to the actual yields and the response to irrigation treatments was the same as the measured yields. The results of the simulation of water productivity under different irrigation and nitrogen fertilizer managements for data from 2005 to 2016 with the aim of evaluating water productivity in a long-term meteorological period revealed that the irrigation intervals of seven days at 100 kg/ha nitrogen fertilizer level was the best irrigation management for the studied years. The irrigation intervals of 14 and 21 days at the level of 100 kg/ha nitrogen fertilizer also had more appropriate physical water productivity.

Conclusion
The results of water productivity modeling showed that a seven days irrigation interval at 100 kg/ha nitrogen fertilizer was the best irrigation interval. Therefore, the development of seven days intermittent irrigation and education and promotion of proper utilization by farmers are recommended to increase water productivity. Therefore, in order to increase water productivity, it is recommended to develop seven days intermittent irrigation and educate and promote proper utilization by farmers.

Keywords

Main Subjects

References

Abrol, I. P., & Sangar, S. (2006). Sustaining Indian agriculture–conservation agriculture the way forward. Current Science, 91(8), 1020-1025.##Akinbile, C. O. (2013). Assessment of the CERES-Rice model for rice production in Ibadan, Nigeria. Agricultural Engineering International: CIGR Journal, 15(1), 19-26.##Amiri, E., Rezaei, M., Rezaei, E. E., & Bannayan, M. (2014). Evaluation of Ceres-Rice, AquaCrop and Oryza2000 models in simulation of rice yield response to different irrigation and nitrogen management strategies. Journal of Plant Nutrition, 37(11), 1749-1769. doi: 10.1080/01904167.2014.888750.##Bouman, B. A. M., & Tuong, T. P. (2001). Field water management to save water and increase its productivity in irrigated lowland rice. Agricultural Water Management, 49(1), 11-30. doi: 10.1016/S0378-3774(00)00128-1.##Bouman, B. A. M., & van Laar, H. H. (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems, 87(3), 249-273. doi: 10.1016/j.agsy.2004.09.011.##Buresh, R. J., Singh, U., Godwin, D. C., Ritchie, J. T., & De Datta, S. K. (1991). Simulating soil nitrogen transformations and crop response to nitrogen using the CERES-Rice model. IRRI research paper series. International Rice Research Institute, Manilla, Philippines.##Dente, L., Satalino, G., Mattia, F., & Rinaldi, M. (2008). Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sensing of Environment, 112(4), 1395-1407. doi: 10.1016/j.rse.2007.05.023.##Esmaelzadeh, M., Esfahani, M., Alami, A., Momeni, A. & Khaledian, M.R. (2021). Profiling the physiological response of upland and lowland rice (Oryza sativa L.) genotypes to water deficit. Journal of Crop Science & Biotechnology, 25, 289-300. doi: 10.1007/s12892-021-00131-3.##FAO. (2000). Crops and drops: Making the best use of water for agriculture. Food and Agriculture Organization of the United Nations. Retrieved 20 April 2024, from https://farm-d.org/document/crops-and-drops-making-the-best-use-of-water-in-agriculture/.##Godwin, D. C., & Singh, U. (1998). Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji, G. Y., Hoogenboom, G., & Thornton, P. K. (Eds.). Understanding Options for Agricultural Production. Systems Approaches for Sustainable Agricultural Development. Vol. 7. Springer, Dordrecht, The Netherlands. pp. 41-54. doi: 10.1007/978-94-017-3624-4_4.##Hoang, L., Ngoc, T. A., & Maskey, S. (2016). A robust parameter approach for estimating CERES-Rice model parameters for the Vietnam Mekong Delta. Field Crops Research, 196, 98-111. doi: 10.1016/j.fcr.2016.06.012.##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., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265. doi: 10.1016/S1161-0301(02)00107-7.##Kiani, M., Ilkai M. N. & Aqayari, F. (2016). Validation of CERES-Rice model in simulating nitrogen fertilizer levels in rice. Journal of Agriculture & Plant Breeding, 12(2), 35-46. [In Persian].##Kim, K., Jeong, H., & Kim, J. (2013). Comparison of crop growth and evapotranspiration simulations between Noah Multi Physics model and CERES-Rice model. Korean Journal of Agricultural & Forest Meteorology, 15(4), 282-290. doi: 10.5532/KJAFM.2013.15.4.282##.Fallah Ghalhari, G. A., Asadi, M., & Entezari, A, R. (2016). Climate mapping of Guilan province by using multivariable methods. Journal of Geography & Planning, 19(54), 235-251.##Mall, R. K., & Aggarwal, P. K. (2002). Climate change and rice yields in diverse agro-environments of India. I. Evaluation of impact assessment models. Climatic Change, 52(3), 315-330. doi: 10.1023/A:1013702105870.##Maniruzzaman, M., Biswas, J. C., Hossain, M. B., Haque, M. M., Naher, U. A., Biswas, A., Choudhury, A. K., Akhter, S., Ahmed, F., Rahman, M. M., & Kalra, N. (2017). Evaluating the CERES-Rice model under dry season irrigated rice in Bangladesh: Calibration and validation. Journal of Agricultural & Crop Research, 5(6), 96-107.##Najafi, S., Khaledian, M. R., & Rezaei, M. (2021). Evaluation of water productivity with three rice genotypes under different irrigation regimes and nitrogen fertilizer treatments in Rasht, northern Iran. Irrigation & Drainage, 70(4), 679-689. doi: 10.1002/ird.2582.##Rezaei, G., Khaledian, M. R., Kavoosi-Kalashami, M., & Rezaei,  M. (2022). Prioritization of areas suitable for rice cultivation based on the economic value of irrigation water. Irrigation & Drainage, 71(3), 776-782. doi: 10.1002/ird.2685.##Rezaei, M., Shahnazari, A., Raeini Sarjaz, M., & Vazifedoust, M. (2015). Large-scale simulation of rice yield and water productivity using CERES-Rice model. Iranian Journal of Irrigation & Drainage, 9(2), 283-291. [In Persian].##Rezayati, S., Khaledian, M. R., Razavipour, T., & Rezaei, M. (2020). Water flow and nitrate transfer simulations in cultivation under different irrigation and nitrogen fertilizer application managements by HYDRUS-2D model. Irrigation Science, 38(4), 353-363. doi: 10.1007/s00271-020-00676-1.##Ritchie, J. T., Alocilja, E. C., Singh, U., & Uehara, G. (1987). IBSNAT and the CERES-Rice model. Proceedings of the International Workshop on the Impact of Weather Parameters on Growth and Yield of Rice. 7-10 April, 1986, Manilla, Philippines. pp. 271-281.##Ritchie, J. T., Singh, U., Godwin, D. C., & Bowen, W. T.  (1998).  Cereal growth, development, and yield. In: Tsuji, G. Y., Hoogenboom, G., & Thornton, P. K. (Eds.). Understanding Options for Agricultural Production.  Systems Approaches for Sustainable Agricultural Development. Vol. 7. Springer, Dordrecht, The Netherlands. pp. 79-98. doi: 10.1007/978-94-017-3624-4_5.##Singh, U., & Ritchie, J. T. (1993). Simulating the impact of climate change on crop growth and nutrient dynamics using the CERES-Rice model. Journal of Agricultural Meteorology, 48(5), 819-822. doi: 10.2480/agrmet.48.819.##Singh, U., Tsuji, G. Y., & Godwin, D. C. (1990). Planting new ideas in DSSAT: The CERES-Rice model. Agrotechnology Transfer, 10, 1-7. University of Hawaii, Honolulu, Hawaii, USA.##Toset, H. P. W., Gleditsch, N. P., & Hegre, H. (2000). Shared rivers and interstate conflict. Political Geography, 19(8), 971-996. doi: 10.1016/S0962-6298(00)00038-X.##van Laar, H. H., Goudriaan, J., & van Keulen, H. (1997). SUCROS97: Simulation of Crop Growth for Potential and Water-Limited Production Situations. As Applied to Spring Wheat. Quantitative Approaches in Systems Analysis, No. 14. AB-DLO, Wageningen, 52 p.##Vilayvong, S., Banterng, P., Patanothai, A., & Pannangpetch, K. (2015). CSM-CERES-Rice model to determine management strategies for lowland rice production. Scientia Agricola, 72(3), 229-236. doi: 10.1590/0103-9016-2013-0380.##Wikarmpapraharan, C., & Kositsakulchai, E. (2010). Evaluation of ORYZA2000 and CERES-rice models under potential growth condition in the central plain of Thailand. Thai Journal of Agricultural Science, 43(1), 17-29.##Zare Abyaneh, H., Aram, M., & Akhavan, S. (2015). Assessment of virtual water trade volume of main crops in Hamedan province. Iranian Water Research Journal, 9(3), 151-161. [In Persian].
Cereal Research
Volume 14, Issue 4 - Serial Number 53
February 2025
Pages 329-345

Files

Share

How to cite

Statistics