Application of the CSM-CERES-Rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment
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The objectives of this study were to evaluate the performance of the cropping system model (CSM)-CERES-Rice to simulate growth and development of an aromatic rice variety under irrigated conditions in a semiarid environment of Pakistan and to determine the impact of various plant densities and nitrogen (N) application rates on grain yield and economic return. The crop simulation model was evaluated with experimental data collected in experiments that were conducted in 2000 and 2001 in Faisalabad, Punjab, Pakistan. The experimental design was a randomized complete block design with three replications and included three plant densities (one seedling hill−1, PD1; two seedlings hill−1, PD2; and three seedlings hill−1, PD3) and five N fertilizer regimes (control, N0; 50 kg ha−1, N50; 100 kg ha−1, N100; 150 kg ha−1, N150; and 200 kg ha−1, N200). To determine the most appropriate combination of plant density and N levels, four plant densities from one seedling hill−1 to four seedlings hill−1 and 13 N levels ranging from 0 to 300 kg N ha−1 (52 scenarios) were simulated for 35 years of historical daily weather data under irrigated conditions. The evaluation of CSM-CERES-Rice showed that the model was able to simulate growth and yield of irrigated rice in the semiarid conditions, with an average error of 11% between simulated and observed grain yield. The results of the stimulation analysis result showed that two seedlings hill−1 along with 200 kg N ha−1 (PD2N200) produced the highest yield as compared to all other scenarios. Furthermore, the economic analysis through the mean gini dominance also showed the dominance of this treatment (PD2N200) compared to the other treatment combinations. Thus, the management scenario that consisted of two seedlings hill−1 and 200 kg N ha−1 was the best for high yield and monitory return of irrigated rice in the semiarid environment. The mean monetary returns ranged from 291 US $ ha−1 to 1 460 US $ ha−1 among the 52 production options that were simulated. This approaching was demonstrated as effective way to optimize the density and N management for high yield and monetary return. It will help the rice production.
KeywordsCrop management Crop modeling Biomass Decision support system for agrotechnology transfer Grain yield
Cropping system model
Gross domestic product
Root mean square error
Leaf area index
The research was supported in part by a Post Doctorate Fellowship Program of the Higher Education Commission (HEC), Islamabad, Pakistan, for the first author of this article. The author particularly is also thankful to the administration of Bahauddin Zakariya University (BZU), Multan, Pakistan for the approval of study leave.
- Ahmad, S., Ahmad, A., Zia-ul-Haq, M., Ali, H., Khaliq, T., Anjum, M. A., et al. (2009). Resources use efficiency of field grown transplanted rice (Oryza sativa L.) under irrigated semiarid environment. Journal of Food, Agriculture and Environment, 7, 487–492.Google Scholar
- Ahmad, S., Zia-ul-Haq, M., Ali, H., Shad, S. A., Ahmad, A., Maqsood, M., et al. (2008). Water and radiation use efficiencies of transplanted rice (Oryza sativa L.) at different plant density and irrigation regimes under semi-arid environment. Pakistan Journal of Botany, 40, 199–209.Google Scholar
- Banterng, P., Hoogenboom, G., Patannothai, A., Singh, P., Wani, S. P., Pathak, P., et al. (2010). Application of the cropping system model (CSM)-CROPGRO-Soybean for determining optimum management strategies for soybean in tropical environments. Journal of Agronomy and Crop Science, 196, 231–242.CrossRefGoogle Scholar
- Buccola, S. T., & Subaei, A. (1984). Mean-Gini analysis, stochastic efficiency and weak risk aversion. Australian Journal of Agricultural Economics, 28, 77–86.Google Scholar
- Buresh, R. J., Singh, U., Godwin, D. C., Ritchie, J. T., & de Datta, S. K. (1991). Simulation soil nitrogen transformations with CERES-Rice. Agrotechnology Transfer, 13, 7–10.Google Scholar
- Cheyglinted, S., Ranamukhaarachchi, S. L., & Singh, G. (2001). Assessment of the CERES-RICE model for production in the central plain of Thailand. Journal of Agricultural Sciences, 137, 289–298.Google Scholar
- FAO. (2009). FAOSTAT, Agriculture and food trade. http://www.faostat.fao.org. Accessed 5 November 2009.
- FAO. (2011). FAOSTAT Agricultural price statistics. http://faostat.fao.org/site/351/default.aspx. Accessed 28 May 2011.
- Godwin, D. C., & Singh, U. (1998). Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In G. Y. Tsuji, G. Hoogenboom, & P. K. Thornton (Eds.), Understanding options for agricultural production (pp. 55–78). Dordrecht: Kluwer Academic Publishers.Google Scholar
- GOP. (2007). Economic survey of Pakistan 2006–2007, finance division. Islamabad: Economic Advisory Wing, Finance Division, Govt. of Pakistan.Google Scholar
- GOP. (2009). Economic Survey of Pakistan 2008–2009, finance division. Islamabad: Economic Advisory Wing, Finance Division, Govt. of Pakistan.Google Scholar
- Hoogenboom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Batchelor, W. D., Hunt, L. A., et al. (2004). Decision support system for agrotechnology transfer (DSSAT) version 4.0. Honolulu: University of Hawaii, CD-ROM.Google Scholar
- Hoogenboom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Batchelor, W. D., Hunt, L. A., et al. (2010). Decision support system for agrotechnology transfer, (DSSAT) version 4.5. Honolulu: University of Hawaii, CD-ROM.Google Scholar
- Hunt, L. A., & Boote, K. J. (1998). Data for model operation, calibration, and evaluation. In G. Y. Tsuji, G. Hoogenboom, & P. K. Thornton (Eds.), Understanding options for agricultural production (pp. 9–39). Dordrecht: Kluwer Academic.Google Scholar
- IRRI. (2009). Atlas of rice & world rice statistics. In: IRRI (Ed.), Atlas of rice & world rice statistics. Metro Manila: International Rice Research Institute (IRRI). http://www.irri.org/science/ricestat/index.asp. Accessed 29 October 2009.
- Kropff, M.J., van Laar, H.H., & Matthews, R. (1994). ORYZA1: An ecophysiological model for irrigation rice production. In: SARP research proceedings (p. 110). Wageningen: DLO Research Institute for Agrobiology and Soil Fertility.Google Scholar
- Kumar, R., & Sharma, H. L. (2004). Simulation and validation of CERES-Rice (DSSAT) model in north-western Himalayas. Indian Journal of Agricultural Sciences, 74, 133–137.Google Scholar
- Niaz, A., Ibrahim, M., & Ishaq, M. (2003). Assessment of nitrate leaching in wheat-maize cropping system: A lysimeter study. Pakistan Journal of Water Resources, 7, 1–6.Google Scholar
- Ritchie, J. T., Singh, U., Godwin, D. C., & Bowen, W. T. (1998). Cereal growth, development and yield. In G. Y. Tsuji, G. Hoogenboom, & P. K. Thornton (Eds.), Understanding options for agricultural production (pp. 79–98). Dordrecht: Kluwer Academic.Google Scholar
- Singh, U. (1994). Nitrogen management strategies for lowland rice cropping systems. In: Proceedings of the international conference on fertilizer usage in the tropics (FERTROP) (pp. 293–306). Kuala Lumpur: Malaysian Society of Soil Science.Google Scholar
- Singh, Y., Singh, B., Ladha, J. K., Khind, C. S., Gupta, R. K., Meelu, O. P., et al. (2004). Long-term effects of organic inputs on yield and soil fertility in the rice-wheat rotation. Soil Science Society of America Journal, 68, 845–853.Google Scholar
- Singh, U., Wilkens, P.W., Chude, V., & Oikeh, S. (1999a). Predicting the effect of nitrogen deficiency on crop growth duration and yield. In: Proceedings of the fourth international conference on precision agriculture (pp. 1379–1393). Madison: ASA-CSSA-SSSA.Google Scholar
- St’astna, M., Trnka, M., Kren, J., Dubrovsky, M., & Zalud, Z. (2002). Evaluation of the CERES models in different production regions of the Czech Republic. Rostlina Vyroba, 48, 125–132.Google Scholar
- Tahir, M. A., & Rasheed, H. (2008). Distribution of nitrate in the water resources of Pakistan. African Journal of Environmental Science and Technology, 11, 397–403.Google Scholar
- Thornton, P. K., Hoogenboom, G., Wilkens, P. W., & Jones, J. W. (1998). Seasonal analysis. In G. Y. Tsuji, G. Uehara, & S. Balas (Eds.), DSSAT version 3, vol. 3–2 (pp. 1–65). Honolulu: University of Hawaii.Google Scholar
- Tsuji, G. Y., Hoogenboom, G., & Thornton, P. K. (1998). Understanding options for agricultural production. Systems approaches for sustainable agricultural development. Dordrecht: Kluwer Academic.Google Scholar
- USDA (2009). United States Department of Agriculture-National Agricultural Statistics Service, USDA. PSD Online http://www.agcensus.usda.gov. Accessed 12 December 2009.