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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 52 issue 2 (april 2018) : 140-145,   Doi: 10.18805/IJARe.A-321

Application of CSM–CERES–Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield 

T
T. Hussain
J
J. Anothai
C
C. Nualsri
W
W. Soonsuwon
1Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, 90112, Thailand
Cite article:- Hussain T., Anothai J., Nualsri C., Soonsuwon W. (2018). Application of CSM–CERES–Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield. Indian Journal of Agricultural Research. 52(2): 140-145. doi: 10.18805/IJARe.A-321.

ABSTRACT

Crop models can provide rapid and cost effective means to deal with rice crop management. The objectives of this study included, exploring the ability of CSM–CERES–Rice in scheduling irrigation and to simulate the effect of drought stress on upland rice yield. Irrigation treatments 100, 70 and 50 % of field capacity (FC) were applied from 80 days after planting (DAP) at flowering stage until maturity and CSM–CERES–Rice was used to predict irrigation amount for each water regime for treatment duration. Results showed that, at 70 and 50 % FC, performance of an upland rice genotype, Dawk Pa-yawm was decreased significantly as compared to 100 % FC. Normalized root mean square error (RMSEn) values less than 10 % for each treatment indicated a strong agreement between simulated and observed grain yield (GY) and biomass. d-index approaching to unity and RMSEn less than 10 % indicated a good agreement between simulated and observed soil moisture contents (SMC) for all irrigation treatments. Overall, it was concluded that drought stress had negative correlation with GY and CSM–CERES–Rice was able to predict irrigation amount for all treatments assuring that, model has potential for its use as a tool to schedule irrigation for experiments under water limited conditions.

REFERENCES

  1. Anothai, J., Soler, C.M.T., Green, A., Trout, T.J. and Hoogenboom, G. (2013). Evaluation of two evapotranspiration approaches simulated with the CSM–CERES–Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions. Agric. For. Meteorol. 176: 64-76. https://doi.org/10.1016/    j.agrformet.2013.03.001
  2. Chen, C., Wang, E. and Yu, Q. (2010). Modeling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric. Water Manag. 97(8): 1175-1184. https://doi.org/10.1016/    j.agwat.2008.11.012
  3. Davatgar, N., Neishabouria, M.R., Sepaskhahb, A.R., Soltanic, A. (2009). Physiological and morphological responses of rice (Oryza sativa L.) to varying water stress management strategies. Int. J. Plant Prod. 3(4): 19-32. https://doi.org/10.22069/IJPP.2012.660
  4. Droogers, P., Bastiaanssen, W.G.M., Beyazgül, M., Kayam, Y., Kite, G.W. and Murray-Rusta, H. (2000). Distributed agro-hydrological modeling of an irrigation system in western Turkey. Agric. Water Manag. 43(2):183-202. https://doi.org/10.1016/S0378-    3774(99)00055-4
  5. Goyal, V., Malik, R.S. and Jhorar, B.S. (2012). Modeling groundwater recharge from the lined farm canal under shallow water table condition. Indian J. Agric. Res. 46(3): 217-225.
  6. Jat, S.R., Gulati, I.J., Soni, M.L., Kumawat, A., Yadava, N.D., Nangia, V., Glazirina, M. and Rathor, V.S. (2017). Water productivity and yield analysis of groundnut using CropSyst simulation model in hyper arid partially irrigated zone of Rajasthan. Legume Res. 40(4): 1-6. https://doi.org/10.18805/lr.v40i04.9003
  7. Kumari, P., Kumar, P.V., Kumar, R., Wadood, A. and Tirkey, D.A. (2017). Effect of weather on grain yield of direct seeded upland rice varieties in Jharkhand, India. Indian J. Agric. Res. 51(6): 562-567. https://doi.org/10.18805/IJARe.A-4714
  8. Loague, K. and Green, R.E. (1991). Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7(1-2): 51-73. https://doi.org/10.1016/0169-7722(91)90038-3
  9. Majid, S.A., Asghar, R. and Murtaza, G. (2007). Yield stability analysis conferring adaptation of wheat to pre-and post-anthesis drought conditions. Pak. J. Bot. 39(5): 1623-1637.
  10. Oyeogbe, A.I., Oluwasemire, K.O. and Akinbola, G.E. (2012). Modelling soil water characteristics of an inland valley soil. Indian J. Agric. Res. 46(4): 317-323.
  11. Passioura, J. (2007). The drought environment: physical, biological and agricultural perspectives. J. Exp. Bot. 58(2): 113-117. https:/    /doi.org/10.1093/jxb/erl212
  12. Patel, N., Rajput, T.B. (2008). Dynamics and modeling of soil water under subsurface drip irrigated onion. Agric. Water Manag. 95(12): 1335-1349. https://doi.org/10.1016/j.agwat.2008.06.002
  13. Patel, D.P., Das, A., Munda, G.C., Ghosh, P.K., Bordoloi, J.S. and Kumar, M. (2010) Evaluation of yield and physiological attributes of high-yielding rice varieties under aerobic and flood-irrigated management practices in mid-hills ecosystem. Agric. Water Manag. 97(9): 1269-1276. https://doi.org/10.1016/j.agwat.2010.02.018
  14. Patel, C., Nema, A. K., Singh, R. S., Yadav, M. K., Singh, S. K. and Singh, S. M. (2017). Evaluation of DSSAT-CERES model for irrigation scheduling of wheat crop in Varanasi region of Uttar Pradesh. J. Agrometerol. 19(2): 120-124.
  15. Soler, C.M.T., Suleiman, A., Anothai, J., Flitcroft, I. and Hoogenboom, G. (2013). Scheduling irrigation with a dynamic crop growth model and determining the relation between simulated drought stress and yield for peanut. Irrigation sci. 31(5): 889-901. https://doi.org/10.1007/s00271-012-0366-9
  16. Tsuji, G.T., Uehara, G. and Salas, S. (1994). DSSAT v3.0, Vol. 3. University of Hawaii, Honolulu, Hawaii.
  17. Vilayvong, S., Banterng, P., Patanothai, A. and Pannangpetch, K. (2015). CSM-CERES-Rice model to determine management strategies for lowland rice production. Scientia Agricola. 72(3): 229-236. https://dx.doi.org/10.1590/0103-9016-2013-0380
  18. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’donnell, J. and Rowe, C.M. (1985). Statistics for the evaluation and comparison of models. J. Geophys. Res. 90(C5): 8995-9005. https://dx.doi.org/10.1029/    JC090iC05p08995
  19. Yoshida, S. (1978). Tropical climate and its influence on rice [periodicity, productivity and stability]. IRRI, Los Baños, Philippines. IRRI Res. Paper Series 20.
  20. Zain, N.A.M, Ismail, M.R., Puteh, A., Mahmood, M., Islam, M.R. (2014). Impact of cyclic water stress on growth, physiological responses and yield of rice (Oryza sativa L.) grown in tropical environment. Ciência Rural. 44(12): 2136-2141. https://    dx.doi.org/10.1590/0103-8478cr20131154
  21. Zulkarnain, W.M., Ismail, M.R., Ashrafuzzaman, M., Saud, H.M. and Haroun, I.C. (2009). Rice growth and yield under rain shelter house as influenced by different water regimes. Int. J. Agric. Biol. 11: 566-570.
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Indian Journal of Agricultural Research
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    Research Article

    volume 52 issue 2 (april 2018) : 140-145,   Doi: 10.18805/IJARe.A-321

    Application of CSM–CERES–Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield 

    T
    T. Hussain
    J
    J. Anothai
    C
    C. Nualsri
    W
    W. Soonsuwon
    1Department of Plant Science, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, 90112, Thailand
    Cite article:- Hussain T., Anothai J., Nualsri C., Soonsuwon W. (2018). Application of CSM–CERES–Rice in scheduling irrigation and simulating effect of drought stress on upland rice yield. Indian Journal of Agricultural Research. 52(2): 140-145. doi: 10.18805/IJARe.A-321.

    ABSTRACT

    Crop models can provide rapid and cost effective means to deal with rice crop management. The objectives of this study included, exploring the ability of CSM–CERES–Rice in scheduling irrigation and to simulate the effect of drought stress on upland rice yield. Irrigation treatments 100, 70 and 50 % of field capacity (FC) were applied from 80 days after planting (DAP) at flowering stage until maturity and CSM–CERES–Rice was used to predict irrigation amount for each water regime for treatment duration. Results showed that, at 70 and 50 % FC, performance of an upland rice genotype, Dawk Pa-yawm was decreased significantly as compared to 100 % FC. Normalized root mean square error (RMSEn) values less than 10 % for each treatment indicated a strong agreement between simulated and observed grain yield (GY) and biomass. d-index approaching to unity and RMSEn less than 10 % indicated a good agreement between simulated and observed soil moisture contents (SMC) for all irrigation treatments. Overall, it was concluded that drought stress had negative correlation with GY and CSM–CERES–Rice was able to predict irrigation amount for all treatments assuring that, model has potential for its use as a tool to schedule irrigation for experiments under water limited conditions.

    REFERENCES

    1. Anothai, J., Soler, C.M.T., Green, A., Trout, T.J. and Hoogenboom, G. (2013). Evaluation of two evapotranspiration approaches simulated with the CSM–CERES–Maize model under different irrigation strategies and the impact on maize growth, development and soil moisture content for semi-arid conditions. Agric. For. Meteorol. 176: 64-76. https://doi.org/10.1016/    j.agrformet.2013.03.001
    2. Chen, C., Wang, E. and Yu, Q. (2010). Modeling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric. Water Manag. 97(8): 1175-1184. https://doi.org/10.1016/    j.agwat.2008.11.012
    3. Davatgar, N., Neishabouria, M.R., Sepaskhahb, A.R., Soltanic, A. (2009). Physiological and morphological responses of rice (Oryza sativa L.) to varying water stress management strategies. Int. J. Plant Prod. 3(4): 19-32. https://doi.org/10.22069/IJPP.2012.660
    4. Droogers, P., Bastiaanssen, W.G.M., Beyazgül, M., Kayam, Y., Kite, G.W. and Murray-Rusta, H. (2000). Distributed agro-hydrological modeling of an irrigation system in western Turkey. Agric. Water Manag. 43(2):183-202. https://doi.org/10.1016/S0378-    3774(99)00055-4
    5. Goyal, V., Malik, R.S. and Jhorar, B.S. (2012). Modeling groundwater recharge from the lined farm canal under shallow water table condition. Indian J. Agric. Res. 46(3): 217-225.
    6. Jat, S.R., Gulati, I.J., Soni, M.L., Kumawat, A., Yadava, N.D., Nangia, V., Glazirina, M. and Rathor, V.S. (2017). Water productivity and yield analysis of groundnut using CropSyst simulation model in hyper arid partially irrigated zone of Rajasthan. Legume Res. 40(4): 1-6. https://doi.org/10.18805/lr.v40i04.9003
    7. Kumari, P., Kumar, P.V., Kumar, R., Wadood, A. and Tirkey, D.A. (2017). Effect of weather on grain yield of direct seeded upland rice varieties in Jharkhand, India. Indian J. Agric. Res. 51(6): 562-567. https://doi.org/10.18805/IJARe.A-4714
    8. Loague, K. and Green, R.E. (1991). Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7(1-2): 51-73. https://doi.org/10.1016/0169-7722(91)90038-3
    9. Majid, S.A., Asghar, R. and Murtaza, G. (2007). Yield stability analysis conferring adaptation of wheat to pre-and post-anthesis drought conditions. Pak. J. Bot. 39(5): 1623-1637.
    10. Oyeogbe, A.I., Oluwasemire, K.O. and Akinbola, G.E. (2012). Modelling soil water characteristics of an inland valley soil. Indian J. Agric. Res. 46(4): 317-323.
    11. Passioura, J. (2007). The drought environment: physical, biological and agricultural perspectives. J. Exp. Bot. 58(2): 113-117. https:/    /doi.org/10.1093/jxb/erl212
    12. Patel, N., Rajput, T.B. (2008). Dynamics and modeling of soil water under subsurface drip irrigated onion. Agric. Water Manag. 95(12): 1335-1349. https://doi.org/10.1016/j.agwat.2008.06.002
    13. Patel, D.P., Das, A., Munda, G.C., Ghosh, P.K., Bordoloi, J.S. and Kumar, M. (2010) Evaluation of yield and physiological attributes of high-yielding rice varieties under aerobic and flood-irrigated management practices in mid-hills ecosystem. Agric. Water Manag. 97(9): 1269-1276. https://doi.org/10.1016/j.agwat.2010.02.018
    14. Patel, C., Nema, A. K., Singh, R. S., Yadav, M. K., Singh, S. K. and Singh, S. M. (2017). Evaluation of DSSAT-CERES model for irrigation scheduling of wheat crop in Varanasi region of Uttar Pradesh. J. Agrometerol. 19(2): 120-124.
    15. Soler, C.M.T., Suleiman, A., Anothai, J., Flitcroft, I. and Hoogenboom, G. (2013). Scheduling irrigation with a dynamic crop growth model and determining the relation between simulated drought stress and yield for peanut. Irrigation sci. 31(5): 889-901. https://doi.org/10.1007/s00271-012-0366-9
    16. Tsuji, G.T., Uehara, G. and Salas, S. (1994). DSSAT v3.0, Vol. 3. University of Hawaii, Honolulu, Hawaii.
    17. Vilayvong, S., Banterng, P., Patanothai, A. and Pannangpetch, K. (2015). CSM-CERES-Rice model to determine management strategies for lowland rice production. Scientia Agricola. 72(3): 229-236. https://dx.doi.org/10.1590/0103-9016-2013-0380
    18. Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Klink, K.M., Legates, D.R., O’donnell, J. and Rowe, C.M. (1985). Statistics for the evaluation and comparison of models. J. Geophys. Res. 90(C5): 8995-9005. https://dx.doi.org/10.1029/    JC090iC05p08995
    19. Yoshida, S. (1978). Tropical climate and its influence on rice [periodicity, productivity and stability]. IRRI, Los Baños, Philippines. IRRI Res. Paper Series 20.
    20. Zain, N.A.M, Ismail, M.R., Puteh, A., Mahmood, M., Islam, M.R. (2014). Impact of cyclic water stress on growth, physiological responses and yield of rice (Oryza sativa L.) grown in tropical environment. Ciência Rural. 44(12): 2136-2141. https://    dx.doi.org/10.1590/0103-8478cr20131154
    21. Zulkarnain, W.M., Ismail, M.R., Ashrafuzzaman, M., Saud, H.M. and Haroun, I.C. (2009). Rice growth and yield under rain shelter house as influenced by different water regimes. Int. J. Agric. Biol. 11: 566-570.
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