Determination of Upland Rice Cultivar Coefficient Specific Parameters for DSSAT (Version 4.7)-CERES-Rice Crop Simulation Model and Evaluation of the Crop Model under Different Temperature Treatments conditions
To develop basis for strategic or arranged decision
making towards crop yield improvement in Thailand, a new method in which crop
models could be used is essential. Therefore, the objective of this study was
to measure cultivar specific parameters by using DSSAT (v4.7) Cropping
Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai
Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a
Completely Randomized Design (CRD) with split plot design. Results showed that five
upland rice genotypes had significantly affected each other by different
temperature treatments (28°C, 30°C, 32°C) with grain yield, tops weight,
harvest index, flowering, and maturity date. At the same time, all the
phenological traits had highly significant variation with the genotypes. The
cultivar specific parameters obtained by using a temperature tolerant cultivar
(Basmati 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model
evaluation results indicated that utilizing the estimated cultivar coefficient
parameters, model simulated well with varying temperature treatments as
indicated by the agreement index (d-statistic) closer to unity. Hence, it was
estimated that model calibration and evaluation was realistic in the limits of
test cropping seasons and that CSM fitted with cultivar specific parameters can
be used in simulation studies for investigation, farm managing or decision
making. This electronic document is a “live” template. The various components
of your paper [title, text, heads, etc.] are already defined on the style
sheet, as illustrated by the portions given in this document.
References
[1]
Mourice, S.K., Rweyemamu, C.L., Tumbo, S.D. and Amuri, N. (2014) Maize Cultivar Specific Parameters for Decision Support System for Agrotechnology Transfer (DSSAT) Application in Tanzania. American Journal of Plant Sciences, 5, 821-833.
http://doi.org/10.4236/ajps.2014.56096
[2]
Dias, M.P.N.M., Navaratnea, C.M., Weerasinghea, K.D.N. and Hettiarachchib, R.H.A.N. (2016) Application of DSSAT Crop Simulation Model to Identify the Changes of Rice Growth and Yield in Nilwala River Basin for Mid-Centuries under Changing Climatic Conditions. Procedia Food Science, 6, 159-163.
https://doi.org/10.1016/j.profoo.2016.02.039
[3]
Kant, K., Telkar, S.G., Gogoi, M. and Kumar, D. (2017) Crop Simulation Models. Biomolecule Reports, 1-5.
[4]
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 Crop Syst Simulation Model in Hyper Arid Partially Irrigated Zone of Rajasthan. Legume Research, 40, 1-6.
[5]
(2015) World Wheat, Maize (Corn), Rice, and Cotton. Oklahoma State University, Stillwater.
http://www.nue.okstate.edu/Crop_Information/World-Wheat-Production.htm
[6]
Nayak, A.K., Shahid, M., Nayak. A.D., Dhal, B., Moharana, K.C., Mondal, B., Tripathi, R., Mohapatra, S.D., Bhattacharyya, P., Jambhulkar, N.N., Shukla, A.K., Fitton, N., Smith, P. and Pathak, H. (2019) Assessment of Ecosystem Services of Rice Farms in Eastern India. Ecological Processes, 8, Article No, 35.
https://doi.org/10.1186/s13717-019-0189-1
[7]
Jalota, S.K. (2010) Integrated Effect of Transplanting Date, Cultivar and Irrigation on Yield, Water Saving and Water Productivity of Rice (Oryza sativa L.) in Indian Punjab: Field and Simulation Study. Agricultural Water Management, 96, 1096-1104. https://doi.org/10.1016/j.agwat.2009.02.005
[8]
Fahad, S., Bajwa, A.A., Nazir, U., Shakeel, A.A., Farooq, A., Zohaib, A., Sadia, S., Wajid, N., Chao, W., Wang, D. and Huang, J. (2017) Crop Production under Drought and Heat Stress: Plant Responses and Management Options. Frontiers of Plant Sciences, 8, Article No. 1147. https://doi.org/10.3389/fpls.2017.01147
[9]
Yuliawana, T. and Handokob, I. (2016) The Effect of Temperature Rise to Rice Crop Yield in Indonesia Uses Shierary Rice Model with Geographical Information System (GIS) Feature. Procedia Environmental Sciences, 33, 214-220.
https://doi.org/10.1016/j.proenv.2016.03.072
[10]
Araus, J.L. and Slafer, G.A. (2011) Crop Stress Management and Global Climate Change. CABI, Cambridge. https://doi.org/10.1079/9781845936808.0000
[11]
Anothai, J., Patanothai, A., Jogloy, S., Pannangpetch, K., Boote, K. J. and Hoogenboom, G. (2008) A Sequential Approach for Determining the Cultivar Coefficients of Peanut Lines Using End-of-Season Data of Crop Performance Trials. Field Crops Research, 108, 169-178. https://doi.org/10.1016/j.fcr.2008.04.012
[12]
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. and Ritchie, J.T. (2010) The DSSAT Cropping System Model. European Journal of Agronomy, 18, 235-265.
https://doi.org/10.1016/S1161-0301(02)00107-7
[13]
Hoogenboom, G., Jones, J.W., Traore, P.C.S. and Boote, K.J. (2012) Experiments and Data for Model Evaluation and Application. In: Kihara, J., Fatondji, D., Jones, J.W., Hoogenboom, G., Tabo, R. and Bationo, A., Eds., Improving Soil Fertility Recommendations in Africa Using the Decision Support System for Agrotechnology Transfer (DSSAT), Springer Science+Business Media, Dordrecht, 9-18.
https://doi.org/10.1007/978-94-007-2960-5_2
[14]
Mendiburu, F.D. and Simon, R. (2007) Agricolae—A Free Statistical Library for Agricultural Research. Iowa State University, Ames.
[15]
Akinbile, C.O. (2013) Assessment of the CERES-Rice Model for Rice Production in Ibadan, Nigeria. Agricultural Engineering International: CIGR Journal, 15, 19-26.
[16]
Kiniry, J., Williams, R., Vanderlip, J., Atwood, D., Muuliken, W., Cox, H. and Weibold, W. (1997) Evaluation of Two Maize Models for Nine U.S. Locations. Agronomy Journal, 89, 421-426.
https://doi.org/10.2134/agronj1997.00021962008900030009x
