Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T22:39:59.925Z Has data issue: false hasContentIssue false
  • English
  • Français

Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

Published online by Cambridge University Press:  31 July 2018

Chitnucha Buddhaboon ,
Attachai Jintrawet  and
Gerrit Hoogenboom
Chitnucha Buddhaboon
Affiliation:
Rice Department, Bureau of Rice Research and Development, Ubonratchathani Rice Research Centre, Mueang district, Ubonratchathani 34000, Thailand
Attachai Jintrawet*
Affiliation:
Plant and Soil Sciences Department, Centre for Agricultural Resource System Research, Faculty of Agriculture, Chiang Mai University50200, Thailand
Gerrit Hoogenboom
Affiliation:
Department of Agricultural and Biological Engineering, Institute for Sustainable Food Systems, Gainesville, FL 32611, USA
*
Author for correspondence: Attachai Jintrawet, E-mail: attachai.j@cmu.ac.th
Get access Rights & Permissions [Opens in a new window]

Abstract

Prior to applying the cropping system model-CERES-Rice model to deep water rice (DWR), it is important to estimate the rice genetic coefficients (GC). The goal of the current study was to compare two methods for estimating GC using a GC calculator (GENCALC) and generalized likelihood uncertainty estimation (GLUE) for three flooded rice (FDR) varieties. Data from a field experiment on the effect of planting date and variety on FDR production was conducted in 2009 on a DWR area in Bang Taen His Majesty's Private Development Project, Prachin Buri, Thailand. The experimental design was split-plot with four main plots (planting dates) and three sub-plots (FDR varieties) with four replications. The simulated values for anthesis date, maturity date and grain weight using GENCALC produced normalized root mean square errors (RMSEn) of 3.97, 3.69 and 3.68, while using GLUE produced RMSEn of 3.67, 2.50 and 3.68, respectively. The simulated grain number and grain yield under GENCALC GC were not significantly different from the observed values but were higher than simulated values for GLUE GC. Simulated values of above-ground biomass for both GENCALC (11 727 kg/ha) and GLUE GC (11 544 kg/ha) were overestimated compared to observed values (8512 kg/ha). In addition, good agreements of leaf N values were found with D-index values of 0.94 and 0.96 using GENACALC and GLUE GC simulations, respectively. Therefore, the GENCALC and GLUE GC estimators of DSSAT can both be used for estimating GC of FDR in the DWR area in Thailand and similar agro-ecosystems in Southeast Asia.

Keywords

Anthesis datematurity daterice grain yieldrice top weight
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 4 , May 2018 , pp. 482 - 492
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anothai, J, Patanothai, A, Jogloy, S, Pannangpetch, K, Boote, KJ 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.Google Scholar
Bao, Y, Hoogenboom, G, McClendon, RW and Paz, JO (2015) Potential adaptation strategies for rainfed soybean production in the south-eastern USA under climate change based on the CSM-CROPGRO-Soybean model. Journal of Agricultural Science, Cambridge 153, 798–824.Google Scholar
Bao, Y, Hoogenboom, G, McClendon, RW and Vellidis, G (2017) A comparison of the performance of the CSM-CERES-MAIZE and EPIC models using maize variety trial data. Agricultural Systems 150, 109–119.Google Scholar
Bray, RH and Kurtz, LT (1945) Determination of total organic and available form of phosphorus in soil. Soil Science 59, 39–45.Google Scholar
Buddhaboon, C, Jintrawet, A and Hoogenboom, G (2011) Effects of planting date and variety on flooded rice production in the deep water area of Thailand. Field Crops Research 124, 270–277.Google Scholar
Cheyglinted, S, Ranamukhaarachchi, SL and Singh, G (2001) Assessment of the CERES-Rice model for rice production in the central plain of Thailand. Journal of Agricultural Science, Cambridge 137, 289–298.Google Scholar
Department of Agriculture (2004 a) Recommendation for Chemical Fertilizer Application in Rice Field Base on Soil Analysis. Bangkok, Thailand: Department of Agriculture. (in Thai).Google Scholar
Department of Agriculture (2004 b) Thai Rice Check. The Agricultural Co-Operative Federation of Thailand, Bangkok. Bangkok, Thailand: Department of Agriculture. (in Thai).Google Scholar
Gao, L, Jin, Z, Huang, Y and Zhang, L (1992) Rice clock model – a computer model to simulate rice development. Agricultural and Forest Meteorology 60, 1–16.Google Scholar
Graves, AR, Hess, T, Matthews, RB, Stephens, W and Middleton, T (2002) Crop simulation models as tools in computer laboratory and classroom-based education. Journal of Natural Resources and Life Sciences Education 31, 48–54.Google Scholar
He, J, Jones, JW, Graham, WD and Duke, MD (2010 a) Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation methods. Agricultural Systems 103, 256–264.Google Scholar
He, J, Porter, CH, Wilkens, PW, Marin, F, Hu, H and Jones, JW (2010 b) Generalized likelihood uncertainty analysis tool for genetic parameter estimation (GLUE Tool). In Jones, JW, Hoogenboom, G, Wilkens, PW, Porter, CH and Tsuji, GY (eds), Decision Support System for Agrotechnology Transfer Version 4.5, vol. 3, Chapter 2. Honolulu, HI, USA: University of Hawaii, pp. 21–32.Google Scholar
Hoogenboom, G, Jones, JW, Wilkens, PW, Porter, CH, Boote, KJ, Hunt, LA, Singh, U, Lizaso, JL, White, JW, Uryasev, O, Royce, FS, Ogoshi, R, Gijsman, AJ and Tsuji, GY (2011) Decision Support System for Agrotechnology Transfer Version 4.5. Honolulu, HI, USA: University of Hawaii.Google Scholar
Hoogenboom, G, Jones, JW, Porter, CH, Wilkens, PW, Boote, KJ, Hunt, LH and Tsuji, GY (2013) Decision Support System for Agrotechnology Transfer Version 4.5 Volume 1: Overview. Honolulu, HI, USA: University of Hawaii.Google Scholar
Hunt, LA and Boote, KJ (1998) Data for model operation, calibration, and evaluation. In Tsuji, GY, Hoogenboom, G and Thornton, PK (eds), Understanding Options for Agricultural Production. Honolulu, HI, USA: Kluwer Academic Publishers and ICASA, pp. 9–39.Google Scholar
Hunt, LA, Pararajasingham, S, Jones, JW, Hoogenboom, G, Imamura, DT and Ogoshi, RM (1993) GENCALC: software to facilitate the use of crop model for analyzing field experiments. Agronomy Journal 85, 1090–1094.Google Scholar
Hunt, LA, Kuchar, L and Swanton, CJ (1998) Estimation of solar radiation for use in crop modeling. Agricultural and Forest Meteorology 91, 293–300.Google Scholar
Hunt, LA, White, JW and Hoogenboom, G (2001) Agronomic data: advances in documentation and protocols for exchange and use. Agricultural Systems 70, 477–492.Google Scholar
Jackson, ML (1965) Soil Chemical Analysis: Advanced Course. Madison, WI, USA: University of Wisconsin.Google Scholar
Jiang, M and Jin, Z (2009) A method for upscaling genetic parameters of CERES-Rice in regional applications. Rice Science 16, 292–300.Google Scholar
Jintrawet, A and Chinvanno, S (2011) Assessing impacts of ECHAM4 GCM climate change data on main season rice production systems in Thailand. APN Science Bulletin 1, 29–34.Google Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235–265.Google Scholar
Kammen, DM and Hassenzahl, DM (2001) Should We Risk It? Exploring Environmental, Health, and Technological Problem Solving. Princeton, NJ, USA: Princeton University Press.Google Scholar
Knörzer, H, Grözinger, H, Graeff-Hönninger, S, Hartung, K, Piepho, H-P and Claupein, W (2011) Integrating a simple shading algorithm into CERES-wheat and CERES-maize with particular regard to a changing microclimate within a relay-intercropping system. Field Crops Research 121, 274–285.Google Scholar
Kundu, SS, Skogerboe, GV and Walker, WR (1982) Using a crop growth simulation model for evaluating irrigation practices. Agricultural Water Management 5, 253–268.Google Scholar
Loague, K and Green, RE (1991) Statistical and graphical methods for evaluation solute transport models: overview and application. Journal of Contaminant Hydrology 7, 51–73.Google Scholar
Mahmood, R, Meo, M, David, RL and Mark, LM (2003) The CERES-Rice model-based estimates of potential monsoon season rainfed rice productivity in Bangladesh. The Professional Geographer 55, 259–273.Google Scholar
Matthews, RB, Stephens, W, Hess, T, Middleton, T and Graves, A (2002) Applications of crop/soil simulation models in tropical agricultural systems. Advances in Agronomy 76, 31–124.Google Scholar
Meier, U (2001) Growth Stages of Mono-and Dicotyledonous Plants: BBCH Monograph. Federal Biological Research Centre for Agriculture and Forestry, Berlin and Braunschweig, Germany.Google Scholar
Myung, IJ (2003) Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47, 90–100.Google Scholar
Pabico, JP (2008) Optimizing the cultivar coefficients in CERES-Rice model using simulated breeding. The Joint 19th Philippines Agricultural Engineering Week, 58th Philippine Society of Agricultural Engineers Annual National Convention, and 6th International Agricultural Engineering Conference and Exhibition, Laguna (on CD-ROM).Google Scholar
Phakamas, N, Jintrawet, A, Patanothai, A, Sringam, P and Hoogenboom, G (2013) Estimation of solar radiation based on air temperature and application with the DSSAT v4.5 peanut and rice simulation models in Thailand. Agricultural and Forest Meteorology 180, 182–193.Google Scholar
Pratt, PF (1965) Potassium. In Black, CA (ed.) Method of Soil Analysis. Part II, Chemical and Microbiological Properties. Agronomy Monograph no. 9. Wisconsin, Madison, USA: America Society Agronomy Inc., pp. 1022–1030.Google Scholar
Rice Department (2009) Rice Knowledge Bank [Online]. Available at http://www.ricethailand.go.th/Rkb/ (Accessed 22 June 2018).Google Scholar
Ritchie, JT, Alocilja, EC, Singh, U and Uehera, G (1987) IBSNAT and CERES-Rice model. In IRRI (ed.) Weather and Rice. Proceedings of the International Workshop on the Impact of Weather Parameter on Growth and Yield of Rice. Los Baños, the Philippines: International Rice Research Institute, pp. 271–281.Google Scholar
Singh, H, Singh, KN and Hasan, B (2007) Evaluation of CERES-Rice Model (V.4.0) under temperate conditions of Kashmir Valley, India. Cereal Research Communications 35, 1723–1732.Google Scholar
Timsina, J and Humphreys, E (2006 a) Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: a review. Agricultural Systems 90, 5–31.Google Scholar
Timsina, J and Humphreys, E (2006 b) Application of CERES-Rice and CERES-Wheat in research, policy and climate change studies in Asia: a review. International Journal of Agricultural Research 1, 202–225.Google Scholar
Wallach, D and Goffinet, B (1987) Mean squared error of prediction in model for studying ecological and agronomic systems. Biometrics 43, 561–573.Google Scholar
Yao, F, Xu, Y, Lin, E, Yokozawa, M and Zhang, J (2007) Assessing the impacts of climate change on rice yields in the main rice areas of China. Climatic Change 80, 395–409.Google Scholar