Evaluating Genetic Coefficients of KUML4 Mung Bean Variety for a Crop Simulation Model

Tawatchai Inboonchuay, Audthasit Wongmaneeroj, Napaporn Phankamolsil, Sirinapa Chungopast, Sahaschai Kongthon, Prakit Somta

Abstract


The Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model is a part of the management module that processes user inputs describing crop management. The precision and accuracy of cropping models require recent research to calibrate and validate models according to climate changes and new cultivars. This study aims to determine the genetic coefficient (GC) of the mung bean variety KUML4 for the CSM-CROPGRO Model and compare predicted data from the model with observed data in the phenology, growth, yield and yield component used in DSSAT. Mung bean is planted in two seasons (dry and rainy seasons) at two locations. Plant growth is monitored at V4, R3, R6 and R7. DSSAT CROPGRO-cowpea model is used to calibrate the GC with the generalized likelihood uncertainty estimation (GLUE). Results show that the GC evaluation of mung bean by using the second planting date in the highest growth and yield plot, then the genetic coefficient of KUML4 was calibrated by GLUE until predicted values of plant growth and development were close to observed values. The GC of KUML4 mung bean could estimate growth, such as shoot weight, leaf area index, and plant height. The prediction of mung bean yield is acceptable.


Keywords


DSSAT; Genetic coefficient; Mung bean

Full Text:

PDF

References


Ahmad, S., Ahmad, A., Ali, H., Hussain, A., Garcia y Garcia, A., Khan, M. A., Zia-Ul-Haq, M., Hasanuzzaman. M., & Hoogenboom, G. (2013). Application of the CSM-CERES-Rice model for evaluation of plant density and irrigation management of transplanted rice for an irrigated semiarid environment. Irrigation Science, 31(3), 491-506. https://doi.org/10.1007/s00271-012-0324-6

Anothai, J., Patanothai, A., Jogloy, S., Pannangpetch, K., Boote, K. J., & 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(2), 169-178. https://doi.org/10.1016/j.fcr.2008.04.012

Banterng, P., Hoogenboom, G., Patanothai, A., Singh, P., Wani, S. P., Pathak, P., Tongpoonpol, S., Atichart, S., Srihaban, P., Buranaviriyakul, S., Jintrawet, A., & Nguyen, T. C. (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(3), 231–242. https://doi.org/10.1111/j.1439-037X.2009.00408.x

Banterng, P., Patanothai, A., Pannangpetch, K., Jogloy, S., & Hoogenboom, G. (2003). Seasonal variation in the dynamic growth and development traits of peanut lines. The Journal of Agricultural Science, 141(1), 51-62. https://doi.org/10.1017/S0021859603003435

Banterng, P., Patanothai, A., Pannangpetch, K., Jogloy, S., & Hoogenboom, G. (2004). Determination and evaluation of genetic coefficients of peanut lines for breeding applications. European journal of agronomy, 21(3), 297-310. https://doi.org/10.1016/j.eja.2003.10.002

Biswas, J. C., Kalra, N., Maniruzzaman, M., Choudhury, A. K., Jahan, M. A. H. S., Hossain, M. B., Ishtiaque, S., Haque, M. M. & Kabir, W. (2018). Development of mungbean model (MungGro) and its application for climate change impact analysis in Bangladesh. Ecological Modelling, 384, 1-9. https://doi.org/10.1016/j.ecolmodel.2018.05.024

Bohn, H. L., Acneal, B. L. & O’connor, G. A. (2001). Soil Chemistry. 3rd ed. Wiley, New York.

Boote, K. J., Jones, J. W., Batchelor, W. D., Nafziger, E. D., & Myers, O. (2003). Genetic coefficients in the CROPGRO–Soybean model: Links to field performance and genomics. Agronomy Journal, 95(1), 32-51. https://doi.org/10.2134/agronj2003.3200

Boote, K. J., Jones, J. W., Hoogenboom, G., & Pickering, N. B. (1998). Simulation of crop growth: CROPGRO MODEL. In R. M. Peart & R. B. Curry (Eds), Agricultural systems modeling and simulation (pp. 651–692). Marcel Dekker, New York. https://doi.org/10.1201/9781482269765-18

Brady, N. C. & Weil, R. R. (2008). The nature and properties of soils. 14th ed. Prentice Hall, Upper Saddle River, New Jersey.

Buddhaboon, C., Jintrawet, A., & Hoogenboom, G. (2018). Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators. The Journal of Agricultural Science, 156(4), 482-492. https://doi.org/10.1017/S0021859618000527

Buol, S. W., Southard, R. J., Graham, R. C & McDaniel, P. A. (2011). Soil genesis and classification. 6th edition. Iowa State Press. A Blackwell Pub Co., Ames, Iowa, USA.

Casanova, D., Goudriaan, J., & Bosch, A. D. (2000). Testing the performance of ORYZA1, an explanatory model for rice growth simulation, for Mediterranean conditions. European Journal of Agronomy, 12(3-4), 175-189. https://doi.org/10.1016/S1161-0301(00)00048-4

Dahiya, P. K., Linnemann, A. R., Van Boekel, M. A. J. S., Khetarpaul, N., Grewal, R. B., & Nout, M. J. R. (2015). Mung bean: Technological and nutritional potential. Critical Reviews in Food Science and Nutrition, 55(5), 670-688. https://doi.org/10.1080/10408398.2012.671202

Eswaran, H., Almaraz, R., van den Berg, E. & Reich, P. (1997). An assessment of the soil resources of Africa in relation to productivity. Geoderma, 77(1), 1-18. https://doi.org/10.1016/S0016-7061(97)00007-4

Fernandes, E. C. M., Motavalli, P. P., Castilla, C., & Mukurumbira, L. (1997). Management control of soil organic matter dynamics in tropical land-use systems. Geoderma, 79(1-4), 49-67.https://doi.org/10.1016/S0016-7061(97)00038-4

Geng, S., Hess, C. E., & Auburn, J. (1990). Sustainable agricultural systems: concepts and definitions. Journal of Agronomy and Crop Science, 165(2‐3), 73-85. https://doi.org/10.1111/j.1439-037X.1990.tb00837.x

Guerra, L. C., Hoogenboom, G., y Garcia, A. G., Banterng, P., & Beasley Jr, J. P. (2008). Determination of cultivar coefficients for the CSM-CROPGRO-Peanut model using variety trial data. Transactions of the American Society of Agricultural and Biological Engineers, 51(4), 1471-1481. https://doi.org/10.13031/2013.25227

Hartkamp, A.D., Hoogenboom, G.& White, J.W. (2002). Adaptation of the CROPGRO growth model to velvet bean (Mucuna pruriens): I. Model development. Field Crops Research, 78(1), 9-25. https://doi.org/10.1016/S0378-4290(02)00091-6

He, J., Jones, J. W., Graham, W. D., & Dukes, M. D. (2010a). Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agricultural Systems, 103(5), 256-264. https://doi.org/10.1016/j.agsy.2010.01.006

He, J., Porter, C. H., Wilkens, P. W., Marin, F., Hu, H., Jones, J. W., Hoogenboom, G. & Tsuji, G. Y. (2010b). Generalized likelihood uncertainty analysis tool for genetic parameter estimation (GLUE Tool). In J. W. Jones, G. Hoogenboom, P. W. Wilkens, C. H. Porter, & G. Y. Tsuji (Eds), Decision support system for agrotechnology transfer version 4.5 (pp. 21–32.). vol. 3, Chapter 2. Honolulu, HI, USA: University of Hawaii.

Hoogenboom, G., Jones, J., Porter, C., Wilkens, P., Boote, K., Hunt, L., & Tsuji, G. (2010). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.51 (Vol. 1).

Hoogenboom, G., Jones, J.W., Wilkens, P. W., Porter, C. H., Batchelor, W. D., Hunt, L. A., Boote, K. J., Singh, U., Uryasev, O., Bowen, W. T., Gijsman, A. J., Du Toit, A. S., White, J. W. & Tsuji, G. Y. (2004). Decision support system for agrotechnology transfer version 4.0. [CD-ROM]. University of Hawaii, Honolulu, HI.

Hoogenboom, G., Porter, C. H., Shelia, V., Boote, K. J., Singh, U., White, J. W., Hunt, L. A., Ogoshi, R., Lizaso, J. I., Koo, J. & Asseng, S. (2017) Decision support system for agrotechnology transfer version (DSSAT) 4.7 [CD-ROM]. University of Hawaii, Honolulu, Hawaii.

Hoogenboom, G., Wilkens, P. W. & Tsuji, G. W. (1999) DSSAT version 3. Vol. 4. Honolulu, Hawaii: University of Hawaii.

Phakamas, N., Patanothai, A., Pannangpetch, K., Jogloy, S., & Hoogenboom, G. (2008). Dynamic patterns of components of genotype× environment interaction for pod yield of peanut over multiple years: A simulation approach. Field crops research, 106(1), 9-21. https://doi.org/10.1016/j.fcr.2007.10.008

Phankamolsil, N., Chungopast, S., Sonsri, K., Duangkamol, K., Polfukfang, S., & Somta, P. (2023). Decision support system for selecting mung bean cultivation sites in central Thailand based on soil suitability class. Agronomy, 13, 1030. https://doi.org/10.3390/agronomy13041030

Robertson, G. P. & Groffman, P. M. (2007). Nitrogen transformation. In E. A. Paul, (Ed). Soil microbiology, biochemistry and ecology (pp. 341–364). Springer, New York, USA. https://doi.org/10.1016/B978-0-08-047514-1.50017-2

Sanchez, P. A. (2019). Properties and management of soils in the tropics. 2nd edition. Cambridge University Press, UK. https://doi.org/10.1017/9781316809785

Sequeros, T., Ochieng, J., Schreinemachers, P., Binagwa, P. H., Huelgas, Z. M., Hapsari, R. T., Juma, M. O., Kangile, J. R., Karimi, R., Khaririyatun, N., Mbeyagala, E. K., Mvungi, H., Nair, R. M., Sanya, L. N., Nguyen, T. T. L., Phommalath, S., Pinn, T., Simfukwe, E., & Suebpongsang, P. (2021). Mungbean in Southeast Asia and East Africa: Varieties, practices and constraints. Agriculture & Food Security, 10(1), 2. https://doi.org/10.1186/s40066-020-00273-7

Soil Survey Division Staff. (1993). Soil survey manual. united states department of agriculture handbook no. 18. United States Department of Agriculture, United States Government Printing Office, Washington, DC.

Thongthip, N., Kongsil, P., Somta, P., & Chaisan, T. (2023). Identification of important morphology for waterlogging tolerance from developed mung bean F2 population. Chilean journal of agricultural research, 83(2), 236-247. http://dx.doi.org/10.4067/s0718-58392023000200236

Tongyai, C. (1994) Impact of climate change on simulated rice production in Thailand. In C. Rosenzweig & A. Iglesian (Eds), Implications of climate change for international agriculture: Crop modelling study (pp. 371–386). US Environmental Protection Agency. EPA 230-B-94-003, Washington DC.

Tsuji, G. Y., Hoogenboom, G., & Thornton, P. K. (1998) Understanding options for agricultural production. systems approaches for sustainable agricultural development. Kluwer Academic Publishers, Dordrecht, the Netherlands. https://doi.org/10.1007/978-94-017-3624-4

Wallach, D., & Goffinet, B. (1987). Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics, 43(3), 561-573. https://doi.org/10.2307/2531995

Weil, R.R., & Brady, N.C. (2017). The nature and properties of soils. 15th edition. Pearson Education, Inc., New Jersey, USA.

Willmott, C. J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63(11), 1309-1313. https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2

Zhuang, J. X., Jiang, H. Y., Liu, L. L., Wang, F. F., Tang, L., Zhu, Y., & Cao, W. X. (2013). Parameters optimization of rice development stage model based on individual advantages genetic algorithm. Scientia Agricultura Sinica, 46(11), 2220-2231. https://doi.org/10.3864/j.issn.0578-1752.2013.11.005




DOI: http://doi.org/10.17503/agrivita.v46i3.4324

Copyright (c) 2024 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.