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

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

doi:10.17503/agrivita.v46i3.4324

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

AGRIVITA, Journal of Agricultural Science · Vol. 46 No. 3 (2024) · hlm. 425–438

DOI: 10.17503/agrivita.v46i3.4324
ISSN: 2477-8516, 0126-0537
Terbit: 2024-09-05
Penerbit: Faculty of Agriculture University of Brawijaya in collaboration with PERAGI
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc/4.0

Abstrak

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.

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Kasetsart University
Ministry of Agriculture and Cooperatives
University of Brawijaya
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Rekan penulis	Organisasi	Artikel bersama
Kuswanto Kuswanto	University of Brawijaya	2
Chaisit Thongjoo	Kasetsart University	2
Peerasak Srinives	Kasetsart University	1
Kiattisak Sonsri	Kasetsart University	1
Pongpet Pongsivapai	Kasetsart University	1
Pranee Chomun	Afiliasi tidak diketahui	1
Sujitra Thaithet	Afiliasi tidak diketahui	1
Sutdacha Khunthong	Afiliasi tidak diketahui	1
Natcha Sornhiran	Afiliasi tidak diketahui	1
Sunita Sangthong	Afiliasi tidak diketahui	1
Sunchai Phungern	Afiliasi tidak diketahui	1
Kanokkorn Sinma	Afiliasi tidak diketahui	1
Suphachai Amkha	Afiliasi tidak diketahui	1

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Sumber metadata: OAI-PMH (metadata jurnal) https://agrivita.ub.ac.id/index.php/agrivita/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Evaluating Genetic Coefficients of KUML4 Mung Bean Variety for a Crop Simulation Model</dc:title>
	<dc:creator>Inboonchuay, Tawatchai</dc:creator>
	<dc:creator>Wongmaneeroj, Audthasit</dc:creator>
	<dc:creator>Phankamolsil, Napaporn</dc:creator>
	<dc:creator>Chungopast, Sirinapa</dc:creator>
	<dc:creator>Kongthon, Sahaschai</dc:creator>
	<dc:creator>Somta, Prakit</dc:creator>
	<dc:subject xml:lang="en-US">agronomy and Soil sciences</dc:subject>
	<dc:subject xml:lang="en-US">DSSAT; Genetic coefficient; Mung bean</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Agriculture University of Brawijaya in collaboration with PERAGI</dc:publisher>
	<dc:contributor xml:lang="en-US">This work was supported by funding from the Kasetsart University Research and Development Institute (KURDI), Bangkok, Thailand (FF(KU)1.64).</dc:contributor>
	<dc:date>2024-09-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://agrivita.ub.ac.id/index.php/agrivita/article/view/4324</dc:identifier>
	<dc:identifier>10.17503/agrivita.v46i3.4324</dc:identifier>
	<dc:source xml:lang="en-US">AGRIVITA Journal of Agricultural Science; Vol 46, No 3 (2024); 425-438</dc:source>
	<dc:source>2477-8516</dc:source>
	<dc:source>0126-0537</dc:source>
	<dc:source>10.17503/agrivita.v46i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/view/4324/1751</dc:relation>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/downloadSuppFile/4324/2960</dc:relation>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/downloadSuppFile/4324/2961</dc:relation>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/downloadSuppFile/4324/3242</dc:relation>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/downloadSuppFile/4324/3243</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 The Author(s)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
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