An Integrated IDOCRIW-MCDM Framework for the Evaluation and Selection of Indian Mustard (Brassica juncea) Genotypes
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Authors
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Nidahamteinsar Papang
North Eastern Hill University, Shillong
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Priya Sharma
University of Delhi, Delhi
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Bishal Gurung
Fortune Institute of International Business, New Delhi
Keywords:
Multi-criteria decision making (MCDM); TOPSIS; VIKOR; COPRAS; Genotypes; IDOCRIWAbstract
Identifying the best-performing genotypes of Indian mustard (Brassica juncea) across multiple agronomic criteria is rarely straightforward, as no
single genotype tends to excel across the board. This study developed a systematic approach to this challenge, drawing on two independent datasets
of genotypes information recorded under well-defined evaluation conditions. Rather than relying on intuition, this study builds a structured decision
framework using three well-established Multi-Criteria Decision-Making (MCDM) methods: the Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS), ViseKriterijumsaOptimizacija I KompromisnoResenje (VIKOR), and Complex Proportional Assessment (COPRAS). Since
neither dataset provides predefined criterion weights, the Integrated Determination of Objective Criteria Weights (IDOCRIW) method is applied
to derive them objectively from the data itself. For the first dataset, TOPSIS and COPRAS arrive at identical rankings (ρ = 1.000), while VIKOR
diverges sharply (ρ = −1.000), reflecting its compromise-based logic. Results obtained that genotype A5 (IC212031) consistently emerges as the top
performer. For the second dataset, VIKOR and COPRAS both placed A17 (NRC YS 5-2) at the top and A25 (Anuradha) at the bottom, an agreement
at both extremes that highlights the reliability and robustness of the proposed framework
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References
Johannsen, W. (1911). The genotype conception of heredity. The
American Naturalist, 45(531), 129-159.
Przełęcki, M. (1964). On the concept of genotype. In Form and Strategy
in Science: Studies Dedicated to Joseph Henry Woodger on the
Occasion of his Seventieth Birthday (pp. 315-329). Dordrecht:
Springer Netherlands.
Sur, B., Rout, S., Singla, S., Mandal, R., Nath, S., Maying, B., ...and
Roy, S.K. (2023). Evaluation of mustard genotypes [Brassica
juncea (L.) Czern and Coss] for quantitative criteria and character
association of seed yield and yield components at sub Himalayan
region of West Bengal (India). Plant Science Today, 10(1),
166-178.
Rout, S. (2021). Studies on combining ability for seed yield along
with its attributing characters and aphid resistance in mustard
[Brassica juncea (L.) Czern&Coss.] (Doctoral dissertation,
UTTAR BANGA KRISHI VISWAVIDYALAYA).
Gomes, C.F.S., Costa, H.G., and de Barros, A.P. (2017). Sensibility
analysis of MCDA using prospective in Brazilian energy sector.
Journal of Modelling in Management, 12(3), 475-497.
Hezer, S., Gelmez, E., &Özceylan, E. (2021). Comparative analysis
of TOPSIS, VIKOR and COPRAS methods for the COVID-19
Regional Safety Assessment. Journal of infection and public
health, 14(6), 775-786.
de Matos Lessa, M.S.C., Amaral, T.M., Leão, P.C.S., and Oliva, J.T.
(2024). Multi-criteria decision analysis applied to Brazilian
grapevine genotype selection. Journal of Food Composition and
Analysis, 130, 106126.
de Oliveira, B.R., Zuffo, A.M., Aguilera, J.G., Steiner, F., Ancca, S.M.,
Flores, L.A.P., and Gonzales, H.H.S. (2022). Selection of soybean
genotypes under drought and saline stress conditions using
Manhattan distance and TOPSIS. Plants, 11(21), 2827.
Mondal, P., Biswas, T., Maji, P., and Majumder, A. (2024). Application
of multiple criteria decision-making approach for ranking lentil
genotypes.International Journal of Agricultural and Applied
Sciences, 5(1), 31-36.
Gholizadeh Vazvani, M., Dashti, H., SaberiRiseh, R., and Loit, E.
(2025). Association analysis of response to take-all disease with
agronomic criteria and molecular markers and selection ideal
genotypes in bread wheat (Triticum aestivum L.) genotypes.
Molecular Breeding, 45(4), 36.
Prakash Kumar, P.K., Bhar, L.M., Paul, A.K., Samarendra Das, S.D.,
and Roy, H.S. (2018). Development of composite stability
measure using Multi Criteria Decisions Making (MCDM)
techniques, Journal of the Indian Society of agricultural Statistics,
72(2), 121-127.
Zavadskas, E.K., and Podvezko, V. (2016). Integrated determination
of objective criteria weights in MCDM. International Journal of
Information Technology & Decision Making, 15(02), 267-283.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method.
Omega, 53, 49-57.
Diakoulaki, D., Mavrotas, G., and Papayannakis, L. (1995). Determining
objective weights in multiple criteria problems: The critic method.
Computers & operations research, 22(7), 763-770.
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis,
Z., and Antucheviciene, J. (2021). Determination of objective
weights using a new method based on the removal effects of
criteria (MEREC). Symmetry, 13(4), 525.
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z.,
and Antucheviciene, J. (2018). Simultaneous evaluation of criteria
and alternatives (SECA) for multi-criteria decision-making.
Informatica, 29(2), 265-280.
331
decision making algorithm for decisive electric vehicle battery
recycling method. Results in engineering, 22, 102272.
Trinkūnienė, E., Podvezko, V., Zavadskas, E.K., Jokšienė, I.,
Vinogradova, I., and Trinkūnas, V. (2017). Evaluation of quality
assurance in contractor contracts by multi-criteria decision
making methods. Economic research-Ekonomskaistraživanja,
30(1), 1152-1180.
Alao, M.A., Popoola, O.M., and Ayodele, T.R. (2021). Selection of
waste-to-energy technology for distributed generation using
IDOCRIW-Weighted TOPSIS method: A case study of the City
of Johannesburg, South Africa. Renewable Energy, 178, 162-183.
Parthasarathy, T.N., Narayanamoorthy, S., Thilagasree, C.S.,
Marimuthu, P.R., Salahshour, S., Ferrara, M., and Ahmadian, A.
(2024). An enhanced fuzzy IDOCRIW-COCOSO multi-criteria
Rousseeuw, P.J. (1987). Silhouettes: a graphical aid to the interpretation
and validation of cluster analysis. Journal of computational and
applied mathematics, 20, 53-65.
Hwang, C.L., andYoon, K. (1981). Methods for multiple criteria
decision making. In Multiple criteria decision making: methods
and applications a state-of-the-art survey (pp. 58-191). Berlin,
Heidelberg: Springer Berlin Heidelberg.
Zavadskas, E.K., Kaklauskas, A., Peldschus, F., and Turskis, Z. (2007).
Multi-criteria assessment of road design solutions by using
the COPRAS method. The Baltic journal of Road and Bridge
engineering, 2(4), 195-203.
Opricovic, S., and Tzeng, G.H. (2002). Multicriteria planning of post
earthquake sustainable reconstruction. Computer‐Aided Civil and
Infrastructure Engineering, 17(3), 211-220
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