Assessment of rhizome yield of local Indonesian turmeric (Curcuma longa L.) during two growing seasons

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REVIANA AULIA
HARIS MAULANA
https://orcid.org/0000-0002-0446-6786
YOSHUA LIBERTY FILIO
NURUL AMELIA SHAFIRA
PUTRI ARDHYA ANINDITA
TARKUS SUGANDA
https://orcid.org/0000-0003-2143-9605
VERGEL CONCIBIDO
https://orcid.org/0000-0003-3313-055X
AGUNG KARUNIAWAN

Abstract

Abstract. Aulia R, Maulana H, Filio YL, Shafira NA, Anindita PA, Suganda T, Concibido V, Karuniawan A. 2022. Assessment of rhizome yield of local Indonesian turmeric (Curcuma longa L.) during two growing seasons. Biodiversitas 23: 2534-2543. In Indonesia, rhizome yield evaluation across diverse growing seasons is very useful for selecting turmeric genotypes that have the potential to be developed into commercial varieties. Evaluation using multiple measurements has high accuracy. This study aimed to select turmeric genotypes based on differences in agro-morphological traits, identify genotypes by season interactions (GEIs), and select stable and high-yielding turmeric during two seasons. The study was conducted for two growing seasons (Planting Season 1 in January - October 2019 and Planting Season 2 in January - October 2020) using an augmented design. Cluster analysis based on agro-morphological traits was used to select turmeric genotypes based on their proximity. A combined analysis of variance (ANOVA) was used to estimate the effect of GEIs on rhizome yield and yield attributes. Rhizome yield evaluation was analyzed using parametric and nonparametric measurements. The results showed that twenty-five turmeric genotypes were selected based on agro-morphological differences. GEIs caused the variation of rhizome yield with a contribution of 34.88%, and yield attributes of 7.04% for weight per plant (WPP), 10.97% for rhizome width (RW), 11.95% for Petiole length (PL), 47.25% for lamina length (LL), and 25.44% for lamina width (LW). The results of parametric and nonparametric measurements selected six genotypes of turmeric into ideal groups, namely G4 (CL-12), G6 (CL-20), G9 (CL-30), G12 (CL-37), G22 (CL-82), and G26 (Cek 1). They can be developed as superior local commodities and used as breeding materials for turmeric plants in the future.

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References
Ahmadi, J., Vaezi, B., Shaabani, A., Khademi, K., Fabriki Ourang, S., Pour-Aboughadareh, A., 2015. Non-parametric measures for yield stability in grass pea (Lathyrus sativus L.) advanced lines in semi warm regions. J. Agric. Sci. Technol. 17:1825–1838.
Anandaraj, M., Prasath, D., Kandiannan, K., Zachariah, T.J., Srinivasan, V., Jha, A.K., Singh, B.K., Singh, A.K., Pandey, V.P., Singh, S.P., Shoba, N., Jana, J.C., Kumar, K.R., Maheswari, K.U., 2014. Genotype by environment interaction effects on yield and curcumin in turmeric (Curcuma longa L.). Ind. Crops Prod. 53: 358–364. https://doi.org/10.1016/j.indcrop.2014.01.005
Anindita, P.A., Putri, T.K., Ustari, D., Maulana, H., Rachmadi, M., Concibido, V., Suganda, T., Karuniawan, A., 2020. Dataset of agromorphological traits in early population of turmeric (Curcuma longa L.) local accessions from Indonesia. Data Br. 33, 106552. https://doi.org/10.1016/j.dib.2020.106552
Changizi, M., Choukan, R., Heravan, E.M., Bihamta, M.R., Darvish, F., 2014. Evaluation of genotype × environment interaction and stability of corn hybrids and relationship among univariate parametric methods. Can. J. Plant Sci. 94: 1255–1267. https://doi.org/10.4141/CJPS2013-386
Eberhart, S.A., Russell, W.A., 1966. Stability Parameters for Comparing Varieties 1. Crop Sci. 6:36–40. https://doi.org/10.2135/cropsci1966.0011183x000600010011x
Federer, W.T., Reynolds, M., Crossa, J., 2001. Combining results from augmented designs over sites. Agron. J. 93: 389–395. https://doi.org/10.2134/agronj2001.932389x
Food and Agriculture Organization of The United Nation, 2019. http://www.fao.org/economic/est/trade-and-markets-home/en/#.YMWDo_kzbIU 2019.
Francis, T.R., Kannenberg, L.W., 1978. Yield stability studies in short-season maize: I. A descriptive method for grouping genotypes. Can. J. Plant Sci. 5: 1029–1034.
Gauch, H.G., 2013. A simple protocol for AMMI analysis of yield trials. Crop Sci. 53:1860–1869. https://doi.org/10.2135/cropsci2013.04.0241
Goksoy, A.T., Sincik, M., Erdogmus, M., Ergin, M., Aytac, S., Gumuscu, G., Gunduz, O., Keles, R., Bayram, G., Senyigit, E., 2019. The parametric and non-parametric stability analyses for interpreting genotype by environment interaction of some soybean genotypes. Turkish J. F. Crop. 24: 28–38. https://doi.org/10.17557/tjfc.562637
Hosseini, S.M.S., Maleki, A., Gholamian, M.R., 2010. Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Syst. Appl. 37: 5259–5264. https://doi.org/10.1016/j.eswa.2009.12.070
Huehn, M., 1990. Nonparametric measures of phenotypic stability . Part 1?: Theory. Euphytica 47:189–194.
Jayarathne, S., Koboziev, I., Park, O.H., Oldewage-Theron, W., Shen, C.L., Moustaid-Moussa, N., 2017. Anti-Inflammatory and Anti-Obesity Properties of Food Bioactive Components: Effects on Adipose Tissue. Prev. Nutr. Food Sci. 22:251–262. https://doi.org/10.3746/pnf.2017.22.4.251
Kang, M.S., 1988. A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res. Commun. 16:113–115.
Karuniawan, A., Maulana, H., Ustari, D., Dewayani, S., Solihin, E., Solihin, M.A., Amien, S., Arifin, M., 2021. Yield stability analysis of orange - Fleshed sweet potato in Indonesia using AMMI and GGE biplot. Heliyon 7, 1–10. https://doi.org/10.1016/j.heliyon.2021.e06881
Khalili, M., Pour-Aboughadareh, A., 2016. Parametric and non-parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. J. Agric. Sci. Technol. 18: 789–803.
Maulana, H., Dewayani, S., Solihin, M.A., Arifin, M., Amien, S., Karuniawan, A., 2020. Yield stability dataset of new orange fleshed sweet potato (Ipomoea batatas L. (lam)) genotypes in West Java, Indonesia. Data Br. 32, 106297. https://doi.org/10.1016/j.dib.2020.106297
Nassar, R., Huhn, M., 1987. Studies on Estimation of Phenotypic Stability: Tests of Significance for Nonparametric Measures of Phenotypic Stability. Biometrics 43:45. https://doi.org/10.2307/2531947
Plaisted, R.L., 1960. A Shorter Method for Evaluating the Ability of Selections to Yield Consistently Over Locations. Am. Potato J. 37: 166–172.
Plaisted, R.L., Peterson, L.C., 1959. A technique for evaluating the ability of selections to yield consistently in different locations or seasons. Am. Potato J. 36:381–385. https://doi.org/10.1007/BF02852735
Pour-Aboughadareh, A., M. Yousefian, H. Moradkhani, P. Poczai, and K.H.M.S., 2019. STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits. Appl. Plant Sci. 7. https://doi.org/10.1002/aps3.1211
Ruswandi, D., Syafii, M., Maulana, H., Ariyanti, M., Indriani, N.P., Yuwariah, Y., 2021. GGE Biplot Analysis for Stability and Adaptability of Maize Hybrids in Western Region of Indonesia. Int. J. Agron. 2021. https://doi.org/10.1155/2021/2166022
Sandeep, I.S., Kuanar, A., Akbar, A., Kar, B., Das, S., Mishra, A., Sial, P., Naik, P.K., Nayak, S., Mohanty, S., 2016. Agroclimatic zone based metabolic profiling of turmeric (Curcuma Longa L.) for phytochemical yield optimization. Ind. Crops Prod. 85: 229–240. https://doi.org/10.1016/j.indcrop.2016.03.007
Shakeri, A., Zirak, M.R., Wallace Hayes, A., Reiter, R., Karimi, G., 2019. Curcumin and its analogues protect from endoplasmic reticulum stress: Mechanisms and pathways. Pharmacol. Res. 146:104335. https://doi.org/10.1016/j.phrs.2019.104335
Sharifi, P., Aminpanah, H., Erfani, R., Mohaddesi, A., Abbasian, A., 2017. Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Sci. 24:173–180. https://doi.org/10.1016/j.rsci.2017.02.001
Shrishail, D., Handral Harish, K., Ravichandra, H., Tulsianand, G., Shruthi, S.D., 2013. Turmeric: Nature’s precious medicine. Asian J. Pharm. Clin. Res. 6: 10–16.
Shukla, G.K., 1972. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity (Edinb). 29:237–245. https://doi.org/10.1038/hdy.1972.87
Swanson, C.., Laughlin, L.., Finlay, D., Robinson, M.., Reichling, T.., Matheny, H., Bushnell, D.., 2010. Biomarker analysis confirms the antioxidant and antiinflammatory activity of a colorless turmeric extract in vitro. J. Am. Acad. Dermatol. 62:AB23. https://doi.org/10.1016/j.jaad.2009.11.133
Tanvir, E.M., Hossen, M.S., Hossain, M.F., Afroz, R., Gan, S.H., Khalil, M.I., Karim, N., 2017. Antioxidant properties of popular turmeric (Curcuma longa) varieties from Bangladesh. J. Food Qual. 2017. https://doi.org/10.1155/2017/8471785
Thennarasu, K., 1995. On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Diss. Univ. New Delhi.
Vaezi, B., Pour-Aboughadareh, A., Mohammadi, R., Mehraban, A., Hossein-Pour, T., Koohkan, E., Ghasemi, S., Moradkhani, H., Siddique, K.H.M., 2019. Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes. Euphytica 215. https://doi.org/10.1007/s10681-019-2386-5
Verma, R.K., Kumari, P., Kumar, V., Verma, R., Rani, N., Kumar, R., 2018. Principal component analysis in turmeric (Curcuma longa). J. Pharmacogn. Phytochem. 1097–1101. https://doi.org/10.13140/RG.2.2.32827.85281
Wang, B., Mao, J.F., Zhao, W., Wang, X.R., 2013. Impact of Geography and Climate on the Genetic Differentiation of the Subtropical Pine Pinus yunnanensis. PLoS One 8. https://doi.org/10.1371/journal.pone.0067345
Wricke, G., 1962. Übereine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Zeitschrift für Pflanzenzüchtung 47:92–96.
You, F.M., Duguid, S.D., Thambugala, D., Cloutier, S., 2013. Statistical analysis and field evaluation of the type 2 modified augmented design ( MAD ) in phenotyping of flax ( Linum usitatissimum ) germplasms in multiple environments. Aust. J. Crop Sci. 7:1789–1800.

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