Combined Tall and Short Genotypes Performance of Multi-canopy Rice in Dry and Wet Planting Seasons

Authors

DOI:

https://doi.org/10.29244/jtcs.13.02.467-478

Keywords:

combined analysis, cropping system, MGIDI, multi-canopy, rice breeding

Abstract

The productivity of existing rice varieties has plateaued, while the area of rice fields continues to decline and the population continues to grow. Efforts to develop higher-yielding rice varieties have been made, one of which is the development of the multi-canopy rice system. This study aimed to assess the agronomic performance of tall and short rice genotypes in a multi-canopy (MC) system across two planting seasons. This study evaluated 22 MC combinations and two monoculture checks (‘Inpari 32’ and ‘Ciherang’) using a randomized complete block design with 3 replications, across dry and wet seasons at IPB University’s Babakan station. The results of the study showed that a significant C×S interaction was found only for the number of filled grains (NFG) trait. A better performance is generally observed in the dry season. The best performing combination was D15 (IPB200-F-60 and IPB200-F-12), which produced the highest grain yield of 7.27 tons/ha, outperforming the check varieties. Total number of tillers (TNT), number of productive tillers (NPT), and number of filled grains (NFG) were positively and significantly correlated with yield. MGIDI analysis identified multi-canopy combinations D15, D6, D20, D19, D1, D12, and D9 as potential based on six traits. These combinations should be tested across a wider range of environments to further elucidate the effect of genotype-by-environment interaction.

References

Abdelrahman, M. A. E., Engel, B. S. M., Eid, M. M., & Aboelsoud, H. (2022). A new index to assess soil sustainability based on temporal changes of soil measurements using geomatics: An example from El- Sharkia, Egypt. All Earth, 34(1), 147–166. https://doi.org/10.1080/27669645.2022.2103953

Aryana, I. G. P. M. (2009). Adaptasi dan stabilitas hasil galur-galur padi beras merah pada tiga lingkungan tumbuh. Indonesian Journal of Agronomy, 37(2), 95–100.

Blanche, S. B., Utomo, H. S., Wenefrida, I., & Myers, G. O. (2009). Genotype × environment interaction of hybrid and varietal rice cultivars for grain yield and milling quality. Crop Science, 49(6), 2011– 2018.

Brown, D., Van den Bergh, I., De Bruin, S., Machida, L., & Van Etten, J. (2020). Data synthesis for crop variety evaluation: A review. Agronomy for Sustainable Development, 40(6), 1–20. https://doi.org/10.1007/s13593-020-00630-7

Cao, Y. J., Wang, L. C., Gu, W. R., Wang, Y. J., & Zhang, J. H. (2021). Increasing photosynthetic performance and post-silking N uptake by moderately decreasing the leaf source of maize under high planting density. Journal of Integrative Agriculture, 20, 494–510. https://doi.org/10.1016/S2095-3119(20)63378-0

Debnath, P., Chakma, K., Bhuiyan, M. S. U., Thapa, R., Pan, R., & Akhter, D. (2024). A novel multi-trait genotype ideotype distance index (MGIDI) for genotype selection in plant breeding: Application, prospects, and limitations. Crop Design, 3(4), 1–10. https://doi.org/10.1016/j.cropd.2024.100074

Fitrawaty, Hermawan, W., Yusuf, M., & Maipita, I. (2023). A simulation of increasing rice price toward the disparity of income distribution: Evidence from Indonesia. Heliyon, 9(3), 1–14. https://doi.org/10.1016/j.heliyon.2023.e13785

Gupta, V., Kumar, M., Singh, V., Chaudhary, L., Yashveer, S., & Sheoran, R. (2022). Genotype by environment interaction analysis for grain yield of wheat (Triticum aestivum L.) genotypes. Agriculture, 12(7), 1–15. https://doi.org/10.3390/agriculture12071002

He, D., Wang, E., Wang, J., & Lilley, J. M. (2017). Genotype × environment × management interaction of canola across China: A simulation study. Agricultural and Forest Meteorology, 247, 424–433. https://doi.org/10.1016/j.agrformet.2017.08.027

Hidayah, U. F., Suwarno, W. B., & Aswidinnoor, H. (2022). Genotype by environment analysis on multi-canopy cropping system in rice: Effects of different types of flag leaves. Agronomy Journal, 114(1), 1–10. https://doi.org/10.1002/agj2.20959

Hilmarsson, H. S., Rio, S., & Sánchez, J. I. (2021). Genotype by environment interaction analysis of agronomic spring barley traits in Iceland using AMMI, factorial regression model and linear mixed model. Agronomy, 11(3), 1–15. https://doi.org/10.3390/agronomy11030499

Listiyanto, R. (2023). Uji daya hasil pendahuluan 35 galur padi (Oryza sativa L.) generasi F5 dan F6 sebagai genotipe pendek galur multikanopi [Skripsi, IPB University, Faculty of Agriculture]. IPB University Repository.

Muthayya, S., Sugimoto, J. D., Montgomery, S., & Maberly, G. F. (2014). An overview of global rice production, supply, trade, and consumption. Annals of the New York Academy of Sciences, 1324(1), 7–14. https://doi.org/10.1111/nyas.12540

Olivoto, T., Diel, M. I., Schmidt, D., & Lucio, A. D. (2022). MGIDI: A powerful tool to analyze plant multivariate data. Plant Methods, 18(121), 1–13. https://doi.org/10.1186/s13007-022-00952-5

Olivoto, T., & Nardino, M. (2021). MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37(10), 1383–1389. https://doi.org/10.1093/bioinformatics/btaa981

Omar, S. C., Shaharudin, A., & Tumin, S. A. (2019). The status of the paddy and rice industry in Malaysia. Khazanah Research Institute.

Riswanto, A. (2025). Analisis daya pasang genotipe dan seleksi multi-karakter pada padi multi-kanopi [Undergraduate thesis, IPB University]. IPB University Repository.

Roy, D., Gaur, A. K., & Pandey, I. D. (2022). Estimation of G × E interaction by AMMI model in ‘antenna panel’ genotypes of rice (Oryza sativa L.). Brazilian Archives of Biology and Technology, 65, 1–14. https://doi.org/10.1590/1678-4324-2022220202

Rozaki, Z. (2020). COVID-19, agriculture, and food security in Indonesia. Review of Agricultural Science, 8, 243–261. https://doi.org/10.7831/ras.8.0_243

Saltz, J. B., Bell, A. M., Flint, J., Gomulkiewicz, R., Hughes, K. A., & Keagy, J. (2018). Why does the magnitude of genotype-by-environment interaction vary? Ecology and Evolution, 8, 6342–6353. https://doi.org/10.1002/ece3.4128

Samsuddin, S. S., Abdul Maulud, K. N., Sharil, S. A., Karim, O., & Pradhan, B. (2023). Monitoring of three stages of paddy growth using a multi-spectral vegetation index derived from UAV images. Egyptian Journal of Remote Sensing and Space Science, 26(4), 989–998. https://doi.org/10.1016/j.ejrs.2023.11.00

Satoto, Rumanti, I. A., & Widyastuti, Y. (2016). Yield stability of new hybrid rice across locations. Agrivita Journal of Agricultural Science, 38(1), 33–39. https://doi.org/10.17503/agrivita.v38i1.675

Sharifi, P. H., Aminpanah, R., Erfani, A., Mohaddesi, A., & Abbasian. (2017). Evaluation of genotype × environment interaction in rice based on the AMMI model in Iran. Rice Science, 24(3), 173–180. https://doi.org/10.1016/j.rsci.2017.05.002

Shrestha, J., Subedi, S., Subedi, N. R., Subedi, S., Kushwaha, U. K. S., Maharjan, B., & Subedi, M. (2021). Assessment of variability, heritability, and correlation in rice (Oryza sativa L.) genotypes. Natural Resources for Sustainable Development, 11(2), 181–192. https://doi.org/10.31924/nrsd.v11i2.077

Sholehah, M., Suwarno, W. B., Hapsari, V. P., Sulistyo, N. N., Marwiyah, S., & Aswidinnoor, H. (2024). Rice breeding for multi-canopy system: Estimations of genetic parameters and response to selection. Agronomy Journal, 116(5), 2129–2140. https://doi.org/10.1002/agj2.21629

Sitaresmi, T., Barokah, U., Mulyani, E., & Nugroho, R. J. (2025). Growth responses of superior varieties of rice in South Coast of Kebumen Regency. Akta Agrosia, 28(1), 1–7. https://doi.org/10.31186/aa.28.1.1-7

Smith, A., Norman, A., Kuchel, H., & Cullis, B. (2021). Plant variety selection using interaction classes derived from factor analytic linear mixed models: Models with independent variety effects. Frontiers in Plant Science, 12, 1–17. https://doi.org/10.3389/fpls.2021.737462

Stansfield, W. (1991). Schaum’s outline of theory and problems of genetics (3rd ed.). McGraw-Hill.

Statistic Indonesia. (2025). Luas panen dan produksi padi Indonesia 2024. BPS-Statistics Indonesia. https://www.bps.go.id/id/pressrelease/2025/02/03/2414/pada-2024-luas-panen-padi-mencapai-sekitar-10-05-juta-hektare-dengan-produksi-padi-sebanyak-53-14-juta-ton-gabah-kering-giling-gkg-.html

Sution, S., Sugiarti, T., Hartono, H., & Lehar, L. (2019). Pengaruh dua musim tanam berbeda dan beberapa varietas terhadap pertumbuhan dan produktivitas padi gogo. Agriekstensia, 18(1), 24–31. https://doi.org/10.34145/agriekstensia.v18i1.24

Uwiringiyimana, X., & Antriyandarti, E. (2025). Declining rice production in Indonesia: A case study of Central Java, challenges and food security strategies. Journal of Scientific Reports, 9(1), 177–192. https://doi.org/10.58970/JSR.1103

Widyastuti, L. P. Y., Suwarno, W. B., & Aswidinnoor, H. (2020). Genotype by environment analysis on a multi-canopy cropping system towards vertical harvest space in rice. Agronomy Journal, 112(6), 4568–4577. https://doi.org/10.1002/agj2.20405

Yang, R. C. (2014). Analysis of linear and non-linear genotype × environment interaction. Frontiers in Genetics, 5, 1–7. https://doi.org/10.3389/fgene.2014.00227

Yoshida, S. (1981). Fundamentals of rice crop science. International Rice Research Institute.

Zobel, B. J., & Talbert, J. T. (1984). Applied forest tree improvement. John Wiley & Sons.

Downloads

Published

2026-06-25

How to Cite

Rahayu, D. P., Suwarno, W. B., & Aswidinnoor, H. (2026). Combined Tall and Short Genotypes Performance of Multi-canopy Rice in Dry and Wet Planting Seasons. Journal of Tropical Crop Science, 13(02), 467–478. https://doi.org/10.29244/jtcs.13.02.467-478