Phenotypic evaluation to define optimal sowing time for upland rice lines in the second crop in Campo das Vertentes region
Abstract
Rice is a staple food for more than half of the world’s population. In Brazil, upland rice cultivation in the southeastern region faces competition from soybean due to its higher profitability in recent years. In this context, developing more competitive rice lines and expanding the sowing window, such as incorporating rice into the second season, can enhance its integration into cropping systems. This study aimed to evaluate the performance of elite upland rice lines under different sowing dates during the second crop season. Field experiments were conducted in Lavras, Minas Gerais State, Brazil, across 4 sowing dates in 7-day intervals, starting on January 28, 2022. Eight genotypes were evaluated in a randomized block design with a two-way factorial scheme (8 genotypes × 4 sowing dates). The assessed traits included the number of days to flowering (NDFL), plant height (PH), tolerance to Helminthosporium oryzae, and grain yield (GY). The data were analyzed using mixed models based on the restricted maximum likelihood/best linear unbiased predictor. The results revealed significant genetic variability for NDFL and PH as well as significant sowing date effects on NDFL, PH, and GY. A sharp decline in performance was observed across sowing dates, with an increase of 24 days in NDFL, a 14% reduction in PH, and sterility rates reaching 100% at the last sowing. These findings highlight the importance of genotype selection and optimal sowing timing to sustain upland rice production during the second crop season.
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References
Abbas, A., Yu, P., Sun, L., Yang, Z., Chen, D., Cheng, S., & Cao, L. (2021). Exploiting genic male sterility in rice: From molecular dissection to breeding applications. Frontiers in Plant Science, 12(629314), 1-27. https://doi.org/10.3389/fpls.2021.629314
Abo-Yousef, M. I., Abd Elaty, M. S., Sorour, F. A., Salem, M., Talha, I. A., & Bahgt, M. M. (2024). Environmental stability and adaptation of several rice (Oryza Sativa L.) cultivars with different climate change. Egyptian Journal of Agronomy, 46(1), 103-113. https://doi.org/10.21608/agro.2024.268409.1416
Anilkumar, C., Azharudheen, T. P. M., Sah, R. P., Sunitha, N. C., Devanna, B. N., Marndi, B. C., & Patra, B. C. (2023). Gene based markers improve precision of genome-wide association studies and accuracy of genomic predictions in rice breeding. Heredity, 130(5), 335-345. http://doi.org/10.1038/s41437-023-00599-5
Cao, Y., Cai, H., & Sun, S. (2021). Effects of growth‐stage‐based limited irrigation management on the growth, yields, and radiation utilization efficiency of winter wheat in northwest China. Journal of the Science of Food and Agriculture, 101(14), 5819-5826. https://doi.org/10.1002/jsfa.11233
Carlos, F. S., Denardin, L. G. O., Martins, A. P., Anghinoni, I., Carvalho, P. C. F., Rossi, I., Burchain, M. P., Cereza, T., Carmona, F. C., & Camargo, F. A. O. (2020). Integrated crop–livestock systems in lowlands increase the availability of nutrients to irrigated rice. Land Degradation & Development, 31(18), 2962-2972. https://doi.org/10.1002/ldr.3653
Colombari Filho, J. M., & Rangel, P. H. N. (2015). Cultivares. In A. Borém & P. H. N. Rangel (Eds.), Arroz: do plantio à colheita (pp. 84-121). Ed. UFV.
Companhia Nacional de Abastecimento. (2024). Séries históricas das safras: grãos. CONAB. https://www.conab.gov.br/info-agro/safras/serie-historica-das-safras#gr%C3%A3os-2
Costa, N. H. A. D., Seraphin, J. C., & Zimmermann, F. J. P. (2002). Novo método de classificação de coeficientes de variação para a cultura do arroz de terras altas. Pesquisa Agropecuária Brasileira, 37(3), 243-249. https://doi.org/10.1590/S0100-204X2002000300003
Counce, P. A., Keisling, T. C., & Mitchell, A. J. (2000). A uniform, objective, and adaptive system for expressing rice development. Crop Science, 40(2), 436-443. https://doi.org/10.2135/cropsci2000.402436x
Dantas, A. A. A., Carvalho, L. G., & Ferreira, E. (2007). Classificação e tendências climáticas em Lavras, MG. Ciência e Agrotecnologia, 31(6), 1862-1866. https://doi.org/10.1590/s1413-70542007000600039
Garcia, D. A., Mulhanga, A. D. I., Berchembrock, Y. V., Cardoso, F. P., Botelho, F. B. S., Santos, H. O., & Ribeiro, A. O. (2024). Performance of elite upland rice lines at low temperatures. Pesquisa Agropecuária Brasileira, 59, 1-8. https://doi.org/10.1590/S1678-3921.pab2024.v59.03605
Goulart, R. Z., Reichert, J. M., & Rodrigues, M. F. (2020). Cropping poorly-drained lowland soils: Alternatives to rice monoculture, their challenges and management strategies. Agricultural Systems, 177, 102715. https://doi.org/10.1016/j.agsy.2019.102715
Heinemann, A. B., Stone, L. F., Silva, G. C. C., Matta, D. H., Justino, L. F., & Silva, S. C. (2024). Climate drivers affecting upland rice yield in the central region of Brazil. Pesquisa Agropecuária Tropical, 54, 1-9. https://doi.org/10.1590/1983-40632024v5477222
Instituto Nacional de Meteorologia. (2022). Todos os dados meteorológicos. INMET. http://portal.inmet.gov.br/
International Rice Research Institute. (1996). Standard evaluation system for rice (4th ed.). INGER Genetic Resources Center.
Li, J., Xiao, Q., Wu, H., & Li, J. (2024). Unpacking the global rice trade network: Centrality, structural holes, and the nexus of food insecurity. Foods, 13(4), 1-26. https://doi.org/10.3390/foods13040604
Liu, L.-W., Hsieh, S.-H., Lin, S.-J., Wang, Y.-M., & Lin, W.-S. (2021). Rice blast (Magnaporthe oryzae) occurrence prediction and the key factor sensitivity analysis by machine learning. Agronomy, 11(4), 1-15. http://doi.org/10.3390/agronomy11040771
Niu, Y., Chen, T., Zhao, C., & Zhou, M. (2021). Improving crop lodging resistance by adjusting plant height and stem strength. Agronomy, 11(12), 1-14. https://doi.org/10.3390/agronomy11122421
Peng, S., Khush, G. S., Virk, P., Tang, Q., & Zou, Y. (2008). Progress in ideotype breeding to increase rice yield potential. Field Crops Research, 108(1), 32-38. https://doi.org/10.1016/j.fcr.2008.04.001
Pimentel Gomes, F. (2000). Curso de estatística experimental (14. ed.). Degaspari.
R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Rubaiyath Bin Rahman, A. N. M., & Zhang, J. (2022). Trends in rice research: 2030 and beyond. Food and Energy Security, 12(2), 1-17. https://doi.org/10.1002/fes3.390
Ramirez-Villegas, J., Heinemann, A. B., Castro, A. P., Breseghello, F., Navarro-Racines, C., Li, T., Rebolledo, M. C., & Challinor, A. J. (2018). Breeding implications of drought stress under future climate for upland rice in Brazil. Global Change Biology, 24(5), 2035-2050. https://doi.org/10.1111/gcb.14071
Resende, M. D. V., & Alves, R. S. (2022). Statistical significance, selection accuracy, and experimental precision in plant breeding. Crop Breeding and Applied Biotechnology, 22(3), 1-19. https://doi.org/10.1590/1984-70332022v22n3a31
Resende, M. D. V., & Duarte, J. B. (2007). Precisão e controle de qualidade em experimentos de avaliação de cultivares precision and quality control in variety trials. Pesquisa Agropecuária Tropical, 37(3), 182-194. https://revistas.ufg.br/pat/article/view/1867
Ribas, G. G., Zanon, A. J., Streck, N. A., Pilecco, I. B., Souza, P. M., Heinemann, A. B., & Grassini, P. (2021). Assessing yield and economic impact of introducing soybean to the lowland rice system in southern Brazil. Agricultural Systems, 188, 103036. https://doi.org/10.1016/j.agsy.2020.103036
Scott, A. J., & Knott, M. (1974). A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30(3), 507-512. https://doi.org/10.2307/2529204
Soratto, R. P., Guidorizzi, F. V. C., Sousa, W. S., Gilabel, A. P., Job, A. L. G., & Calonego, J. C. (2022). Effects of previous fall–winter crop on spring–summer soybean nutrition and seed yield under no-till system. Agronomy, 12(12), 1-17. https://doi.org/10.3390/agronomy12122974
Streck, N. A., Bosco, L. C., Michelon, S., Walter, L. C., & Marcolin, E. (2006). Duração do ciclo de desenvolvimento de cultivares de arroz em função da emissão de folhas no colmo principal. Ciência Rural, 36(4), 1086-1093. https://doi.org/10.1590/S0103-84782006000400007
United States Department of Agriculture. (2023). Agricultural Research Service, Global Production. Data Production. USDA-FAS. https://fas.usda.gov/data/production
Wani, S. A., Qayoom, S., Bhat, M. A., Lone, B. A., & Nazir, A. (2016). Influence of sowing dates and nitrogen levels on growth, yield and quality of scented rice cv. Pusa Sugandh-3 in Kashmir valley. Journal of Applied and Natural Science, 8(3), 1704-1709. https://doi.org/10.31018/jans.v8i3.1026
Yu, K., Zhao, S., Sun, B., Jiang, H., Hu, L., Xu, C., Yang, M., Han, X., Chen, Q., & Qi, Z. (2024). Enhancing food production through modern agricultural technology. Plant, Cell & Environment. http://dx.doi.org/10.1111/pce.15299
Zhang, J., Tong, T., Potcho, P. M., Huang, S., Ma, L., & Tang, X. (2020). Nitrogen effects on yield, quality and physiological characteristics of giant rice. Agronomy, 10(11), 1-16. https://doi.org/10.3390/agronomy10111816
Zhao, H., Huang, X., Yang, Z., Li, F., & Ge, X. (2023). Synergistic optimization of crops by combining early maturation with other agronomic traits. Trends in Plant Science, 28(10), 1178-1191. http://doi.org/10.1016/j.tplants.2023.04.011
Zheng, Z., Hey, S., Jubery, T., Liu, H., Yang, Y., Coffey, L., Miao, C., Sigmon, B., Schnable, J. C., Hochholdinger, F., Ganapathysubramanian, B., & Schnable, P. S. (2019). Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant Physiology, 182(2), 977-991. https://doi.org/10.1104/pp.19.00752
Copyright (c) 2026 Arsénio Daniel Ivo Mulhanga, Dionatas Alex Garcia, Yasmin Vasques Berchembrock, Ivan Natividade Júlio Zevo, Marcelo Araújo Junqueira Ferraz, Flávia Barbosa Silva Botelho (Autor)
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Funding data
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Conselho Nacional de Desenvolvimento Científico e Tecnológico
Grant numbers 1
