Grain yield predictor model using agronomic aspects and vegetative indices of soybean
Abstract
The objective of this work was to evaluate, through a predictive model, which factors influence soybean yield, using agronomic aspects and vegetative indices, in addition to identifying the best soybean cultivar for the northwest of Rio Grande do Sul. The experimental design used was strips with randomized blocks, consisting of 10 cultivars and five blocks. Analyzing the quantitative characters, the positive contributions to yield came from the grain weight of vegetables with two grains, and the grain weight of the plant, having a strong influence on the average grain yield. The vegetable grain weight with three grains contributed negatively to the yield, as the average grain weight was lower than expected, lowering the average grain yield. For the vegetation indices that contributed positively, the GRAY, IGB and RGRI index stand out, while the BGI, GLI2, GRAY2, IGR, IRB, NRBDI and NG indices had negative contributions to the average grain yield. The NEO 581 E cultivar showed better yield performance, reaching 5780 kg ha⁻1, followed by the SOYTECH 541 I2X cultivar, which reached a yield of 5356 kg ha⁻1. The predictive model identified the main variables that influenced final yield, with Cercospora sojina and Corynespora casiicola, grain weight in three-grain legumes, plant grain weight, GRAY index, IGB, NGBDI and RGRI, the variables that contributed positively to grain yield.
Downloads
References
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L.M., & Sparovek, G. (2014). Köppen's climate classification map for Brazil. Meteorologische Zeitschrift, 6, 711–728.
Balest, D. S., Bisognin, D. A., Uhry, D. F., Badinelli, P. G., Waldow, D. A. G., & Zanon, A.J. (2022). Adaptability and Stability of Soybean Cultivars in Lowland Production System. Revista Ceres, 69(5), 506-512. https://doi.org/10.1590/0034-737X202269050002
Bruce, R. W., Rajcan, I., & Sulik, J. (2021). Classification of soybean pubescence from multispectral aerial imagery. Plant Phenomics, 4:2021:9806201. https://doi.org/10.34133/2021/9806201
Castro, D. G., Bruzzi, A. T., Zambiazzi, E. V., Rezende, P. M, Zuffo, A. M., Sales, A. P., & Bianchi, M. C. (2017). Physiological quality and enzymatic expression of RR® soybean seeds. Journal of Agricultural Sciences, 40(1), 222-235.
CONAB – Companhia Nacional de Abastecimento. (2024). Colheita de soja 2023/2024. Brasília, DF: CONAB.
Lana, F. D., Ziegelmann, P. K., Maia, A. D. H., Godoy, C. V., & Ponte, E. M. (2015). Meta-analysis of the relationship between crop yield and soybean rust severity. Phytopathology, 105(3), 307-315. https://doi.org/10.1094/PHYTO-06-14-0157-R
Degani, P. C., Leitner, C. P. S., Baggenstoss, S., Torkomian, A. L. V., & Alves Filho, A. G. (2021). Innovation in cultivars overview of soybean protections and registrations in Brazil. Navus: Management and Technology Journal, 11, 1-16. https://doi.org/10.22279/navus.2021.v11.p01-16.1524
Deshmukh, R., Sonah, H., Patil, G., Chen, W., Prince, S., Mutava, R., Vuong, T., Valliyodan, B, & Nguyen, H. T. (2014). Integrating omic approaches for abiotic stress tolerance in soybean. Frontiers in Plant science, 5, 2-12. https://doi.org/10.3389/fpls.2014.00244
Ishikawa, M. S., Ribeiro, N. R., Oliveira, E. C., Almeida, A. A. D., & Balbi-Peña, M. I. (2018). Selection of soybean cultivars for resistance to black root rot (Macrophomina phaseolina). Summa phytopathologica, 44(1), 38-44. https://doi.org/10.1590/0100-5405/178653
Lenhardt, G. M., Carvalho, I. R., Loro, M. V., Port, E. D., Lautenchleger, F. & Pradebon, L. C. (2023). Agronomic performance in different segregating soybean progenies. Revista Agropecuária Catarinenses, 36(1), 73-80. https://doi.org/10.52945/rac.v36i1.1341
Mengistu, A., Arelli, P., Bond, J., Nelson, R., Rupe, J., Shannon, G., & Wrather, A. (2013). Identification of soybean accessions resistant to Macrophomina phaseolina by field screening and laboratory validation. Plant Health Progress, 14(1), 1-20. https://doi.org/10.1094/PHP-2013-0318-01-RS
Oliveira, Z. B., Knies, A. E., Rodrigues, L. R., Schmidt, D. A., & Kury, A. G. (2021). Soybean productivity as a function of sowing time and supplementary irrigation in the central region of RS. IRRIGA Journal (Revista Brasileira de Irrigação e Drenagem), 26(4), 774-786. https://doi.org/10.15809/irriga.2021v26n4p774-786
Parmley, K., Nagasubramanian, K., Sarkar, S., Ganapathysubramanian, B. & Singh, AK. (2019). Development of optimized phenomic predictors for efficient plant breeding decisions using phenomic-assisted selection in soybean. Plant Phenomics. 28:2019:5809404. DOI: 10.34133/2019/5809404
Porta, F. S., Streck, N. A., Alberto, C. M., Silva, M. R., Tura, E. F., Uliana, G. F., & Zanon, A. J. (2024). Improving understanding of the plastochron of determinate and indeterminate soybean cultivars1. Revista Brasileira de Engenharia Agrícola e Ambiental, 28(10), p. e278299. https://doi.org/10.1590/1807-1929/agriambi.v28n10e278299
Pradebon, L. C., Carvalho, I. R., Silva, J. A. G., Loro, M. V., Pettenon, A. L., Roza, J. P. D., Schulz, A. D., & SILVA, T. B. (2024). Selection based on the phenomenic approach and agronomic ideotic of white oat. Agronomy Journal, 116(3), 1275-1289. https://doi.org/10.1002/agj2.21569
R Core Team. (2023). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org
Ribeiro, F. C., Rocha, F. S., Erasmo, E. A. L., Matos, E. P., & Costa, S. J. (2016). Management with insecticides aimed at controlling brown stink bug in intact soybeans. Revista de Agricultura Neotropical, 3(2), 48-53. https://doi.org/10.32404/rean.v3i2.1132
Siqueira, C. B., Oliveira, F. S., Peixoto, P. M. C., & Amaral, A. A. D. (2021). Importance and management of spontaneous plants from the perspective of agroecology-review. Nucleus, (16786602), 18(2).
Vasconcelos, E. S., Reis, M. S., Sediyama, T., & Cruz, C. D. (2015). Grain productivity, adaptability and stability of early and mid-cycle soybean genotypes. Seminar: Agricultural Sciences, 36(3), 1203-1214.
Teixeira, W. W. R., Battisti, R., Sentelhas, P. C., Moraes, M. F., & Oliveira Junior, A. (2019). Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups. Journal of Agronomy and Crop Science, 205(5), 533-544. https://doi.org/10.1111/jac.12343
Zanon, A. J., Streck, N. A., Rocha, T. S. M. D., Alberto, C. M., Bartz, A. C., Paula, G. M. D., & Bexaira, K. P. (2016). Effect of growth type on the development of modern soybean cultivars after the beginning of flowering in Rio Grande do Sul. Bragantia, 75(4), 445-458. https://doi.org/10.1590/1678-4499.535
Copyright (c) 2024 Agronomy Science and Biotechnology
This work is licensed under a Creative Commons Attribution 4.0 International License.
doi.org/10.33158/ASB