Leaf area index and light interception relationship with seed yield of soybean cultivars under reduced seeding rates
DOI:
https://doi.org/10.5433/1679-0359.2024v45n5p1639Keywords:
Glycine max L. (Merril). , Minimum optimal plant population, Normalized difference vegetation index, Plant density, Plant population.Abstract
Owing to the recent increase in the cost of germplasm, biotechnology royalties, and seed treatments, studies have been conducted to analyze the capacity of modern cultivars to maintain yield under reduced seeding rates (SR). This study elucidated the effect of reduced SR on the leaf area index (LAI) and light interception by the canopy of soybean cultivars with contrasting branching plasticity and identified the association of these variables with seed yield. Field experiments were conducted in randomized blocks using BRS 1010IPRO (high plasticity) and NS 5959IPRO (medium plasticity) cultivars, with five SRs: 100, 80, 60, 40, and 20% of the recommended SR. The SR reduction did not reduce the seed yield to the point where the LAI and light interception in the reproductive phase were similar to those obtained with the recommended SR. Higher LAI and light interception in cultivars with higher branching plasticity confer greater potential for reducing the SR. The minimum optimal SR (MOSR) for cumulative LAI, Normalized Difference Vegetation Index (NDVI), and intercepted photosynthetic active radiation (IPAR) in the reproductive phase was closer to the MOSR for seed yield than in the vegetative phase or the total crop cycle, indicating “luxury growth” in the vegetative phase at the recommended SRs. Cumulative LAI, NDVI, and IPAR in the reproductive phase had a greater correlation with yield than those in the vegetative phase or the total cycle. The cumulative NDVI had a higher correlation with seed yield than cumulative LAI and IPAR.
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Agudamu, T. Y., & Shiraiwa, T. (2016). Branch development responses to planting density and yield stability in soybean cultivars. Plant Production Science, 19(3), 331-339. doi: 10.1080/1343943X.2016.1157443 DOI: https://doi.org/10.1080/1343943X.2016.1157443
Andrade, T. G., Andrade, A. S. D., Jr., Souza, M. O., Lopes, J. W. B., & Vieira, P. F. D. M. J. (2022). Soybean yield prediction using remote sensing in southwestern Piauí State, Brazil. Revista Caatinga, 35(1), 105-116. doi: 10.1590/1983-21252022V35N111RC DOI: https://doi.org/10.1590/1983-21252022v35n111rc
Balbinot, A. A., Jr., Oliveira, M. C. N. de, Franchini, J. C., Debiasi, H., Zucareli, C., Ferreira, A. S., & Werner, F. (2018). Phenotypic plasticity in a soybean cultivar with indeterminate growth type. Pesquisa Agropecuária Brasileira, 53(9), 1038-1044. doi: 10.1590/S0100-204X2018000900007 DOI: https://doi.org/10.1590/s0100-204x2018000900007
Board, J. (2000). Light interception efficiency and light quality affect yield compensation of soybean at low plant populations; light interception efficiency and light quality affect yield compensation of soybean at low plant populations. Crop Science, 40(5), 1285-1294. doi: 10.2135/cropsci2000.4051285x DOI: https://doi.org/10.2135/cropsci2000.4051285x
Board, J. E. (2004). Soybean cultivar differences on light interception and leaf area index during seed filling. Agronomy Journal, 96(1), 305-310. doi: 10.2134/AGRONJ2004.3050 DOI: https://doi.org/10.2134/agronj2004.3050
Bueno, A. F., Panizzi, A. R., Hunt, T. E., Dourado, P. M., Pitta, R. M., & Gonçalves, J. (2021). Challenges for adoption of integrated pest management (ipm): the soybean example. Neotropical Entomology, 50(1), 5-20. doi: 10.1007/s13744-020-00792-9 DOI: https://doi.org/10.1007/s13744-020-00792-9
Carciochi, W. D., Schwalbert, R., Andrade, F. H., Corassa, G. M., Carter, P., Gaspar, A. P., Schmidt, J., & Ciampitti, I. A. (2019). Soybean seed yield response to plant density by yield environment in North America. Agronomy Journal, 111(4), 1923-1932. doi: 10.2134/AGRONJ2018.10.0635 DOI: https://doi.org/10.2134/agronj2018.10.0635
Carpenter, A. C., & Board, J. E. (1997). Branch yield components controlling soybean yield stability across plant populations. Crop Science, 37(3), 885-891. doi: 10.2135/CROPSCI1997.0011183X003700030031X DOI: https://doi.org/10.2135/cropsci1997.0011183X003700030031x
Corassa, G. M., Amado, T. J. C., Strieder, M. L., Schwalbert, R., Pires, J. L. F., Carter, P. R., & Ciampitti, I. A. (2018). Optimum soybean seeding rates by yield environment in Southern Brazil. Agronomy Journal, 110(6), 2430-2438. doi: 10.2134/AGRONJ2018.04.0239 DOI: https://doi.org/10.2134/agronj2018.04.0239
Edwards, J. T., Purcell, L. C., & Karcher, D. E. (2005). Soybean yield and biomass responses to increasing plant population among diverse maturity groups: II. Light interception and utilization. Crop Science, 45(5), 1778-1785. doi: 10.2135/CROPSCI2004.0570 DOI: https://doi.org/10.2135/cropsci2004.0570
Esquerdo, J. C. D. M., Zullo, J., & Antunes, J. F. G. (2011). Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil. International Journal of Remote Sensing, 32(13), 3711-3727. doi: 10.1080/01431161003764112 DOI: https://doi.org/10.1080/01431161003764112
Fehr, W. R., & Caviness, C. E. (1977). Stages of soybean development. Iowa State University. https://dr.lib.iastate.edu/entities/publication/58c89bfe-844d-42b6-8b6c-2c6082595ba3
Ferreira, A. S., Zucareli, C., Werner, F., Fonseca, I. C. de B., & Balbinot, A. A., Jr. (2020). Minimum optimal seeding rate for indeterminate soybean cultivars grown in the tropics. Agronomy Journal, 112(3), 2092-2102. doi: 10.1002/AGJ2.20188 DOI: https://doi.org/10.1002/agj2.20188
Gaspar, A. P., & Conley, S. P. (2015). Responses of canopy reflectance, light interception, and soybean seed yield to replanting suboptimal stands. Crop Science, 55(1), 377-385. doi: 10.2135/CROPSCI2014.03.0200 DOI: https://doi.org/10.2135/cropsci2014.03.0200
Glier, C. A. S., Duarte, J. B., Jr., Fachin, G. M., Costa, A. C. T. da, Guimarães, V. F., & Mrozinski, C. R. (2015). Defoliation percentage in two soybean cultivars at different growth stages. Revista Brasileira de Engenharia Agrícola e Ambiental, 19(6), 567-573. doi: 10.1590/1807-1929/AGRIAMBI.V19N6P567-573 DOI: https://doi.org/10.1590/1807-1929/agriambi.v19n6p567-573
Hayashida, R., Godoy, C. V., Hoback, W. W., & Freitas, A. B. de. (2021). Are economic thresholds for IPM decisions the same for low LAI soybean cultivars in Brazil? Pest Management Science, 77(3), 1256-1261. doi: 10.1002/ps.6138 DOI: https://doi.org/10.1002/ps.6138
Li, T. (2019). Interdependent dynamics of LAI-ET across roofing landscapes: the mongolian and tibetan plateaus. Journal of Resources and Ecology, 10(3), 296-306. doi: 10.5814/J.ISSN.1674-764X.2019.03.008 DOI: https://doi.org/10.5814/j.issn.1674-764x.2019.03.008
Mathew, J. P., Herbert, S. J., Zhang, S., Rautenkranz, A. A. F., & Litchfield, G. V. (2000). Differential response of soybean yield components to the timing of light enrichment. Agronomy Journal, 92(6), 1156-1161. doi: 10.2134/AGRONJ2000.9261156X DOI: https://doi.org/10.2134/agronj2000.9261156x
Monteith, J. L. (1977). Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London B, Biological Sciences, 281(980), 277-294. doi: 10.1098/RSTB.1977.0140 DOI: https://doi.org/10.1098/rstb.1977.0140
Müller, M., Rakocevic, M., Caverzan, A., Chavarria, G., Müller, M., Rakocevic, M., Caverzan, A., & Chavarria, G. (2017). Grain yield differences of soybean cultivars due to solar radiation interception. American Journal of Plant Sciences, 8(11), 2795-2810. doi: 10.4236/AJPS.2017.811189 DOI: https://doi.org/10.4236/ajps.2017.811189
Nandan, R., Bandaru, V., He, J., Daughtry, C., Gowda, P., & Suyker, A. E. (2022). Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean. Remote Sensing, 14(21), 5301. doi: 10.3390/RS14215301/S1 DOI: https://doi.org/10.3390/rs14215301
Pereyra, V. M., Bastos, L. M., Borja Reis, A. F. de, Melchiori, R. J. M., Maltese, N. E., Appelhans, S. C., Vara Prasad, P. V., Wright, Y., Brokesh, E., Sharda, A., & Ciampitti, I. A. (2022). Early-season plant-to-plant spatial uniformity can affect soybean yields. Scientific Reports, 12(1), 1-10. doi: 10.1038/s41598-022-21385-z DOI: https://doi.org/10.1038/s41598-022-21385-z
Purcell, L. C. (2000). Soybean canopy coverage and light interception measurements using digital imagery. Crop Science, 40(3), 834-837. doi: 10.2135/CROPSCI2000.403834X DOI: https://doi.org/10.2135/cropsci2000.403834x
Rigsby, B., & Board, J. E. (2003). Identification of soybean cultivars that yield well at low plant populations. Crop Science, 43(1), 234-239. doi: 10.2135/CROPSCI2003.2340 DOI: https://doi.org/10.2135/cropsci2003.2340
Roznik, M., Boyd, M., & Porth, L. (2022). Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks. Remote Sensing Applications: Society and Environment, 25, 100693. doi: 10.1016/J.RSASE.2022.100693 DOI: https://doi.org/10.1016/j.rsase.2022.100693
Stepanov, A., Dubrovin, K., & Sorokin, A. (2022). Function fitting for modeling seasonal normalized difference vegetation index time series and early forecasting of soybean yield. The Crop Journal, 10(5), 1452-1459. doi: 10.1016/J.CJ.2021.12.013 DOI: https://doi.org/10.1016/j.cj.2021.12.013
Tagliapietra, E. L., Streck, N. A., Rocha, T. S. M. da, Richter, G. L., Silva, M. R. da, Cera, J. C., Guedes, J. V. C., & Junior Zanon, A. (2018). Optimum leaf area index to reach soybean yield potential in subtropical environment. Agronomy Journal, 110(3), 932-938. doi: 10.2134/AGRONJ2017.09.0523 DOI: https://doi.org/10.2134/agronj2017.09.0523
Thompson, N. M., Larson, J. A., Lambert, D. M., Roberts, R. K., Mengistu, A., Bellaloui, N., & Walker, E. R. (2015). Mid-South soybean yield and net return as affected by plant population and row spacing. Agronomy Journal, 107(3), 979-989. doi: 10.2134/AGRONJ14.0453 DOI: https://doi.org/10.2134/agronj14.0453
Umburanas, R. C., Yokoyama, A. H., Balena, L., Dourado, D., Neto, Teixeira, W. F., Zito, R. K., Reichardt, K., & Kawakami, J. (2019). Soybean yield in different sowing dates and seeding rates in a subtropical environment. International Journal of Plant Production, 13(2), 117-128. doi: 10.1007/S42106-019-00040-0/FIGURES/3 DOI: https://doi.org/10.1007/s42106-019-00040-0
Xiao, Z., Liang, S., Sun, R., Wang, J., & Jiang, B. (2015). Estimating the fraction of absorbed photosynthetically active radiation from the MODIS data based GLASS leaf area index product. Remote Sensing of Environment, 171, 105-117. doi: 10.1016/J.RSE.2015.10.016 DOI: https://doi.org/10.1016/j.rse.2015.10.016
Zhou, Z., Ding, Y., Liu, S., Wang, Y., Fu, Q., & Shi, H. (2022). Estimating the applicability of NDVI and SIF to gross primary productivity and grain-yield monitoring in China. Remote Sensing, 14(13), 3237. doi: 10.3390/RS14133237 DOI: https://doi.org/10.3390/rs14133237
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Copyright (c) 2024 André Sampaio Ferreira, Claudemir Zucareli, Inês Cristina de Batista Fonseca, Gabriel Danilo Shimizu, Flavia Werner, Douglas Mariani Zeffa, Alvadi Antonio Balbinot Junior
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