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Performance of soybean varieties differs according to yield class: a case study from Southern Brazil

  • G. M. Corassa
  • A. L. Santi
  • T. J. C. Amado
  • G. B. Reimche
  • R. Gaviraghi
  • M. B. Bisognin
  • J. L. F. Pires
Article
  • 86 Downloads

Abstract

Considering environmental conditions in the selection of soybean (Glycine max L.) varieties is a key strategy in ensuring high crop yield. Recently, the new technology of multi-hybrid planters has been making it more practical for farmers to plant different varieties together. However, there remains a gap in understanding how different varieties perform in terms of yield class; this knowledge is essential for technology adoption. The objectives of this study were to: (i) evaluate the agronomic performance of six soybean varieties at varying yield class (YC); (ii) quantify the economic return of within-field varieties arrangement; and (iii) propose guidelines for multi-variety soybean planting in Southern Brazil. The experimental design comprised a factorial split-plot set up in a randomized complete block design, with three YC [low (LY), medium (MY) and high yielding (HY)] and six varieties, replicated three times. The main findings were: (a) soybean variety performance differed according to YC; (b) the farmer-selected variety performed well for HY and MY; (c) varieties with high plant height (PH) should be placed in LY, where PH reduction and an increase in the number of pods and yield were recorded; (d) varieties with low PH should be placed in HY, avoiding excessive plant growth and yield penalty; (e) within-field variety arrangement increased yield by 2.10% and 11.50% and economic return by US$ 26 and 137 ha−1 for HY and LY, respectively. The results support the emergent concept of within-field multi-variety soybean planting in Southern Brazil.

Keywords

Glycine max L. Multi-varieties Seed yield Economic return 

Notes

Acknowledgments

The authors wish to thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; process number 88881.132762/2016-01), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; process numbers 140508/2015-5, 140275/2016-9 and 141144/2017-3), and Programa de Pós-Graduação em Agronomia: Agricultura e Ambiente (PPGAAA) for providing financial support to this research. Thanks to Eliseu José Schaedler and Carlos Eduardo Dauve for providing data and experimental fields for this study and the LAPSul team for the technical support.

References

  1. Adamchuk, V., Dobermann, A., & Ping, J. (2004). Listening to the story told by yield maps. Precision Agriculture extension circular EC 04-704. Lincoln, NE, USA.Google Scholar
  2. Amado, T. J. C., & Santi, A. L. (2011). Using precision farming to overcome yield-limiting factors in Southern Brazil Oxisols: a case study. In D. E. Clay & J. Shanahan (Eds.), GIS applications in agriculture—Nutrient management for improved energy efficiency (pp. 31–60). Boca Raton, FL, USA: CRC Press.CrossRefGoogle Scholar
  3. Anthony, P., Malzer, G., Sparrow, S., & Zhang, M. (2012). Soybean yield and quality in relation to soil properties. Agronomy Journal, 104(5), 1443–1458.  https://doi.org/10.2134/agronj2012.0095.CrossRefGoogle Scholar
  4. Assefa, Y., Vara Prasad, P. V., Carter, P., Hinds, M., Bhalla, G., Schon, R., et al. (2016). Yield responses to planting density for US modern corn hybrids: A synthesis-analysis. Crop Science, 56(5), 2802–2817.  https://doi.org/10.2135/cropsci2016.04.0215.CrossRefGoogle Scholar
  5. Blackmore, S., & Moore, M. (1999). Remedial correction of yield map data. Precision Agriculture, 1, 53–66.  https://doi.org/10.1023/A:1009969601387.CrossRefGoogle Scholar
  6. Board, J. E. (2000). Light interception efficiency and light quality affect yield compensation of soybean at low plant populations. Crop Science, 40(5), 1285–1294.  https://doi.org/10.2135/cropsci2000.4051285x.CrossRefGoogle Scholar
  7. Board, J. E., & Harville, B. G. (1992). Explanations for greater light interception in narrow- vs. wide-row. Crop Science, 32(1), 198–202.  https://doi.org/10.2135/cropsci1992.0011183X003200010041x.CrossRefGoogle Scholar
  8. Board, J. E., & Maricherla, D. (2008). Explanations for decreased harvest index with increased yield in soybean. Crop Science, 48(5), 1995.  https://doi.org/10.2135/cropsci2008.02.0098.CrossRefGoogle Scholar
  9. Brasil, (2009). Regras para analises de sementes (Rules for Seed Analysis). (Ministério da Agricultura Pecuária e Abastecimento, Ed.) (1st ed.). Brasília: Secretaria de Defesa Agropecuária.Google Scholar
  10. Bronick, C. J., & Lal, R. (2005). Soil structure and management: A review. Geoderma, 124(1–2), 3–22.  https://doi.org/10.1016/j.geoderma.2004.03.005.CrossRefGoogle Scholar
  11. Bunselmeyer, H. A., & Lauer, J. G. (2015). Using corn and soybean yield history to predict subfield yield response. Agronomy Journal, 107, 558–562.  https://doi.org/10.2134/agronj14.0261.CrossRefGoogle Scholar
  12. Carpenter, A. C., & Board, J. (1997). Branch yield components controlling soybean yield stability across plant populations. Crop Science, 37(3), 885–891.  https://doi.org/10.2135/cropsci1997.0011183X003700030031x.CrossRefGoogle Scholar
  13. Conab. (2015). Acompanhamento da safra brasileira de grãos (Monitoring of the Brazilian grain crop). Safra 2014/15. Monitoramento Agrícola, 2(9), 134.Google Scholar
  14. Conley, S. P., Abendroth, L., Elmore, R., Christmas, E. P., & Zarnstorff, M. (2008). Soybean seed yield and composition response to stand reduction at vegetative and reproductive stages. Agronomy Journal, 6(9), 1666–1669.  https://doi.org/10.2134/agronj2008.0082.CrossRefGoogle Scholar
  15. Corrêa, J. C., Mauad, M., & Rosolem, C. A. (2004). Fósforo no solo e desenvolvimento de soja influenciados pela adubação fosfatada e cobertura vegetal (Phosphorus in soil and soybean growth as affected by phosphate fertilization and cover crop residues). Pesquisa Agropecuaria Brasileira, 39(12), 1231–1237.  https://doi.org/10.1590/S0100-204X2004001200010.CrossRefGoogle Scholar
  16. Cox, W. J., & Cherney, J. H. (2011). Growth and yield responses of soybean to row spacing and seeding rate. Agronomy Journal, 103(1), 123–128.  https://doi.org/10.2134/agronj2010.0316.CrossRefGoogle Scholar
  17. De Bruin, J. L., & Pedersen, P. (2008). Soybean seed yield response to planting date and seeding rate in the upper Midwest. Agronomy Journal, 100(3), 696–703.  https://doi.org/10.2134/agronj2007.0115.CrossRefGoogle Scholar
  18. Egli, D. B. (2013). The relationship between the number of nodes and pods in soybean communities. Crop Science, 53(4), 1668–1676.  https://doi.org/10.2135/cropsci2012.11.0663.CrossRefGoogle Scholar
  19. Evans, L. T., & Fisher, R. A. (1999). Yield potential: Its definition, measurement, and significance. Crop Science, 39(6), 1544–1551.  https://doi.org/10.2135/cropsci1999.3961544x.CrossRefGoogle Scholar
  20. Fehr, W. R., Caviness, E. C., Burmood, D. T., & Pennington, J. S. (1971). Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Science, 11(6), 929–931.  https://doi.org/10.2135/cropsci1971.0011183X001100060051x.CrossRefGoogle Scholar
  21. Franzluebbers, A. J. (2002). Soil organic matter stratification ratio as an indicator of soil quality. Soil and Tillage Research, 66(2), 95–106.  https://doi.org/10.1016/S0167-1987(02)00018-1.CrossRefGoogle Scholar
  22. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science (New York, N.Y.), 327(5967), 828–831.  https://doi.org/10.1126/science.1183899.CrossRefGoogle Scholar
  23. Hargreaves, P. (2003). Evaluating soil microbial biomass carbon as an indicator of long-term environmental change. Soil Biology & Biochemistry, 35(3), 401–407.  https://doi.org/10.1016/S0038-0717(02)00291-2.CrossRefGoogle Scholar
  24. Heiffig, L. S., Câmara, G. M. D. S., Marques, L. A., Pedroso, D. B., & Piedade, S. M. D. S. (2006). Fechamento e índice de área foliar da cultura da soja em diferentes arranjos espaciais (Closed canopy and leaf area index of soybean in different space arrangements). Bragantia, 65(2), 285–295.CrossRefGoogle Scholar
  25. Hörbe, T. A. N., Amado, T. J. C., Ferreira, A. O., & Alba, P. J. (2013). Optimization of corn plant population according to management zones in Southern Brazil. Precision Agriculture, 14(4), 450–465.  https://doi.org/10.1007/s11119-013-9308-7.CrossRefGoogle Scholar
  26. Jaynes, D. B., Colvin, T. S., & Kaspar, T. C. (2005). Identifying potential soybean management zones from multi-year yield data. Computers and Electronics in Agriculture, 46, 309–327.  https://doi.org/10.1016/j.compag.2004.11.011.CrossRefGoogle Scholar
  27. Jeschke, M., & Shanahan, J. (2015). Strategies and considerations for multi-hybrid planting. Crop insights. Retrieved April 12, 2018, fom https://www.pioneer.com/home/site/us/agronomy/library/multi-hybrid-planting/.
  28. Kaiser, M., Ellerbrock, R. H., & Gerke, H. H. (2008). Cation exchange capacity and composition of soluble soil organic matter fractions. Soil Science Society of America Journal, 72(5), 1278.  https://doi.org/10.2136/sssaj2007.0340.CrossRefGoogle Scholar
  29. Kasperbauer, M. J. (1987). Far-red light reflection from green leaves and effects on phytochrome-mediated assimilate partitioning under field conditions. Plant Physiology, 85(2), 350–354.  https://doi.org/10.1104/pp.85.2.350.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Khosla, R., Inman, D., Westfall, D. G., Reich, R. M., Frasier, M., Mzuku, M., et al. (2008). A synthesis of multi-disciplinary research in precision agriculture: Site-specific management zones in the semi-arid western Great Plains of the USA. Precision Agriculture, 9, 85–100.  https://doi.org/10.1007/s11119-008-9057-1.CrossRefGoogle Scholar
  31. Liu, B., Liu, X., Wang, C., Jin, J., Herbert, S. J., & Hashemi, M. (2010). Responses of soybean yield and yield components to light enrichment and planting density. International Journal of Plant Production, 4(1), 1–10.Google Scholar
  32. Ludwing, M. P., Dutra, L. M. C., Filho, O. A. L., Zabot, L., Uhry, D., & Lisboa, J. I. (2010). Produtividade de grãos da soja em função do manejo de herbicida e fungicidas (Soybean grain yield response to herbicide and fungicides). Ciência Rural, 40(7), 1–7.Google Scholar
  33. Maddonni, G. A., Otegui, M. E., & Cirilo, A. G. (2001). Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crops Research, 71(3), 183–193.  https://doi.org/10.1016/S0378-4290(01)00158-7.CrossRefGoogle Scholar
  34. Maluf, J. R. T. (2000). Nova classificação climática do Estado do Rio Grande do Sul (A new climatic classification for the state of Rio Grande do Sul, Brazil). Revista Brasileira de Agrometeorologia, 8, 141–150.Google Scholar
  35. Mantovani, E. C., Matoso, M. J., Oliveira, A. C. D. E., & Avellar, G. D. E. (2007). Management crop production system using precision farming concept for decision making. Revista Brasileira de Engenharia de Biossistemas, 1(2), 127–136.CrossRefGoogle Scholar
  36. Menegatti, L. A. A., & Molin, J. P. (2004). Remoção de erros em mapas de produtividade via filtragem de dados brutos (Removal of erros in yield maps through raw data filtering). Revista Brasileira de Engenharia Agrícola e Ambiental, 8(1), 126–134.CrossRefGoogle Scholar
  37. Molin, J. P. (2002). Definição de unidades de manejo a partir de mapas de produtividade (Zone management definition based on yield maps). Engenharia Agrícola, 22(1), 83–92.Google Scholar
  38. Navarro Junior, H. M., & Costa, J. A. (2002). Contribuição relativa dos componentes do rendimento para produção de grãos em soja (Relative contribution of yield components for grain production in soybean). Pesquisa Agropecuária Brasileira, 37(3), 269–274.CrossRefGoogle Scholar
  39. Norsworthy, J. K., & Shipe, E. R. (2005). Effect of row spacing and soybean genotype on mainstem and branch yield. Agronomy Journal, 97(3), 919–923.  https://doi.org/10.2134/agronj2004.0271.CrossRefGoogle Scholar
  40. Pedersen, P., & Lauer, J. G. (2004). Soybean growth and development in various management systems and planting dates. Agronomy Journal, 44, 508–515.Google Scholar
  41. Popp, M. P., Keisling, T. C., McNew, R. W., Oliver, L. R., Dillon, C. R., & Wallace, D. M. (2002). Planting date, cultivar, and tillage system effects on dryland soybean production. Agronomy Journal, 94(1), 81–88.  https://doi.org/10.2134/agronj2002.8100.CrossRefGoogle Scholar
  42. Lima, S. F. de, Alvarez, R. de C. F., Theodoro, G. D. F., Bavaresco, M., & Silva, K. S. (2012). Efeito da semeadura em linhas cruzadas sobre a produtividade de grãos e a severidade da ferrugem asiática da soja (Effect of sowing in crossed lines on grain yield and the severity of asian soybean rust). Bioscience Journal, 28(6), 954–962.Google Scholar
  43. QGIS Development Team. (2015). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Retrieved April 11, 2018, from http://www.qgis.org.
  44. Rawls, W. J., Pachepsky, Y. A., Ritchie, J. C., Sobecki, T. M., & Bloodworth, H. (2003). Effect of soil organic carbon on soil water retention. Geoderma, 116(1–2), 61–76.  https://doi.org/10.1016/S0016-7061(03)00094-6.CrossRefGoogle Scholar
  45. Robertson, M. J., Llewellyn, R. S., Mandel, R., Lawes, R., Bramley, R. G. V., Swift, L., et al. (2012). Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture, 13, 181–199.  https://doi.org/10.1007/s11119-011-9236-3.CrossRefGoogle Scholar
  46. Salmeron, M., Gbur, E. E., Bourland, F. M., Buehring, N. W., Earnest, L., Felix, B., et al. (2014). Soybean maturity group choices for early and late plantings in the midsouth. Agronomy Journal, 106(5), 1893–1901.  https://doi.org/10.2134/agronj14.0222.CrossRefGoogle Scholar
  47. Sangoi, L., Gracietti, M. A., Rampazzo, C., & Bianchetti, P. (2002). Response of brazilian maize hybrids from different eras to changes in plant density. Field Crops Research, 79, 39–51.  https://doi.org/10.1016/S0378-4290(02)00124-7.CrossRefGoogle Scholar
  48. Santi, A. L., Amado, T. J. C., Eitelwein, M. T., Cherubin, M. R., Da Silva, R. F., & Da Ros, C. O. (2013). Definição de zonas de produtividade em áreas manejadas com agricultura de precisão (Definition of yield zones in areas managed with precision agriculture). Revista Brasileira de Ciências Agrarias, 8(3), 510–515.  https://doi.org/10.5039/aqraria.v8i3a2489.CrossRefGoogle Scholar
  49. SAS Institute. (2016). SAS version 9.4 - University Edition. SAS Institute Inc. Cary, NC, USA.Google Scholar
  50. Schepers, A. R., Shanahan, J. F., Liebig, M. A., Schepers, J. S., Johnson, S. H., & Luchiari, A. (2004). Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal, 96(1), 195.  https://doi.org/10.2134/agronj2004.0195.CrossRefGoogle Scholar
  51. Shanahan, J. F., Doerge, T. A., Johnson, J. J., & Vigil, M. F. (2004). Feasibility of site-specific management of corn hybrids and plant densities in the great plains. Precision Agriculture, 5(3), 207–225.  https://doi.org/10.1023/B:PRAG.0000032762.72510.10.CrossRefGoogle Scholar
  52. Smidt, E. R., Conley, S. P., Zhu, J., & Arriaga, F. J. (2016). Identifying field attributes that predict soybean yield using random forest analysis. Agronomy Journal, 108(2), 637–646.  https://doi.org/10.2134/agronj2015.0222.CrossRefGoogle Scholar
  53. Soil Survey Staff. (2014). Keys to soil taxonomy. Soil Conservation Service (Vol. 12).Google Scholar
  54. Souza, C. A., Figueiredo, B. P., Coelho, C. M. M., Casa, R. T., & Sangoi, L. (2013). Arquitetura de plantas e produtividade da soja decorrente do uso de redutores de crescimento (Plant architecture and productivity of soybean affected by plant growth retardants). Bioscience Journal, 29(3), 634–643.Google Scholar
  55. Tedesco, M. J., Volkweiss, S. J., & Bohnen, H. (1995). Análise de solo, plantas e outros materiais (Analysis of soil, plants and other materials) (2nd ed.). Porto Alegre, RS, Brasil: Departamento de solos - UFRGS.Google Scholar
  56. Thomas, A. L., & Costa, J. A. (2010). Desenvolvimento da planta de soja e potencial de rendimento de grãos (Soybean plant development and yield potential. In A. L. Thomas & J. A. Costa (Eds.), Soja: Manejo para alta produtividade de grãos (Soybean: management for high yield) (pp. 13–33). Porto Alegre, RS, Brasil: Evangraf.Google Scholar
  57. van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance—A review. Field Crops Research, 143, 4–17.  https://doi.org/10.1016/j.fcr.2012.09.009.CrossRefGoogle Scholar
  58. van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52(3), 197–208.  https://doi.org/10.1016/S0378-4290(97)00037-3.CrossRefGoogle Scholar
  59. Van Roekel, R. J., Purcell, L. C., & Salmerón, M. (2015). Physiological and management factors contributing to soybean potential yield. Field Crops Research, 182, 86–97.  https://doi.org/10.1016/j.fcr.2015.05.018.CrossRefGoogle Scholar
  60. Walker, E. R., Mengistu, A., Bellaloui, N., Koger, C. H., Roberts, R. K., & Larson, J. A. (2010). Plant population and row-spacing effects on maturity group III soybean. Agronomy Journal, 102(3), 821–826.  https://doi.org/10.2134/agronj2009.0219.CrossRefGoogle Scholar
  61. Zanon, A. J., Streck, N. A., & Grassini, P. (2016). Climate and management factors influence soybean yield potential in a subtropical environment. Agronomy Journal, 108(4), 1447–1454.  https://doi.org/10.2134/agronj2015.0535.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • G. M. Corassa
    • 1
  • A. L. Santi
    • 2
  • T. J. C. Amado
    • 3
  • G. B. Reimche
    • 3
  • R. Gaviraghi
    • 2
  • M. B. Bisognin
    • 2
  • J. L. F. Pires
    • 4
  1. 1.Department of Agricultural EngineeringFederal University of Santa MariaSanta MariaBrazil
  2. 2.Department of Agricultural and Environmental SciencesFederal University of Santa MariaFrederico WestphalenBrazil
  3. 3.Department of SoilFederal University of Santa MariaSanta MariaBrazil
  4. 4.Embrapa TrigoPasso FundoBrazil

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