Abstract
Due to the importance of Brazil as one of the main world soybean producers and providers, studies to understand the yield reducing factors are of major. Asian soybean rust (ASR) is one of the main reducing factors affecting soybean production. Thus, this study simulated the ASR effects in 24 locations in three Brazilian producing mega-regions, considering different sowing dates, from 15 Sept to 25 Nov for the South and Central mega-regions, and from 05 Oct to 15 Dec for the North mega-region for a historical series of 31 years (1988–2018). Still, we also evaluated the effects of El Niño Southern Oscillation (ENSO) events on disease severity and hence on soybean yield. The approach used in the present study was based on the combination of two models: a disease development model (DDM) for estimating ASR severity, and the process-based crop simulation model (CSM) DSSAT-CROPGRO-Soybean for yield estimation. We compared the yields of affected soybean crops by ASR with disease-free crop yields (ΔYASR). On average, the ASR reduced soybean yield between 13 and 33%, with the Central mega-region being the most affected by ASR, followed by North and South mega-regions. ENOS events affected ASR severity, which in turn impacted soybean yields. In the South mega-region, El Niño showed higher impacts on disease than La Niña and Neutral events. In contrast, La Niña and Neutral events impacted more the soybean yield in the North, whereas in the Central mega-region there was no evidence of ENOS impacts on ASR severity and soybean yields.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507
Bai, J., Chen, X., Dobermann, A., Yang, H., Cassman, K. G., & Zhang, F. (2010). Evaluation of NASA satellite-and model-derived weather data for simulation of maize yield potential in China. Agronomy Journal, 102, 9–16. https://doi.org/10.2134/agronj2009.0085
Baastians, L. (1991). Ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology, 81(6), 611–615. https://doi.org/10.1094/Phyto-81-611
Battisti, R. (2016). Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil. Doctoral Dissertation. University of São Paulo, Piracicaba, 2016. https://www.teses.usp.br/teses/disponiveis/11/11152/tde-03102016-162340/publico/Rafael_Battisti_versao_revisada.pdf. Accessed 4 Apr 2021.
Battisti, R., & Sentelhas, P. C. (2019). Characterizing Brazilian soybean-growing regions by water deficit patterns. Field Crops Research, 240, 95–105. https://doi.org/10.1016/j.fcr.2019.06.007
Battisti, R., Sentelhas, P. C., & Boote, K. J. (2017). Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Research, 200, 28–37. https://doi.org/10.1016/j.fcr.2016.10.004
Beruski, G. C., Sentelhas, P. C., Pereira, A. B., Câmara, G. M. S., Junior, A. I. P., & Schiebelbein, L. M. (2020). Soybean rust epidemics as affected by weather conditions in Brazilian. Journal of Agricultural Science, 12(1), 213–225. https://doi.org/10.5539/jas.v12n1p213
CONAB-Companhia Nacional de Abastecimento. (2021). Acompanhamento da Safra Brasileira. Grãos safra 2020/21, Sétimo Levantamento, Brasilia. https://www.conab.gov.br/component/k2/item/download/36566_871e4d2210be5fcac5a6c7bfd2b400a5. Accessed 10 Apr 2021.
Dalla Lana, F., Paul, P. A., Godoy, C. V., Utiamada, C. M., Silva, L. H. C. P., Siqueri, F. V., Forcelini, C. A., Jaccoud-Filho, D. Z., Miguel-Wruck, D. S., Borges, E. P., Juliatti, F. C., Campos, H. D., Nunes, J., Jr., Carneiro, L. C., Canteri, M. G., Ito, M. F., Meyer, M. C., Martins, M. C., Balardin, R. S., … Del Ponte, E. M. (2018). Meta-analytic modeling of the decline in performance of fungicides for managing soybean rust after a decade of use in Brazil. Plant Disease, 102(4), 807–817. https://doi.org/10.1094/PDIS-03-17-0408-RE
Dalla Lana, F., Ziegelmann, P. K., Maia, A. H. N., Godoy, C. V., & Del Ponte, E. M. (2014). 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
Del Ponte, E. M., Godoy, C. V., Li, X., & Yang, X. B. (2006). Predicting Severity of Asian Soybean Rust Epidemics with Empirical Rainfall Models. Phytopathology, 96(7), 797–803. https://doi.org/10.1094/PHYTO-96-0797
Del Ponte, E. M., & Esker, P. D. (2008). Meteorological factors and Asian soybean rust epidemics: A systems approach and implications for risk assessment. Scientia Agricola, 65, 88–97. https://doi.org/10.1590/S0103-90162008000700014
Del Ponte, E. M., Maia, A. H. N., Santos, T. V., Martins, E. J., & Baethgen, W. E. (2011). Early-season warning of soybean rust regional epidemics using El Niño Southern/Oscillation information. International Journal of Biometeorology, 55(4), 575–583. https://doi.org/10.1007/s00484-010-0365-6
Dijk, V., & Haan, L. (1990). On the estimation of the exceedance probability of a high level. Technical Report n. 911. School of Operations Research and Industrial Engineering. College of Engineering, Cornell University. Ithaca, NY.
Duarte, Y. C. N., & Sentelhas, P. C. (2020). NASA/POWER and daily gridded weather datasets - how good they are for estimating maize yields in Brazil? International Journal of Biometeorology, 64, 319–329. https://doi.org/10.1007/s00484-019-01810-1
Echeveste da Rosa, C. R. (2015). Asian soybean rust resistance: An overview. Journal of Plant Pathology and Microbiology, 6(9), 1–7. https://doi.org/10.4172/2157-7471.1000307
Gabriel, M., Muraro, D. S., Da Rosa, G. M., Wastowski, A. D., Kulczynski, S. M., Silva, J. C., Carvalho, I. R., Szareski, V. J., Silva, V. A., & Stumm, J. O. (2018). Chemical control of Asian soybean rust and its effect in the yield and quality of soybean seeds. Journal of Agricultural Science, 10(10), 518–526. https://doi.org/10.5539/jas.v10n10p518
Godoy, C. V., Seixas, C. D. S., Soares, R. M., Marcelino-Guimarães, F. C., Meyer, M. C., & Costamilan, L. M. (2016). Asian soybean rust in Brazil: Past, present, and future. Pesquisa Agropecuária Brasileira, 51(5), 407–421. https://doi.org/10.1590/S0100-204X2016000500002
Hartman, G., Wang, T., & Tschanz, A. T. (1991). Soybean rust development and the quantitative relationship between rust severity and soybean yield. Plant Disease, 75(6), 596–600. https://doi.org/10.1094/PD-75-0596
Hinnah, F. D., Sentelhas, P. C., Gleason, M. L., Dixon, P. M., & Zhang, X. (2020). Assessing biogeography of coffee rust risk in Brazil as affected by the El Niño Southern Oscillation. Plant Disease, 104, 1013–1018. https://doi.org/10.1094/PDIS-01-19-0207-SR
Hoogenboom, G., Porter, C. H., Boote, K. J., Shelia, V., Wilkens, P. W., Singh, U., White, J. W., Asseng, S., Lizaso, J. I., Moreno, L. P., Pavan, W., Ogoshi, R., Hunt, L. A., Tsuji, G. Y., & Jones, J. W. (2019). The DSSAT crop modeling ecosystem. In K. J. Boote (Ed.), Advances in crop modeling for a sustainable agriculture (pp. 173–216). Burleigh Dodds Science Publishing.
IBGE–Instituto Brasileiro de geografia e Estatística. (2021). Sistema de produção Agrícola Municipal. https://sidra.ibge.gov.br/pesquisa/pam/tabelas. Accessed 5 May 2021.
Jones, J., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Mailer, R. J. (2016). Oilseeds, overview. In: Reference module in food science. Elsevier. https://doi.org/10.1016/B978-0-08-100596-5.00026-3.
Megeto, G. A. S., Oliveira, S. R. M., Del Ponte, E. M., & Meira, C. (2014). Árvore de decisão para classificação de ocorrências de ferrugem asiática em lavouras comerciais com base em variáveis meteorológicas. Engenharia Agrícola, 34(3), 590–599. https://doi.org/10.1590/S0100-69162014000300021
Monteiro, L. A., Sentelhas, P. C., & Pedra, G. U. (2018). Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation. International Journal of Climatology, 38(1571–1581), 2018. https://doi.org/10.1002/joc.5282
Nóia Júnior, R. S., Schwerz, F., Safanelli, J. L., Rodrigues, J. C., & Sentelhas, P. C. (2019). Eucalyptus rust climatic risk as affected by topography and ENSO phenomenon. Australasian Plant Pathology, 48, 131–141. https://doi.org/10.1007/s13313-018-0608-2
Rodrigues, R. Á., Pedrini, J. E., Fraisse, C. W., Fernandes, J. M. C., Justino, F. B., Heinemann, A. B., Costa, L. C., & Vale, F. X. R. (2012). Utilization of the cropgro-soybean model to estimate yield loss caused by Asian rust in cultivars with different cycle. Bragantia, 71(2), 308–317. https://doi.org/10.1590/S0006-87052012000200021
Savary, S., & Willocquet, L. (2020). Modeling the impact of crop diseases on global food security. Annual Review of Phytopathology, 58, 313–341. https://doi.org/10.1146/annurev-phyto-010820-012856
Silva, H. F. M., Boote, K. J., Hoogenboom, G., Gonçalves, A. O., Andrade Junior, A. S., & Marin, F. R. (2021). Performance of the CSM-CROPGRO-soybean in simulating soybean growth and development and the soil water balance for a tropical environment. Agricultural Water Management, 252(106929), 2021. https://doi.org/10.1016/j.agwat.2021.106929
Tsukahara, R. Y., Hikishima, M., & Canteri, M. G. (2008). Relationship between climate and the progress of the Asian soybean rust (Phakopsora pachyrhizi) in two micro-regions of Paraná State. Semina: Ciências Agrárias, 29(1), 47–52. http://www.uel.br/revistas/uel/index.php/semagrarias/article/view/2852/2420. Accessed 4 Apr 2021.
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
Yang, X. B., Dowler, W. M., & Tschanz, A. T. (1991). A simulation model for assessing soybean rust epidemics. Journal of Plant Pathology, 133(3), 187–200. https://doi.org/10.1111/j.1439-0434.1991.tb00153.x
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of Interest
The authors declare there are no conflicts of interest.
Rights and permissions
About this article
Cite this article
Fattori, I.M., Sentelhas, P.C. & Marin, F.R. Assessing the Impact of Climate Variability on Asian Rust Severity and Soybean Yields in Different Brazilian Mega-Regions. Int. J. Plant Prod. 16, 17–28 (2022). https://doi.org/10.1007/s42106-021-00169-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42106-021-00169-x