Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019

Abstract

The study of the soybean yield variability influenced by the climate contributes to the planning of strategies to mitigate its negative effects. Thus, our aim was to calibrate agrometeorological models for soybean yield forecast and identify the weather variables that most influence soybean yield. This study used historical series of climate and soybean yield data from soybean-producing locations in the Mato Grosso do Sul state, Brazil. The historical climate series was 20 years (2000–2019). The soybean production, yield, and planted area data of the localities were in the period from 2009–2018. Multiple linear regression analysis was the statistical tool used for data modeling. The models from the north and central regions forecast of anticipation of 2 months since the final data necessary to apply the model were EXCJANc and PJANc, respectively. The models calibrated for the southern region reported anticipation of one month since the final data necessary to apply the model was EXCFEVc. The calibrated models used to forecast soybean yield as a function of climatic conditions have a high degree of significance (p < 0.05), high accuracy and errors lower. The models for the northern and central regions show a prevision of anticipation of 2 months before soybean harvest, a period that is essential for producers to be able to conduct pre- and post-harvest planning. The climate variable with the greatest negative influence (r = − 0.54) on soybean yield in Mato Grosso do Sul state was water stress in December.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes, G., Leonardo, J., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift,22(6), 711–728.

    Article  Google Scholar 

  2. Aparecido, L. E. D. O., Rolim, G. D. S., Moraes, J. R. D. S. C., Rocha, H. G., Lense, G. H. E., & Souza, P. S. (2018). Agroclimatic zoning for urucum crops in the state of Minas Gerais, Brazil. Bragantia,77(1), 193–200.

    Article  Google Scholar 

  3. Battisti, R., Sentelhas, P. C., Boote, K. J., Câmara, G. M. S., Farias, J. R. B., & Basso, C. J. (2017). Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil. European Journal of Agronomy, 83, 1–14.

    Article  Google Scholar 

  4. Bonato, E. R., Bertagnolli, P. F., Ignaczak, J. C., Tragnago, J. L., & Rubin, S. (1998). Performance of soybean cultivars in three sowing dates in Rio Grande do Sul, Brazil. Pesquisa Agropecuaria Brasileira (Brazil),33(6), 879–884.

    Google Scholar 

  5. Camargo, A. P. (1971). Water balance in the State of São Paulo (3rd ed., p. 24). Campinas: Instituto Agronômico. (Boletim, 116).

  6. Camargo, A. P., & Sentelhas, P. C. (1997). Performance evaluation of different methods of estimating potential evapotranspiration in the State of São Paulo, Brazil. Revista Brasileira de Agrometeorologia, Santa Maria, 5(1), 89–97.

    Google Scholar 

  7. CONAB. (2019). Monitoring of the Brazilian harvest: Grains, V.4, Setembro 2019. Brasília: Conab, 2019. Retrieved December 22, 2019, https://www.conab.gov.br/info-agro/safras

  8. Dourado Neto, D., Sparovek, G., Figueredo Júnior, L. G. M., Fancelli, A. L., Manfron, P. A., & Medeiros, S. L. P. (2004). Model for estimating the productivity of depleted corn grains based on soil water balance. Revista Brasileira de Agrometeorologia,12(2), 359–367.

    Google Scholar 

  9. Draper, N. R., & Smith, H. (1980). Applied regression analysis (2nd ed.).

  10. Fietz, C. R., & Urchei, M. A. (2002). Deficiência hídrica da cultura da soja na região de Dourados, MS. Revista Brasileira de Engenharia Agrícola e Ambiental,6(2), 262–265.

    Article  Google Scholar 

  11. Fontana, D. C., Berlato, M. A., Lauschner, M. H., & Mello, R. W. (2001). Estimated soybean yield model in the State of Rio Grande do Sul. Pesquisa Agropecuária Brasileira, Brasília,36(3), 399–403.

    Article  Google Scholar 

  12. Franke, A. E. (2000). Need for supplemental irrigation in soybeans in the edaphoclimatic conditions of the Planalto Médio and Missões, RS. Pesquisa Agropecuaria Brasileira (Brazil), 35(8), 1675–1683.

  13. Gujarati, D. N., & Porter, D. C. (2011). Basics econometrics (5th ed., p. 872). McGraw-Hill Education.

  14. Lasdon, L. S., & Waren, A. D. (1982). GRG2 user’s guide. Austin: Depto of general Business, Shchoool of Business Administration, University of Texas.

    Google Scholar 

  15. NASA/POWER. (2019). https://power.larc.nasa.gov/data-access-viewer/.

  16. Martins, E., Aparecido, L. E. O., Santos, L. P. S., Mendonça, J. M. A., & Souza, P. S. (2015). Influence of climatic conditions on the productivity and quality of coffee produced in the southern region of Minas Gerais. Coffee Science,10, 499–506.

    Google Scholar 

  17. Martorano, L. G., Bergamaschi, H., Dalmago, G. A., Faria, R. T., Mielniczuk, J., & Comiran, F. (2009). Soil water status indicators with soybean under no-tillage and conventional tillage. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande,13(4), 397–405.

    Article  Google Scholar 

  18. Martorano, L. G., Bergamaschi, H., Faria, R. T., Dalmago, G. A. (2012). Decision strategies for soil water estimations in soybean crops subjected to no-tillage and conventional systems, in Brazil. In M. Kumar (Org.). Problems, Perspectives and Challenges of Agricultural Water Management. Croácia: InTech (pp. 439–456).

  19. Moreto, V. B., & Rolim, G. S. (2015). Estimation of annual yield and quality of “Valência” orange related to monthly water deficiencies. African Journal of Agricultural Research,10(6), 543–553.

    Article  Google Scholar 

  20. Rolim, G. S., Ribeiro, R. V., Azevedo, F. A., Camargo, M. B. P., & Machado, E. C. (2008). Prediction of the number of fruits based on the amount of reproductive structures in orange trees. Revista Brasileira de Fruticultura,30(1), 48–53.

    Article  Google Scholar 

  21. Sentelhas, P. C., Battisti, R., Câmara, G. M. S., Farias, J. R. B., Hampf, A., & Nendel, C. (2015). The soybean yield gap in Brazil-magnitude, causes and possible solutions for a sustainable production. Journal of Agricultural Science, 153, 1394–1411.

    Article  Google Scholar 

  22. USDA. (2018). http://www.usdabrazil.org.br/pt-br/.

  23. Valeriano, T. T. B., Rolim, G. S., & Aparecido, L. E. O. (2017). A method to determine agro-climatic zones based on correlation and cluster analyses. Theoretical and Applied Climatology,134(3–4), 1355–1364.

    Google Scholar 

Download references

Acknowledgements

We thank the Federal Institute of Mato Grosso Sul, Campus Naviraí, for funding this research.

Funding

This research was supported by the IFMS - Federal Institute of Education, Science and Technology of Mato Grosso do Sul - Campus of Naviraí, Naviraí, Brasil.

Author information

Affiliations

Authors

Contributions

LEOA conceived of the project and together with GBT designed the study. GBT, JRSCM and KCM were responsible for collected the data and carried out the statistical analyses. JAL and PAL were responsible for the field work. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Lucas Eduardo de Oliveira Aparecido.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Oliveira Aparecido, L.E., Torsoni, G.B., da Silva Cabral de Moraes, J.R. et al. Modeling the impact of agrometeorological variables on soybean yield in the Mato Grosso Do Sul: 2000–2019. Environ Dev Sustain (2020). https://doi.org/10.1007/s10668-020-00807-w

Download citation

Keywords

  • Crop modeling
  • Climate
  • Yield zoning
  • Spatial error model
  • Glycine max L.