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Performance of the CSM-CROPGRO-soybean in simulating soybean growth and development and the soil water balance for a tropical environment

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  • Henrique Figueiredo Moura da Silva, Evandro
  • Boote, Kenneth J.
  • Hoogenboom, Gerrit
  • Gonçalves, Alexandre Ortega
  • Junior, Aderson Soares Andrade
  • Marin, Fabio Ricardo

Abstract

Continuous monitoring of soil water content is a crucial element for sustainable agricultural water management. The goal of this study was to use the Cropping System Model (CSM)-CROPGRO-Soybean model in conjunction with field data to determine the impact of different irrigation regimes, soil texture, and tillage practices on soybean [Glycine max (L.) Merr.] growth, development, and yield for tropical conditions. Field experiments were conducted at two sites: (i) Piracicaba with conventional tillage (PI-1, season 2016–2017), and no-tillage practices (PI-2, season 2017–2018), where the experiments were irrigated with full water requirements; and (ii) Teresina under conventional tillage (season 2019) with two irrigation treatments of full (TE-1) and 50% (TE-2) water requirements. Soil water content was measured for all experiments using an electromagnetic probe installed at several depths. The results showed that the model was able to simulate soybean growth and development for the different sites, with a very good agreement (D-statistic > 0.8) between the simulated and observed data. In addition, the soil water content was simulated with satisfactory accuracy (D-statistic > 0.5). Following model evaluation, long-term hypothetical scenarios for different soil tillage practices and water management regimes were simulated for Piracicaba and Teresina sites. The results showed that the use of no-tillage could reduce the average amount of irrigation in Piracicaba by 30% and in Teresina by 17%, achieving the same yield level as conventional tillage. Thus, the CSM-CROPGRO-Soybean can be used as a tool for determining optimum water management practices for tropical environments.

Suggested Citation

  • Henrique Figueiredo Moura da Silva, Evandro & Boote, Kenneth J. & Hoogenboom, Gerrit & Gonçalves, Alexandre Ortega & Junior, Aderson Soares Andrade & Marin, Fabio Ricardo, 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, Elsevier, vol. 252(C).
  • Handle: RePEc:eee:agiwat:v:252:y:2021:i:c:s0378377421001943
    DOI: 10.1016/j.agwat.2021.106929
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    References listed on IDEAS

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    1. Alagarswamy, G. & Singh, P. & Hoogenboom, G. & Wani, S. P. & Pathak, P. & Virmani, S. M., 2000. "Evaluation and application of the CROPGRO-Soybean simulation model in a Vertic Inceptisol," Agricultural Systems, Elsevier, vol. 63(1), pages 19-32, January.
    2. Andales, A. A. & Batchelor, W. D. & Anderson, C. E. & Farnham, D. E. & Whigham, D. K., 2000. "Incorporating tillage effects into a soybean model," Agricultural Systems, Elsevier, vol. 66(2), pages 69-98, November.
    3. Liu, H.L. & Yang, J.Y. & Tan, C.S. & Drury, C.F. & Reynolds, W.D. & Zhang, T.Q. & Bai, Y.L. & Jin, J. & He, P. & Hoogenboom, G., 2011. "Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model," Agricultural Water Management, Elsevier, vol. 98(6), pages 1105-1111, April.
    4. Zhou, Hong & Zhao, Wen zhi, 2019. "Modeling soil water balance and irrigation strategies in a flood-irrigated wheat-maize rotation system. A case in dry climate, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 286-302.
    5. Jones, J. W. & Keating, B. A. & Porter, C. H., 2001. "Approaches to modular model development," Agricultural Systems, Elsevier, vol. 70(2-3), pages 421-443.
    6. Candogan, Burak Nazmi & Sincik, Mehmet & Buyukcangaz, Hakan & Demirtas, Cigdem & Goksoy, Abdurrahim Tanju & Yazgan, Senih, 2013. "Yield, quality and crop water stress index relationships for deficit-irrigated soybean [Glycine max (L.) Merr.] in sub-humid climatic conditions," Agricultural Water Management, Elsevier, vol. 118(C), pages 113-121.
    7. Salmerón, Montserrat & Purcell, Larry C., 2016. "Simplifying the prediction of phenology with the DSSAT-CROPGRO-soybean model based on relative maturity group and determinacy," Agricultural Systems, Elsevier, vol. 148(C), pages 178-187.
    8. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    9. da Silva, Evandro H.F.M. & Gonçalves, Alexandre O. & Pereira, Rodolfo A. & Fattori Júnior, Izael M. & Sobenko, Luiz R. & Marin, Fábio R., 2019. "Soybean irrigation requirements and canopy-atmosphere coupling in Southern Brazil," Agricultural Water Management, Elsevier, vol. 218(C), pages 1-7.
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    2. Park, Sugyeong & Chun, Jong Ahn & Kim, Daeha & Sitthikone, Mounlamai, 2022. "Climate risk management for the rainfed rice yield in Lao PDR using APCC MME seasonal forecasts," Agricultural Water Management, Elsevier, vol. 274(C).

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