CSM-CROPGRO-Dry bean

Dry bean (common bean, Phaseolus vulgaris) is the most important food legume grown globally. The CROPGRO-Dry bean model was developed from the BEANGRO model (Hoogenboom et al., 1994; White et al., 1995), which was adapted from SOYGRO using datasets from the International Center for Tropical Agriculture (CIAT), the University of Florida and published papers (Hoogenboom et al., 1994; White et al., 1995). The overall physiology of soybean and common bean proved very similar, so the main modifications related to differences in phenology, plant composition, and organ sizes (especially to allow for large-seeded bean cultivars).  The CSM-CROPGRO-Dry bean model shares the same source code as the other CROPGRO-legume models (Hoogenboom et al., 1992; Boote et al., 1998; Jones et al., 2003), but has its own species and cultivar trait files.  It uses the same hedge-row light interception model (Boote and Pickering, 1994) combined with a leaf-scale photosynthesis model based on the Farquhar approach for simulating response to CO2.

Performance of the original BEANGRO model (Hoogenboom et al., 1994) was assessed for a series of field trials conducted at CIAT (White et al., 1995). A subsequent application was to demonstrate the importance of photoperiod sensitivity in adaptation of common bean lines to rainfed systems in the highlands of Mexico (Acosta-Gallegos and White, 1995).  The temperature responsiveness of the CSM-CROPGRO-Dry bean model was evaluated and improved based on data collected in elevated temperature experiments in sunlit, controlled-environment chambers (Boote et al., 2018).

The BEANGRO model was also used as a platform to estimate genotype-specific parameters (GSPs) from genetic information, resulting in the GENEGRO model (White and Hoogenboom, 1996; Hoogenboom et al., 1997). The approach was subsequently extended to CROPGRO (Hoogenboom and White, 2003; Boote et al., 2016) and has been applied in soybean, sorghum and wheat. For further information see the discussions of estimating GSPs (Boote et al., 2016; Wallach et al., 2018).

The CSM-CROPGRO-Dry bean model has been applied for irrigation and drought management (Hoogenboom et al., 1988; Heinemann et al., 2000; Nunez-Barrios et al., 2005), salinity (Webber et al., 2010), weed management (Saberali et al., 2012, 2016), agroclimatic zoning (Belay et al., 1998; Meireles et al., 2002), planting date evaluation (Dallacort et al., 2008), and yield and evapotranspiration prediction (Dallacort et al., 2010, 2011)

References

  • Acosta-Gallegos, J.A., and J.W. White. 1995. Phenological plasticity as an adaptation by common bean to rainfed environments. Crop Science 35, 199-204. 
  • Belay, S., C. Wortman, and G. Hoogenboom. 1998. Haricot bean agroecology in Ethiopia: Defining using agroclimatic and crop growth models. African Crop Science Journal 6(1):9 18.
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  • Boote, K.J., and N.B. Pickering. 1994. Modeling photosynthesis of row crop canopies. HortScience 29:1423–34.
  • Boote, K.J., V. Prasad, L.H. Allen Jr, P. Singh, and J.W. Jones. 2018. Modeling sensitivity of grain yield to elevated temperature in the DSSAT crop models for peanut, soybean, dry bean, chickpea, sorghum, and millet. European Journal of Agronomy 100:99-109. 
  • Boote, K. J., C. E. Vallejos, J. W. Jones, and M. J. Correll.  2016.  Crop modeling approaches for predicting phenotype of grain legumes with linkage to genetic information.  Chapter 8. Pp. 163-192. In: X. Yin & P.C. Struik (eds.), Crop Systems Biology, Springer International Publishing, Switzerland.
  • Dallacort, R., R. Rezende, P.S.L. de Freitas, R.T. de Faria, T.L. de F. Azevedo, and J.B. Tolentino Júnior. 2008. Utilização do modelo CROPGRO-Drybean na determinação das melhores épocas de semeadura da cultura do feijão para a região de Maringá, Estado do Paraná, Brasil. Acta Scientiarum Agronomy 27(2):349-355. https://doi.org/10.4025/actasciagron.v27i2.1855
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  • Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT Cropping System Model. European Journal of Agronomy 18:235–65.
  • Meireles, E.J.L., A.R. Pereira, P.C. Sentelhas, L.F. Stone,  and F.J.P. Zimmermann. 2002. Calibration and test of the CROPGRO-Dry bean model for edaphoclimatic conditions in the savanas of Central Brazil. Scientia Agricola 59(4):723-729. https://doi.org/10.1590/S0103-90162002000400016
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  • Saberali, S.F., S.A.M. Modarres-Sanavy, M. Bannayan, M. Aghaalikhani, G. Haghayegh, and G. Hoogenboom. 2016. Dry bean canopy characteristics and N assimilation as affected by weed pressure and nitrogen rate. The Journal of Agricultural Science 154(4):598-611.
  • Wallach, D., C. Hwang, M.J. Correll, J.W. Jones, K.J. Boote, G. Hoogenboom, S. Gezan, M. Bhaktae, and C.E. Vallejos. 2018. A dynamic model with QTL covariables for predicting flowering time of common bean (Phaseolus vulgaris) genotypes. European Journal of Agronomy 101(1):200-209.
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