CSM-CROPGRO-Soybean

Soybean (Glycine max) is widely grown for its oil and protein-rich seed, which are consumed directly by humans as a vegetable and in processed forms and by poultry and livestock as various processed feed components. It is among the five most important crops globally. As a legume, the crop can fix nitrogen via symbiosis so is also prized for soil fertility benefits in rotations, especially with corn.

The soybean module of CROPGRO was based on the model SOYGRO V4.2 (Wilkerson et al., 1983) but has been substantially modified from the original version (Hoogenboom et al., 1992; Jones et al., 2003). Growth is driven by a 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  (Alagarswamy et al., 2006). Vegetative and reproductive development is driven by temperature and photoperiod calculated at an hourly time step (Boote et al., 1998).  The genetic coefficients for reproductive development are well-parameterized for the full range of soybean maturity groups from 00 to X (Grimm et al., 1993, 1994; Piper et al., 1998).

The model requires inputs of management practices, environmental conditions and cultivar-specific traits (genetic coefficients) to predict daily growth and development (Boote et al., 1998). The species file describes characteristics assumed constant across cultivars such as tissue composition, partitioning, and sensitivity of processes to temperature, light, plant water deficit, and plant N deficiency. The required ecotype and cultivar data include lengths of developmental phases, vegetative traits, leaf traits, reference seed size, and seed composition (Boote et al., 2001; Jones et al., 2003).

As a process-oriented model, CSM-CROPGRO-Soybean can be used to study soybean response to management (Egli and Bruening, 1992; Boote et al., 1997), crop rotation (Alagarswamy et al., 2000; Singh et al., 1999a, b; Liu et al., 2013), environmental conditions (Banterng et al., 2010; Curry et al., 1995; Nielsen et al., 2002; Sau et al., 1999; Ruiz-Nogueira et al., 2001), and varietal response (Mavromatis et al., 2001, 2002), including genetic yield potential (Boote and Tollenaar, 1994; Boote et al., 2003). This includes causes of spatial yield variability (Paz et al., 1998; Nijbroek et al., 2003), remote sensing applications (Richetti et al., 2019), and potential impacts of climate change (Bao et al., 2015 a, b; Boote, 2011; Boote et al., 2018; Heinemann et al., 2006).

References

  • Alagarswamy, G., K.J. Boote, L.H. Allen, Jr., and J.W. Jones.  2006.  Evaluating the CROPGRO-Soybean model ability to simulate photosynthesis response to carbon dioxide levels.  Agronomy J. 98:34-42.
  • Alagarswamy, G., P. Singh, G. Hoogenboom, S.P. Wani, P. Pathak, and S.M. Virmani. 2000. Evaluation and application of the CROPGRO-Soybean simulation model in Vertic Inceptisols of Peninsular India. Agricultural Systems 63(1):19-32.
  • Banterng, P., G. Hoogenboom, A. Patanothai, P. Singh, S. P. Wani, P. Pathak, S. Tongpoonpol, S. Atichart, P. Srihaban, S. Buranaviriyakul, A. Jintrawet, and T. C. Nguyen. 2010. Application of the Cropping System Model (CSM)-CROPGRO-Soybean for determining optimum management strategies for soybean in tropical environments. Journal of Agronomy and Crop Science 196(3):231-242.
  • Bao, Y., G. Hoogenboom, R.W. McClendon, and P. Urich. 2015. Soybean production in 2025 and 2050 in the southeastern USA based on the SimCLIM and the CSM-CROPGRO-Soybean models. Climate Research 63(1):73-89.
  • Bao, Y., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2015. Potential adaptation strategies for rainfed soybean production in the southeastern USA under climate change based on the CSM-CROPGRO-Soybean model. The Journal of Agricultural Science 153(5):798-824.
  • Boote, KJ. 2001. Physiology and modeling of traits in crop plants: Implications for genetic improvement. Agric. Syst. 70:395–420.
  • Boote, K. J.  2011.  Improving soybean cultivars for adaptation to climate change and climate variability.  Chapter 17.  pp 370-395.  IN:  S. S. Yadav, R. J. Redden, J. L. Hatfield, H. Lotze-Campen, and A. E. Hall (Eds.) Crop adaptation to climate change.  Wiley-Blackwell, West Sussex, U.K. 
  • Boote, K.J., and N.B. Pickering. “Modeling Photosynthesis of Row Crop Canopies.” HortScience 29 (1994): 1423–34.
  • Boote, KJ, and M Tollenaar. 1994. Modeling genetic yield potential. p. 533–565. In K.J. Boote et al. (ed.) Physiology and determination of crop yield. ASA, CSSA, and SSSA, Madison, WI.
  • Boote, K. J., J. W. Jones, G. Hoogenboom, and G. G. Wilkerson.  1997.  Evaluation of the CROPGRO-soybean model over a wide range of experiments.  pp. 113-133.  In  M. J. Kropff et al. (eds.).  Systems Approaches for Sustainable Agricultural Development:  Applications of Systems Approaches at the Field Level.  Kluwer Academic Publishers, Dordrecht, The Netherlands. 
  • Boote, KJ, JW Jones, G Hoogenboom, and NB Pickering. 1998. The CROPGRO Model for Grain Legumes In Understanding Options for Agricultural Production. pp. 99–128. Springer, 1998.
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  • Boote, K. J., J. W. Jones, W. D. Batchelor, E. D. Nafziger, and O. Myers.  2003.  Genetic coefficients in the CROPGRO-soybean model: Links to field performance and genomics.  Agron. J. 95: 32-51.
  • Boote, K.J., Prasad, V., Allen Jr, L.H., Singh, P. and Jones, J.W., 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.
  • Egli, DB and Bruening. Planting date and soybean yield: Evaluation of environmental effects with a crop simulation model SOYGRO. Agric. For. Meteorol. 1992. 62:19–29.
  • Grimm, S. S., J. W. Jones, K. J. Boote, and J. D. Hesketh.  1993.  Parameter estimation for predicting flowering date of soybean cultivars.  Crop Sci. 33:137-144.
  • Grimm, S. S., J. W. Jones, K. J. Boote, and D. C. Herzog.  1994.  Modeling the occurrence of reproductive stages after flowering for four soybean cultivars.  Agron. J. 86:31-38
  • Heinemann, A.B., A.de H.N. Maia, D. Dourado_Neto, K.T. Ingram, and G. Hoogenboom. 2006. Soybean (Glycine Max [L.] Merr.) growth and development response to CO2 enrichment under different temperature regimes. European Journal of Agronomy 24(1):52-61.
  • Hoogenboom, G. J. W. Jones, and K. J. Boote. 1992. Modeling growth, development and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: A Review. Transactions of the ASAE 35(6):2043-2056.
  • 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. “The DSSAT Cropping System Model.” European Journal of Agronomy 18 (2003): 235–65.
  • Liu, S., J.Y. Yang, XY. Zhang, C.F. Drury, W.D. Reynolds, and G. Hoogenboom. 2013. Modelling crop yield, soil water content and soil temperature for a soybean-maize rotation under conventional, reduced and conservation tillage systems in Northeast China. Agricultural Water Management 123(1):32-44.
  • Mavromatis, T., K. J. Boote, J. W. Jones, A. Irmak, D. Shinde, and G. Hoogenboom.  2001.  Developing genetic coefficients for crop simulation models with data from crop performance trials.  Crop Sci. 41:40-51.
  • Mavromatis, T., K.J. Boote, J.W. Jones, G.G. Wilkerson, and G. Hoogenboom. 2002. Repeatability of model genetic coefficients derived from soybean performance trials across different states. Crop Science 42(1)76-89.
  • Nielsen, D.C., L. Ma, L.R. Ahuja, and G. Hoogenboom. 2002. Simulating soybean water stress effects with Nijbroek, R., G. Hoogenboom, and J.W. Jones. 2003. Optimal irrigation strategy for a spatially variable soybean field: a modeling approach. Agricultural Systems 76(1):359-377.RZWQM and CROPGRO models. Agronomy Journal 94(6):1234-1243.
  • Nijbroek, R., G. Hoogenboom, and J.W. Jones. 2003. Optimal irrigation strategy for a spatially variable soybean field: a modeling approach. Agricultural Systems 76(1):359-377.
  • Piper, E. L., K. J. Boote, and J. W. Jones.  1998.  Evaluation and improvement of crop models using regional cultivar trial data.  Applied Engineering in Agriculture 14:435-446.
  • Richetti, J., K.J. Boote, G. Hoogenboom, J. Judge, J.A. Johann, and M.A. Uribe-Opazo. 2019. Remotely sensed vegetation index and LAI for parameter determination of the CSM-CROPGRO-Soybean model when in situ data are not available. International Journal of Applied Earth Observations and Geoinformation 79(1):110-115.
  • Ruiz-Nogueira, K. J. Boote, and F. Sau.  2001.  Calibration and use of CROPGRO-soybean model for improving soybean management under rainfed conditions in Galicia, Northwest Spain.   Agricultural Systems 68:151-173.
  • Sau, F., K. J. Boote, and B. Ruiz-Nogueira.  1999.  Evaluation and improvement of CROPGRO-soybean model for a cool environment in Galicia, northwest Spain.  Field Crops Res. 61:273-291.
  • Singh, P., G. Alagarswamy, G. Hoogenboom, P. Pathak, S.P. Wani, and S.M. Virmani. 1999a. Soybean-chickpea rotation on Vertic Inceptisols: 2. Long-term simulation of water balance and crop yields. Field Crops Research 63(3):225-236.
  • Singh, P., G. Alagarswamy, P. Pathak, S.P. Wani, G. Hoogenboom, and S.M. Virmani. 1999b. Soybean-chickpea rotation on Vertic Inceptisols: 1. Effect of soil depth and landform on light interception, water balance and crop yields. Field Crops Research 63(3):211-224.
  • Wilkerson, G.G., Jones, J.W., Boote, K.J., Ingram, K.T., Mishoe, J.W., 1983. Modeling soybean growth for crop management. Transactions of the American Society of Agricultural Engineers 26, 63–73.