CERES-Sorghum

The current CERES-Sorghum model is based on the model first described by Joe Ritchie and colleagues in an ICRISAT modeling workshop report (Virmani et al., 1989). The model uses light interception and radiation use efficiency (RUE) to estimate daily growth, which is then partitioned to different organs based on factors that vary with growth stage. Routines for climate, soil, crop management, and model controls are shared with other crops in CSM.

In 2015, several revisions were incorporated to improve modeling of phenology, leaf area development and partitioning (White et al., 2015). Modeled responses for eight real-world and hypothetical cases were described. These included growth under well-managed conditions, responses to row-spacing, population, sowing date, irrigation, defoliation, and increased atmospheric carbon dioxide concentration ([CO2]), and a long-term sorghum and winter wheat (Triticum aestivum L.) rotation. Among traits and experiments considered, model accuracy was high for phenology (r2 = 0.96, P < 0.01 for anthesis and r2 = 0.91, P < 0.01 for maturity), moderate for grain yields (r2 values from 0.30 to 0.52, P < 0.01), depending on the simulated experiments, and low for unit grain weight (r2 = 0.02, NS) and leaf area index for forage sorghum (r2 = 0.18, NS).

CERES-Sorghum has been adapted for modeling cultivar differences in response to phosphorus, with emphasis on West African sorghums (Adam et al., 2018). Akinseye et al. (2017) recently compared CERES-Sorghum with two other sorghum models for the same region. Initial explorations for known maturity loci (Ma) to predict phenology have also shown promise (White et al., 2007).

Tropically-adapted sorghums are often very responsive to photoperiod and difficulties have been noted in simulating phenology in West African germplasm. The method of Folliard et al. (2004) is available as an option with CERES-Sorghum and is activated simply by providing the additional required cultivar coefficients (see examples in the *. CUL file).

Besides the above-mentioned research, recent applications of the model include:

  • Examining the impact of deficit irrigation for western Kansas (Araya et al., 2017).
  • Quantifying the value of incorporating drought and heat tolerance in cultivars types at two sites each in India (cv. CSV 15 at both Akola and Indore) and Mali (cv. CSM 335 at Samanko and cv. CSM 63E at Cinzana), West Africa.

References

Adam, M., Dzotsi, K., Hoogenboom, G., Traoré, P., Porter, C., Rattunde, H., Nebie, B., Leiser, W.L., Weltzien, E., Jones, J.W., 2018. Modelling varietal differences in response to phosphorus in West African sorghum. European Journal of Agronomy (in press).

Akinseye, F.M., Adam, M., Agele, S.O., Hoffmann, M.P., Traore, P.C.S., Whitbread, A.M., 2017. Assessing crop model improvements through comparison of sorghum (sorghum bicolor L. moench) simulation models: A case study of West African varieties. Field Crops Research 201, 19-31.

Araya, A., Kisekka, I., Gowda, P.H., Prasad, P.V.V., 2017. Evaluation of water-limited cropping systems in a semi-arid climate using DSSAT-CSM. Agricultural Systems 150, 86-98.

Arkin, G.F., Ritchie, J.T., Maas, S.J., 1978. A model for calculating light interception by a grain sorghum canopy. Trans. ASAE 21: 303-308. Virmani, S.M., Tandon, H.L.S.,

Alagarswamy, G., 1989. Modelling the Growth and Development of  Sorghum and Pearl Millet. ICRISAT, Patancheru, Andhra Pradesh, India.

Folliard, A., Traoré, P.C.S., Vaksmann, M., Kouressy, M., 2004. Modeling of sorghum response to photoperiod: a threshold-hyperbolic approach. Field Crops Research 89, 59-70.

Singh, P., Nedumaran, S., Traore, P.C.S., Boote, K.J., Rattunde, H.F.W., Prasad, P.V.V., Singh, N.P., Srinivas, K., Bantilan, M.C.S., 2014. Quantifying potential benefits of drought and heat tolerance in rainy season sorghum for adapting to climate change. Agricultural and Forest Meteorology 185, 37-48.

White J.W., Alagarswamy G., Ottman M.J., Porter C.H., Singh U., Hoogenboom G. 2015. An overview of CERES-Sorghum as implemented in the Cropping System Model Version 4.5. Agronomy Journal 107:1987-2002. DOI: 10.2134/agronj15.0102

White, J., Hoogenboom, G., Ottman, M., 2007. Modeling phenology of sorghum based on known maturity (Ma) loci. Farming Systems Design 2007. Proc. Int. Conf., Catania, Italy, pp. 10-12.