Determination of genetic coefficients of three spring wheat varieties under a Mediterranean environment applying the DSSAT model
|Isaac Maldonado-Ibarra1*, Gabriel R. Rodríguez2, and Dalma Castillo-Rosales1|
|The impact of climate change requires developing and validating models that help to project possible scenarios that must adapt to new varieties. This study seeks to validate and calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model as a tool that facilitates the characterization of the behavior of new varieties in the face of new scenarios generated by climate change. The determination of genetic coefficients of three bread wheat (Triticum aestivum L.) varieties was considered in the methodology; this was done with the database of the historical records of the Instituto de Investigaciones Agropecuarias (INIA) Wheat Breeding Program for the 2000 to 2011 period. Once the adjustment level of the model was dealt with, it was feasible to obtain genetic coefficients of three spring wheat varieties (Pandora-INIA, Kipa-INIA, and Millán-INIA); days from planting to anthesis variable exhibited RMSE values fluctuating between 3.5 and 9.7 depending on the variety. For total duration of days to maturity, 'Millán-INIA' exhibited a very good adjustment (RMSE = 0.25) as compared to 'Pandora-INIA' (RMSE = 1.35) and 'Kipa-INIA' (12.22). Furthermore, the coefficient of determination of the genetic coefficients indicates that the varieties have minimum vernalization requirements; these are similar to the photoperiod between 'Pandora-INIA' and 'Millán-INIA' and lower in the case of 'Kipa-INIA'. Thermal requirements for grain filling, biomass production, and plant height did not exhibit any important differences among varieties. Finally, the methodology allowed calibrating the DSSAT model and achieving a good predictive level of yields for the three varieties. The plant development parameters must be studied in greater detail because of the low association between simulated and observed values.|
|Keywords: Climate change, DSSAT model, genetic coefficients, simulation, Triticum aestivum.|
|1Instituto de Investigaciones Agropecuarias, INIA Quilamapu, Casilla 426, Chillán, Chile. *Corresponding author (firstname.lastname@example.org).|
2Instituto Nacional de Tecnología Agropecuaria INTA, Instituto de Clima y Agua, N. Repeto y de los Reseros SN (1686) Hurlingham, Buenos Aires, Argentina.