This modelling approach simulates the setback viscosity of rice as function of air temperature perceived by the crop during the R8  stage of the ripening period (Counce et al., 2009), when the main culm turns to brown (Lanning et al., 2011) . In particular this viscoamilographic index is differently determined for medium and long grain varieties, using respectively a linear and quadratic function of the 95-th percentile of nighttime air temperature values observed during the abovementioned phenological phase.

The general equation developed for Japonica varieties (Lanning et al., 2012) are given below:

Where:

SVlong (Rapid Visco Unit) is the setback viscosity of long grain rice starch ;

Tn95th (°C) is the 95th percentiles of nighttime air temperature frequencies that occurred during the R8 phase of rice ripening;

ai, bi, ci(unitless) are the coefficients of the equation, specific for long grain Japonica varieties.

Where:

SVmedium (Rapid Visco Unit) is the setback viscosity of medium grain rice starch;

Tn95th (°C) is the 95th percentiles of nighttime air temperature frequencies that occurred during the R8 phase of rice ripening;

ai, bi (unitless) are the coefficients of the equation, specific for medium grain Japonica varieties.

Basically, once a DVS equal to 2.5 is reached, the model for the simulation of HRY begins to receive as input the hourly values of air temperature. Thus, each day, the HRY model populate a list object by using the hourly temperature data included between Sunset and Sunrise hour. These latter are exogenous variables that vary depending on the latitude of the site where simulations are performed. At last, when physiological maturity stage (DVS = 3) is achieved, the values stored in the above mentioned list object are used to calculate the 95-th percentile of nighttime air temperature, which represents the input of  the equation listed above.

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