This model calculates the protein fraction accumulated in the rice grain during the ripening phase, in response to two environmental factors: temperature and latitude. Each of these  factors multiplies a reference protein content, peculiar of the tested varieties, increasing or decreasing its value, with a specific effect. The impact factors are a-dimensional and they can assume values ​​ranging from 0 (maximum impact) and 1 (no impact).

The model was developed by considering a potential production level and two ecotypes, Japonica and Indica, characterized by different parameterizations.

The protein content is therefore calculated via the following equation (Li et al., 2005a):

Where:

PC (%) is the protein content of rice kernel as percentage of grain dry weight (D.W.);

PPC (%) is the peculiar protein content (D.W.) of the simulated rice variety;

LF (unitless) is the latitude factor;

TavgF (unitless) is the factor related to average air temperature;

TmaxF (unitless) is the factor related to maximum air temperature;

TminF (unitless) is the factor related to minimum air temperature;

pa, pc, pi, pl (unitless) are the relative weights of the above mentioned factors.

All the above mentioned environmental factors are derived using a quadratic function of corresponding input experienced by the rice crop during the entire ripening phase (e.g. minimum air temperature to calculate minimum temperature factor) as shown in Figure 2.

Figure 2. Generic quadratic function used to determine the impact of minimum air temperature on rice protein content.

The relative weights of considered factors are derived using the following equation:

Where:

Ri2 is the value of regression coefficient between the i-th input taken into account and the protein content from field campaign data.

This model was evaluated in different sites of China, Japan and Thailand, reporting values ​​of RMSE equal to 0.4% and 0.5% for Indica and Japonica varieties respectively.    

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