*3.1. Model Development and Estimation Efficiency*

The general, country-wide productivity model using biophysical explanatory variables explains up to 40% of the biomass productivity in the country (R2 = 0.402). This model fit can be considered adequate for a country scale assessment, especially for a country with a wide variety of soil types from salt-affected soils to Arenosols, Luvisols and chernozems. The efficiency of the crop-specific models is best for wheat, followed by maize and sunflowers,

in the order of the available sample size, respectively (Figure 3 and Table 2). Results were statistically significant at the 0.01 level.

**Figure 3.** Scatter plot of observed vs estimated biomass productivity of total cropland area (**A**), wheat (**B**), maize (**C**) and sunflowers (**D**). Results were significant at the 0.01 level.

**Table 2.** Test validation results of all cropland, wheat, maize and sunflowers. R2: correlation coefficient, R: Pearson correlation, MAPE: mean absolute percentage error, MAE: mean absolute error, N: number of pairs.


The combination of measured and satellite-driven data for the general productivity model development gave almost the same model fit as the crop-specific one for wheat, which was based on a large sample size of measured yields. The descriptive power of sunflower productivity estimation was not as strong (Figure 3D). The MAPE results are as follows: all cropland 19.28%, wheat 18.07%, maize 19.17% and sunflowers 29.81%. The most accurate prediction based on the MAPE and MAE results was for wheat followed by the maize and sunflower predictions.
