*4.7. Models Based on Crops Production Value vs. GVA and GDP Economic Indicators*

As expected, the R-square value of the model depicting the relation between the agricultural production value and agriculture sector GDP is very high at 92.70 (Figure 13). The adjusted R-square is similarly large, the residuals displaying a constant variance and independence (Figure A9). This emphasizes that the agriculture sector's GDP is mostly influenced by the agriculture production value and less influenced by taxes. This facilitates the control of size and growth rate of agriculture economy.

**Figure 13.** TotalPlantsVal–GDP\_Agriculture.

At first glance, as represented in the table fitted line plot, the relation between the wheat production value and total GVA is linear (Figure 14). Still, due to a special case, in which for a high wheat production value there was a low total GVA, the linear model fails to perform very well. The regression *p*-value is 0.07 (Figure A12), close to the 0.05 significance level, but according to the R-square the model is explaining 38.00% of the variance. The quadratic model fits the model much better. Still, future observations are required in order to more clearly asses the overall context (Figure 14a). The model emphasizes the important share of wheat production value from the total GVA of the Republic of Moldova. Therefore, the wheat market has a significant impact on all of the Moldavian economy and can be used as an instrument of macro-economic control. The total agriculture plant production value–agriculture GVA displays a positive linear trend and the linear model can explain around 43.00% of the agricultural GVA based on the total plants value (Figure 15). The regression *p*-value is 0.05 (Figure A14), while the residuals displays almost constant variance, independence and a good normal probability plot (Figure A13). This model confirms the transparency between total production value and GVA in the agriculture sector.

**Figure 14.** (**a**) Wheat Value–GVA economy quadratic. (**b**) Wheat value–GVA economy.

**Figure 15.** TotalPlantsVal–GVA\_Agriculture.

#### *4.8. Models Based on GVA and GDP Economic Indicators*

The relation between agriculture GVA and total GVA is strongly linear (Figure 16); the model shows independent normally distributed residuals, with constant variance, a high 91.80 R-square, a very low S-value at 0.05 and a regression *p*-value at 0 (Figures A15 and A16). As such we can determine the value for total GVA relying on agriculture GVA. As we used natural logarithms for the terms of the model, a 1.00% increase of the agriculture GVA will lead to a 0.59% increase of total

GVA. This model emphasizes the importance of agriculture sector for the Republic of Moldova's economy, as this sector can be used as a control tool to induce long term economic development.

**Figure 16.** GVA\_Agriculture–GVA\_Economy.

As noticed in the Figure 17, the relation between GDP (as predictor) and agricultural GVA (as explained variable) can be linearly expressed, having for residuals a slightly visible variation and no correlation (Figure A17). As for the equation coefficients, they are showing that for a 1% increase in agricultural GVA, the GDP value will increase by 0.45%. This model completes the previous model, thereby emphasizing the importance of the agriculture sector in the Moldavian macro-economy.

**Figure 17.** GVA\_Agriculture–GDP.
