# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
## 3 Results and Discussion
### Type 1 Maize  
Double smoothing and ARIMAX models were applied to estimate the type 1 maize. Weather measures were found to be significantly influential in the models, contributing to the explanatory power of the proposed models. Equation (7) presents the estimation model for type 1 maize considering the interpolation obtained by means of the single exponential smoothing method. \[ \begin{array}{l}S_{t}=0.87\ y_{t}+(1-0.87)S_{t-1}\\ D_{t}=0.87\ S_{t}+(1-0.87)D_{t-1}\end{array}  \tag{7}\]  
The Sum of Squared Residuals (SSR) associated value for the model presented in Equation (6) is 0.709, and the Root Mean Squared Error (RMSE) is 0.111. The associated mean value for the model presented in Equation (7) is 0.5284, and the trend is \(-0.0014\). Since \(\alpha=0.87\) is the weight used in the level component of the smoothed estimate, the high weight gives more weight to the recent data. Considering the dynamic structure of the time series and by means of transference functions, Equation (8) presents the estimation model for type 1 maize. The best selected model was ARIMAX(2,0,0)X(1,0,0) considering the smallest AIC value. \[Y_{t}=0.39+0.0116\mathrm{T}+0.117\mathrm{P}+0.657Y_{t-1}+0.356Y_{t-2}+0.414Y_{ t-12}+\epsilon_{t} \tag{8}\]  
where \(Y_{t}\) is type 1 maize at time \(t\), T is the average temperature and P is the precipitation. All estimated parameters were significant at the 0.05 level. The SSR associated value for the model presented in Equation (8) is 0.629, and the Akaike info criterion (AIC) is \(-1.369\). We can observe the positive impact of temperature and precipitation over type 1 maize and a dynamic dependence. Ljung Box autocorrelation proof validates the hypothesis of white noise in the residuals of the model, confirming the adequacy and reliability of the estimationmodel. Figure 3 (Equation (8)) presents the estimation model as a dynamic explanatory response in time and the residuals of the estimation model given by Equation (8). A similar model considering the dynamic structure of the time series and by means of transference functions is presented in Equation (9). The selected model was ARIMAX(2,0,0)x(1,0,0) considering the smallest AIC value. \[Y_{t}=0.34+0.011\text{R}+0.723Y_{t-1}+0.351Y_{t-2}+0.449Y_{t-12}+\varepsilon_{t} \tag{9}\]  
where \(Y_{t}\) is type 1 maize at time \(t\) and R is radiation. All estimated parameters were significant at the 0.05 level. The Sum of Squared Residuals (SSR) associated value for the model presented in Equation (9) is 0.608, and the Akaike info criterion (AIC) is \(-1.38\). We can observe the positive impact of the radiation over type 1 maize and a dynamic dependence. Ljung Box autocorrelation proof validates the hypothesis of white noise in the residuals of the model, confirming the adequacy and reliability of the estimation model. Figure 3 (Equation (9)) presents the estimation model as a dynamic explanatory response in time and the residuals of the estimation model given by Equation (9). A similar model considering the dynamic structure of the time series and by means of transference functions is presented in Equation (10). The selected model was ARIMAX(2,0,0)X(1,0,0). \[Y_{t}=0.82+0.004\text{H}+0.761Y_{t-1}+0.372Y_{t-2}+0.466Y_{t-12}+\varepsilon_{t} \tag{10}\]  
where \(Y_{t}\) is type 1 maize at time \(t\) and H is humidity. All estimated parameters were significant at the 0.05 level. The Sum of Squared Residuals (SSR) associated value for the model presented in Equation (10) is 0.598, and the Akaike info criterion (AIC) is \(-1.39\). We can observe the positive impact of the average humidity over type 1 maize and a dynamic  
Figure 3: Representation for Maize 1 variety, estimation model and residuals given by Equations (8)–(11). dependence. Ljung Box autocorrelation proof validates the hypothesis of white noise in the residuals of the model, confirming the adequacy and reliability of the estimation model.