# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 3 Results
### Maize Yield Predictions  
This chapter presents the results of the AquaCrop model analysis, utilizing both in-situ data and NDVI satellite data for maize cultivation from 2018 to 2023 (Figure 5).  
Upon comparing the results obtained in 2018 with those of other years, it becomes evident that the simulated values, with an RMSE of 0.01 t\(\cdot\)ha\({}^{-1}\), closely align with the measured values, indicating a high level of precision in the output. However, simulation results for the years 2020 and 2023 suggest less reliable outcomes with RMSE values of 0.48 t\(\cdot\)ha\({}^{-1}\) and 0.75 t\(\cdot\)ha\({}^{-1}\), respectively, compared to other years. Conversely, the results for 2019 and 2021, with RMSE values of 0.21 t\(\cdot\)ha\({}^{-1}\) and 0.09 t\(\cdot\)ha\({}^{-1}\), respectively, adhere to the criteria necessary for considering the model successful.  
The research indicates (see Table 3) that results based on in-situ LAI data yield higher error values than those based on satellite NDVI. However, this trend is not consistent across all years, as there are instances where the RMSE values for LAI are lower than those for NDVI. This suggests that while a predictive model relying on ground-based LAI data may exhibit a relatively limited level of accuracy compared to a model associated with satellite-derived NDVI, the relationship between the two datasets can vary depending on the specific year. Notably, the year 2020 exhibits a significantly higher RMSE compared to other years, indicating an elevated level of forecast errors for that specific year.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**Yield (t\(\cdot\)ha\({}^{-1}\))**} \\ \cline{2-7}
**Year** & \multicolumn{2}{c}{**RMSE (t\(\cdot\)ha\({}^{-1}\))**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-7}  & **LAI** & **NDVI** & **LAI** & **NDVI** & **LAI** & **NDVI** \\ \hline
2018 & 0.463 & 0.399 & 0.294 & 0.252 & 9.412 & 8.732 \\
2019 & 0.898 & 0.871 & 0.646 & 0.564 & 10.918 & 9.845 \\
2020 & 1.453 & 1.501 & 0.744 & 0.787 & 5.411 & 5.776 \\
2021 & 0.201 & 0.192 & 0.162 & 0.137 & 2.163 & 1.847 \\
2022 & 0.531 & 0.513 & 0.404 & 0.390 & 5.058 & 4.846 \\
2023 & 0.106 & 0.169 & 0.033 & 0.049 & 0.624 & 2.333 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of model results based on in-situ and satellite data for winter wheat cultivation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**Yield (t\(\cdot\)ha\({}^{-1}\))**} \\ \cline{2-7}
**Year** & \multicolumn{2}{c}{**RMSE (t\(\cdot\)ha\({}^{-1}\))**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-7}  & **LAI** & **NDVI** & **LAI** & **NDVI** & **LAI** & **NDVI** \\ \hline
2018 & 0.011 & 0.056 & 0.011 & 0.056 & 0.212 & 1.039 \\
2019 & 0.210 & 0.198 & 0.210 & 0.198 & 4.319 & 4.073 \\
2020 & 0.486 & 0.428 & 0.486 & 0.428 & 6.674 & 5.875 \\
2021 & 0.089 & 0.333 & 0.089 & 0.333 & 1.214 & 4.530 \\
2022 & 0.131 & 0.311 & 0.131 & 0.311 & 1.917 & 4.535 \\
2023 & 0.750 & 0.642 & 0.718 & 0.603 & 1.514 & 0.817 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of model results based on in-situ and satellite data for maize cultivation.