# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 3 Results
### Sugar Beet Yield Predictions  
The simulation results for sugar beet cultivation (refer to Figure 7) in 2018 and 2021 indicate a lower level of accuracy, with RMSE values of 0.95 t\(\cdot\)ha\({}^{-1}\) and 0.78 t\(\cdot\)ha\({}^{-1}\), respectively. These results suggest a deviation from actual values. However, upon analyzing the simulation results for sugar beet cultivation in 2023, the recorded RMSE value of 0.20 t\(\cdot\)ha\({}^{-1}\) attests to a high level of accuracy for the predictive model, based on both in-situ measurements and satellite data. This observation underscores the significant ability of the simulation to approximate actual values and, compared to other years, it seems to characterize a higher level of reliability. Examining the simulation results for sugar beet cultivation in the years 2019, 2020, and 2022, the respective RMSE values of 0.61, 0.30, and 0.72 t\(\cdot\)ha\({}^{-1}\) indicate that the model has achieved the expected predictive precision.  
The study demonstrates a consistent downward trend in the analysis results based on LAI over the examined period (Table 5). It is noteworthy that the annual comparison indicates that error values derived from LAI are comparable to those obtained from NDVI, with no consistent trend of one being consistently higher than the other. This suggests that the predictive model associated with LAI may exhibit similar accuracy compared to the NDVI-based model.  
This visual representation illuminates the significant potential of using high-resolution satellite data in enhancing the predictability and management of crop yields within the dynamic agricultural landscape of Wielkopolska, particularly amidst the challenges brought forth by climate change. The results reveal a distinct advantage in utilizing satellite data for predicting yields of specific crops such as wheat and sugar beets. Conversely, the analysis across other crops like winter rapeseed and maize demonstrates a remarkable consistency in prediction errors, underscoring the widespread applicability and promise of satellite data in agricultural yield forecasting. This consistency across various crop types suggests that the integration of satellite data into yield forecasting models is not only feasible, but also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{5}{c}{**Yield (t-ha\({}^{-1}\))**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RMSE (t-ha\({}^{-1}\))**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-7}  & **LAI** & ** NDVI** & **LAI** & **NDVI** & **LAI** & **NDVI** \\ \hline
2018 & 0.396 & 0.363 & 0.396 & 0.363 & 17.149 & 15.694 \\
2019 & 0.061 & 0.118 & 0.061 & 0.118 & 2.430 & 4.667 \\
2020 & 0.153 & 0.172 & 0.153 & 0.172 & 4.561 & 5.115 \\
2021 & 0.019 & 0.029 & 0.019 & 0.029 & 0.578 & 0.899 \\
2022 & 0.259 & 0.249 & 0.259 & 0.249 & 7.524 & 7.248 \\
2023 & 0.077 & 0.116 & 0.077 & 0.116 & 2.330 & 3.512 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model results based on in-situ and satellite data in winter rapeseed cultivation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{5}{c}{**Yield (t-ha\({}^{-1}\))**} \\ \cline{2-7}  & \multicolumn{2}{c}{**RMSE (t-ha\({}^{-1}\))**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**MAPE**} \\ \cline{2-7}  & **LAI** & **NDVI** & **LAI** & **NDVI** & **LAI** & **NDVI** \\ \hline
2018 & 0.954 & 0.872 & 0.954 & 0.872 & 1.653 & 1.510 \\
2019 & 0.612 & 0.613 & 0.612 & 0.613 & 1.079 & 1.081 \\
2020 & 0.306 & 0.402 & 0.306 & 0.402 & 0.489 & 0.642 \\
2021 & 0.789 & 0.673 & 0.789 & 0.673 & 1.277 & 1.089 \\
2022 & 0.725 & 0.632 & 0.725 & 0.632 & 1.125 & 0.980 \\
2023 & 0.265 & 0.298 & 0.257 & 0.289 & 0.453 & 0.508 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model results based on in-situ and satellite data for sugar beet cultivation.  
holds substantial promise for improving agricultural outcomes across a broad spectrum of practices.