## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Crop**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Cross-Validation**} & \multicolumn{3}{c}{**Final Model Fit**} \\ \cline{3-10}  & & & **R\({}^{2}\)** & **RMSE** & **MAE** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline \multirow{8}{*}{Maize} & \multirow{4}{*}{2019} & RF & **0.409** & **1139.7** & 894.4 & **0.898** & **574.8** & **447.1** \\  & & SVM & 0.367 & 1199.7 & **878.7** & 0.452 & 1111.6 & 788.6 \\  & & MARS & 0.347 & 1195.6 & 938.1 & 0.359 & 1183.4 & 931.2 \\  & & BRNNs & 0.351 & 1193.2 & 927.4 & 0.397 & 1147.7 & 897.2 \\ \cline{2-10}  & \multirow{4}{*}{2020} & RF & **0.319** & **1170.2** & **916.8** & **0.908** & **563.3** & **437.0** \\  & & SVM & 0.279 & 1210.2 & 930.1 & 0.365 & 1131.2 & 847.1 \\  & & MARS & 0.257 & 1221.6 & 957.5 & 0.270 & 1209.5 & 949.2 \\  & & BRNNs & 0.271 & 1211.6 & 946.7 & 0.321 & 1166.6 & 917.1 \\ \cline{2-10}  & \multirow{4}{*}{2021} & RF & **0.310** & **981.7** & 767.3 & **0.910** & **464.6** & **358.0** \\  & & SVM & 0.268 & 1024.2 & **764.2** & 0.365 & 952.2 & 688.6 \\  & & MARS & 0.262 & 1014.4 & 790.7 & 0.284 & 998.8 & 777.9 \\  & & BRNNs & 0.262 & 1016.1 & 786.5 & 0.311 & 979.3 & 762.2 \\ \cline{2-10}  & \multirow{4}{*}{2022} & RF & **0.371** & **845.3** & 674.1 & **0.914** & **407.4** & **321.3** \\  & & SVM & 0.328 & 879.0 & **670.8** & 0.404 & 823.5 & 611.3 \\  & & MARS & 0.316 & 877.4 & 696.0 & 0.331 & 867.0 & 689.8 \\  & & BRNNs & 0.318 & 877.9 & 690.4 & 0.363 & 845.6 & 671.9 \\ \hline \multirow{8}{*}{Soybean} & \multirow{4}{*}{2019} & RF & **0.398** & **232.1** & **181.4** & **0.913** & **109.9** & **85.0** \\  & & SVM & 0.336 & 243.5 & 188.2 & 0.419 & 227.7 & 172.5 \\ \cline{1-1}  & & MARS & 0.326 & 245.3 & 191.8 & 0.328 & 244.5 & 192.2 \\ \cline{1-1}  & & BRNNs & 0.296 & 251.1 & 194.8 & 0.349 & 240.7 & 188.9 \\ \cline{1-1} \cline{2-10}  & \multirow{4}{*}{2020} & RF & **0.393** & **291.3** & **223.9** & **0.904** & **140.9** & **107.0** \\  & & SVM & 0.317 & 310.6 & 235.0 & 0.425 & 284.1 & 205.2 \\ \cline{1-1}  & & MARS & 0.306 & 311.3 & 241.9 & 0.317 & 308.4 & 239.9 \\ \cline{1-1}  & & BRNNs & 0.267 & 320.8 & 247.3 & 0.314 & 309.2 & 239.3 \\ \cline{1-1} \cline{2-10}  & \multirow{4}{*}{2021} & RF & **0.297** & **214.9** & 170.3 & **0.908** & **102.0** & **79.9** \\ \cline{1-1}  & & SVM & 0.231 & 231.4 & **166.3** & 0.319 & 216.4 & 149.7 \\ \cline{1-1}  & & MARS & 0.212 & 227.6 & 181.1 & 0.236 & 223.6 & 178.6 \\ \cline{1-1}  & & BRNNs & 0.222 & 226.7 & 178.6 & 0.263 & 219.7 & 174.9 \\ \cline{1-1} \cline{2-10}  & \multirow{4}{*}{2022} & RF & **0.298** & **214.7** & 170.2 & **0.908** & **101.9** & **79.9** \\ \cline{1-1}  & & SVM & 0.230 & 231.7 & **166.6** & 0.324 & 215.6 & 148.9 \\ \cline{1-1}  & & MARS & 0.211 & 227.7 & 181.1 & 0.236 & 223.6 & 178.6 \\ \cline{1-1}  & & BRNNs & 0.221 & 227.0 & 178.8 & 0.285 & 216.4 & 171.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy assessment results of the evaluated machine learning methods for crop yield potential prediction, expressed using cross-validation and final model fit metrics. Figure 4 presents the relative importance of phenological metrics used in maize and soybean yield prediction across the four study years (2019-2022) based on the most accurate machine learning model per dataset. Phenological metrics related to the vegetation index values at POS were the most important for predicting the yield potential for both maize and soybean, closely followed by maturity and senescence. These covariates were related to flowering in maize and pod fill in soybean [79], and likely reflect the cumulative canopy development at the peak of canopy vigor, producing the highest vegetation index values during the maize and soybean vegetative period [80]. Similarly, [PERSON] [30] emphasized the relevance of late-season data, reinforcing the observation that indices derived around the pod filling (soybean) or flowering to grain-fill stages (maize) had the most predictive value.