## 3 Analysis of Original Research Articles
### Ensemble Learning Techniques  
The application of ensemble learning techniques to agricultural commodity price prediction has been effective and has also shown potential for both short- and long-term forecasting (Table 4). [PERSON] and [PERSON] [25] analyzed 60 years of deflated price data for corn, soybean, and cocoa, finding that TBATS outperformed short-term forecasts, while Gradient Boosting Machines (GBM) excelled at long-term projections. Similarly, in a different study, [PERSON] and [PERSON] [26] emphasized the dominance of Northern America's corn production in influencing global prices and highlighted the superior performance of Random Forest (RF) and GBM for long-term predictions, while TBATS remained effective for horizons of 2-5 months. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Refs.** & **Year** & **Commodity Name** & **Dataset** & **Technique** & **Findings** & **Limitation** \\ \hline \multirow{4}{*}{[25]} & \multirow{4}{*}{2024} & \multirow{4}{*}{2022} & The World Bank’s commodity market database, & \multirow{4}{*}{TBATS, GBM, CART, LM, RF price data} & \multirow{4}{*}{TBATS, GBM, CART, LM, RF GBM: RA: 60\%, and (1960–2020)} & & Asymmetrical price responses to \\  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \multirow{4}{*}{[26]} & \multirow{4}{*}{2022} & \multirow{4}{*}{Corn} & FAOSTAT corn yield/production (1961–2019), World Bank corn price & \multirow{4}{*}{FAOSTAT corn yield/production (1961–2019), World Bank corn price} & \multirow{4}{*}{RF and GBM, MLR, VAR, TBATS} & \multirow{4}{*}{Difficulty in (1961–2019), World Bank corn price} & \multirow{4}{*}{RF} & \multirow{4}{*}{RF} & \multirow{4}{*}{RF} \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \multirow{4}{*}{[27]} & \multirow{4}{*}{2023} & \multirow{4}{*}{Rice} & Average Rice Price dataset at the Indonesian Wholesale Trade Level (2010–2022) & & & The model’s performance was from 0.0093573 & \multirow{4}{*}{RF} & \multirow{4}{*}{RF} \\ \cline{1-1} \cline{5-6}  & & & & & & 0.0089389 & & \\ \hline \multirow{4}{*}{[28]} & \multirow{4}{*}{2021} & \multirow{4}{*}{Corn, Sugar} & \multirow{4}{*}{Daily prices (2003–2019)} & ARIMA, SARIMA, SVR, AdaBoost, LSTM, Ensemble models & Best performing models: SVR, followed by SVR/LSTM & \multirow{4}{*}{Models did not capture significant volatility and non-stationary data trends in the datasets} \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \multirow{4}{*}{[29]} & \multirow{4}{*}{2024} & \multirow{4}{*}{Corn} & Planting land area, crop price, crop yield, rainfall, import and export volumes, import and export values, and price in a total of 53 variables (2002–2023) & & & Focus on a specific \\ \cline{1-1} \cline{5-6}  & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Overview of ensemble learning techniques in wheat, rice, and corn price prediction. Ensemble learning methods have also been further explored to increase the accuracy of price prediction in other studies. [PERSON] et al. (2017) used Random Forest with hyperparameter tuning to forecast Indonesian rice prices, with MAPE reduced from 0.0093573 to 0.0089389 and R\({}^{2}\) increased from 0.9916805 to 0.9921578. [PERSON] et al. (2018) showed that combining support vector regression (SVR) with LSTM or AdaBoost provided better prediction performance for corn and sugar prices in Brazil in comparison with traditional econometric models, and the SVR model outperformed all. [PERSON] and [PERSON] (2019) reported that the Bagging-SVR model had the highest accuracy in predicting corn prices in Thailand, with an R\({}^{2}\) of 0.961, an MAE of 0.234, and an RMSE of 0.315, which entails the merit of feature importance in promoting forecasting performance. [PERSON] et al.