## 4 Discussion
### Challenges and Limitations
#### 4.4.4 Interaction of Limitations  
The limitations discussed--data availability, model complexity, and interpretability--often interact in ways that compound the overall challenge. For example, limited and region-specific datasets reduce the volume and variety of input features available for training, which in turn pressures researchers to use more complex models (e.g., deep learning with multiple layers or hybrid structures) to extract patterns. These more complex models tend to require greater computational resources and longer training times, increasing operational costs and making them much more difficult to deploy. Furthermore, such models are often opaque or \"black box\" in nature, making them uninterpretable, especially when trained on sparse or noisy datasets. This lack of transparency undermines user trust and limits adoption, particularly in high-stakes agricultural policy contexts. Thus, these limitations form a cycle, where attempting to solve one issue may adversely affect others.  
In addition to the methodological challenges already addressed, the forecasting of agricultural commodity prices remains difficult due to the broad range and variability of influencing factors. These include oil prices [18], global market linkages [19], and weather-related variables such as rainfall, temperature, humidity, wind speed, and sunshine [30; 68; 31]. Macroeconomic indicators, as well as domestic and international demand and supply conditions, have also been identified as significant contributors to price fluctuations [21]. The increasing financialization of agricultural markets has introduced further volatility and complexity [43].  
These factors, and their interactions, influence both the selection and performance of forecasting models. As highlighted by [PERSON] and [PERSON] [25], no single model can be expected to perform best across all contexts. Model effectiveness varies depending on the specific crop, forecasting horizon, data availability, and preprocessing strategies [27; 29]. A notable limitation across the reviewed studies is the absence of standardized datasets and consistent evaluation metrics, which prevents direct comparison of model performance [53].  
Several additional considerations, such as the selection of time lags, the configuration of model parameters (e.g., learning rate, window length), and the choice of input features, were also shown to significantly affect forecasting results [33; 35; 36]. Furthermore, the integration of external data--such as sentiment indicators, macroeconomic variables, and open-source weather data--has shown potential to improve predictive performance [26; 41; 42], though systematic validation remains limited.  
Given the volatility of grain commodity prices and their implications for producers, markets, and consumers [41, 42], the development of forecasting models that are both accurate and interpretable is essential. Priority should be given to the advancement of explainable AI approaches and hybrid frameworks that support informed decision-making in real-world applications [45, 49].  
One of the main limitations encountered in this review is the lack of a standardized framework for evaluating and comparing forecasting models under common conditions. A potential direction for future research involves the development of controlled, game-like simulation environments, like those used in supply chain forecasting competitions [74]. Such platforms would allow researchers to test their models using the same datasets and forecasting scenarios, facilitating more realistic, transparent, and reproducible performance comparisons. This approach could complement existing empirical evaluations and contribute to the establishment of benchmarking standards in the field.