<p>Accurate forecasting of agricultural commodity prices is a critical task for market participants and policymakers, particularly in contexts characterized by multiple sources of uncertainty. Meteorological conditions are often considered potential drivers of price dynamics and are therefore frequently included in predictive models. This study examines the case of fine durum wheat by comparing Machine Learning and Deep Learning approaches under different feature configurations, with and without meteorological variables. Using long-term monthly data, we evaluate eXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks within a unified out-of-sample forecasting framework. The empirical results show that, at the monthly frequency, including weather-related variables does not yield systematic improvements in forecasting accuracy. In several cases, models that exclude meteorological inputs achieve comparable or superior performance while reducing complexity. In particular, XGBoost trained on price-based covariates yields the most robust and accurate predictions, whereas LSTM models capture long-term trends but do not consistently benefit from climatic information. These findings highlight the importance of careful feature selection and suggest that the predictive value of weather variables is highly dependent on data granularity and market structure.</p>