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Agriculture, especially cereals, is important in sustaining economies and food security globally. This study delves into the Moroccan agricultural landscape, specifically focusing on predicting livestock feed sales to assist cereal company producers in optimizing production, streamlining supply chain operations, and enhancing customer satisfaction. Data collected from various markets across Morocco, including sales dates and locations, was combined with climate data and analyzed using advanced machine learning techniques, particularly the gradient boosting regression (GBR) algorithm, which achieved high accuracy with a mean absolute error (MAE) of 0.0203 and a root mean square error (RMSE) of 0.0281. The evaluation of multiple regression models revealed promising results, demonstrating the effectiveness of predictive models in accurately forecasting sales. These findings contribute valuable insights to sales forecasting in the cereal industry by considering weather conditions, production methods, and livestock-related variables, highlighting the importance of leveraging advanced machine learning techniques for optimizing production processes and meeting market demands efficiently in the agribusiness sector. [ABSTRACT FROM AUTHOR]

This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. [ABSTRACT FROM AUTHOR]