Browsing by Subject "Forecasting model"

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Using machine learning for supply and demand predictions in the German milk market
(2023) Baaken, Dominik; Hess, Sebastian
The German milk market is driven by various ongoing trends on both the domestic supply and the international demand side. This results in increasingly volatile prices, as well as increasing production costs, and both risks continue to induce dairy farms going out of business. Therefore market participants have expressed a desire for reliable forecasting tools at the regional level in order to be able to make strategic and operational decisions with greater planning certainty. However, such forecasting models at the farm or regional level do not currently exist or are not publicly available. This dissertation fills this research gap by developing a forecasting model for predicting regional milk production in Lower Saxony. The first of four research chapters, Chapter 3, compares five different Machine Learning (ML) models and a traditional linear regression (OLS) model based on time trends, direct and indirect weather influences, and price events. The ML models show advantages in forecast accuracy, in particular ML methods outperform econometric modelling in predicting non-linear developments induced by investment. Furthermore, differences in the efficiency of the methods are apparent: while comparable estimation approaches achieve similar accuracies, the training speed of the models varies considerably. Chapter 4 presents the relationship between seasonal weather conditions and seasonal milk production. This chapter incorporates the influences of direct and indirect weather conditions as well as time and price trends into the model. A Fixed Effects (FE) estimator is used to model quarterly milk production for a panel dataset from Lower Saxony. The results mainly illustrate the influence of farm decisions on milk production, which is stronger than the influence of weather conditions. Contrary to expectations, the influence of weather conditions during the growing season cannot be significantly demonstrated. Instead, there is a positive effect of warmer and drier weather in almost all quarters except autumn. Chapters 5 and 6 shift the focus to the demand side of the German milk market, examining in particular the sale of raw milk from vending machines. As farmers seek alternative sales channels, on-farm vending machines offer an opportunity for additional income. Chapter 5 develops a forecasting model based on a nationwide survey and the Xtreme Gradient Boosting (XGB) algorithm. The model achieves sufficiently accurate values to qualify as a practical tool, allowing indecisive farm managers to input their own values into the model and thus secure their investment decision. The influence of the variables on the prediction is investigated using SHapley Additive exPlanation (SHAP) values, indicating that sales of raw milk from vending machines are influenced less by individual marketing measures than by various location factors such as population density, proximity to a city, and location along a road with commuter traffic. It can be concluded that there is additional sales potential if farmers would be allowed to place the vending machine in an optimal location away from the farm. Chapter 6 analyses consumer behaviour through a survey in Germany, using seemingly unrelated regression (SUR) to model willingness to pay (WTP) and frequency of purchase. The results suggest that in this form of marketing, consumers especially value a ‘fair’ price for the producer and are less price-sensitive. On average, customers’ WTP is higher than the current milk price and varies between consumer groups. Consumers with a closer connection to milk production are willing to pay more for raw milk but purchase it less frequently. It also appears that as consumers get older, they are more likely to buy raw milk but are less willing to pay for it. Tailoring marketing activities based on consumer characteristics can increase the efficiency of additional sales channels. Overall, this dissertation demonstrates the potential applications and limitations of ML methods for considering supply and demand in the German milk market. The forecasting models can serve as a potential tool for farmers to better weight strategic and operational decisions, thus contributing to more efficient agriculture.