Food price dynamics and regional clusters: machine learning analysis of egg prices in China

Abstract

PurposeThe study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.Design/methodology/approachThe study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.FindingsThe study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.Practical implicationsThe machine learning techniques could help governments make more precise policies and help producers make better investment decisions.Originality/valueThis is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

Editorial: Prediction, pattern recognition and machine learning in agricultural economics

“…Liu et al . (2023) used the dynamic time warping (DTW) method to cluster time-series data of egg prices across provinces in China and identified three different clusters.…”

“…For instance, Graskemper et al (2021) used a clustering algorithm and found three types of farms in Germany, and suggested different policies that should be made toward each group. In this special section, two papers (Zeng and Chen, 2023;Liu et al, 2023) used different clustering algorithms to study two important policy issues: rural-urban integration and egg price clustering at the provincial level of China. Given the sheer size of China and the heterogeneous development levels between regions, such studies could help make more precise policies in these fields.…”

“…In contrast to the classical clustering algorithm of K-means, which has a restricting application to continuous variables, PAM can take into account discrete variables. Liu et al (2023) used the dynamic time warping (DTW) method to cluster time-series data of egg prices across provinces in China and identified three different clusters. Within each cluster, the trends of food price dynamics are very similar between these provinces.…”

Clustering of agriculture in the Republic of Uzbekistan

“…Combining investments and innovations into one system will lead to the successful development of the level of economic recovery. One of the new directions of agricultural formations is agricultural clusters [1][2][3][4][5].…”

NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization

“…Reference [2] made a chicken egg incubator connected to the Internet of things (IoT) and supplied solar energy. In machine learning, [3] predicted the price of chicken eggs using dynamic time warping (DTW) to make investing easier for chicken egg businesspeople. Reference [4] used a support vector machine (SVM) to predict the category of chicken eggs based on a combination of images and sensor metrics.…”