Machine Learning & AI in Agricultural Economics: Examples and Explanations

Prof. Dr. Xiaohua Yu

(Virtual Book)

This page introduces my recent research papers on agricultural economics with applications of machine learning and AI.

Chapter 1, Feature Engineering

(1). LASSO

Meister S., X. Yu (2025) Forecasting Egg Price Inflation in Germany with Machine Learning: A Comparative Study with ARIMAX and LSTM.

Li Y., and X. Yu (2025) Attribute Non-Attendance in the Choice Experiment with Machine Learning: WTP for Organic Apples in Germany. International Food and Agribusiness Management Review, https://doi.10.22434/IFAMR.1133

Maruejols L., L. Hoeschle, X. Yu (2022) Vietnam between economic growth and ethnic divergence: A LASSO examination of income-mediated energy consumption. Energy Economics. 106222. https://doi.org/10.1016/j.eneco.2022.106222

(2) IV LASSO

Höschle, L.,Maruejols, L., and Yu, X. (2025) The impact of energy justice on local economic outcomes: Evidence from the bioenergy village program in Germany,Energy Economics. https://doi.org/10.1016/j.eneco.2025.108432

(3) Shapley Values

Meister S., X. Yu (2025) Forecasting Egg Price Inflation in Germany with Machine Learning: A Comparative Study with ARIMAX and LSTM.

(4) Other methods

Wang H., L. Maruejols, and X.Yu (2021) Predicting energy poverty with combinations of remote-sensing and socioeconomic survey data in India: Evidence from machine learning. Energy Economics. Vol. 102, 105510. https://doi.org/10.1016/j.eneco.2021.105510

Chapter 2, Supervised Machine Learning

(1) Random Forest

Zhong, X. and X. Yu (2025) “Who Buy Food Products from Online Influencers? Predictions with Machine Learning”, forthcoming in International Food and Agribusiness Management Review. https://doi.org/10.22434/IFAMR.1130

Maruejols, L., Höschle, L. and Yu, X. (2025) ‘Energy independence, rural sustainability and potential of bioenergy villages in Germany: machine learning perspectives’, International Food and Agribusiness Management Review, https://doi.org/10.22434/ifamr1132.

(2) Gradient Boosting Classification (GBM)

Zhong, X. and X. Yu (2025) “Who Buy Food Products from Online Influencers? Predictions with Machine Learning”, International Food and Agribusiness Management Review. https://doi.org/10.22434/IFAMR.1130

(3) Support Vector Machine

Maruejols, L., Höschle, L. and Yu, X. (2025) ‘Energy independence, rural sustainability and potential of bioenergy villages in Germany: machine learning perspectives’, International Food and Agribusiness Management Review, Available at: https://doi.org/10.22434/ifamr1132.

(4) Logit

Zhong, X. and X. Yu (2025) “Who Buy Food Products from Online Influencers? Predictions with Machine Learning”, International Food and Agribusiness Management Review. https://doi.org/10.22434/IFAMR.1130

Li Y., and X. Yu (2025) Attribute Non-Attendance in the Choice Experiment with Machine Learning: WTP for Organic Apples in Germany. International Food and Agribusiness Management Review, https://doi.10.22434/IFAMR.1133 .

(5) Neural Network Analysis (Deep Learning, ANN, CNN, RNN, LSTM)

Yu, X. and S. Liu. 2024. "No Free Lunch Theorem"and Algorithm Selection in Policy Research: Predicting Hog Price with Machine Learning (In Chinese). Issues in Agricultural Economy. 202(5):20-32.

Meister S., X. Yu (2025) Forecasting Egg Price Inflation in Germany with Machine Learning: A Comparative Study with ARIMAX and LSTM.

(6) Other methods

e.g. regression based method, Bayesian Learning

Chapter 3, Unsupervised Machine Learning

(1) K-means

Ölkers Tim, Liu S., X. Yu, O. Musshoff (2024) Patterns and Heterogeneity in Credit Repayment Performance: Evidence from Malian Farmers. Applied Economics Perspectives and Policy. https://doi.org/10.1002/aepp.13484

Wang H., J. Han, X. Yu (2024) Who performs better? The heterogeneity of grain production eco-efficiency: Evidence from unsupervised machine learning. Forthcoming in Environmental Impact Assessment Review 106, 107530. https://doi.org/10.1016/j.eiar.2024.107530

Wang H. , X. Yu (2023) Carbon Dioxide Emission Typology and Policy Implications: Evidence from Machine Learning. China Economic Review. Volume 78, April 2023, 101941 https://doi.org/10.1016/j.chieco.2023.101941

Wang H., J. F. Feil and X. Yu (2023) Let the Data Speak about the Cut-off Values for Multidimensional Index: Classification of Human Development Index with Machine Learning. Socio-economic Planning Sciences. Volume 87, Part A, June 2023, 101523. https://doi.org/10.1016/j.seps.2023.101523

(2) PAM (partition around medoids)

Graskemper V., X. Yu and Jan-Hennting Feil (2021). Farmer Typology and Implications for Policy Design – an Unsupervised Machine Learning Approach. Land Use Policy. Volume 103, April 2021, 105328. https://doi.org/10.1016/j.landusepol.2021.105328

Graskemper V., X. Yu and Jan-Henning Feil (2022) Values of Farmers-Evidence from Germany, Journal of Rural Studies. Vo. 89:13-24. https://doi.org/10.1016/j.jrurstud.2021.11.005

(3) DTW (dynamic time warping)

Liu C., Zhou L., Hoeschle L. And X. Yu (2023), Food Price Dynamics and Regional Clusters: Machine Learning Analysis of Egg Prices in China. China Agricultural Economic Review. Vol. 15 No. 2, pp. 416-432. https://doi.org/10.1108/CAER-01-2022-0003

(4) PCA

Forthcoming

(5) Market Basket Analysis

Forthcoming

Chapter 4, Text Mining

Hoeschle L., Shuang Liu, X. Yu (2025) "Let the Poor Talk about “Poverty”: Revisiting Poverty Alleviation in Rural China with Machine Learning”, Public Policy & Poverty. https://doi.org/10.1002/pop4.7000

Chapter 5, Time series analysis

Meister S., X. Yu (2025) Forecasting Egg Price Inflation in Germany with Machine Learning: A Comparative Study with ARIMAX and LSTM.

Chapter 6, Reinforcement Learning

e.g. Markov Reward Process

Chapter 7, Methodological Comments

Yu X. and L. Maruejols. Prediction, pattern recognition and machine learning in agricultural economics. China Agricultural Economic Review, 2023. Vol. 15(2):375-378. https://doi.org/10.1108/CAER-05-2023-307

Yu, X., Tang Z. and Bao T. 2019. Machine Learning and Renovation of Agricultural Policy Research. Journal of Agrotechnical Economics (in Chinese). 2019 (2): 4-9.