Machine Learning & AI in Agricultural Economics: Examples and Explanations

(Virtual Book by Prof. Dr. Xiaohua Yu, University of Göttingen)

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

Machine Learning & AI will prevail in agricultural and applied economics with the increasing volume of data and powerful computation. Causality analysis with traditional econometrics and prediction with machine learning & AI have different logics, but the later will be better to serve the policy analysis.

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. Qopen. https://doi.org/10.1093/qopen/qoaf015

Li Y., and X. Yu (2025) Attribute Non-Attendance in the Choice Experiment with Machine Learning: WTP for Organic Apples in Germany. Forthcoming in 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, forthcoming in 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

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, Available at: https://doi.org/10.22434/ifamr1132.

(2) Gradient Boosting Classification (GBM)

(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

Maruejols, L., Höschle, L. and X. Yu (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.

Li Y., and X. Yu (2025) Attribute Non-Attendance in the Choice Experiment with Machine Learning: WTP for Organic Apples in Germany. Forthcoming in 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

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

Liu S., J. Wehner, J.H. Feil and X. Yu, 2025, Harmony, Conflict, and Evolution of the Common Agricultural Policy in Europe: A Text Mining Survey. Modern Agriculture. https://doi.org/10.1002/moda.70020 .

(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

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”, forthcoming in Public Policy & Poverty. https://doi.org/10.1002/pop4.7000

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. Qopen. https://doi.org/10.1093/qopen/qoaf015

6, Reinforcement Learning

e.g. Markov Reward Process

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.