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Machine learning in agricultural and applied economics

Citations

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Cited by:

  1. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
  2. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
  3. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
  4. Daniel Cooley & Steven M. Smith, 2022. "Center Pivot Irrigation Systems as a Form of Drought Risk Mitigation in Humid Regions," NBER Chapters, in: American Agriculture, Water Resources, and Climate Change, pages 135-171, National Bureau of Economic Research, Inc.
  5. David Wuepper & Robert Finger, 2023. "Regression discontinuity designs in agricultural and environmental economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(1), pages 1-28.
  6. repec:ags:aaea22:335590 is not listed on IDEAS
  7. Feras Batarseh & Munisamy Gopinath & Ganesh Nalluru & Jayson Beckman, 2019. "Application of Machine Learning in Forecasting International Trade Trends," Papers 1910.03112, arXiv.org.
  8. Feras A. Batarseh & Munisamy Gopinath & Anderson Monken, 2020. "Artificial Intelligence Methods for Evaluating Global Trade Flows," International Finance Discussion Papers 1296, Board of Governors of the Federal Reserve System (U.S.).
  9. Tobias Dalhaus & Linda Steinhübel & Bernhard Dalheimer & Liesbeth Colen, 2024. "The future of research on sustainable food systems: Building an early‐career network of agricultural economists in Europe," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 319-324, April.
  10. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
  11. Kresova, Svetlana & Hess, Sebastian, 2021. "Determinants of Regional Raw Milk Prices in Russia," 2021 Conference, August 17-31, 2021, Virtual 315064, International Association of Agricultural Economists.
  12. Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 235-260, February.
  13. Ancín, María & Pindado, Emilio & Sánchez, Mercedes, 2022. "New trends in the global digital transformation process of the agri-food sector: An exploratory study based on Twitter," Agricultural Systems, Elsevier, vol. 203(C).
  14. Svetlana Kresova & Sebastian Hess, 2022. "Identifying the Determinants of Regional Raw Milk Prices in Russia Using Machine Learning," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
  15. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
  16. Ziesmer, Johannes & Jin, Ding & Mukashov, Askar & Henning, Christian, 2023. "Integrating fundamental model uncertainty in policy analysis," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
  17. Arslan, Aslihan & Cavatassi, Romina & Hossain, Marup, 2022. "Research Series 69: Structural and rural transformation and food systems: a quantitative synthesis for LMICs," IFAD Research Series 320669, International Fund for Agricultural Development (IFAD).
  18. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
  19. Milad Abbasiharofteh & Jan Kinne & Miriam Krüger, 2024. "Leveraging the digital layer: the strength of weak and strong ties in bridging geographic and cognitive distances," Journal of Economic Geography, Oxford University Press, vol. 24(2), pages 241-262.
  20. Silvia Coderoni & Roberto Esposti & Alessandro Varacca, 2024. "How Differently Do Farms Respond to Agri-environmental Policies? A Probabilistic Machine-Learning Approach," Land Economics, University of Wisconsin Press, vol. 100(2), pages 370-397.
  21. Seidel, Claudia & Shang, Linmei & Britz, Wolfgang, 2023. "A critical assessment of neural networks as meta-model of a farm optimization model," Discussion Papers 338200, University of Bonn, Institute for Food and Resource Economics.
  22. Schaefer, David & Britz, Wolfgang & Kuhn, Till, 2020. "Modelling policy induced manure transports at large scale using an agent-based simulation model," Discussion Papers 305270, University of Bonn, Institute for Food and Resource Economics.
  23. Ladina Knapp & David Wuepper & Robert Finger, 2021. "Preferences, personality, aspirations, and farmer behavior," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 901-913, November.
  24. Luigi Biagini & Simone Severini, 2021. "The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool," Papers 2104.14188, arXiv.org.
  25. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
  26. Baaken, Dominik & Hess, Sebastian, 2021. "Regionale Milchmengenprognose: Regressionsmodelle und Maschinelles Lernen im Vergleich," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317056, German Association of Agricultural Economists (GEWISOLA).
  27. Muhammed Sehid Gorus & Erdal Tanas Karagol, 2023. "Factors affecting per capita ecological footprint in OECD countries: Evidence from machine learning techniquesa," Energy & Environment, , vol. 34(7), pages 2601-2618, November.
  28. Luigi Biagini & Simone Severini, 2022. "Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis," Papers 2212.03092, arXiv.org, revised Dec 2022.
  29. Abbasiharofteh, Milad & Kinne, Jan & Krüger, Miriam, 2021. "The strength of weak and strong ties in bridging geographic and cognitive distances," ZEW Discussion Papers 21-049, ZEW - Leibniz Centre for European Economic Research.
  30. Man-, ZuyiKeunZuyi Wang & Takagi, Chifumi & Kim, Man-Keun & Chung, Anh, 2022. "Uncover Drivers Influencing Consumers' WTP Using Machine Learning: Case of Organic Coffee in Taiwan," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322150, Agricultural and Applied Economics Association.
  31. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
  32. Tymoteusz Miller & Grzegorz Mikiciuk & Anna Kisiel & Małgorzata Mikiciuk & Dominika Paliwoda & Lidia Sas-Paszt & Danuta Cembrowska-Lech & Adrianna Krzemińska & Agnieszka Kozioł & Adam Brysiewicz, 2023. "Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
  33. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.
  34. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
  35. Aslihan Arslan & Romina Cavatassi & Marup Hossain, 2022. "Food systems and structural and rural transformation: a quantitative synthesis for low and middle-income countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(1), pages 293-320, February.
  36. Rao, Amar & Talan, Amogh & Abbas, Shujaat & Dev, Dhairya & Taghizadeh-Hesary, Farhad, 2023. "The role of natural resources in the management of environmental sustainability: Machine learning approach," Resources Policy, Elsevier, vol. 82(C).
  37. Yacoubou Djima, Ismael & Kilic, Talip, 2024. "Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data," Journal of Development Economics, Elsevier, vol. 168(C).
  38. Cao, An N.Q. & Gebrekidan, Bisrat Haile & Heckelei, Thomas & Robe, Michel A., 2022. "County-level USDA Crop Progress and Condition data, machine learning, and commodity market surprises," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322281, Agricultural and Applied Economics Association.
  39. Jianghong Xu & Chenguang Wang & Xukang Yin & Weixin Wang, 2024. "Digital economy and rural household resilience: Evidence from China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(5), pages 244-263.
  40. Dorothee Weiffen & Ghassan Baliki & Tilman Brück, 2022. "Violent conflict moderates food security impacts of agricultural asset transfers in Syria: A heterogeneity analysis using machine learning," HiCN Working Papers 381, Households in Conflict Network.
  41. Baaken, Dominik & Hess, Sebastian, 2021. "Forecasting Regional Milk Production Quantity: A Comparison of Regression Models and Machine Learning," 2021 Conference, August 17-31, 2021, Virtual 315117, International Association of Agricultural Economists.
  42. Louise O Fresco & Floor Geerling-Eiff & Anne-Charlotte Hoes & Lan van Wassenaer & Krijn J Poppe & Jack G A J van der Vorst, 2021. "Sustainable food systems: do agricultural economists have a role? [Interdisciplinary collaboration between natural and social sciences–status and trends exemplified in groundwater research]," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 694-718.
  43. Mika Ylinen & Mikko Ranta, 2024. "Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 247-276, March.
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