IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v30y2025i2d10.1007_s13253-025-00700-x.html
   My bibliography  Save this article

Editorial for the special issue on New Perspectives in Statistics, Data Science and Econometrics for Agriculture, Land Use and Forestry

Author

Listed:
  • Felicetta Carillo

    (Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria - Centro di ricerca Politiche e Bioeconomia (CREA-PB))

  • Paolo Maranzano

    (University of Milano-Bicocca)

  • Philipp Otto

    (University of Glasgow)

Abstract

No abstract is available for this item.

Suggested Citation

  • Felicetta Carillo & Paolo Maranzano & Philipp Otto, 2025. "Editorial for the special issue on New Perspectives in Statistics, Data Science and Econometrics for Agriculture, Land Use and Forestry," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 255-260, June.
  • Handle: RePEc:spr:jagbes:v:30:y:2025:i:2:d:10.1007_s13253-025-00700-x
    DOI: 10.1007/s13253-025-00700-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-025-00700-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13253-025-00700-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Paul A. Parker & Bruno Sansó, 2025. "Soil Carbon Mapping of the Contiguous US Using VNIR Spectra Within A Heterogeneous Spatial Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 517-539, June.
    2. Silvia Emili & Federica Galli, 2025. "Modelling Spatio-Temporal Dynamics in Multi-Output Stochastic Frontiers for the European Agribusiness Industry," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 540-575, June.
    3. Roy Cerqueti & Paolo Maranzano & Raffaele Mattera, 2025. "Spatially-Clustered Spatial Autoregressive Models with Application to Agricultural Market Concentration in Europe," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 431-465, June.
    4. Pavel Cudlin & Alessandro Muolo & Kostas Rontos & Luca Salvati, 2025. "Von Thunen Revisited? An Econometric Analysis of Metropolitan Expansion and Land-Use Change in Greece," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 491-516, June.
    5. Mario Figueira & Carmen Guarner & David Conesa & Antonio López-Quílez & Tamás Krisztin, 2025. "Unveiling Land Use Dynamics: Insights from a Hierarchical Bayesian Spatio-Temporal Modelling of Compositional Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 283-308, June.
    6. Chiara Di Maria, 2025. "Investigating the Causal Effect of Deforestation on Infant Health Through Soil Characteristics: A Comparison of Traditional and Machine Learning Mediation Analysis Using Simulated and Real Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 466-490, June.
    7. Mario Figueira & Carmen Guarner & David Conesa & Antonio López-Quílez & Tamás Krisztin, 2025. "Correction: Unveiling Land Use Dynamics: Insights from a Hierarchical Bayesian Spatio-Temporal Modelling of Compositional Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 309-309, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:30:y:2025:i:2:d:10.1007_s13253-025-00700-x. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.