Recovering forecast distributions of crop composition: Method and application to kentucky agriculture

Gwan Seon Kim, Mehdi Nemati, Steven Buck, Nicholas Pates, Tyler Mark

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a novel application of the multinomial logit (MNL) model using Cropland Data Layer and field-level boundaries to estimate crop transition probabilities, which are used to generate forecast distributions of total acreage for five major crops produced in the state of Kentucky. These forecasts distributions have a wide range of applications that, besides providing interim acreage estimates ahead of the June Acreage Survey, can inform the ability of producers to incorporate new crops in the land-use rotation, investments in location-specific capital and input distribution as well informing the likelihood of adverse water quality events from nutrient run-off.

Original languageEnglish
Article number2917
JournalSustainability (Switzerland)
Volume12
Issue number7
DOIs
StatePublished - Apr 1 2020

Bibliographical note

Funding Information:
Funding: This research was funded by National Science Foundation (NSF), grant number 1355438.

Publisher Copyright:
© 2020 by the authors.

Keywords

  • Forecast distribution
  • Multinomial logit model
  • Simulation
  • Transition probability

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law

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