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

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

doi:10.3390/su12072917

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

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

Agricultural Economics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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 language	English
Article number	2917
Journal	Sustainability (Switzerland)
Volume	12
Issue number	7
DOIs	https://doi.org/10.3390/su12072917
State	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/su12072917

Cite this

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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.",

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AB - 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.

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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