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Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture

GwanSeon Kim (), Mehdi Nemati (), Steven Buck (), Nicholas Pates () and Tyler Mark ()
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GwanSeon Kim: College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA
Mehdi Nemati: School of Public Policy, University of California, Riverside, CA 92521, USA
Steven Buck: Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA
Nicholas Pates: Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA
Tyler Mark: Department of Agricultural Economics, University of Kentucky, Lexington, KY 40546, USA

Sustainability, 2020, vol. 12, issue 7, 1-1

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.

Keywords: forecast distribution; multinomial logit model; simulation; transition probability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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