Predicting Soybean Yield with NDVI using a Flexible Fourier Transform Model

Chang Xu and Ani Katchova

No 266693, 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida from Southern Agricultural Economics Association

Abstract: We study how to incorporate the Normalized Difference Vegetation Index (NDVI) derived from remote sensing satellites to improve soybean yield predictions in ten major producing states in the United States. Unlike traditional methods which assume that a global OLS model applies to all observations, we account for geographical heterogeneity by using the Flexible Fourier Transform (FFT) model. Results show that there is considerable heterogeneity in how responsive soybean yield is to NDVI over the growing season. Out-of-sample cross-validation indicates that accounting for geographical heterogeneity improves the forecasts in terms of smaller prediction error compared to models assuming away geographical heterogeneity.

Keywords: Crop Production/Industries; Productivity Analysis; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 29
Date: 2018-01-17
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/266693/files/SoybeanYieldNDVI.pdf (application/pdf)
https://ageconsearch.umn.edu/record/266693/files/S ... I.pdf?subformat=pdfa (application/pdf)

Related works:
Journal Article: Predicting Soybean Yield with NDVI Using a Flexible Fourier Transform Model (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:saea18:266693

DOI: 10.22004/ag.econ.266693

Access Statistics for this paper

More papers in 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida from Southern Agricultural Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().