Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258501 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 9 [Article No.:] 397 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-17
Publisher: 
MDPI, Basel
Abstract: 
In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.
Subjects: 
categorical data
neural networks
precision agriculture
statistical moments
yield
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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