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.
Highlights
The International Society of Precision Agriculture adopted the following definition of precision agriculture in 2019 (see The International Society of Precision Agriculture (n.d.)): “Precision Agriculture is a management strategy that gathers, processes, and analyzes temporal, spatial, and individual data and combines it with other information to support management decisions according to estimated variability for improved resource using efficiency, productivity, quality, profitability, and sustainability of agricultural production”
In Addey et al (2021) the authors examine the implications of risks, uncertainties and random events on the prediction of crop yields
Motivated by all these studies, we propose a general mathematical model for analyzing yield data
Summary
The International Society of Precision Agriculture adopted the following definition of precision agriculture in 2019 (see The International Society of Precision Agriculture (n.d.)):. It is shown that a data-science based near sensor neural network algorithms can be implemented to automatically detect the codling moth The performance of this system is evaluated, and power consumption ideas are discussed for achieving the zero energy balance of the system. In Addey et al (2021) the authors examine the implications of risks, uncertainties and random events on the prediction of crop yields Motivated by all these studies, we propose a general mathematical model for analyzing yield data. We derive expressions for statistical moments from the underlying stochastic model This model allows 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.
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