High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Tesfaye, Kindie; Takele, Robel; Shelia, Vakhtang; Lemma, Esayas; Dabale, Addisu; Sibiry Traore, Pierre C.; Solomon, Dawit; Hoogenboom, Gerrit

doi:10.1016/j.cliser.2023.100425

Climate Services

2023

DOI: 10.1016/j.cliser.2023.100425

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High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Kindie Tesfaye,

Robel Takele,

Vakhtang Shelia

et al.

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2024

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Cited by 1 publication

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On the Limitations of Univariate Grey Prediction Models: Findings and Failures

Jeutsa,

Kibong,

Diboma

et al. 2024

Computational and Mathematical Methods

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Grey systems theory can be used to predict the evolution of a system with insufficient information. Unfortunately, the most used version of the grey model (GM), namely, GM(1,1), works best when the system series have an increasing exponential rate. In any other case, the GM(1,1) produces inaccurate predictions. In this paper, we examine the mathematical formulation of the conventional GM(1,1) in order to propose a new GM that addresses its shortcomings through a new time response function. Examples are presented to demonstrate the flexibility and accuracy of the new model when implemented with series of various natures. Comparisons with other intelligent GM(1,1) show that the proposed model performs better than the reference models.

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On the Limitations of Univariate Grey Prediction Models: Findings and Failures

Jeutsa,

Kibong,

Diboma

et al. 2024

Computational and Mathematical Methods

View full text Add to dashboard Cite

show abstract

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High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Cited by 1 publication

References 40 publications

On the Limitations of Univariate Grey Prediction Models: Findings and Failures

On the Limitations of Univariate Grey Prediction Models: Findings and Failures

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