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March 18, 20260 citationsOpen Access

Methodological Evaluation of Regional Monitoring Networks in Kenya: Time-Series Forecasting for Risk Assessment

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WMW. MwangiOAOchieng Agnes

Key Points

  • The aim is to evaluate monitoring networks and develop a robust time-series forecasting model for risk assessment in agriculture.
  • Employs an analytical approach integrating formal modeling with domain evidence
  • Establishes a time-series forecasting model for measuring risk reduction
  • Defines verifiable assumptions and derivations that provide practical implications
  • Demonstrates a convergent estimation process under specified assumptions
  • Establishes a stable link between the proposed forecasting metric and observed outcomes
  • Provides a reproducible analytical framework for risk assessment in agriculture

Abstract

This study addresses a current research gap in Agriculture concerning Methodological evaluation of regional monitoring networks systems in Kenya: time-series forecasting model for measuring risk reduction in Kenya. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of regional monitoring networks systems in Kenya: time-series forecasting model for measuring risk reduction, Kenya, Africa, Agriculture, comparative study This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.

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Cite This Study

Mwangi et al. (2014) studied this question.

synapsesocial.com/papers/69ba43d84e9516ffd37a5723https://doi.org/10.5281/zenodo.19054845
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