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March 18, 20260 citationsOpen Access

Time-Series Forecasting Model Evaluation of Regional Monitoring Networks in Kenya: An Assessment of Yield Improvement Systems

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DWD. J. WoodwardAGAmber Green-SmithMWMrs Megan Williams

Key Points

  • The aim is to evaluate and improve time-series forecasting models for agricultural yield in Kenya.
  • Used formal modelling integrated with domain evidence.
  • Established verifiable assumptions for the forecasting model.
  • Applied a structured analytical approach.
  • Utilized a formula for empirical specification and inference.
  • Achieved bounded error under perturbation in forecasting.
  • Identified a stable connection between the proposed metric and actual yield outcomes.
  • Provided a reproducible analytical framework for future research.

Abstract

This study addresses a current research gap in Environmental Science concerning Methodological evaluation of regional monitoring networks systems in Kenya: time-series forecasting model for measuring yield improvement 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 yield improvement, Kenya, Africa, Environmental Science, working paper 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

Woodward et al. (2014) studied this question.

synapsesocial.com/papers/69ba43e94e9516ffd37a59a6https://doi.org/10.5281/zenodo.19048584
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