PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 13, 20260 citationsOpen Access

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

View Full Paper
NONairobi Peter OgutuKMKisii William MbogoAGAmbalaibau David Gitonga

Key Points

  • The research aims to evaluate the effectiveness of regional monitoring networks in predicting agricultural yield and sustainability risks.
  • Conducted a systematic review of existing data from multiple monitoring networks.
  • Applied a time-series forecasting model to analyze trends.
  • Measured yield predictions and accuracy rates across regions.
  • The forecasting model predicted yield fluctuations with an average accuracy of 85%.
  • Confidence interval for predictions ranged from 70% to 95%.
  • Notable variability in model performance was observed across different regions.

Abstract

Regional monitoring networks have been established in Kenya to assess agricultural productivity and environmental sustainability. A systematic review of existing studies was conducted, including data from multiple monitoring networks. A time-series forecasting model was applied to analyse trends and predict future scenarios. The analysis revealed that the monitoring networks successfully predicted yield fluctuations with a mean accuracy rate of 85% (95% confidence interval: 70-95%). While the models showed high predictive power, there was variability in network performance across different regions. Further research is recommended to improve model robustness and integrate additional environmental factors for more comprehensive risk assessment. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ogutu et al. (2012) studied this question.

synapsesocial.com/papers/69b3ab9102a1e69014ccc7bfhttps://doi.org/10.5281/zenodo.18953105
Ask AI
Helpful
Bookmark
Share
View Full Paper