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| Main Authors: | , |
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| Formato: | Recurso digital |
| Idioma: | inglês |
| Publicado em: |
Zenodo
2001
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| Assuntos: | |
| Acesso em linha: | https://doi.org/10.5281/zenodo.18729509 |
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Sumário:
- <p>Field research stations in Ghana have been established to monitor agricultural productivity and environmental health. These systems aim to assess risk reduction measures through Bayesian hierarchical models. This study employs a Bayesian hierarchical linear regression model (Equation: $y_{ij} = eta_0 + eta_1X_{ij} + u_i + v_j + e_{ij}$) to analyse data collected from the field stations. The model accounts for spatial and temporal variability, incorporating random effects for individual research stations ($u_i$) and years of observation ($v_j$). Robust standard errors are used to quantify uncertainty in parameter estimates. The analysis revealed that the Bayesian hierarchical model provides a significant improvement in risk reduction predictions over traditional models. Specifically, it detected a 15% higher reduction rate in pest infestation across all stations compared to previous studies using simpler models. This replication study confirms the efficacy of the Bayesian hierarchical model for evaluating field research station systems in Ghana and suggests its potential as a standard methodological tool for future agricultural risk assessments. Given the demonstrated effectiveness, it is recommended that all future studies in Ghana adopt this statistical approach to enhance the reliability and accuracy of their findings. Additionally, further investigation into the model's sensitivity to different data types and scenarios should be conducted. Bayesian hierarchical model, field research stations, risk reduction, agricultural productivity, environmental health</p>