Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Tag: agricultural development
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Integrating natural resource education through national networks: experiences from Thailand and Indonesia
Integrating learning approaches for agricultural R&D
The African Highlands initiative has a challenging mandate. While charged with developing approaches to operationalize participatory natural resource management at farm and landscape levels, approaches must also be researched so that others may learn from AHI’s experiences. Futhermore, in recognition that agricultural development is not only about enabling the mobilization of local-level resources, AHI must learn how to add value to local initiatives to enable farmers to overcome critical development bottlenecks. One of the most significant challenges to operationalizing this complex agenda lies in balancing quality research with quality development. This brief shares progress made toward this end.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.
Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)
Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II.