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.
Proceedings of the workshop on voices of poor livestock keepers in Vihiga district district information documentation centre, Vihiga, Kenya 17th December 2002
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.
Myths and science of soils of the tropics: proceedings of an international symposium sponsored by Division A-6 of the American Society of Agronomy, the World Association of Soil and Water Conservation, and the Soil and Water Conservation Society, in Las V
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.