Aim of Monitoring, Evaluation work packages; Climate Smart Coaches and Advisors; and practice. Key Building Blocks Based on the CSA Theory of Change, the ME&L conceptual framework is focussed on three building blocks: 1. Capacity development: The specific knowledge, attitudes and skills advisors need to support farmers and accelerate the adoption of climate smart farming practices. 2. The role of the advisor: Supporting the implementation of climate smart farming measures and acting as change agents, navigating the transition to CSF. 3. Climate Action: The actions taken to combat climate change and subsequent impacts. Instruments for ME&L The ME&L approach employs three interconnected instruments to understand the effects and enhance the effectiveness of interventions within the CSA network: 1. Evaluation of interventions (TTT and CoP) - A generic instrument has been developed to evaluate and refine the key capacity development interventions in the network, with a specific evaluation of the Training the Trainers (TTT) and an instrument for the annual self-evaluation of each Community of Practice (CoP). This last instrument will be used to prepare for the annual CoP reflection and planning session during the National Annual Meeting at country level (NAM). 2. Climate Smart Advice Capacity Assessment Tool (CSA-CAT) – Focusses on the development of CS advisory capacity and is aimed at monitoring and evaluation. It has been developed specifically for monitoring and supporting the development of CSA capacity within the network. 3. Dynamic Learning Agenda (DLA) - Overarching ME&L instrument that is employed for coordinating, facilitating and enriching learning across various actors, activities and components of the network. The aim is to deepen and guide learning, to document lessons learned and to foster collaboration and exchange between CoPs, countries, thematic areas, Work Packages, and Projects, Initiatives and Programs (PIPs) beyond the CSA network. The ME&L framework and approach is iterative and dynamic. It provides the first step in fostering the CSA network as a dynamic learning system for the continuous improvement of network functionality and the strengthening of climate-smart advisory capacity to accelerate the adoption of climate smart farming.
Collins et al. (Tue,) studied this question.