Development and content validation of indicators for assessing the compatibility of conventional agricultural systems with climate smart agriculture: a systems thinking and causal mapping approach

Climate smart agriculture (CSA) is an integrated approach to ecosystem management and is one of the global development goals for transforming and reorienting dynamic agricultural systems. Customizing this approach for conventional agricultural systems depends on identifying indicators to measure adaptation. This study aimed to develop and validate an indicator framework for assessing the compatibility of conventional agricultural systems with CSA at the farm and farming-community levels and to organize the validated components within a systems-thinking-based causal framework. The study employed a sequential multi-phase methodological design. First, an initial set of indicators and sub-indicators was developed through a critical review of CSA and agricultural sustainability literature and subsequently refined through expert consultation. Second, content validity was assessed using the Content Validity Ratio (CVR) and Content Validity Index (CVI). A total of 18 agricultural experts from various disciplines participated in this assessment. Experts evaluated the necessity of the proposed components using a binary essentiality judgment and a five-point Likert scale. Finally, the retained indicators and sub-indicators were organized into a conceptual causal map using Vensim. All seven main indicators met the predefined content-validity criterion, although their CVR values were not uniformly equal to one. The economic indicator received the highest mean score (4.58), followed by the environmental (4.47) and social (4.46) indicators, whereas the ethical indicator received the lowest mean score (3.17) while remaining valid. At the sub-indicator level, six items did not meet the predefined CVR threshold and were excluded from the final farm-level framework. The causal map subsequently organized the validated dimensions and sub-indicators into a hierarchical conceptual structure, with normalized expert-assessed importance scores used solely for visual comparison. The findings indicate that compatibility with CSA is a multidimensional and interconnected construct rather than the outcome of a single technological or managerial factor. Economic feasibility, environmental sustainability, social capacity, technological readiness, and managerial capability received particularly high levels of expert importance, while behavioral-cognitive and ethical dimensions remained relevant complementary components. The resulting systems thinking framework attempts to provide a valid conceptual basis for assessing farm-level adaptation to CSA and to inform future empirical research and quantitative modeling of system dynamics.