An Assessment of Climate Smart Agriculture Policy Priorities in Zimbabwe using Natural Language Processing (NLP) Techniques

Climate Smart Agriculture (CSA) has become a cornerstone of Zimbabwe's strategy to address food security and climate change. However, the translation of CSA policy commitments into coherent implementation remains a critical challenge. This study applies Natural Language Processing (NLP) to systematically assess the thematic emphasis of Zimbabwe's CSA policy framework. We analyze a corpus of 47 national policy documents spanning agriculture, climate, environment, and economic development sectors. Using keyword based proportion scoring, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis, we quantify the relative emphasis on the FAO's three CSA pillars: Productivity, Adaptation, and Mitigation. Our findings reveal a striking imbalance: productivity and adaptation receive equal emphasis (approximately 2\% each), while mitigation is virtually absent from the entire corpus (0\%). Sentiment analysis shows uniform neutrality across all documents, indicating technical, non-emotional framing. LDA topics are dominated by administrative and resilience terminology, with no distinct mitigation cluster. These results highlight a systemic mitigation gap in Zimbabwe's CSA policy discourse, a finding validated by farmer surveys showing that approximately 12\% have received mitigation related training. Building on the governance insights of Marenya et al. (2025), we propose a "One Government Approach" to address inter ministerial fragmentation and ensure mitigation targets are integrated into agriculture sector planning. This study demonstrates NLP's utility for policy monitoring and offers a replicable methodology for other climate vulnerable nations.