Sustainable smart agriculture and climate resilience are significant factors for maintaining food security with environmental management. Existing agricultural systems target predicting and generating reports but lack detailed explanations as to why the phenomenon occurs. The examples of such why are like “Why there is a significant decline in the yield?”, “Why the soil is degrading?”, “Why the level of water is declining?” and so on. To address this challenge, the paper presents a prototype for Explainable LLM based Why-QA framework for sustainable smart agriculture. The implemented prototype integrates domain-enriched LLM with knowledge graph, causal inference engine with explainability layer to provide detailed explanations to complex “why-questions”. Domain-specific LLMs are used to support the domain knowledge with knowledge graph analyzing the sustainable relationships in the answer, causal reasoning to produce the causes of events and explainability module to provide the detailed reasoning supported with benchmark sustainable metrics incorporating the soil, climate and crop data. The prototype implementation of framework is evaluated on a dataset of 500 annotated agricultural why-type questions constructed from FAOSTAT, USDA, and NOAA sources. Results clearly demonstrate promising improvements over five baseline systems developed across causal reasoning and explanation quality metrics, which help validating the architectural feasibility of the proposed framework.