Climate adaptation strategies are proposed in response to climate change. They are practised in agriculture to sustain food production. These strategies can be found in unstructured data (for example, scientific literature from the Elsevier website) or structured (heterogeneous climate data via government APIs). We present Climate Adaptation question-answering with Improved Readability and Noted Sources (CAIRNS), a framework that enables experts -- farmer advisors -- to obtain credible preliminary answers from complex evidence sources from the web. It enhances readability and citation reliability through a structured ScholarGuide prompt and achieves robust evaluation via a consistency-weighted hybrid evaluator that leverages inter-model agreement with experts. Together, these components enable readable, verifiable, and domain-grounded question-answering without fine-tuning or reinforcement learning. Using a previously reported dataset of expert-curated question-answers, we show that CAIRNS outperforms the baselines on most of the metrics. Our thorough ablation study confirms the results on all metrics. To validate our LLM-based evaluation, we also report an analysis of correlations against human judgment.

翻译：气候适应策略是为应对气候变化而提出的方案，在农业领域实践中用于维持粮食生产。这些策略可见于非结构化数据（例如爱思唯尔网站上的科学文献）或结构化数据（如通过政府API获取的异构气候数据）。我们提出了具备改进可读性与标注来源的气候适应问答框架（CAIRNS），该框架使专家——农业顾问——能够从网络复杂证据源中获取可信的初步答案。它通过结构化ScholarGuide提示增强可读性与引证可靠性，并利用基于模型间专家一致性的加权混合评估器实现稳健评估。这些组件共同实现了无需微调或强化学习的可读、可验证且领域扎根的问答系统。基于先前报道的专家策问答数据集，我们证明CAIRNS在多数评估指标上优于基线方法。详尽的消融实验在所有指标上验证了该结果。为验证基于大语言模型的评估方法，我们还报告了与人工判断相关性的分析。