地理问答（GeoQA）正从传统的基于规则和检索的方法，向由大语言模型驱动的智能范式转变。作为地理人工智能的重要组成部分，GeoQA旨在提供可靠且可解释的地理知识服务。然而，现有的检索增强生成（RAG）方法在处理需要结构化知识和连贯逻辑推理的地理问题时仍显不足。为应对这些挑战，本研究提出了 GeoQA-PG，一个面向土壤地理学的问答框架。具体而言，通过整合层次化知识框架与细粒度知识图谱，构建了一种树状网络知识体系。在此知识体系基础上，开发了一种名为 SoilKnowRAG 的双通道检索机制，结合文本检索与图检索以缓解知识碎片化问题。基于 GeoQA-PG，实现了 SoilGPT，并使用问答数据集对其进行了评估。实验结果表明，在客观和主观任务上，SoilGPT 均显著优于参数规模相当的基线模型；同时，对 SoilKnowRAG 的对比评估进一步证实了其相较于其他 RAG 方法的有效性。总体而言，GeoQA-PG 为知识驱动的 GeoQA 提供了一个可迁移的框架，推动了地理知识的智能表征、推理与服务。

Geographical Question Answering (GeoQA) is transitioning from traditional rule-based and retrieval-based approaches to intelligent paradigms driven by large language models. As an important component of geographic artificial intelligence, GeoQA aims to provide reliable and interpretable geographic knowledge services. However, existing Retrieval-Augmented Generation (RAG) methods remain inadequate for geographic questions requiring structured knowledge and coherent logical reasoning. To address these challenges, this study proposes GeoQA-PG, a question answering framework for pedogeography. Specifically, a tree-network knowledge system is constructed by integrating a hierarchical knowledge framework with a fine-grained knowledge graph. Building on this knowledge system, a dual-channel retrieval mechanism, named SoilKnowRAG, is developed. It combines text and graph retrieval to mitigate knowledge fragmentation. Based on GeoQA-PG, SoilGPT is implemented and evaluated using a question answering dataset. Experimental results demonstrate that SoilGPT significantly outperforms baseline models with comparable parameter scales on both objective and subjective tasks, while the comparative evaluation of SoilKnowRAG further confirms its effectiveness compared with other RAG methods. Overall, GeoQA-PG provides a transferable framework for knowledge-driven GeoQA and advances the intelligent representation, reasoning, and services of geographic knowledge.