The deployment of information retrieval (IR) systems in agriculture remains particularly challenging in low-resource environments, where limited access to expert knowledge, data resources, and digital infrastructure constrains effective information access. Recent advances in large language models (LLMs) have enabled conversational interfaces that generate direct answers to user queries, increasingly functioning as retrieval outputs in IR systems. However, little is known about how such generated answers behave in low-resource language settings. This study examines Swahili maize-related question answering as a low-resource agricultural IR task and investigates whether LLM-generated responses are detectably different from human-authored answers. Using a dataset of 127 questions with responses from human experts, ChatGPT, and Gemini, we analyze textual characteristics including answer length, lexical diversity, and TF-IDF cosine similarity. A multi-class TF-IDF and logistic regression classifier is then used to evaluate source detectability. Results show that LLM-generated answers are systematically longer, less lexically diverse, and exhibit moderate similarity to human responses. The classifier achieves 0.98 accuracy, indicating that generated responses exhibit consistent and learnable patterns. These findings highlight detectability as a useful diagnostic signal for evaluating generative IR systems, particularly in low-resource environments where ground-truth evaluation is limited.

Despite major advances in information retrieval (IR) and language technologies, speakers of extremely low-resource ethnic community languages (ECLs) remain largely excluded from digital information access. With Tanzania having over 120 languages, yet most IR systems and digital assistants primarily support English and, to a limited extent, Kiswahili. This disproportionately affects rural populations (over 65%), where ECLs are used alongside Kiswahili in daily life. This paper presents the ongoing Ethnic Voices initiative, which addresses this gap by building foundational text and speech resources explicitly relevant to IR research in extremely low-resource multilingual settings. The project targets six ECLs in Tanzania, with current data collection completed for Hehe and Luguru. Data collection from the two ECLs has produced over 800 Kiswahili-ECL parallel sentences per language and over 95 hours of speech. Preliminary survey results show strong demand for multilingual and voice-based access to information. It further identifies language barriers as a primary limitation of existing English-centric IR systems. This also paper discusses the IR implications of these findings and outlines directions toward inclusive retrieval and evaluation in extremely low-resource languages. Beyond resource creation, we position the collected dataset as IR-enabling infrastructure that supports the formulation of retrieval tasks, the development of baseline systems, and the design of evaluation protocols in benchmark-scarce, extremely low-resource multilingual settings.