  Decisions in agriculture are increasingly data-driven; however, valuable
agricultural knowledge is often locked away in free-text reports, manuals and
journal articles. Specialised search systems are needed that can mine
agricultural information to provide relevant answers to users' questions. This
paper presents AgAsk -- an agent able to answer natural language agriculture
questions by mining scientific documents.
  We carefully survey and analyse farmers' information needs. On the basis of
these needs we release an information retrieval test collection comprising real
questions, a large collection of scientific documents split in passages, and
ground truth relevance assessments indicating which passages are relevant to
each question.
  We implement and evaluate a number of information retrieval models to answer
farmers questions, including two state-of-the-art neural ranking models. We
show that neural rankers are highly effective at matching passages to questions
in this context.
  Finally, we propose a deployment architecture for AgAsk that includes a
client based on the Telegram messaging platform and retrieval model deployed on
commodity hardware.
  The test collection we provide is intended to stimulate more research in
methods to match natural language to answers in scientific documents. While the
retrieval models were evaluated in the agriculture domain, they are
generalisable and of interest to others working on similar problems.
  The test collection is available at:
\url{https://github.com/ielab/agvaluate}.
