In this document we aim at composing a high level overview of the use of semantics in the production, exchange and use of agricultural data. In particular, we focus on the use of semantics for data management, and the resources, tools and services available for its production. We also look at the current research trends in the area. Semantics refers to the description of the meaning of data, made possible by “semantic resources” (aka “semantic structures”) aiming at making explicit the information that may help to find, understand, and reuse data(sets). Semantics may also make explicit the entities and relations the data embody. Granted that no data is ever produced or distributed without some attempts to describe its meaning (all databases have column names, all documents have a title and often some ways to indicate what topics they are about), the level of semantic richness, accuracy, shareability and reusability of the resources used vary greatly. The goal of our this document is to indicate the main tasks where semantics is used or could be used for the treatment of agricultural data and highlight current bottlenecks, limitations and impact on interoperability of the current situation. The intended readers of this document are managers, project coordinators, data scientists, and researchers interesting in getting the big picture of semantics in agriculture. In particular, it aims at being readable and useful to the various communities involved, touching on both the data management and the agricultural side. We use the phrase “semantic resources” to collectively refer to structures of varying nature, complexity and formats used for the purpose of expressing the “meaning” of data. However, we acknowledge the fact that not all semantic resources equally contribute to the achieving effective data interoperability, and wherever possible we highlight the current use of them and identify limitations. The concept of agricultural data is generic for data produced or used in agriculture, including data on agricultural production, or data relative to lab and field experiments, environmental conditions or climate, just to mention a few relevant areas of data productions). Being aware of the width of the sector, we make no claims on comprehensiveness. In terms of formats, we are interested in both textual/semi-structured documents and structured data, including georeferenced data. This landscaping exercise is based on (1) expertise of the group members (2) previous/ongoing initiatives and (3) a bibliometric analysis of the scientific literature. It lays the basis for the two following activities of the RDA Agrisemantics Working Group - the collection of real-life use cases where semantics is useful or needed, and the compilation of a set of recommendations for future infrastructural component supporting data management and semantics. This document is organized in the following way. The Introduction (Sec.1) sets the context and introduces the terminology adopted in the course of the document. Sec. 2 (Semantics and Data Management) presents a high level account of possible different users of semantics in agriculture, with a discussion on state-of-the-art interoperability related work. Sec. 3 (Research Trends) analyzes the research trends in semantics for agriculture and nutrition in the past 10 years. Sec. 4 (Semantics Structures in the Agricultural Domain) describes the landscape of existing vocabularies for data specification in agriculture. Sec. 5 (The Semantic Expert Toolkit) describes the tools and services currently available for the creation of maintenance of semantic resources. As they are mostly generic tools, we look specifically at practices in the agriculture and food community. Finally, we draw our conclusions in Sec 6 (Conclusions and Next Steps).