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• The higher the customer concentration, the lower the willingness of management to disclose voluntary earnings previews. • The higher the customer concentration, the higher the management's willingness to disclose optimistic performance forecasts. • The economic policy uncertainty plays a regulatory role in the impact of customer concentration on earnings forecasts. This paper examines the impact of customer concentration on the surplus forecasts of listed companies by using the data of China's A-share listed companies from 2012 to 2022. It is found that the higher the customer concentration, the lower the voluntariness of management's disclosure of surplus forecasts; when firms' customers are highly concentrated, management tends to disclose optimistic earnings forecasts; the economic policy uncertainty and the quality of firms' internal control moderated the relationship. [ABSTRACT FROM AUTHOR]

• Summary of recent techniques in agricultural question answering systems. • Discussion on key knowledge storage approaches: corpora, knowledge graph, and LLM. • Overview of the application and future of agricultural question answering systems. The increasing application of artificial intelligence in agriculture production and management has generated a large amount of data, leading to a demand for processing this data. This review focuses on the knowledge storage approaches in agricultural question answering systems, namely corpora, knowledge graphs, and large language models. These systems are built on massive amounts of data and aim to process and retrieve information effectively in the context of sustainable agriculture. Corpora refer to large collections of diverse documents that serve as foundational resources for training and fine-tuning question answering systems. Knowledge graphs capture structured and interconnected knowledge by representing entities, relationships, and attributes, enabling efficient organization and querying of information. Large language models, such as GPT-4, enhance the capacity of question answering systems to provide accurate and relevant responses. By exploring these three prominent knowledge storage approaches, this review analyses the methodology and impact of agricultural question answering systems, highlighting their applications in the production process. The findings provide important implications for future research in agriculture, and potential directions for further exploration. [ABSTRACT FROM AUTHOR]