Appendix A Data and Code Availability 17 A.1 Code 17 A.2 Data 17 A.3 Result 17 B Dataset Documentation

The robust ability of LLMs to generate and acquire domain-specific knowledge has been a significant factor in this potential [17]. While researchers have explored the use of LLMs in answering agriculture-related exams [55], their performance in certain crop cultivation scenarios, such as pest management, has been less than satisfactory [66]. Moreover, there remains a considerable gap between the ability to answer exam questions and the application of this knowledge in real-world situations. To bridge the gap and thoroughly assess LLMs in supporting the crop science field, we introduce CROP. CROP comprises an instruction tuning dataset that equips LLMs with the necessary skills to aid tasks in crop production, along with a carefully designed benchmark to evaluate the extent to which LLMs fulfill the demands of real-world agricultural applications. We anticipate that CROP will serve the research community and also provide practical benefits to industry practitioners. E.2 LLM-based Multi-turn Dialogue Generation In recent research, several LLM-based approaches have emerged for constructing multi-turn dialogues.