Agricultural decision-making involves complex interdependencies between goals, risks, resources, and environmental factors. Traditional support systems often lack the contextual awareness and adaptability needed to assist farmers in navigating dynamic conditions. This paper introduces FarmerLikeMe, a novel framework to support goal- and risk-aware decision-making in agriculture. FarmerLikeMe combines three key components: (i) a goal-oriented model using the i* model and RiskML to explicitly capture learning experiences ( \(\mathcal {L}E\) ) as farming intentions, farm operations, environmental data, agronomic practices, and ecological risks; thereby enabling farmers to interact through a controlled interface; (ii) a causal knowledge graph, which serves as a collective knowledge base used to seek and share \(\mathcal {L}E\) of farming practices, fostering experience sharing and collective responses to climate challenges; and (iii) An explaining module leveraging LLM-enhanced knowledge graphs and Graph-based Retrieval-Augmented Generation (Graph RAG) to produce decisions that align with individual farmer goals while accounting for potential risks such as climate variability, crop diseases, and resource constraints. A mobile proof-of-concept shows real-world applicability, bridging knowledge representation, and natural language interaction to support intelligent, explainable, and sustainable farming.