||Civil data is often expressed in semi-structured or unstructured text making it difficult to extract knowledge for integration within the Common Operational Picture. Our research objective is to develop a conceptual model using Controlled Natural Language (CNL) for fact extraction and human-agent reasoning about civil considerations, and 2nd and 3rd order effects essential to the analysis of civil information. A logic problem was developed on the topic of food insecurity to illustrate coalition collaborative planning to coordinate resources and thereby reduce redundant efforts in support of reestablishing stability within a host nation. We use Controlled English, a machine readable and human understandable language to represent the concepts, facts, and logical inference rules indicating relationship constraints within this logic problem. Our approach provides a means to evaluate the rationale, reasoning steps leading to conclusions and to identify inconsistent hypotheses. It is worth noting that some forms of cognitive biases, such as the confirmation bias and mirror-imaging may be identified by using CNL to capture assumptions. Potentially, CNL could be used to support the extraction of facts from natural language documents contained within a civil data repository for use in reasoning about food insecurity and other civil considerations in support of coalition collaborative planning.