MaizeEviReason for Auditable Evidence Applicability and Conservative Release in Maize Disease Decision Support

Agricultural evidence can be topically relevant yet conflict with the conditions of a query, or lack the information needed to support an action. We present MaizeEviReason, an auditable interface that keeps source conditions, distinguishes applicable, inapplicable and undetermined states, and separates applicability assessment from deterministic release checks. The core evaluation measures how much information is lost when conditions are projected onto fixed fields and what coverage is given up when supplied contracts are enforced. Matched learning experiments evaluate applicability prediction, and a public-pipeline diagnostic examines whether the interface is activated. On 49 constructed assignments from five documents, a six-field projection wrongly accepted 13 conflicting or unresolved assignments and reached an accuracy of 0.7347, against 0.8367 for direct interpretation of the text contract. The complete rule agrees perfectly with its reference, as expected, because those labels were derived from the same contracts. On 120 constructed release records, deterministic gating reduced observed symbolic violations from 7 to 0, while coverage fell from 55.83% to 50.00%. The prespecified source-family comparison of decision accuracy was not significant (Holm-adjusted $p = 0.75$), and the eight-record source-bound test covered only one family. On ten permanently held-out documents, the condition-differential evidence adapter (CDEA) obtained a macro-F1 of 0.4949 with the state loss and 0.2518 with its full objective. In the public evaluation the condition interface was never activated, so its effect under usable condition inputs remains untested. Taken together, these descriptive diagnostics locate information loss in the evaluated condition representation and mark the operating scope of applicability assessment and output checking; they are not evidence of general decision-support effectiveness.