Pasture degradation assessment is important for sustainable rangeland management, while conventional field surveys can be labor-intensive and remote-sensing approaches may require specialized sensing platforms or additional environmental data. This study proposes EcoPastureNet, a field-guided deep learning framework for four-level pasture degradation classification from ground-level RGB images. The framework uses DINOv3-L/16 as the visual backbone and incorporates overall vegetation cover, pasture productivity, grassland type, soil type, and salinity as privileged ecological information available only during training. Ecological knowledge is transferred through auxiliary prediction tasks and representation-level alignment, while inference requires only an RGB image. The model was evaluated on 419 pasture sampling points from 14 administrative districts and territories across four regions of southern and southeastern Kazakhstan using a strictly district-level geographic split. Among the image-only baselines, DINOv3-L/16 achieved a Macro-F1 of 0.6575. The full EcoPastureNet improved Macro-F1 to 0.7378, balanced accuracy to 0.7214, and achieved a quadratic weighted kappa of 0.8633. The largest class-specific improvement was observed for slight degradation, where the F1-score increased from 0.3333 to 0.6250. These results indicate that field-derived ecological supervision can improve image-based pasture degradation classification while preserving RGB-only inference.