理解休耕地的空间分布对优化食物-水（FW）纽带至关重要，因其在作物轮作与水资源保护中发挥关键作用。休耕地在美国农业部作物土地数据层（USDA Cropland Data Layer, CDL）中属于低精度类别。地理空间基础模型（Geospatial Foundation Model, GFM）Prithvi-EO 在多种计算机视觉任务中展现出强迁移能力。然而，其视觉Transformer（ViT）骨干网络仅在单一空间尺度上生成特征，难以满足目标检测头对多尺度特征的需求。现有方法通过缩放单步长（single-stride）token构建多尺度金字塔，但牺牲了空间异质性；而对整个骨干网络进行微调在计算上对GFM而言不可行。我们评估了一种休耕地检测流程，该流程融合两种参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）策略：低秩适应（LoRA）与一种混合PEFT，并结合三种颈部（neck）设计：伪多尺度（pseudo multi-scale）、轻量级ViT-Adapter（Lite ViT-Adapter）与全量ViT-Adapter（Full ViT-Adapter）。最优配置——采用Lite ViT-Adapter与单阶段检测头、并使用Diou损失函数——实现了0.9479的mAP@50，表明中心感知定位对不规则休耕地田块检测的有效性。在LoRA下、无ViT-Adapter的单阶段检测相较无Adapter的锚点式基线方法提升6.42%；最优配置相较该基线方法提升25.70%。结果表明，轻量级空间先验融合与选择性骨干网络解冻，可使Prithvi-EO更有效地捕获局部休耕地模式，性能优于依赖重整形单步长ViT token的方法。

Understanding spatial distribution of fallow land is important for optimizing the food-water (FW) nexus, given fallowing's role in crop rotation and water conservation. Fallow is a low accuracy class in USDA Cropland Data Layer (CDL). Geospatial foundation model (GFM), Prithvi-EO has shown strong transferability across computer vision tasks. However, its Vision Transformer (ViT) backbone produces features at a single spatial scale that are ill-suited for the multi-scale features required by object detection heads. Existing approaches synthesise multi-scale pyramids through scaling of single stride tokens, sacrificing spatial heterogeneity, and full backbone fine-tuning is computationally prohibitive for GFMs. We evaluate a fallow detection pipeline combining two parameter-efficient fine tuning (PEFT) schemes: Low-Rank Adaptation (LoRA) and a hybrid PEFT, with three neck designs: pseudo multi-scale, Lite ViT-Adapter, and Full ViT-Adapter. Our best configuration, Lite ViT-Adapter with a one-stage head, achieves a mAP@50 of 0.9479 with the Diou loss, suggesting the effectiveness of center-aware localization for irregular fallow field detection. ViT-Adapter free one-stage detection under LoRA improves the adapter-free anchor-based approach by 6.42%, and the best configuration improves baseline adapter-free anchor-based approach by 25.70%. These results demonstrate that lightweight spatial prior fusion and selective backbone unfreezing enable Prithvi-EO to capture local fallow patterns more effectively, outperforming approaches that rely on reshaped single-stride ViT tokens.