This article analyzes the data processes that render US agricultural landscapes knowable at scale as objects of anthropological inquiry. We focus our inquiry on the US Department of Agriculture's Cropland Data Layer (CDL), a widely used, moderate‐resolution raster data set classifying national agricultural land use annually. The CDL's crop categories are based upon—but depart significantly from—those of another federal agricultural office, the Farm Service Agency (FSA). We visualize several transformations from the FSA's data categories to the CDL's to identify which crop varieties are preserved during this process and which are coarsened into higher‐level categories. These patterns illustrate the logics underlying the CDL's data categorization schema. Constrained by the technical limits of remote sensing technology, these data most often obscure the presence of specialty, native, and food crops, rendering them unknowable at a national scale and entrenching long‐standing productivist values into the country's agri‐data infrastructure. Bounded by the same path dependencies shaping the very agricultural landscapes they codify, these data themselves become barriers to recognizing where agricultural transformations may already be under way.

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我们怎么知道我们种植的是什么？询问美国农业景观的数据化

本文分析了使美国农业景观作为人类学研究对象在规模上可知的数据过程。我们将调查重点放在美国农业部的农田数据层 （CDL） 上，这是一个广泛使用的中等分辨率栅格数据集，每年对国家农业用地进行分类。CDL 的作物类别基于另一个联邦农业办公室农业服务局 （FSA） 的作物类别，但与农场服务局 （FSA） 的作物类别大相径庭。我们将从 FSA 的数据类别到 CDL 的几个转换可视化，以确定在此过程中保留了哪些作物品种，哪些被粗化为更高级别的类别。这些模式说明了 CDL 数据分类架构的基础逻辑。受遥感技术技术限制的限制，这些数据往往掩盖了特种作物、本地作物和粮食作物的存在，使它们在全国范围内不可知，并将长期存在的生产主义价值观根植于该国的农业数据基础设施中。这些数据受制于塑造它们编纂的农业景观的相同路径依赖关系的约束，这些数据本身成为识别可能已经在进行农业转型的地区的障碍。