Pasture land occupies extensive areas and is increasingly of interest for sustainable intensification, land use diversification, greenhouse gas emission mitigation, and bioenergy expansion. Accurate maps of pasture and other managed land covers are needed for monitoring, intercomparison, assessing potential uses, and planning. Yet, land maps can be generated from different types of classification datasets – i.e. as a land use or land cover type – as well as different sources. In this study our aim was to assess and compare land use and land cover definitions for pasture, and examine variability in the resulting pasture land classification maps. First, we conducted a review of pasture definitions in commonly used mapping databases. We then performed a case study involving Brazil, a dominant global producer of pasture-based livestock. Six geospatial databases were harmonized and compared to each other and to MODIS land cover for Brazil including the Cerrado and Amazon biomes, which are internationally recognized for their ecological value. Total pasture area estimates for Brazil ranged by a factor greater than four, from about 430,000 km² to over 1.7 million km². Our analysis showed high variability in pasture land maps depending on the definitions, methods and underlying datasets used to generate them. The results are illustrative of a symptomatic problem for all manage land datasets, demonstrating the need for land categories studies and geospatial data resources that fully define land terms and describe measurable management attributes. Additionally, the suitability of individual geospatial datasets for different types of land mapping must be better described and reported. These recommendations would help bring more consistency in the consideration of managed lands in research, reporting, and policy development, as demonstrated here for pasture land using six case study datasets from multiple sources.

牧场占地广阔，对可持续集约化，土地利用多样化，减少温室气体排放和生物能源扩展的兴趣日益增加。监测，比对，评估潜在用途和规划需要准确的牧场和其他管理土地覆盖图。但是，可以从不同类型的分类数据集（即作为土地用途或土地覆盖类型）以及不同来源生成土地图。在这项研究中，我们的目的是评估和比较牧场的土地利用和土地覆被定义，并检查生成的牧场土地分类图的变异性。首先，我们回顾了常用制图数据库中的牧场定义。然后，我们进行了一项涉及巴西的案例研究，巴西是草场牲畜的主要全球生产国。对六个地理空间数据库进行了协调，并相互比较，并与巴西的CDIS，亚马逊生物群落等MODIS土地覆盖进行了比较，这两个生态数据库因其生态价值而得到国际认可。巴西的牧场总面积估计约为四十三万公里，相差四倍以上²超过170万公里²。我们的分析表明，牧场地图的高度可变性取决于生成它们的定义，方法和基础数据集。结果说明了所有管理土地数据集的症状问题，表明需要土地类别研究和地理空间数据资源来完全定义土地术语并描述可测量的管理属性。此外，必须更好地描述和报告各个地理空间数据集对不同类型的土地制图的适用性。这些建议将有助于使研究，报告和政策制定中对管理土地的考虑更加一致，如此处使用多个来源的六个案例研究数据集对牧场的示范所示。