Estimation of land productivity and availability is necessary to predict land production potential, especially for the emerging bioenergy crop production, which may compete land with food crop production. This study provides land productivity estimates in the contiguous United States (CONUS) through a machine learning approach. Land productivity is defined as the potential in producing agricultural outputs given biophysical properties including climate, soil, and land slope. The land productivity is approximated by the potential yields of six major crops in the CONUS, i.e. corn, soybean, winter wheat, spring wheat, cotton, and alfalfa. This quantitative relationship is then applied to estimating the availability of marginal land for bioenergy crop production in the CONUS. Furthermore, the levels of uncertainties associated with land productivity and marginal land estimates are quantified and discussed. Based on the modeling results, the total marginal land of the CONUS ranges 55.0–172.8 mha, but the 95% inter-percentile distance of the estimated productivity index reaches up to 60% of its expected value in data-scarce regions. Finally, in a cross-check analysis, marginal lands estimated based on biophysical criteria are found to be comparable to those based on an economic criterion.

中文翻译：

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使用生物物理预测器基于机器学习估算美国本土的土地生产力

土地生产力和可用性的估计对于预测土地生产潜力是必要的，特别是对于新兴的生物能源作物生产，可能会与粮食作物生产竞争土地。本研究通过机器学习方法提供了美国本土 (CONUS) 的土地生产力估计值。土地生产力被定义为在给定生物物理特性（包括气候、土壤和土地坡度）的情况下生产农业产品的潜力。土地生产力由 CONUS 六种主要作物的潜在产量估算，即玉米、大豆、冬小麦、春小麦、棉花和苜蓿。然后将这种定量关系应用于估计 CONUS 用于生物能源作物生产的边际土地的可用性。此外，对与土地生产力和边际土地估计数相关的不确定性水平进行了量化和讨论。根据建模结果，CONUS 的总边际土地范围为 55.0-172.8 mha，但在数据稀缺地区，估计生产力指数的 95% 百分间距达到其预期值的 60%。最后，在交叉检查分析中，发现基于生物物理标准估计的边际土地与基于经济标准的边际土地具有可比性。