Deriving Comprehensive County-Level Crop Yield and Area Data for U.S. Cropland
Deriving Comprehensive County-Level Crop Yield and Area Data for U.S. Cropland
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获取美国农田的综合县级农作物产量和面积数据
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
K. Paustian
中科院分区：
文献类型：
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作者：
E. Lokupitiya;F. Breidt;R. Lokupitiya;S. Williams;K. Paustian
美国农作物生产的地面数据是通过国家农业统计局（NASS）和农业普查（AgCensus）进行的调查提供的。这些调查的统计数据被广泛用于经济分析、政策设计和其他目的。然而，调查数据的缺失对需要全面数据进行空间分析的研究提出了限制。我们通过填补NASS和AgCensus报告的现有数据的空白，创建了美国9种主要作物16年的综合县级数据库。我们结合了NASS和农业普查报告的回归分析和线性混合效应模型，其中包括与不同农业生态区相关的县级环境、管理和经济变量。预测产量和作物面积与NASS报告数据非常接近，相对误差在10%以内。线性混合效应模型方法对所有作物的产量缺口总量的84%和作物面积缺口的83%的补全效果最好。AgCensus数据的回归分析填补了NASS报告的主要作物产量和种植面积的16%的空白。
Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses. We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.