Overcoming data barriers in spatial agri-food systems analysis: A flexible imputation framework

• 全文链接

克服农业粮食体系空间分析中的数据障碍

关键词：

来源：

Journal of Agricultural Economics

全文链接：

//agri.nais.net.cn/topic/downloadFile/6a6dd9c6-11f2-41a5-9423-f1f8d95abef4

来源地址：

https://onlinelibrary.wiley.com/doi/full/10.1111/1477-9552.12523

资源所属：

一带一路农业专题

类型：

学术文献

语种：

英语

原文发布日期：

2023-01-04

摘要：

Suppressions in public data severely limit the usefulness of spatial data and hinder research applications. In this context, data imputation is necessary to deal with suppressed values. We present and validate a flexible data imputation method that can aid in the completion of under-determined data systems. The validations use Monte Carlo and optimisation modelling techniques to recover suppressed data tables from the 2017 US Census of Agriculture. We then use econometric models to evaluate the accuracy of imputations from alternative models. Various metrics of forecast accuracy (i.e., MAPE, BIC, etc.) show the flexibility and capacity of this approach to accurately recover suppressed data. To illustrate the value of our method, we compare the livestock water withdrawal estimations with imputed data and suppressed data to show the bias in research applications when suppressions are simply dropped from analysis.