《AJAE》| 中西部玉米生产的信息输入和技术效率:来自农民使用产量和土壤图的证据

摘要：

人们对大数据将如何影响美国的作物生产越来越感兴趣，但对“小型”(即单个农场)数据在田间层面的影响知之甚少。 我们通过研究中西部玉米产量与产量和土壤图中所包含的信息之间的关系来填补这一空白。关于这种关系的研究是缺乏的，也许是因为地图是信息输入，它可能无法以与传统输入相比较的方式进入生产函数。利用美国农业部详细的调查数据，我们实施了随机前沿分析来评估绘图技术如何影响田间生产率。在控制农户技术内生选择的基础上，我们发现了直接(前沿转移)和间接(效率提升)生产率效应的证据。 根据模型，由于地图的采用，田间产量增加了5.6%或11.9%。产量图使预期效率平均提高8.5%，土壤图使预期效率平均提高7.2%。 这些影响因运营商的人口统计数据(如种田的经验年限)和结构特征(如田地是否投保以及是否为运营商所有)而异。考虑到产量图和土壤图并未得到普遍采用，我们的结果表明通过田间信息的使用仍有机会提高生产率。

Abstract：

There is increasing interest in how big data will affect U.S. crop production, yet little is known about the field-level effects of “small” (i.e., individual farm) data. We help to fill this void by studying the relationship between Midwest corn production and the information contained in yield and soil maps. Research on this relationship is lacking, perhaps because maps are information inputs that may not enter the production function in a way comparable to conventional inputs. Using detailed USDA survey data, we implement a stochastic frontier analysis to evaluate how mapping technologies influence field productivity. Controlling for farmers' endogenous choice of technologies, we find evidence of direct (frontier-shifting) and indirect (efficiency-enhancing) productivity effects. Depending on model, field output increases by 5.6% or 11.9% as a result of map adoption. Yield maps increase expected efficiency by 8.5%, and soil maps increase expected efficiency by 7.2%, on average. These effects differ by operator demographics, such as years of experience with the field, and structural characteristics, such as whether the field is insured and if it is owned by the operator. Given that yield and soil maps are not universally adopted, our results suggest there remain opportunities to increase productivity through field-level information use.

《AJAE》全称为《American Journal of Agricultural Economics》，《美国农业经济学杂志》旨在为世界各地的农业和食品经济学、自然资源和环境、以及农村和社区发展等领域的创造性学术工作讨论提供平台,最新影响因子为4.082，刊载方向为管理科学-农业经济与政策。

原文链接：

https://doi.org/10.1111/ajae.12251

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