利用卫星和手机评估并促进农业技术采用：来自印度小农户的证据

中文摘要

本文评估了印度一项低成本、定制化的土壤养分管理咨询服务。作为方法上的贡献，我们考察卫星测量能否以及在何种情境下有效估计农业产量和处理效应。该干预改善了农户自报的施肥管理实践，但改善幅度尚不足以对产量产生可测量的影响。利用普通最小二乘（OLS）校准的卫星测量所得点估计比基于农户自报数据的估计更精确，表明其可提升统计功效。然而，文献中常用的线性模型可能产生有偏估计。我们提出了一种使用两阶段最小二乘（2SLS）的替代程序。在不存在样本流失的情境下，该方法的统计功效低于使用自报产量的方法；在存在差异性样本流失的情境下，该方法可能大幅提升统计功效。我们提供了一份“操作指南”和代码，以便其他研究者利用遥感进行产量估计和项目评估。

Abstract

This paper evaluates a low-cost, customized soil nutrient management advisory service in India. As a methodological contribution, we examine whether and in which settings satellite measurements may be effective at estimating both agricultural yields and treatment effects. The intervention improves self-reported fertilizer management practices, though not enough to measurably affect yields. Satellite measurements calibrated using OLS produce more precise point estimates than farmer-reported data, suggesting power gains. However, linear models, common in the literature, likely produce biased estimates. We propose an alternative procedure, using two-stage least squares. In settings without attrition, this approach obtains lower statistical power than self-reported yields; in settings with differential attrition, it may substantially increase power. We include a “cookbook” and code that should allow other researchers to use remote sensing for yield estimation and program evaluation.