Estimating the Impact of Weather on Agriculture

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel...

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Main Authors:	Michler, Jeffrey D., Josephson, Anna, Kilic, Talip, Murray, Siobhan
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2021
Subjects:	REMOTE SENSING AGRICULTURAL PRODUCTIVITY CROP YIELD WEATHER IMPACTS PRECIPITATION TEMPERATURE
Online Access:	http://documents.worldbank.org/curated/undefined/235241638281693198/Estimating-the-Impact-of-Weather-on-Agriculture http://hdl.handle.net/10986/36643

id	okr-10986-36643
recordtype	oai_dc
spelling	okr-10986-366432021-12-04T05:10:42Z Estimating the Impact of Weather on Agriculture Michler, Jeffrey D. Josephson, Anna Kilic, Talip Murray, Siobhan REMOTE SENSING AGRICULTURAL PRODUCTIVITY CROP YIELD WEATHER IMPACTS PRECIPITATION TEMPERATURE This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially linked with a range of geospatial weather data sources and related metrics. The paper provides systematic evidence on measurement error introduced by (1) different methods used to obfuscate the exact GPS coordinates of households, (2) different metrics used to quantify precipitation and temperature, and (3) different remote sensing measurement technologies. First, the analysis finds no discernible effect of measurement error introduced by different obfuscation methods. Second, it finds that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, the analysis finds substantial amounts of measurement error based on remote sensing products. In extreme cases, the data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature drawn from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. The paper concludes with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data. 2021-12-03T14:44:35Z 2021-12-03T14:44:35Z 2021-11 Working Paper http://documents.worldbank.org/curated/undefined/235241638281693198/Estimating-the-Impact-of-Weather-on-Agriculture http://hdl.handle.net/10986/36643 English Policy Research Working Paper;No. 9867 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa Sub-Saharan Africa
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	REMOTE SENSING AGRICULTURAL PRODUCTIVITY CROP YIELD WEATHER IMPACTS PRECIPITATION TEMPERATURE
spellingShingle	REMOTE SENSING AGRICULTURAL PRODUCTIVITY CROP YIELD WEATHER IMPACTS PRECIPITATION TEMPERATURE Michler, Jeffrey D. Josephson, Anna Kilic, Talip Murray, Siobhan Estimating the Impact of Weather on Agriculture
geographic_facet	Africa Sub-Saharan Africa
relation	Policy Research Working Paper;No. 9867
description	This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially linked with a range of geospatial weather data sources and related metrics. The paper provides systematic evidence on measurement error introduced by (1) different methods used to obfuscate the exact GPS coordinates of households, (2) different metrics used to quantify precipitation and temperature, and (3) different remote sensing measurement technologies. First, the analysis finds no discernible effect of measurement error introduced by different obfuscation methods. Second, it finds that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, the analysis finds substantial amounts of measurement error based on remote sensing products. In extreme cases, the data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature drawn from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. The paper concludes with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.
format	Working Paper
author	Michler, Jeffrey D. Josephson, Anna Kilic, Talip Murray, Siobhan
author_facet	Michler, Jeffrey D. Josephson, Anna Kilic, Talip Murray, Siobhan
author_sort	Michler, Jeffrey D.
title	Estimating the Impact of Weather on Agriculture
title_short	Estimating the Impact of Weather on Agriculture
title_full	Estimating the Impact of Weather on Agriculture
title_fullStr	Estimating the Impact of Weather on Agriculture
title_full_unstemmed	Estimating the Impact of Weather on Agriculture
title_sort	estimating the impact of weather on agriculture
publisher	World Bank, Washington, DC
publishDate	2021
url	http://documents.worldbank.org/curated/undefined/235241638281693198/Estimating-the-Impact-of-Weather-on-Agriculture http://hdl.handle.net/10986/36643
_version_	1764485671784808448

Estimating the Impact of Weather on Agriculture

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