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dc.contributor.authorKrell, N.
dc.contributor.authorDavenport, F.
dc.contributor.authorHarrison, L.
dc.contributor.authorTurner, W.
dc.contributor.authorPeterson, S.
dc.contributor.authorShukla, S.
dc.contributor.authorMarter-Kenyon, J.
dc.contributor.authorHusak, G.
dc.contributor.authorEvans, T.
dc.contributor.authorCaylor, K.
dc.date.accessioned2022-03-17T01:57:04Z
dc.date.available2022-03-17T01:57:04Z
dc.date.issued2022
dc.identifier.citationKrell, N., Davenport, F., Harrison, L., Turner, W., Peterson, S., Shukla, S., Marter-Kenyon, J., Husak, G., Evans, T., & Caylor, K. (2022). Using real-time mobile phone data to characterize the relationships between small-scale farmers’ planting dates and socio-environmental factors. Climate Risk Management.
dc.identifier.issn2212-0963
dc.identifier.doi10.1016/j.crm.2022.100396
dc.identifier.urihttp://hdl.handle.net/10150/663588
dc.description.abstractAccurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect. © 2022 The Author(s)
dc.language.isoen
dc.publisherElsevier B.V.
dc.rightsCopyright © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCentral Kenya
dc.subjectMobile phone data
dc.subjectPlanting dates
dc.subjectRandom forests
dc.subjectSmall-scale food producers
dc.subjectStart of season
dc.titleUsing real-time mobile phone data to characterize the relationships between small-scale farmers’ planting dates and socio-environmental factors
dc.typeArticle
dc.typetext
dc.contributor.departmentSchool of Geography, Development and Environment, University of Arizona
dc.identifier.journalClimate Risk Management
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleClimate Risk Management
refterms.dateFOA2022-03-17T01:57:04Z


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Copyright © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license.
Except where otherwise noted, this item's license is described as Copyright © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license.