A High-Frequency Mobile Phone Data Collection Approach for Research in Social-Environmental Systems: Applications in Climate Variability and Food Security in Sub-Saharan Africa. Giroux, S., A., Kouper, I., Estes, L., D., Schumacher, J., Waldman, K., Greenshields, J., T., Dickinson, S., L., Caylor, K., K., & Evans, T., P. Environmental Modelling & Software, Elsevier, 5, 2019. ![A High-Frequency Mobile Phone Data Collection Approach for Research in Social-Environmental Systems: Applications in Climate Variability and Food Security in Sub-Saharan Africa [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Collecting high-frequency social-environmental data about farming practices in sub-Saharan Africa can provide new insight into environmental changes that farmers face and how they respond within smallholder agro-ecosystems. Traditional data collection methods such as agricultural censuses are costly and not useful for understanding intra-annual and real-time decisions. Short-message service (SMS) has the potential to transform the nature of data collection in coupled social-ecological systems. We present a system for collecting, managing, and synthesizing weekly data from farmers, including data infrastructure for management of big and heterogeneous datasets; probabilistic data quality assessment tools; and visualization and analysis tools such as mapping and regression techniques. We discuss limitations of collecting social-environmental data via SMS and data integration challenges that arise when linking these data with other social and environmental data. In combination with high-frequency environmental data, such data will help ameliorate issues of scale mismatch and build resilience in environmental systems.
@article{
title = {A High-Frequency Mobile Phone Data Collection Approach for Research in Social-Environmental Systems: Applications in Climate Variability and Food Security in Sub-Saharan Africa},
type = {article},
year = {2019},
websites = {https://www.sciencedirect.com/science/article/pii/S1364815218303207?via%3Dihub},
month = {5},
publisher = {Elsevier},
day = {20},
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last_modified = {2020-05-11T14:43:33.993Z},
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abstract = {Collecting high-frequency social-environmental data about farming practices in sub-Saharan Africa can provide new insight into environmental changes that farmers face and how they respond within smallholder agro-ecosystems. Traditional data collection methods such as agricultural censuses are costly and not useful for understanding intra-annual and real-time decisions. Short-message service (SMS) has the potential to transform the nature of data collection in coupled social-ecological systems. We present a system for collecting, managing, and synthesizing weekly data from farmers, including data infrastructure for management of big and heterogeneous datasets; probabilistic data quality assessment tools; and visualization and analysis tools such as mapping and regression techniques. We discuss limitations of collecting social-environmental data via SMS and data integration challenges that arise when linking these data with other social and environmental data. In combination with high-frequency environmental data, such data will help ameliorate issues of scale mismatch and build resilience in environmental systems.},
bibtype = {article},
author = {Giroux, Stacey A. and Kouper, Inna and Estes, Lyndon D. and Schumacher, Jacob and Waldman, Kurt and Greenshields, Joel T. and Dickinson, Stephanie L. and Caylor, Kelly K. and Evans, Tom P.},
doi = {10.1016/J.ENVSOFT.2019.05.011},
journal = {Environmental Modelling & Software}
}
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