IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i24p16480-d998213.html
   My bibliography  Save this article

Benefits and Challenges of Making Data More Agile: A Review of Recent Key Approaches in Agriculture

Author

Listed:
  • Elena Serfilippi

    (The Committee on Sustainability Assessment (COSA), Philadelphia, PA 19147, USA)

  • Daniele Giovannucci

    (The Committee on Sustainability Assessment (COSA), Philadelphia, PA 19147, USA)

  • David Ameyaw

    (International Center for Evaluation and Development, Sakumono JWCP+XJ7, Ghana)

  • Ankur Bansal

    (GDi Partners (GDi), New Delhi 110065, India)

  • Thomas Asafua Nketsia Wobill

    (International Center for Evaluation and Development, Sakumono JWCP+XJ7, Ghana)

  • Roberta Blankson

    (International Center for Evaluation and Development, Sakumono JWCP+XJ7, Ghana)

  • Rashi Mishra

    (GDi Partners (GDi), New Delhi 110065, India)

Abstract

Having reliable and timely or ongoing field data from development projects or supply chains is a perennial challenge for decision makers. This is especially true for those operating in rural areas where traditional data gathering and analysis approaches are costly and difficult to operate while typically requiring so much time that their findings are useful mostly as learning after the fact. A series of innovations that we refer to as Agile Data are opening new frontiers of timeliness, cost, and accuracy. They are leveraging a range of technological advances to do so. This paper explores the differences between traditional and agile approaches and offers insights into costs and benefits by drawing on recent field research in agriculture conducted by diverse institutions such as the World Bank (WB), World Food Program (WFP), United States Agency for International Development (USAID), and the Committee on Sustainability Assessment (COSA). The evidence collected in this paper about agile approaches—including those relying on internet and mobile-based data collection—contributes to define a contemporary dimension of data and analytics that can contribute to more optimal decision-making. Providing a theoretical, applied, and empirical foundation for the collection and use of Agile Data can offer a means to improve the management of development initiatives and deliver new value, as participants or beneficiaries are better informed and can better respond to a fast-changing world.

Suggested Citation

  • Elena Serfilippi & Daniele Giovannucci & David Ameyaw & Ankur Bansal & Thomas Asafua Nketsia Wobill & Roberta Blankson & Rashi Mishra, 2022. "Benefits and Challenges of Making Data More Agile: A Review of Recent Key Approaches in Agriculture," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16480-:d:998213
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/24/16480/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/24/16480/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    2. Kilic, Talip & Moylan, Heather & Koolwal, Gayatri, 2021. "Getting the (Gender-Disaggregated) lay of the land: Impact of survey respondent selection on measuring land ownership and rights," World Development, Elsevier, vol. 146(C).
    3. Dillon, Andrew & Rao, Lakshman Nagraj, 2018. "Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Satellite Data," ADB Economics Working Paper Series 540, Asian Development Bank.
    4. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.
    5. Carletto, Calogero & Savastano, Sara & Zezza, Alberto, 2013. "Fact or artifact: The impact of measurement errors on the farm size–productivity relationship," Journal of Development Economics, Elsevier, vol. 103(C), pages 254-261.
    6. Bevis, Leah EM. & Barrett, Christopher B., 2020. "Close to the edge: High productivity at plot peripheries and the inverse size-productivity relationship," Journal of Development Economics, Elsevier, vol. 143(C).
    7. Abay, Kibrom A. & Abate, Gashaw T. & Barrett, Christopher B. & Bernard, Tanguy, 2019. "Correlated non-classical measurement errors, ‘Second best’ policy inference, and the inverse size-productivity relationship in agriculture," Journal of Development Economics, Elsevier, vol. 139(C), pages 171-184.
    8. Kilic, Talip & Moylan, Heather & Ilukor, John & Mtengula, Clement & Pangapanga-Phiri, Innocent, 2021. "Root for the tubers: Extended-harvest crop production and productivity measurement in surveys," Food Policy, Elsevier, vol. 102(C).
    9. Zilberman, David & Khanna, Madhu & Lipper, Leslie, 1997. "Economics of new technologies for sustainable agriculture," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 41(1), pages 1-18.
    10. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    11. Benson, Todd, 2019. "Can mobile phone-based household surveys in rural Papua New Guinea generate information representative of the population surveyed?," Project notes 2, International Food Policy Research Institute (IFPRI).
    12. Brian Dillon, 2012. "Using mobile phones to collect panel data in developing countries," Journal of International Development, John Wiley & Sons, Ltd., vol. 24(4), pages 518-527, May.
    13. Noam Angrist & Peter Bergman & Caton Brewster & Moitshepi Matsheng, 2020. "Stemming Learning Loss During the Pandemic: A Rapid Randomized Trial of a Low-Tech Intervention in Botswana," CSAE Working Paper Series 2020-13, Centre for the Study of African Economies, University of Oxford.
    14. Robert Morello and Benjamin Leo, 2016. "Practical Considerations with Using Mobile Phone Survey Incentives: Experiences in Ghana and Tanzania - Working Paper 431," Working Papers 431, Center for Global Development.
    15. Calogero Carletto, 2021. "Better data, higher impact: improving agricultural data systems for societal change [Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity r," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 719-740.
    16. Furbush,Ann & Josephson,Anna Leigh & Kilic,Talip & Michler,Jeffrey David, 2021. "The Evolving Socioeconomic Impacts of COVID-19 in Four African Countries," Policy Research Working Paper Series 9556, The World Bank.
    17. Robert Garlick & Kate Orkin & Simon Quinn, 2020. "Call Me Maybe: Experimental Evidence on Frequency and Medium Effects in Microenterprise Surveys," The World Bank Economic Review, World Bank, vol. 34(2), pages 418-443.
    18. Desiere, Sam & Jolliffe, Dean, 2018. "Land productivity and plot size: Is measurement error driving the inverse relationship?," Journal of Development Economics, Elsevier, vol. 130(C), pages 84-98.
    19. Elinor Benami & Michael R. Carter, 2021. "Can digital technologies reshape rural microfinance? Implications for savings, credit, & insurance," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1196-1220, December.
    20. Gourlay, Sydney & Kilic, Talip & Lobell, David B., 2019. "A new spin on an old debate: Errors in farmer-reported production and their implications for inverse scale - Productivity relationship in Uganda," Journal of Development Economics, Elsevier, vol. 141(C).
    21. George W Pariyo & Abigail R Greenleaf & Dustin G Gibson & Joseph Ali & Hannah Selig & Alain B Labrique & Gulam Muhammed Al Kibria & Iqbal Ansary Khan & Honorati Masanja & Meerjady Sabrina Flora & Saif, 2019. "Does mobile phone survey method matter? Reliability of computer-assisted telephone interviews and interactive voice response non-communicable diseases risk factor surveys in low and middle income coun," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-25, April.
    22. Michael Carter & Alain de Janvry & Elisabeth Sadoulet & Alexandros Sarris, 2017. "Index Insurance for Developing Country Agriculture: A Reassessment," Annual Review of Resource Economics, Annual Reviews, vol. 9(1), pages 421-438, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Calogero Carletto, 2021. "Better data, higher impact: improving agricultural data systems for societal change [Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity r," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 719-740.
    2. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    3. Abay,Kibrom A. & Barrett,Christopher B. & Kilic,Talip & Moylan,Heather G. & Ilukor,John & Vundru,Wilbert Drazi, 2022. "Nonclassical Measurement Error and Farmers’ Response to Information Reveal Behavioral Anomalies," Policy Research Working Paper Series 9908, The World Bank.
    4. Carletto,Calogero & Dillon,Andrew S. & Zezza,Alberto, 2021. "Agricultural Data Collection to Minimize Measurement Error and Maximize Coverage," Policy Research Working Paper Series 9745, The World Bank.
    5. Mensah, Edouard R. & Kostandini, Genti, 2020. "The inverse farm size-productivity relationship under land size mis-measurement and in the presence of weather and price risks: Panel data evidence from Uganda," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304477, Agricultural and Applied Economics Association.
    6. Kibrom A. Abay & Leah E. M. Bevis & Christopher B. Barrett, 2021. "Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 498-522, March.
    7. Abate, Gashaw T. & de Brauw, Alan & Hirvonen, Kalle & Wolle, Abdulazize, 2023. "Measuring consumption over the phone: Evidence from a survey experiment in urban Ethiopia," Journal of Development Economics, Elsevier, vol. 161(C).
    8. Zezza,Alberto & Mcgee,Kevin Robert & Wollburg,Philip Randolph & Assefa,Thomas Woldu & Gourlay,Sydney, 2022. "From Necessity to Opportunity : Lessons for Integrating Phone and In-Person Data Collectionfor Agricultural Statistics in a Post-Pandemic World," Policy Research Working Paper Series 10168, The World Bank.
    9. Aragón, Fernando M. & Restuccia, Diego & Rud, Juan Pablo, 2022. "Are small farms really more productive than large farms?," Food Policy, Elsevier, vol. 106(C).
    10. William J. Burke & Stephen N. Morgan & Thelma Namonje & Milu Muyanga & Nicole M. Mason, 2023. "Beyond the “inverse relationship”: Area mismeasurement may affect actual productivity, not just how we understand it," Agricultural Economics, International Association of Agricultural Economists, vol. 54(4), pages 557-569, July.
    11. Fang Xia & Lingling Hou & Songqing Jin & Dongqing Li, 2020. "Land size and productivity in the livestock sector: evidence from pastoral areas in China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(3), pages 867-888, July.
    12. Kibrom A. Abay & Tesfamicheal Wossen & Jordan Chamberlin, 2023. "Mismeasurement and efficiency estimates: Evidence from smallholder survey data in Africa," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 413-434, June.
    13. Helfand, Steven M. & Taylor, Matthew P.H., 2021. "The inverse relationship between farm size and productivity: Refocusing the debate," Food Policy, Elsevier, vol. 99(C).
    14. Taylor, Matthew P.H. & Helfand, Steven M., 2021. "The Farm Size – Productivity Relationship in the Wake of Market Reform: An Analysis of Mexican Family Farms," 2021 Conference, August 17-31, 2021, Virtual 315138, International Association of Agricultural Economists.
    15. Abay, Kibrom A. & Barrett, Christopher B. & Kilic, Talip & Moylan, Heather & Ilukor, John & Vundru, Wilbert Drazi, 2023. "Nonclassical measurement error and farmers’ response to information treatment," Journal of Development Economics, Elsevier, vol. 164(C).
    16. C. S. C. Sekhar & Namrata Thapa, 2023. "Rural market imperfections in India: Revisiting old debates with new evidence," Development Policy Review, Overseas Development Institute, vol. 41(5), September.
    17. Paola Mallia, 2022. "You reap what (you think) you sow? Evidence on farmers’behavioral adjustments in the case of correct crop varietal identification," PSE Working Papers hal-03597332, HAL.
    18. Kosmowski, Frederic & Chamberlin, Jordan & Ayalew, Hailemariam & Sida, Tesfaye & Abay, Kibrom & Craufurd, Peter, 2021. "How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia," Food Policy, Elsevier, vol. 102(C).
    19. Joachim De Weerdt & John Gibson & Kathleen Beegle, 2020. "What Can We Learn from Experimenting with Survey Methods?," Annual Review of Resource Economics, Annual Reviews, vol. 12(1), pages 431-447, October.
    20. Gourlay, Sydney & Kilic, Talip & Martuscelli, Antonio & Wollburg, Philip & Zezza, Alberto, 2021. "Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions," Food Policy, Elsevier, vol. 105(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16480-:d:998213. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.