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A comparison of global agricultural monitoring systems and current gaps

Author

Listed:
  • Fritz, Steffen
  • See, Linda
  • Bayas, Juan Carlos Laso
  • Waldner, François
  • Jacques, Damien
  • Becker-Reshef, Inbal
  • Whitcraft, Alyssa
  • Baruth, Bettina
  • Bonifacio, Rogerio
  • Crutchfield, Jim
  • Rembold, Felix
  • Rojas, Oscar
  • Schucknecht, Anne
  • Van der Velde, Marijn
  • Verdin, James
  • Wu, Bingfang
  • Yan, Nana
  • You, Liangzhi
  • Gilliams, Sven
  • Mücher, Sander
  • Tetrault, Robert
  • Moorthy, Inian
  • McCallum, Ian

Abstract

Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data.

Suggested Citation

  • Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    • Handle: RePEc:eee:agisys:v:168:y:2019:i:c:p:258-272
    DOI: 10.1016/j.agsy.2018.05.010
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    1. Deepak K. Ray & Navin Ramankutty & Nathaniel D. Mueller & Paul C. West & Jonathan A. Foley, 2012. "Recent patterns of crop yield growth and stagnation," Nature Communications, Nature, vol. 3(1), pages 1-7, January.
    2. Anderson, Weston & You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike & Wu, Wenbin, 2014. "A comparative analysis of global cropping systems models and maps:," IFPRI discussion papers 1327, International Food Policy Research Institute (IFPRI).
    3. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    4. Stehfest, Elke & Heistermann, Maik & Priess, Joerg A. & Ojima, Dennis S. & Alcamo, Joseph, 2007. "Simulation of global crop production with the ecosystem model DayCent," Ecological Modelling, Elsevier, vol. 209(2), pages 203-219.
    5. Egelkraut, Thorsten M. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "An Evaluation of Crop Forecast Accuracy for Corn and Soybeans: USDA and Private Information Agencies," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 35(1), pages 79-95, April.
    6. Headey, Derek & Fan, Shenggen, 2010. "Reflections on the global food crisis: How did it happen? How has it hurt? And how can we prevent the next one?," Research reports 165, International Food Policy Research Institute (IFPRI).
    7. David P. Rogers & Vladimir V. Tsirkunov, 2013. "Weather and Climate Resilience : Effective Preparedness through National Meteorological and Hydrological Services," World Bank Publications - Books, The World Bank Group, number 15932, December.
    8. Christopher Adam & Olu Ajakaiye, 2011. "Causes, Consequences and Policy Implications of Global Food Price Shocks: Introduction and Overview," Journal of African Economies, Centre for the Study of African Economies, vol. 20(suppl_1), pages -11, May.
    9. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    10. Mingjun Ding & Qian Chen & Xiangming Xiao & Liangjie Xin & Geli Zhang & Lanhui Li, 2016. "Variation in Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Data," Sustainability, MDPI, vol. 8(11), pages 1-16, November.
    11. Nathaniel D. Mueller & James S. Gerber & Matt Johnston & Deepak K. Ray & Navin Ramankutty & Jonathan A. Foley, 2012. "Closing yield gaps through nutrient and water management," Nature, Nature, vol. 490(7419), pages 254-257, October.
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