IDEAS home Printed from https://ideas.repec.org/p/ags/aaea06/21299.html
   My bibliography  Save this paper

Generating global crop distribution maps: from census to grid

Author

Listed:
  • You, Liangzhi
  • Wood, Stanley
  • Wood-Sichra, Ulrike

Abstract

In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services. This paper proposes a spatial allocation model for generating highly disaggregated, crop-specific production data by a triangulation of any and all relevant background and partial information. This includes national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. This information is compiled and integrated to generate "prior" estimates of the spatial distribution of individual crops. Priors are then submitted to an optimization model that uses cross-entropy principles and area and production accounting constraints to simultaneously allocate crops into the individual pixels of a GIS database. The result for each pixel (notionally of any size, but typically from 25 to 100 square km) is the area and production of each crop produced, split by the shares grown under irrigated, high-input rainfed, low-input rainfed conditions (each with distinct yield levels). Tested in Latin America and sub-Saharan Africa, the spatial allocation model is applied here to generate a global distribution of crop area and production for 20 major crops (wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soybean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops). The detailed spatial datasets represent a truly unique and extremely rich platform for exploring the social, economic and environmental consequences of agricultural production in a strategic policy context.

Suggested Citation

  • You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike, 2006. "Generating global crop distribution maps: from census to grid," 2006 Annual meeting, July 23-26, Long Beach, CA 21299, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea06:21299
    DOI: 10.22004/ag.econ.21299
    as

    Download full text from publisher

    File URL: http://ageconsearch.umn.edu/record/21299/files/sp06wo01.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. You, Liangzhi & Wood, Stanley, 2006. "An entropy approach to spatial disaggregation of agricultural production," Agricultural Systems, Elsevier, vol. 90(1-3), pages 329-347, October.
    2. Genti Kostandini & Bradford F. Mills & Steven Were Omamo & Stanley Wood, 2009. "Ex ante analysis of the benefits of transgenic drought tolerance research on cereal crops in low‐income countries," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 477-492, July.
    3. You, Liangzhi & Ringler, Claudia & Wood-Sichra, Ulrike & Robertson, Richard & Wood, Stanley & Zhu, Tingju & Nelson, Gerald & Guo, Zhe & Sun, Yan, 2011. "What is the irrigation potential for Africa? A combined biophysical and socioeconomic approach," Food Policy, Elsevier, vol. 36(6), pages 770-782.
    4. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    5. Bruno Losch & Sandrine Fréguin-Gresh & Eric Thomas White, 2012. "Structural Transformation and Rural Change Revisited : Challenges for Late Developing Countries in a Globalizing World
      [Transformations rurales et développement : Les défis du changement structurel
      ," World Bank Publications, The World Bank, number 12482.
    6. Nelson, Gerald C., 2002. "Introduction to the special issue on spatial analysis for agricultural economists," Agricultural Economics, Blackwell, vol. 27(3), pages 197-200, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Van Dijk, M. & You, L. & Havlik, P. & Palazzo, A. & Mosnier, A., 2018. "Generating high-resolution national crop distribution maps: Combining statistics, gridded data and surveys using an optimization approach," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276038, International Association of Agricultural Economists.
    2. Correa, Diego F. & Beyer, Hawthorne L. & Fargione, Joseph E. & Hill, Jason D. & Possingham, Hugh P. & Thomas-Hall, Skye R. & Schenk, Peer M., 2019. "Towards the implementation of sustainable biofuel production systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 250-263.
    3. Lutz, Femke & Stoorvogel, Jetse J. & Müller, Christoph, 2019. "Options to model the effects of tillage on N2O emissions at the global scale," Ecological Modelling, Elsevier, vol. 392(C), pages 212-225.
    4. Xavier, Antonio & Martins, Maria de Belem Costa Freitas & Fragoso, Rui Manuel de Sousa, 2011. "Recovery of Incomplete Data of Statistical Livestock Number Applying an Entropy Approach," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115790, European Association of Agricultural Economists.
    5. Kazi Ahmed & Guiling Wang & Miao Yu & Jawoo Koo & Liangzhi You, 2015. "Potential impact of climate change on cereal crop yield in West Africa," Climatic Change, Springer, vol. 133(2), pages 321-334, November.
    6. World Bank, 2017. "ICT in Agriculture (Updated Edition)," World Bank Publications, The World Bank, number 27526.
    7. Yu, Qiangyi & Wu, Wenbin & You, Liangzhi & Zhu, Tingju & van Vliet, Jasper & Verburg, Peter H. & Liu, Zhenhuan & Li, Zhengguo & Yang, Peng & Zhou, Qingbo & Tang, Huajun, 2017. "Assessing the harvested area gap in China," Agricultural Systems, Elsevier, vol. 153(C), pages 212-220.
    8. Xie, Hua & You, Liangzhi & Takeshima, Hiroyuki, 2017. "Invest in small-scale irrigated agriculture: A national assessment on potential to expand small-scale irrigation in Nigeria," Agricultural Water Management, Elsevier, vol. 193(C), pages 251-264.
    9. António Xavier & Rui Fragoso & Maria Belém Costa Freitas & Maria Socorro Rosário, 2019. "An Approach Using Entropy and Supervised Classifications to Disaggregate Agricultural Data at a Local Level," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 763-779, December.
    10. Iimi,Atsushi & You,Liangzhi & Wood-Sichra,Ulrike & Humphrey,Richard Martin, 2015. "Agriculture production and transport infrastructure in east Africa : an application of spatial autoregression," Policy Research Working Paper Series 7281, The World Bank.
    11. Fjelde, Hanne, 2015. "Farming or Fighting? Agricultural Price Shocks and Civil War in Africa," World Development, Elsevier, vol. 67(C), pages 525-534.
    12. Grados, D. & García, S. & Schrevens, E., 2020. "Assessing the potato yield gap in the Peruvian Central Andes," Agricultural Systems, Elsevier, vol. 181(C).

    More about this item

    Keywords

    Research Methods/ Statistical Methods;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land

    Statistics

    Access and download statistics

    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:ags:aaea06:21299. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.