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Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications

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  • Jones, Peter G.
  • Thornton, Philip K.

Abstract

Agricultural modellers often need detailed soil profile data with which to run their models. We combine an extensive soil profile database with the Harmonized World Soil Database, a 30arcsecond raster database of soil information worldwide, and describe a statistical process to identify representative soil profiles for each of its 188 distinct soil types at different spatial resolutions. We then outline a method to cluster the soils in the Harmonized World Soil Database to produce soil maps at coarser resolution, and we describe derived global soil maps at spatial resolutions of 5 and 10arcmin, which may be more practical for some large-scale modelling studies. The derived data files allow a user to select any point or area on land and then to access the set of soil profiles pertaining to the mapping unit selected, which are available in a format suitable for use in modelling applications. In situations where the user has little or no other information about the soils in the region of study, the methods described can be used to produce plausible soil profile information based on the most up-to-date global soil map currently available.

Suggested Citation

  • Jones, Peter G. & Thornton, Philip K., 2015. "Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 139(C), pages 93-99.
  • Handle: RePEc:eee:agisys:v:139:y:2015:i:c:p:93-99
    DOI: 10.1016/j.agsy.2015.07.003
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    References listed on IDEAS

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    1. Hertel, Thomas W. & Lobell, David B., 2014. "Agricultural adaptation to climate change in rich and poor countries: Current modeling practice and potential for empirical contributions," Energy Economics, Elsevier, vol. 46(C), pages 562-575.
    2. Jones, Peter G. & Thornton, Philip K., 2013. "Generating downscaled weather data from a suite of climate models for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 114(C), pages 1-5.
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    2. Fraga, H. & García de Cortázar Atauri, I. & Santos, J.A, 2018. "Viticultural irrigation demands under climate change scenarios in Portugal," Agricultural Water Management, Elsevier, vol. 196(C), pages 66-74.
    3. Parisa Paymard & Mohammad Bannayan & Reza Sadrabadi Haghighi, 2018. "Analysis of the climate change effect on wheat production systems and investigate the potential of management strategies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 1237-1255, April.
    4. Fraga, Helder & Santos, João A., 2018. "Vineyard mulching as a climate change adaptation measure: Future simulations for Alentejo, Portugal," Agricultural Systems, Elsevier, vol. 164(C), pages 107-115.
    5. Global Commission on Adaptation, 2019. "Adapt Now," World Bank Publications - Books, The World Bank Group, number 32362, December.
    6. Yang, Chenyao & Fraga, Helder & Ieperen, Wim Van & Santos, João Andrade, 2017. "Assessment of irrigated maize yield response to climate change scenarios in Portugal," Agricultural Water Management, Elsevier, vol. 184(C), pages 178-190.
    7. Zunfu Lv & Yan Zhu & Xiaojun Liu & Hongbao Ye & Yongchao Tian & Feifei Li, 2018. "Climate change impacts on regional rice production in China," Climatic Change, Springer, vol. 147(3), pages 523-537, April.
    8. Henderson, Benjamin & Cacho, Oscar & Thornton, Philip & van Wijk, Mark & Herrero, Mario, 2018. "The economic potential of residue management and fertilizer use to address climate change impacts on mixed smallholder farmers in Burkina Faso," Agricultural Systems, Elsevier, vol. 167(C), pages 195-205.
    9. Dunnett, A. & Shirsath, P.B. & Aggarwal, P.K. & Thornton, P. & Joshi, P.K. & Pal, B.D. & Khatri-Chhetri, A. & Ghosh, J., 2018. "Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions," Ecological Modelling, Elsevier, vol. 381(C), pages 23-35.
    10. Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    11. Shirsath, Paresh B. & Aggarwal, P.K. & Thornton, P.K. & Dunnett, A., 2017. "Prioritizing climate-smart agricultural land use options at a regional scale," Agricultural Systems, Elsevier, vol. 151(C), pages 174-183.
    12. Amit Kumar Basukala & Livia Rasche, 2022. "Model-Based Yield Gap Assessment in Nepal’s Diverse Agricultural Landscape," Land, MDPI, vol. 11(8), pages 1-25, August.

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    More about this item

    Keywords

    WISE 3.1; Soil profile; DSSAT; Cluster; MarkSimGCM; CMIP5;
    All these keywords.

    JEL classification:

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