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Operational in-season rice area estimation through Earth observation data in Nepal - working paper

  • Faisal M. Qamer
  • Sravan Shrestha
  • Kiran Shakya
  • Birendra Bajracharya
  • Shib Nandan Shah
  • Ram Krishna Regmi
  • Salik Paudel
  • Pragya Shrestha
  • Santosh Paudel
  • Padma Pokhrel
  • Liping Di
  • Zhiqi Yu
  • Sreten Cvetojevic
  • Liying Guo
  • Timothy J. Mayer
  • Meryl Kruskopf
  • Aparna Phalke
  • Summary

In an effort to adopt emerging technologies in food security assessment through a codevelopment approach, the Government of Nepal’s Ministry of Agriculture and Livestock Development (MoALD) and the International Centre for Integrated Mountain Development’s (ICIMOD) SERVIR-HKH Initiative undertook a pilot study in Chitwan District in 2019 to jointly develop methods for satellite remote sensing and machine learning-based in-season crop assessment. MoALD experts and relevant stakeholders thoroughly reviewed the approach before the honourable minister approved it for formal use in the national-level assessment for 2020 and onwards.

For wider adoption of the advanced data science methods established in the pilot study, we customised the technology by developing a digital suite of software, including GeoFairy (a mobile app to facilitate field data collection by field extension professionals at the district level) and RiceMapEngine (a simplified platform for machine learning-based crop classification to facilitate crop area map production by MoALD’s GIS Section).

In the current federal governance structure of Nepal, high-quality crop maps and yield estimates will not only bridge information needs among the federal and subnational institutions but also provide a means for consistent cross-country crop status assessments and communication.

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  • Publisher Name:
    International Centre for Integrated Mountain Development (ICIMOD)
  • Publisher Place:
    Kathmandu, Nepal