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Machine learning-based agricultural assessment helps reduce knowledge gaps for better-informed policy decisions in Nepal
From June to November 2020, 130 staff members from district offices and central departments within the Ministry of Agriculture and Livestock Development (MoALD), Nepal participated in a remote sensing- and information and communication technology-based field data collection and crop area estimation exercise spanning 22 districts.
Towards the end of the year, as the Ministry deliberated on the procurement and subsidy process, they turned to the intermediate results of the 2020 crop area estimates derived from the exercise to access the information they needed at the ‘palika’ level.
The assessment method the estimates were built on – approved for use at the national level by the Ministry this year – was co-developed by the MoALD Statistics Unit and our SERVIR-HKH initiative during a joint pilot in Chitwan district in 2019.
The MoALD leadership had revitalized the institution’s GIS Unit with new staff to institutionalize the approach, and we had extended our support in organizing trainings for staff at the local and central institutional levels. We are now working jointly with MoALD to extend this remote sensing-based approach to incorporate other major cereal crops in Nepal.
Incorporating remote sensing and machine learning algorithms to map in-season crops substantially improves the accuracy of crop area and makes yield assessments more efficient. And while relatively new, it is already recording and making relevant information available at the province level, helping reduce information gaps among Nepal’s federal and sub-national institutions, and further enabling planning processes at the ‘palika’ level within Nepal’s federal structure.
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