Rice Yield Estimation for Individual Rice Plots: What We Can Do and Observe Under Lockdowns and In-between


Rice Yield Estimation for Individual Rice Plots: What We Can Do and Observe Under Lockdowns and In-between

13 October 2021

The Rural Development and Food Security (Agriculture) Thematic Group, Sustainable Development and Climate Change Department, ADB; Cambodia Resident Mission, ADB; and the government of Cambodia shared the data collection methodology, results and their potential use, and other insights from the crop survey carried out in 2020 to support the Rice Sector Development Program (SDP) project in Cambodia. The study covered close to 400 households, aimed at mapping rice plots and crop yields for three seasons (early wet, wet, and dry seasons) using geotagging, earth observation and machine learning. The results are presented in color graduation in the rice plot map at https://adb-crop-yield.web.app. The crop yield map and gender disaggregation data can be further used in focus group discussions with project beneficiaries to validate crop yield gaps and gender inequalities.

The study confirms the potential use of remote sensing in data collection for future planning amidst lockdowns and movement restrictions due to the COVID-19 pandemic.

Program and Learning Materials: 
Date Session / Activity Presentation Material Speaker(s)
13 Oct 2021 Opening Introduction Michiko Katagami, Principal Natural Resources and Agriculture Economist, SDCC, ADB
13 Oct 2021 Opening Welcome Remarks HE Seilava, Project Director, Rice SDP, Ministry of Economy and Finance
13 Oct 2021 Presentations Crop Survey and Geo Tagging
This presentation gives an overview on rural development using new technologies in agriculture.
Biranchi Kumar Choudhury, Edward Maningo
13 Oct 2021 Presentations Remote Mapping of Rice Paddy Boundaries in Cambodia
This presentation gives an overview on the usage of remote mapping for agricultural development.
William Wu
13 Oct 2021 Presentations Rice Yield Estimation with Satellite Imagery and Machine Learning
This presentation gives an overview on the use of machine learning model and satellite imagery for development in agriculture.
Ren Avell Flores
13 Oct 2021 Comments and Result Discussions Speakers/Panelists Chanthou Hem, Senior Project Officer CARM, ADB Representative, MAFF/Rice SDP; Leonard Heung, Natural Resources Economist, SEER, ADB; Navin Twarakavi, Senior Digital Agriculture Specialist, SDCC, ADB
13 Oct 2021 Best Practices Sharing Integration of Remote Sensing and Crop Model for Yield Estimation
This presentation gives an overview on the integration of remote sensing and crop model to IRRI's rice yield estimation projects for agricultural advancement...
Emma Quicho


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