Technological innovation for agricultural statistics : key indicators for Asia and the Pacific 2018 special supplement.

Format:

Book

Author/Creator:

Asian Development Bank

Language:

English

Subjects (All):

Agriculture--Asia--Statistics.

Agriculture.

Physical Description:

1 online resource (62 pages)

Edition:

1st ed.

Place of Publication:

Metro Manila, Philippines : Asian Development Bank, [2018]

Summary:

This special supplement to the Key Indicators for Asia and the Pacific 2018 showcases the role that technology can play in improving the quality, timeliness, and frequency of agricultural statistics. The first part presents a summary of existing methods for collecting land area, production, and yield data in the region. The second part discusses measurement errors associated with the existing data collection methods. The third part presents ways to address these measurement errors using remote sensing technology by showcasing results from three methodological research activities undertaken by the Asian Development Bank in three countries: the Lao People's Democratic Republic, Thailand, and Viet Nam. This report concludes with a summary of how other innovations, such as drones, computer-assisted personal interviewing, and artificial intelligence hold promise in transforming the field of agricultural statistics.

Contents:

Cover

Title

Copyright

Contents

Foreword

Abbreviations

Highlights

Introduction

Existing Methods for Collecting Agricultural and Rural Statistics in Asia and the Pacific

Administrative reporting systems

Sample surveys

Census of agriculture

Data Collection Activities in Project Areas

Measurement Error in Land Area, Yield, and Production Estimates

Land area

Production

Yield

Technology for Agricultural Statistics: A Potential Game-Changer

Remote sensing for land area measurement

Estimating rice yields from space

Remote sensing for developing a sampling frame

Other Technological Innovations for Agricultural and Rural Statistics

Drones

Computer-assisted personal interviewing

Artificial intelligence

Conclusion

References

Figures

Figure 1: Land Area Measurement Bias, Self-Reported versus Global Positioning System (ha)

Figure 2: Distribution of Differences in Plot Area

Figure 3: Production Measurement Bias, Self-Reported versus Objectively Measured

Figure 4: Distribution for Differences in Yield

Figure 5: Mapping Plot Boundaries Using Google Earth Images

Figure 6: Distribution for Differences in Area

Figure 7: Normalized Difference Vegetation Index Time Series

Figure 8: Classified Land Cover Map for Thai Binh Province, Viet Nam

Figure 9: Growth Cycle of Paddy Rice: A Conceptual Framework to Model Crop Yield

Figure 10: Plotting Time Series of Normalized Difference Vegetation Index Values

Figure 11: Linear Regression Model between the Peak of Vegetation Indices and Crop Yield

Figure 12: Spatially Explicit Yield Map Based on Normalized Difference Vegetation Index

Figure 13: Sample Mesh

Figure 14: Crop-Cutting on a Subplot.

Figure 15: Area Estimate Comparison from Remote Sensing and Official Statistics for Savannakhet, Lao People's Democratic Republic

Figure 16: Area Estimate Comparison from Remote Sensing and Official Statistics for Ang Thong, Thailand

Figure 17: Area Estimate Comparison from Remote Sensing and Official Statistics for Thai Binh, Viet Nam

Figure 18: Drone Image of Small-Scale Farms in Ghana

Figure 19: Drone Image of Small-Scale Farms in Uganda Indicating Crop Health

Figure 20: Computer-Assisted Personal Interviewing as Part of Project Activities

Figure 21: Artificial Intelligence for Compiling Potential and Actual Crop Yield

Boxes

Box 1: The Century Old Mystery of the Relationship Between Plot Size and Productivity for Small-Scale Farmers

Box 2: Land Area Measurement Using Survey Solutions

Box 3: Explaining Spatial and Temporal Resolution from a Remote Sensing Perspective

Table

Table 1: Distribution of Meshes in the Sampling Frame.

Notes:

Description based on print version record.

ISBN:

9789292613136

9292613138