Skip to main content

Food insecurity, hunger, and widening inequality are some of the most pressing issues in the world today. Extreme weather and climate change will further exacerbate the challenges that the agriculture sector faces: undermining sustainable development and worsening poverty. Prioritizing agricultural development and sustainability through timely and evidence-based policies will aid in securing enough resources for the next generations and time to adapt to calamities.

Why are agricultural statistics important for ending hunger, food insecurity and malnutrition?

SDG 2 aims to end hunger, food insecurity, and malnutrition through strategic investments in agriculture. Indicator 2.4.1 under this goal is fully dedicated to related economic, social, and environmental issues, as it measures the proportion of agricultural area under productive and sustainable use. One way to measure this indicator is through high-quality and up-to-date farm surveys. Obtaining good data is paramount in identifying challenges, priorities, and progress toward this goal.

High-quality, reliable, and timely agricultural statistics enable governments to anticipate vulnerabilities and take action early. Furthermore, statistics can shed light on whether agriculture is able to meet the current and future needs of the world efficiently. Agricultural statistics are compiled mainly via traditional sources like administrative records, manual measurements, and field surveys. Although these are less costly compared to conducting agricultural surveys and censuses, they can be prone to measurement bias and errors. Due to this, more countries are using alternative technological approaches. These include the use of remote sensing technology and Geographic Information System (GIS) techniques.

Remote sensing technology allows for the observation of areas such as crop fields and water surfaces through aerial cameras, satellites, or image sensors. GIS techniques analyze geographical data and organize it to produce visualizations. Statistical agencies are using these technological advances in agricultural statistics to collect timely, more reliable, and more precise estimates relative to traditional methods.

Developing countries in Asia and the Pacific, however, continue to rely on traditional surveys or administrative data to generate crop statistics. This is due to a lack of detailed cadastral maps, advanced computing infrastructure, personnel trained in remote sensing applications, GIS tools, probability sampling techniques, and overall institutional support such as government funding to integrate these technologies into regular national statistical data collection systems. To address this, the Asian Development Bank (ADB) engages in initiatives to develop agricultural statistics in Asia and the Pacific, especially in areas where agriculture remains a major source of livelihood.

Collaborations, innovations, and advocacy

ADB supported the Global Strategy to Improve Agricultural and Rural Statistics developed by the World Bank, the Food and Agricultural Organization of the United Nations (UN), and the United Nations Statistical Council (UNSC) in 2010. It championed three key activities: the development of the Asia-Pacific Regional Action Plan to improve the agricultural and rural statistics in the region; conducting methodological studies; and training of implementing agencies’ staff on statistical concepts, techniques, and agricultural statistics production.

In 2013, ADB supported a massive capacity development project to promote the use of remote sensing technologies in monitoring and formulating food security policies in Asia and the Pacific. The project commenced with a study conducted in four pilot locations: Savannakhet (Lao PDR); Nueva Ecija (Philippines); Ang Thong (Thailand); and Thai Binh (Viet Nam). The project explored different approaches in estimating crop area and production data through modern technological approaches:

  • area frame sampling techniques to collect paddy rice area and crop cutting exercises to estimate production data;
  • Global Positioning System (GPS), high-resolution Google Earth images, and GIS techniques to stratify areas into rice-growing parcels;
  • use of a customized software application to estimate paddy rice statistics using radar satellite imagery;
  • fusion of satellite data to measure rice yield; and
  • remote sensing techniques to study land measurement bias

To introduce the implementing agencies to these approaches, ADB also developed and provided learning programs on remote sensing concepts, field validation techniques, crop cutting methodology and geospatial analysis using a combination of satellite and field data. ADB also arranged free access to satellite data through special agreements between the pilot countries and the Japan Aerospace Exploration Agency (JAXA).

Following the study, ADB spearheaded initiatives in collaboration with the national statistics offices (NSOs) and agriculture ministries of these countries. One included the launch of knowledge products, including a massive open online course (MOOC) on the use of remote sensing technology for estimating rice paddy area and production. Meant for NSOs and all interested users, this course ran for two iterations in 2017 and was accompanied by a handbook released the following year. Another was the pilot testing of methodologies using sampling techniques, remote-sensing technology, and GIS applications.

To further support the SDGs, ADB launched another capacity development project in 2018 to enhance the data-driven monitoring of the SDGs in developing member countries. With a focus on Pacific countries, current initiatives include a methodology to validate national parcel area estimates through the application of sampling techniques and employment of land measurement methods using GPS in the Cook Islands. Other ongoing activities include the development of materials for the launch of a MOOC, and a handbook on GIS intended to support capacity building among NSOs on census and survey mapping.

With more accurate and timely agricultural statistics and the help of NSOs, ADB and member governments can create long-term solutions to combat hunger and inequality.

Infographic describing the importance of agricultural statistics


Download the infographic "Why are agricultural statistics important?"



A. Dillon and L.N. Rao. 2018. Land Measurement Bias: Comparisons from Global Positioning System, Self-Reports, and Satellite Data. ADB Economics Working Paper Series. No. 540. Manila: Asian Development Bank.

K. Guan, et al. 2018. Measuring Rice Yield from Space: The Case of Thai Binh Province, Viet Nam. ADB Economics Working Paper Series. No. 541. Manila: Asian Development Bank.

ADB. 2018. Technological Innovation for Agricultural Statistics: Special Supplement to Key Indicators for Asia and the Pacific 2018. Manila: Asian Development Bank.

ADB. 2018. Completion Report: Innovative Data Collection Methods for Agricultural and Rural Statistics. Asian Development Bank.

A.C. Durante, et al. 2018. Improving Paddy Rice Statistics Using Area Sampling Frame Technique. ADB Economics Working Paper Series. No. 565. Manila: Asian Development Bank.

L.N. Rao. 2018. Results from Lao People’s Democratic Republic, Philippines, Thailand, Viet Nam [PowerPoint slides]. Economic Research and Regional Cooperation Department, Asian Development Bank.

A. Dillon, et al. 2017. Land Measurement Bias and its Empirical Implications: Evidence from a Validation Exercise. Economic Development and Cultural Change.

L. Rotairo, et al. 2019. Use of Remote Sensing to Estimate Paddy Area and Production: A Handbook. Manila: Asian Development Bank.

ADB. 2021. Japan Fund for Poverty Reduction: Sector Highlights.

ADB. 2015. Completion Report: Improving Agricultural and Rural Statistics for Food Security. Manila.

P. Lapitan and A.C. Durante. 2019. Harvesting the good data that Asia’s farmers need. Asian Development Blog.

L.N. Rao, J.D. Roque, A.C. Durante. 2018. Technological innovation is a game-changer for agricultural statistics. Asian Development Blog.

L.N. Rao. 2018. Land measurement bias revisited. Asian Development Blog.

L.N. Rao. 2016. The curious case of area measurement in surveys (I). Asian Development Blog.


Cover photo credit: Nesly Mateo@Shutterstock


Copyright © 2021 PARIS21 - All Rights Reserved