Harvesting the good data that Asia’s farmers need

Published on Friday, 31 May 2019

Published by Pamela Lapitan and Anna Christine D. Durante on Friday, 31 May 2019

Farmers need objective, apolitical agriculture data to make the most of their harvest. Photo: ADB
Farmers need objective, apolitical agriculture data to make the most of their harvest. Photo: ADB

Farmers and governments need reliable agricultural data to prepare for extreme weather and climate change. It is also vital for helping to maximize the development potential of the agriculture sector. Without good data, farmers and planners are working in the dark.

One of the most effective ways of gathering this important data is the combination of two powerful methods: area frame sampling – in which land is segregated into defined areas for analysis – and remote sensing, often involving GPS and satellites systems to gather data on the areas. This form of data gathering is considered highly effective due to its versatility, completeness of coverage, and accuracy.

In a recent study, areas were stratified into rice-growing parcels using satellite data. Estimates of paddy rice areas were calculated using GPS devices, high resolution Google Earth images, and Geographic Information System techniques. The results suggest that the area frame sampling methodology significantly improves the level of precision of paddy rice statistics — an advantage over existing administrative data collection in most countries for which precise measures are not publicly available and where statistics reported may be politically motivated, to some extent.

The advances in remote sensing technology holds promise for the development of a complete and updated area frame sampling procedure. While remote sensing served as a prime contributor to the localization and geocoding of sampling units, these advantages are yet to be fully utilized for statistical purposes and ultimately, for policymaking.

  Capturing the changing face of agriculture in Asia’s developing countries

In 2021, the Philippines and Viet Nam will implement their census of agriculture. Done every five or ten years, the census is the largest data collection effort in the agriculture sector in most developing countries. It is an effort to capture the changing face of agriculture in these countries. But they will not be using the internationally accepted area frame sampling method.

So, what’s keeping the Philippines and Viet Nam from using this method? One reason is the lack of technical capacity in government statistical service. The development of the area frame sampling method is a major task that requires investment of technical capabilities and other resources.

Substantial work is still needed to strengthen the statistical systems and improve the methodologies needed for the use of area frame sampling and remote sensing. This is being addressed in part by the Global Strategy to Improve Agricultural and Rural Statistics, which was established in order to respond to the emerging data requirements for decision making in the 21st century. This is also the same call that prompted the ADB to support programs that aim to strengthen the countries’ capacities to use innovative approaches for data collection.

The utility of area frames for improving crop statistics is one of the methodologies explored by ADB to prepare countries to develop a foundation for data collection based on sample surveys and censuses.

The first phase of the Global Strategy has concluded with the hope that the main successes, lessons learned, and good practices achieved shall contribute to the creation of new programs for the government statistics agencies. As we pave the way for the second phase of the Global Strategy, the development community, including ADB, remains committed to the needs of modern agriculture while responding to emerging data requirements linked to challenges of climate change, food security and supply chains, price volatility, and production structures.

In developing countries across Asia, where data gathering is becoming increasingly important, this support is particularly significant.