Agricultural land is a key asset for farmers—the base of their economic livelihood. From a policy perspective, agricultural land size is a key statistic for understanding farming structures within a country. From a macroeconomic standpoint, agricultural land area influences the calculation of potential economic output.
Accurate measurements of agricultural land are essential for assessing farm productivity, planning agricultural growth, and formulating environmental, rural, urban, and social plans and policies. For example, overestimating or underestimating agricultural land area—and, consequently, production— can impact a country's ability to meet its caloric and nutrient needs sustainably. In regions where food security is a challenge, improved agricultural area statistics enable governments to better plan imports of key commodities to meet these needs.
Traditional agricultural land estimates rely on farmers’ recall, but this method often lacks accuracy. Global Positioning System (GPS) and satellite data provide objective alternatives. A study published by the Asian Development Bank (ADB) provides guidance when considering the most suitable method for GPS land measurement.
This article is adapted from that study.
Measurements based on farmers' subjective recall assume their understanding of the term "operational agricultural land," a concept that can introduce non-sampling biases if unclear. These challenges are compounded by variations in agricultural practices across the region. In some countries, communal areas are commonly shared for agricultural production, and land tenure is often undefined. This method also assumes farmers have precise knowledge of their land's size, typically based on formal land titles or deeds that provide exact measurements. Without such documents, farmers' estimates are often speculative.
Additionally, unfamiliarity with standardized units of measurement increases the response burden, leading to inaccurate conversions or speculative answers. Physical characteristics of the land further complicate accurate estimation. In many Asian and Pacific countries, particularly in smallholder mixed cropping systems, agricultural parcels often have irregular shapes and may lie on steep, mountainous terrain, affecting farmers' perception of their size.
One approach to mitigating this is by adapting objective area measurement. Traditionally, this involves land surveying to delineate agricultural parcels using the tape-and-compass method. This technique entails measuring each side of the parcel with a tape measure and using a compass to determine the angles between sides. However, it is time-consuming, resource-intensive, and requires highly skilled workers to perform precise measurements and calculate the area using complex trigonometric functions. When done correctly, however, the tape-and-compass method is considered the “gold standard” for agricultural area measurement (Carletto et al., 2016).
The growing accessibility of geospatial technologies is reshaping how agricultural statistics are gathered, processed, and disseminated. Advanced technologies like remote sensing using satellite imagery, GPS, and unmanned aerial vehicles (UAVs) offer the potential for more efficient methods to monitor changes in agriculture with greater precision and frequency.
When considering the most suitable method for GPS land measurement, several critical factors—such as the size, shape, and terrain of the parcel—must be considered, along with available resources.
Walking method: The common method involves an enumerator, usually guided by the farmer, physically walking the perimeter of a parcel while carrying a GPS device, which automatically tracks and calculates the area. This approach reduces the need for multiple pieces of surveying equipment and extensive training for field staff. Furthermore, the time required for measurement is limited to the duration of walking the parcel’s perimeter, significantly streamlining the overall process. It is recommended when the highest positional accuracy and measurement precision are required.
Moreover, GPS measurement methods integrated into tablets can be advantageous in certain cases, particularly due to their convenience and potential integration with other data collection tools.
The walking method, whether using a dedicated handheld GPS device or an on-tablet GPS sensor, is particularly effective for smaller parcels with complex shapes and easily navigable terrain. It allows for precise boundary capture but can be time-consuming for larger parcels, potentially taking up to one hour for areas exceeding 10,000 m².
Digitization method: Conversely, the digitization method is more suitable for large, monocropped areas. This method involves the farmer tracing the boundary of their parcel directly over a satellite image, negating the need for the farmer and enumerator to walk the boundary physically. Key to the success of this approach is the ability of the farmer to accurately recognize their land from an aerial perspective and the assumption that the satellite imagery is up-to-date and reflects the current agricultural season.
Parcel corner GPS: The parcel corner GPS method involves an enumerator identifying and marking only the corners of the parcel using the Survey Solutions geometry multi-point question type to speed up the data input process. The goal is to capture the essential boundaries of the parcel more easily. The key challenge in using this method is the difficulty in accurately identifying corner points, particularly in irregularly shaped parcels. Significant inaccuracies in area measurement may also occur if enumerators are not properly trained and well-versed in using the field instruments.
Scaling GPS area measurement methodologies nationally requires addressing several factors: building geospatial capacity in national statistics offices and agricultural line ministries; sufficiently training field staff in GPS technology usage; and managing the logistical challenges of data transfer between the tablet, where information is recorded, and the GPS device, where area boundary files are stored. It is crucial to ensure that data are effectively transferred, reviewed, and consolidated at all levels.
After data collection, developing a knowledge management plan is essential for utilizing this information in agricultural statistics. This typically involves deriving adjustment factors—ratios of GPS-measured areas to farmer-reported estimates—to account for biases in farmer-reported data. These factors can be tailored by parcel size, region, or agricultural practice to accurately reflect variations across a country. These adjustments, in turn, could help policymakers improve resource allocation through extension services to farmers and better plans to meet food security requirements.
- Standardize objective measurement practices and integrate them into agricultural surveys.
- Depending on the reliability of farmer-reported data, national statistical offices or line ministries involved in agricultural statistics should conduct objective measurement surveys to compile a set of conversion or adjustment factors addressing biases in farmer-reported data. These adjustment factors—calculated as the ratio of GPS-measured area to farmer-reported area—can be provided in aggregate or disaggregated by parcel size or geographic area. They can be applied in relevant analyses and help inform policies for improved planning.
- Select the most appropriate measurement method—walking, digitization, or parcel corner GPS—based on specific local characteristics such as parcel size, shape, and terrain. Tailored approaches will ensure more accurate and reliable data.
- Develop detailed, clear guidelines for each measurement method, addressing diverse terrains and parcel sizes while providing strategies for challenges like low internet connectivity in remote areas.
- Invest in technology and human resources, including high-quality GPS devices and updated satellite imagery, coupled with comprehensive training for enumerators and farmers to ensure accurate data collection.
- Encourage ongoing research and development in land measurement methods, exploring new technologies and advanced imaging techniques to improve accuracy and efficiency.
Asian Development Bank. 2024. Plotting from Above: Enhancing Agricultural Mapping in Asia and the Pacific.
Joseph leads data development and statistical capacity-building initiatives in the System of National Accounts (SNA), global value chains, and statistical business registers. He started his career at Statistics Canada in 1999 has specialized in SNA and input-output economics. Joseph has considerable experience producing critical data and analysis for evidence-based policymaking.
Pamela has a degree in Statistics from the University of the Philippines Los Baños. Prior to joining ADB, she was officer-in-charge and statistical coordination officer of Regional Division IV of the National Statistical Coordination Board. She was also an instructor at the Rural High School and the Institute of Statistics of UPLB and worked for Bayer Philippines. Her interests include statistical analysis, planning and conducting surveys and experimental designs, geographic information systems, poverty statistics, national accounts, and small area estimation.
Anthony is an agricultural statistics consultant with the ADB, offering more than16 years of expertise in the design and implementation of agricultural censuses and surveys across the Asia-Pacific region. His work leverages cost-effective technologies like digital data collection using tablets and drones, geographic information systems, and remote sensing. Anthony holds degrees in Economics from the University of California, Berkeley, and Chulalongkorn University.
Anna Christine has been involved in agricultural statistics projects for nearly 13 years, specializing in innovative methodologies to support policymaking in the agriculture sector. She holds a degree in Agricultural Engineering from the University of the Philippines, and a master’s degree in Environment and Natural Resources Management from the same institution. Prior to joining ADB, she worked on agricultural policy analysis and development assistance projects at the Philippine Department of Agriculture.
Arturo is a survey statistician and former professor of Statistics and director at the Institute of Statistics, University of the Philippines Los Baños, with extensive experience in the design and analysis of sample surveys over the last 20 years. He has worked on the design and analysis of sample surveys across various subject areas, such as labor and employment, food and nutrition, income and expenditure, agricultural surveys, price surveys, ethnic minorities, and violence against women.
Mashal holds a MS in Remote Sensing and GIS from the National University of Sciences and Technology, Islamabad, and a B.Sc in Mining Engineering from the University of Engineering and Technology, Peshawar. Her areas of interest include remote sensing, GIS, disaster risk reduction, multi-hazard vulnerability and risk assessment, and agricultural statistics.
