Technical ReportPDF Available

Agricultural Master Sampling Frames in Practice Lessons learned from international field experiments and case studies Agricultural Master Sampling Frames in Practice Lessons learned from international field experiments and case studies

Authors:

Abstract and Figures

Following the 2015 publication of the Handbook on Master Sampling Frames for Agricultural Statistics: Frame Development, Sample Design and Estimation (hereafter, the Handbook), the Global Strategy to improve Agricultural and Rural Statistics (GSARS) initiated a series of field test experiments aiming to answer technical questions raised by the national statistical institutes on the practical aspects of implementing the concept of the Master Sampling Frame (MSF) in their statistics production processes. Experiments were implemented in Nepal, Brazil and Rwanda, and the respective results were published in Delincé (2017), Ferraz (2018) and Lemoine (unpublished). GSARS-supported activities in Morocco, China, India, Côte d’Ivoire and Malawi also provided useful material, reported in this publication in the form of case studies. The building process and use of an agricultural MSF has been considered as a starting point to integrate agricultural and rural statistics into national statistical systems. This report is part of a series of publications that not only provides support in this respect but also adds to the goal of fostering the sustainability of countries’ statistical systems, and statistical capacity building. Readers interested in exploring MSF concepts and methods related to agricultural surveys are directed to the numerous publications available on the GSARS website (http://www.gsars.org)
Design effect by cluster information.................................................... 42 Figure 4.2. Example of PSU in Nepal's area frame................................................. 43 Figure 5.1. Efficiency analysis: dual-frame design................................................. 52 Figure 5.2. AGRO list frame coordinates of sampled units.................................... 54 Figure 6.1. Example of points within a segment in Goiana-PE.............................. 60 Figure 6.2. Stratification of the area frames for Goiana and Santos Dumont....... 63 Figure 6.3. Photointerpretation tool based on Google Earth and Collect Earth.... 63 Figure 6.4. Distribution of the sample in Kavre (left, hilly region) and Chitawan (right, plain region), overlaid onto Google imagery............................. 64 Figure 6.5. Relative efficiency of stratification at province level in Morocco as a function of the importance of the non-agricultural stratum............... 69 Figure 6.6. Boxplots of estimator performance by stratum, for 10 000 Monte Carlo replicates. Sample size: 120........................................................ 89 Figure 6.7. Boxplots of estimator performance, for 10 000 Monte Carlo replicates. Sample size: 120................................................................. 89 Figure 6.8. High-resolution coverage of Rwanda in the period running from 1 January 2017 to 2 February 2017......................................................... 95 Figure 6.9. Sentinel-2A of 11 August and Sentinel-1 stack, 10 m resolution, overlaid with one of the survey segments.......................................... 96 Figure 6.10. Image coverage of the Nyagatare region, overlaid with the survey segments (green polygons)............................................................... 97 Figure 6.11. Stratification of the Northern Nyagatare regions from unsupervised clustering of the Sentinel-2 data stack…………………….. 100
… 
Content may be subject to copyright.
A preview of the PDF is not available
Article
This paper intends to contribute to an up-to-date discussion of dual frame designs in agricultural surveys. It starts by reviewing historical scenarios of applications to envision new perspectives, and ends by presenting a modern approach to the problem. A dual frame sampling design is proposed that has the appeal of relying on low-cost technological resources. The design has enough generality to allow for applications not only on agricultural but also on rural and environmental surveys, or any other survey related to the use of soil. Unbiased estimations based on domain and multiplicity approaches are presented and their major differences are discussed. Design parameters, design feasibility by different sample size allocations, as well as the statistical performance of several dual frame estimators are investigated using a Monte Carlo simulation study that is built on information from the Brazilian agricultural census of 2006 and FAO’s Global Strategy’s field experiences in the city of Goiana, Pernambuco. The results show dual frames present a gain in precision when compared to a single area frame survey. In addition, the choice of the best design and estimator depends upon scenarios with different types of allocation and different sizes of area frame segments.
Article
Full-text available
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
Article
Full-text available
Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM < 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative's general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors-Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI-and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%-91% of the requirements for a view at least 70% clear, and 45%-68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring.
Chapter
Area frame sampling is an important tool for area change estimation in agricultural and environmental problems. The cost-efficiency of the unit size chosen for the survey can be assessed through the intracluster correlations of some ancillary variates on elementary units. Intracluster correlations can be written as weighted average of correlogram values on the same units. This approach is being applied in two large scale projects: crop area change estimation in the European Union, where the current sampling design may be improved with the information collected in previous years, and forest assessmen by photo-interpretation of a sample of satellite images in the area including Europe and the former USSR. This operation should be carried out for the first time in 1999–2000.
Article
The available multiple frame estimation methods do not deal with the case of mixed frame level information where units from the same sample are allowed to have mixed information. That is, some units may have only basic (possibly due to privacy concerns or lack of memory on the part of the respondent) while others may have more than basic information, where basic is defined as having known selection probability for each unit from the sampled frame and the number of frames the unit could have been selected from but not knowing the frame identification except, of course, for the sampled frame. To address this new problem, we first propose a unified approach based on multiplicity-adjusted estimation which encompasses all the proposed estimators (classified in this article as either combined or separate) as well as new estimators obtained by combining simple and complex multiplicity estimators. We also propose hybrid multiplicity estimators to account for mixed information. The methods discussed here are limited to the combined frame approach only because of their ability to deal with the case of mixed information. Simulation results are presented to compare various methods in terms of relative bias and relative root mean squared error of point and variance estimators.
Article
In many surveys, reliable estimates are required both at the national level and for subnational areas. When the subnational areas vary considerably in population size or importance, problems can arise in the use of standard allocations. In this article, a simple allocation method is suggested for achieving reliable national and subnational estimates.
Article
The construction of sample designs and estimators under a linear regression superpopulation model is considered. The anticipated variance, the variance of the predictor computed with respect to the sampling design and the superpopulation model, is used as a criterion for evaluating probability designs and model-unbiased predictors. Regression predictors that are model unbiased and design consistent are constructed.