Regional Workshop

Strategic Planning for

Agricultural and Rural Statistics

Bangkok 17-19 March 2015

Recent developments in the research component

Naman KEITA, Michael RAHIJA , Piero FALORSI, Carola FABI

Angela PIERSANTE Christophe DUHAMEL Dalip SINGH

Recent Developments in Research

Component

GS pillar 2 recommends building and using Master Sampling Frames (MSF) as a key

technical tool for integration of agriculture in national statistical systems.

The Research programme of the Action Plan to Implement the Global Strategy includes

several research activities on methods for developing and using the MSF for Agricultural

Surveys:

– Technical Report on Identifying the most appropriate sampling frame for

specific landscape types (published)

– Technical Report on Integrated Survey Framework (published)

– Improving methods for linking area frames with list frames (Technical

paper to be published by April 2015)

– Improving the use of GPS, GIS, RS for setting up a master sampling frame

(published in Jan 2015)

=>These reports are inputs to a Handbook on building and using Master

Sampling Frames for Agricultural Surveys: Frame development, sample

design and estimation (to be published by April 2015)

Topics presented

• Integrated Survey Framework,

• Identifying the most appropriate sampling frame for

specific landscape types,

• Cost of Production

• AGRIS

Integrated Survey Framework

The GS recommends three methods to support integration of agriculture into NSS (pillar 2):

o Integrated survey framework o Master sampling frame o Integrated database

Integrated statistical systems can help in addressing recurring problems in many countries of duplications, conflicting statistics, and ensure best use of resources.

Integrated Survey Framework CONCEPT OF DATA INTEGRATION

Data Integration is defined as “the process of combining data from two or more sources to produce statistical outputs” (Definition of UN Glossary) Data integration is a multidisciplinary issue involving Statistics, Management and Governance Integration can happen at any stage of statistical processes 1. Before data collection 2. During field operation 3. During data dissemination The scope of this Report is limited to Statistical methods of integration Purpose of integration: 1. Estimating one or more phenomena based on multiple sources 2. Estimating interpretive models of relation between phenomena 3. Frame Construction

Integrated Survey Framework SCOPE

Types of Data Integration: 1. Exact Matching 2. Statistical Matching

In many countries, there is a multitude of “ad hoc” surveys, pilot or evaluation studies etc with different objectives, not always coordinated and compatible The three different target populations to be interlinked are: o Farms (for the economic dimension). o Households (for the social aspect); o Agricultural plots (for environmental dimension);

o

Integrated Survey Framework 3 Cases of data integration

o Case A: Integration through the introduction of a multi-purpose survey. o Joint collection of a number of phenomena on same ( related units) o + reduced sample size - cyclical/seasonal factors may not be captured o More suited to countries with inadequate survey resources o Example: LSMS, Ag Census conducted in conjunction with PHC

o Case B: Ex post integration of data from different sources:

o Case B1-Through Record Linkage if enumeration units are same/same type-. o Case B2: Linking different enumeration units

o Case B2.1: aggregating at domain level (EA, districts) o Case B 2.2: Linking based on logical relationships

o one to one, one to many, many to many

o Case C: Planned data integration: o each survey may be conducted separately but with a common goal /database

Integrated Survey Framework

Technical report and guidelines

The statistical methods proposed are:

o Record linkage (how to exploit a record linkage procedure with data referred to different statistical units)

o Sampling (how to carry out a sample for a joint observation on farms and households, considering a multiple frame context)

o Estimation (how to carry out coherent estimates when different surveys produce conflicting estimates of the same phenomena).

• For more details and use, Technical Report is avallabile on the GS website www.gsars.org • Guide Lines to be finalised by April 2015

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

CONTENT OF THE TECHNICAL REPORT

1. Purpose and scope

2. Master frame of agriculture

3. Master frame of household surveys

4. Appropriate master frames: country examples (frame based on an agricultural

census, frame based on land cover maps or satellite images)

5. Area sampling and data collection

6. Modeling survey costs

7. Modeling survey errors: relative efficiency of some area sampling strategies

(Simple random sampling, Replicated samples by zones, Systematic replicated

samples, Point sampling, Two-stage sampling)

8. Choosing an area sampling strategy and optimizing the sample design

9. Multipurpose sample design

10. Concluding remarks

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

Points made / Conclusions of the Report

1. Review of Literature: – King (1945), Bureau of Ag Economics, USDA

– A good number of research papers have been listed for interested reader

2. Two streams of Work

– Master frame of household surveys UNSD (1986, 2005, 2008, 2009)

• Development of high quality MSF is expensive and could not be justified if it

is used only once

– Master frame of agriculture (FAO 1996, 1998)

• Area Survey Frame Approach

3. Recommended Master Frame : Most cost efficient

– Use of Dual Frames and selection of replicated samples

– country examples (Chile, Uruguay, Fiji - CA as basis for MSF)

– Guatemala, Nicaragua- Land use maps/satellite imagery as basis

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

Construction of Mater Sample Frame

1. LIST FRAMES

• a recent agricultural census with a good coverage is available

• a recent agricultural census with low coverage is available

• a non-recent agricultural census is available

• a recent population census with a few questions on agriculture is available

• based on administrative registers

2. AREA FRAMES

• segments with physical boundaries

• segments with geometrical boundaries

• points

3. MULTIPLE FRAMES

– Dual frame composed of Master Frame of Agriculture (A) and Master Frame of

Households (B) as the Most Appropriate MSF for Agriculture and household

surveys

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

4. Approach to identify an appropriate Sampling Strategy

– One of the parameters in cluster sampling variance formula is the correlation

between observations within eash cluster.

– Existence of positive correlation among near observations (area sampling)

– There is no uniform optimum sampling scheme for a general class of

correlograms

– Model asssumptions are made related to the correlation structure and expected

variance is minimized

– Sampling strategy that achieves minimum sampling error among the set of

alternative strategies for fixed cost

– For Multi-purpose sample, weighted average of sampling errors is minimized

5. Choosing an area sampling strategy and optimizing the sample design

– Modeling survey costs

– Modeling survey errors: – Evaluating relative efficiency of some area sampling strategies (Simple random sampling, Replicated

samples by zones, Systematic replicated samples, Point sampling, Two-stage sampling)

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

CRITERIA FOR SELECTING A SAMPLING STRATEGY

1. APPLICABILITY:

• If fixed cost is higher than the budget, the strategy is not feasible

2. RELATIVE EFFICIENCY

• It is assumed that the sampling variance is more important than the bias

• The efficiency of each feasible strategy is identified by minimizing its expected sampling

variance subject to budget constraints

• The relatively most efficient strategy is identified by comparing these computed efficiencies

3. MULTIPURPOSE SURVEYS

• Relative values of importance (weights) are assigned to all purposes

• A lost function is defined as a weighted sum of the single purpose sampling variances and

minimized to give compromise solution

• The relatively most efficient strategy is identified by comparing these compromise

efficiencies

Identifying the Most Appropriate Sampling

Frame for Specific Landscape Types

Conclusions

• Area sampling frames are recommended as they protect against non-coverage bias.

• Cost of area frame is generally fixed and depends on type of segment boundaries

• Efficiency of sampling design depends on correlation structure of the survey variable

• There is no uniform optimal strategy for a general class of correlation structure

• The report identifies a cost –efficient sampling strategy by comparing a set alternative

strategies which has the minimum sampling error

• The report identifies assessment of correlation structure as one of the difficulties in

identifying a suitable strategy and suggests development of abacusses.

• Another difficulty relates to optimizing multi-purpose samples where an importance

value needs to be assigned to each individual purpose so that mean of the sampling

variances can be minimized.

• Methodology relates to the case where list frame is 100% sampled and only area

sample needs to be designed. Future developments will treat the general case .

Cost of Production

Objectives

Provide developing countries with concrete and

applicable recommendations for improving or

undertaking Cost of Production (CoP) data collection and

compilation

Provide methodological guidance on the analytical uses

of CoP data: derived economic indicators and economy-

environment indicators

Cost of Production

• Global survey of cost of production statistics (2012)

– Helped identify how many countries had experience in CoP

– The methods used, the uses and users of the data

• Literature review

– Published on the GS website

– Prepared by the EU-JRC, revised by FAO/ESS

• Handbook

– First draft published on the GS website (received and integrated

comments from our group of experts throughout 2013-2014)

– Published in English and Spanish

Cost of production

Field Tests

Several meetings and field visits to Colombia

– Agreement with coffee producers to document their approach and

evaluate it against the Handbook (field test)

– A round-table on pilot tests in Colombia (national initiative)

Workshop in Tunisia

– Determination of criteria for product selection and identification of

priority commodities (cereals, milk and meat)

– Workshop to design questionnaires and tested in November

Philippines

– Draft on the Cost and Returns program and its comparison against the

Handbook has been prepared

Cost of production

Handbook (contents) – Due in May 2015

• Purpose

• Uses and benefits of cost of production statistics

• Outputs, indicators and analytical framework

• Considerations on the data collection approach

• Guideline for data collection and estimation

• Data dissemination, reporting and international comparisons

• Conclusion and key Challenges

AGRicultural Integrated

Survey - AGRIS

AGRIS is a farm-based modular multi-year survey program

• designed for countries that do not collect regular data as a cost-

effective way to accelerate the production of (disaggregated) data

• to complement the Agricultural Census as part of an integrated

Census Programme

• covering the technical, economic, environmental and social

dimensions of agricultural holdings (MSCD)

Develop an AGRIS Toolkit to provide resources in terms of

• methodology

• survey tools and instruments using available knowledge and

technology to cover all survey steps,

• budget and institutional framework guidelines

AGRIS data collection

strategy

• Collection of questions classifiable in two possible categories: a core

section and a rotating section

• Annual core module and several rotating thematic modules

• Modules frequency will depend on country capacity and priorities

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Agricultural Census o

AGRIS Core Module

AH Roster • • • • • • • • •

Crop production • • • • • • • • •

Livestock production • • • • • • • • •

AGRIS Rot. Module 1 Economy • • • • •

AGRIS Rot. Module 2 Labour force • • • • •

AGRIS Rot. Module 3 Machinery and equipment • •

AGRIS Rot. Module 4 Production methods and

environment • •

AGRIS – sampling and

dissemination

Data Collection Mode

– Face-to-face interviews, log-books (multiple seasons, CoP, livestock), using

CAPI?

Sampling strategy

– Stratified multistage random sample for household farms, based on a list frame

or on an area frame (points or segments)

– Stratified simple random sample for farms of the non-household sector

– Panel sampling to enable longitudinal analyses

– Subsampling for rotating modules

Data access

– Consistent with national dissemination policies in place

– Through a DDI-compliant AGRIS Central catalogue developed by FAO (based

on IHSN practice and tool)

AGRIS - Toolkit

AGRIS planning and design [ GSBPM 1.x, 2.x, 3.x ]

– Planning AGRIS

– Questionnaires

– Sampling

AGRIS data collection [ GSBPM 4.x ]

AGRIS data processing [ GSBPM 5.x ]

AGRIS data analysis [ GSBPM 6.x ]

– Generic tabulation plan

– Sampling errors calculations

– Analyses for the core module: guidelines

– Analyses for the thematic modules: guidelines

AGRIS data dissemination [ GSBPM 7.x ]

AGRIS data documentation and archiving [ GSBPM cross-cutting]

Thank you