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IMPROVING THE METHODOLOGY FOR
USING ADMINISTRATIVE DATA IN AN
AGRICULTURAL STATISTICS SYSTEM
Critical Analysis of Agricultural
Administrative Sources Being
Currently Used By Developing
Countries
June 2015
Working Paper No. 6
Global Strategy Working Papers
Global Strategy Working Papers present intermediary research outputs (e.g.
literature reviews, gap analyses etc.) that contribute to the development of
Technical Reports.
Technical Reports may contain high-level technical content and consolidate
intermediary research products. They are reviewed by the Scientific Advisory
Committee (SAC) and by peers prior to publication.
As the review process of Technical Reports may take several months, Working
Papers are intended to share research results that are in high demand and should
be made available at an earlier date and stage. They are reviewed by the Global
Office and may undergo additional peer review before or during dedicated
expert meetings.
The opinions expressed and the arguments employed herein do not necessarily
reflect the official views of Global Strategy, but represent the author’s view at
this intermediate stage. The publication of this document has been authorized
by the Global Office. Comments are welcome and may be sent to
Improving the Methodology for Using Administrative Data in an Agricultural Statistics System
Technical Report 3
Critical Analysis of
Agricultural Administrative Sources Being Currently
Used By Developing Countries
Submitted to
the Food and Agriculture Organization of the United Nations
(FAO)
Under
the Global Strategy to improve Agriculture and Rural Statistics
By
the School of Statistics and Planning (SSP)
the College of Business and Management Sciences (CoBAMs)
Makerere University
Uganda
and
the Centre for Survey Statistics and Methodology (CSSM)
Iowa State University (ISU)
the United States of America
i
Table of Contents Acronyms and Abbreviations ii
List of Tables v
1. Introduction………………………………………………………………………. 1
1.1. Background to Task 3…………………………………………………… 1
1.2. Overview of Objectives and Approaches……………………………….. 1
1.3. Reports Produced So Far………………………………………………… 2
1.4. Structure of the Report…………………………………………………… 2
2. Analysis of Results of Country Assessments Reports……………………………………… 4
2.1. Institutional Capacity (Pre-requisites) …………………………………. 5
2.2. Resources Dimension (Input) …………………………………………... 5
2.3. Throughput………………………………………………………………. 6
2.4. Output…………………………………………………………………… 6
2.5. Country Assessment Conclusions……………………………………….. 7
2.6. Additional Country Assessments……………………………………....... 7
3. Structural Issues in Administrative Data Systems for Agricultural
Statistics………………………………………………………………………....... 31
3.1. Organizations Collecting and Managing Agricultural Administrative
Data……………………………………………………………………... 33
3.2. Institutional Home, Coordination and Geographical Coverage………… 41
3.3. Core Items and Core Data Items Covered………………………………. 44
3.4. Human Resource/Incentives to ADSAS staff…………………………... 48
4. Conduct Issues in the ADSAS…………………………………………………… 53
4.1. Uganda - Infra-structural Development…………………………………. 53
4.2. Data Collection Methods and Technologies Used……………………… 54
4.3. Sources of Funding and Sustainability Strategies………………………. 65
5. Performance Issues, or Outcomes, in the ADSAS……………………………… 66
5.1. Quality Control Procedures…………………………………………….... 67
5.2. Issues on Multiple Data Sources……………………………………….... 69
5.3. Uses in Forming the Statistical Product………………………………… 79
5.4. Uses by Non-Statisticians of the Final Statistical Product……………… 81
6. Strengths and Weaknesses (Challenges) and Recommendations…………........ 86
6.1. Analysis of the Results of Country Assessment Reports……………...... 86
6.2. Structural Issues in the ADSAS………………………………………… 88
6.3. Conduct Issues in the ADSAS………………………………………....... 101
6.4. Performance Issues……………………………………………………… 104
6.5. Challenges on Data Uses………………………………………………... 110
References……………………………………………………………………………. 112
Annex…...…………………………………………………………………………….. 116
A1: Country Reports………………………………………………………………... 116
A1.1 UGANDA…………………………………………………………… 116
A1.2 TANZANIA…………………………………………………………. 121
A1.3 MOZAMBIQUE…………………………………………………….. 122
A2: Quality Assessments……………………………………………………………. 126
A2.1 Quality Assessment on the ARDS for Tanzania……………………. 126
A2.2 Data Quality Assessment for some agencies in Uganda……………. 132
A3: The ADSAS Questionnaire…………………………………………………….. 134
ii
Acronyms and Abbreviations ABIOVE Associação Brasileira das Indústrias de Óleo Vegetal/ Brazilian
Association of Vegetable Oil Industries
ADSAS Administrative Data Systems for Agricultural Statistics
ADB Asian Development Bank
AfDB African Development Bank
AGMARK Agricultural Market Development Trust – Africa
APCAS Asia Pacific Commission on Agricultural Statistics
ARDS Agricultural Routine Data System
ARIS Animal Resources Information System
ASDP Agricultural Sector Development Plan
ASLMs Agricultural Sector Lead Ministries
AU-IBAR Inter-African Bureau for Animal Resources of the African Union
BoG Bank of Ghana
CAADP Comprehensive African Agriculture Development Program
CAPE Crop Acreage and Production Estimation
CDL Cropland Data Layer
CEPAGRI Center for the Promotion of Agriculture (previously GPSCA)
CIS Community Information System
COMESA Common Market for Eastern and Southern Africa
CPI Consumer Price Index
CSAs Census Supervisory Areas
CSO Central Statistics Office (generic term for INE)
CTA Technical Centre for Agiculture & Rural Cooperation
CWIQ Core Welfare Indicator Questionnaires (CWIQ)
DAO District Agricultural Officers
DFID Department for International Development
DoA Department of Agriculture
DoE Department of Economics
DRC Democratic Republic of Congo
GSS Democratic Republic of Congo
DVO District Veterinary Officer
EAFRO East Africa Fisheries Organization
ESCOM Electricity Supply Commission of Malawi
ESS European Social Survey
EU European Union
EWS Early Warning System (also known as Aviso Previo in Portuguese)
FAO Food and Agriculture Organization of the United Nations
FAS Food and Agricultural Statistics
FBS Food Balance Sheet
FEWS Famine Early Warning Systems
FRA Food Reserve Agency
iii
FSA Farm Services Agency
GCES General Crop Estimation Surveys - India
GDP Gross Domestic Product
GMM Generalised Method of Moment
GoI Government of India
GoM Government of Mozambique
GOM Government of Mali
GPS Global Positioning System
IACS Integrated Administrative and Control System
IBGE Brazilian Institute of Geography and Statistics
ICAS International Conference on Agricultural Statistics
ICBT International Conference on Agricultural Statistics
IFDC Informal Cross Border Trade
IFPRI International Fertiliser Development Centre
INE International Food Policy Research Institute
INFOCOM Information System of the Ministry of Commerce and Industry
ISTAT Italian Statistical Institute
JICA Japan International Cooperation Agency
LGA Local Government Authority
LGMD Local Government Authority
LIMS Livestock Information Management System
LLG Lower Local Governments
MAAIF Ministry of Agriculture Animal Industry and Fisheries
MDA Ministries Departments and Agencies
MDG Millennium Development Goals
MFPED Ministry of Finance Planning and Economic Development
MoA Ministry of Agriculture (generic term for MINAG, MADER)
MoW&E Ministry of Water and Environment
NAADS National Agricultural Advisory Services
NAGRIC National Animal Genetics Resources Centre
NAP National Agricultural Policy
NARO National Agricultural Research Organization
NDVI Normalized Difference Vegetation Index
NEDA National Economic and Development Authority
NEWU National Early Warning Unit
NGOs Non-Governmental Organizations
NHB National Horticultural Board
NPA National Planning Authority
NRI National Resources Inventory
NSI National Statistical Institutes
NSO National Statistics Office
NSS National Statistics System
iv
OIE World Organisation for Animal Health
PARP Action Plan for the Reduction of Poverty (Plano de Acção para
Redução da Pobreza)
PDA Personal Digital Assistants
PNSD Plan for National Statistical Development
PS Propensity Score
RAAD Routine Administrative Agricultural Data
RECs Regional Economic Communities
RELMA Regional Land Management Unit
RS Remote Sensing
SADC Southern Africa Development Community
SAP Système d’Alerte Précoce/Early Warning system
SAR Synthetic Aperture Radar
SCP Structure, Conduct and Performance
SEA Standard Enumeration Areas
SIDA Swedish International Development Cooperation Agency
SSPS Sector Strategic Plan for Statistics
TADs Trans-boundary Animal Diseases
TIA Trabalho de Inquérito Agrícola (Te Annual Agricultura
Statistics Surrey)
TUEKSTAT Turkish Statistical Institute
UBOS Uganda Bureau of Statistics
UNBS Uganda National Bureau of Standards
UNECE United Nations Economic Commission for Europe
UNFFE Uganda National Farmers Federation
URA Uganda Revenue Authority
USAID United States Agency for International Development
USDA United States Department of Agriculture
VAEO Village Agricultural Extension Officer – Tanzania
VAC Vulnerability Assessment Committee
VAT Value Added Tax
WAEO Ward Agricultural Extension Officer – Tanzania
WFP World Food Programme
WRSI Water Requirements Satisfaction Index
v
List of Tables TABLE 2.1: MAIN SOURCES OF AGRICULTURAL STATISTICS IN AFRICA 8 TABLE 2.2: MAIN SOURCES OF AGRICULTURAL INPUTS DATA IN AFRICA 10 TABLE 2.3: MAIN SOURCES OF EXTERNAL TRADE, STOCK OF CAPITAL AND RESOURCES DATA IN AFRICA 11 TABLE 2.4: MAIN SOURCES OF PRICE DATA, INVESTMENT/SUBSIDIES DATA, RURAL INFRASTRUCTURE AND SERVICES DATA IN AFRICA
12
TABLE 2.5: MAIN SOURCES OF STATISTICS ON DEMOGRAPHIC AND ENVIRONMENTAL CHARACTERISTICS OF AGRICULTURE IN AFRICA
13
TABLE 2.6: GENERAL PERCEPTION OF QUALITY, RELIABILITY, & CONSISTENCY OF ADMINISTRATIVE AGRICULTURAL STATISTICS DATA IN AFRICA
14
TABLE 2.7: GENERAL PERCEPTION OF QUALITY, RELIABILITY, & CONSISTENCY OF ADMINISTRATIVE AGRICULTURAL STATISTICS DATA IN AFRICA CONTINUED
15
TABLE 2.8: GENERAL PERCEPTION OF QUALITY, RELIABILITY, & CONSISTENCY OF ADMINISTRATIVE AGRICULTURAL STATISTICS DATA IN AFRICA CONTINUED
16
TABLE 2.9: GENERAL PERCEPTION OF QUALITY, RELIABILITY, & CONSISTENCY OF ADMINISTRATIVE AGRICULTURAL STATISTICS DATA IN AFRICA CONTINUED
17
TABLE 2.10: MAIN SOURCES OF AGRICULTURAL STATISTICS IN ASIA AND PACIFIC REGION 18 TABLE 2.11: MAIN SOURCES OF AGRICULTURAL INPUTS DATA IN ASIA PACIFIC 19 TABLE 2.12: MAIN SOURCES OF EXTERNAL TRADE, STOCK OF CAPITAL AND RESOURCES DATA IN ASIA PACIFIC REGION 20 TABLE 2.13: MAIN SOURCES OF PRICE DATA, INVESTMENT/SUBSIDIES DATA, RURAL INFRASTRUCTURE AND SERVICES DATA IN ASIA PACIFIC
21
TABLE 2.14: MAIN SOURCES OF STATISTICS ON DEMOGRAPHIC AND ENVIRONMENTAL CHARACTERISTICS OF AGRICULTURE IN THE ASIA PACIFIC REGION
22
TABLE 3. 1: STRUCTURE, CONDUCT, AND PERFORMANCE (SCP) DESIGN ISSUES OF ANY ADSAS 32 TABLE 3. 2: NUMBER OF ORGANIZATIONS COLLECTING AND MANAGING AGRICULTURAL ADMINISTRATIVE DATA IN SELECTED AFRICAN COUNTRIES
34
TABLE 3. 3: COORDINATION, INSTITUTIONAL HOME AND GEOGRAPHICAL COVERAGE OF ADSAS IN SELECTED AFRICAN COUNTRIES
42
TABLE 3. 4: CORE DATA ITEMS BY COUNTRY 45 TABLE 3. 5: CROP CORE ITEMS AND ASSOCIATED DATA 46 TABLE 3. 6: LIVESTOCK CORE ITEMS AND ASSOCIATED DATA 47 TABLE 3. 7: NUMBER OF PROFESSIONALS (STATISTICIANS), SUPPORT STAFF AND STATISTICIANS SPONSORED FOR TRAININGS IN THE ORGANIZATION
49
TABLE 3. 8: REGULARITY OF TRAINING PROGRAMMES FOR STATISTICAL STAFF 50 TABLE 4. 1: METHODS OF DATA COLLECTION 55 TABLE 4. 2: LIST OF INSTITUTION PRODUCING AGRICULTURAL STATISTICS IN COTE D’IVOIRE 60 TABLE 4. 3: TECHNOLOGIES USED 61 TABLE 4. 4: SOURCES OF FUNDING OF ADSAS 65 TABLE 5. 1: MECHANISMS USED TO ASSURE GOOD DATA QUALITY 68 TABLE 5. 2: COLLECTION OF ROUTINE AGRICULTURAL ADMINISTRATIVE DATA AND METHODS OF RECONCILIATION 71 TABLE 5. 3: UGANDA AGRICULTURAL PRODUCTION DATA (THOUSAND TONS) 77 TABLE 5. 4: UGANDA LIVESTOCK NUMBERS (THOUSAND ANIMALS) 77 TABLE 5. 5: ADMINISTRATIVE USES OF ADSAS: USES IN CONSTRUCTING STATISTICS 80
vi
TABLE 5. 6: ADMINISTRATIVE USES OF ADSAS: USES OF FINAL STATISTICS 81 TABLE 5. 7: MAIN USERS OF DATA GENERATED FROM ADSAS 82 TABLE 5. 8: FREQUENCY OF USE AND ACCESSIBILITY TO ADSAS 83 TABLE A 1: LIST OF CORE ITEMS AND CORE DATA COVERED IN UGANDA 120 TABLE A 2: REVIEW OF DATA USE IN UGANDA 121 TABLE A 3: VAEO/WAEOS MONTHLY REPORT 126 TABLE A 4: VAEO/WAEOS MONTHLY REPORT 128 TABLE A 5: VAEO/WAEOS MONTHLY REPORT 129 TABLE A 6: VAEO/WAEOS MONTHLY REPORT 130 TABLE A 7: VAEO/WAEOS MONTHLY REPORT 131 TABLE A 8: QUALITY OF DATA FROM SOME AGENCIES IN UGANDA 132
1
1 Introduction 1.1. BACKGROUND TO TASK 3
The Global Strategy to Improve Agriculture and Rural Statistics adopted by the
United Nations Statistical Commission in 2010 aims to improve statistics in
agriculture, livestock, aquaculture, small-scale fisheries and forestry production
in developing countries and ensure the sustainability of their maintenance. Its
main objective is building statistical capacity in developing countries for key
basic food and agricultural statistics.
One of the key components of the Global Action Plan is its Research Plan
which aims at developing cost-effective methods that will serve as the basis for
preparing technical guidelines, handbooks and training material to be used by
consultants, country statisticians and training centres. One of the key priorities
of the Research Plan, which was to be implemented in 2014 was “Improving the
methodology for using administrative data in agricultural statistics”.
1.2. OVERVIEW OF OBJECTIVES AND APPROACHES
The aim of the research is to develop strategies and methodologies for the
improvement of the collection and management of data from administrative
sources and of their use in an integrated agricultural statistics system in
developing countries. This will involve investigation of cost-effective
approaches and methods for the production of annual and geographically
disaggregated reliable agricultural data, including the combination of surveys
and high-frequency administrative data. The expected primary products of this
research will include (i) a technical report that includes a country-tested and
validated methodology to improve and make available administrative data for
producing agricultural statistics in developing countries and (ii) a proposed
strategy on how to use administrative data in cost effective agricultural statistics
systems. The technical report and proposed strategies will develop sound
methodology for improving and using administrative sources for agricultural
statistics in developing countries, taking into consideration the existing
2
approaches regarding administrative information systems in the different
countries (e.g. differences in approaches for collecting and using administrative
data between on one side French-Portuguese-Spanish speaking countries and on
the other side English speaking countries). Any new potential sources of
administrative data will also be examined.
1.3. REPORTS PRODUCED SO FAR
1) Technical Report 1: Reviewing the Relevant Literature and Studies on
the Quality and Use of Administrative Sources for Agricultural Data
This document reviews relevant literature and studies on, first, quality and,
second, use of administrative sources for producing agricultural data and
proposes a conceptual framework for the use of administrative data in
agricultural statistics (FAO 2015a) .
2) Technical Report 2: The Role of Administrative Data in Developed
Countries: Experiences and Ongoing Research
The Second Technical Report reviewed and analysed relevant country
experiences and ongoing research in developed countries (including Europe,
where important research is being carried out) on the use of administrative
sources for producing agricultural data and lessons for developing countries
(FAO 2015b).
The objective of Task 3 is to analyse the results of country assessments and
other relevant documentation on administrative sources being currently used by
developing countries, and evaluate their strengths, weaknesses and suitability
for use in agricultural statistics within an integrated and cost-effective
agricultural statistics system. Technical Report 3 is therefore the Critical
Analysis of Agricultural Administrative Sources Being Currently Used By
Developing Countries.
1.4. STRUCTURE OF THE REPORT
The information in this report was obtained from literature review of
documents, especially the Africa country assessment report 2014, Asia-Pacific
Country Assessment, internet searches, analysis of data on Africa country
assessments from AfDB, analysis of data on Asia-Pacific country assessment
data and a survey of sources and use of Administrative Data which was emailed
to all Director Generals of Statistics who attended the African Symposium for
Statistical Development (ASSD) which took place in Kampala, Uganda
3
between 12th – 14th January, 2015. In-Depth Country Assessments for Bhutan,
Ghana and Uganda were also referred to.
The report is organized in six broad themes: namely an introduction, an analysis
of the results of country assessments of sources of core agricultural data,
structural issues, conduct issues, performance issues, and challenges and
recommendations. In the structural issues, the report presents a synthesis of the
organizations collecting and managing administrative agricultural data, the core
items and core data items covered and human resource/incentives to ADSAS
staff. In the conduct issues, the report gives a synthesis of agricultural data
collection methods and technologies used, and the sources of funding and
sustainability strategies of the ADSAS in developing countries. In the
performance issues, the report presents a synthesis of agricultural data quality
and data use for both statistical and administrative uses. The last chapter
presents the strength and weakness (challenges) and recommendations.
4
2 Analysis of Results of
Country Assessment
Reports
As part of implementation plan of the Global Strategy, FAO and its regional
Partners have conducted comprehensive Country Assessments of countries,
using a standard questionnaire and covering information on the main sources of
core agricultural data, including administrative sources. The results of this
assessment are available for the Africa and Asia-Pacific Regions. These reports
have been reviewed.
The Africa country assessment report 2014 presents results of the Agricultural
Statistics Capacity Indicators (ASCIs) from the Country Assessment to improve
agricultural and rural statistics in Africa that was carried out in 2013. According
to the report, “There are four dimensions of the ASCIs which are Institutional
infrastructure Dimension (Prerequisites); Resources Dimension (Input);
Statistical Methods and Practices Dimension (Throughput); and Availability of
Statistical Information Dimension (Output). Each dimension is a result of an
aggregation of explaining number of elements. The four dimensions are, in
turn, aggregated into a composite indicator to measure the country capacity as
a whole to produce agricultural statistics; hence the measurement of the
development level of the national agricultural statistics systems as a
whole. The ASCIs puts emphasis on the strengths and weaknesses that exist in
specific areas of the national statistical systems especially in agricultural
statistics in Africa that contribute to the quality level of information produced
and used on regular basis”. There are also In-Depth Country Assessments for
Ghana and Uganda in Africa and Bhutan in Asia-Pacific Regions.
5
2.1. INSTITUTIONAL CAPACITY (PRE - REQUISITES)
Findings from the Africa country assessment report 2014 show that the
continent is quite weak in resources but has a lot of strength in institutional
infrastructure and availability of statistical information(AfDB 2014). Africa is
weak in the resources dimension as well as in the Statistical Methods and
Practices dimension.
The African Development Bank noted that the institutional capacity indicator
provides assessments on five main elements of the institutional infrastructure
dimension of the country capacity to produce agriculture statistics (AfDB
2014). “These elements are the Legal framework, Coordination in the National
Statistical System, Strategic Vision and Planning for Agriculture Statistics,
Integration of agriculture in the National Statistical System and Relevance of
data”. Though marked country differences exist, generally countries were rated
above average on almost all the elements of institutional infrastructure except
relevance of data. Research findings show that most countries had not
established the interface for dialogue between data producers and users. Where
the interface existed, channels of communications were not well set up and for
some of the countries, they did not use the forum on a regular basis as required,
(AfDB 2014). It was reported that 16 of the African countries, namely, South-
Sudan, Zambia, Sierra Leone, Angola, Equatorial Guinea, Congo Republic,
Swaziland, Gabon, Madagascar, Zimbabwe, Seychelles, Comoros, Chad,
Guinea, Guinea Bissau and Libya, were operating below average of the
expected level of the primary institutional infrastructure to produce agricultural
statistics. The 16 countries would therefore need a lot of technical support to
improve on their institutional infrastructure.
Best practices can, however, be drawn from the Asia-Pacific countries of
Australia, Japan, Mongolia, New Zealand which were reported to be excellent
in terms of the institutional capacity to produce agricultural statistics (APCAS
2012).
2.2. RESOURCES DIMENSION (INPUT)
The input dimension indicators on resources show the strength of a country in
deploying adequate resources to execute statistical activities. The three essential
resources involved under the input dimension are the existence of qualified
permanent personnel which includes both quantity (i.e. number of staff
available in the workforce) and quality (i.e. the depth of their knowledge,
training and experience); financial resources; and the physical infrastructure
for planning and execution of statistical activities. The resource indicator of
6
each country capacity is a permutation of these elements.” Countries were rated
below average on this indicator as they had a generally low level (below 50%)
of resources in the area of finances, human resource and physical
infrastructure to run the agricultural statistics systems effectively and efficiently
in Africa” (AfDB 2014). The only exceptions in Africa were 9 countries
namely, Mauritius, Rwanda, Namibia, Cape Verde, Malawi, Ghana, Zambia,
South Africa, and Botswana (AfDB 2014).
2.3. THROUGHPUT
The “Throughput” Dimension – Indicators on Statistical methods and practices
reflects on 9 different elements. The first three which relate to the use of
information technology include statistical software capability, data collection
technology and information technology infrastructure. The others focus on the
adoption of statistical standards, statistical activities, analysis and use of the
data collected, agricultural surveys, agricultural, market price information and
quality consciousness”. Analysis of the countries shows that though countries
in Africa performed well in terms of statistical software capability and
averagely in data collection technology and information technology
infrastructure, they were below average in terms of adoption of statistical
standards; statistical activities; the analysis and use of the data
collected; agricultural surveys; agricultural and market price
information,(AfDB 2014). There were 24 out of the 54 African countries rated
as worst countries in terms of statistical methods and practices. These countries
need both funding and technical assistance for them to be able to adopt and/or
improve their agricultural statistical methods and practices”, (AfDB 2014).
2.4. OUTPUT
The output dimension “considers the minimum set of core data as determined
by the Global Strategy. The indicator on Core Data Availability gives an idea
of the extent to which a statistical system is producing the minimum core set of
data for the country. It signifies the strength of data availability, their
timeliness and accessibility as well as on how their overall quality is perceived
among countries.” The Country assessments revealed that African countries
were performing above average in terms of core data availability, overall data
quality perception and timeliness but were below average in terms of data
accessibility. The eight worst countries in terms of the minimum set of core
data indicators were: Angola, Libya, Somalia, South-Sudan, Equatorial Guinea,
Chad, Swaziland and Comoros. Financial and technical assistance would be
required for these countries to produce agricultural statistics and make it
available to users.
7
2.5. COUNTRY ASSESSMENT CONCLUSIONS
Overall, it was concluded that Africa is quite weak in terms of dimensions
related to resources for statistical activities and implementation of statistical
practices, while Africa is relatively strong in dimensions associated with
institutional capacity and availability of statistical information. The eight
African countries that were rated highest in terms of all the four quality
dimensions (Institutional Capacity; Resources; Statistical Methods and
Practices; and Availability of Statistical Information) were Ethiopia, South
Africa, Ghana, Namibia, Egypt, Rwanda, Uganda, and Mauritius; the least rated
countries were Guinea-Bissau, and Libya.
For the Asia and Pacific region, the Asia-Pacific Commission on Agricultural
Statistics (APCAS 2012) report rated Australia, Japan, Mongolia, New Zealand
as excellent in terms of Institutional infrastructure Dimension (Prerequisites);
Resources Dimension (Input); Statistical Methods and Practices Dimension
(Throughput); and Availability of Statistical Information Dimension (Output) in
the Asia & Pacific region.
2.6. ADDITIONAL COUNTRY ASSESSMENTS
Original data for Africa and the Asia Pacific region was obtained from the
African Development Bank (AfDB) and the Asian Development Bank (ADB),
respectively, to complement the assessment. Further, it was decided to carry out
another review during the African Symposium for Statistical Development
(ASSD) which coincidentally took place in Kampala, Uganda between 12th
–
14th
January, 2015. The questionnaire used is attached as Annex A3.
2.6.1. AFRICA ADDITIONAL COUNTRY ASSESSMENTS
During the Country Assessments of Agricultural Statistical systems in Africa,
countries were asked to mention the main sources of data for compilation of
agricultural statistics for the major crop, livestock, fishery and forestry products
determined on the basis of its share in GDP or agricultural area. Table 2.1
shows that in Africa, crop data is mainly collected through surveys while
forestry, fisheries and aquaculture is mostly obtained through administrative
sources. The specific data which African countries mainly obtain through
sample surveys include: crop yield per area data by 70.7% of the countries,
area harvested data by 68.8% of the countries, crop planted area data by 68.4%
of the countries, crop production quantity data by 62.2% of the countries, crop
production quantity data by 36.8% of the countries. The relatively high
8
importance of survey data for crop statistics likely refers only to national and
possibly regional levels. Surveys and even censuses rarely collect data with
acceptable accuracy at lower administrative levels, like districts.
Table. 2.1: Main Sources of Agricultural Statistics in Africa
PRODUCTION
Main Sources of Data (%)
Census Sample
Surveys Administrative
Records
Estimates/
Forecasts Special
Study
Expert
Opinion/
Assessment
No. of
countries
Crop Crop production:
quantity 13.3 62.2 11.1 13.0 0.0 0.0 45
Crop production:
value 13.2 36.8 23.7 21.1 2.6 2.6 38
Crop yield per
area 9.8 70.7 4.9 14.6 0.0 0.0 41
Area planted 10.5 68.4 10.5 7.9 2.6 0.0 38
Area harvested 6.2 68.8 9.4 9.4 3.1 3.1 32
Livestock
Livestock
production:
quantity
11.4 38.6 27.3 22.7 0.0 0.0 44
Livestock
production:
value
13.5 35.1 29.7 21.6 0.0 0.0 37
Fishery
Fishery and
aquaculture
production:
quantity
10.0 30.0 47.5 10.0 2.5 0.0 40
Fishery and
aquaculture
production:
value
11.1 25.0 47.2 16.7 0.0 0.0 36
Forestry
Forest production
of wood1:
quantity
5.9 11.8 55.9 17.6 5.9 2.9 34
Forest production
of wood: value 7.1 14.3 53.6 17.9 7.1 0.0 28
Forest production
of non wood1:
quantity
10.5 5.3 63.2 10.5 10.5 0.0 19
Forest production
of non wood:
value
12.5 6.2 62.5 12.5 6.2 0.0 16
Source: Computed from the Africa Country Assessment data
Footnotes: 1 Wood products include industrial wood (timber), fuel wood, charcoal and small woods, and
other type of wood, such as fire wood, charcoal, wood chips and round wood which are used in an
unprocessed form (e.g. pulpwood).1 Non-wood forest products include both food and non-food items. For
example, food products include game meat, insects, insect eggs, etc. Non-food products are like gums
which are collected freely from forest trees. The responses here refer to major crop, livestock, fishery and
forestry products. The basis for deciding the “major product” is the share in GDP or agricultural area.
9
The types of data for which over half of the African countries use
administrative sources as their major data source include Forest production of
wood value data by 53.6% of the African countries, Forest production of wood
quantity data by 55.9% of the countries, Fishery and aquaculture production
value data by close to half (47.2%) of the countries and Fishery and aquaculture
production quantity data also by close to half (47.5%) of the countries. About
one quarter of the countries use administrative data sources as their major
source of data for crop production value data (23.7%), livestock production
quantity data (27.3%) and livestock production value data (29.7%).
Ivory Coast, Kenya, Namibia, Sierra Leone, and Tanzania were the only
countries in Africa whose main source of crop production quantity data was
from administrative sources.
Algeria, Ivory Coast, Guinea, Mali, Morocco, Sierra Leone, South Africa,
Tanzania, and Tunisia were the only countries in Africa whose main source of
crop production value data was from administrative sources. Only 4.9% of
African countries obtain crop yield per area data from administrative sources.
Only two countries, Sierra Leone and Tanzania, use administrative data as the
main source of crop yield per area data. Only 4 countries, Ivory Coast, Kenya,
Sierra Leone and Tanzania use administrative data as the main source of crop
planted area data. Less than a tenth (9.4%) of African countries obtain area
harvested data from administrative sources. Only three countries, Ivory Coast,
Kenya, and Sierra Leone, use administrative data as the main source of area
harvested data. Twelve African countries including Benin, Burundi, Cameroon,
Ivory Coast, Guinea, Kenya, Madagascar, Mali, Namibia, Rwanda, Seychelles
and Sierra Leone, use administrative data as the main source of data on
livestock production quantity.
Over a fifth of the African countries (21.6%) obtain their livestock production
value data from estimates/forecasts (see Table 2.1). Findings showed that 12
countries were using administrative data as the main source of data on livestock
production value data and these were: Benin, Cameroon, Ivory Coast, Guinea,
Kenya, Mali, Namibia, Rwanda, Sierra Leone, South Africa, and Sudan. For
fisheries and aquaculture production and value, there are 19 countries that use
administrative data as the main source of data and they include: Benin, Burundi,
Cameroon, Ivory Coast, Ethiopia, Guinea, Kenya, Madagascar, Mali, Mauritius,
Morocco, Mozambique, Namibia, Niger, Rwanda, Seychelles, Sierra Leone,
Sudan and Tanzania.
Table 2.1 also shows that the following 20 countries use administrative data as
the main source of data on Forest production of wood quantity and value:
Benin, Burkina Faso, Cameroon, Congo Republic, Ivory Coast, Ethiopia,
10
Gambia, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritius, Morocco,
Mozambique, Niger, Sao Tome Principe, Sierra Leone, Burundi, and Tanzania.
Table 2.2 presents the main sources of agricultural inputs data in Africa.
Administrative records stand out as the main source of agricultural inputs data.
Table. 2.2: Main Sources of Agricultural Inputs Data in Africa
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
countries
INPUTS
Fertilizer
quantity 6.1 30.3 54.5 9.1 0.0 0.0 33
Fertilizer
value 3.2 32.3 51.6 9.7 3.2 0.0 31
Pesticide
quantity 3.8 34.6 57.7 3.8 0.0 0.0 26
Pesticide
value 4.5 31.8 54.5 4.5 4.5 0.0 22
Seeds
quantity 8.7 26.1 56.5 8.7 0.0 0.0 23
Seeds
value 13.0 30.4 47.8 8.7 0.0 0.0 23
Animal
Feed
quantity
6.2 25.0 56.2 12.5 0.0 0.0 16
Animal
Feed
value
14.3 21.4 64.3 0.0 0.0 0.0 14
Forage
quantity 0.0 22.2 66.7 11.1 0.0 0.0 9
Forage
value 0.0 28.6 57.1 14.3 0.0 0.0 7
Animal
vaccines and
drugs
quantity
4.5 18.2 72.7 4.5 0.0 0.0 22
Animal
vaccines and
drugs value
9.1 13.6 72.7 4.5 0.0 0.0 22
Aquatic
seeds
quantity
0.0 16.7 83.3 0.0 0.0 0.0 6
Aquatic
seeds value 0.0 16.7 83.3 0.0 0.0 0.0 6
AGRO-PROCESSING
Main
crops 0.0 38.9 33.3 27.8 0.0 0.0 18
Post harvest
losses 20.0 20.0 20.0 40.0 0.0 0.0 5
Main
livestock 5.9 29.4 41.2 23.5 0.0 0.0 17
Fish:
Quantity 5.9 23.5 52.9 17.6 0.0 0.0 17
Fish:
value 0.0 33.3 53.3 13.3 0.0 0.0 15
Source: Computed from the Africa Country Assessment data
11
The response rate for the section of the questionnaire on agricultural inputs
(Table 2.2) is low relative to the response rate for the section on sources of
agricultural statistics (Table 2.1). Among those countries that responded,
administrative sources were rated as the major source-over 50%-of data on
agricultural inputs and agro-processing in almost all instances.
Administrative sources are the major source of data on external trade in over
85% of the African countries (Table 2.3). In many countries, it is the Informal
Cross Border Trade (ICBT) data that is collected through surveys. For those
countries that provided information concerning stock of capital and resources,
administrative sources were rated at least second as the major source data in
Africa.
Table 2.3: Main Sources of External Trade, Stock of Capital and Resources Data
in Africa
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
countries
EXTERNAL TRADE
Export:
quantity 9.3 2.3 86.0 2.3 0.0 0.0 43
Export:
Value 9.3 2.3 86.0 2.3 0.0 0.0 43
Import:
quantity 7.1 2.4 90.5 0.0 0.0 0.0 42
Import:
Value 7.1 2.4 90.5 0.0 0.0 0.0 42
STOCK OF CAPITAL AND RESOURCES
Livestock
Inventories 17.9 25.0 35.7 21.4 0.0 0.0 28
Agricultural
machinery 19.0 38.1 38.1 0.0 4.8 0.0 21
Stocks of
main crops:
quantity
4.3 47.8 43.5 4.3 0.0 0.0 23
Land
and use 4.2 41.7 29.2 12.5 12.5 0.0 24
Water-
related:
Irrigated
areas 14.3 52.4 23.8 9.5 0.0 0.0 21
Types of
irrigation 15.0 50.0 30.0 5.0 0.0 0.0 20
Irrigated
crops 10.5 73.7 10.5 5.3 0.0 0.0 19
Quantity
of water
used
0.0 40.0 40.0 20.0 0.0 0.0 5
Water
quality 0.0 62.5 25.0 0.0 12.5 0.0 8
Source: Computed from the Africa Country Assessment data
12
Table 2.4 pertains to data on prices, investments/subsidies, taxes, rural
infrastructure and services. As for the section on agricultural inputs, response
rates for this section of the questionnaire were relatively low. For countries that
provided responses on the main sources of price data, investment/subsidies
data, and rural infrastructure and services data, administrative sources were the
main source of data on investment subsidies or taxes and rural infrastructure
and services. Administrative sources were also the main source of data for
agricultural inputs, in addition to being the main source of data for agricultural
export and import prices, see Table 2.4.
Table 2.4: Main Sources of Price Data, Investment/Subsidies Data, Rural Infrastructure
and Services Data in Africa
Source: AfDB Database-Africa Country Assessment of Agricultural Statistics Systems
Table 2.5 presents the main sources of agricultural statistics on social related
indicators in Africa. The major sources of data are censuses and administrative
sources.
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
Countries
PRICES
Producer prices 8.3 54.2 29.2 8.3 0.0 0.0 24
Wholesale prices 10.0 60.0 25.0 0.0 0.0 5.0 20
Consumer prices 7.7 74.4 10.3 0.0 7.7 0.0 39
Agric. Input prices 0.0 40.9 50.0 9.1 0.0 0.0 22
Agric. Export prices 0.0 16.0 80.0 4.0 0.0 0.0 25
Agric. Import prices 0.0 7.7 88.5 3.8 0.0 0.0 26
INVESTMENT SUBSIDIES OR TAXES
Public investment in
agriculture 0.0 9.5 81.0 4.8 4.8 0.0 21
Agricultural
subsidies 0.0 14.3 71.4 9.5 4.8 0.0 21
Fishery access fees 0.0 9.1 90.9 0.0 0.0 0.0 11
Public expenditure
for fishery
management
0.0 6.7 86.7 0.0 6.7 0.0 15
Fishery subsidies 0.0 7.1 85.7 0.0 7.1 0.0 14
Water
Pricing 0.0 10.0 90.0 0.0 0.0 0.0 10
RURAL INFRASTRUCTURE AND SERVICES
Area equipped for
irrigation 0.0 40.0 53.3 6.7 0.0 0.0 15
Crop markets 16.7 22.2 55.6 0.0 5.6 0.0 18
Livestock markets 13.0 17.4 65.2 0.0 4.3 0.0 23
Rural roads (Km) 0.0 11.1 83.3 0.0 5.6 0.0 18
Railways
(Km) 0.0 6.2 93.8 0.0 0.0 0.0 16
Communication 0.0 4.8 95.2 0.0 0.0 0.0 21
Banking and
insurance 5.0 5.0 90.0 0.0 0.0 0.0 20
13
Table 2.5: Main Sources of Statistics on Demographic and Environmental Characteristics
of Agriculture in Africa
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
Countries
SOCIAL
Population
dependent on
agriculture
51.4 37.8 5.4 5.4 0.0 0.0 37
Agricultural
workforce (by
gender)
38.2 52.9 2.9 5.9 0.0 0.0 34
Fishery
workforce (by
gender)
42.1 26.3 21.1 10.5 0.0 0.0 19
Aquaculture
workforce (by
gender)
44.4 11.1 33.3 11.1 0.0 0.0 9
Household
income 30.8 57.7 3.8 7.7 0.0 0.0 26
ENVIRONMENTAL
Soil
degradation 0.0 11.1 33.3 33.3 0.0 22.2 9
Water pollution
due to
agriculture
0.0 0.0 100.0 0.0 0.0 0.0 3
Emissions due
to agriculture 0.0 12.5 25.0 25.0 37.5 0.0 8
Water pollution
due to
aquaculture
0.0 0.0 100.0 0.0 0.0 0.0 2
Emissions due
to aquaculture 0.0 0.0 100.0 0.0 0.0 0.0 2
GEOGRAPHICAL LOCATION
Geo-coordinate
of the statistical
unit (parcel,
province,
region, country)
51.9 25.9 7.4 3.7 11.1 0.0 27
Source: AfDB Database-Africa Country Assessment of Agricultural Statistics Systems
The general perception of quality, reliability and consistency of administrative
agricultural statistics data in Africa is presented in Tables 2.6 and Table 2.7.
14
Table 2.6: General Perception of Quality, Reliability, & Consistency of Administrative
Agricultural Statistics Data in Africa
PRODUCTION
General Perception of Quality, Reliability, & Consistency of Data (%)
Highly
Reliable Reliable Acceptable Workable Unacceptable
Number of
Countries
Crop production:
quantity 0.0 40.0 40.0 20.0 0.0 5
Crop production:
value 14.3 28.6 57.1 0.0 0.0 9
Crop yield per
area 0.0 50.0 50.0 0.0 0.0 2
Area planted 0.0 25.0 50.0 25.0 0.0 4
Area harvested 0.0 0.0 66.7 33.3 0.0 3
Livestock
production:
quantity
0.0 25.0 41.7 33.3 0.0 12
Livestock
production: value 9.1 9.1 63.6 18.2 0.0 11
Fishery and
aquaculture
production:
quantity
5.6 33.3 50.0 11.1 0.0 19
Fishery and
aquaculture
production: value
0.0 23.5 64.7 11.8 0.0 17
Forest production
of wood1: quantity 5.3 21.1 57.9 15.8 0.0 19
Forest production
of wood: value 6.7 6.7 73.3 13.3 0.0 15
Forest production
of non wood2:
quantity
8.3 25.0 58.3 8.3 0.0 12
Forest production
of non wood:
value
10.0 10.0 70.0 10.0 0.0 10
1 Wood products include industrial wood (timber), fuel wood, charcoal and small woods, and other type of
wood, such as fire wood, charcoal, wood chips and round wood which are used in an unprocessed form
(e.g. pulpwood).
2 Non-wood forest products include both food and non-food items. For example, food products include
game meat, insects, insect eggs, etc. Non-food products are like gums which are collected freely from
forest trees.
15
Table 2.7: General Perception of Quality, Reliability, & Consistency of Administrative
Agricultural Statistics Data in Africa Continued
General Perception of Quality, Reliability, & Consistency of Data (%)
Highly
Reliable Reliable Acceptable Workable Unacceptable
Number of
Countries
Export: quantity 18.9 51.4 27.0 2.7 0.0 37
Export:
Value 18.9 54.1 24.3 2.7 0.0 37
Import: quantity 18.4 50.0 28.9 2.6 0.0 38
Import:
Value 18.4 52.6 26.3 2.6 0.0 38
Livestock
Inventories 20.0 20.0 40.0 10.0 10.0 10
Agricultural
machinery 12.5 37.5 37.5 12.5 0.0 8
Stocks of main
crops: quantity 40.0 30.0 30.0 0.0 0.0 10
Land use 14.3 42.9 42.9 0.0 0.0 7
Irrigated areas 20.0 20.0 60.0 0.0 0.0 5
Types of
irrigation 16.7 33.3 50.0 0.0 0.0 6
Irrigated crops 0.0 0.0 100.0 0.0 0.0 2
Quantity of
water used 100.0 0.0 0.0 0.0 0.0 2
Water quality 0.0 50.0 50.0 0.0 0.0 2
Source: AfDB Database-Africa Country Assessment of Agricultural Statistics Systems
All data on exports and imports, livestock inventories, stocks of main crops,
land use, irrigation and water usage was considered to be of at least workable
quality by those countries that used administrative sources as the main source of
data, see Table 2.7. Only one of the ten Countries that use administrative data
as the main source of livestock inventories data consider it to be of
unacceptable quality.
Almost all data on fertilizers, pesticides, seeds, animal feeds, forage, animal
vaccines & drugs, aquatic seeds, main crops, post-harvest losses, main
livestock and fish was considered to be of at least workable quality by those
countries that used administrative sources as the main source of data, see Table
2.8.
16
Table 2.8: General Perception of Quality, Reliability, & Consistency of Administrative
Agricultural Statistics Data in Africa Continued
General Perception of Quality, Reliability, & Consistency of Data (%)
Highly
Reliable Reliable Acceptable Workable Unacceptable
Number
of
Countries
Fertilizer quantity 11.1 38.9 27.8 16.7 5.6 18
Fertilizer value 12.5 31.3 31.3 18.8 6.3 16
Pesticide quantity 6.7 33.3 33.3 20 6.7 15
Pesticide value 8.3 33.3 33.3 16.7 8.3 12
Seeds quantity 7.7 46.2 23.1 15.4 7.7 13
Seeds Value 0.0 45.5 27.3 18.2 9.1 11
Animal Feed quantity 0.0 33.3 33.3 22.2 11.1 9
Animal Feed Value 0.0 22.2 44.4 22.2 11.1 9
Forage quantity 16.7 50 16.7 16.7 6
Forage Value 0.0 25.0 25.0 25.0 25.0 4
Animal vaccines and
drugs quantity 12.5 18.8 43.8 18.8 6.3 16
Animal vaccines and
drugs value 6.3 25.0 50.0 12.5 6.3 16
Aquatic seeds quantity 0.0 40.0 60.0 0.0 0.0 5
Aquatic seeds value 0.0 40.0 60.0 0.0 0.0 5
Main Crops 50.0 16.7 16.7 16.7 0.0 6
Post harvest losses 0.0 0.0 100.0 0.0 0.0 1
Main livestock 14.3 28.6 28.6 28.6 0.0 7
Fish: Quantity 11.1 33.3 22.2 33.3 0.0 9
Fish:Value 25.0 25.0 25.0 25.0 0.0 8
Source: AfDB Database-Africa Country Assessment of Agricultural Statistics Systems
Almost all price data; investment/subsidies data; and rural infrastructure and
services data was considered to be of at least workable quality by those
countries that used administrative sources as the main source of data, see Table
2.9.
17
Table 2.9: General Perception of Quality, Reliability, & Consistency of Administrative
Agricultural Statistics Data in Africa Continued
General Perception of Quality, Reliability, & Consistency of Data (%)
Highly
Reliable Reliable Acceptable Workable Unacceptable
Number of
Countries
Producer prices 14.3 28.6 57.1 0.0 0.0 7
Wholesale prices 0.0 20.0 80.0 0.0 0.0 5
Consumer prices 50.0 0.0 25.0 25.0 0.0 4
Agric. Input
prices 27.3 45.5 27.3 0.0 0.0 11
Agric. Export
prices 10.0 60.0 30.0 0.0 0.0 20
Agric. Import
prices 17.4 39.1 43.5 0.0 0.0 23
Public
investment in
agriculture
35.3 41.2 23.5 0.0 0.0 17
Agricultural
subsidies 33.3 46.7 20.0 0.0 0.0 15
Fishery access
fees 20.0 60.0 20.0 0.0 0.0 10
Public
expenditure for
fishery
management
38.5 38.5 23.1 0.0 0.0 13
Fishery
subsidies 33.3 41.7 25.0 0.0 0.0 12
Water
Pricing 33.3 66.7 0.0 0.0 0.0 9
Area equipped
for irrigation 12.5 75.0 12.5 0.0 0.0 8
Crop markets 20.0 70.0 10.0 0.0 0.0 10
Livestock
markets 13.3 46.7 33.3 6.7 0.0 15
Rural roads
(Km) 0.0 53.3 40.0 6.7 0.0 15
Railways
(Km) 33.3 46.7 20.0 0.0 0.0 15
Communication 20.0 50.0 20.0 10.0 0.0 20
Banking and
insurance 38.9 38.9 11.1 11.1 0.0 18
Source: AfDB Database-Africa Country Assessment of Agricultural Statistics Systems
2.6.2. ASIA PACIFIC COUNTRY ASSESSMENTS
During the Country Assessments of Agricultural Statistical systems in Asia and
Pacific region, countries were asked to mention the main sources of data for
compilation of agricultural statistics for the major crop, livestock, fishery and
forestry products determined on the basis of its share in GDP or agricultural
area. Table 2.10 shows that in the Asia and Pacific region; crop, livestock and
fisheries data is mainly collected through surveys while forestry data is mostly
obtained through administrative sources.
18
Table 2.10: Main Sources of Agricultural Statistics in Asia and Pacific Region
PRODUCTION
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
No. of
countries
CROP
Crop production:
quantity 21.1 57.9 15.8 10.5 2.6 0.0 383
Crop production:
value 18.4 52.6 13.2 13.2 2.6 0.0 38
Crop yield per
area 16.7 52.8 16.7 11.1 2.8 0.0 36
Area planted 21.6 56.8 13.5 8.1 0.0 0.0 37
Area harvested 23.5 55.9 11.8 5.9 2.9 0.0 34
Livestock
Livestock
production:
quantity
24.3 56.8 10.8 5.4 2.7 0.0 37
Livestock
production:
value
16.1 51.6 12.9 12.9 6.5 0.0 31
FISHERY
Fishery and
aquaculture
production:
quantity
15.4 46.2 30.8 7.7 0.0 0.0 26
Fishery and
aquaculture
production:
value
10.0 50.0 30.0 10.0 0.0 0.0 20
FORESTRY
Forest production
of wood1:
quantity
16.7 27.8 44.4 5.6 5.6 0.0 18
Forest production
of wood: value 0.0 30.0 50.0 10.0 10.0 0.0 10
Forest production
of non wood2:
quantity
20.0 26.7 40.0 6.7 6.7 0.0 15
Forest production
of non-wood:
value
11.1 22.2 44.4 11.1 11.1 0.0 9
Source: Computed from the Asia Pacific Country Assessment data
Footnotes: 1 Wood products include industrial wood (timber), fuel wood, charcoal and small woods, and
other type of wood, such as fire wood, charcoal, wood chips and round wood which are used in an
unprocessed form (e.g. pulpwood).1 Non-wood forest products include both food and non-food items. For
example, food products include game meat, insects, insect eggs, etc. Non-food products are like gums
which are collected freely from forest trees. The responses here refer to major crop, livestock, fishery and
forestry products. The basis for deciding the “major product” is the share in GDP or agricultural area
3 The total percentage exceeds 100% because some countries mentioned more than one data
source
19
Table 2.11: Main Sources of Agricultural Inputs Data in Asia Pacific
Main Sources of Data (%)
Census
Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
countries
INPUTS
Fertilizer
quantity 12.0 28.0 52.0 4.0 0.0 4.0 25
Fertilizer
value 14.3 23.8 57.1 4.8 0.0 0.0 21
Pesticide
quantity 13.0 21.7 56.5 4.4 0.0 4.4 23
Pesticide
value 15.0 25.0 55.0 5.0 0.0 0.0 20
Seeds
quantity 10.0 20.0 60.0 10.0 0.0 0.0 10
Seeds
value 11.1 22.2 66.7 0.0 0.0 0.0 9
Animal Feed
quantity 11.1 22.2 44.4 11.1 0.0 11.1 9
Animal Feed
value 14.3 28.6 42.9 0.0 0.0 0.0 7
Forage
quantity 12.5 25.0 50.0 0.0 0.0 12.5 8
Forage
value 14.3 28.6 57.1 0.0 0.0 0.0 7
Animal
vaccines and
drugs quantity
16.7 0.0 66.7 16.7 0.0 0.0 6
Animal
vaccines and
drugs value
14.3 28.6 42.9 14.3 0.0 0.0 7
Aquatic seeds
quantity 33.3 33.3 33.3 0.0 0.0 0.0 3
Aquatic seeds
value 25.0 50.0 25.0 0.0 0.0 0.0 4
AGRO-PROCESSING
Main
crops 14.3 71.4 14.3 0.0 0.0 0.0 7
Post harvest
losses 25.0 25.0 0.0 25.0 25.0 0.0 4
Main
livestock 25.0 50.0 25.0 0.0 0.0 0.0 8
Fish:
Quantity 0.0 75.0 25.0 0.0 0.0 0.0 4
Fish:
value 0.0 100.0 0.0 0.0 0.0 0.0 2
Source: Computed from the Asia Pacific Country Assessment data
Table 2.11 presents the main sources of agricultural inputs data in the Asia
Pacific region. Administrative records stand out as the main source of
agricultural input data. A number of countries did not provide responses to
questions in this section of the questionnaire. However, for those countries that
responded, administrative sources were rated as the major source-over 40%-of
data on agricultural inputs in almost all instances (Table 2.11).
20
Administrative sources are the major source of data on external trade in over
80% of the countries in the Asia Pacific region (Table 2.12). This is similar to
the situation in Africa.
Table 2.12: Main Sources of External Trade, Stock of Capital and Resources Data in Asia
Pacific Region
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
countries
EXTERNAL TRADE
Export:
quantity 3.1 9.4 84.4 3.1 0.0 0.0 32
Export:
Value 2.9 8.8 85.3 2.9 0.0 0.0 34
Import:
quantity 3.3 10.0 83.3 3.3 0.0 0.0 30
Import:
Value 3.0 9.1 87.9 0.0 0.0 0.0 33
STOCK OF CAPITAL AND RESOURCES
Livestock
Inventories 17.7 47.1 23.5 5.9 5.9 0.0 17
Agricultural
machinery 31.8 36.4 27.3 4.6 0.0 0.0 22
Stocks of
main crops:
quantity
11.1 44.4 22.2 22.2 0.0 0.0 9
Land
and use 29.0 41.9 25.8 0.0 3.2 0.0 31
Water-
related:
Irrigated
areas 22.7 54.6 22.7 0.0 0.0 0.0 22
Types of
irrigation 20.0 46.7 33.3 0.0 0.0 0.0 15
Irrigated
crops 15.4 53.9 30.8 0.0 0.0 0.0 13
Quantity
of water
used
0.0 62.5 25.0 12.5 0.0 0.0 8
Water
quality 0.0 50.0 50.0 0.0 0.0 0.0 6
Source: Computed from the Asia Pacific Country Assessment data
Table 2.11 presents the main sources of agricultural inputs data in the Asia
Pacific region. Administrative records stand out as the main source of
agricultural input data. A number of countries did not provide responses to
questions in this section of the questionnaire. However, for those countries that
responded, administrative sources were rated as the major source-over 40%-of
data on agricultural inputs in almost all instances (Table 2.11).
For countries that provided responses on the main sources of: price data;
investment/subsidies data; and rural infrastructure and services data,
21
administrative sources were the main source of data on investment subsidies or
taxes and rural infrastructure and services. Administrative sources were also the
main source of data for agricultural export and import prices, see Table 2.13.
Table 2.13: Main Sources of Price Data, Investment/Subsidies Data, Rural Infrastructure
and Services Data in Asia Pacific
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
Countries
PRICES
Producer prices 5.6 83.3 11.1 0.0 0.0 0.0 18
Wholesale
prices 0.0 64.3 35.7 0.0 0.0 0.0 14
Consumer
prices 4.0 84.0 12.0 0.0 0.0 0.0 25
Agric. Input
prices 19.1 57.1 14.3 9.5 0.0 0.0 21
Agric. Export
prices 20.0 30.0 45.0 5.0 0.0 0.0 20
Agric. Import
prices 15.0 30.0 50.0 5.0 0.0 0.0 20
INVESTMENT SUBSIDIES OR TAXES
Public
investment in
agriculture
0.0 14.3 85.7 0.0 0.0 0.0 7
Agricultural
subsidies 0.0 0.0 100.0 0.0 0.0 0.0 7
Fishery access
fees 0.0 0.0 100.0 0.0 0.0 0.0 2
Public
expenditure for
fishery
management
0.0 16.7 83.3 0.0 0.0 0.0 6
Fishery
subsidies 0.0 0.0 100.0 0.0 0.0 0.0 5
Water
Pricing 0.0 0.0 66.7 33.3 0.0 0.0 3
RURAL INFRASTRUCTURE AND SERVICES
Area equipped
for irrigation 28.6 14.3 57.1 0.0 0.0 0.0 7
Crop markets 16.7 16.7 66.7 0.0 0.0 0.0 6
Livestock
markets 0.0 0.0 75.0 25.0 0.0 0.0 4
Rural roads
(Km) 0.0 0.0 83.3 16.7 0.0 0.0 6
Railways
(Km) 0.0 0.0 75.0 25.0 0.0 0.0 4
Communication 0.0 0.0 100.0 0.0 0.0 0.0 4
Banking and
insurance 0.0 0.0 100.0 0.0 0.0 0.0 6
Source: ADB Database-Asia Pacific Country Assessment of Agricultural Statistics Systems
Table 2.14 presents the main sources of agricultural statistics on social related
indicators in the Asia and Pacific region. The major sources of data are
22
censuses and surveys for social data, while administrative records are the major
sources for environmental data.
Table 2.14: Main Sources of Statistics on Demographic and Environmental
Characteristics of Agriculture in the Asia Pacific Region
Main Sources of Data (%)
Census Sample
Surveys
Administrative
Records
Estimates/
Forecasts
Special
Study
Expert
Opinion/
Assessment
Number
of
Countries
SOCIAL
Population
dependent on
agriculture
47.1 35.3 17.7 0.0 0.0 0.0 17
Agricultural
workforce (by
gender)
45.0 35.0 20.0 0.0 0.0 0.0 20
Fishery
workforce (by
gender)
46.2 53.9 0.0 0.0 0.0 0.0 13
Aquaculture
workforce (by
gender)
57.1 42.9 0.0 0.0 0.0 0.0 7
Household
income 25.0 68.8 0.0 6.3 0.0 0.0 16
ENVIRONMENTAL
Soil
degradation 0.0 25.0 25.0 25.0 25.0 0.0 4
Water pollution
due to
agriculture
0.0 0.0 66.7 33.3 0.0 0.0 3
Emissions due
to agriculture 0.0 0.0 50.0 0.0 25.0 25.0 4
Water pollution
due to
aquaculture
0.0 0.0 100.0 0.0 0.0 0.0 1
Emissions due
to aquaculture 0.0 0.0 100.0 0.0 0.0 0.0 1
GEOGRAPHICAL LOCATION
Geo-coordinate
of the statistical
unit (parcel,
province,
region, country)
40.0 10.0 50.0 0.0 0.0 0.0 10
Source: ADB Database-Asia Pacific Country Assessment of Agricultural Statistics Systems
23
2.7. FINDINGS OF THE IN-DEPTH COUNTRY
ASSESSMENTS
These have been carried out for Ghana, Uganda and Bhutan4.
2.7.1. STATUS OF AGRICULTURAL STATISTICS IN GHANA
At the institutional level, the responsibility for agricultural statistics in Ghana is
held by the Statistics, Research and Information Directorate (SRID) in the
Ministry of Food and Agriculture (MOFA) and the Ghana Statistical Service
(GSS). Other ministries producing agricultural statistics at the moment include
the following:
Ministry of Fisheries
Ministry of Lands and Natural Resources
Ministry of Local Government and Rural Development
Ministry of Finance
National Development Planning Commission
Acceptable data on forest production of wood and non- wood quantities and
value are available. These are obtained annually from administrative records
across the country. External trade consists of quantity and value data for
exports and imports. These are collated monthly from administrative records
throughout the country. The data is acceptably reliable. The following
institutions are responsible for the data collection: Ghana Statistical Services
(GSS), the Line Ministries, Customers/Revenue Authority and Other agencies.
Data on fertilizers and pesticides are collected nationwide every year by
accessing available administrative records. This exercise is overseen by:
MOFA, GSS, the Line Ministries and Customers/Revenue Authority. The most
recent data available is that for 2011 and is considerably reliable. Data on seeds
and animal vaccines and drugs are obtained in the same manner as the inputs
above. The only exception is that the institutions responsible for the data
collection are only the following two: the MOFA and the Producers’
Association. Another input on which data is available is animal feed. This
information is compiled by the MOFA, Producers’ Association and GSS. The
necessary data is collected quarterly from administrative records nationwide. As
for fertilizers and pesticides, the most recent available data on animal feed is for
2011, and the data are considered reliable. The last input to be considered is
4 FAO, 2014; Mubiru, J, 2014; Kencho Thinley, 2014
24
aquatic fish seed. Data is produced monthly by the line ministries. This is made
possible by accessing administrative records across the country.
Reliable information is also available on area equipped for irrigation, crop
markets, livestock markets, rural roads, railways and communication.
Administrative records are stated to be the main source of data for the listed
items. The frequency of collection is yearly and it covers the whole of Ghana.
The producers of this data are listed as follows:
Area equipped for Irrigation: MOFA, Line Ministries and Producers’
Association.
Crop markets, livestock markets: MOFA, Line Ministries and other
related agencies.
Rural roads: MOFA, Line ministries.
Railways, Communication: Line ministries.
The data on the Banking and Insurance sector is highly reliable and is produced
by the Line Ministries, BoG, and other agencies. Just like the others, the data is
produced annually from administrative records.
There is no information on water pricing. On the other hand, all the other items
under investment subsidies have data available on them. Data on these are all
collected annually from administrative records across the nation thus are
reliable. The institutions responsible for the data collection are as follows:
Public Investment in Agriculture - MOFA, other Line Ministries and
BoG.
Agricultural subsidies - MOFA, other Line Ministries, BoG and
Customers/Revenue Authority.
Fishery Access fees – line Ministries
Public expenditure for fishery management – line ministries, other
agencies
Fishery subsidies – line ministries, Customers/Revenue Authority.
On environment acceptable data is available on soil degradation and water
pollution due to agriculture for 2011. This is produced every year and the data
is sourced from administrative records in the entire nation. MOFA, line
ministries and other agencies like the EPA are responsible for this task. Data on
emissions due to agriculture, emission due to aquaculture and water pollution
due to aquaculture were not available at all.
25
The breakdown of the human resource of the MOFA is as follows:
12 regular professional staff at headquarters, all of whom are for
agricultural statistics
10 regular professional staff at regional/ local offices. All of these
people do work on agricultural statistics.
13 regular support staff at headquarters for agricultural statistics.
6 professional staff and 2 support staff have been trained in national
training institutions over the last 12 months.
It was reported that out of the 106 regular professional staff at the GSS
headquarters, only one person was for agricultural statistics. There was no non-
professional staff at headquarters nor regional/district GSS staff working on
agricultural statistics.
2.7.2. STATUS OF AGRICULTURAL STATISTICS IN UGANDA
The in-depth assessment of the agricultural statistics system in Uganda was
carried out by a team consisting of an International Consultant, the National
Strategy Coordinator, the National Consultant in collaboration with a mission
of USDA consultants [Mark R. Miller Director, International Programs Officer
USDA; Cheryl Chritensen, Branch Chief, Food Security and Development
Branch]. Together, the team visited the selected institutional managers where
agricultural statistics is considered critical but already identified as fairly
constrained. The task involved:
1. The assessment of key data source capacities (institutional
arrangements, human, technical and financial resources, statistical
infrastructures, methods of data collection, processing and
dissemination); then
2. Identifying capacity gaps within the institutional arrangements and
evaluating needs for improvements and proposing requirements.
As mentioned elsewhere, MAAIF is the main source of administrative
agricultural data. However, there are a number of Commodity Boards that
operate outside the usual MAAIF structure. They are legally established and
some of them have clauses that allow them to collect data but with obligations
to supply the information to the head office. These Boards are basically
required to promote production, marketing the produce and in some cases
provide extension services. They are: Uganda Coffee Development Authority
(UCDA), Cotton Development Authority (CDA), NAGRIC handling artificial
insemination matter, NARO, NAADS providing extension services except
26
Uganda Tea Authority which is a private entity but provides data whenever
asked to do so.
Further, a number of ministries also collect and manage various agriculture-
related administrative data. For instance, the Ministry of Water and
Environment needs quality statistical data to inform policy design and facilitate
planning, implementation, monitoring and measuring the impact of
development interventions in the sector.
A Sector Statistics Committee (SSC), with representatives from technical
departments, was established, to guide the statistics function. Mainly
administrative data, compiled at lower levels, with technical assistance (TA)
from the centre is generated. The Ministry embraced the “Operational DB” and
“Data-ware House” concept. Each department is responsible for collection and
maintenance of its datasets. The SSC is expected to periodically discuss, guide
and monitor statistical programmes, regarding availability, quality and use of
sector statistics. The data is mainly disseminated through reports to MFPED,
OPM, NPA, MoW&E Website, sector input into UBOS Statistical Abstracts,
Ad-hoc briefs to State House, private firms and academicians.
Other stakeholders in the generation of agricultural statistics are:
1. The Uganda Coffee Development Authority (UCDA) was established
by statutory mandate in 1991 after the liberalization process. It is
expected to promote and oversee the development of the entire coffee
industry through research, quality assurance, improved marketing and
providing for any other matters incidental to activities of coffee
production. It is one of the statutory bodies that operate under the
overall guidance of the Ministry of Agriculture, Animal industry and
Fisheries. The UCDA Act provides for the production of all relevant
statistics, sharing and dissemination of the statistics. During the initial
stages focus was on marketing the produce but after sometime it became
necessary to obtain and manage statistics to meet the emerging
demands.
Human resources are available at regional and sub-regional field
offices, each of which handles a number of districts. Information is
obtained on monthly production levels. Quarterly monitoring is based
on the information of the 2008 baseline survey that was carried out in
20 out of 52 coffee producing districts of Uganda at that time. By the
year 2014, the survey was in 82 coffee producing districts out of 112.
While the statistical system exists to collect the data from farmers, the
required numbers of staff are still inadequate since some extension
27
workers have too much work before them to be able to focus more on
this subsector. Financial resources to manage statistical operations are
inadequate.
From administrative processes the number of coffee farmers, acreage
including weather conditions and farmers estimates area obtainable.
Once in a while surveys are conducted. Sometimes yield estimates are
obtained from demonstration fields and at times from selected plots to
estimate yields. Ugandan coffee is about 80% Robusta and the
agricultural input requirement is not high. Yield estimates of 700 Kgs of
clean coffee per hectare were common in the past but there have been
improvements of 2.5 – 3 or even 4 metric tonnes per hectare in some
areas.
2. The National Forestry Authority:
The Uganda Government Forestry Policy (2002) summarises the
mandate of the National Forestry Authority (NFA) as sustainable
management of the government’s Central Forest Reserves (CFRs) then
promotion and development of private forestry. The methods of data
collection relating to forest activities and operations are for generating
internal data collection which later is a made available for public use.
There are seven ranges each with a Manager and specialized staff to
collect:
Inventories,
Number of Mother trees, and
Number of replacement trees
Data processing is done at the head office of NFA because the field
offices do not have the necessary facilities.
3. Local Government office
a) Jinja District
The relationship with the mother ministry was described as weak and
poorly coordinated but has seen some improvement in the recent past.
The District is doing well on data collection within the means available.
The Veterinary Department has been given a template on the chain of
data to collect.
A format has been developed by the district for all types of data: crop,
livestock, fish and entomology data. Information based on that format is
28
often received from the DVO and DAO. However, the district has more
area to cover than staff can manage.
There are 6 rural sub-counties, 3 rural town councils, 3 divisions in Jinja
Municipality and 59 parishes that are guided on how to do their
planning and forward their drafts to the higher levels of desegregation
into bigger chapters of planning. However, indicators at this level are
scarce they are therefore highly dependent on community wishes of the
area.
b) Wakiso District
Pre-1987 there were established extension workers down at sub-county
and the parishes that furnished monthly data on crops and livestock with
a known format. The system continued until in 1992 – 1997 the re-
structuring of government service force did away with the field assistant
(most of the extension workers) and left the task to the districts.
However, the districts were not empowered to manage the extension
workers. Later, in 2006 the NAADS program was established and tried
to re-establish the extension.
The International Consultant on the In-depth Country Assessment
observed that the agricultural system in Uganda exists but it is still
experiencing several weaknesses that need to be addressed before the
system can properly function. They included but are not limited to the
following:
i. There is no unified agricultural production statistics database housed
and linked to other sectors and sub-sectors engaged in agricultural
activities. It was recommended that UBOS house this database and
linked to other producers and users.
ii. The critical Meteorological data for the Early Warning System has not
been included in the SSPS of MAAIF and yet the up-to-date
information impacts on many policy matters that affect the agricultural
sector production. At the same time it provides guidance to both
extension workers and farmers for better production programmes.
Management at UBOS shall initiate meetings and discussions with both
the MAAIF and MoW&E to ensure that they as users and producers
guarantee the availability of resources for the compilation and inclusion
of the important indicators in the statistical schemes devised under the
PNSD.
iii. While the NAADS programme provides inputs to the farmers and takes
records of the deliveries, it has neither capacity nor any arrangement to
29
record statistics of areas, yields and production of the outcomes of the
initiated activities whether for crops or animals. This is a major
weakness that can be addressed by encouraging NAADS to obtain the
data for all the processes of agricultural production. However, it will be
very useful if the NAADS activities are properly streamlined to take
place under the overall supervision of the Ministry of Agriculture and
avoid duplication of effort.
iv. Some institutions do not produce suitable budgets for agricultural
statistical activities. Management promised to encourage MDA’s to
provide budgets for early inclusion in their ministerial financial
requests.
v. It has been observed that many times, budget provisions for statistical
activities have been subjected to serious reductions or cuts to the extent
that whatever remains cannot help in producing sensible data for
compiling appropriate indicators. As a remedy the workshop therefore
recommended that all statistical budgets once approved wherever they
are should be ring fenced as a protective measure to ensure that
data/information flows to the decision makers at all times. It was agreed
that UBOS should lead the crusade of protecting the budgets through
their coordination role of PNSD. Management of UBOS promised to
make contacts with the Ministry of Finance and other related organs to
reinforce this recommendation since it had already been proposed and
discussed. This will more vigorously fooled up when the new Act
comes in force.
vi. There is a strong demand for training at all levels but more particularly
in sampling techniques for agricultural statisticians and limited training
for enumerators at the primary data collection levels. Similarly, training
is required for monitoring and evaluation specialists.
vii. Lack of suitable and outdated equipment is another setback in getting
agricultural statistics from many of the institutions or agencies that
would otherwise be powerful sources.
2.7.3. STATUS OF AGRICULTURAL STATISTICS IN BHUTAN
Bhutan has a relatively decentralized statistical system with the National
Statistical Bureau (NSB) as its apex body. The Ministry of Agriculture and
Forests (MoAF) generates agriculture statistics through surveys, census and
administrative records.
Harvested area and crop production data are captured annually through sample
surveys conducted by the Department of Agriculture (DoA). Livestock data,
including fisheries, are collected through the livestock census undertaken
30
annually by the Department of Livestock (DoL). The forestry data is compiled
by the Department of Forests and Park Services (DoFPS) annually from an
administrative reporting system. The DoFPS has an automated database system
that records data from field offices to the department headquarters. Data on
market and trade is maintained by Department of Agricultural Marketing and
Cooperatives (DAMC) through administrative records.
The MoAF Policy and Planning Division (PPD) compile and analyze the data
produced by various agencies within the ministry as well as the other ministries
and publish the Bhutan RNR Statistics annually. The statistical data captured
through surveys and censuses are available at geog (sub-districts), dzongkhag
(districts) and national levels while most of the compiled administrative and
secondary data are available at dzongkhag and national levels. The NSB does
not directly collect agricultural data but calculates Renewable Natural
Resources (RNR) statistics including the gross domestic product (GDP) and
growth using the statistical data submitted by the MoAF.
The major issues/challenges with the agricultural statistical system are mainly
due to lack of coordination, funding and professionals. In addition inadequate
and poor quality statistics negatively affect the agricultural statistical system.
These and other weaknesses impact greatly on the ability of data gathering,
processing, storage, dissemination and use of agricultural statistics.
31
3 Structural Issues in
Administrative Data Systems
for Agricultural Statistics Chapter 2 of Technical Report 1 discusses the proposed analytical framework,
structure, conduct and performance for assessment of the Administrative Data
System for Agricultural Statistics (ADSAS) and a quality framework for
assessing data quality. The design framework given in Table 2.1 of Technical
Report 1 is used for the review of the ADSAS systems. This discusses the
structural issues, including organizations collecting administrative data, their
structure, the core data items collected and staffing levels and qualifications
(FAO 2015a). Table 3.1 presents the structure, conduct and performance design
issues of any ADSAS. This section discusses structural issues in the ADSAS,
while discussions related to conduct and performance are provided in Sections
4 and 5, respectively.
32
Table 3.1: Structure, Conduct, and Performance (SCP) Design Issues of Any ADSAS
Structural Design Issues Conduct Design Issues
Performance
(Quality of core data items
covered)
1. ADSAS’s perceived mandate (and
clientele)
Aims and objectives
Clientele (e.g., farmers,
traders, consumers,
government, donors)
2. Institutional home, organization, and
coordination
Public-, private-, farmer
organization, or trader and
NGO-based ADSAS
Provides complementary
services that generate or
increase value of information
Geographic coverage and
range of commodities
Assuring coordination among
stages
o Integration of ADSAS
Activities
o Centralized or decentralized
ADSAS activities
o Specialization in ADSAS
Products
Design of incentives for
ADSAS staff
Profit orientation of the
ADSAS
3. Nature of core data items covered
(crop items livestock items, poultry,
aquaculture and fisheries products, agro-
forestry production, agricultural inputs,
land cove)
1. Information provided
Raw data
Analysis of raw
data
Analytical reports
2. ICT used in the collection
and
dissemination
Traditional ICT
(e.g., radio,
television, and fax)
Modern ICT (e.g.,
email, internet,
SMS)
PDAs and GPSs
3. Funding strategies
4. Data collection methods
used
Structured
questionnaire and
enumerators
Wiki approach
(users SMS or
update web)
5. Quality control methods
used
6. Feedback mechanism used
1. Coverage
2. Comprehensiveness
3. Timeliness
4. Punctuality
5. Completeness
6. Relevance
7. Accuracy
8. Reliability
9. Integrity/ Credibility
10. Accessibility to different
clientele
11. Clarity/interpretability
12. Comparability
Consistency/ Coherence/
13. Sustainability of ADSAS
Financial support
User support
Cost minimization
Adapted from Kizito, A. M. (2011) “The Structure, Conduct, and Performance of Agricultural Market
Information Systems in Sub-Saharan Africa”; Agricultural, Food, and Resource Economics, East Lansing,
Michigan, Michigan State University, Ph.D. Dissertation.
33
3.1. ORGANIZATIONS COLLECTING AND MANAGING
AGRICULTURAL ADMINISTRATIVE DATA
Many sources of administrative data have application to agricultural statistics,
in particular. These include the regular returns or reports by agricultural
field/extension staff, (for various agricultural items, including crops and
livestock), tax data, land ownership records, information on government
subsidies, import/export data, data on agricultural production and inputs from
manufacturers and distributors, administrative farm registers and other
registration or licensing systems, records on agri-tourism, farmers’ associations,
private businesses’ data, and meteorological data.
In most countries the basic agricultural administrative data, i.e. crops, livestock,
fisheries, forestry; is collected and managed under the ministries of agriculture,
livestock, fisheries or forestry. However, in many countries there are parastatal
organizations collecting administrative data especially on commercial or cash
crops. Private sector agencies or organizations also often administratively
collect and manage various data. These agencies sometimes collect and manage
the data without any direct participation of the Central or National Statistics
Office (NSO). A summary of the organizations collecting and managing
administrative data in many African countries is included in Table 3.2. In-
depth analyses of Uganda, Tanzania, and India follow.
34
Table 3.2: Number of organizations collecting and managing agricultural administrative
data in selected African countries
TYPE OF
ORGANIZATION
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIA
MA
UR
ITIU
S
RS
A
SO
UT
H S
UD
AN
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L
CSO/ Other
7
4
11
Information center
(MLFR) 3
3
Input Association
1
1
MDA 4
1
5
Min of Environment
1
1
Min. of Ocean
1
1
Min. of Agriculture
6
4
2 1
1 1 4 19
Min. of Fishing
1
1
Min. of Housing
1
1
Min. of Land
2 2
Min. of Livestock
1
1
None
4
1
5
Parastatal/ Authority 1
2
2 3 6
14
Producer Association
4
4
Research Organization
2
2
Total 5 6 7 5 6 4 4 7 10 6 4 1 6 71
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
3.1.1. ORGANIZATIONS INVOLVED IN AGRICULTURAL
STATISTICS IN TANZANIA
In Tanzania the Agricultural Routine Data System (ARDS) was developed to
meet the data needs for monitoring and evaluation of the Agricultural Sector
Development Programme (ASDP). A pilot study for the improved version of
ARDS was carried out in Kondoa and Mpwapwa districts in Dodoma Region
and Kilosa and Morogoro District Council in Morogoro Region in Tanzania
during 2008 to 2010.
The main features of the improved ARDS were:
Harmonized Village Agricultural Extension Officer/Ward Agricultural
Extension Officer (VAEO/WAEO) format,
District level integrated data collection format, and
35
A data management software Local Government Management Database
(LGMD-2). This was expected to transmit/generate a harmonized
database for agriculture.
Agricultural Routine Data System (ARDS) plays an important role in delivering
field level agricultural information to districts, regions and Agricultural Sector
Lead Ministries (ASLMs). But effective monitoring, supervision, planning, and
policy formulation has been difficult partly because the ARDS has not been
functioning properly. For example, the following shortcomings have been
identified:
The content of the monthly report prepared by village / ward
agricultural extension officers (VAEO/WAEO) is different from one
report to the other, which makes data consolidation to the district level
difficult.
Submission rate of the monthly reports by extension officers is low.
The quality of the report is low.
There are many villages / wards which have no extension officers.
Few reports produced by district officers are delivered to regions or
ASLMs.
One of the purposes of ARDS improvement was to revive the flow of routine
reporting which originates in the Crop and Livestock Development Report (now
VAEO/WAEO format) from the Local Government Authority (LGA) to
Agricultural Sector Lead Ministries (ASLMs) via Regions using a
Village/Ward formats.
3.1.2. ORGANIZATIONS INVOLVED IN AGRICULTURAL
STATISTICS IN UGANDA
In Uganda, the Ministry of Agriculture Animal Industry and Fisheries (MAAIF)
designed a standard template to facilitate regular reporting on agricultural data
generated from administrative records. MAAIF has collaboration with the local
governments at all levels mainly, district and sub county. The local
administrative level staff collects agricultural data, including crop and
livestock-related data, on a regular basis – such as monthly or quarterly. They
report to the District Production Coordinator at district level, where the data is
summarized further and utilized when necessary. The District Production
Coordinator is supposed to share the agricultural data with MAAIF every
quarter.
36
3.1.3. ORGANIZATIONS INVOLVED IN AGRICULTURAL
STATISTICS IN INDIA
India has one of most elaborate systems for the collection and management of
data, in general, and more specifically agricultural statistics, including
administrative data. Therefore, it offers a number of lessons. It is a
decentralized system with the State Governments. The National Sample Survey
Organisation is responsible for the planning and operations of the scheme for
Improvement of Crop Statistics (ICS) and employs full-time staff for field
supervision. It shares the fieldwork with the designated State agencies, which
carry out the field supervision in about half the number of sample villages.
Institutions involved are State Agricultural Statistics Authorities (SASAs),
which operates at the State level, and the Directorate of Economics and
Statistics, Ministry of Agriculture (DESMOA), which is responsible for
compilation of data at the national level. A summary of these three institutions
and their primary responsibilities is as follows:
i. Agricultural Statistics Authorities (SASAs): Involved in the collection
and compilation of Agricultural Statistics at the State level.
ii. Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA): Operates at the Centre and is the pivotal agency for such
compilation at the all-India level.
iii. The National Sample Survey Organization (NSSO), and the State
Directorates of Economics and Statistics (DESs): Planning and
operations of the scheme for Improvement of Crop Statistics (ICS).
The States and Union Territories can be classified into three broad groups
(structure): (a) States and Union Territories which have been surveyed in a
cadastral manner and where area and land use statistics form a part of the land
records maintained by the revenue agency (referred to as “temporarily settled
States”). (b) “Permanently settled” States, where there is no land revenue
agency at the village level and crop area and land use statistics are collected
through a scheme of sample surveys. (c) Districts and Union Territories for
which only “conventional” estimates are available.
The Institutions involved are:
The Directorate of Economics & Statistics, Ministry of Agriculture
(DESMOA)
Market Intelligence Units,
Meteorological Department
The Crop Weather Watch Group (CWWG).
37
The State Agricultural Statistics Authorities (SASAs)
National Crop Forecasting Centre (NCFC)
The Space Application Centre (SAC)
3.1.3.1. Statistics on Crops and Horticulture in India
Final estimates of crop production are a product of area estimates and yield
estimates. Area estimates obtained through complete enumeration, and yield is
obtained through crop-cutting experiments. The estimates become available
much after the crop is harvested.
Administrative data sources include the National Horticultural Board (NHB)
and the State Directorates of Horticulture and Agriculture. There are two main
sources that generate statistics of production of horticultural crops namely: (i)
The Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) operates a centrally sponsored scheme “Crop Estimation Survey on
Fruits and Vegetables” in 11 States covering 7 fruit and 7 vegetable and spice
crops for estimating area and production. The fruit crops covered are mango,
banana, apple, citrus, grapes, pineapple and guava. The vegetable and spice
crops are potato, onion, tomato, cabbage, cauliflower, ginger and turmeric. (ii)
The National Horticultural Board (NHB) compiles and publishes estimates of
area, production and prices of all important fruit and vegetable crops based on
reports furnished by the State Directorates of Horticulture and Agriculture.
These estimates are based on the informed assessment of local level officials
dealing with horticulture and the reports of market arrivals in major wholesale
fruit and vegetable markets.
Estimates of cotton production are collected and published by the Cotton
Advisory Board (CAB) and those for oilseeds by the Central Organization for
Oil Industry and Trade (CODIT). The DESMOA estimates are based on the
girdawari for area and crop cutting experiments under the GCES for yield,
whereas the estimates of COOIT mainly depend on the feedback received from
important markets about arrivals, trend of crop and the additional information
provided by members of the industry.
The main reasons for divergence, in this case too, are differences in
methodology, post-harvest losses, incomplete market arrivals and the
inclination of the oilseeds industry to underestimate production in order to
lobby for larger imports.
3.1.3.2. Land Use Statistics in India
Institutions involved are Agricultural Statistics Authorities (SASAs) at the State
level, Directorate of Economics and Statistics, Ministry of Agriculture
38
(DESMOA) and the National Remote Sensing Agency (NRSA). Statistics of
land use are compiled from the village land records maintained by the patwari.
The information is available according to each survey number and recorded
under nine categories: (a) Forests, (b) Area under Non-Agricultural use, (c)
Barren and Uncultured Land, (d) Permanent Pastures and other Grazing Land,
(e) Miscellaneous Tree Crops, (f) Culturable Waste Land, (g) Fallow Land
other than Current Fallows, (h) Current Fallows, and (i) Net Area Sown.
Land use statistics are also being collected through nationwide land use or
cover mapping by the National Remote Sensing Agency (NRSA) according to a
22-fold classification. The categories are much more detailed and provide
useful information for land development programmes. However, these details
are still not available at the local levels of block and panchayat.
3.1.3.3. Irrigation Statistics in India
Irrigation statistics mainly relate to data on area irrigated by different sources
and under different crops. The principal sources of irrigation statistics are the
crop statistics compiled by the Directorate of Economics and Statistics,
Ministry of Agriculture (DESMOA), and the publications of the Ministry of
Water Resources. Besides these, some data on irrigated area are available from
the administrative reports of State Government departments and the
Agricultural Census. Rainfall and weather data are available from the India
Meteorological Department (IMD). Groundwater is the principal source for
minor irrigation and the Central Ground Water Board (CGWB) is responsible
for generation and dissemination of statistics on ground water which inter-alia
include statistics on minor irrigation. The Minor Irrigation Division of the
Ministry of Water Resources also compiles information on minor irrigation at
the national level on the basis of statistics furnished by nodal offices designated
for the purpose in individual States. The Command Area Development
Division of the Ministry compiles and disseminates data on Command Area
Development Programme (CADP) furnished by State Command Area
Development Authorities (CADAs). A summary of institutions involved is as
follows:
Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) Ministry of Water Resources
The India Meteorological Department (IMD)
The Central Water Commission (CWC)
The Central Ground Water Board (CGWB)
The Minor Irrigation Division of the Ministry of Water Resources
39
The Command Area Development Division of the Ministry of Water
Resources compiles and disseminates data on Command Area
Development Programme (CADP) furnished by State Command Area
Development Authorities (CADAs).
Strengths, weaknesses and suitability for use in agricultural statistics within an
integrated and cost-effective agricultural statistics system
The biggest problem with the collection and management of agricultural
administrative data in many developing countries has been the many and
frequent changes in the administrative structure itself. For example, in Uganda
there have been many changes in the number and boundaries of districts.
Further, in the 1980s there was a shift from the purely administrative chiefs to
the semi-political local council leaders. The latter were not used to collecting
data. Similarly, the decentralization policy meant that the extension staff were
no longer answerable to the central the governments. Finally, the restructuring
policies have meant that services like production and marketing or distribution
of agricultural inputs, which were originally controlled by the central
governments, or at least parastatals, are now in the private sector.
The other problem is divergence in figures from different sources on the same
data item. The fact that most administrative data collectors are government
institutions implies that administrative data collection can be sustainable.
3.1.3.4. Statistics on Agricultural Prices in India
The Institutions involved are: The Directorate of Economics and Statistics,
Ministry of Agriculture (DESMOA), State Directorates of Economics and
Statistics (DESs), State Market Intelligence Units, State Department of Food
and Civil Supplies and State revenue departments.
The Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) is responsible for the collection, compilation and dissemination of
the price data of agricultural commodities. The price data are collected in terms
of (a) weekly and daily wholesales prices, (b) retail prices of essential
commodities, and (c) farm harvest prices.
Retail prices of essential commodities are collected on a weekly basis for about
80 commodities by the staff of the State Market Intelligence Units, State
Directorates of Economics and Statistics (DESs) and State Department of Food
and Civil Supplies. However, the flow of data from these agencies is not
considered satisfactory. Farm Harvest Prices are collected by the field staff of
the State revenue departments for about 30 commodities at the end of each crop
40
season and published by the DESMOA. It brings out a periodical publication
entitled, Farm Harvest Prices of Principal Crops in India.
3.1.3.5. Statistics on Agricultural Market Intelligence in India
The Institutions involved are the State Agricultural Market Intelligence Units
whose mandate is to help the DESMOA in the formulation, implementation and
review of the agricultural price policy relating to procurement, marketing,
storage, transportation, import, export and credit, etc. They furnish regular
reports on market arrivals, off-takes, stocks, crop prospects, and outlook of
market prices and periodically do appraisal of production of various crops per
season to inform preparation of crop forecasts.
3.1.3.6. Livestock and Fisheries Statistics in India
Livestock statistics are mainly obtained through Livestock census and fisheries
statistics is obtained through sample surveys. However, data on deep-sea
fishing are obtained through reports required to be furnished by trawlers and
other deep-sea fishing vessels.
3.1.3.7. Forestry Statistics in India
The Institutions involved are: The Directorate of Economics and Statistics,
Ministry of Agriculture (DESMOA), State Forest Departments and the Council
of Forestry Research and Education (ICFRE). Forestry statistics are collected
mainly as a by-product of administrative reports of the State Forest
Departments. The data on the forestry are obtained through a set of periodical
reports (45 in number) furnished by the State Forest Departments and other
concerned agencies. In addition to details of forest area, the reports provide
information on forest products (wood and non-wood), forest land under
cultivation, and grazing land, etc. The Forest Survey of India (FSI) monitors
the forest resources at a macro level, storing and retrieving forestry related data,
designing methodology for forest surveys while the Indian Council of Forestry
Research and Education (ICFRE) is mandated to collect, collate and compile
primary and secondary data generated by the State Forest Departments and
various Central ministries.
Since 1987, the FSI has begun using Remote Sensing (RS) technology to
collect data on forest cover under three broad classes (dense forest, open forest
and mangroves). Introduction of digital interpretation has helped in reducing
the time lag in the availability of the area estimates to just a few months after
the completion of the survey.
41
3.1.3.8. Agricultural Inputs Statistics in India
The Institutions involved include the Fertilizer Association of India (FAI)
which collects information on production, distribution and stocks of fertilizers
held though it does not provide details of actual consumption. The FAI is made
up of the following 6 institutions: The Agricultural Implements and Machinery
Division of the Department of Agriculture, the Tractors Manufacturing
Association and Manufacturers of Tractors and Power Tillers, the Directorate of
Plant Protection, Quarantine and Storage (PPQ&S) in the Ministry of
Agriculture. The Agricultural Implements and Machinery Division of the
Department of Agriculture which compiles and maintains statistics on
production and sale of tractors and power tillers from Tractors Manufacturing
Association and Manufacturers of Tractors and Power Tillers. The Directorate
of Plant Protection, Quarantine and Storage (PPQ&S) in the Ministry of
Agriculture collects plant protection, quarantine and storage data. This data are
not compiled and maintained as an organized database. The Locust Warning
Organization of the Directorate collects information on locust development and
movement together with related aspects. This information is centrally collated
and a fortnightly locust situation bulletin is brought out and circulated to
various organizations
3.2. INSTITUTIONAL HOME, COORDINATION AND
GEOGRAPHICAL COVERAGE
The Ministries, Departments or Agencies (MDAs) collecting administrative
data often have staff at headquarters and then field (or extension) staff;
otherwise, in some countries data is collected by chiefs. For these, data
collection is often not their primary job. They have other jobs. Data on
institutional home, coordination and geographical coverage is given in Table
3.3.
42
Table 3.3: Coordination, institutional home and geographical coverage of ADSAS in
selected African countries
Bu
rund
i
Eg
yp
t
Gh
ana
Les
oth
o
Lib
eria
Lib
ya
Mau
rita
nie
Mau
riti
u
So
uth
Su
dan
So
uth
Afr
ica
Su
dan
Ug
and
a
Zam
bia
To
tal/
13
Institutional home
Public (Government) 1 1 1 1 1 1 1 1 1 1 1 1 1 13
Private 0 0 0 1 0 0 0 0 0 1 0 0 0 2
Farmer organization 0 0 0 1 0 0 0 0 0 0 1 0 0 2
Trader organization 0 0 0 1 0 0 0 0 0 1 1 0 0 3
NGO 0 0 0 0 0 0 0 0 0 0 1 0 0 1
Research Organization 0 0 0 1 0 0 0 0 0 0 1 0 0 2
Coordination
Centralized 1 1 0 1 1 1 0 1 0 1 0 0 1 8
Partially Decentralized 0 0 1 1 0 0 1 1 1 0 1 1 1 8
Fully Decentralized 0 0 0 0 0 0 0 0 0 1 0 0 0 1
Geographic coverage
Sub-national (Part of
country) 0 1 0 1
0 0 0 0 0 1 0 0 3
National (entire country) 1 1 1 1
1 1 1 1 1 1 1 1 12
Regional (many countries) 0 1 0 1
0 0 0 0 0 0 0 0 2
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
3.2.1. INSTITUTIONAL HOME
Institutional home refers to whether the ADSAS is housed in the public, private
or other sectors. Other sectors may include farmer or trader organizations or
NGOs. Table 3.3 shows that most ADSAS are housed in government ministries,
departments and agencies. In terms of institutional home, most ADSAS are
housed in government. It is usually argued that housing statistical activities in
autonomous authorities and the private sector comes with high-powered
incentives that lead to better performance outcomes (e.g., in terms of the quality
attributes) than when housed in traditional hierarchical organizations such as
government departments and ministries with low-powered incentives. The
success of the statistical activities in Uganda is partly attributed to moving the
statistical activities from the departmental level under the Ministry of Finance
and Planning to an independent and autonomous Bureau of Statistics.
3.2.2. COORDINATION
Complementary services refer to whether the ADSAS conducts other activities
besides collecting administrative data. This is actually always the case by
43
default, since administrative data is always a byproduct of another activity. In
this case, organizations that generate administrative data as a result of activities
that are mandatory or activities that generate user fees are likely to be
sustainable than those whose activities are voluntary or without user fees. For
example, active commodity exchanges generate quantity and price information
as a byproduct of commodity trading at no data collection costs, thus improving
on sustainability due to low data production costs. Commodity exchanges also
generate accurate data without sampling or non-sampling errors as the
information collected is related to actual trading and not on surveys. The
customs bodies collect reasonably accurate import data because they get
revenue from imports. This is in contrast to exports where many countries often
do not collect revenue.
Coordination can be categorized into three groups: i) integration of ADSAS
activities ii) centralized or decentralized ADSAS activities, and iii)
specialization in ADSAS products. The main question is what are the
advantages and disadvantages of having all activities of the ADSAS being
conducted in one organization compared to many organizations, or in a
centralized system compared to decentralized system, or to produce specialized
administrative data or information products on many.
Integration of ADSAS activities refers to whether all the activities of the
ADSAS such as collection, analysis and diffusion of administrative agricultural
statistics data are conducted in one organization or in several.
Having all activities in one organization can be viewed as vertical integration of
activities, which reduces possibilities of hold-up by strategic partners when
activities are conducted by many partners, where delays, for example by one
partner may lead to delays in the release of statistical data and reports. In this
case, centralization secures timelines and reputation of the ADSAS. On the
other side, however, centralization may be associated with bureaucracy.
Furthermore, the ADSAS may produce too many core data items and associated
data that it is not possible to collect all information under one organization. This
may result in a need for more systems or involvement of many organizations
but with more coordination.
Centralization versus decentralization refers to whether activities of
administrative data production are in one place or decentralized in many places.
In terms of coordination, Table 3.3 shows that most ADSAS interviewed have
centralized or one national office. Others are partially decentralized with many
sub-national offices and a central office. Only one reported to be full
decentralized with many sub-national without central offices. The advantages
of decentralizing administrative data collection are obtaining more feedback,
44
the ease of providing customized information to local users, and speed or
timeliness of obtaining information. Centralization is associated with
economies of scale in production and diffusion of administrative data. For
example there will be only need for one computer to process and diffuse the
information. The disadvantages of centralization may be too much expansion
thus bringing in agency problems such as shirking.
For specialization, the ADSAS may be organized in such a way that different
activities are conducted by different organizations, for example by core items or
data item. One organization may be in charge of crop data items and another in
charge of livestock data items but all dealing with collection and diffusion.
Another arrangement could be that one organization deals with collection of all
data items and another in charge of diffusion for all data items. The advantage
is gain in comparative advantage in activities and the disadvantages are
potential delays and hold-up associated with different organizations specializing
in different activities.
3.2.2.1. Coverage: Geographical and Range of Commodities
The geographical coverage relates to whether the ADSAS covers the whole
country, just parts of the country or many countries. Another aspect can be
range of commodities covered by the ADSAS, which is discussed in the section
on Core Items and Core Data Items Covered below. This is also related to the
range of data items monitored by the ADSAS. By default, Table 3.3 shows that
most ADAS cover the entire countries. This is most likely because they are
mostly located in government, which normally has to design programs to cover
the whole country so as to obtain information which represents the whole
country in terms of production, marketing and consumption (food security).
The geographic coverage may influence timeliness of the release of data and
also the costs of data collection, which in turn affects sustainability. National
coverage may be associated with economies of scale in collection and diffusion.
The most common weakness in developing countries is normally the lack of co-
ordination between the NSO and the various administrative agricultural data
collection and management systems. India possesses an excellent infrastructure.
With most parts of the country having detailed cadastral survey maps,
frequently updated land records and the institution of a permanent village
reporting agency, the country has all the necessary means to produce reliable
and timely statistics.
45
3.3. CORE ITEMS AND CORE DATA ITEMS COVERED
The data being currently covered in the developing countries has to be reviewed
for comparison with what is recommended in Chapter 3 of the Global Strategy.
However, this section stresses what is covered using administrative data. These
were discussed in Section 7 of Technical Report 1 (FAO 2015a).
Table 3.4 shows data items by country; the extent of use of administrative data
for agricultural statistics in developing countries is quite high especially for
cash/commercial crops, crop forecasting/early warning, livestock and poultry,
inputs and trade data.
Table 3.4: Core Data Items by Country
India Zambia Bhutan Malawi Mozambique Uganda Mali
Number
of
Countries
out of a
total of 7
Crop area 1 1
1 1 1 1 6
Crop
production 1 1
1 1 1 1 6
Land use 1
1 1
3
Irrigation 1
1 2
Agric prices 1
1
2
Market
intelligence 1
1
Fisheries 1
1
1 3
Forestry 1
1
1 1 4
Forecasts 1 1
1 1 1
5
Agricultural
inputs 1 1
1 1 1
5
Exports and
imports 1 1 1 1 1 1 1 7
Meteorological
data 1 1
1 1 4
Food reserves
1
1
Livestock
& Poultry
Production
1 1 1 3
Source: Country Assessments 2014
Footnote: For crop production and area, this is mostly for commercial crops
It is important that the ADSAS collects information that is valued or needed by
the main stakeholders, which in most developing countries are governments.
From Table 3.5 through Table 3.6, we compare the core data items and
associated data being currently covered in selected developing countries with
46
what is recommended in Chapter 3 of the Global Strategy. However, this
section stresses what is covered using administrative data. These were
discussed in Section 7 of Technical Report 1.
Table 3.5 presents the crop core items and associated data. For example, Table
3.5 indicates that most of the crop core items such as Wheat, Maize, Barley,
Sorghum, Rice, Sugar Cane, Soybeans, and Cotton are actually monitored in
selected countries’ studies. The table further indicates that the most associated
data items under crops are area planted, area harvested, production, yield, and
amounts in storage.
Table 3.5: Crop Core Items and Associated Data
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L/1
3
Crop Items
Wheat 0 1 1 1
1 1 1 0 1 1
1 9
Maize 0 1 1 1 1 0 1 1 1 1 1
1 10
Barley
1 0
1 1 0 0 1
1 5
Sorghum 0 1 1 1
0 1 0 1 1 1
1 8
Rice 0 1 1
1 0 1 1 1 0 1
1 8
Sugar Cane 1 1 1
1 0
1 0 1
1 7
Soybeans 0 1 1 1
0
0 0 1
1 5
Cotton 1 1 1
0
0 1 0 1
1 6
Others1 1
1
1
2
Associated Data
Area Planted 1 1 0 1 1 1 1 1 1 0 1
1 10
Area Harvested 0 1 0 1 1 1 1 1 1 0 1
1 9
Production 1 1 0 1 1 0 1 1 1 0 1
1 9
Yield 1 1 0 1 1 0 1 1 1 0 1
0 8
Amounts In Storage 0 1 0 1
0 1 1 1 1 1
1 8
Producer/ Consumer
Prices 1 1 0
1 0 0 1 1 1 1
0 7
Area Irrigated 0 1 0 1 1 0 1 1 0 0 1
0 6
Employment and Labor 1
0
1 0 0 1 1 0 1
1 6
Disposition
(Sales, Food, Seed,
Feeds)
0
0
0
1 1 0 1
1 4
Early Warning
Indicators 0
0 1
0 1 0 1 0
1 4
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
47
The livestock core items and associated data are presented in Table 3.6. Table
3.6 shows that cattle, sheep, pigs, goats, and poultry as the main livestock core
items monitored by many countries. The table further indicates that Net Trade
Imports and Exports, Production of Products, Producer and Consumer Prices,
Inventory and Annual Births are the main associated data collected under
livestock.
Table 3.6: Livestock Core Items and Associated Data
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L/1
3
Livestock
Cattle 1
1 1
1 1 1 1 1
1 1 10
Sheep 1
1 1
1 1 1 1 1
1 1 10
Pigs 1
1
0
1 1 1
1 1 7
Goats 1
1 1
1 1 1 1 1
1 1 10
Poultry 1
1
0 1 1 1 1
1 1 8
Others (Lapins, Apiary,
Turkey) 1
1
Associated Data
Net Trade Imports and
Exports 0 1 1
1 0 1 1 1 1
1 1 9
Production of Products 1 1 1
0
1 1 1
1 1 8
Producer and Consumer
Prices 0 1 0
1 0
1 1 1
1 1 7
Inventory 1
0 1
1
0 1 0
1 1 6
Annual Births 0 1 0 1
0
0 1 0
1 1 5
Common Disease
1
1
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
For FOODNET5 an MIS Project which used to collect both primary and
secondary market information, the key variables included:
Off lorry, wholesale and retail prices.
Trade volumes in major commodity markets.
Demand and supply conditions in markets.
Quality of produce in markets.
General weather conditions.
Production and price projections.
5 FOODNET stopped and its activities were taken up by Farmgain.
48
Market news from Uganda, the East African region and around the
world.
The livestock-data spreadsheet that Districts compile and submit to the
Ministries for Agriculture and Livestock, includes some general information on
rainfall, water availability and grazing conditions in the District. It then reports
livestock data on a variety of domains, including:
– ‘Outbreaks of contagious diseases’;
– ‘Rabies’;
– ‘Other clinical cases handled’;
– ‘Tick control’;
– ‘Dip wash testing’;
– ‘Laboratory activities’;
– ‘Vaccine stocks’;
– ‘Veterinary inspection services’;
– ‘Internal animal movements in relation to animal laws’;
– ‘Artificial insemination’;
– ‘Veterinary regulatory activities’;
– ‘Meat inspection’;
– ‘Vaccination’;
– ‘Animal quarantine and other restrictions’;
– ‘Animal production’;
– ‘Types of livestock farming systems in the district’;
– ‘Livestock markets’;
– ‘Hides and Skins’;
– ‘Staff disposition and vehicle strength’
3.4. HUMAN RESOURCE/INCENTIVES TO ADSAS STAFF
The key issue in the collection and management of administrative data is the
staff. Therefore their number, qualifications or experience and training are very
important. Table 3.7 summarizes human resources available for production of
statistics related to various segments of the agricultural sector.
49
Table 3.7: Number of professionals (Statisticians), support staff and statisticians
sponsored for trainings in the organization
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H
SU
DA
N
SO
UT
H
AF
RIC
A
SU
DA
N
UG
AN
DA
ZA
MB
IA
Number of professionals
(statisticians) in organization
Crops 4
4 1
60
1 3
0 3
Livestock 0
4
60
1 3
0
Aquaculture and fisheries 0
4
0
1 3
15 1
Agro-Forestry production 0
4
0
0 3
0
Agricultural inputs
4
60
1 3
0
Land cover
60
1
0 4
Total 4 0 20 1 0 240 0 5 15 0 0 15 8
Number of support staff in organization
Crops
3 1
6
3 0
4
Livestock
3 1
6
3 0
4
Aquaculture and fisheries 3 1
1 0
Agro-Forestry production 3
2 0
Agricultural inputs
3 1
6
3 0
Land cover
6
2
Total 0 0 15 4 0 24 0 12 0 0 0 0 10
Number of statistical staff sponsored for short training courses?
Crops 0
1
2 600
0 0
0
Livestock 0
1
2 600
0 0
Aquaculture and fisheries 0
1
0 0
0
Agro-Forestry production 0
1
0 0
Agricultural inputs
1
600
0 0
Land cover
600
0
0
Total 0 0 5 0 4 240 0 0 0 0 0 0 0
3.4.1. NUMBER OF PROFESSIONALS (STATISTICIANS) IN
ORGANIZATION
Many countries have professional and semi-professional statisticians and
related staff involved in the collection of administrative agricultural data in the
various institutions. For example in Uganda, the Ministry of Agriculture has
about 15 professional statisticians who work on collection of Administrative
data on production, area, and yield of crops and livestock. In addition, most of
50
the MDAs have recruited statisticians who are involved in administrative data
collection. Other countries such as Ghana, South Sudan and Zambia also
reported to have ample professional statisticians. In Uganda still, UCDA has 4
statisticians and Bank of Uganda has a statistics division with several
statisticians.
3.4.2. NUMBER OF SUPPORT STAFF IN ORGANIZATION
The number of support staff engaged in collecting administrative data varied
considerably between countries under this review. For example, countries such
as Ghana, Mauritius and Zambia reported to have modest support staff.
3.4.3. STATISTICAL STAFF SPONSORED FOR SHORT TRAINING
COURSES
Apart from Libya, many countries did not report to provide short term training
(one week or more) abroad in the last 12 months to their staff. It is possible
however that there was underreporting or over reporting such as in Libya. In
Uganda for example, statistical training is conducted for MAAIF staff and the
LGs. In the past there used to be Assistant Agricultural Officers (in charge of
Statistics) at District Level. These often had Certificates or even Diplomas from
the Eastern Africa Statistical Training Center (EASTC), Dar es Salaam,
Tanzania.
3.4.4. REGULAR TRAINING PROGRAMME FOR STATISTICAL
STAFF
Some countries have regular training programmes for their staff, which can lead
to an increase in the quality of data produced by the ADSAS.
Table 3.8: Regularity of Training Programmes for Statistical Staff
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L/1
3
Subject
Crops 0 1 0 0 1 1 0 1 0
0 4
Livestock 0 1 0 0 1 1 0 1 0
4
Aquaculture And
Fisheries 0 1 0 0 1
0 1 0
1 4
Agro-Forestry
Production 0 1 0
0 1 0
2
Agricultural Inputs 1 0 0 1 1 0 1 0
4
Land Cover
1 1 0 1
0 3
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
51
In Uganda an in-service statistical training is conducted for MAAIF staff and
the Local Governments. In the past there used to be Assistant Agricultural
Officers (in charge of Statistics) at District Level. These often had Certificates
or even Diplomas from the Eastern Africa Statistical Training Center (EASTC),
Dar es Salaam, Tanzania. Another good example of a regional training
programme can be taken from the Tanzania Agricultural Routine Data System
(ARDS). A brief outline of trainings for 2012/13 is described as follows:
i. Training for regional officials and district officers on the common
reporting formats:
Regional officials and district officers are trained on the common
reporting formats appropriate for the VAEO/WAEO format and
Integrated Data Collection Format.
ii. Training for VAEO / WAEO on VAEO / WAEO format:
Once the training of district officers is completed, the training for
VAEO / WAEO on the VAEO/ WAEO format is conducted in each
LGA with district officers being facilitators under the supervision of
regional officials and M&E TWG.
iii. Training of Regional officials and IT Specialist on Excel & LGMD-2:
Regional officials and IT specialists of the regions where the ARDS is
rolled-out receive training on Excel and technical aspects of LGMD2.
To reduce the number of trips and the roll out time, officers from
several regions are trained together in the same venue.
iv. Training of district officers on LGMD-2 and Excel:
The training for district officers (in charge of agricultural statistics and
M&E) on Excel and LGMD2 is carried out immediately after the one
for regional officials (IT specialists and agricultural officers). The
regional officers and ASDP M&E TWG members will be the trainers
(facilitators). Like the trainings for regional officers, district officers in
a few regions may be trained together. Before VAEO / WAEO submit
filled-in VAEO/WAEO formats to districts, district officers should be
familiar with the functions of LGMD2 and Excel techniques. Certain
Excel techniques are necessary to calculate district level data from the
ward level data stated in the VAEO/WAEO format.
There is a Training Guide for District Officers on Data Consolidation, Analysis
and Feedback in ARDS which provides guidelines for data handling and
analysis at district level.
52
The biggest challenge in many developing countries is the lack of staff and low
staff retention mainly due to poor working conditions. Generally, poor behavior
such as shirking among employees of institutions supposed to collect
administrative information has been blamed on poor incentive structures among
employees. As mentioned in institutional home section, it has been argued that
some government statistical departments do not offer competitive salaries to
their employees, which may lead to poor quality indicators such as irregular
collection, lack of supervision and late release of statistical data and reports.
So the rate at which the trained and experienced staff leave the service is often
very high necessitating continuous training which is often not possible. Regular
training is not common in most countries.
53
4 Conduct Issues in the
ADSAS The analysis of conduct issues follows the layout given in Table 2.1 of
Technical Report 1. This covers the data collection methods used; the
technologies used in the data collection and dissemination; plus the funding
with stress on the sustainability of the system. Conduct issues from the frame
include Information provided, ICT used in the collection and dissemination,
Funding strategies, data collection methods used, quality control methods used
and feedback mechanism used.
4.1. UGANDA INFRA-STRUCTURAL DEVELOPMENT
Almost all Ministries/Agencies participating in the first phase of developing the
Plan for National Statistical Development (PNSD) raised concerns relating to
the inadequacy of physical and statistical infrastructure, which was
compromising efficiency and effectiveness in the process of producing and
disseminating statistics (UBOS 2014).
4.1.1. PHYSICAL INFRASTRUCTURE
Fortunately, MAAIF already has a building put up for FAS under the 1990/91
NCAL. However, there is need to provide the Ministries/Agencies with IT
equipment. Provision of such equipment will depend on the specific needs of
each Ministry/Agency. Other equipment may also be required. Such equipment
will include: transportation vehicles, computers and networking equipment, IT
accessories and software, survey equipment and accessories; and office
furniture and supplies. Development of the replacement and maintenance plan
is a pre-requisite to provision of equipment. For FAS, area and yield
measurement equipment will specially be required apart from the IT equipment.
54
4.1.2. STATISTICAL INFRASTRUCTURE
This embraces the improvement of the basic elements of the statistical
infrastructure such as statistical registers, sampling frames, classifications and
methodologies, statistical computer packages for analysis of survey data and
Geographical Information Systems (GIS) for statistical mapping.
4.1.3. GEOGRAPHICAL INFORMATION SYSTEM
This will aim at establishing and or maintaining the existing GIS capability,
setting of standards, protocols of GIS data collection and exchange by different
data producers and setting up a national GIS data repository and providing users
access to all available layers of geographic information.
4.1.4. NATIONAL MASTER SAMPLE FOR HOUSEHOLD SURVEYS
UBOS will develop and maintain a National Master Sample Frame for guiding
household survey programmes for generation of data to inform government,
development partners and the entire public about the progress in meeting PEAP
and MDG goals. For FAS, the PHC 2002 Agricultural Module was expected to
form a basis for the sampling frame6.
4.1.5. STATISTICAL METHODOLOGIES AND CLASSIFICATIONS
Global classifications will be adopted to improve harmonization and
consistency among various data sets in Uganda and to ensure international
comparability.
4.2. DATA COLLECTION METHODS AND TECHONOLGIES
USED Various methods and equipment need to be used to measure land and crop
areas; estimate production and yield; etc. Further, structured questionnaires are
used with enumerators or the Wiki approach (users SMS or update web), etc.
These are discussed in this section.
Data collection methods vary depending on the parameter of interest. The
methods for collecting production and area estimates sometimes differ from
those used in obtaining price information. In Uganda for example, estimates
made by the Department of Agriculture were guess estimates extracted from
Annual and Monthly Reports compiled by District Agricultural Officers. The
6 There has been a PHC 2014 which also had an agricultural module. The sampling frame is therefore
expected to be up-dated.
55
information collected was on area, yield, production, prices, marketing etc of
the main food and cash crops. Table 4.1shows that self-administered
questionnaires and routine reporting are the main methods used to collect
administrative data reported in selected ADSAS.
Table 4.1: Methods of Data Collection
B
UR
UN
DI
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H
AF
RIC
A
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
Methods of data collection
Self-administered
questionnaires 1 1 1 1 1 1 1 1 1 1 1 1 1
1
3
Wiki approach (users SMS or
update web) 0 0 0 0 0 0 0 0 1 0 1 0 1 3
Routine reporting 1 0 1 1 0 0 1 1 0 1 1 0 1 8
Special Forms of Ag Census
1
1
Crop Cutting
1
1
Eye estimation
1
1
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
4.2.1. METHODS OF DATA COLLECTION
Typical examples of administrative agricultural data collection and flow can be
given.
1) Tanzania’s Experience
The Agricultural Routine Data System (ARDS) which was implemented since
2009 as a pilot in four districts in Morogoro region was formed as a result of a
need to create a monitoring component for the Agricultural Sector Development
Plan. The three key features of the monitoring system are the data collection
tools used by the Village/Ward Agricultural Extension Officers; the integrated
data collection tools for aggregation of the collected data at District level and
the local government monitoring data base 2 (LGMD-2) in which data is
captured and transmitted from the local government via the regions to the
Agricultural Line Ministries.
Data forms filled in by the Village Agricultural Extension Workers are
compiled by the Ward Agricultural Extension Workers who check for
completeness and also discuss any issues that need clarification. In order to
ensure completed forms are received, the submission of Ward level data to
Districts and receipt of blank VAEO/WAEO formats is normally linked to
distribution of salaries to VAEO/WAEOs from the District. At the district data
56
entry is done in Excel using the Village/Ward data collection format. Training
is provided to equip the district officers with skills of data entry, analysis and
giving feedback to the data collectors. Technical backstopping provided for the
district officers is a team of two competent M&E Technical Working Group
members and one Regional Officer.
Provision of backstopping is very important to ensure that district officials are
fully conversant with ARDS operation. A backstopping team consists of two
competent M&E TWG members and one regional officer. All LGA officers
gather to regional towns and report their progress and challenges.
2) Uganda’s Experience
In Uganda for example, estimates made by the Department of Agriculture were
guess estimates extracted from Annual and Monthly Reports compiled by
District Agricultural Officers who will have obtained the data from the
Agricultural Extension staff at the lower administrative units – usually sub
counties. The information collected was on area, yield, production, prices,
marketing etc of the main food and cash crops, namely: cotton, coffee, tobacco,
tea, sugar, cocoa, citrus, plantains, sweet potatoes, Irish potatoes, cassava,
finger millet, sorghum, maize, wheat, rice, field peas, pigeon peas, cow peace,
beans, groundnut, simsim, castor and vegetables.
Two methods were used in the estimation of annual crop areas (Uganda Bureau
of Statistics 2007): (a) "Buganda Method"; randomly selected villages would be
supposedly completely enumerated (Mitala Survey) in respect of areas of all
important crops. The area under each crop obtained by pacing and/or eye-
estimation or pure guess work would be aggregated and then divided by a
"refined" number of tax-payers belonging to the sample villages. The average
derived there from would then be multiplied into the total number of tax-payers
in the entire district to get estimated area under the crop in the district.
(b)"Outside Buganda Method"; returns of plots counts would be carried out by
chiefs and compiled for the two major seasons in the year. These plot counts
would be aggregated and multiplied by a general plot mean size, supposedly,
derived from pacing by the extension staff, to obtain district crop area totals.
Some variation of this which was much more widely applied than the "Buganda
Method" was to have both plot counts and mean plot sizes obtained by the
extension staff rather than chiefs. Production was then estimated as the product
of area and yield. The yield estimates were always arrived at subjectively by
the respective District Agriculture Officer with the help of his/her staff lead by
an Assistant Agricultural Officer, These systems unfortunately broke down in
the late 1970s so that currently most of the data is made-up at the district
headquarters by the extension staff without any consultation with the chiefs.
57
The methodology used did not yield reliable estimates (Uganda Bureau of
Statistics 2007). Sadly this system completely broke down, thus exemplifying
the issue of sustainability.
The FOODNET data collection strategy involved market agents recording daily
wholesale and retail price data from four markets in Kampala (Kisenyi, Owino,
Kalerwe and Nakawa) and also collecting weekly prices of 28 commodities
from 19 district markets across the country. Off-lorry prices were also
collected.
3) India’s Experience
India provides good examples on data collection. Statistics of crop area are
compiled with the help of the village revenue agency (commonly known as
patwari agency) in the temporarily settled parts of the country and by specially
appointed field staff in the permanently settled States under a scheme known as
“Establishment of an Agency for Reporting Agricultural Statistics (EARAS)”.
The remaining eight States in the North-Eastern Region and two other Union
Territories do not have a reporting system, though the States of Tripura and
Sikkim (except some minor pockets) are cadastrally surveyed. They compile
what are called conventional crop estimates based on personal assessment of
the village chowkidars.
In the states that have a patwari agency, a complete enumeration of all fields
(survey numbers) called girdawari is made in every village during each crop
season to compile land use, irrigation and crop area statistics. In the States
covered by EARAS, the girdawari is limited to a random sample of 20 per cent
villages of the State, which are selected in such a way that during a period of
five years, the entire State is covered.
Under the Improvement of Crop Statistics (ICS) scheme, an independent
agency of supervisors carries out a physical verification of the patwari’s
girdawari in a sub-sample of the TRS sample villages (in four clusters of five
survey numbers each); and makes an assessment of the extent of discrepancies
between the supervisor’s and patwari’s crop area entries in the sample clusters.
The first crop forecast relating to the kharif crops is mostly based on reports
prepared by the States mainly guided by the visual observation of field officials.
The second forecast covering both the kharif and rabi crops takes into account
additional information obtained from various sources including agricultural
inputs, incidence of pests and diseases, and weekly reports of State departments
of agriculture regarding area coverage, conditions of standing crops. Results of
Remote Sensing data are also considered at this stage. In the third forecast, the
earlier advance estimates of both the kharif and rabi seasons are strengthened,
58
again taking into account information received from sources such as Market
Intelligence Units, Meteorological Department and the Crop Weather Watch
Group (CWWG). The fourth forecast is based on firm figures supplied by State
Agricultural Statistics Authorities (SASAs) who are by then in a position to
obtain fairly dependable estimates of yield rates through GCES.
In addition to the four forecasts, the Directorate of Economics and Statistics,
Ministry of Agriculture (DESMOA) issues the “Final Estimates” of crop area
and production in December. As a few States continue to revise their data on
delayed receipt of information, the all-India crop statistics are brought out as
“Fully Revised” in the next crop year in the following December. This is a very
good example of systematically combining data from different sources to
strengthen the final crop forecasts.
The Ministry of Agriculture has set up a National Crop Forecasting Centre
(NCFC) with the objective of examining the existing mechanism of building
forecasts of principal crops and developing more objective techniques. The
NCFC takes into account information on weather conditions, supply of
agricultural inputs, pests, diseases and related aspects including the proceedings
of CWWG in the formulation of scientific and objective forecasting methods to
replace the present system.
The DESMOA compiles the production estimates on the basis of reports
received from State Governments. These are obtained as the product of area
sown under the crop through complete enumeration and the yield rate from crop
cutting experiments. The CAB estimates are based on inputs from the Cotton
Corporation of India, East India Cotton Association, Indian Cotton Mills
Federation, etc. and these, in turn, depend on data on market arrivals, volume of
cotton ginned and pressed in all ginning mills irrespective of the area sown or
condition of the crop.
The data are collected by price reporters appointed by the State Governments or
Agricultural Marketing Committees and forwarded to the State Directorates of
Economics and Statistics (DESs). Daily wholesale prices cover 12
commodities (rice, paddy, wheat, jowar, bajra, ragi, maize, barley, gram, sugar,
gur and khandsari) from over 600 market centres. On receipt of the prices from
various State agencies, the Directorate of Economics and Statistics, Ministry of
Agriculture (DESMOA) forwards the same to the Economic Adviser, Ministry
of Commerce and Industry for monitoring wholesale prices. Wholesale prices
of certain important cereals, gram and sugar are also sent to the Cabinet
Secretary on alternate days for direct monitoring.
59
4) Côte d’Ivoire Experience
In Côte d’Ivoire (Ivory Coast), production and dissemination of agricultural
statistics are provided by the Department of Statistics and Documentation of the
Ministry of Agriculture and Animal Resources (MINAGRA). This Department
usually puts two publications per year, namely, the "Agricultural Statistics
Yearbook" and "Food Balance Sheets". The data for these two publications
come from two major sources of collection: the census and administrative data
collection.
The last census of agriculture was carried out in 2001. Due to the political crisis
in the country, no census of agriculture has been carried out. The process for the
preparation of the next census is actually ongoing.
Hence, the administrative agricultural data collection is increasingly essential
for the production of agricultural statistics as the estimates based on the
sampling frame and input from the 2001 census is actually obsolete. The
administrative data collection is carried out by 18 national institutions in the
agricultural sector. For example, some of the institutions involved in the
agricultural statistics collection in the sector include: le Comité de Gestion de la
Filière Café-Cacao/Conseil Café Cacao (CGFCC/CCC), l’Association
Interprofessionnelle de la Filière Coton (INTERCOTON), Office d’aide à la
commercialisation des Produits Vivriers (OCPV), Société d’Exploitation et de
Développement Aéroportuaire et Météorologique (SODEXAM) etc. Table 4.2
presents the key stakeholders.
The following variables are covered: rainfall, maximum and minimum
temperatures, land area cultivated and/or harvested, production, yields,
agricultural products sold, prices, quantities (produced, export, processed) etc.
Unfortunately, food crops (like maize, millet, sorghum etc.) are not covered in
the administrative data collection, conducted by OCPV, except for the food
crops prices monitoring (including rice). Estimates continue to be from the
database of RNA 2001 taking into account the climatic data of SODEXAM. For
rice, since 2008, the Department of Statistics and Documentation mandated the
data collection to the National Office of Rice Development (ONDR).
60
Table 4.2: List of Institution Producing Agricultural Statistics in Cote d’Ivoire
N° Acronyms Definitions Requested data
1 CGFCC/CCC
Management Committee of
the Coffee-Cocoa Pathway /
Coffee Cocoa Council
Production-Export-Processing-Price-
Distribution of exports by destination and by
port of embarkation
2 INTERCOTON Inter-professional Association
of Cotton Sector
Seed cotton production-Areas planted- yields-
Cotton fibre production-yield-Producer prices
3 OCPV Office Assistance Food
marketing of products Prices of food in the markets
4 SODEXAM
Operating Company and
Airports Meteorological and
Development
Temperatures and rainfall per station
5 OCAB
Central Organisation of
producers- exporters of
Pineapple and Banana
Production- Export-Areas - yields
6 DCPE Direction of Economic
Conjuncture and Forecast
Foreign trade of agricultural products for export
and import
7 DGD General Direction of Customs Idem
8 PALM-CI Export Corporation of Palm
oil in Ivory Coast Production of palm oil-Palmiste-Fine and cake
9 PALMAFRIQUE
Palm oil plantations of
African company in Ivory
Coast
Production regimes (Industrial & village
plantations) - Areas (Industrial & village
plantations)
10 MINEF/DISA/DPIF
Ministry of Environment,
Water and Forests / Direction
of Information, Statistics and
Archives / Direction of
Production and Forest
Industries
volume of wood export, volume processed
wood (lumber, veneer-slicing)
11 MIPARH/DPP
Ministry of Animal
Husbandry and Fishery
Resources / Direction of
Planning and Programs
Animal products (national herd and production
of meat and offal, milk and eggs by species-
import of meat, offal and derivatives by espèce-
Import milks, products and derivatives by
species- fishery products (import fishery
products and derivatives)
12 ARECA Cotton and Cashew
Regulatory Authority
Areas planted-Production of seed cotton- yields
-Production of cotton fiber-purchase price to
producers-exportation
13 APROMAC
Professional Association of
Natural Rubber in Ivory
Coast
Areas Planted (village plantations & Research
Station) -Production rubber- Purchase price to
producers
14 AIPH Inter-professional Association
of Oil Palm
Production regimes (industrial & village
plantations) Total -Production (industrial &
village plantations) -Production palm kernel
(by framed structure)
15 SUCRIVOIRE Operating Company of sugar
cane plantations
Area-Sugar production, yields, marketed
production,
Production of sugarcane (industrial & village
plantations)
16 SUCAF-CI Sweets Africa
Area-Sugar production, yields, marketed
production,
Production of sugarcane (industrial & village
plantations)
17 DOPA Direction of Professional
Agricultural Organizations
Number of Cooperatives by region and
speculation-list of Cooperatives
18 ONDR National Office of Rice
Development Production- yields- Areas-Price
61
4.2.2. TECHNOLOGIES USED IN ADMINISTRATIVE DATA
COLLECTION
Use of some technologies in administrative data collection leads to increased
quality especially the timeliness. For example, the use of GPS in distance and
area measurements increase accuracy while reducing the time of data collection,
although they might also increase the costs of information collection, thus
affecting the sustainability of the ADSAS. There is likely to be a trade-off, with
the reduction of time for data collection leading to less time for the
enumerators. On the other hand, the GPS are generally more expensive than the
other measurement equipment. Table 4.3 shows that the use of modern
technologies like GPS, PDAs, computer-assisted telephonic interviews and
scanning questionnaires is still low in Africa. The most common technologies
mentioned are personal interview (mentioned by 10 out of 13 countries) and
manual data entry into computer (mentioned by 8 out of 13 countries). African
countries need to migrate to using new technologies.
Table 4.3: Technologies Used
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H
SU
DA
N
SO
UT
H
AF
RIC
A
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
Technologies used
Personal interview 1 1 0 1 1 1 1 1 1 0 1
1 1
0
Computer Assisted Telephonic
Interview (CATI) 0 0 0 0 0 0 0 0 0 0 0
0 0
Manual data entry into computer 0 1 1 1 1 0 0 1 0 1 1
1 8
Scanning of questionnaires. 0 1 0 1 1 0 0 0 0 0 0
0 3
Personal Data Assistant (PDA)
and 0 0 0 0 0 0 0 0 0 0 1
0 1
Computer Assisted Personal
interview (CAPI) 0 0 0 0 0 0 0 0 0 0 0
0 0
Geographical Position System
(GPS) 0 1 0 1 1 0 0 0 0 0 0
1 4
Compass as Measuring Tapes 0 0 0 0 0 0 0 0 0 0 1
1 2
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
Crop Area Statistics: Through a Centrally sponsored scheme, “the Crop
Acreage and Production Estimation (CAPE)”, since 1990, an attempt has been
underway to use Remote Sensing (RS) technology for estimation of crop areas
and land use in India. The objective of CAPE, among others, is to provide
State-level crop area estimates, meeting a 90/90 accuracy goal using the remote
sensing data covering mainly the crop growing parts of the States.
62
4.2.3. METHODS OF DATA STORAGE AND
DISSEMINATION/DIFFUSION
This covers the ICT used, i.e. Hand-held equipment, Telephones, etc.
Traditional ICT (e.g. radio, television and fax); Modern ICT (e.g. e-mail,
internet, SMS); PDAs, etc.
As discussed in Technical Report 1, metadata are vital for informing both
producers and users about data quality. It is recommended that the metadata
should be present at all the stages. Incoming data should be accompanied by
sufficient metadata to fully understand them, and to ensure that values are
correctly allocated to the relevant variables. Metadata are at the heart of the
management of the interpretability indicator. An example was given of the
Integrated Metadata Base is (IMDB) Statistics Canada’s single source of
metadata information describing surveys and programs. The quality of the
IMDB information has to be monitored regularly to ensure completeness and
accuracy. It was stressed that it is important for statistical agencies to publish
good metadata because by doing so they show openness and transparency and
breed trust with data users (Dion 2007).
When introducing new data storage or dissemination technology, the agency is
to advised consider the associated merits as well as the risks. A new technology
may benefit certain dimensions, while creating costs in other areas. Issues to
consider with respect to reliability, accessibility, timeliness, and sustainability
are as follows:
Reliability: Can a technology reduce the accuracy of the information
diffused? Use of some technologies such as SMS, email, reduces
diffusion errors.
Accessibility: Some illiterate users may not be able to read SMS and
Timeliness: Some technologies may be able to transmit administrative
data / information faster (E.g., prices over SMS compared to updating
website).
Sustainability: The costs involved with the use of a technology to store
or disseminate information. Some may be fast but expensive, e.g., use
of IPADS to collect information is associated with high fixed costs.
May also not be feasible where electricity and security are an issue.
63
In Tanzania even when improved ARDS was not introduced, a number of
routine reports were produced at all administrative levels (village, wards,
districts and regions).
VAEO Report (monthly, quarterly, annual) at village level (paper).
These reports are based on VAEO/WAEO formats and are canvassed by
VAEOs (WAEOs in case there is no VAEO in the village).
Integrated Data Collection Report (quarterly, annual) at district level
(stored in LGMD-2)
Integrated Regional Report (quarterly, annual) at regional level (stored
in LGMD-2)
Integrated National Report (quarterly, annual) at national level (stored
in LGMD-2)
Thus, Integrated National level quarterly and annual reports are expected to be
provided by the Improved ARDS.
In Uganda databases are owned by the agencies which generate the data, but
data is shared with the Uganda Bureau of Statistics, according to need
especially when generating data for the Annual Statistical Abstract. Data
dissemination is in hardcopies (publications), CDs and summaries are posted on
the website www.ubos.org
64
Box 4.1: An Example of Metadata for Sector—MAAIF Uganda
Data type/ Indicator: This refers to the type of data or indicator produced by the sector
Definition and standard classification; Definition of data type or indicator and the standard of
classification
Scope/coverage of data; Scope of data /indicator produced. Total coverage of data information
collected and the target population.
Sources of data produced; data sources used
Compilation practices in the data production; methods used in data collection/ compilation,
validation of statistical data, revision policy, periodicity wit which studies and analysis of revisions are
carried out; whether and how they are used internally to inform the statistical process.
Method of computation; process of computation of data and how the data/ indicator is computed.
Accessibility and availability of data;
- Statistical presentation
- Dissemination: media and format
- Advance release calendar
- Simultaneous release(degree to which statistics are made available to all users at the same
time, and modalities used to achieve this)
- Dissemination on request (dissemination on request of unpublished but non confidential
statistics to the public).
Accounting conventions: reference period (frequency of statistical production: daily, weekly, monthly,
quarterly or annually) Recording of transactions (budget estimates for collection of statistics and
expenditure recordings)
Collection and limitations: Comments and limitations involved in the production of data/ key
indicators.
Uganda - Radio dissemination for market information
- Three types of radio programmes have been used to disseminate market information on crop
and commodities.
- Two short radio broadcasts, each of two minutes on Tuesdays and Thursdays.
- A 15 minute programme at the end of each week giving an overview to the national market
and a learning programme discussing market opportunities.
- A learning programme for market information will be incorporated and run at the national
level.
Information dissemination
Analyzed data is relayed to farmers on a weekly basis via FM Radios in eight local languages. The data
is also sent to policy makers, traders and development agencies through, E-Mail, internet, SMS,
WORLDSPACE, newspapers and workshops. Dissemination through SMS is instant and very helpful
for those with no electronic mail list. One needs to download the message menu, write the item required
e.g. MAIZE and send to 198. Information on the daily prices can be received instantly. This is a joint
effort between MTN and FOODNET.
65
FOODNET and the National Agricultural Advisory Development Services
(NAADS) covered the districts of Arua, Soroti, Kibaale, Tororo and Mukono.
The aim was to develop a localised agricultural market information service
(AIMS) that met the marketing needs of the farming and trading community at
the district level. The data collection covered 19 districts. The data was
collected from quotations by the respondents.
4.3. SOURCES OF FUNDING AND SUSTAINABILITY
STRATEGIES
Many ADSAS face limited or insufficient funding, which leads to late or
irregular collection of information, inability to hire well trained staff, and lack
of sustainability in many cases. This can lead to poor quality in terms of
timeliness. Table 4.4 shows that all the ADSAS interviewed were funded by
government and four out of thirteen by donors. One ADSAS reported to be
funded by charity organizations and another in South Africa by the private
sector. By default, where data is collected by a Government Ministry or Agency
(MDA), the funding is from government (could of course be donor-funded) and
is more certain than if it is a private organization or survey or census. There is
however, the example of the FOODNET market information programme
activities in Uganda that were funded by a consortium of donors including
USAID through ACDI-VOCA; Government of Uganda through MAAIF and
NAADS; CTA; RELMA. What did not come out of the studies and literature
review is the possibility of raising funds through subscription fees and
information sales by ADSAS. This could be because they are mostly
government departments that have to provide information as a “public good”.
Table 4.4: Sources of funding of ADSAS
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L/1
3
Funding Source
Government 1 1 1 1 1 1 1 1 1 1 1 1 1 13
Charity Organizations 0 0 0 0 0 0 0 0 0 0 1 0 0 1
Donors 1 0 0 0 0 0 0 0 1 0 1 0 1 4
Private Sector 0 0 0 0 0 0 0 0 0 1 0 0 0 1
Farmer Or Trader
Organization 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank
66
5 Performance Issues, or
Outcomes, in the ADSAS The third pillar of the ADSAS encompasses performance issues and outcomes.
These are outlined in Table 2.1 of Technical Report 1 and discussed in Section
5 of Technical Report 2. For ready reference, the performance dimensions from
Table 2.1 of Technical Report 1 are summarized below:
Coverage
Comprehensiveness
Timeliness
Punctuality
Completeness
Relevance
Accuracy
Reliability
Integrity/ Credibility
Accessibility to different clientele
Clarity/interpretability
Comparability
Consistency/ Coherence
Sustainability of ADSAS
Financial support
User support
Cost minimization
The usability of the data product ultimately dictates the sustainability of the
ADSAS. If the administrative data are a useful – ideally, indispensable -- to
data users and statisticians, continued support for maintaining a high quality
product is expected. The above indicators target usability from different
directions. The concepts of coverage, comprehensiveness, and completeness
67
refer to the extent to which the administrative data source captures the
population and concepts of interest for statistical purposes. Timeliness and
punctuality indicate the frequency with which administrative data are released
and the time lag between the date of release and the measured reference period.
Relevance describes the alignment between the concept measured by the
administrative data source and the concept of interest to the data user.
Reliability, accuracy, and integrity/credibility are colloquial descriptors for the
statistical concept of mean squared error – difference between the quantity
measured on the administrative source and the target concept of interest. The
notions of comparability and consistency/coherence refer to the level of
agreement between different sources of data that intend to measure the same
concept. The dimensions of accessibility and clarity/interpretability describe the
ease with which a data user can acquire and understand the data.
Although the various performance dimensions are intrinsically linked, a
classification of the dimensions into two broad categories simplifies the
discussion. The first category contains dimensions pertaining to efficiency of
the basic data, and the second category consists of dimensions pertaining to use.
This section reviews issues with these two categories consecutively. In terms
of efficiency of the basic data, this section begins with an overview of
mechanisms for quality control used in developing countries and then discusses
the important issue of comparability across multiple data sources. With respect
to data use, this section first discusses uses of administrative data in forming the
statistical product and then discusses uses by non-statisticians as a final
statistical product.
5.1. QUALITY CONTROL PROCEDURES
Table 5.1 presents the methods that some ADSAS reported to use in order to
ensure high data quality. They include eyeballing, data entry control /validation
programs, random supervision visits to collectors, comparing collected data
with alternative data sources, regular training of collectors in good data
collection skills, and recruitment of highly skilled or good professional staff.
Other data quality control mechanisms reported include use of service
contracts, obtaining feedback from data users, establishing monitoring and
supervisory committees, and formation of advisory panels and boards to advise
on quality issues.
68
Table 5.1: Mechanisms used to assure good data quality
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
S
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
TO
TA
L/1
3
Eyeballing
1 1
0
1
1 4
Data entry control /validation
programs 1 1
1
1
1 5
Random visits to collectors
1 1
0
1
1 4
Comparing with alternative
data sources 1 1
0
1
1 4
Regular Training collectors
1 1
1
1
1 5
Recruiting profession al staff
1 1
1
1
1 5
Use of service contracts
1 1
0
1
1 4
Feedback from data users
1 1
0
1
1 4
Monitoring and supervisory
committee 1 1
1
1
1 5
Advice from advisory panel
and boards 1 1
0
1
1 4
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
Quality assessments for agricultural administrative data systems in developing
countries are rarely done. It seems most ADSAS in developing countries do not
put emphasis on documenting agricultural data quality parameters, and where
they exist, they are subjective (See Annexes A2.1 and A2.2). In Table 5.1,
eight out of the 13 countries surveyed provide no information on the use of
quality control procedures in their countries. Among the five countries that
responded, four indicated that they employ all of the suggested quality control
mechanisms. One country (Libya) indicated use of a subset of the quality
control processes. This indicates that in systems of administrative data
collection in developing countries are dichotomized into two groups: countries
that employ a high degree of quality control, and countries that employ no
quality control, with very few countries in the intermediate range.
5.1.1. QUALITY CONTROL IN THE INDIAN AGRICULTURAL
STATISTICS SYSTEM
India employs two main mechanisms for quality control of crop related
statistics based on administrative data. The Improvement of Crop Statistics
(ICS) scheme involves supervision of data collectors to verify the accuracy of
the basic data. The Timely Reporting Scheme (TRS) is an effort to improve the
69
timeliness of the data. These two approaches to quality control in India are
discussed in more detail below.
The revenue agency in India maintains a complete enumeration of land
ownership records (cadastral register) in 18 states, designated as temporarily
settled. In these temporarily settled states, the leader of the village revenue
agency (patwari) completely enumerates all fields. This complete enumeration,
called girdawari, is judged “fairly reliable” due to the patwari’s “intimate
knowledge of local agricultural and his ready availability in the village”
(Agriwatch 2013). However, a concern exists that an increase in the extent and
variety of the patwari’s responsibilities will diminish both the accuracy and the
timeliness of the girdawari.
To improve the timeliness of the crop area statistics, India implemented the
Timely Reporting Scheme (TRS) in 18 States and Union Territories. “Under the
TRS, the patwari is required to complete the girdawari on a priority basis in a
20 percent random sample of villages and to submit the village crop statements
to higher authorities by a stipulated date for the preparation of advance
estimates of the area under major crops…The TRS sample of villages is also
selected in such a way that the entire temporarily settled parts of the country are
covered over a period of five years.”
To verify the accuracy of the girdawari, India implemented the Improvement of
Crop Statistics (ICS) scheme: “Under the ICS scheme, an independent agency
of supervisors carries out a physical verification of the patwari’s girdawari in a
subsample of TRS sample villages (in four clusters of five survey numbers
each)…and makes an assessment of the extent of discrepancies between the
supervisor’s and patwari’s crop area entries in the sample clusters. The
supervisor also scrutinizes the village crop abstract prepared by the patwari an
checks whether it is free from totaling errors and whether it has been dispatched
to the higher authorities by the stipulated time” (India 2013).
5.2. ISSUES ON MULTIPLE DATA SOURCES
An important set of quality dimensions consists of consistency, coherence, and
comparability. These terms refer to the agreement among different data sources
that measure closely related concepts. Whenever statistics are generated for the
same purpose from different sources, some variation in the results is expected.
Disparities may arise from multiple causes. Mistakes or incentives may lead to
reporting or collection errors in administrative data. Differences in data
collection procedures or phrasing of questions can lead to systematic differences
in responses. Conceptual differences often exist between quantities collected
70
through administrative processes and quantities of analytical interest to a
statistical agency.
Because administrative data files are often stored in different formats, they can
have different identifying variables, and may have internal errors or
inconsistencies, making a one-to-one match generally impossible. Probabilistic
matching is one technique for unifying disparate data sources. Several software
tools for probabilistic record linkage have been developed to meet this end
(ISAD 2008c).
Methodology for record linkage and evaluation of measurement error is needed
to maintain high quality databases that integrate multiple administrative files.
Examples of applications discussed include combining census and
administrative data to create efficient frames for health surveys, improvements
to sub-national estimates, and validating financial survey data using tax data.
Administrative registers of sufficiently high quality can be used for direct
tabulation of agricultural statistics. Even if register systems are not of sufficient
quality or completeness to support direct tabulation, administrative data can be
used to reduce respondent burden or provide population control totals, which
can be used to construct more efficient survey estimators through calibration
(i.e., (Deville 1993).
In a number of developing countries, data are available from three kinds of
sources. First, Routine Administrative Agricultural Data (RAAD) collection is
often administered through the Ministries for Agriculture, Livestock, Forestry and
Fisheries (MALF) on a regular (weekly, monthly or annual) basis. The RAAD
often provides data on even the smallest administrative units, say districts or
villages. Second, data are often collected in Annual Agricultural Surveys (AGS).
In many of the countries that we have reviewed, surveys are less common than
data collected through RAAD and are often discontinued periodically due to
budget cuts. Nonetheless, surveys are in portant source of data to consider because
of their potential to yield unbiased estimators with a quantifiable measure of
sampling variance. The third source is a Census of Agriculture and Livestock
(ACAL), ideally conducted on a decennial or multiannual basis. Like surveys,
censuses suffer irregularity in many of the countries that we reviewed due to
insufficient funding or political instability. The AGS and the ACAL are often
jointly carried out by the National Statistics Offices (NSO) and MALF. Both the
AGS and also the ACAL often only give useable data at the national and regional
levels, leaving out districts. Inevitably, the three data series differ.
Table 5.2 shows which of the countries that participated in the survey collect data
through RAAD and which countries attempt to reconcile the RAAD information
with data from other sources. The table indicates that although most countries
71
collect data using RAAD, very few reconcile this information with data from other
sources. Furthermore, the reconciliation process is often ad hoc and needs clear
recommendations. Many statistical agencies publish estimates based on
administrative, survey, and census data individually, without any guidance to the
eventual user on how to reconcile them.
If differences between data sources are systematic or can be understood, then
inconsistencies across data sources do not necessarily prohibit the use of
multiple sources of information. Statistical models and manual review
processes have been used by statistical offices in developed countries to
reconcile differences across data sources. Probabilistic record linkage has been
used to combine data with inconsistent identifying variables. The subsequent
sections review various uses of multiple data sources.
Table 5.2: Collection of routine agricultural administrative data and methods of
reconciliation
Q14count
Collection of
routine
agricultural
administrative
data
Reconciliation
done Methods of data reconciliation
Burundi 0
Egypt 1 1 The statement collection from the same source
Ghana 0 0
Lesotho 1 1 The final and correct statistics comes from the
bureau as per statistic
Liberia 0 0
Libya 1 1
Mauritanie 1 0
Mauritius 1 0
South Sudan 1 0
South Africa 1 1 Mainly for census data confrontation and
comparison of collected and re??
Sudan 0 0
Sudan
1
Uganda 1 1 Fitting the data into the overall GDP growth
Zambia 1 1 Comparing routine agricultural administrative
data collected with census
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
5.2.1. USE OF MULTIPLE DATA SOURCES FOR SMALL AREA
ESTIMATION
Due to sample size constraints, the results from agricultural annual sample
surveys and even ACAL are not likely to be available at lower levels, say
72
district level. Ancillary information from ARDS data can be used for scaling
down the higher level estimates from sample surveys to district level estimates,
using Small Area Estimation Techniques. In Africa, one such application has
been made in Ethiopia ((Statistics 2012) and (Abaye 2009). The agricultural
annual surveys conducted by Central Statistical Agency (CSA) were providing
crop-wise area estimates at regional and zone levels only. Due to small sample
sizes, District (werada) level estimates were not available. On the other hand,
Ministry of Agriculture and Rural Development (MoARD) was generating area
estimates through an approach which was very much similar to ARDS bottom-
up approach. The Small Area Estimation approach was used to develop district
level estimates for crop area from annual surveys, using MoARD data as an
auxiliary variable. A werada level crop area estimate is done. Two auxiliary
variables, data from MoARD and data from the census of agriculture are used
in model based estimation for weradas. Area level model (Fay 1979) is used for
estimation.
5.2.2. USE OF MULTIPLE DATA SOURCES IN INDIA
In the India Agricultural Statistics System, apart from the estimates of
production compiled and published by the Directorate of Economics and
Statistics, Ministry of Agriculture (DESMOA), a separate series is also
available for some major commercially important crops prepared by the trade
organizations especially for cotton and oilseed crops. For example, estimates of
cotton production are published by the Cotton Advisory Board (CAB) and those
for the oilseeds by the Central Organization for Oil Industry and Trade
(COOIT).
The Indian Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) and Trade series differ widely from each other causing confusion
among users and debate over the veracity of either series.
The divergence between the two series is as a result of the following:
The DESMOA compiles the production estimates on the basis of reports
received from State Governments. These are obtained as the product of area
sown under the crop through complete enumeration and the yield rate from crop
cutting experiments. The CAB estimates are based on inputs from the Cotton
Corporation of India, East India Cotton Association, Indian Cotton Mills
Federation, etc. and these, in turn, depend on data on market arrivals, volume of
cotton ginned and pressed in all ginning mills irrespective of the area sown or
condition of the crop.
73
The two series of estimates differ from each other, the DESMOA estimates
being consistently less than the CAB estimates. The main reasons for
divergence are seen to be:
Shortcomings of the girdawari on which the official estimate of area is
based and the inadequacy of the GCES to give due representation and
weight in its sample to different factors such as irrigated and un-
irrigated, hybrid and local varieties of crop.
Cotton is harvested through several pickings spread over time and it is
possible that the primary agency is not careful to follow the prescribed
procedure of the crop cutting experiments;
The CAB estimates on the other hand, are of a subjective nature being
compiled on the basis of reports from several agencies without proper
attention to full coverage and standard procedures.
The DESMOA has been making consistent efforts to reduce the divergence
between the two estimates by holding discussions with the concerned agencies.
The following measures have been suggested in this connection:
The sample of crop cutting experiments may be suitably increased and
made representative of various types of cotton cultivation;
The primary agencies responsible for area enumeration and crop cutting
experiments should be trained thoroughly;
The methodology followed by CAB should be improved by a careful
review of the data from sources like market arrivals, ginning factories,
Annual Survey of Industries (ASI), unorganized manufacturing units,
etc. in respect of cotton and the use of appropriate models.
There are also differences in the data on Fisheries between the Livestock
Census and State reports with regard to data on fishermen, fishing craft and
gear, due to use of different concepts and definitions. There are similar
problems with Forestry data on the area under forest cover as published by FSI
and by DESMOA.
5.2.3. USE OF MULTIPLE DATA SOURCES IN MOZAMBIQUE
In Mozambique, there are various agricultural data sources like the Annual
Agricultural Statistics Survey (Trabalho de Inquérito Agrícola -TIA), Aviso
Previo, administrative data on crops and livestock, and the censuses of
agriculture and livestock. The two main objectives of TIA are to collect data on
agricultural production, area cultivated and livestock. The TIA data collection
methodology includes the use of GPS equipment for measuring the farm size and
74
area planted in crops, as well as measuring tape and compass for measuring
smaller plots. The production data are dependent on respondent recall. The nature
of recall bias is an area that needs to be studied further, but in the case of crops
that are sold, the farmers appear to provide more accurate information.
In order to provide forecasts and preliminary crop estimates, the Department of
Early Warning undertakes a Crop Forecast Survey (Aviso Prévio) which was
designed around three field visits to sampled farms. The first visit in December
- January is right after the planting of the crop to check crop progress, measure
fields and select two 7-meter square plots for crop cutting. The second visit in
February-March is scheduled to check the status of the crop. The third and final
visit in April -May is for crop cutting.
The Arrolamento Pecuário data from the Livestock Directorate of MINAG are
based on the number of livestock that are vaccinated in national vaccination
programs. That program reports that about one million cattle were vaccinated in
the most recent program and DNSV believes that about 10 to 15 percent were
not vaccinated but has no means to determine the reliability of that estimate.
Censuses of Agriculture and Livestock (Censo Agro-Pecuário-CAP) have been
carried in Mozambique, CAP 1 in 1999/2000 and CAP II in 2009/2010. These
have been jointly organized and carried out by the National Statistics Institute
(Instituto Nacional de Estatística - INE) and MINAG. The Ministry of
Agriculture (Ministério de Agricultura- MINAG) carries out annual National
Agricultural Surveys (Trabalho de Inquérito Agrícola-TIA). TIA has been
conducted in 1996, 2002, 2003, 2005 and 2006, 2007, and 2008. No survey was
conducted in 2004 due to Presidential and Parliamentary elections. There was
also no survey in 2009 or 2010 because of the CAPII.
Trant (2011)7, recommended that Mozambique adopt a new methodology for
official crop statistics to be based on the most recent Census estimate, “Census
Benchmark”, multiplied by the cumulative change estimated, from one year to
the next, by the various annual surveys taken throughout the growing season.
This is a “Best Practices Procedure”8 recommended by FAO
9.
The “benchmark data” for the revised estimates should be from the Censo
Agro-Pecuário (CAPII). Aviso Prévio and TIA would be used to measure
change between years to be used to provide the annual estimates, preliminary
7 Michael Trant, Agricultural Statistician (FAO Consultant); Mission Report, Master Plan Project for
Agriculture Statistics; April 17 to 29, 2011; Maputo, Mozambique.
8 Global Strategy for Improving Agriculture and Rural Statistics, United Nations/World Bank/FAO, 2010.
9 Ibid.
75
(Aviso Prévio), and final (TIA). It was argued that the proposed change in the
methodology for establishing the “official estimates” would provide a
substantive improvement in the quality, reliability, and coherence of MINAG’s
statistics for agriculture. According to Trant, annual estimates based on such a
methodology are able to take advantage of the precision and accuracy of a
Census or large probability survey such as CAPII and the timeliness of the
reasonably reliable measures of change from the annual sample surveys. The
approach also minimizes the variability of the annual survey estimates resulting
from their relatively small sample size or sample rotation. The methodology is
as follows:
The proposed changes in the methodology for establishing the “official
estimates” would provide a substantive improvement in the quality, reliability,
and coherence of MINAG’s statistics for agriculture even if Aviso Prévio
estimates cannot be based on sound statistical practices in the short term. Trant
concluded that “The most likely impact of not improving the Aviso Prévio
program would be that the preliminary estimates would continue to be
overstated, and there would likely be substantive downward revisions to crop
area, yield, and production when the post-harvest estimates from TIA are
available”.
Now rather than using TIA and Aviso Previo, we could use the RAAD,
livestock or fishery data.
5.2.4. USE AND RECONCILIATION OF MULTIPLE DATA SOURCES
IN UGANDA
In Uganda, two sources of information on agriculture are available. The
MAAIF collects administrative data through RAAD annually. The Uganda
Census of Agriculture (UCA) provides information for the period 2008-2009.
Tables 5.3 and 5.4 allow a comparison of MAAIF and UCA data on crops and
livestock, respectively. The MAAIF administrative data are available for 2007,
2008, and 2009, and the UCA data are available only for the period 2008-2009.
The differences between the estimates based on the two data sources are of a
substantively important magnitude
76
In Uganda, annual agricultural production data has been produced basically as
projections made and agreed upon by the Ministry of Agriculture, Animal
Industry and Fisheries (MAAIF) and the Uganda Bureau of Statistics (MAAIF
Data). These projections have been based on a number of factors like
population growth, weather conditions/rainfall pattern, prices of agricultural
commodities collected for CPI computation, external trade data, pests and
diseases, and other general conditions. On a quarterly basis, a few farmers
across the country, are observed.
The Uganda Census of Livestock was carried out in 2008, while the Uganda
Census of Agriculture and Livestock (UCA) was carried out in 2008/9.
77
Table 5.3: Uganda Agricultural Production Data (Thousand Tons)
MAAIF Data Estimates based on UCA 2008/09 Data
Crop 2007 2008 2009 2008 UCA 2008/09 2009
Plantain Banana 9,233 9,371 9,512 4,229 4,300 4,522
Cereals
Finger millet 732 783 841 275 277 250
Maize 1,262 1,266 1,266 2,315 2,362 2.355
Sorghum 458 477 497 342 351 374
Rice 162 171 181 178 183 206
Wheat 19 19 20 19 20
Root Crops
Sweet Potatoes 2,654 2,707 2,766 1,794 1,819 1,943
Irish Potatoes 650 670 689 147 154 162
Cassava 4,973 5,072 5,179 2,876 2,894 2,952
Pulses
Beans 430 440 452 912 929 925
Field Peas 16 16 17 15 16 17
Cow Peas 75 79 84 9 10 11
Pigeon Peas 89 90 91 10 11 13
Legumes
Ground Nuts 162 173 185 230 237 258
Sim-sim 168 173 178 99 101 115
Sunflower 217 234
Source: UBOS Statistical Abstract
Table 5.4: Uganda Livestock Numbers (thousand animals)
Species
MAAIF Data Livestock Census 2008
2007 2008 2008 2009 2010 2011
Cattle 7,182 7,398 11,408 11,751 12,104 12,467
Sheep 1,697 1,748 3,413 3,516 3,621 3,730
Goats 8,275 8,523 12,450 12,823 13,208 13,604
Pigs 2,122 2,186 3,184 3,280 3,378 3,496
Poultry 26,950 27,508 37,404 39,270 43,201 47,520
Source: UBOS Statistics Abstract
As shown in the tables, the MAAIF Data, the results from UCA 2008/09 and
Livestock Census 2008, did not give the same results. Secondly, the MAAIF
data is for the calendar years, while the UCA 2008/09 data was for the second
season (July –December) 2008 and first season (January – June) 2009.
Efforts were made to construct and evaluate estimates for years after 2009 and
before 2007 were constructed. These procedures rely on implicit model
assumptions. The work described below is still in progress and the preliminary
estimates have not yet been adopted.
I. Ratios of production were created by crop in order to get estimates of
Season One 2008 and Season Two 2009. These led to the computation
of annual estimates of 2008 and 2009 calendar years basing on the UCA
2008/09 data. For calendar year 2009, the growth rates of the old
78
MAAIF series were maintained. In addition, adjustments for selected
crops to maintain the growth rates as in the MAAIF data
II. UBOS estimated forward series of production up to 2012 using the
growth rates in the old MAAIF data series to extrapolate the 2009
calendar estimates obtained in (1) above. They maintain levels given by
UCA 2008/09 and project based on the MAAIF growth rates.
III. UBOS has also tried to reconcile the production estimates of 2009/10
using the Supply equal to Use concept during the rebasing of National
Accounts estimates to 2009/10 Base year.
IV. UBOS has tried to create back series of crop production and area to
2000 using UCA 2008/09.
V. In the creation of the backward series two basic approaches have been
used:
Procedure 1:
Taking yield and crop area growth rates constant
Maintaining the crop yield in the series 1998-2008 and applying the
area growth rates (of the same period) on crop area of census year for
the specific crop, new area was obtained for 1998-2008. The yield of
that period was then applied on the new area to obtain new production.
Although this method was good especially for area- giving consistent
area, under production there were crops that had issues and these
included: Maize, Rice, Cassava, Beans, and Pigeon peas (these are
highlighted red in worksheet). Some of issues were the differences
between census year and 2008, and also the difference between the
previous series and the adjusted estimated.
Procedure 2:
Applying the series’ production growth rates on the census data
while assuming that the yield is constant
After obtaining the crop production growth rates for the period, these
were applied on the census production and new production was
obtained for the period. Using the new production estimates and
dividing it by the previous yield, new area was obtained.
Advantages of this method
i. Production estimates get close to the census year production for all
periods
79
ii. The series is smoother.
Although cassava production gets close to the census year production, it
remains a challenge to explain the over estimation in the previous years
since it reduces by about 85% with the new series.
VI. National accounts estimates for food crop growing activities for 2009-
2012 were recomputed using the new projections based on UCA
2008/09 and the estimates had differences (from published estimates)
ranging from -1.4% to 5.6%.
VII. An important lesson is the intention to try to fit the UCA data in the
prevailing national accounts series.
VIII. There is a group/team of staff from UBOS and MAAIF who discuss and
agree on the figures.
Clearly, in Uganda administrative data is not being collected. However,
reconciliation of the existing projected MAAIF data with the census data has
been attempted. The outstanding work would be to make the factors considered
in the projections more explicit, possibly eventually leading to developing a
model for making the projections.
5.3. USES IN FORMING THE STATISTICAL PRODUCT
The review in Technical reports 1 and 2 found that administrative data serve
multiple purposes in the national statistical systems of developed countries.
Administrative data aid in data collection, sample design, and estimation. For
example, administrative data are used to identify farm operators, create
selection probabilities for sample designs, impute missing data, edit erroneous
information, construct weights for model-assisted calibration estimators, and
provide auxiliary information for model-based small area estimators. These
uses of administrative data are intended to improve the overall efficiency of the
final statistical product. This section informs on the extent to which statistical
offices in developing nations utilize administrative data to this end.
Table 5.5 summarizes the use of administrative data for statistical purposes in
countries that participated in the survey of ADSAS. The majority of countries
use administrative data for direct tabulation, frame preparation, survey design,
and forecasting. Only two countries use administrative data in formal statistical
estimation procedures, such as calibration and imputation.
80
Table 5.5: Administrative Uses of ADSAS: Uses in Constructing Statistics
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
Statistical Uses
Direct Tabulation 0 1 1 0 1 1 1 1 0 0 1 0 1 8
Frame
Construction/improvement 0 1 1 1 0 1 0 1 0 1 1 1 1 9
Survey Design 0 1 1 1 0 0 0 0 1 0 1 1 1 7
Model-Assisted Calibration
Estimators 0 1 0 0 0 0 0 0 0 0 1 0 0 2
Nonresponsive Adjustments
(weighting) 0 1 0 0 0 0 0 0 0 0 1 1 0 3
Imputation for Missing Survey
data 0 1 0 0 0 0 0 0 0 0 1 0 0 2
Small Area Estimation 0 1 0 0 1 0 0 0 0 0 1 1 1 5
Forecasting 0 1 1 1 0 0 0 1 0 1 1 1 1 8
Survey Data Integration 0 1 0 0 1 0 0 0 0 0 1 0 1 4
Further reporting 1 1 0 1 0 0 0 0 0 0 1 1 1 6
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
81
5.4. USE BY NON-STATISTICIANS OF THE FINAL
STATISTICAL PRODUCT
As indicated in Table 5.4, many statistical offices in developing countries use
the administrative data directly as the final statistical product. Such
publications serve a variety of purposes for policy, business, farming, etc. The
utility of the final statistical product depends critically on the timeliness of the
data and the accessibility of the publication. Table 5.6 summarizes the various
uses of administrative data in developing countries that participated in the
Survey of ADSAS.
Table 5.6: Administrative Uses of ADSAS: Uses of Final Statistics
Non- Statistical Uses
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
Policy formulation
implementation
and monitoring
1 1 1 1 1 0 1 1 1 1 1 1 1 12
Supporting
investment decisions 1 1 1 1 0 1 1 1 0 1 1 1 1 11
Food security
planning and
monitoring
1 1 1 1 1 0 1 1 0 1 1 0 1 10
Providing
information to users 1 1 1 1 1 1 1 1 0 0 1 0 1 10
Measuring progress
of international
agreements and goals
1 1 1 1 1 0 1 0 0 0 1 1 1 9
Attainment of
efficient markets 0 1 1 0 0 0 0 1 0 0 1 0 0 4
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
The table shows that information from ADSAS is important for policy
formulation, implementation and monitoring in most countries where the survey
response was received. The information is also used in supporting investment
decisions, food security planning and monitoring, providing information to
users for a number of various uses, and for measuring progress of international
agreements and goals.
In terms of users, Table 5.7 shows that ADSAS serve a broad spectrum of
clients, notably governments, researchers, farmers, donors and traders. The
objectives and aims of the ADSAS may in a way influence some parts of the
82
performance such as the information provided and the frequency at which it is
provided. For example, although information on area, production and yield is
very useful in government policy formulation and food security monitoring and
planning, the frequency at which policy makers need it is less than for example
how traders would need it.
These survey results demonstrate that administrative records provide a major
source of information to facilitate decision making for the agricultural sector.
With regular reporting, policy makers and implementers at both national and
local government levels will be equipped with data to make meaningful
decisions. Agricultural statistics are essential for service delivery and
monitoring of development in the sector. Indeed, most of the routine
administrative agricultural statistics are collected for monitoring the agricultural
sector developing plans.
Table 5.7: Main users of data generated from ADSAS
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
SO
UT
H
SU
DA
N
SO
UT
H
AF
RIC
A
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
User /Clientele
Donors 1
1 0 0 0 1 1 0 0 1 1 1 7
Education 0
1 1 0 1 0 1 0 0 1 1 0 6
Farmers 1
1 1 0 0 0 1 0 1 1 1 1 8
Government (MDA) 1
1 1 1 1 1 1 1 1 1 1 1 12
Researchers 1
1 1 1 1 1 1 0 1 1 1 1 11
Traders 0
1 1 0 0 0 1 0 1 1 0 1 6
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
The data user needs to understand meaning of the data and the nature of the
data collection processes so that he/she can draw appropriate inferences and
conclusions. Provision of thorough and understandable metadata is essential to
protect against misinterpretations and incorrect uses. The Integrated Meta-
database of Statistics Canada is a good example of a formal system for
dissemination and storage of complete information about the nature of the data
characteristics and collection processes (Dion 2007).
Data must also be accessible to be useful, rendering critical issues of data
access, storage and dissemination. Table 5.8 summarizes the frequency of use
and accessibility of ADSAS in countries participating in the Survey of ADSAS.
Administrative data are most commonly accessed on an annual basis and
83
through open-access forums, namely the Internet. Though annual data access
seems infrequent compared to daily or weekly access rates, it is important to
remember that annual data collection through Censuses is generally impractical,
and developing countries may not have the resources to conduct annual surveys
of agriculture. Administrative data, therefore, may be the primary source of
annual information on agricultural activity in developing countries.
Table 5.8: Frequency of Use and Accessibility to ADSAS
Source: Survey of ADSAS 2014. 1=Yes, 0=No, Blank=Partial Non-response
BU
RU
ND
I
EG
YP
T
GH
AN
A
LE
SO
TH
O
LIB
ER
IA
LIB
YA
MA
UR
ITA
NIE
MA
UR
ITIU
SO
UT
H S
UD
AN
SO
UT
H A
FR
ICA
SU
DA
N
UG
AN
DA
ZA
MB
IA
To
tal/
13
Frequency of use
Daily 0 1 0 1 0 0 0 0 0 1 1 0 0 4
Weekly 0 1 0 0 0 0 0 0 0 0 1 0 0 2
Bi Weekly 0 1 0 0 0 0 0 0 0 0 1 0 0 2
Monthly 0 1 1 0 0 0 0 1 1 0 1 0 1 6
Bi-Monthly 0 1 0 0 0 0 0 0 0 0 1 0 0 2
Quarterly 0 1 1 1 1 0 0 1 0 1 1 1 1 9
Semi-Annual 0 1 1 0 0 0 0 0 0 0 1 0 0 3
Annually 1 1 1 1 1 0 1 1 0 1 1 0 1 10
Ad-hoc 0 1 1 0 0 1 0 0 0 0 1 0 1 5
Accessibility
Open access Internet /
web 0 1 1 0 1 1 1 1 1 1 0 1 1 10
Website with password 0 1 0 0 0 0 0 0 0 0 0 0 0 1
Email 0 1 1 1 0 0 0 1 0 0 1 0 1 6
Telephone 0 1 0 1 0 1 0 0 0 0 1 0 1 5
Hard cards 0 1 0 0 0 0 0 0 0 0 1 0 0 2
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5.4.2. NON-STATISTICAL USES OF ADMINISTRATIVE
AGRICULTURAL DATA IN INDIA
1) Crop and Land Use Statistics
Planners and policy makers use administrative data for efficient agricultural
development and for making decisions on procurement, storage, public
distribution, export, import and many other related issues. With increasing
decentralized planning and administration, these statistics are needed with as
much disaggregation as possible, down to the level of villages
2) Crop Forecasts
The Timely Reporting Scheme (TRS) has the principal objective of reducing
the time lag in making available the area statistics of major crops in addition to
providing the sampling frame for selection of crop-growing fields for crop
cutting experiments. Under the TRS, the patwari is required to complete the
girdawari on a priority basis in a 20 per cent random sample of villages and to
submit the village crop statements to higher authorities by a stipulated date for
the preparation of advance estimates of the area under major crops. The
advance estimates are used in the framing of crop forecasts. This provides the
Government with advance estimates of production for various decisions relating
to pricing, distribution, export and import.
3) Forestry Statistics
Reliable forestry statistics are required for planning, policy-making, analysis
and decision-making on forestry investment and development programmes.
4) Agricultural Inputs Statistics
For a comprehensive appraisal of the agricultural economy, information on
inputs is as important as the data on production. The Directorate of Plant
Protection, Quarantine and Storage (PPQ&S) in the Ministry of Agriculture
advises and assists the Union Government on all matters relating to plant
protection including international obligations, besides assisting the State
Governments in their plant protection activities.
5) Use of market information
Farmers can make use of market information for the following purposes:
I. Negotiation for better prices.
II. Decide where to sell.
III. Check on prices they are getting.
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IV. Decide whether or not to store.
V. Decide whether to grow “out of season.”
VI. Decide whether to grow different crops.
VII. Decide whether to add value through processing.
VIII. Work with other farmers to bulk up commodities.
IX. Decide when to sell their commodities.
5.4.2. NON-STATISTICAL USES OF ADMINISTRATIVE
AGRICULTURAL DATA IN UGANDA
An important user of administrative data in Uganda was FOODNET.
FOODNET in Uganda developed methods, information and interventions that
lead towards greater market efficiency and value-added processing in the
agricultural sector. FOODNET mainly focused on market analysis studies,
market information and agro-enterprise development and related business
development support services.
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6 Strengths and Weaknesses
(Challenges) and
Recommendations The objective of Task 3 was to analyze the results of the Country assessments
and other relevant documentation on administrative sources being used in
developing countries and evaluate their strengths, weaknesses and suitability for
use in agricultural statistics within an integrated and cost-effective agricultural
statistics system. A number of weaknesses and strengths have been given in the
review above. We highlight and summarize these below with some
recommendations in preparation for Task 4 on identifying and analyzing gaps
and remaining methodological issues for improving the quality and use of
administrative sources for agricultural statistics, and propose possible solutions
to fill the gaps. These proposals will be presented at an Expert meeting that will
be organized by FAO in Rome with the participation of country representatives.
6.1 ANALYSIS OF THE RESULTS OF COUNTRY
ASSESSMENT REPORTS
The Africa country assessment report 2014 concluded that overall, Africa is
quite weak in terms of resources for statistical activities; and statistical methods
and practices dimensions and strong in the institutional capacity and availability
of statistical information dimensions. However, these are general analyses and
not specifically for agricultural administrative data. It was therefore decided to
request for the original data for Africa from AfDB. Further, it was decided to
carry out another review during the African Symposium for Statistical
Development (ASSD) which coincidentally took place in Kampala, Uganda
between 12th
– 14th
January, 2015.
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For the Asia and Pacific region, the Asia-Pacific Commission on Agricultural
Statistics(APCAS 2012) report rated Australia, Japan, Mongolia, New Zealand
as excellent in terms of Institutional infrastructure Dimension (Prerequisites);
Resources Dimension (Input); Statistical Methods and Practices Dimension
(Throughput); and Availability of Statistical Information Dimension (Output) in
the Asia & Pacific region.
The In-Depth Country Assessment for Bhutan (Thinley 2014) concluded that
some of the major issues and challenges in agriculture statistics can be viewed
from different perspectives such as from the eyes of producers and users. The
producers mainly face the challenges of poor coordination, lack of professionals
and funding while the users face the difficulties of inadequacy, poor quality and
irregular release of data.
a) Poor coordination
Multiple agencies both within and outside the MoAF are involved in generating
RNR-statistics. For instance, the major agencies outside authority of the MoAF
are the Department of Revenue and Customs (DRC) under the Ministry of
Finance (MoF) involved in recording of the trade statistics; the Natural
Resources Development Corporation Limited (NRDCL) involved in recording
of the forestry related statistics; the Food Corporation of Bhutan Limited
(FCBL) involved in recording the food related statistics especially the imports,
exports and food reserves. There are also numerous agencies within the MoAF
responsible for production of RNR statistics.
b) Inadequate and poor quality data
The general experiences are there are no adequate data available and the
existing data are of poor quality. The problems are attributed by lack of
professional and full time statisticians, and adequate funds. The existing staff
involved in generation and handling the RNR statistics are from non-statistics
backgrounds and also have multiple mandates to be fulfilled back in their
offices. At times, they spent majority of their time doing non-statistical
activities. Further, in the absence of adequate government funding training of
staff is a challenge and certain statistical activities cannot be carried out as
deemed necessary.
c) Irregular release of data
Owing to lack of adequate funding support, timely release of data is greatly
hindered. In the absence of regular funding support we have to depend on
funding supports of the donors and development partners. If no supports are
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available, we have to wait for such favourable time. At times, the collected field
data takes too long to release for want of funds and experts and become
irrelevant.
d) Lack of professional manpower
The RNR statistical works are coordinated by the RNR statistical coordination
section (RNR-SCS) housed in PPD. The RNR-SCS itself does not have
qualified statisticians except some have availed short trainings and hands on
experiences at job.
Most of the field data collections are done by field extension officials
supervised by district RNR sector heads under the overall coordination of
respective subject matter departments (agriculture, livestock and forestry). The
extension officials who serve as enumerators for almost all RNR data collection
activities do not have statistical backgrounds and skills.
e) Lack of adequate funding
In the absence of strong statistical law and adequate funds with the government,
it would remain difficult for the government to allocate enough funds for the
statistical activities.
6.2. STRUCTURAL ISSUES IN THE ADSAS
In this analytical framework the structural design issues refer to the relatively
stable features of the administrative sources related to agriculture. These
include: (a) the perceived mandate (aims, objectives, and clientele) of the
system, (b) the institutional home, organization, and coordination of the
sources, and (c) the nature of the commodities to be covered. We shall also
review how the administrative sources fit in the overall integrated food and
agricultural statistics system. (FAO 2015a). A number of weaknesses were
identified in the structural issues of the ADSAS, including in the organizations
collecting the administrative data, their structure, the core data items collected
and the staffing levels and qualifications. These are outlined below.
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6.2.1. ORGANIZATIONS COLLECTING AND MANAGING
ADMINISTRATIVE AGRICULTURAL DATA
It was found out that in most developing countries the basic agricultural
administrative data, i.e. crops, livestock, fisheries, forestry; is collected and
managed under the ministries of agriculture, livestock, fisheries or forestry.
However, in many countries there are parastatal organizations collecting
administrative data especially on commercial or cash crops. Private sector
agencies or organizations also often administratively collect and manage
various data, especially after the restructuring policies followed in many of
these countries. These agencies sometimes collect and manage the data without
any direct participation of the Central or National Statistics Office (NSO). They
often use different concepts and definitions, this leads to the data, even on the
same item being different.
In this respect, India provides a good example on how to co-ordinate the
various federal and state institutions.
6.2.2. STRUCTURE OF ORGANIZATIONS COLLECTING
ADMINISTRATIVE AGRICULTURAL DATA
A number of MDAs collecting and managing the data have staff at headquarters
and in the field (extension staff and sometimes chiefs or even enumerators).
The weakness is that often, well qualified staff cannot be retained. In many
developing countries, there is the lack of staff and low staff retention mainly
due to poor working conditions and incentives. In a number of countries the
otherwise good data collection systems have not been sustained. Examples are
the “Buganda” and “Outside Buganda” methods and the FOODNET market
information systems in Uganda. Often the problem is that these systems are
donor-funded and stop as soon as donor funding ends. Also, about 15 years ago
all districts were reporting on a regular basis, however currently very few
districts are reporting as per the desired schedule.(MAAIF Verbal
Communication).
A second weakness is that the field staff are often not well supervised. The
Tanzanian ARDS offers a good example of supervision, or backstopping. A
backstopping Team consists of two competent M&E TWG members and one
regional officer. Further, all Local Government officers gather at regional towns
and report their progress and challenges.
The biggest problem with the collection and management of agricultural
administrative data in many developing countries has been the many and
90
frequent changes in the administrative structure itself. For example, in Uganda
there have been many changes in the number and boundaries of districts.
Further, in the 1980s there was a shift from the purely administrative chiefs to
the semi-political local council leaders. The latter were not used to collecting
data. Similarly, the decentralization policy meant that the extension staff were
no longer answerable to the central the governments.
6.2.3. THE CORE ITEMS COVERED AND GEOGRAPHICAL
COVERAGE
There is generally a lack of data on food crops and coverage is often at the
national and regional levels. This leaves out the lower administrative levels
which are important in the light of the decentralization policies in most of the
developing countries. We also look at specific weaknesses for some core items.
Lessons from the India Agricultural Statistics System
a) Crop Area Statistics
Challenges
As noted earlier, the main purpose of the ICS scheme is to monitor the
performance of the primary reporting agency in the TRS and EARAS
villages. The findings of the ICS over a number of years reveal a high
degree of negligence in carrying out the girdawari, thereby casting
doubt on the reliability of crop area statistics.
Another deficiency of crop area statistics came in with the development
and modernization of agriculture where several new short duration
crops are grown. Although the patwari is required to undertake
intermediate crop inspection between the two major seasons, this does
not appear to be done regularly. Even if short duration crops like
vegetables, flowers, mushroom, etc. are covered during the crop
inspection, they are not listed separately in the final crop abstract but
clubbed together under “other crops”.
The major reason for the poor quality of area statistics is the failure of
the patwari agency to devote adequate time and attention to the
girdawari. The patwari agency is overburdened with many functions
and has to cope with a large geographical area.
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Recommendations
The patwari’s jurisdiction should be reduced wherever it is excessive
and intensive supervision through normal revenue and statistical staff
should be organized over their area of enumeration.
The ICS continues to assess the quality of crop area statistics and
highlighting the deficiencies.
Some southern states have replaced the hereditary system of appointing
patwaris by a state-wide cadre of transferable officials, a strategy that is
reported to have worked quite well. However, it was found desirable
that the states concerned keep staff transfers to the minimum and see
that when an officer is posted at a place, s/he remains there sufficiently
long to take advantage of familiarity with the local conditions in
discharging his functions.
The patwari agency and the girdawari, which has stood the test of time
and proved to be cost effective and efficient in generating crop and land
use statistics down to the village level, should be restored to its past
level of performance. There should be intensive supervision of the
patwari’s work by higher-level revenue officials as well as by the
technical staff of the ICS and the former should be made accountable
for any lapses.
Once the TRS is put on a sound footing, it is possible to use its results
for coming up not only the advance estimates but also the final
estimates of crop area. By ensuring that the girdawari in the TRS
sample is carried out under strict operational and technical control, area
estimates based on the TRS data will be of high quality in terms of
reliability and timeliness.
The North Eastern States and Union Territories that prepare crop area
estimates based on personal assessment of village chowkidars need to
improve the method of data collection. Some efforts have been made to
extend EARAS to some of these States but in the absence of cadastral
survey and detailed records it is not possible to use EARAS type of area
estimation. The progress made by Remote Sensing Technology (RST)
in area estimation holds out a promise to deal with this problem.
One aspect that deserves consideration is the desirability of adding to
the current year’s TRS sample, a small sub-sample of the preceding
year’s TRS sample. Data for two consecutive years from the same set
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of villages prove useful to improve the precision of the survey estimates
and estimates of change over time, in particular.
The patwari and the primary staff employed in Establishment of an
Agency for Reporting Agricultural Statistics (EARAS) should be
imparted with systematic and periodic training and the fieldwork should
be subjected to intensive supervision by the higher-level revenue
officials as well as by the technical staff.
Timely Reporting Scheme (TRS) and Establishment of an Agency for
Reporting Agricultural Statistics (EARAS) scheme should be regarded
as programmes of national importance and the Government of India at
the highest level should prevail upon the State Governments to give due
priority to them, deploy adequate resources for the purpose and ensure
proper conduct of field operations in time.
A Statistical Study should be made to examine whether the data
collected in the ICS can be used for working out a correction or
adjustment factor to be applied to official statistics of Crop Area to
provide an alternative all-India estimate of crop area as a cross check on
official statistics compiled from the States’ reports. If this is technically
feasible, the design of the ICS can be modified and the scheme
strengthened to generate such correction factors.
b) Crop Forecasts
Challenges
The present system of crop forecasts being based mostly on subjective
appraisal at various levels does not reflect the ground situation
correctly. This is specially the case with regard to the preliminary
forecasts, which have to be fairly reliable for taking several policy
decisions.
The Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) is handicapped due to non-receipt of timely information
from the States and it often has to prepare such forecasts based on
incomplete data.
Frequent changes in the production figures especially of food grains
between one forecast and another, and the “final” and “fully revised”
estimates cause confusion and doubt among the users. While releasing
these figures, the DESMOA may indicate the reasons for the change.
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Recommendations
There is need for more objective forecasting based on timely and
detailed information on crop condition, meteorological parameters,
water availability, crop damage, etc. The system of forecasting crop
production in the country by the Ministry of Agriculture needs to be
replaced as soon as possible by an objective method that can assimilate
information received from various sources using appropriate statistical
techniques. The recent establishment of the NCFC, which has been
assigned the responsibility of streamlining and improving the quality of
forecasting, should go a long way in accomplishing this objective.
The NCFC needs additional professional support, comprising
statisticians and multi-disciplinary team of experts to devise scientific
techniques of crop forecasting.
Remote Sensing technology can also provide a satisfactory means of
developing reliable estimates of crop area and condition of the crop at
various stages of growth for forecast purposes. The Space Application
Centre (SAC) is already at an advanced stage of experimenting with the
approach of Remote Sensing to estimate the area under principal crops
through the scheme known as “Forecasting Agricultural output using
Space, Agro-meteorology and Land based observations” (FASAL).
Incidentally, this will form an important input in the forecasting
methodology to be developed by NCFC. The land-based observations
should be used to measure quantitative changes in crop growth besides
discriminating one crop from another.
The States should be assisted by the Centre in adopting the objective
techniques to be developed by the National Crop Forecasting Centre
(NCFC).
c) Production of Horticultural Crops
Challenges
The Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) pilot survey are based on sound technical methodology.
However, the survey procedures are complex, time consuming and
rather difficult to implement in practice. Further, the survey is limited to
11 States and its extension to the remaining States will take a long time
94
due to the fact that many of them do not possess the necessary staff
resources to carry out the fieldwork.
The estimates furnished by the NHB relate to the entire country but they
are of doubtful reliability being essentially based on subjective reports
received from the ground-level staff. There is, in fact, considerable
divergence between the NHB and the DESMOA estimates for the States
and the crops covered.
Neither NHB nor DESMOA provide estimates of production of crops
such as mushroom, herbs and floriculture that are of emerging
commercial importance (coverage/completeness).
Recommendations
Since the methodology used in the Directorate of Economics and
Statistics, Ministry of Agriculture (DESMOA) survey for estimation of
production is complex, time consuming and not cost-effective and it has
been observed that the field staff does not always follow the procedures
laid down for collection of data, it is important that an alternative and
more feasible methodology needs to be developed for estimating
production of horticultural crops.
One possibility is to use the flow of data from sources concerned with
horticultural crops such as wholesale markets (market arrivals), growers
associations, fruit and vegetable processing plants, export trade, etc. in
order to develop a suitable model for estimation. It should be tried out
on a pilot basis before actually implementing it on a large scale.
Special studies need to be carried out in this connection, which may be
entrusted to a team comprising representatives of the Indian
Agricultural Statistics Research Institute (IASRI), Directorate of
Economics and Statistics, Ministry of Agriculture (DESMOA), Field
Operations Division of National Sample Survey Organisation (NSSO
(FOD)) and one or two major States growing horticultural crops.
d) Land Use
Challenges
The nine-fold classification of land use based on village records is not
adequate and does not, for instance, provide information on such
characteristics as social forestry, marshy and water logged land, built-up
land, etc. which are important for local development plans.
95
It is also out of question to introduce the 22-fold classification in the
village records. The patwari cannot, in most cases, identify the
characteristics of various categories not to speak of the heavy burden
this work imposes.
Recommendations
It is suggested that the nine-fold classification may be slightly enlarged
to cover two or three categories of land use which are of common
interest to the Centre and States, and which can be easily identified by
the patwari through visual observation. Such addition increases his
workload only marginally.
The categories to be added may be decided by joint consultation
between the Centre and the States.
There is need to consider the rationalization and simplification of the
Village Crop Register (Khasra Register) and other records maintained
by patwari. The records have remained almost the same for a long
time. There are also marked differences in the content and format of the
records among the States.
Cropping practices have also changed over time and new crops
especially of short duration are sown and harvested. The list of crops
covered by the Village Crop Abstract (Jinswar) needs a review that may
also result in some changes in the manual of instructions for the
girdawari.
Computerization of land records is another major effort in progress to
modernize the land record system. Under this programme, plot-wise
details of ownership are maintained in the computer and periodically
updated so that each owner is able to obtain readily his ownership
record. Computerization reduces the workload of the patwari.
e) Irrigation Statistics
Lack of a sound database for the minor irrigation sector has made it necessary
to conduct a periodical Census of Minor Irrigation works throughout the
country under the scheme of Rationalization of Minor Irrigation Statistics
(RMIS). The primary fieldwork of the census is entrusted to the patwari and
the village level worker (of C.D. block) under the supervision of block-level
officials who also exercise a five per cent sample check in randomly selected
villages. The results of the sample check are used to apply a correction factor
to the main census data. Validation of data takes place at the district level and
96
further compilation and tabulation at the State level with the help of software
provided by the National Informatics Centre.
The Central Water Commission (CWC), which is the nodal agency for water
resource development in the country, is responsible for statistics of water
resources pertaining to major and medium irrigation projects. The River
Management Wing of CWC is engaged in hydrological data collection relating
to all the important river systems in the country.
Statistics compiled by CWC on major and medium irrigation projects and those
compiled by the Minor Irrigation Division, especially the irrigation potential
created and actually being utilized are the alternative sources of estimates of
total irrigated area.
Challenges
There is a large variation between the statistics of “area irrigated”
published by the DESMOA and the “irrigation potential utilized”
published by the Ministry of Water Resources. Both data series are
available with a considerable time lag.
The existing system of generation and dissemination of data in respect
of major and medium irrigation projects does not permit real time
monitoring of inflows of water and its utilization through canals and the
distributary system. Reluctance on the part of the States to furnish the
data in view of their vested interest in the sharing of water is another
stumbling block.
A large volume of useful data is available with the CWC on various
aspects of irrigation without any statistical analysis. These data need to
be put to use by the statistical machinery for better management of
water resources.
Recommendations
In view of the wide variation between the data on irrigated area
provided by the DESMOA and the Ministry of Water Resources, it
becomes essential that State Governments make a special effort to
minimize the divergence through appropriate interaction among the
departments concerned. This is better attempted at the local level
(panchayat or village).
It is desirable to have statistics of irrigated area with cross-classification
by source of irrigation (major, medium and minor) and by individual
97
crop. As this involves laborious tabulation at the village level, this may
be done once in five years as part of the Agricultural Census.
In order to reduce the time lag between the generation and
dissemination of data in respect of irrigation projects for real time
monitoring of water resources and proper and efficient water
management, it is necessary that the major and medium irrigation
projects are provided with computer facilities as well as appropriate
Geographical Information Systems (GIS).
The State Directorates of Economics and Statistics (DESs) should be
made the nodal agencies in respect of irrigation statistics and they
should establish direct links with the State and Central agencies
concerned to secure speedy data flow. The State DESs need to be
strengthened for this purpose.
The divergence between the two series of irrigated area published by
the Ministry of Agriculture and Ministry of Water Resources is
inevitable due to different concepts and definitions used by them. The
data users should be made aware of these differences for proper
understanding and analysis of data.
Statistical monitoring and evaluation cells with trained statistical
personnel should be created in the field offices of the Central Water
Commission (CWC) in order to generate a variety of statistics relating
to water use.
The Central Statistical Organisation (CSO) should designate a senior
level officer to interact with the Central and State irrigation authorities
in order to promote an efficient system of water resources statistics and
oversee its activities.
f) Agricultural Prices
Challenges
Wholesale prices data are received in the DESMOA mostly through
postal mail, which entails delay. Supply of data through post is stated to
be the reason for delay.
The State Governments generally use part time reporters who are not
fully conversant with the connotations of the different terms used in
price data collection and they do not pay adequate attention to the
reporting work.
The main deficiency in the collection of price data arises due to large
non-response.
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There is no coordination among the State agencies concerned nor an
adequate supervisory check over price collection.
Recommendations
Wholesale prices are primarily used to monitor the weekly price
movements. It is, therefore, essential to have quality data on prices by
ensuring representative price collection centres and commodity-wise
quotations of prices. For this purpose, a well-documented manual of
instructions on collection of prices is required.
The price collectors should be given thorough training on concepts,
definitions and the methods of data collection. The training courses
should be repeated periodically.
A mechanism to ensure timely data flow is an immediate need. For
this, the latest tools of communication technology like e-mail should be
availed of. Further, the system should ensure simultaneous data flow
from lower levels to the State as well as to the Centre.
The State agencies at the district level and below should follow up cases
of non-response. The quality of data should be determined on the basis
of systematic analysis of the price data both by the Centre and the
States. Workshops and training courses should be an integral part of
quality improvement.
The number of essential commodities should be reduced to an absolute
minimum, especially the non-food crops, in consultation with Ministry
of Consumer Affairs and Cabinet Committee on Prices.
The centres of price collection should, as far as possible, be the same
for the essential commodities as for those of wholesale prices.
g) Agricultural Market Intelligence
Challenges
Though the data to be supplied by the market intelligence units are of
great utility, the units have ceased to be effective in discharging their
functions mainly due to a lack of proper direction and control of their
activities.
The staff strength of the units has been considerably reduced resulting
in even worse performance.
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Recommendations
Agricultural Market Intelligence is an important and useful instrument,
and it should be strengthened and extended to all the States.
The functions, activities and the staff requirements of the Agricultural
Market Intelligence Units should be re-evaluated and appropriate
measures taken to streamline the units.
Full advantage of their services should be availed of to provide advance
estimates of crop production, to collect auxiliary information required
for framing “small area” estimates of crop production and several other
studies.
h) Fisheries Statistics
Challenges
There are problems in the flow of data from States and consequently
much delay in the compilation of all-India statistics. As far as the deep-
sea sector is concerned, though only a small number of licensed vessels
are in operation, the data on fish catch do not flow in a regular manner.
There is a need to put in place a proper mechanism of reporting for this
purpose.
The data on fish production from the inland sector are collected by the
State Governments. It is noticed that the resources required for regular
data collection are quite large and the cost incurred is not commensurate
with the actual volume of fish production. Inland fisheries pose several
problems due to the vast and diverse nature of water sources and it is
necessary to develop a cost-effective methodology.
The data on fish production from aqua culture, supplied by the States,
similarly suffer from poor quality and become available with
considerable time lag. The types of culturing methods are not reflected
in the data.
The data on fisherman population, fishing craft and gear are available
from both the State Governments and the Livestock Census, while data
on workers engaged in fishing are also available from the population
census. However, the data from these sources are not comparable due
to differences in concepts and definitions and their application across
States.
100
There is an apparent inconsistency between the value of the output and
the export earnings, the latter being much higher. An exploratory study
is required to reconcile the discrepancy.
Recommendations
It has been observed that the present system is operating satisfactorily in
the case of marine fisheries but a lot still needs to be done to evolve a
suitable methodology with regard to inland fisheries.
In the marine sector, there is a need to impart regular, training to field
staff and impose adequate supervision to ensure quality of data.
Use of modern tools of Information Technology for data
communication and storage will improve the quality and timeliness of
fisheries statistics.
The States should improve the recording of area under still water by
appropriate modification of land use statistics.
The discrepancies between the two sources of data namely, Livestock
Census and State reports with regard to data on fishermen, fishing craft
and gear should be reconciled by adoption of uniform concepts and
definitions and review of these statistics at the district and State levels.
i) Forestry Statistics
Challenges
The main drawback in the compilation of forestry statistics (as in the
case of several other sectors) is the inordinate delay in the availability of
data. Except the area under forest cover now being assessed by the
biennial RS satellite survey, all the other published data have long time
lags. The FSI faces the problem of delayed transmission of data by the
States, which tend to accord low priority to the reporting work. Nearly
half the States do not furnish the statistics in time which delays the
national compilation.
The present contribution of the forest sector to the GDP is considered as
an underestimate therefore not accurate as it does not take into account
several important items such as head loads of fire wood, wood used for
power generation, eco-tourism, etc.
There is a large discrepancy between the area under forest cover as
published by FSI and by DESMOA mainly due to the differences in the
concepts and definitions followed by the two agencies.
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Recommendations
Forest area statistics are generated through two sources, the FSI and
DESMOA, each using different sets of concepts and definitions
resulting thereby in a wide divergence between the two estimates. It is
desirable to reconcile these differences to the extent possible, which can
be attempted only at the micro level. It is necessary to have the FSI
survey data at the village level for this purpose.
Early measures are required to cover all forest products in the State
reports in order to improve the GDP estimates of the forest sectors.
To obviate delay in the transmission and to reduce the time lag in the
availability of forestry statistics, it is desirable to set up statistical units
under the State Conservators of Forests to oversee collection and
compilation of forest statistics from diverse sources on forest products
including timber and non-timber forest products.
The latest tools of Information and Communication Technology should
be used for storage, retrieval and rapid transmission of data.
In view of the unavoidable nature of the divergence between statistics
from the two sources – land records and State Forest Departments –
because of different coverage and concepts, the two series should
continue to exist; but the reasons for divergence should be clearly
indicated to help data users in interpreting the forestry statistics.
Statistics Division in the Ministry of Environment and Forests with
adequate statistical manpower should be created for rationalization and
development of proper database on forestry statistics.
J) Agricultural Inputs Statistics
Challenges
With structural adjustment in several countries, the production,
marketing, export/import of agricultural inputs, is in the private sector.
This makes data collection much more difficult. Though some data on
fertilizers are available from the input survey and from publications of
the Fertiliser Association of India, they are incomplete and not available
in time.
The collection and compilation of data with reference to agricultural
implements and machinery is limited to tractors and power tillers and
that too depends only on the data supplied by the manufacturers. The
information is very often not complete and there is no scientific
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mechanism for collecting statistics in this area. Data on farm practices
and farm management are not available, though these are very much
required for an understanding of the farm practices.
Though a lot of statistics on plant protection, quarantine and storage
flow into the headquarters of the Directorate of PPQ&S, they are not
being fully compiled. The data have also not been organized for
effective long-term use. The Directorate does not have enough
statistical support.
Recommendations
The Directorate of Economics and Statistics, Ministry of Agriculture
(DESMOA) should collect, compile and maintain a complete database
on State-wise production, sale of tractors, power tillers, harvesters and
other agricultural implements, density of such implements per hectare,
investment made, level of mechanization, adoption of water saving
devices, etc.
The Directorate of Plant Protection, Quarantine and Storage (PPQ&S)
being the apex body for plant protection should act as a depository of
information on plant protection. Efforts should be made to design,
develop and maintain a comprehensive database on plant protection for
effective long-term uses.
The Statistics and Computer Unit of the Directorate of Plant Protection
Quarantine and Storage (PPQ&S) should be strengthened both in terms
of statistical and computer personnel as well as computer equipment.
Information collected through General Crop Estimation Surveys
(GCES) and the scheme for Improvement of Crop Statistics (ICS)
should be compiled to generate estimates on various inputs such as
fertilizers, pesticides, multiple cropping, etc.
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6.3. CONDUCT ISSUES IN THE ADSAS
In the context of this study, conduct refers to the behaviour of the
administrative systems. In a way, the conduct issues are highly related to
processes. The conduct or process include the following: (a) the information
provided by the administrative system (including data sharing agreements and
processes), (b) the ICT used in transmission and diffusion of the administrative
data (data management process), (c) the funding strategies, (d) the data
collection methods, (e) the quality control methods used, and (f) the feedback
mechanism used by the administrative systems (FAO 2015a).
There are weaknesses in the data collection methods used; the technologies
used in the data collection, analysis, management and dissemination; funding
especially the issue of sustainability.
6.3.1. METHODOLOGY
Different methods are used in the developing countries, including equipment in
area measurement (eye-estimates, measuring equipment, GPS, etc.) and yield or
production estimation (e.g. farmers’ estimates and crop-cutting). Several
weaknesses have been observed.
In Uganda, there is no reliable and documented method of data collection in the
districts. There is also no clear data collection infrastructure. The district
officials are not obliged to work for or send reports to MAAIF. The Fisheries
section in these districts established Beach Management Units as well as their
Fish catch forms (Form I BMU, Form II Parish, Form III Sub-county), but
implementation is still lacking. UBOS designed enumeration areas throughout
the country but they are not being used in the data collection exercise (Uganda
Bureau of Statistics 2007).
The exercise of data collection is found to have been usually thrown to the
Extension officers, Parish Chiefs and LC officials without any facilitation. This
renders the collected data completely inaccurate since the exercise lacks morale
right from the top. The collected data whenever it occurred would be stored as
hard copies, only few instances where one would find this data stored in a
computer or its accessories.
Analysis of this data is reported not to have taken place on so many occasions;
one would occasionally find basic analysis done in simple Excel formats.
Unfortunately, this is the data reported to have been used for planning and
reporting purposes.
104
The way forward is for UBOS to support the development of administrative
data as a reliable source through standardization of data collection instruments,
and continuous coordination with the respective Ministries Agencies. Under the
Integrated Framework for the Development of Agricultural Statistics, there is a
proposal for the Development of Village Registration System and Agricultural
Reporting Service.
6.4. PERFORMANCE ISSUES
In the context of the ADSAS, this analytical framework looks at performance in
terms of: (a) coverage, (b) comprehensiveness, (c) timeliness, (d) punctuality,
(e) completeness, (f) relevance, (g) accuracy, (h) reliability, (i) integrity/
credibility (j) accessibility to different clientele, (k) clarity/interpretability, (l)
comparability, (m) consistency/ coherence, and (n) sustainability of ADSAS.
Sustainability is examined in three aspects: (i) financial support, (ii) user
support, and (iii) cost minimization.(FAO 2015a)
Quality assessments for agricultural administrative data systems in developing
countries are rarely done. It seems most ADSAS in developing countries do not
put emphasis on documenting agricultural data quality parameters. The general
feeling is, however, that the quality of most administrative agricultural data is
very and thus need improvement.
A study was carried out in 2012 with the objective of rigorously assessing the
improved Tanzania ARDS. The study focused on relevance, effectiveness,
efficiency and sustainability of data collection system and to identify its
strengths and weaknesses. The assessment also aimed at providing insights on
what data are collected through the ARDS versus other data collection
instruments such as the multiannual agricultural sample censuses and a
proposed annual agricultural survey. The assessment was conducted by three
consultants with oversight of the Agricultural Sector Development Programme
(ASDP) Monitoring and Evaluation (M&E) Thematic Working Group (TWG)
which is composed of officials of the ASLMs and Development Partners (DPs).
6.4.1. OBSERVATIONS FROM THE STUDY
The following were observed from the study.
6.4.1.1 Relevance - Appropriateness of the Design
The ARDS emerged as a requirement for monitoring the ASDP. Before ARDS,
the data needs for project monitoring were met through traditional data
collection approach. The maincontribution of Improved ARDS has been to
systematize the data generation process. For monitoring the ASDP activities,
105
several performance indicators (particularly output indicators) are obtained
from the VAEO/WAEO reports generated at LGA level.
The design of ARDS for performance monitoring of agriculture sector is
appropriate. However, there are some inherent difficulties in the
implementation process. Regarding the questions whether ARDS is appropriate
for delivering data needed for data transmission to FAO and as inputs into the
system of national accounts it may be mentioned that the design of ARDS is
quite comprehensive in terms of its coverage, but ARDS is not yet fully
operative to provide the data at national level.
6.4.1.2. Effectiveness
One of the important aspects of effectiveness of data collection system is the
quality of data. There are some inherent limitations with the approach of data
collection methods in ARDS as compared to those of alternative methods in
sample surveys. For instance supervision of the data collection is not adequate.
Also respondents tend to give subjective responses to qualitative questions. For
example when a question is asked about a variable like area under a crop in a
village, for which there are no authentic records, the response may be
subjective.
One of the recommendations is more intensive supervision. An example is India
which has put in place a scheme for Improvement of Crop Statistics (ICS)
which employs full time staff for field supervision. Another recommendation is
improvement of record keeping by farmer groups and farm input providers.
6.4.1.3. Efficiency (Cost effectiveness)
As compared to other methods of data collection, ARDS is less costly, but in
terms of quality of data, sample surveys have got better control. One of the
main limitations is that it is not possible to associate any objective measures of
reliability with the results in contrast to sample surveys in which the sampling
errors provide an objective measure of reliability. In ARDS, data is collected
from all the villages and the errors associated with the results are in the form of
non-sampling errors which cannot be measured objectively. One of the general
perceptions about improved ARDS is that that the system is cost effective and
has facilitated data collection, uniform reporting and improved data
accessibility.
The recommendation is that, in order to reduce non sampling errors and
improve efficiency, there should be close supervision in the data collection
process.
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6.4.1.4. Sustainability
Sustainability of ARDS is linked with its relevance, effectiveness and
efficiency. Improved
ARDS has put the routine data collection on a systematic track. But it is still in
a rolling out stage. It is well designed to serve the purpose for which it was
initiated. On the resource side, minimum needs for data collection are to be
maintained. In the long run, the system has to sustain on the basis of internal
resources.
The recommendation is to expand the scope to cover the all administrative units
in the country.
a) Tanzania – ARDS
Recommendations
Based on the observations and discussions above, key recommendations
regarding the Improved ARDS are presented on the following (Statistics. 2012):
I. Data Collection Methods (Data collection for VAEO format),
II. Data Flow (From VAEO format to LGMD2) and Accessibility,
III. Data Quality and Reliability,
IV. Data Management,
V. Resources (Funding/Budget),
VI. Human Resource and Capacity Building,
VII. ARDS Data Use,
VIII. Sustainability and
IX. ARDS-Future Perspective
With an integrated approach of censuses and agricultural annual sample
surveys in view, the role of ARDS will have to be refocused. The
number of items in VAEO/WAEO formats may have to be reduced.
ARDS is to be maintained for routine administrative monitoring
purposes. Towards agricultural statistics system with its refocused
format, it may be used for providing early warning of crop conditions,
food shortages, surpluses and other anecdotal information that could be
used to develop/improve the master sample frame.
Once the agricultural annual sample surveys are fully operational on a
regular basis providing reliable estimates for core crop and livestock
products at national and regional levels, the ARDS data should be
helpful in generating district level estimates using Small Area
Estimation Techniques.
107
b) Uganda - Administrative Data Challenges
There are still challenges with regard to the compilation of agricultural statistics
from administrative records. First, farmers do not keep records on area planted,
animals kept and production levels. Secondly, the quality and timeliness of the
data is generally poor. Financial and human resources are limited at the local
level to support administrative data generation. For instance, the number of
local governments compiling administrative data has been on a decline,
although MAAIF has been developing the capacity of the staff involved in the
generation of agricultural statistics in the local governments.
One of the key challenges for the NSS is the generation and utilization of
administrative data. A lot of administrative data is being produced but its
quality leaves a lot to be desired due to the following reasons:
Poor data flow due to unclear reporting mechanisms;
Submission of incomplete returns/ reports;
Failure of some units to submit returns;
Data may be collected but never used for planning;
Poor documentation of the data production processes;
The reporting mechanisms of different Sectors/Institutions vary
considerably and this delays the data collection process;
Limited capacity in terms of skills of the staff involved in data
management;
High turnover of the professional staff; and
Low level demand for agricultural statistics especially at lower levels of
administration.
Recommendations
Establish a system for linking administrative units with NSO data
collection units (Enumeration Areas);
Recruit and train more data collectors in addition to improved support
supervision by higher level officials;
Standardization of data collection instruments, and continuous
coordination with the respective Ministries Agencies. Under the
Integrated Framework for the Development of Agricultural Statistics,
108
there is a proposal for the Development of Village Registration System
and Agricultural Reporting Service;
Raise visibility of the statistics unit (define minimum statistics staff
structure across the NSS, identify high profile/senior staff to
champion/advocate the statistics function;
Improve on and address capacity gaps at all levels of administration
through a comprehensive statistical capacity development programme;
Estimates of production are still a big challenge that needs to be
improved. The high dependence on farmer’s estimates for production
well knowing that they are always underestimates should be abandoned.
The improvement of extension services can be accompanied by the
introduction of the crop card system or at least the recording of monthly
production data on some main crops and livestock numbers.
c) Côte d’Ivoire Experience
Difficulties in the production of agricultural statistics
In the vast majority of African countries, the administrative provision for data
collection of data in general, particularly in the agricultural sector is faced with
major constraints. Several problems also been reported on the quality of this
data in Côte d’Ivoire (Ivory Coast).
Due to the very long period since the last census of agriculture (13 years for
Côte d’Ivoire), the forecast and estimates from agricultural census data are
often poorly adapted. In addition, the ministry for agriculture (MINAGRI) does
not have any quality assessment process for the administrative data. However,
since 2008, as part of the FAO CountryStat programme, an annual workshop
for validation of statistics received by FAO, is held and all stakeholders are
supposed to participate.
Following discussions with the Department of Statistics and Documentation,
the main difficulties encountered in the production of agricultural statistics
relate to:
inadequate or lack of material resources;
unskilled human resources in agricultural data processing;
lack of capacity building program for staff;
High turnover of agricultural statisticians or skilled staff looking for
better working and living conditions;
lack of required information (especially disaggregated), poor
organization of the data collection and archiving;
109
methodologies are not always adequate (specially for the food crops);
lack of national strategy for the production of statistics in general and
specifically for agricultural statistics;
A regulatory framework long governed by an old law. The decrees and
orders have not yet been taken for the new act;
poor coordination of the several stakeholders and a lack of national
classifications (sometimes two different structures involved in the same
industry statistics transmitted to one year to the same variables differ)
are involved in the collection and production of agricultural statistics
with
Recommendations
To improve the reliability of statistics, the Department of Statistics and
Documentation suggested the following actions:
definition of a strategy for the production and publication of agricultural
statistics, including the required human, financial and material
provision;
Establish a discussion platform between the institutions involved in the
collection, production and management of data;
Promote producers’/users’ workshops in order to make the data
collected ,best meet the information needs for the development of the
agricultural sector;
cover all relevant areas in the production of agricultural statistics;
promote the dissemination of agricultural statistics ;
harmonize concepts, definitions and methods used by the producers of
agricultural statistics;
Include ICT and new technologies (Electronic Data collection,
Geographic Information Systems, Electronic Data Transfer) and use of
the area sampling frame in the production and dissemination of
agricultural statistics.
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6.5. CHALLENGES ON DATA USES
Uses of agricultural administrative data may be classified into two categories.
One category contains uses of administrative data for forming the final
statistical product. The second consists of uses of the final statistical product by
non-statisticians.
Developed countries make extensive use of administrative data for forming the
final statistical product. Examples of these include using administrative data in
frame preparation, imputation models, and small area estimation. A number of
these uses are also employed by developing countries. Uses of administrative
data for formation of the statistical product that are employed to a regular
degree by developing countries include the following:
Direct tabulation of the final statistical product
Use in survey design
Use in frame preparation
Despite the use of administrative data in developing countries for the purposes
listed above, several uses remain that are rarely applied in developing countries.
Examples in the category of forming the final statistical product include the
following;
Model-assisted calibration estimators
Non response adjustments (weighting)
Imputation for missing survey data
Small area estimation (apparently the only known application has been
in Ethiopia; (Abaye 2009)
Survey data integration
The second category of uses contains uses of administrative data as a final
statistical product by non-statisticians. Examples of these include use of an
official statistics for policy, planning, or business decisions. Developing
countries make extensive use of administrative data to guide decision making.
Examples of such uses by developing countries include the following:
Policy formulation implementation and monitoring
Supporting investment decisions
Food security planning and monitoring
Providing information to users
The primary difference between uses of administrative data in developed and
developing countries is that developed countries make extensive use of such
111
data to aid in forming the statistical product, while developing countries
primarily use administrative data as a final product for policy and planning
purposes. Efforts to expand the use of administrative data to improve the
efficiency of survey and census based estimators would be valuable. In
particular, the adjustment of RAAD to produce official agricultural statistics
and the use of small area estimation techniques to produce better estimates for
lower-level administrative areas deserve serious thought.
112
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Michael., T. (2011). "Mission Report, Master Plan Project for Agriculture
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Vientiane, Lao PDR, 18-21 February 2014; Agenda Item 7; (APCAS/14/7.1);
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Year Master Plan For Agricultural Statistics For Mozambique – (2012 – 2022);
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"Agricultural Statistics" (http://mospi.nic.in/nscr/as.htm).
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115
sources and capacities in Uganda – Kampala", 5th - 27th September, 2013; 5th –
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ANNEX A1: Country Reports
A1.1. Uganda
A1.2. Tanzania
A1.3. Mozambique
A2: Quality Assessments
A2.1 Tanzania
A2.2 Uganda
A1: Country Reports
A1.1. UGANDA
There are several agencies involved in collecting various aspects of Food and
Agricultural Statistics (FAS) in Uganda. The main ones are:
Uganda Bureau of Statistics (formerly Statistics Department under the
Ministry of Finance, Planning and Economic Development)
Ministry of Agriculture, Animal Industry and Fisheries (MAAIF)
There are seven semi autonomous bodies that receive policy guidance
from MAAIF and collect some data, most of it administrative, mainly
for its own operations and these include the: National Agricultural
Research Organisation (NARO); Cotton Development Organisation
(CDO); Uganda Coffee Development Authority (UCDA); Diary
Development Authority (DDA); National Animal Genetic Resource
Centre Data Bank (NAGRIC & DB); National Agricultural Advisory
Services (NAADS); Uganda Trypanosomiasis Control Council (UTCC)
Ministries of Trade and Industry: The Marketing, Cooperative and
Planning Departments
Bank of Uganda: The Statistics and Research Departments
Other Parastatal, Regulatory Bodies and Associations:
- Uganda Export Promotion Board
- Uganda Tea Growers Association and Sugar Plantation;
- Uganda Flowers Exporters Association
- Uganda Vanilla Association
- International Institute of Tropical Agriculture (IITA)-FOODNET
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- FEWSNET
The Ministry of Agriculture, Animal Industry and Fisheries
Production of agricultural data started in Uganda way back during colonial
times when the ministry responsible for agriculture established an Agricultural
Reporting Service through which data was administratively reported.
Currently, the Ministry functions through two directorates namely: The
Directorate of Crop Resources and that of Animal Resources. In addition, there
is the Department of Fisheries Resources and an Agricultural Planning
Department.
Department of Agricultural Planning: Statistics Unit
The main source of data is the Statistical Unit within the Department of
Agricultural Planning which is directly under the Monitoring and Evaluation
Section. Its main functions include the following;
Provision of relevant agricultural sector information
Design and implementation of agricultural surveys
Maintenance of a comprehensive database for the Agricultural sector
Monitoring and Evaluation of projects and programmes in the Agricultural
sector
The Statistics Unit also works with UBOS to up-date annual data.
At the local government level the reporting is through the quarterly Output
Based Budgeting Tool (OBT) and the annual District Statistical Abstract. On
the other hand at National level, MAAIF shares the data with other institutions
like, Uganda Bureau of Statistics (UBOS), Bank of Uganda (BOU) and other
ministries.
Directorate of Animal Resources and Fisheries
The current data in the Department of Animal Production and Marketing,
and the Department of Livestock Health and Entomology, under this
directorate, is based on projections that try to take into consideration,
migrations, disease outbreak and droughts as they have occurred over the last
twelve months. Information is also sought from the Farming in Tsetse Fly
Control Areas (FITCA) project in which periodic surveys are carried out.
Information is availed on number of species of animals affected; number of
movement permits issued and quarantine measures undertaken.
118
The Department of Livestock Health and Entomology on the other hand, also
collects data on;
Reported outbreaks and control, affected species etc. and is captured
either actively or passively, and
Veterinary inspections/regulations and entomology yield data from
administrative intervention and participating farmer organizations,
respectively
Department of Fisheries Resources (DFR)
Data collection on fisheries dates back to 1927-33, when a survey of Lake
Victoria and other Lakes like Tanganyika in East Africa was carried out. This
followed a recommendation to start a data collection system for our lakes in
1933 including Lake Victoria. A number of Institutions were put in place
including East Africa Fisheries Organization (EAFRO) later, UFFRO continued
at various stages to find the best approaches to collect good data for
management of the lakes using local fish inspectors- the Fish Guards. Later
Fisheries Assistants were trained to improve this service.
Collected data, in case of Uganda, was submitted by districts. Analysis was
done at Fisheries Headquarters in Entebbe. Supervision of extension officers
was duty of District Fisheries Officers (DFOs) and overseen by MAAIF
Headquarters.
Department of Plant Protection
Crop protection zones have been identified and 34 new inspectors have been
deployed and are charged with collecting data in this respect. The Department
occasionally collects data especially on the disease and pest outbreaks and some
records do exist in the area of phyto-sanitary issues.
Ministry of Lands, Water and Environment; Department of Meteorology
The Department is mandated to monitor weather and climate and advise
planners and decision makers. It has data on rainfall, wind (speed/direction)
sunshine hours and evaporation rates BUT with different levels of complexness.
The data is generated through observations/recording.
There is need to increase reliability of data through increasing scope and
coverage, and installation of automatic weather stations (from 20 to 100).
The Department produces a monthly bulletin for MAAIF which depicts average
amounts of rainfall Vis-à-vis 30-year means and compared with the previous
month. In addition it supplies daily forecasts to MAAIF.
119
There is a linkage between the Meteorology Department and Early Warning
and Agricultural Statistics Unit in MAAIF. MAAIF uses data from
Meteorology to advise farmers on what to plant basing on the critical
minimums for each of the production processes right from planting to
harvesting. The data so far generated is relatively reliable but there is need to
provide more user specific information.
Bank of Uganda
As far as FAS is concerned, Bank of Uganda is participating in the Informal
Cross-Border Trade (ICBT) Survey under the Statistics Department as
discussed above. Secondly, the Bank, through the Statistics and Research
Departments, carries out surveys to construct an Index of Agricultural
Production (IAP).
Other Parastatal and Regulatory Bodies
Uganda Export Promotion Board
Uganda Tea Growers Association and Sugar Plantation; and
Uganda Flowers Exporters Association (UFEA) has a Fresh Handling Facility
which handles the flower exports of all the UFEA members and therefore keeps
data on flower exports
Uganda Vanilla Association
International Institute of Tropical Agriculture (IITA)-FOODNET; FEWSNET
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Table A1: List of Core Items and Core Data Covered in Uganda
Country - Uganda List of core items List of core data
Livestock
Cattle
Sheep
Goats
Pigs
Type
Numbers
Common Diseases
Production
Marketing
Crops
Coffee
Tea
Cotton
Sugar cane
Tobacco
Area under the crop
Production
Volume and value of Exports
Poultry
Chicken
Turkey
Type
Numbers
Common Diseases
Production
Marketing
Aquaculture and fisheries
products
XXXXXXXXXXXXX
Stock
Feeds
Harvest volume
Exports (regional & international)
Agro-Forestry production No information from administrative
sources
Agricultural inputs
Market information
Types of inputs
Sale prices
Location of distributors
Land cover
Forest cover
Available as part of land use/cover
statistics by the National Forest
Authority
Apiary
Honey
Production
Prices
Horticulture No of farmers
Livestock and Poultry
items
Milk
Meat
Eggs
Production
Prices
121
Table A2: Review of Data Use in Uganda
A1.2. TANZANIA
Routine Livestock Data Collection in Tanzania
The Tanzania Ministry of Livestock and Fisheries Development (MLFD) ‘has
the mandate of overall management and development of livestock and fisheries
resources for sustainable achievement of the National Strategy for Growth and
Reduction of Poverty, Improved Livelihood of Livestock and Fisheries
Dependent Communities, Food Safety & Security without compromising
Animal Welfare and Environmental Conservation’ (www.mifugo.go.tz).
Reliable livestock data and statistics are critical for MLFD’s mandate, and are
generated by a variety of sources, including administrative records, surveys and
censuses. Because censuses and surveys data are not available on a continuous
basis and data are disseminated with some delay (the final reports of 2007/2008
Agricultural Sample Census have not been released as of Feburary 2011),
Data Use Criteria Who uses the
data
For what –
current &
potential
Accessibility Frequency of
use
Crop items
MAAIF,
MOFPED,
UBOS, BOU,
LGs
Planning,
Monitoring,
evaluation and
GDP Compilation
Accessible Quarterly
Livestock items
MAAIF,
MOFPED,
UBOS, BOU,
LGs
Planning,
Monitoring,
evaluation and
GDP Compilation
Accessible Quarterly
Poultry
MAAIF,
MOFPED,
UBOS, BOU,
LGs
Planning,
Monitoring,
evaluation and
GDP Compilation
Accessible Quarterly
Aquaculture and fisheries
products
MAAIF,
MOFPED,
UBOS, BOU,
LGs
Planning,
Monitoring,
evaluation and
GDP Compilation
Accessible Quarterly
Agro-Forestry production
Agricultural inputs
MAAIF,
MOFPED,
UBOS, BOU,
LGs
Planning,
Monitoring,
evaluation and
GDP Compilation
Accessible Quarterly
122
MLFD primarily use administrative records livestock data for its daily
activities, as well as for annual planning and budgeting.
The collection of administrative records livestock data in Tanzania involves
staff from the 127 Districts or Local Government Authorities (LGA) which are
responsible for the routine collection of livestock-related data. In particular,
Livestock/Veterinary Officers or Agriculture (Crop) Officers employed by
LGAs provide livestock extension services to rural households at village level,
and are expected to also collect some livestock-related data. Their activities are
directed and supervised by a District Agriculture and Livestock/Veterinary
Development Officer (DALDO). At village level, livestock data are collected
according to a format detailed by LGAs – i.e. there is no a unique format used
throughout the country – as data are primarily collected to meet the data needs
of District Authorities. Livestock/Veterinary Officers or Agriculture (Crop)
Officers Village extension officers deliver the data they collect to the Ward
Extension Officer, who compiles and assembles data from the various villages
and sends them to the District on a monthly basis (the Ward is an administrative
sub-division between the villages and the District). Districts assemble and
analyze the data for planning, monitoring and evaluation and, in turn, share it
with the Regional Governments (there are 26 Regions in Tanzania). Some
Districts send monthly reports on livestock to MLFD, though they are not
mandated to do so.
A1.3. MOZAMBIQUE
Main Systems for Collecting Agricultural Data
The traditional sources of agricultural data are the administrative records,
census of agriculture and livestock and agricultural surveys. However,
increasingly, the Population and Housing Census is becoming an important
source of basic data on agriculture especially for the construction of the
sampling frame (INE 2011).
Traditional Sources of Agricultural Data
Administrative Records
As part of their regular work, extension staffs compile a lot of agricultural data
which they use to file monthly, quarterly, half yearly and annual reports to
district authorities on such things as land utilization, rainfall conditions, crop
plantings and production of food and cash crops, livestock and poultry data.
The reports are collated by the Provincial Agricultural Officers and the reports
from provinces are collated by the Ministry of Agriculture to produce national
123
administrative data on agriculture. One good example of this is the arrolamento
system which the Directorate of Veterinary Services has over the years used to
maintain a frame of livestock producers with a cattle headcount that is updated
periodically. The data from the arrolamento are used by the Directorate of
Veterinary Services in the districts to collect livestock data which the district
uses for programming and operations e.g. disease control. However the quality
of this data has been questioned.
For instance, there is a concern that the arrolamento method is unable to give
accurate livestock numbers because cattle dips were privatized and are not used by
all cattle keepers.
Large and Commercial Farms
Given the relative importance that large farms can have in agricultural
production for particular crops and livestock, the Trabalho de Inquérito
Agrícola (TIA)- the Annual Agricultura Statistics Survey) has a special frame
for large farms. For the TIA sampling frame, each district office is responsible
for compiling a list of all the large farms in the district defined in terms of a
minimum farm size or number of livestock. These large farms are supposed to
be included in the TIA sample with certainty each year. This frame is also
important for the livestock estimates. However, it has been found that the lists
of large farms are not complete and include some farms that no longer exist10
.
Fishery Statistics
Mozambique has a long ocean shore line and several inland lakes and rivers.
Therefore various artisanal, commercial and aquaculture fisheries are practiced
and it is estimated that artisanal and commercial fisheries account for about
90% and 10% of the fisheries, respectively. There is also aquaculture which is
relatively very small, but important. It is currently divided into two commercial
farms and many small-scale ones. The data collection system is made up of the
following agencies:
i. National Directorate for Fisheries Administration
ii. Institute for Aquaculture
iii. Institute for Fisheries Research which collects data on commercial
fisheries
iv. Institute for the Development of Small-scale Fishing which carries out a
census on fisheries every five years
10
One could compare with the tax register now as INE has an agreement with the TAX
Authority
124
Data on aquaculture should be captured in CAP and other household-based
surveys.
The Fisheries sector has a Strategic Plan and a Fishery Sector statistics Master
Plan 2012-19. The current master plan document describes the current
statistical system, the data collected, including the institution in charge, and the
methodology used in the collection of such data. It can be seen that most of the
data are collected using the administrative system. The sampling system is used
for only a few items, including the data on catch and effort of artisanal fishing
and biological statistics. A fishing census is also used as a method to collect
data related to the number of artisanal vessels, gears and number of fishers.
With regard to the catch of artisanal fleet there are two different methods used
to collect these data, the sampling and the administrative method.
A lot of problems have been identified in the following areas: definition of the
indicators, registration of the fishing aquaculture companies and fishing
processing plants, registration of fishing vessels and fishing gears, statistics of
production, export and import of fishing and aquaculture products, fishing
prices and others. Particularly, the administrative methods used to collect all the
information need to be reviewed and harmonized.
The other weakness identified is the lack of an integrated data base. All the
information collected by the institutions and sent to the National Directorate
For Economy and Fisheries Policies (DNEPP) are being sent through sheets in
Excel format or raw data. DNEPP does not have any data base that links to
other data bases. Once the information is received through those sheets, it is
again punched manually to Excel, resulting in a loss of information, and makes
difficult their dissemination.
There is also scarcity of technical personnel specialized in statistics.
Forestry Statistics
Forestry statistics is collected under the Directorate of Forestry and Wildlife.
Data is available on land cover, forests, water, etc. Most administrative data are
collected and compiled primarily for internal use, and usually without using
standard statistical procedures or personnel who have had training in statistical
methods.
125
Agricultural Market Information
There are two main sources of market information. There is the Ministry of
Commerce (MIC) and MINAG Agricultural Markets Information System
(SIMA). MIC provides market information that is intended to meet the needs of
traders (upstream), whereas SIMA intends to meet the needs of farmers (down
stream – agricultural prices). MIC covers the later part of trade which involves
exports and imports, wholesale prices from among larger traders and millers as
well as current stocks. MIC is one of the main users of early warning data.
Other Institutions Collecting Agricultural Data
Data on large scale commercial farms are also provided by;
i. Provincial Directorate of Agriculture under the Department of
Economics(DE), and
ii. Some specialized agencies under MINAG, e.g. Cotton Institute, Cashew
nut Institute, Centre for Promotion of Commercial Agriculture
(CEPAGRI), INFOCOM, etc.
126
A2: Quality Assessments
A2.1 Quality Assessment on the ARDS for Tanzania
Table A3: Tanzania Quality Assessment for Variables VAEO/WAEO
Format
(Key: XXX- Good; XX- Fair; X- Poor)
(Data quality assessment is based on feasibility of good response and difficulty level in
capturing good quality data. The rating subjective in nature and is a result of interactions with
different functionaries (village executives, VAEO/WAEOs etc.)
Table A 3: VAEO/WAEOS Monthly Report
Attribute Variables Data Quality
Assessment Remarks
Weather
Condition
Number of days XXX Numbers of days are easy
to quantify. Most of the
villages have no Rain
Gauges Amount of rain (mm) X
Target Implementation
and Crop Prices
Target
Priority
Crops XX
For priority crops Targets
are set at higher levels
(Region or districts). Non
priority crops targets are
not set at any level. The
same applies for
collecting data for
achieved targets. On
price, the ARDS may
take advantage of data
from District Market
Monitors
Non priority
crops X
Implementation
Priority
Crops XX
Non priority
crops X
Crop Prices XX
Plant Health (Chemical
Control)
Name of pests/Disease XXX
It is not easy to capture
the Amount of Pesticide
applied, unless under
subsidy. Capturing area
is thru estimation.
Name of crop Affected XXX
Severity (Large, Average,
Small) XXX
Affected Area XX
Number of Villages Affected y XXX
Pesticide Applied XX
Amount of pesticide Applied
(kg/litre) X
Number of Villages served XXX
Number of House hold served XX
Area Rescued (ha XX
Livestock Slaughtered
(Number Slaughtered)
Cattle XX Fees are charged for all
animals in official
slaughter points.
Subjective reporting is
likely. It is not easy to get
data from non official
slaughter points
Sheep XX
Goat XX
Pig XX
Chicken (Local) X
Chicken (improved) X
Others X
Average Retail Price XXX
Attribute Variables Data Quality Remarks
127
Attribute Variables Data Quality
Assessment Remarks
Assessment
Meat Inspection
Name of Place for
Slaughter/Inspection XXX
Recording is done on
major cases only i.e.
condemnation.
Type of Animal XXX
Number of Animals affected XX
Reasons for Condemnations XXX
Number of cases XX
Livestock Products
(Milk)
Milk – Indigenous Cattle (litre) X
Only part of marketed
products is capture by
ARDS.
Milk Dairy Cattle (litre) X
Cheese (kg) X
Butter (kg) X
Ghee (kg) X
Livestock Products
(Hides and Skin)
Dry suspended XX Data fairly captured
under commercial off
take. ARDS not capable
to capture hides and skins
from traditional off take
Dry salted XX
Wet Blue XX
Livestock Health
(Medication)
Type of livestock XXX Only disease clinical
signs can be captured,
Laboratory confirmation
not done by most
extension officers. Not
easy to capture data from
non extension
practitioners.
Type of disease XX
Number Affected XX
Number Treated XX
Number Recovered XX
Number Died XX
Treatment/Medicine Applied X
Dipping , Spraying and
vaccination
Type of Livestock XXX
Not easy to get data from
none subsidized vaccines.
No mechanism available
to capture data from
pastoralists
Number dipped XX
Medicine Applied XX
Number sprayed XX
Medicine Applied XX
Number vaccinated XX
Vaccine Applied XX
Livestock service
(Cattle, Goat, Sheep,
Pig, Duck, Chicken)
Cutting hoof X
Data on AI can be fairly
captured. Not easy to
capture other data under
extensive livestock
keeping system.
Castration XX
AI XX
Cutting Horn X
Branding X
Cutting tail X
Cutting teeth X
Cutting bill/beak X
128
Table A4: VAEO/WAEOs Monthly Report
Attribute Variables Data Quality
Assessment Remarks
Village Food Situation Food Situation XXX Positive/Negative trend
observable.
Farmers
groups/Associations
Number of SACCOs XXX System and mechanism is
in place. Number of Members XXX
Amount of Loans (Tsh) XXX
Other Farmer groups
(production Processing
Marketing Crop,
Livestock & Fisheries)
Number of Associations/Groups XXX
Data easily captured and
verified.
Number of Members XXX
Total number Registered XXX
Total number with Bank
Account XXX
Total number of farmers
trained XXX
Training method XXX
Training providers XXX
Plant health
(biological Control
Measures)
Type of disease XXX
Good figures can be
captured when control is
done massively under
government intervention.
Type of Crop XXX
Control Measures XX
Area Controlled (ha) XX
Number of Households
involved XX
Irrigation
(Crops Harvested
under irrigation)
Type of Crops harvested under
irrigation XXX
Records are kept and
standard measurements
are applied.
Planted area (ha) (i) XXX
Yield (ton/ha) (ii) XXX
Production XXX
Soil Erosion
Type of erosion(i) XX How and what mechanism
to quantity the area
observed to be under soil
erosion
Name of village(s) Involved XXX
Area Destroyed (ha) X
Type of Control Measures XX
Area Cultivated by
Village/Ward and
means of Cultivation
By Tractors/power tillers (ha) XX Fairly captured
Area not captured
properly
By Draught Animals (ha) XX
By hand hoes/hand (ha) XX
No tillage (ha) XX
129
Table A5: VAEO/WAEOs Monthly Report
Attribute Variables Data Quality
Assessment Remarks
Basic Information of
Village/Ward
Population XXX Basic data is available.
By applying projection
best estimates can be
arrived.
Number of Household XXX
Male Headed household XXX
Female headed household XXX
Number of household engaging
in agriculture XXX
Number of
Smallholder
Households
Participating in
Contracting
Production and Out
growers schemes
Contracting Production XXX
Verifiable Out-growers scheme XXX
Irrigation (Irrigation
scheme)
Name of the Scheme XXX
Verifiable
Name of water source XXX
Potential Area (ha) XXX
Area under improved irrigation
(ha) XXX
Season irrigated XXX
Status of the scheme XXX
Number of farmers using
irrigation infrastructures (both
members and non members of
IO)
XXX
Number of
agricultural, livestock
and fishery machines
Working
Individually-
owned XX
Group owned easily
verifiable. Record
keeping available.
Group-owned XXX
Not Working
Individually-
owned XX
Group-owned XXX
Machinery Drawn
(tractors/Power
Tillers)
Working
Individually-
owned XX
Group owned easily
verifiable. Record
keeping available.
Group-owned XXX
Not Working
Individually-
owned XX
Group-owned XXX
Animal Drawn
(Draught Animals) Working
Individually-
owned XX
Fairly captured. Group-owned XX
Number of Hand
operated Implements
Hand Hoes XXX
Verifiable. Spray pump (Plant/Livestock) XXX
Flaying Knives XXX
Branding Iron XXX
Number of Agro-
processing machines
Working
Individually-
owned XXX
Verifiable Group-owned XXX
Not Working XX
XXX
130
Table A6: VAEO/WAEOs Monthly Report
Attribute Variables Data Quality
Assessment Remarks
Extension Services
(Farmers Field
School (FFS)
Number of Field School XXX
Group Owned easily
verifiable. Record
Keeping available
Number of Farmers Started XXX
Average Duration (days) XXX
XXX
XXX
XXX
Input Use Inorganic
Fertilizer
Annual requirement XX Under subsidy
requirement can be
established. Difficult to
establish used inputs. Amount used per year (ton) X
Agro Chemicals
(Generic or Trade) Name of
Chemicals XXX
Difficult to verify amount
used. Unit (kg/litre) XXX
Amount used per year X
Improved Seeds
Annual Requirement for the
reporting year (kg) XXX
Gender subsidy
requirement can be
established. Difficult to
establish seeds used by
types/variety.
Name of Improved Variety XX
Amount used
in the
reporting year
(Kg)
Quality
Declared Seed X
Certified Seed X
Livestock Population
Number of
indigenous
Cattle
Total XX
Total number can be fairly
captured. Going further
into categorization, class
and gender not easy under
routine data collection.
Category and
Class X
Number of
Improved -
Cattle
Meat XX
Diary XXX
Sheep Gender
Total
Goat Gender
Total
Other
Livestock,-
Pig, horse,
Camel, Dog XX
Avian
Indigenous XX
Improved-
Broilers/Layers
XX
Livestock
Infrastructure
Working XXX
Easy to verify and
capture.
Not Working XXX
Number Required XXX
Number of Registered XXX
131
Table A7: VAEO/WAEOs Monthly Report
Attribute Variables Data Quality
Assessment Remarks
15)Grazing Land
Number of
Animals Total XX Under Extensive
livestock keeping system
all types of animals share
same piece of grazing
and grazing resources.
Communal grazing is
practiced and no
demarcation in place,
movement of animals not
restricted and movements
of animals is subject to
the availability of pasture
and water.
Leased/ demarcated areas
are under private
management. Basically
these may be poultry
farms, ranch or dairy
farms.
Total Grazing
Land in the
Village (ha)
Total XXX
By type of
livestock
(Cattle, Goat,
Sheep…)
X
Utilized Land
(ha)
Total XX
By type
(Cattle, Goat,
Sheep…)
X
Total
Demarcated Area
(ha)
Total XXX
By type
(Cattle, Goat,
Sheep…)
X
Total Area
Leased (ha)
Total Area XXX
By type
(Cattle, Goat,
Sheep…)
X
16)Pasture
Improved
Pasture
Number of farms/plots XXX
It is a farming business.
Data can be captured and
verified.
Area (ha) XXX
Seed Production (kg) XXX
Amount of Hay Bales/bundles
produced (Hay XXX
17)Crop Residue
Type of crop XXX If carried for commercial
purposes data can fairly
be captured. Otherwise it
is difficult to capture
under communal grazing
system.
Amount of Hay Bales/Bundles
Produced (Hay) X
Area of farms/Plots Grazed in Situ
(ha) X
132
A2.2. Data Quality Assessment for some agencies in Uganda
Table A8: Quality of Data from Some Agencies in Uganda
Organization Attribute Score
(1-5) Reason for score
CDO Relevancy 4 Are on demand for contribution to GDP
Accuracy 2 Lack of adequate personnel in marketing & monitoring
Completeness 3 Data are available but not complete
Consistency 3 There are some conflicts with other data sources e.g. URA,
UBOS)
Timeliness 3 Lack of adequate personnel at ginneries and in the
Marketing & Monitoring Dept.
Data Gaps 3
UCDA Relevancy 5 Demand driven by different users including IMF/World
bank, BOU, MFPED, MTTI and contribution to GDP
Accuracy 3
Data on prices, sales registrations and loadings/exports,
quality analysis is very accurate while data on survival
rates and district production potential is not.
Completeness 3
Data is available and not complete as facilitation is up to
district level although data are collected by extension
officers at sub-county level with no facilitation from
UCDA
Consistency 4 There are some conflicts with other data sources e.g. URA
on border/exit points
Timeliness 4
UCDA provides daily market information and also
registration and loadings/shipments. Some information
from districts delay to be disseminated to headquarters
Data Gaps 2
Lack of adequate information on mean plot sizes of
different coffee (Clonal Robusta, traditional Robusta &
Arabica); organic coffee-productivity & profitability;
district survival rates of new planted coffee; insufficient
data on level of domestic consumption and supply capacity
of local roasters. Also lack of information on number of
coffee farmers by farm size by district
Livestock
Health &
Entomolgy
Relevancy 5 Are on demand for contribution to GDP
Accuracy 4 Any disease outbreak is always followed up by the dept.
and appropriate action taken
Completeness 1
Low coverage since endemic diseases are not covered fully
by local govts. which sometimes do not report timely the
outbreaks. Dept. has complete coverage on epidemic
diseases of major importance.
Consistency 3
Depends on the coverage
Different Organizations give different figures (FITCA,
UBOS).
Timeliness 4 Is always demand driven of adequate personnel at ginneries
and in the Marketing & Monitoring Dept.
Data Gaps 1 Failure to report, low coverage, funding, weak linkage with
grass root technical personnel
Animal
Production Relevancy 5 Are on demand for contribution to GDP
Accuracy 2 Based on estimates
Completeness 1 Information is up to the district level and not parish level as
133
Organization Attribute Score
(1-5) Reason for score
RDS warrants.
Consistency 3 Different Organizations give different figures (FITCA,
UBOS).
Timeliness 1 Delay in submission of data collected from the field.
Data Gaps 1 Failure to report, low coverage, funding, weak linkage with
grassroots technical personnel
Fisheries
Relevance to
stake
stakeholders
5 Its so relevant although it has not been analysed
Accuracy 2 most data is merely estimates
Completeness 2 We may not fill up gaps where data is completely missing
because of lack of resources.
Timeliness 1 Available data is accessible in hardcopy and sometimes
soft copy.
Consistency 2 There is a normally conflicting data report where data is
gathered from other non government agencies.
Data Gaps 1 Data from other producers is not yet being gathered and
there are no methods, standards for that capture.
Directorate of
Crop
Resources
Relevance to
stake
stakeholders
5 Some times feedback is recieved from data users
Accuracy 3 Methodology not defined
Completeness 3 Where data exists, there are only a few variables in it
Timeliness 3 No release calendar
Consistency 2 Methodology not defined
Data Gaps 2 No standard definitions
134
A3: The ADSAS Questionnaire
Analysis of agricultural administrative sources being currently used by
developing countries
Introduction and consent
The Global Strategy to Improve Agriculture and Rural Statistics adopted by the
United Nations Statistical Commission in 2010 aims to improve statistics in
agriculture, livestock, aquaculture, small-scale fisheries and forestry production
in developing countries and ensure the sustainability of their maintenance. Its
main objective is building statistical capacity in developing countries for key
basic food and agricultural statistics.
One of the key components of the Global Action Plan is its Research Plan
which aims at developing cost-effective methods that will serve as the basis for
preparing technical guidelines, handbooks and training material to be used by
consultants, country statisticians and training centres. One of the key priorities
of the Research Plan, which was to be implemented in 2014 is “Improving the
methodology for using administrative data in agricultural statistics”. This
research aims at developing strategies and methodologies for the improvement
of the collection and management of data from administrative sources and of
their use in an integrated agricultural statistics system in developing countries.
The School of Statistics and Planning (formerly ISAE) of Makerere University,
Uganda and Iowa State University, USA are undertaking this research funded
by FAO.
The major challenge being faced by the research team is lack of comprehensive
and up to date information on the methods of data collection, the quality and
use of administrative data in agricultural statistics for the developing countries.
For this reason, the team has come up with this questionnaire to collect the
required information for the said purpose.
Your response to this questionnaire will be greatly appreciated.
135
1. Identification (You may attach a business card-For
Kampala Munyonyo interviews).
1.1. Name of respondent
1.2. Title of Respondent
1.3. Organization
1.4. Country
1.5. Email address
1.6. Telephone
1.7. Occupation/Profession
135
2. Organizations collecting administrative data
2.1. In your Country, do you compile administrative data to generate agricultural statistics? (Yes/No)
1.8. In case you compile administrative data on agriculture, what are the sources of agricultural administrative data currently used in your country for the
following core items?
(1) List of core items (2) Name and website of organization
collecting administrative data
(3) Name of
contact person
(4) Email of contact
person
(5) Phone of contact
person
2.2.1. Crops
2.2.2. Livestock
2.2.3. Aquaculture and
fisheries
2.2.4. Agro-Forestry
production
2.2.5. Agricultural inputs
2.2.6. Land cover
2.3. Was routine agricultural administrative data collected during the reference period of the last census of agriculture in your country?
1. Yes 2. No
2.4. Was any reconciliation done between the two data sets (i.e., census data and administrative data)?
1. Yes 2.No
2.5. If Yes, how was the data reconciliation done?
136
3. Structure of the organization
3.1. What is the administrative structure of the organizations collecting agricultural administrative data on the following
core items and associated data? (Please list all in the space below)
(1) List of core items (2) Coordination (3) Institutional home (4) Geographic coverage
1. Centralized (One national
office)
2. Partially
Decentralized(many sub-
national with central
offices)
3. Fully Decentralized(many
sub-national without
central offices)
4. Other (specify)_____
1. Public (Government)
2. Private
3. Farmer organization
4. Trader organization
5. NGO
6. Research Organization
7. Other (Specify)___
1. Sub-national (Part of
country)
2. National (entire country)
3. Regional (many countries)
4. Other (Specify)_____
3.1.1. Crops
3.1.2. Livestock
3.1.3. Aquaculture and fisheries
3.1.4. Agro-Forestry production
3.1.5. Agricultural inputs
3.1.6. Land cover
137
4. User and uses
4.1. What is the administrative data on the following core items used for (statistical and non-statistical uses?)(Please
list all in the space below)
(1) List of core items (2) User/ Clientele (3) Statistical Uses (4) Non-Statistical Uses (5) Frequency of Use (6) Accessibility
1. Donors
2. Education
3. Farmers
4. Government
(Ministries,
Departments and
Agencies)
5. Researchers
6. Traders
7. Other (Specify)
1. Direct Tabulation
2. Frame
Construction/impro
vement
3. Survey Design
4. Model-Assisted
Calibration
Estimators
5. Nonresponse
Adjustments
(weighting)
6. Imputation for
Missing Survey
data
7. Small Area
Estimation
8. Forecasting
9. Survey Data
Integration
10. Further reporting
11. Other (specify)____
1. Policy formulation
implementation and
monitoring
2. Food security planning
and monitoring
3. Attainment of efficient
markets
4. Providing information
to users
5. Measuring progress
towards international
agreements and goals
(MDGs, CAADP)
6. Supporting investment
decisions
7. Others
(Specify)_______
1. Daily
2. Weekly
3. Bi Weekly
4. Monthly
5. Bi-Monthly
6. Quarterly
7. Semi-Annual
8. Annually
9. Ad-hoc
10. Other (specify)
1. Open access
Internet / web
2. Website with
password
3. Email
4. Telephone
5. Hard cards
6. Other (specify)
4.1.1 Crops
4.1.2 Livestock
138
(1) List of core items (2) User/ Clientele (3) Statistical Uses (4) Non-Statistical Uses (5) Frequency of Use (6) Accessibility
4.1.3 Aquaculture
and fisheries
4.1.4 Agro-Forestry
production
4.1.5 Agricultural
inputs
4.1.6 Land cover
139
5. Data Collection methods
5.1. What data collection methods are used to collect the core data?
(1) List of core items (2) Methods of data collection
(Please list all in the space below)
(3) Techonolgies used in administrative data collection
(Please list all in the space below)
1. Self-administered questionnaires
2. Wiki approach (users SMS or update web)
3. Routine reporting
4. Other (Specify)_________
1. Personal interview
2. Computer Assisted Telephonic Interview (CATI)
3. Manual data entry into computer
4. Scanning of questionnaires.
5. Personal Data Assistant (PDA) and
6. Computer Assisted Personal interview (CAPI)
7. Geographical Position System (GPS)
8. Compass as Measuring Tapes
9. Others (please name)
5.1.1 Crops
5.1.2 Livestock
5.1.3 Aquaculture and fisheries
5.1.4 Agro-Forestry production
5.1.5 Agricultural inputs
5.1.6 Land cover
140
6. Funding and HR/Incentives to the Administrative Data Systems for Agricultural Statistics
(ADSAS) staff
6.1. What are the sources of funding of the activities?Do you provide statistical training to staff?How many statisticians
are working on the system?
(1) List of core items (2) Sources of
founding
(3) Number of
professionals
(statisticians) in
organizations
(4) Number of
support staff in
organization
(5) Number of statistical
staff sponsored for short
training courses (of one
week or more) abroad in
the last 12 months?
(6) Is there a regular
training programme
for statistical staff?
1=Yes
2=No
1. Government
2. Charity
organizations
3. Donors
4. Private sector
5. Farmer or trader
organization
6. Other (specify)
1.6.1 Crops
1.6.2. Livestock
1.6.3 Aquaculture and
fisheries
1.6.4 Agro-Forestry
production
1.6.5 Agricultural inputs
1.6.6 Land cover
141
7. Data Quality
7.1. How does the organisation collecting agricultural administrative data ensure that the following data quality
attributes are achieved?
Data quality assurance methods
(1) Data quality aspect (2)
Ple
ase
ran
k t
he
mo
st i
mp
ort
ant
dat
a q
ual
ity
asp
ect
for
Ad
min
istr
ativ
e d
ata
(3)
Ey
ebal
lin
g
(4)
Dat
a en
try
co
ntr
ol
/val
idat
ion p
rog
ram
s
(5)
Ran
do
m v
isit
s to
co
llec
tors
(6)
Co
mp
arin
g w
ith
alt
ern
ativ
e d
ata
sou
rces
(7)
Reg
ula
r T
rain
ing c
oll
ecto
rs
(8)
Rec
ruit
ing
pro
fess
ion
al
staf
f
(9)
Use
of
serv
ice
con
trac
ts
(10
) Fee
db
ack
fro
m d
ata
use
rs
(11
) Mo
nit
ori
ng
an
d s
up
erv
iso
ry c
om
mit
tee
(12
) Ad
vic
e fr
om
ad
vis
ory
pan
el a
nd
bo
ard
s
(13) Others (Specify)______
7.1 Coverage
7.2. Comprehensiveness
7.3. Timeliness
142
7.4. Punctuality
7.5. Completeness
7.6. Relevance
7.7. Accuracy
7.8. Reliability
7.9. Integrity/ Credibility
7.10. Accessibility to users
7.11. Clarity/interpretability
7.12. Comparability
7.13. Consistency/ Coherence
143
8. Core items and core data items covered
8.1 Which of the following core data are collected as administrative data
under each core item? (Please tick all appropriate)
SN List of core items
SN List of core data items
(1) Crop items Yes No
(2) Associated Data Yes No
1 Wheat 1 2 1 Area Planted 1 2
2 Maize 1 2 2 Area Harvested 1 2
3 Barley 1 2 3 Yield 1 2
4 Sorghum 1 2 4 Production 1 2
5 Rice 1 2 5 Amounts in storage 1 2
6 Sugar cane 1 2 6 Area irrigated 1 2
7 Soybeans 1 2 7 Producer and or consumer prices 1 2
8 Cotton 1 2 8 Disposition (sales, food, seed, feeds) 1 2
9 Other (Specify) 1 2 9 Employment and labor 1 2
10 Early warning indicators 1 2
11 Other (specify)__. 1 2
(3) Livestock Yes No
(4) Associated Data Yes No
1 Cattle 1 2 1 Inventory 1 2
2 Sheep 1 2 2 Annual births 1 2
3 Pigs, 1 2 3 Production of products (meat, Milk,
Eggs, Wool) 1 2
4 Goats 1 2 4 Producer and or consumer prices 1 2
5 Poultry 1 2 5 Net trade or imports and exports 1 2
6 Other (specify)__. 1 2 6 Other (specify)__. 1 2
(5) Aquaculture and
fisheries products Yes No
(6) Associated Data Yes No
1 Area cultured 1 2
2 Production 1 2
3 Consumer and producer prices 1 2
4 Net trade imports and exports 1 2
5 Quantity landed and discarded 1 2
6 Days fished 1 2
7
Amounts processed for food and non-
food uses 1 2
8 Net trade or imports and exports 1 2
9 Other (specify)_____. 1 2
144
(7) Agro-Forestry
production Yes No
(8) Associated Data Yes No
1
Area in woodlands and forests
agricultural holdings (AH) 1 2
2 Quantities of products removed AH 1 2
3
Prices for products in land associated
with AH 1 2
4
Area in woodlands and forests non-
agricultural holdings NAH 1 2
5 Quantities of products removed NAH 1 2
6
Prices for products in land associated
with NAH 1 2
7 Other (specify)_____. 1 2
(9) Agricultural
inputs Yes No
(10) Associated Data Yes No
1
Quantities of fertilizer and pesticides
utilized 1 2
2 Water and energy consumed 1 2
3
Capital stocks e.g, machinery by
purpose (e.g., tillage or harvesting) 1 2
4
Number of people of working age by
sex 1 2
5
Number of workers hired by
agricultural holders. 1 2
6
Employment of household members
on the agricultural holding
7 Other (specify)_____.
(11) Land cover Yes No
(12) Associated Data Yes No
1 Cropland (Yes/No) 1 2
2 Forestland 1 2
3 Grassland 1 2
4 Wetlands 1 2
5 Settlements 1 2
6 Other land 1 2
7 Water 1 2
8 Employment 1 2
9 Other (specify)_____. 1 2