Critical Analysis of Agricultural Administrative Sources...

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.

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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

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mailto:[email protected]

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


16


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


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


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


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


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.


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


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


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


Agricultural Statistics Data in Africa Continued


Highly


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


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




Highly


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


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




Highly


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


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


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


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


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


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


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


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


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.


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


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.


(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


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


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


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


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


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


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


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


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

email

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

http://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


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


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


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


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


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


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

84

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

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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.


(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


(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

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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.

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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

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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

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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.

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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


(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.

103

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.

106

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.

110

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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FAO (2015b). "Technical Report 2; The Role of Administrative Data in

Developed Countries: Experiences and Ongoing Research." Food and

Agriculture Organization of the United Nations.

Fay, R. E. a. H., R.A. (1979). "Estimation of Income from Small Places: An

Application of James-Stein Procedures to Census Data. ." Journal of the

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India, G. O. (2013). "Report of the Committee on Statistics of Agriculture and

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Program Implementation. New Delhi.

ISAD, E. (2008c)."Report of WP3. Software tools for integration

methodologies.".

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Michael., T. (2011). "Mission Report, Master Plan Project for Agriculture

Statistics; April 17 to 29, 2011; Maputo, Mozambique.".

Statistics., T. B. O. (2012). "Assessment of the Improved Agricultural Routine

Data System (ARDS); December, 2012."

Thinley, K. (2014)."In-depth Country Assessments - Bhutan experience;

Twenty-fifth Session, Asia and Pacific Commission on Agricultural Statistics;

Vientiane, Lao PDR, 18-21 February 2014; Agenda Item 7; (APCAS/14/7.1);

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Other References

Carfagna, E. and Carfagna, A. (2010). "Alternative sampling frames and

administrative data; which is the best data source for agricultural statistics?" In

R. Benedetti, M. Bee, R. Espa and F. Piersimoni, eds. Agricultural Survey

Methods. Chichester, UK, Wiley. 434 pp.

Gopal N., et.al. (2012): "Reliability of agricultural Statistics in Developing

Countries: Reflections from a Comprehensive Village Survey on Crop Area

Statistics", Indian Institute of Management, Bangalore, Working Paper No. 381.

Government of India Planning Commission (2001): "Report of the Working

Group on Agricultural Statistics".

Government of India (2013). "Report of the Committee on Statistics of

Agriculture and Allied Sectors". National Statistical Commission Ministry of

Statistics and Program Implementation. New Delhi.

Informa Economica and Agriwatch (2013): "India – October Crop Review

and 2014 Winter Crop Prospects", Crop – Ind 13-04.

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and MINAG (2012): (Plano Director De Estatísticas Agrárias – PDEA) A Ten-

Year Master Plan For Agricultural Statistics For Mozambique – (2012 – 2022);

31st October, 2011"

Ministry of Statistics and Programme Implementation, India (Undated):

"Agricultural Statistics" (http://mospi.nic.in/nscr/as.htm).

MUBIRU J.W. (2014); "TheIn-Depth Country Statistical Capacity Assessment

and Strategic Planning in Agricultural Statistics and the Medium Term Action

Plan for Ghana; (2014-2016); February – March, 2014"

Pronab Sen (Undated): "Challenges of Using Administrative Data for

Statistical Purposes", India Country paper.

Sharma S. (2002): "Statistical System in India, Regional Seminar for Senior

Managers on Monitoring and Evaluation of poverty Reduction Programmes",

Bangkok.

Srivastava A. K. (Undated): "Agricultural Statistics System in India",

NSSO(FOD) Agricultural Statistics Wing.

Uganda (2013) The International Consultants: Agyeman-Duah Kofi, FAO

Consultant based in Ghana,Mubiru J.W., National Consultant for Uganda and

UBOS Staff; "The in-depth Country Assessment assistance report of key data

http://mospi.nic.in/nscr/as.htm

115

sources and capacities in Uganda – Kampala", 5th - 27th September, 2013; 5th –

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World Bank, FAO, ILRI, AU-IBAR (2013): "Livestock Data Innovation in

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support from the Gates Foundation; Issue 2, 2010 – 2013, Routine Livestock

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World Bank, FAO, ILRI, AU-IBAR (2011): "Livestock Data Innovation in

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http://www.fao.org/documents/card/en/c/0409b308-3dcd-522a-8109-30432e75ee4f/

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http://www.fao.org/ag/againfo/resources/newsletter/docs/LDIA_Brief_2011_02.pdf

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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.

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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.

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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

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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


Poultry

MAAIF,

MOFPED,

UBOS, BOU,

LGs

Planning,

Monitoring,

evaluation and

GDP Compilation


Aquaculture and fisheries

products

MAAIF,

MOFPED,

UBOS, BOU,

LGs

Planning,

Monitoring,

evaluation and

GDP Compilation


Agro-Forestry production

Agricultural inputs

MAAIF,

MOFPED,

UBOS, BOU,

LGs

Planning,

Monitoring,

evaluation and

GDP Compilation


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


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


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



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



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



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


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


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


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


(9) Agricultural

inputs 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


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


Critical Analysis of Agricultural Administrative Sources...

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