potential use of blockchain for livestock statistics in...
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Technical Report Series GO-40-2018
Potential use of Blockchain for
Livestock Statistics in Vietnam
Final report
FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS
Rome, December 2018
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iii
Table of Contents
LIST OF FIGURES ............................................................................................................................................. IV
LIST OF TABLES .............................................................................................................................................. IV
ACRONYMS AND ABBREVIATIONS ............................................................................................................ IV
PREFACE .......................................................................................................................................................... IV
ACKNOWLEDGEMENTS ................................................................................................................................ VII
EXECUTIVE SUMMARY ............................................................................................................................... VIII
1. Introduction ........................................................................................................................................ 1
2. Livestock Statistics of Vietnam......................................................................................................... 4
2.1 Statistical activities of GSO in the livestock sector ..................................................................................................5
2.2 Statistical activities of MARD in the livestock sector ........................................................................................... 11
2.3 Census data collection procedure ............................................................................................................................... 12
2.4 Shortcomings - Identified issues in data quality/accuracy ................................................................................ 15
3. 3 TE-FOOD Technology and Traceability System ........................................................................ 18
3.1 TE-FOOD background ...................................................................................................................................................... 18
3.2 TE-FOOD Traceability system - Ho Chi Minh City Livestock industry ........................................................... 18
3.3 TE-FOOD Livestock Management System ............................................................................................................... 19
4. 4. Recommendations for deployment of blockchain ................................................................. 22
4.1 Identified blockchain solution for traceability in the livestock industry of Vietnam ............................... 22
4.2 Identified issues with the existing system ................................................................................................................ 26
5. 5. Way forward ................................................................................................................................. 28
A. GSO questionnaires for livestock surveys ............................................................................................ 35
B. Farm Registration Form for TE-FOOD registry system ....................................................................... 63
C. Agricultural Census 2016 questionnaire ............................................................................................... 66
iv
List of Figures
Figure 1 Livestock traceability system with big data storage........................................................................................ 24
Figure 2 Centralised Blockchain system ................................................................................................................................. 26
Figure 3 Fully distributed Blockchain system....................................................................................................................... 26
List of Tables
Table 1 Summary of the livestock survey data collection methodology .....................................................................7
Table 2 Categories of pigs in quarterly questionnaires of livestock survey ...............................................................8
Table 3 Population, sampling frame and sample size for GSO livestock surveys ....................................................9
Table 4 Pig-breeding agricultural holdings and number of pigs bred in 2016 ..................................................... 14
Table 5 Comparison of definitions of registration units ................................................................................................. 20
Table 6 Combining sources for number of livestock ........................................................................................................ 30
Table 7 Combining sources for number of slaughtered animals ................................................................................ 31
Acronyms and Abbreviations
BSF Bovine Spongiform Encephalopathy
CSIRO-Data61 Commonwealth Australian Research Centre
DAFF Department of Agriculture, Forestry and Fishery Statistics
DARD Departments of Agricultural and Rural Development
FAO Food and Agriculture Organization of the United Nations
FMD Foot-and-mouth disease
FMRIC Food Marketing Research and Information Center
GSARS Global Strategy to improve Agricultural and Rural Statistics
GSO General Statistics Office of Vietnam
LMS TE-FOOD livestock registry system for pigs developed for Ministry of Agriculture
MAFF Ministry of Agriculture, Forestry and Fisheries of Japan
MARD Ministry of Agriculture and Rural Development of Vietnam
NSIS National Statistical Indicator System
NSSP National Statistical Survey Programme
RAFC Rural, Agricultural and Fishery Census
UNSD United Nations Statistical Commission
v
Preface
At its 41st session, the United Nations Statistical Commission (UNSD) endorsed the Global Strategy to
improve Agricultural and Rural Statistics (GSARS) (FAO, 2010) to address the critical decline in the
availability and quality of agricultural statistics in developing countries. In its Global Action Plan(FAO,
2012), countries identified the improvement of methods for estimating livestock production as a priority
research topic. The objective is to develop cost-effective statistical methods for estimating livestock
production and productivity and to cover the specific issue of nomadic livestock data.
While it is not practical and certainly not economically optimal for countries to obtain complete
information on the livestock sector on a regular basis, many developing countries continue to lack the
capacity to collect and disseminate even the rudimentary set of data required to monitor national trends
or to inform international development discussions. It is therefore increasingly imperative that national
agricultural systems move towards a systematic collection framework to report reliable statistics.
The core livestock indicators to be used in formulating sector policies and for investment purposes are
generated by different data collection systems: censuses of agriculture/livestock, farm sample surveys,
household surveys and administrative records or routine data. Depending on the availability of resources,
countries implement some of these data collection systems. However, a great number of developing
countries mainly rely on the data regularly collected within administrative reporting systems: information
on livestock population, animal movements, production of livestock products, animal diseases and other
animal-health indicators, market prices of main products, etc.
The World Bank and the Food and Agriculture Organization (FAO) state that “applying an appropriate
framework to collect relevant and high priority information while avoiding multiple or non-standardised
collection of data by different government agencies has been recognised as an effective method to assist
in the design and implementation of policies to promote sustainable livestock sectors” (FAO and World
Bank,2014). The same report also mentions the importance of data integration- the use of data originating
from different sources.
A cost-effective approach to enhance the quality and availability of livestock data in developing countries
is to improve the use and quality of administrative or/routine data, which currently draws much criticism
(in terms of quality, timeliness, reporting periods, granularity, use of conversion factors, and lack of
objective measurements). The use of such data to generate official statistics poses a key challenge for
national statisticians. The Global Strategy’s methodological report on the use of administrative data states
that: routine data on livestock and other core data items – such as forestry, fisheries, agricultural inputs,
exports and imports – are often incomplete, obsolete, inconsistent and unreliable due to limited skills in
vi
data-handling and processing, insufficient resources and other reasons. In a growing number of countries,
ministries of agriculture or departments of agriculture maintain livestock traceability records on central
databases, tracing animals from birth to slaughter and to the final consumer (FAO, 2017).
Blockchain technology is growing fast and has enormous potential. Its applications in the agriculture and
food sector are among the most promising, particularly on the overall issue of traceability which has
gained considerable importance in the food context, following a number of food crises, specifically in the
livestock sector: the Bovine Spongiform Encephalopathy (BSF) epidemic, the presence of dioxin in chicken
feed, foot-and-mouth disease (FMD) outbreaks, the severe diffusion of foodborne illnesses, etc.
Blockchain technology is now starting to be implemented to improve traceability in the livestock value
chain and solve a range of other issues, such as consumer trust, supply-chain transparency, product quality,
environmental impact and fraud,. This new approach may allow producers (farmers), veterinary services,
slaughterhouses, wholesalers, retailers and governments/authorities to be connected to one another
through a distributed ledger that guarantees the transparency and incorruptibility of the data. The
established registers and all the data collected along the supply chain are to be considered from a
statistical perspective as administrative records (data collected for the purpose of carrying out non-
statistical programmes).
Official statisticians in the agricultural sector may be interested in using some of the “administrative
records” produced along the supply chain and recorded into the distributed ledger for several reasons:
to use established registers for survey frames, directly as a frame or to supplement and update
an existing frame;
to replace existing data collection (e.g. use of data from slaughterhouses in lieu of specific
surveys);
for use in editing and imputation, direct tabulation and indirect use in estimation.
However, one must be careful in using administrative records, as there may be some limitations to be
aware of, such as the lack of quality control over the data, the possibility of having missing items of records,
the difference in concepts and definitions, the timeliness of the data, and the costs of using administrative
data.
The present report is therefore the result of a study undertaken by the Global Office hosted by FAO, in
partnership with the company TE-FOOD, the General Statistics Office of Vietnam (GSO), The Ministry of
Agriculture and Rural Development of Vietnam (MARD) and the Commonwealth Australian Research
Centre (CSIRO’s Data61).
vii
Acknowledgements
This Technical Report was produced by Mariana Toteva (FAO consultant) and Zubair Baig (CSIRO’s Data61)
in close collaboration with Christophe Duhamel, FAO Statistics Division.
The document benefited from contributions by CSIRO’s Data 61 (Mark Staples), the GSO of Vietnam (Lê
Trung Hieu and his team from the Agricultural Statistics Department) and TE-FOOD (Erik Arokszallasi, and
Dao Ha Trung).
We also wish to extend our gratitude to all the national, regional and local authorities in Vietnam who
have contributed to the study through their active participation in various meetings and workshops in
Hanoi and Ho Chi Minh City, in particular to the Deputy Director of GSO, Nguyen Thi Huong and the
Deputy Director of the Department of Livestock Production of MARD, Nguyen Xuan Duong.
A special thanks also to the FAO representation in Hanoi for their support.
viii
Executive Summary
A study was conducted by the Global Strategy to improve Agricultural Statistics – hosted by FAO - in close
collaboration with the General Statistical Office of Vietnam (GSO), the Ministry of Agriculture and Rural
Development of Vietnam (MARD), TE-FOOD and the Australian Commonwealth Research Centre (CSIRO-
Data 61). The objectives were to:
contribute to a better understanding of the applications of blockchain technology and its
implications for the official statisticians in the livestock sector;
analyse the feasibility and the sustainability of using information available on the blockchain from
an official statistical perspective, on the basis of the systems put in place by TE-FOOD in the pig
sector in Vietnam;
make recommendations, discuss possible improvements and propose a further research agenda
Main findings and recommendations
1. A methodologically sound and efficient statistical system in the livestock sector in Vietnam is
needed
In the field of livestock statistics, GSO applies statistically sound methods to collect data on the number
of animals and animal production. MARD makes its own estimations based on their administrative
reporting system. As in many other countries where two sources of information coexist, the estimates
of the number of animals per district from GSO and MARD sometimes differ significantly.
The data collected by GSO covers main data items related to the number of animals and animal
production. However policies require additional items for the calculation of important livestock
indicators. The large size of the sample makes the statistical process less efficient and the way the sample
design and validation of final results are organised does not allow to calculate the precision of estimates.
The MARD data collection is linked to the administrative functions of the Ministry (e.g. animal health
records and reporting from farmers). The current processes in MARD also leave room for improvement,
in particular it would be valuable to develop strict protocols for data collection and processing,
harmonised with official statistics quality standards and implemented uniformly in all provinces of the
country.
GSO has planned measures to improve the quality of livestock statistics such as i) a new sample design;
ii) the improvement of questionnaires and iii) mechanisms for controlling the non-sampling error. The
collaboration between the GSO and MARD has to focus on the harmonisation of concepts and
definitions used, integration of existing sources, reduction of duplication and increased consistency
between the sources of the two institutions.
ix
2. TE-FOOD systems are to be considered potential additional sources of data for livestock
statistics in Vietnam
The TE-FOOD traceability system put in place in the pig supply chain was developed following the
request from the Ho Chi Minh City government for a farm-to-table fresh food traceability ecosystem -
now on blockchain - covering all logistics and food quality activities and data management of the supply
chain. The livestock management system (LMS) is a pilot pig registration system which started in 2018
through a contract signed between the MARD and TE-FOOD Vietnam for covering 21 provinces.
The study has demonstrated the potential benefits of the use of these new sources of data for official
livestock statistics. The TE-FOOD traceability system can provide highly rated data, particularly on the
number of animals slaughtered in slaughterhouses. The LMS is at a very early stage of testing, however
it seems fit to replace the current reporting from farms at the MARD and could be used as a reliable
frame for specialised surveys although some key quality aspects will need to be further evaluated. The
new registry system is expected to be more efficient as farmers will be able to report online using their
mobile phones. Both sources however have coverage issues (not covering the entire population of
agricultural holdings breeding pigs) but they can nonetheless be used for the sub-population of farms,
cooperatives and enterprises.
In terms of deployment on the blockchain, TE-FOOD has chosen the option where the livestock
traceability data are produced by the data collectors outside the blockchain and the transaction
data validated and stored on the blockchain. The statistics calculation procedures may then be
executed either off-chain or on-chain. One of the main concern could be the credibility of the data when
it is validated for storage on the blockchain: this is also true for data collected through traditional surveys
or reporting systems. The development of validation mechanisms with GSO and MARD in order to
confirm that the data being reported by farmers is actually valid could be an important domain of
collaboration.
Way forward
Several follow-up activities have been identified:
1. launching a complete assessment of the quality of inputs and outputs of multisource statistics
for the pig sector in Vietnam based on field experiments at regional level (in the 4 provinces where
the TE-FOOD LMS system is in place)
2. undertaking additional studies related to increased coverage for other sub-sectors or commodities
(poultry, cattle, fruit-vegetables for example) in Vietnam;
3. elaborating generic guidelines helping official statisticians in developing/setting up new
approaches combining multi-source information;
4. considering a scaling-up to cover other countries wishing to invest in the possible use of the
exponential availability of data coming from traceability systems on blockchain
1
1. Introduction
In the recent years the national statistical authorities worldwide are working in multi-source environment
and are more and more prone to use a part from the conventional statistical surveys, administrative data
sources but also various internet data sources (big data, internet of things etc.). The questions on how to
use and evaluate these sources and the statistical output from them (quality of the estimates) have already
been studied in different contexts. Various quality measures and indicators for multisource statistics have
been analysed at national or even at international level (ESSnet on quality of multisource statistics –
KOMUSO), aiming at providing guidelines to the national statistical authorities using multiple data sources,
primary established for some administrative or private purposes (KOMUSO, 2018).
Administrative sources, such as farm registers, livestock identification systems, etc. established by
different governmental institutions to support their administrative functions have already been largely
used by official statistics for direct tabulation, building and updating statistical registers, or for
combination with statistical surveys. The statistics derived from administrative data have the advantage to
be produced faster and in a cost-efficient way. The use of administrative sources for official statistics also
reduces the respond burden as the respondents would not be asked twice the same information. However,
when using administrative data, there is a serious concern about the quality of the statistical output, in
terms of relevance, reliability, timeliness, accuracy, etc., and the sustainability of the source. A thorough
analysis of the quality of the data from administrative sources, therefore, precedes the decision on their
use for official statistics. Administrative data are usually stored in electronic (and most rarely paper) format
in one central or in more regional locations.
Traceability systems in food supply chains are established with the aim of following the movement of
food through specified stage(s) of production, processing and distribution by documentation of each point
of food handling in order to identify and address food safety risks11. Increasing transparency throughout
the entire supply chain and gaining the consumer trust, the traceability systems are developed by large
number of business operators all over the world. In some cases the traceability systems are required by
law (such as EU Food law, under Regulation (EC) No 178/2002) in others they are initiatives of government
authorities, such as ministries of agriculture, livestock, food safety agencies, etc. or business operators,
such as Walmart (Walmart, 2018), Unilever, Nestle, producer organisations, etc.
Since ensuring traceability of the food supply chains requires connection, trust and transparency between
a large number of actors from all stages of production and often from different locations (including
1 The definition agreed at Codex Alimentarius held in June-July of 2004
2
international), the blockchain technology is quickly gaining an important role in keeping track of all
transaction from farm to table.
The blockchain technology applied to a food supply chain means collection and storage of a large
amount of data related to the transactions made in the chain. Different statistics necessary for the
management of the supply chain can be produced annually, monthly or even daily and on more frequent
basis. Some of them are of interest to official statistics and government as they may replace entirely or
partly traditional data collection techniques for farms and food industry. For livestock statistics, for
example such data are the number of livestock breeding farms, number of animals sold to
slaughterhouses, number of slaughtered animals at different geographical level, quantities of milk
delivered to dairies and processed to different milk products, etc.
Using the food supply chain traceability system developed with blockchain technology to produce official
statistics requires an assessment of the data quality within the quality framework for official statistics. Since
the food traceability systems are often developed and implemented by specific business operators,
it can be expected that more than one system is developed even in the same food supply chain. Thus
some actors of the supply chain (producers, processors etc.) may be part of more than one traceability
system while others may remain out of them. Quality frameworks specific for traceability systems need to
be developed with a set of indicators to assess quality of produced statistics in order to decide if they can
be used to replace or complete official statistics produced with statistical surveys. The Food Marketing
Research and Information Center (FMRIC) together with the Ministry of Agriculture, Forestry and Fisheries
(MAFF) of Japan, for example developed a Handbook for Introduction of Food Traceability Systems
(‘Guidelines for Food Traceability’) (FMRIC and MAFF, 2007) as a reference for food operators and their
associations wishing to develop a food traceability system in the country.
In Vietnam, the GSO has had the opportunity to be involved in the testing and establishment of two
initiatives: i) TE-FOOD traceability system for pigs supply chain in Ho Chi Minh City and ii) TE-FOOD
livestock registry system for pigs (LMS) developed for Ministry of Agriculture, to be tested for possible
deployment in 21 provinces; both with a lot of potential to be used for official statistics in the future. Being
involved in the establishment of these two systems from an early phase would allow the GSO to set some
initial requirements to ensure the quality of the data obtained from them. The comparison of the estimates
produced from GSO livestock surveys, from the TE-FOOD systems or from the combination of both and
the evaluation of their quality would help GSO to decide on whether and how to use the two external
sources of data for official statistics.
3
The introduction of the traceability system is not currently required in Vietnam but rather the initiative of
local government and different food business operators. The TE-FOOD traceability system in pig supply
chain was developed after the request from Ho Chi Minh City government. It is expected that such
traceability systems are developed in other provinces and for other products as well. Considering the
growing interest in introducing traceability in the supply chains in Vietnam, GSO should be involved in the
development of guidelines for the establishment of traceability systems in the food and feed supply chains
together with the Ministry of Agriculture, Ministry of Health and other interested parties. Such a common
framework would contribute to ensuring certain level of quality in particular coherence of new traceability
systems developed by different business operators and their future use for official statistics.
The TE-FOOD registry system is a joint project between TE-FOOD Vietnam and the Ministry of
Agriculture. It aims at supporting the current reporting system from pig farms operated by the Ministry.
Its’ implementation is in very early stage and the decision on its future implementation and maintenance
depends on the Ministry of Agriculture. However, the integration of the two systems at least the possibility
to match units from the two data sources through common (farm) identifiers has to be investigated.
4
2. Livestock Statistics of Vietnam
Producing livestock statistics as part of official statistics in Vietnam is under the competence of the
Department of Agriculture, Forestry and Fishery Statistics (DAFF) of the GSO. The Statistics Law No
89/2015/QH13 endorsed the list of National Statistical Indicator System (NSIS), which in the field of
livestock includes:
Number of cattle, poultry and other domestic animals.
Output of some main livestock products
The National Statistical Survey Programme (NSSP) is decided by the Prime Minister, covering the
surveys to be conducted regularly in order to compute the statistical indicators for the NSIS. Within this
programme the GSO carries out an agricultural census every 5 years and an annual livestock sample survey.
Other surveys that can be used for producing the livestock related indicators are the farm survey carried
out every 2 years2 and the quarterly survey on producer’s price of agricultural, forestry and fishery goods3.
The last Rural, Agriculture and Aquatic Product Census was carried out in 2016.
The Ministry of Agriculture and Rural Development (MARD) is the main user of livestock statistics for
analysis and monitoring of its agricultural policies. The Ministry acts as coordination agency for statistical
surveys from the NSSP, including agricultural census, annual livestock sample survey, farm survey and
survey on producer’s price of agricultural, forestry and fishery goods. MARD through its provincial
Departments of Agricultural and Rural Development (DARD) also collects information for its own purposes.
This information is considered by statistics law either as statistical surveys outside the Statistical Survey
Programme (Art. 30) or as administrative data. Administrative data used for official statistical activities is
considered statistical data (Art. 36). The development of administrative databases that serve both the
management of the relevant institution and the statistical activities of GSO is prioritised by the State (Art.
36 (4)).
TE-FOOD is a company that has recently launched two management systems involving collection,
exchange and storage of information related to pigs supply chain in Vietnam. This information can be
used to produce some pig’s statistics necessary for the purposes of the management of the systems and
to provide feedback to different users. Chapter VIII of the Statistics Law sets the scope and requirements
2 By the national survey program, GSO will not conduct the single farm survey any more, but the farm questionnaire
will be integrated into the agriculture census (every 10 years for next round) and mid-term census agriculture survey
(every 5 years). It means that the information on farms will be available every five years 3 This survey collects information on farm gate’s (or producer) price of agriculture, forestry and fishery goods to
calculate the gross output of livestock.
5
of statistical activities outside official statistics and its use. According to Art. 67 (1) it includes activities to
collect, process, aggregate, analyse, forecast statistical information to serve for research, business
production and legitimate needs of organisations and individuals themselves or other organisations and
individuals. However, according to Art. 69 this statistical information is invalid to replace the official
statistical information for any planning, implementation, monitoring and evaluation of national policies
for socio-economic development, management and direction by the governmental institutions.
During the assessment mission carried out within the project, ‘The potential use of blockchain for livestock
statistics, a case study in Vietnam’, several meetings with central and provincial offices of GSO and MARD,
TE-FOOD and farmers were organised. The discussions on the livestock statistics and other existing
information raised some issues and helped to identify fields of improvements. The information collected
from TE-FOOD was also discussed in the perspective to study its’ possible future use for official statistics.
The details of the GSO and MARD data collection systems are discussed here below. The assessment of
the current state, is completed with the information coming from other sources, namely ‘SPARS Vietnam’
and ‘Designing a Livestock Probability Sample Survey for Vietnam’. Since the study aims among others to
conceptualise and develop a pilot case on the pig statistics sector in Vietnam in close partnership with TE-
FOOD, the GSO and MARD of Vietnam, the assessment of the different data sources of GSO and MARD is
done with focus on pigs statistics. TE-FOOD systems have to be analysed as if they were administrative
sources with possibility to be used for official statistics. Their possible integration and expansion to cover
other livestock types depend on the decision of provincial governments or MARD.
2.1 Statistical activities of GSO in the livestock sector
The GSO is responsible for the directing and organising statistical activities in the whole country, while
the centralised Provincial Statistical Offices are responsible for the organisation of activities at local level.
The main indicators produced on an annual basis in the livestock sector are the number of heads of main
livestock types (buffaloes, cattle, horses, sheep, goats, pigs and poultry) on 1 October of the year, and the
quantity of animal products (live weight of live animals, slaughtered animals, milk and eggs) produced in
the previous 12 months. The results are regularly published in the Statistical Yearbook of Vietnam and are
produced at national, provincial and district levels.
Livestock statistics processes, including those for producing pigs statistics are planned and designed at
central level. The methodology of the main data source and the livestock survey, are developed according
to the Decision No. 882 /QĐ-TCTK dated on August 28th 2013 of General Director of General Statistic Office.
According to the purpose set in this document the survey collects basic data about:
6
number of head of buffaloes, cattle, pigs, poultry (chickens, ducks, swans, geese...) and others
(horses, goats, sheep, stags...) at the survey time;
main livestock production (live-weight, milk, eggs...) in the survey period.
The statistics estimates cover all livestock breeding units: rural and urban households, sub-farms, farms,
enterprises, cooperatives, etc. The data from the survey are processed and the results are disseminated
within 45 days after the reference date. The statistical indicators produced are used for planning,
developing and implementing sector policies. The survey has annual and quarterly component with
different questionnaires and sample design. The annual component reference date is 1 October. The
October data collection represents all the districts of Vietnam, including the rural districts, district-level
towns, urban districts and provincial cities. The quarterly component also has the following reference
dates: 1 January, 1 April and 1 July. The data collection on 1 April represents also all the districts of Vietnam,
while the other two represent the provinces and central cities. Table 1 summarises the data collection
details of the survey.
7
Table 1 Summary of the livestock survey data collection methodology Data collection
reference day
Coverage/
Representativeness
Units of
observation
Reference period Data
collection
period
Type of survey
Comprehensive Sample
01/January/year N Country
Provinces and central
cities
Households, sub-
farms, farm,
enterprises,
cooperatives
01/10 to 31/12/ year N-1 7 days For farms, enterprises,
cooperatives: number of heads
and quarterly production from
pigs/poultry
For rural households/sub-farms:
number of heads and quarterly
production of pigs/poultry
01/April/year N Country
Provinces and central
cities
Districts
Households, sub-
farms, farm,
enterprises,
cooperatives
For households and sub-farms not
surveyed in January: 01/10 to
31/03/ year N
For others: 01/01 to 31/03/ year N
7 days For farms, enterprises,
cooperatives: number of heads
and quarterly production from
pigs/poultry
For rural households/sub-farms:
number of heads and quarterly (or
biannual) production of pigs/
poultry
For urban households: number of
heads of pigs/ poultry
01/July/year N Country
Provinces and central
cities
Households, sub-
farms, farm,
enterprises,
cooperatives
01/04 to 30/06/ year N-1 7 days For farms, enterprises,
cooperatives: number of heads
and quarterly production from
pigs/poultry
For rural households/sub-farms:
number of heads and quarterly
production of pigs/poultry
01/October/year N Country
Provinces and central
cities
Districts
Households, sub-
farms, farm,
enterprises,
cooperatives
Villages
For buffalo/cattle survey in
households and farms: 01/10/ year
N-1 to 30/09/ year N
For households and sub-farms not
surveyed in July: 01/04 to 30/09/
year N
For others: 01/01 to 31/03/ year N
For villages: 01/10/ year N-1 to
30/09/ year N
7 days
15 days
for
establishin
g the
village lists
For farms, enterprises,
cooperatives: number of heads
for all livestock types; pigs and
poultry quarterly production;
other livestock annual
production
For villages: number of heads of
buffaloes, cattle, and other
livestock of households and
farms in the villages
For rural households/sub-farms:
buffaloes and cattle annual
production
For rural households/sub-farms:
number of heads and quarterly or
biannual production of pigs/
poultry
For urban households: number of
heads of pigs and poultry
8
Questionnaires and type of data collected
The questionnaires used for different survey components (annual and quarterly) but also for different units
of observation (households, farms, enterprises, etc.) are harmonised in terms of livestock categories and
statistical data collected. It is important to mention that apart from the full name of the household head
and the village where the household lives, there is no other identification data (ID), such as farm ID or
personal ID of the household head that would enable the identification of the agricultural holding and the
linkage to other sources. For enterprises, cooperatives and farms, the name of the unit, the location and
the phone number is collected but there is no identification such as farm or enterprise ID.
Pig statistics indicators are calculated on the basis of quarterly data collection as part of the livestock
survey of GSO. The total number of pigs is estimated every quarter using the same pig categories in for
the questionnaire for enterprises, farms and sub-farms and households. As shown in Table 2 below, the
pig categories would need some clarifications in the methodology.
Table 2 Categories of pigs in quarterly questionnaires of livestock survey
Pigs categories Assessment comments
1. Pigs (exclude suckling pigs ) Suckling pigs should be included in the total number of pigs and can be
distinguished in a separate category.
1.1 Porkers Definition need to be set in the methodology: e.g. Porkers are all pigs for
fattening, excluding suckling pigs.
1.2 Sows Definition need to be set in the methodology: e.g. Sows are all covered
breeding females.
of which Dry sows Definition need to be set in the methodology: e.g. Dry sows are all not covered
breeding females (including or not the young gilts).
1.3 Breeding pig Definition need to be set in the methodology: e.g. Breeding pig are all male
pigs of 50 kg or more used or to be used for breeding (boars and young boars).
The questionnaire used for husbandry households in urban areas collects information on the total number
of pigs, while the questionnaire used to define the list of households and farm raising livestock in the
villages collects information on the number of breeding pigs.
The pig meat production, that is, number and weight of pigs sold for slaughter, is collected for the
following two categories: porkers and slaughtering sows. It can be assumed that the category
“slaughtering sows” includes all sows and young gilts, sold for slaughter. It is however not clear if the
breeding pigs sold for slaughter are included in the porkers category. Therefore, these categories and their
definitions also need some clarifications.
As mentioned before the farm ID collected with the livestock survey questionnaires is very limited and
makes it impossible to match automatically individual records from different sources, even between
different surveys. It was suggested to include farm ID such as business license number, farm or household
heads personal identification number, other registration numbers if available.
9
Population and sample design
For the purposes of the sample design the population of pig breeding units (agricultural holdings) is
divided into sub-populations each having a different sample size and design4.
Table 3 Population, sampling frame and sample size for GSO livestock surveys
Legal
status Sub-population Description
Number of
agricultural
holdings
from RAFC
2011
Number of
AH
breeding
livestock
(RAFC 2011)
Number of
AH
breeding
pigs
(RAFC
2011)
Sample
Legal
entities
Enterprises
According to
national
legislation
955 189 126 Full-scale
Cooperatives
According to
national
legislation
6 027 150 130 Full-scale
Physical
persons
Farms Having 100 pigs
or more 20 028 6 348 no data Full scale
Sub-farms Having from 30
to 100 pigs
9 591 696 9 207 185 4 130 000
50 000
Rural
households
Husbandry
households in
rural areas with
less than 30 pigs
140 000
Urban
households
Husbandry
households in
urban areas
All
households
in 500
urban
wards
Source: GSO, Vietnam
4 Methodology Livestock Survey (issued with Decision No. 882 /QĐ-TCTK dated on August 28th 2013 of General Director of
General Statistic Office)
10
Large-scale livestock keeping units
A total of around 70.000 villages are surveyed on the number of heads and annual production of
buffaloes, cattle and other livestock (Form No. 07-N/ĐT.CNUOI-Thon) raised by households and farms in
the villages. The list of the large-scale animal raising households for the respective reference period is
established twice a year on 1 April and 1 October
Small-scale livestock keeping units
In addition, to get the total number of small –scale livestock keeping unit, a sample of 7 000 villages (8.6
percent of the villages in the rural areas) is surveyed to generate the total number of households keeping
livestock. This information is used for generate the headcounts and livestock production of small-scale
livestock keeping households The sample of villages is selected by GSO based on Rural, Agricultural and
Fishery Census (RAFC) 2011 list of villages and is sent to the provincial offices. The same sample villages
are used every year. They can change only when the village is merged with others or moved for the
construction of new road, new urban area, etc.
For the reference dates 1 April and 1 October, in each of the 7 000 selected villages, 20 households raising
pigs and/or poultry are selected (rural households). In total 140 000 rural households selected in the
sample are surveyed with respect to the number of heads and quarterly production of pigs and poultry
(Form No. 04-3.6T/ĐT.CNUOI-G.HNT). The list of households raising pigs and poultry are established from
the preceding data collection in the villages on 1 October. The sample households which are not available
in the following survey period because of moving, changing into farm, sub-farm type, that belong to a
comprehensive survey will be replaced.
For the reference dates 1 January and 1 July, the sample size of rural household is about 47 000.
The selected 140 000 rural households are also surveyed on the number of heads and annual production
of buffaloes and cattle for reference day 1 October (Form No. 05-N/ĐT.CNUOI-TT.HNT)
About 500 urban wards are selected in a similar way as villages and rural areas. In the selected wards the
list of urban households raising livestock is established and the number of heads of livestock types is
collected (Form No. 06-6T/ĐT.CNUOI-HTT)
Overall methodology and survey organisation
The methodology and instructions, include for sample design, are prepared by the central GSO, while the
sample design itself and the selection of the sample are done by the provincial statistical offices. The
11
sampling errors for the livestock surveys are not calculated and the precision of the estimates cannot be
assessed. The sample is also designed with the aim of also producing data at district level. The possibility
to improve the sample design in order to be able to estimate the precision of the survey estimates needs
to be more thoroughly studied. In particular, the quality of the district estimates need to be examined and
their replacement by administrative data may be considered.
The provincial statistical offices are responsible for the overall organisation and implementation of the
statistical surveys in their territory. This covers selection and training of enumerators, data collection and
monitoring of the field work, data entry, cleaning and editing. Cleaned data is transmitted to central GSO
for further control and aggregation. If errors are detected at central level the information is sent back to
province level for correction or justification of soft errors. The final results are validated after consultations
with MARD district divisions and provincial departments.
2.2 Statistical activities of MARD in the livestock sector
MARD, through its different departments, collects various information in the field of livestock, which are
required to perform its planning, regulation and prevention functions. The data collection consists of an
administrative reporting system and agricultural and rural surveys conducted by MARD.
Within the administrative reporting system, different reporters such as farmers, veterinaries, etc. report
to the district agricultural divisions and provincial Departments of Agricultural and Rural Development
(DARD). Farmers are supposed to report on the number of livestock on their farms. In particular, pig farms
report to the agricultural division of their district the number of animals every month. The district office
reports to the provincial department which reports to the central office of MARD. Thus the information on
livestock numbers is aggregated at national, provincial and district level. It is however not clear what is the
reporting rate9 and how the quality of the reported data is controlled at district level.
The introduction of e-reporting, using the TE-FOOD registry system is currently tested within the MARD
and TE-FOOD Vietnam project. The following categories of pigs are distinguished for the reporting
purposes: sows, boars, gilts, piglets and commercial pigs. The registration form contains as well farm ID
such as name and legal form of the farm, business license number, name, personal ID, mobile phone and
e-mail of the owner, manager or other person providing the information on the farm. More details on the
project are given in the section on TE-FOOD Livestock Management System of the report.
Apart from the number of live animals, provincial departments for livestock and animal health through the
implementation of their functions to prevent and control animal diseases collect information on the
12
number of vaccinated animals. There are government programmes to support the purchase of vaccines
for small farms and the estimation on number of vaccinated animals from small farms can be derived from
the purchase records, while big farms have to inform about the number of vaccinated animals. The
veterinaries that carry out the vaccinations on the field report to the Livestock Department their
estimations of number of vaccinated animals at commune level.
The agricultural and rural surveys carried out by the MARD are usually done on a small sample and
conducted every two or three years. These surveys produce detailed data which is not collected with the
regular statistical surveys of GSO, such as breeding structure of main livestock types, slaughter weight, etc.
The indicators produced by these surveys can be considered as complementing the regular annual
livestock statistics and are not in duplication with the existing official statistics data collection activities.
MARD does not have an integrated administrative farm register. However, the livestock farms are
supposed to be registered and get a farm number at Provincial Livestock Department of the MARD. This
registration is done with varying degrees of quality in different provinces and needs further evaluation on
the scope and definition of the units of registration, type of data registered, quality control of the
registered data, update etc. However, the farm number obtained through this registration process is an
important element to be considered for future linkage of individual records from different data sources.
As mentioned before the government gives priority to the development of administrative databases
that serve both the management of the relevant institution and the statistical activities of GSO (statistical
law Art. 36 (4)). In light of this, GSO needs to be involved in any future development of administrative farm
register in order to harmonise the concepts and definitions used, the coverage of farms and variables
collected, the conditions for registration and up-date of the registered data and its control.
2.3 Census data collection procedure
GSO carries out agricultural censuses every five years in years ending at one and six, since 1994. The last
agricultural census named Rural, Agriculture and Fishery Census (RAFC) was carried out in 2016. The
results were disseminated in October 20189. The data from the census are used among others to validate
the annual data on the number of livestock collected with annual sample surveys and to define the
sampling frame for the next survey period.
Additionally, the annual Enterprise Survey produces information on enterprises and cooperatives
engaged in agriculture, fishery and forestry. Combining the two data sources helps to obtain a complete
13
picture of the livestock sector. The entire population of livestock and the share of the number of animals
bred in households, enterprises and cooperatives for different livestock types can therefore be analysed.
The RAFC 2016 does not set a definition of agricultural holding, since it collects data from all rural
households, agricultural, forestry and fishery households in urban areas and farms. The scope of the census
covers approximately 16 million rural households and more than 1 million urban households working in
agriculture, forestry, salt production and fishery; and almost 33.5 thousand farms and other surveyed units.
The RAFC definition of agricultural households and farms are given as follows:
Agricultural household: Households with all or most of labourers regularly participating directly or
indirectly into agricultural activities (cultivation, livestock, irrigation services, plough, etc).
Farm: Individuals, households with agriculture, forestry, and aquaculture which gained farming economy
standards must satisfy the following conditions:
For units which have cultivation, aquaculture and general production must be achieved:
- Area above the land area limitation, at a minimum 3.1 ha for the South East and the
Mekong River Delta; and 2.1 ha for the remaining provinces
- The output value of goods reached 700 million VND / year.
For livestock units which have output value of goods from 1 billion VND / year or more;
For forestry production units which have minimum area of 31 hectares and the average output
value of goods reached 500 million VND/year.
According to the RAFC 2016 results there are about 8.5 million households, 21 thousand farms, 1 740
enterprises and 6 646 cooperatives that raise livestock. Currently, there is no threshold applied for
agricultural activities of the rural households and any household in rural area that has at least one head of
livestock would be enumerated in the RAFC. For urban areas, information is collected from households
that have at least one cattle or 30 poultry. This leads to take into account large number of households
with very small number of livestock, whose contribution to the total number of livestock is very small. As
an example, in 2016 there are about 3.4 million households breeding pigs, out of them about 1.5 million
have just 1 or 2 pigs5.
5 Results of the Rural, Agricultural and Fishery Census 2016
14
Table 4 Pig-breeding agricultural holdings and number of pigs bred in 2016
Agricultural holdings by legal
status
Pig-breeding agricultural
holdings
Number of pigs Share of
pigs (%)
Total 3.441.385 29.604.500 100
Households 3.441.100 27.812.300 93.9
of which having 1 or 2 pigs 1.479.000 2.242.000 7.6
of which farms 14.900 6.581.800 22.2
Cooperatives 135 166.800 0.6
Enterprises 150 1.625.400 5.5
Source: Results of the Rural, Agricultural and Fishery Census 2016
Within the RAFC the information is collected from households through a face to face interview with
a 9-page questionnaire that collects information on the household and people living in the households,
labour force, area of crops, number of livestock, area of aquaculture, equipment and machinery and living
environment of the households. Similar to the GSO livestock survey, the questionnaire for the census does
not collect ID, such as farm ID or personal ID of the household head. Furthermore, the livestock categories
of the census questionnaire as those from the annual survey questionnaires need refining and
harmonisation. For pigs in particular, the same categories as for livestock surveys are used ensure the
coherence between the two statistical processes. It is however, necessary to discuss the categories with
the key data users and harmonise the categories between different sources (statistical surveys of GSO,
administrative registry systems in MARD, traceability initiatives in the pig supply chain, etc.).
The RAFC needs to be completed with the Enterprise survey in order to obtain full picture of the
agricultural sector of Vietnam. This requires high level of coherence between the two statistical processes
and in particular in terms of:
same reference dates and periods for collected data;
harmonised concepts and definitions, including livestock categories and livestock statistics
indicators such as total number of livestock per type and category, total agricultural output, total
animal production such as milk, eggs, meat, etc.;
aggregated and disaggregated precision requirements and data accuracy control;
same punctuality of data dissemination.
15
2.4 Shortcomings - Identified issues in data quality/accuracy
The importance of agricultural statistics in Vietnam is conditioned by the role of the agricultural sector in
country’s economy. In 2017 agriculture, fishery and forestry contributed to 15 percent of the gross
domestic product (GDP) of Vietnam6. Though the RAFC 2016 recorded a trend of transition to non-
agricultural activities in rural areas, still 53.7 percent of the rural households worked in agriculture, forestry
or fishery in 2016 providing work occupation to about 16 million people.
The need for methodologically sound, efficient and sustainable rural and agricultural statistical
system in Vietnam has been recognised in the consultation process with key stakeholders within various
technical assistance activities. The agricultural statistics is acknowledged as a key tool for data intensive
policy design, monitoring and evaluation.
In the field of livestock statistics, GSO applies statistically sound methods to collect data on the number
of animals and animal production. However, some methodological and organisational weaknesses were
identified in the statistical processes.
Relevance, accuracy and reliability
According to MARD the current livestock statistics produced by GSO is insufficient for their planning
needs and sometimes difficult to handle. The data collection covers main data items related to the number
of animals and animal production. However, the data-intensive policy requires collection of additional
items for the calculation of other important livestock indicators.
The sample size of the livestock survey varies between 50.000 and 240.000 units depending on the
reference period. Such a large size of the sample makes the statistical process less efficient. The way the
sample design and validation of final results are organised does not allow the calculation of standard error
in order to estimate the precision of estimates. It can be expected that the sampling error is smaller due
to the large size of the sample. However, the gain in having smaller sampling error is often wiped out by
much larger nonsampling errors which are difficult to handle and often impossible to measure.
Considering the needs of MARD as a main data user, GSO produces livestock statistics at the level of
districts, while MARD makes its own estimations based on their administrative reporting system. As in
many other countries where two sources are held concurrently the estimates of the number of animals
per district from GSO and MARD may differ significantly.
GSO has already started discussions and planning of measures to improve the quality of livestock statistics
such as:
6 Statistical Yearbook of Vietnam 2017
16
new sample design, including reducing the sample size, setting provinces as reporting domains
(instead of districts), computation of survey weights and precision of estimates;
improvement of questionnaire, including compilation of official indicators and other policy
relevant data items, such as more detailed livestock categories, animal production and utilisation,
etc.;
provision of mechanisms for controlling the non-sampling errors, including regular training of
enumerators and supervisors, harmonisation of definitions and concepts, use of appropriate
sampling frame and incorporating cross-checks and validation of survey results.
The MARD data collection is primarily a result of the implementation of the ministry administrative
functions (e.g. animal health records) and reporting from farmers. A recent report on “Designing a
Livestock Probability Sample Survey for Vietnam”7 evaluates the administrative reporting of livestock
statistics at MARD as “irregular and held concurrently”. The current processes in MARD also leave room for
improvement, in particular the development of strict protocol for data collection and processing,
harmonised with official statistics quality standards and implemented uniformly in all provinces of the
country. Such protocol should include the scope, definitions, reference dates and periods for reporting,
measures for quality control of reporting and estimates made by the reporters, etc. The collaboration
between the GSO and MARD has to focus on harmonisation of concepts and definitions used, integration
of existing sources, reduction of duplication and increased consistency between the sources of the two
institutions.
Objectivity and independence
The report on “Designing a Livestock Probability Sample Survey for Vietnam” mentions also the subjective
intervention in the final results validation process as an important source of inaccuracy of official statistics.
Such intervention can affect to higher extent the ministry administrative reporting data but also the
indicators estimated with statistical surveys. The validation process may lead to subjective revisions of
estimated indicators. A bias in the official statistics resulting from such revisions may be significant but
very difficult to measure. Professional and scientific independence in the entire process of developing
producing and disseminating livestock statistics need to be strengthened. An integrated agricultural
statistical system using different data sources and establishing trustful links between them shall contribute
to the transparency and objectivity of the official statistics.
7 Results of the Methodological Studies for Agricultural and Rural Statistics
17
New data sources
Currently the Statistics Law of Vietnam do not consider new data sources as a part of official statistics.
However, in the light of the Internet of Things networks that progressively cover the physical world,
statistical activities of third parties based on new technologies develop fast. The expected benefits from
using such sources for official livestock statistics in Vietnam include:
increased information produced by official statistics: data items available from external sources
can be used to produce official statistical indicators provided that they satisfy the data quality
requirements;
cost efficiency of producing official statistics: the direct data collection can be replaced by using
data already available in other sources;
reduced respond burden: respondents will be asked only once for the same information;
increased objectivity and independence of statistics: the use of new technologies and established
links between different data sources would reduce the possibility for subjective revision of
statistical output.
Within the project “The potential use of blockchain for livestock statistics: a case study in Vietnam” the
traceability system established in the pig supply chain of Ho Chi Minh City by TE-FOOD was considered
as a possible third source for official statistics. The systems overall performance is discussed in details
in the next chapter.
Additionally, TE-FOOD is implementing a pilot project with MARD for the establishment of livestock
management system for pigs and poultry. This system among others is expected to replace the current
on-office monthly report from farmers to MARD by e-reporting. TE-FOOD livestock management system
is established as a pilot registry system to be tested in 21 provinces. Its’ future for up-date of the sampling
frame or direct tabulation of statistical output should be tested during the pilot phase and relevant
requirements related to its statistical use should be incorporated in the final rules and procedures of the
system.
18
3. TE-FOOD Technology and Traceability System
3.1 TE-FOOD background
TE-FOOD 8is the largest whole-chain livestock and fresh food traceability system in Southeast Asia. It has
developed products which are in use by more than 6 000 companies constituting in excess of 400 000
transactions per day and serving 34M people. Software developed by TE-FOOD include identification of
tools for supply chain to facilitate following of produce items throughout the entire supply chain life cycle.
Feedback information generated by these systems help ascertain food safety and provide valuable
feedback to support stakeholder activity in the supply chain.
3.2 TE-FOOD Traceability system - Ho Chi Minh City Livestock industry
As a result of food frauds that are actively committed in various parts of the world, data that is flowing
through a supply chain is not trustworthy. The core need of the livestock supply chain industry is the
objectivity in data representation, non-corruptibility and non-modifiability. The main purpose of a
blockchain system for supporting traceability of livestock in the Vietnamese livestock industry is to ensure
that the integrity of data, that is, provenance, is both maintainable and verifiable.
The TE-FOOD traceability system in pig supply chain was developed after the request from Ho Chi Minh
City government, as a farm-to-table fresh food traceability ecosystem - now on blockchain - covering all
logistics and food quality activities and data management of the supply chain. It provides cost effective
software and identification tools to make livestock and also fresh food supply information transparent.
TE-FOOD is integrating supply chain companies, consumers, and governments/authorities to:
help food supply chains to work in a more transparent way,
comply with the import regulations of foreign countries,
improve brand protection against tampering,
mitigate the effects of outbreaks and food frauds,
improve consumer trust and brand exposure.
Identification tools are applied to livestock, transports, and fresh food packages to follow the items
throughout the whole supply chain. Fresh food products in retail can be traced back to their origins
together with food safety related information.
8 https://tefoodint.com/
19
TE-FOOD provides cost effective software and identification tools to facilitate transparent
information flow for livestock and fresh food supply chain industry. The solutions designed and
developed by TE-FOOD take livestock produce from farms to retail all the way to the consumer’s table.
Software process and functions provided by TE-FOOD are customised according to the requirements of
the client, and also comprise a mobile app to allow for livestock data access as it goes through various
stages of the supply chain.
In addition, TE-FOOD provides tools for supply chain participants and consumers; solutions for both
authorities as to improve food safety. The TE-FOOD traceability system is at an early stage of testing. The
traceability system will follow livestock items through the supply chain from farms to slaughterhouses;
cutting to packaging; retail to consumers. All data that flows through the supply chain is stored in big data
repositories for access by the stakeholders. The current traceability volume for TE-FOOD is:
10 000 pigs/day
200 000 chicken/day
2 500 000 eggs/day
3.3 TE-FOOD Livestock Management System
TE-FOOD Livestock Management System, further referred as LMS,is a pilot registration system which
started in 2018 through a contract signed between the MARD and TE-FOOD Vietnam. It shall cover 21
provinces, starting with four provinces (2 two in the North and two in the South of Vietnam). The initial
registration is still ongoing. According to the plan, the households with pigs (agricultural holdings having
less than 100 pigs) will be registered by the end of the year in the four provinces. Currently the most
exhaustive set of data is available for Dong Nai province, where 14 people were involved for the initial
registration of pig farms (agricultural holdings having 100 pigs or more). After the initial registration,
farmers are due to report online, on a fortnightly basis, the status of number of pigs on the farm, using
the TE-FOOD mobile application. The future development of the system will include reporting on
vaccination and epidemic situation, minimum farm management programme, integration with commercial
supply chain and involvement of other key players in the supply chain.
TE-FOOD Livestock Management System is at very early stage of testing. However it seems fit to replace
the current reporting from farms at the MARD. The new registry system is expected to be more efficient
as farmers will be regularly reminded to report and they will be able to do it online using their mobile
phones. In order to assess its’ capability of generating livestock (pig) register that could be used as reliable
20
frame for specialised surveys for the purposes of official statistics some key quality aspects need to be
evaluated.
The registration unit in the LMS is the household or farm having pigs. According to the definition the
farm is the household having at least 100 pigs. The same definition is used by GSO for the sampling
purposes of livestock survey. However, different definition is used for the purposes of RAFC 2016. Within
the census farms for livestock units are considered those which have output value of goods from 1 billion
/ year or more. Though the definition is not harmonised between sources, for the purposes of the sample
frame for annual livestock survey the same definition is used by LMS and GSO. The cooperatives and
enterprises are also registered in the LMS. A cross check with other registers, such as business register
need to be done in order assess the coherence of the definitions.
Future directions of data storage and provenance design include cattle, fruits and vegetables, fish and
seafood, animal antibiotics, and incorporation of IoT9 data into the supply chain traceability system.
Table 5 Comparison of definitions of registration units
Registration unit LMS definition Livestock survey
(GSO)
Other definitions
Household Households breeding
less than 100 pigs
Households breeding
less than 100 pigs
Definition for household in
RAFC 2016 differs due to
different definition of farm
Farm Households breeding
at least 100 pigs
Households breeding at
least 100 pigs
Definition for farm in RAFC 2016
differs
Cooperative Cooperative breeding
at least 1 pig
Cooperative breeding at
least 1 pig
Registration is needed to get
business license from Authority
as enterprise or co-operative. Enterprise Enterprise breeding at
least 1 pig
Enterprise breeding at
least 1 pig
The coverage of the LMS is not defined in the pilot project. However, the objective is that the registry
system covers all pig breeding farms and households. The coverage can be tested for the provinces where
the registration is completed by comparing the number of farms and households from LMS with the
statistical estimation (RAFC 2016 or village lists for livestock surveys) and number of cooperatives and
enterprises with business register data with similar reference period.
9 The Internet of Things (IoT) sensors are becoming important components of the food supply chain: for example
smart temperature sensors can be integrated into the shipping and storage process for controlling the cold chain
and prevent pathogens.
21
The identification data items of the registered units within LMS are very detailed and allow the
unambiguous identification of each unit. As mentioned in the chapter related to GSO, such characteristics
are missing in the current livestock survey questionnaire. It is necessary to foresee them in order to be
able to link records from LMS and livestock surveys. A generation of unique registration number to be
used by both LMS and livestock surveys should be envisaged.
The statistical data items related to pig categories registered in LMS are harmonised with the needs of
MARD. Considering the recommendation to GSO to refine the pig categories in the livestock survey, the
LMS categories can be used by GSO. This will ensure harmonisation between sources and increase the
relevance of data produced by GSO. LMS collects data also on the number of poultry, which makes the
system scalable for poultry sub-sector.
The control of the quality of reported data is currently not tested. Once the unit is initially registered, it
has to report regularly on the actual number of pigs. Within the pilot project the LMS units have to report
every 15 days which seems to be unnecessary often and may discourage many farmers to report on a
regular basis. Two main quality issues may emerge, units do not report regularly or the reported data is
not correct. A number of measures should be foreseen in order to ensure and control the information
provided by farmers, such as:
provision of rules, obligations and motivation for registration and regular reporting from
registered units;
use intermediaries for reporting such as veterinaries or extension officers for very small units
cross-check of reported data (individual and aggregated) with other sources or registries
on-farm inspections: development of rules and procedures for control of the reported data: who,
how often, what, how is controlled
A part from being used as sampling frame, the LMS may be considered as relevant source for statistics
for direct tabulation of indicators such as number of livestock per category, number of agricultural
holdings breeding livestock, number of farms and pigs per size classes of the pigs herds, etc. The LMS is
currently designed to register pig and poultry breeding units and the actual number of animals. In order
LMS to be used as sampling frame for livestock surveys the coverage and quality of the reported data has
to be compared to the current statistics. If the results of the comparison for at least three consecutive
years are satisfactory, the LMS data can even be used to replace the livestock survey data as well.
22
Finally, in order to use the LMS register as sampling frame the access of GSO to the LMS data has to be
ensured. As mentioned before LMS is still in pilot phase, however it is expected that the registry system is
owned by the MARD. The Law on Statistics of Vietnam provides for the use of administrative data for sate
statistics and thus it is expected that there would be no obstacle for GSO to have direct access to all LMS
data, including individual ID.
As mentioned before, the future development of the LMS foresees integration of various aspects of
livestock breeding and management including coverage of other livestock types, development of
management functions of the LMS, integration with existing or developing traceability or other
commercial supply chain systems. It is important also to foresee that the LMS is integrated to the future
farm register to be developed by MARD.
4. Recommendations for deployment of blockchain
4.1 Identified blockchain solution for traceability in the livestock industry of Vietnam
The main use of blockchain in TE-FOOD LMS for livestock is the enabling of data integrity verification for
the collected statistical data. The date once collected at various stages of the supply chain must not be
modifiable or corruptible. By having all transactions placed on blockchain, the integrity of data can be
assured and verified. In addition, through the one-way blockchain update property, it is very hard to
reverse engineer a hash value associated with a blockchain data item, in order to replace it with another
rogue block. Consequently, the verifiability of blockchain data is everlasting. By application of blockchain
for verification of data integrity and its non-modifiability, the overall confidence placed on the system by
the individual stakeholders, namely, suppliers, wholesalers, retailers, slaughterhouses and consumers, is
boosted.
Blockchains in the Vietnamese livestock supply chain traceability system are to comprise two
phases:
Phase 1: the data collection process is executed, wherein, the various stakeholders of the livestock
traceability system may add, update or delete data as stored in the blockchain. The data generation
channels comprise the following stakeholders/applications/platforms:
official Vietnamese livestock website;
official mobile applications;
call centres.
23
The data provided by the official website and the official mobile applications includes the following:
livestock population data (animal type/subtype, gender, farming type, farm, gender);
slaughtering data (animal type/subtype, gender, slaughterhouse data, number of pieces from
farm);
transport to other premises data (animal type/subtype, gender, origin premise, destination, date);
transport for export data (animal type/subtype, gender, origin premise, destination country, date);
death of animals data (animal type/subtype, gender, death cause, date, location);
yield report data for female animals (newborns, farm, date, mother ID);
Weekly and monthly control data (animal type/subtype, farming type, gender).
The data generated only by the official mobile application includes the following:
animal identification tag registration data (animal type/subtype, farm, gender);
traceability by animal identification tag data (animal identification tag, animal type/subtype,
source farm, destination, date);
death report by animal identification tag (animal identification tag, animal type/subtype, death
place, death cause, date).
The data thus collected from the sources identified above, is stored in big data repositories of the livestock
traceability system, as given in Figure 1. The livestock system will provide the numbers associated with
farm animals, these numbers, estimation on the weight, etc. how many kg arrive from slaughter house.
Feeding and vaccination information are also collected, important for food safety, they're fed well, and
vaccinations are up to date. Connection of livestock information to local veterinary companies, can also
estimate what type is needed, etc.
24
Figure 1 Livestock traceability system with big data storage
For Phase 2 of the project, TE-FOOD is also designing a smart contract extension to the fundamental
blockchain system to be developed in Phase 1 above. The smart contract based system will ensure control
of bilateral relations between the supply chain stakeholders. Presently, the livestock traceability system is
mainly paper-based. Through bilateral agreement on the type of data that is to be rendered to a given
stakeholder, the smart contract-based system can run a report on the collected data, electronically, and
provide processed outcomes for the stakeholders to act on. In addition, the other controls including IoT
sensor data emerging from the various livestock supply chains stakeholders can be included in the
generation of customised reports through application of smart contracts. For example, in the supply chain,
the logistic company delivering product to retailer may do so; but the temperature requirement is meat
should be between 8-12 oC. If the truck has an IoT sensor tool to collect the temperature data until delivery,
every minute, every 5 minutes, whilst sending the IoT data to the blockchain, the smart contract, then the
retail side will be unable to accept this transport, if the truck does not feed the temperature readings into
the blockchain.
The overall effect of the blockchain application would be the following:
reduction in the paper-work associated with maintaining bilateral relations between the various
stakeholders of the blockchain;
accuracy in the data is verifiable through provenance guarantees of the blockchain;
25
data processing is carried out through deployment of smart contracts in the blockchain, in order
to provide customised outputs to the various stakeholders;
food security as the assurance given on the quality of the food to the customers is verifiable by
all parties through blockchains;
avoiding food fraud, as the modifiability of data already on the blockchain, is impossible;
health of the nation is improved as the blockchain based solution ascertains that the truth
associated with farm produce is guaranteed, for as long as the farmer and other stakeholders
report valid farm data for storage on the blockchain. For example, if a farm is selling 200 pigs, as
stated to local authorities, after slaughtering, some are gone, but the meat could be 80kg / pig;
sum should equate to 1600 kg. System generates the estimation, based on other information from
the supply chain entries, and verifies the same through comparison between the various data
collection entities;
antibiotics use can be closely followed, and its overall use can be reduced, to improve the quality
of life for the nation.
TE-FOOD has proposed two options for the blockchain livestock traceability system.
Option 1: In this option, as illustrated in Figure2 (see next page), the livestock traceability data as
produced by the data collectors is collected outside the blockchain from the various stakeholders (given
above). The statistics calculation procedure may then be executed on this collected data either off-chain
or on-chain; both options shall be made available. The transaction data is then validated and stored on
the blockchain. The main concern with this approach is the credibility of the data when it is validated for
storage on the blockchain. If the livestock supply chain stakeholder is not trustworthy, the data to be
stored on the blockchain will not be accurate, and consequently all dependencies will use this inaccurate
but validated data, for other supply chain activities.
Option 2: In this option, as illustrated in Figure 3 (see next page), the computing devices of the supply
chain including mobile devices directly update the data on the blockchain. Through the seamless and un-
interfaced connection of all stakeholder devices to the blockchain, the system will be fully decentralized
albeit at the cost of poor computing efficiency. Moreover, such a system will be complex to manage.
26
Figure 2 Centralised Blockchain system
Figure 3 Fully distributed blockchain system
27
4.2 Identified issues with the existing system
The existing system based on big data backend repositories does not have a mechanism in place to
validate data once it has been stored. The purpose of introducing a blockchain based system is for data
provenance albeit with no guarantees that the actual reporting of data by stakeholders is valid or not. For
instance, a farmer may falsify data for storage in the traceability system, but this data will still be considered
as a transaction on the blockchain and will be stored in the system. This could be also of course true for
data collected through traditional surveys or reporting systems. The development of validation
mechanisms with GSO and MARD in order to confirm that the data being reported by farmers is actually
valid could be an important domain of collaboration.
The existing system cannot be efficiently used for confirmation of bilateral transactions between the
various stakeholders, as the data collection and processing is widely paper-based. In order to both
automate the data collection and processing operations, the blockchain-based supply chain traceability
system is a good solution, albeit without the presence of a data provenance component. Consequently,
TE-FOOD is planning to introduce a blockchain component into the Vietnamese livestock supply chain
systems, so as to facilitate data provenance.
28
5. Way forward
Several immediate and medium-term activities have been identified as a direct output of this first
work of investigation in Vietnam:
5. the first action that could help official statisticians in Vietnam to take the appropriate decisions
on the use of these new sources of data would be to launch a complete assessment of the
quality of inputs and outputs of multisource statistics for the pig sector in Vietnam based on
field experiments at regional level (details for this first action are provided below);
6. this could be followed by additional studies related to increased coverage for other sub-sectors
or commodities (poultry, fruit-vegetables for example) in Vietnam;
7. results could be used as an input for elaborating more generic guidelines that would help
official statisticians in developing/setting up new approaches combining multi-source
information;
8. longer-term scaling-up could be envisaged to cover other countries wishing to invest in the
possible use of exponential availability of data coming from traceability systems on blockchain in
the future
Proposed immediate action:
Assessment of quality of inputs and outputs of multisource statistics for the pig sector in Vietnam
In an integrated statistical system, part of the information may come from sources other than statistical
surveys. These sources however have to meet the high quality requirements of official statistics, related to
coherence, reliability, timeliness, accessibility, etc. For example the main statistical indicators on number
of livestock and animal production at national and province level produced with the GSO livestock survey,
can be completed by the district estimates of MARD administrative reporting system. Data on slaughtered
animals can be obtained from specific slaughterhouses reports or surveys or through analysis of the data
from existing traceability systems in the meat supply chains.
Achieving better use of administrative data and other sources requires quality assurance measures to be
set and implemented. GSO being the competent institution for official livestock statistics has to be
29
involved in the concept and development of administrative and other sources which may aim among
others to be used for official statistics. In particular MARD reporting system, the pilot LMS and other
sources such as traceability systems need to be in line with the GSO quality standards, which include:
adoption of common farm identifier (from current registration at MARD provincial departments
and in the future from the administrative farm register at MARD);
harmonisation of the definition of agricultural holding, farm, sub-farm etc. between GSO, MARD
and third parties;
harmonisation of the livestock categories and definitions and reference dates and periods for
different livestock indicators between GSO, MARD and third parties;
identification of core livestock indicators and sources that may produce them;
analysis of the existing sources in terms of population coverage (overlapping of units or
undercoverage of units) and quality of produced indicators in terms of relevance, accuracy,
reliability, coherence, comparability, accessibility, timeliness, etc.
The combination of statistical surveys with administrative data and other existing sources in an integrated
statistical system would decrease the costs of producing statistics and the respond burden as some of the
data items or even entire surveys can be discontinued. Some of the currently missing indicators can be
produced from additional sources which will increase the relevance of statistics to the users need. Finally,
the reliability of the statistics is expected to increase as the links established between sources and the use
of new technologies such as blockchain would reduce the possibility to subjective revision of statistical
indicators.
A procedure for the analysis and approval of third sources to be used for official statistics need to be
developed. Within such procedure, the sources that were analysed by the GSO and proved to meet the
official statistics quality standards but are currently not recognised as administrative sources by the
Statistics Law (e.g. traceability systems) shall be proposed to the government to be recognised as other
sources to be used for official statistics.
The elaboration of common quality guidelines for third sources that may cover the entire or part of the
given product supply chain would guarantee the adoption of official statistics quality standards already at
the development stage of each new data source and would enable its’ future use for official statistics.
30
With Big data increasingly available the question is not if but how it can be used for official statistics in
order to reduce data collection cost and respond burden, increase relevance and provide reliable data to
different types of data users decision makers, researchers, professionals and the general public.
It is clear that moving to multisource livestock statistics would require new design of the livestock statistical
process and new quality frame work. Within the new statistical process each data source can be placed in
particular phase of the process and assessed according to its purpose. In some cases sources have to be
combined to obtain the estimates for the entire population. The sources to be combined usually have
overlapping or missing units, overlapping or incoherent data items, which raises methodological issues to
be solved in order to achieve high quality of the statistical output.
The LMS and TE-FOOD traceability system were already evaluated as suitable sources to be used in
multisource livestock statistics. While the LMS can be used as a sampling frame for the livestock survey,
the TE-FOOD traceability system can provide highly rated data on number of slaughtered animals in
slaughterhouses. Both sources however have coverage issues, that is, they do not cover the entire
population of agricultural holdings breeding pigs, but they can be used for the sub-population of farms,
cooperatives and enterprises.
When LMS is fully operational, it can be used as a source to update the sampling frame at least for the
enterprises, cooperatives and farms and in midterm for the estimation of the total number of pigs.
Experience from other countries shows, that similar registers need some years of implementation of
specific measures to ensure the quality of the registered data itself. The monitoring of the registry quality
has to be completed by a comparison of data from the GSO livestock statistics and the LMS registry data
for the same reference periods for at least three consecutive years before making a decision to replace
the traditional statistical surveys on number of livestock.
Table 6 Combining sources for number of livestock
Indicators
Number of animals in
enterprises
cooperatives
farms
TE-Food Livestock
Management System
Village l ist (GSO)Number of animals in
households
Sources
Livestock
survey (GSO)
31
The strong commitment of the MARD to implement and maintain the LMS is necessary, including rules
and procedures for registration, control and update of the data in the system.
The estimation of the number of slaughtered pigs is currently done within the livestock survey where
the question on number of animals sold for slaughter is collected from agricultural holdings. The
estimation of the number of slaughtered animals per district is not possible as the destination of the
animals sold for slaughter is usually unknown by the farmers. In order to produce this information which
is considered important by the main data users either the data has to be collected with a survey of
slaughterhouses or other emerging sources such as TE-FOOD traceability system can be used.
Table 7 Combining sources for number of slaughtered animals
The use of TE-FOOD traceability system and similar systems that may be developed in other provinces of
Vietnam for official statistics on the number of slaughtered animals has to be completed by other sources.
There is no official obligation that pigs should be slaughtered within a traceability system which means
that the same farm may slaughter part of the animals within the system and part of them outside the
system. Also, the slaughterhouses themselves may slaughter animals in a traceability system but also
outside it. Therefore it is necessary to develop a methodology for combination of the sources considering
the possible undercoverage issues and possible overlapping of sources. Creating rules for establishment
and functioning of traceability systems will contribute to more precise definition of their coverage and will
facilitate their combination with other sources.
IndicatorsSources
Number of
slaughtered animals
in traceabilty systems
Number of
slaughtered animals
out of traceabilty
systems
Livestock
survey (GSO)
TE-Food Traceability
System
Other traceabil ity
systems
32
The assessment of quality of input and output of multisource statistics of pigs in Vietnam would
cover the following tasks:
Assessment of the TE-FOOD registry system and TE-FOOD traceability system as inputs for
official pig statistics (test in 4 provinces) covering:
- total number of units and total number of pigs initially registered per type of units:
households, farms, cooperatives, enterprises vs total number of agricultural holdings and
number of pigs from RAFC 2016 or other more recent data (livestock survey 2017 and
2018);
- new units registered after the initial registration (number of units, how they were
identified and registered);
- reporting rate after first registration for every 15-day period (number of actual reports
divided by the number of due reports per type of units);
- number of people involved in the initial registration and the follow-up;
Developing and testing of methodology for combination of statistical surveys and external
sources for production of required estimates for pigs statistics (definition of population frame
and sub-frames, set of indicators to be estimated);
Calculation of the estimates obtained from different sources and combination of them and
evaluation of their quality (coherence, accuracy, timeliness, accessibility, etc.);
Definition of quality measures and indicators for multisource pigs statistics.
33
References
European Commission – Eurostat – Komuso Project
https://ec.europa.eu/eurostat/cros/content/essnet-quality-multisource-statistics-komuso_en
FAO and WB. 2010. Global Strategy to Improve Agricultural and Rural Statistics, REPORT NUMBER
56719-GLB, September 2010. http://www.fao.org/docrep/015/am082e/am082e00.pdf
FAO. 2012. Action plan of the global strategy to improve agricultural and rural statistics
FAO and World Bank. 2014. Investing in the livestock sector - why good numbers matter – as
sourcebook for decision makers on how to improve livestock data, 2014
FAO. 2017. Improving the Methodology for Using Administrative Data in an Agricultural Statistics
System, Technical Report Series GO-24-2017, June 2017 http://gsars.org/wp-
content/uploads/2017/06/TR-07.06.2017-Improving-the-methodology-for-using-administrative-data-in-
an-agricultural-statistics-system.pdf
Food Marketing Research& Information Centre, Ministry of Agriculture and Forestry of Japan
2007. Handbook for introduction of traceability systems
http://www.fmric.or.jp/trace/en/
General Statistics Office of Vietnam. 2018.
Results of the Rural, Agricultural and Fishery Census 2016
http://www.gso.gov.vn/default_en.aspx?tabid=515&idmid=5&ItemID=18966
Walmart. 2018. Food Traceability Initiative, Fresh Leafy Greens, 2018
https://corporate.walmart.com/media-library/document/blockchain-supplier-letter-september-
2018/_proxyDocument?id=00000166-088d-dc77-a7ff-4dff689f0001
34
Appendices
A. GSO questionnaires for livestock surveys
B. Farm Registration Form for TE-FOOD registry system
C. Agricultural Census 2016 questionnaire
35
A. GSO questionnaires for livestock surveys
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with Decision no.
882/QĐ-TCTK dated August 28th 2013 of General Director of
General Statistics Office; used and kept confidential under the
Statistical Law.
YEARLY QUESTIONNAIRE USED FOR ENTERPRISES/COOPERATIVES RAISING BUFFALOES,
CATTLES AND OTHERS
(Applied for annual survey period October 1st)
October 1st year 20…...
I. Information of survey unit
Name of survey unit Statistics Office fill
Address Province
District
Commune
Village
Telephone number Landline Mobile
Type of ownership State Non-state Foreign investment Cooperatives
Form No 01-N/ĐT.CNUOI-DN
II. Information about buffaloes, cattles and others raising activites of survey unit
Number and the production of livestock for sale and slaughtered in the last 12 months
Species Code
Quantity (head) Production of buffaloes, cattles and others in the last 12 months (include enterprises
directly produce and rent households raising)
Total
In which:
renting
households
raising
Number of buffaloes, cattle and others sold for
slaughtering purpose (unit)
The production of sold heads for
slaughtering purpose (kg)
Total In which: renting households
raising
Total In which: renting
households raising
A B 1 2 3 4 5 6
1. Buffaloes 01
+ In which: under one year old buffaloes 02
2. Cattle (Total) 03
Of
which:
- Cross-bred cows 04
+ In which: under one year old
cows
05
- Milk cows 06
+ Dairy cows 07
3. Horses 08
Species Code
Quantity (head) Production of buffaloes, cattles and others in the last 12 months (include enterprises
directly produce and rent households raising)
Total In which:
renting
Number of buffaloes, cattles and others
sold for slaughtering purpose (unit)
The production of sold heads for slaughtering
purpose (kg)
households
raising Total
In which: renting
households raising
Total In which: renting
households raising
A B 1 2 3 4 5 6
4. Deer 09
5. Stags 10
6. Goats 11
7. Sheeps 12
8. Rabbits 13
9. Dogs 14
10. Python 15
11. Snakes 16
12. Bees (hives) 17
13. Ostrich 18
B. Non-slaughtering products in the last 12 months
Code Unit
Quantity
Total In which: renting households
raising
A B 1 2 3
1. Cow milk 19 Liter
2. Honey 20 Liter
4. Silkworm 21 Ton
Enumerator
(Signature, full name)
Day….. month …... year 200..…
Director
(Signature, stamp, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with Decision no.
882/QĐ-TCTK dated August 28th 2013 of General Director of
General Statistics Office; used and kept confidential under the
Statistical Law.
QUARTERLY QUESTIONNAIRE USED FOR ENTERPRISES/COOPERATIVES RAISING PIGS AND
POULTRY
(Applied for survey periods January 1st, April 1st, July 1st and October 1st)
Time …../….. year 20.....
I. Information of survey unit
Name of survey unit Statistics Office fill
Address Province
District
Commune
Village
Telephone number Landline Mobile
Type of ownership State Non-state Foreign investment Cooperatives
II. Information about raising activities of survey unit
The number of pigs, poultry
Cod
e
Quantity
(head)
In which: renting
household raising Code
Quantity
(head)
In which: renting
household raising
A B 1 2 A B 1 2
1. Pigs (exclude sucking pigs ) 01 3. Ducks (Total) 10
1.1 Porkers 02 In which: Ducks for eggs 11
Form No. 02-Q/ĐT.CNUOI-DN
1.2 Sows 03 4. Swan (Total) 12
Tr. đó: Dry sows 04 In which: Swans for eggs 13
1.3 Breeding pig 05 5. Geese (Total) 14
2. Chickens (Total) 06 In which: Geese for eggs 15
In which: Commercial chickens 07 6. Quails (Total) 16
2.1 Hens 08 In which: Quails for eggs 17
In which: Commercial hens 09 7. Pigeons 18
B. Production of pigs, poultry in the period :
Cod
e
Production of pigs and poultry
Code
Non-slaughtering products
Number of pigs and
poultry sold for
slaughtering purpose
(head)
The production of sold heads
for slaughtering purpose (kg)
Unit
Quantity
Total
In which: renting
household
raising
Total
In which: renting
household
raising
Total In which: renting
household raising
A B 1 2 3 4 A B 5 6 7
1. Porkers 19 1. Hen eggs 28 Egg
2.Slaughtering Sows 20 In which: Commercial hens 29 Egg
3. Chickens 21 2. Duck eggs 30 Egg
In which: Commercial
chickens 22 3.Swan eggs 31
Egg
4.Ducks 23 4.Goose eggs 32 Egg
5. Swan 24 5. Quail eggs 33 Egg
6. Geese 25
7. Quails 26
8. Pigeons 27
Enumerator
(Signature, full name)
Day….. month …... year 200..…
Director
(Signature, stamp, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with
Decision no. 882/QĐ-TCTK dated August 28th 2013 of
General Director of General Statistics Office; used and
kept confidential under the Statistical Law.
QUARTERLY QUESTIONNAIRE USED FOR FARMS
RAISING PIGS AND POULTRY
(Applied for survey periods January 1st, April 1st, July 1st and
October 1st)
Time …../….. year 20.....
I. Information of farm
Full name of the farm
heads
Statistics Office fill
Address Province
District
Commune
Village
Telephone number Landline Mobile
II. Information about raising activites of the farm
A. Number of pigs, poultry
Code
Quantity
(head)
In which:
renting
household
raising
Code
Quantity
(head)
In which:
renting
household
raising
A B 1 2 A B 1 2
1. Pigs (exclude sucking pigs
) 01 3. Ducks (Total) 10
1.1 Porkers 02 In which: Ducks for eggs 11
1.2 Sows 03 4. Swan (Total) 12
Tr. đó: Dry sows 04 In which: Swans for eggs 13
1.3 Breeding pig 05 5. Geese (Total) 14
2. Chickens (Total) 06 In which: Geese for eggs 15
In which: Commercial
chickens 07 6. Quails (Total) 16
2.1 Hens 08 In which: Quails for eggs 17
In which: Commercial hens 09 7. Pigeons 18
B. Production of pigs, poultry in the period : (Include the directly raising farm and household raising products)
Code Production of pigs and poultry Code Non-slaughtering
products
Form No. 03-Q/ĐT.CNUOI-TT
Number of
livestock &
poultry sold
for
slaughtering
purpose
(unit)
The
production of
sold heads
for
slaughtering
purpose (kg)
Unit Quantity
A B 1 2 A B C 1
1. Porkers 19 1. Hen eggs 28 Egg
2.Slaughtering Sows 20 In which: Commercial
hens 29
Egg
3. Chickens 21 2. Duck eggs 30 Egg
In which: Commercial
chickens 22 3.Swan eggs 31
Egg
4.Ducks 23 4.Goose eggs 32 Egg
5. Swan 24 5. Quail eggs 33 Egg
6. Geese 25
7. Quails 26
8. Pigeons 27
Enumerator
(Signature, full name)
Day….. month …... year 20..…
The head of farm
(Signature, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with
Decision no. 882/QĐ-TCTK dated August 28th 2013 of
General Director of General Statistics Office; used and
kept confidential under the Statistical Law.
SAMPLE QUESTIONNAIRE USED FOR SUB-
FARMS/HOUSEHOLDS RAISING PIGS AND POULTRY
(Applied for survey periods January 1st, April 1st, July 1st and
October 1st)
Time …../….. year 20.....
I. Information of sub-farm/household
Full name of the head of
sub-farm/household
Statistics Office fill
Address Province
District
Commune
Village
Sector of unit Sub-Farm
Household
Data collecting period 3 months 6 months
II. Information about raising activites of the sub-farm/household
A. Number of pigs, poultry
Code
Quantity
(head)
In which:
renting
household
raising
Code
Quantity
(head)
In which:
renting
household
raising
A B 1 2 A B 1 2
1. Pigs (exclude sucking pigs ) 01 3. Ducks (Total) 10
1.1 Porkers 02 In which: Ducks for eggs 11
1.2 Sows 03 4. Swan (Total) 12
Tr. đó: Dry sows 04 In which: Swans for eggs 13
1.3 Breeding pig 05 5. Geese (Total) 14
2. Chickens (Total) 06 In which: Geese for eggs 15
In which: Commercial
chickens 07 6. Quails (Total) 16
2.1 Hens 08 In which: Quails for eggs 17
In which: Commercial hens 09 7. Pigeons 18
B. Production of pigs, poultry in period (Include the production of sub-farm/household and renting household raising)
Form No. 04-3.6T/ĐT.CNUOI-G.HNT
Code
Production of pigs and
poultry
Code
Non-slaughtering
products
Number of
livestock &
poultry sold
for
slaughtering
purpose
(unit)
The
production of
sold heads
for
slaughtering
purpose (kg)
Unit Quantity
A B 1 2 A B C 1
1. Porkers 19 1. Hen eggs 28 Egg
2.Slaughtering Sows 20 In which: Commercial
hens 29
Egg
3. Chickens 21 2. Duck eggs 30 Egg
In which: Commercial
chickens 22 3.Swan eggs 31
Egg
4.Ducks 23 4.Goose eggs 32 Egg
5. Swan 24 5. Quail eggs 33 Egg
6. Geese 25
7. Quails 26
8. Pigeons 27
Enumerator
(Signature, full name)
Day….. month …... year 20..…
The sub-farm/household heads
(Signature, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with
Decision no. 882/QĐ-TCTK dated August 28th 2013 of
General Director of General Statistics Office; used and
kept confidential under the Statistical Law.
YEARLY SAMPLE QUESTIONNAIRE USED FOR
PRODUCTION OF BUFFALOES AND CATTLES OF
HOUSEHOLDS/FARMS
(Applied for annual survey period October 1st)
Time October 1st, 20.....
I. Information of household/farm
Full name of
household/farm heads
Statistics Office fill
Address Province
District
Commune
Village
Sector of unit
Household
Farm Telephone
number
Landline Mobile
II. Information about raising activites of the farm/household (Include information of the quantity and the products produced by
farms/households and renting household raising)
Code Quantity
(head)
Production of buffaloes and cattles
in the last 12 months Amount of cow
milk in the last
12 months
(liters)
Number of
buffaloes and
cattles sold for
slaughtering
purpose (unit)
The production
of sold heads for
slaughtering
purpose (kg)
A B 1 2 3 5
1. Buffaloes 01 x
+ In which: under one year old buffaloes 02 x
2. Cattles 03 x
Form No. 05-N/ĐT.CNUOI-TT.HNT
Of which:
- Cross-bred cow 04 x
+ In which: under one year old
buffaloes
05
x
- Milk cows 06 x
+ Dairy cows 07
Enumerator
(Signature, Full name)
Day... month .... year 200..…
The head of farm/household
(Signature, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with Decision
no. 882/QĐ-TCTK dated August 28th 2013 of General Director
of General Statistics Office; used and kept confidential under
the Statistical Law.
QUESTIONNAIRE USED FOR HUSBANDRY HOUSEHOLDS IN URBAN
(Applied for survey periods April 1st and October 1st)
Time …../….. year 20.....
I. Identification information
Province
District, urban district, town
Commune, ward
Civil group
II. Quantity
Order Full name of householder Quantity10 Householder signature
Buffaloes Cattles Pigs Chickens Ducks Swans Geese Quails
A B 1 2 3 4 6 7 8 9 10
10 Survey period April 1st only collects data about pigs, chickens, ducks, swans, geese, quails
Form No. 06-6T/ĐT.CNUOI-HTT
Enumerator
(Signature, full name)
Day... month .... year 20..…
Chairman of district/town …………
(Signature, full name)
GENERAL STATISTICS OFFICE LIVESTOCK SURVEY
Information collected in this survey is complied with
Decision no. 882/QĐ-TCTK dated August 28th 2013 of
General Director of General Statistics Office; used and
kept confidential under the Statistical Law.
QUESTIONNAIRE USED FOR HOUSEHOLDS, FARMS
RAISING BUFFALOES, CATTLES AND OTHERS IN THE
VILLAGE
(Applied for survey period October 1st)
Time …../….. year 20.....
I. Identification information
Province
District
Commune
Village
II. Information about raising activites of village
Code Quantity
(head)
Production of buffaloes, cattles and others in
the last 12 months
Number of
buffaloes and
cattles sold for
slaughtering
purpose (unit)
The production of sold
heads for slaughtering
purpose (kg)
A B 1 2 3
1. Buffaloes 01 x x
+In which: under one year old buffaloes 02 x x
2. Cattles 03 x x
In which:
- Cross-bred cow 04 x x
+ In which: under one year old
cows
05
x x
- Milk cows 06 x x
+ Dairy cows 07 x x
3. Horses 08
4. Deer 09
5. Stags 10
Form No. 07-N/ĐT.CNUOI-Thon
6. Goats 11
7. Sheep 12
8. Rabbits 13
9. Dogs 14
10. Python 15
11. Snakes 16
12. Bees (hives)11 17 x
13. Ostrich 18
14. Breeding pigs 19
Enumerator
(Signature, full name)
Day... month .... year 200..…
The head of village
(Signature, full name)
11 Specific bees: Quantity is the number of the hives; Production is the amount of honey (liters)
B. Farm Registration Form for TE-FOOD registry system
C. Agricultural Census 2016 questionnaire
31
EVERYTHING WRITTEN ON THIS FORM SHALL BE KEPT
CONFIDENTIAL
CENTRAL STEERING COMMITTEE ON RURAL, AGRICULTURE AND AQUATIC PRODUCT CENSUS 2016
HOUSEHOLD QUESTIONNAIRE
Form 01/TĐTNN-HO
No. Household:
NO. QUESTIONNAIRE IN QUESTIONNAIRES
Province, city:..............................................................................................
District, urban district, town: ....................................................................
Commune, ward: ..................................................... ..................................
Village: ........................................................................................................
Name of enumeration area: ....................................................................... Number of enumeration area:
Enumeration area is (Team leader mark X in one siutable box) 1 Urban 2 Rural
Fullname of HHs head: ______________________________________________________________ Ethinic group: ________________________________
When household are workers at industrial zones, export processing zones, enterprises; pupils,
students rent house in rural area, fill x in the box 1
PART I: HOUSEHOLD, PEOPLE LIVING IN HOUSEHOLD
(Question 1 and 2 are filled by team leader – Team leader based on the list of poor household to identify)
1. Was the household considered a poor household in 2015 by their commune/ward?
(Mark one x in suitable box)
1 YES >> Ques. 3
2 NO
5. Number of people aged above 15 years live in household? (Born before 2002)
Sample number
32
2. Was the household considered a nearest poor household in 2015 by the
commune/ward? (Mark one x in suitable box)
1 YES
2 NO
In which:
5.1. Number of students (over 15 years old)?
3. Number of people in household? (People is living together in household) 5.2. Number of female aged above 55 years that
are not in labor force?
4. Number of people received health insurance?
(Include children under 6 years old)
5.3. Number of male aged above 60 years that are
not in labor force?
PART II. LABOR FORCE, MAIN SOURCE INCOME AND MAJOR ECONOMIC ACTIVITIES OF HOUSEHOLD
I. HOUSEHOLD HEAD II. PEOPLE IN LABOR AGE AND PAST LABOR AGE WHO ARE STILL WORKING (EXCLUDING PUPILS AND
STUDENTS)
Order
Question
MEMBER 1 MEMBER 2 MEMBER 3 MEMBER 4 MEMBER 5 MEMBER 6
6. Name _____________ _____________ _____________ _____________ _____________ _____________
7. Which year [Member] was born?
(WHEN MEMBER UNIDENTFY BORN-YEAR, ASKING QUESTION 7.1)
>>Question 8
>> Question 8
>> Question 8
>> Question 8
>> Question 8
>> Question 8
7.1. How old is [Member]?
8. Sex (Mark one x in suitable box) 1 Male
2 Female
1 Male
2 Female
1 Male
2 Female
1 Male
2 Female
1 Male
2 Female
1 Male
2 Female
9. Professional and technical qualification of [Member]?
(Fill a suitable code in the box)
1=
2=
3=
4=
NO TRAINING
ATTEND A TRAINING COURSE WITHOUT A CERTIFICATE
ATTEND A TRAINING COURSE WITH CERTIFICATE
PRIMARY, TECHNICAL WORKER
6=
7=
8=
9=
VOCATIONAL COLLEGE
COLLEGE
UNIVERSITY
POST-GRADUATE
33
5= VOCATIONAL SCHOOL 10= OTHERS (RELIGION...)
10. Level of political theory of [Member]?
1=
2=
NO TRAINING
PRIMARY
3=
4=
SECONDARY
SENIOR
11. Does [Member] do anything to make money in last 12 months? (DURATION IS OVER 30 DAYS)
1 Yes
2 No >>
NEXT MEMBER
1 Yes
2 No >>
NEXT MEMBER
1 Yes
2 No >>
NEXT MEMBER
1 Yes
2 No >>
NEXT MEMBER
1 Yes
2 No >>
NEXT MEMBER
1 Yes
2 No >>
NEXT MEMBER
12. Which is the main activity of work of [Member] in last 12
months? (BRIEF DESCRIPTION OF WORK, EX : CULTIVATION/ HUSBANDARY/ MILLING/ TEACHER..., then choose a suitable
code to fill in the box)
_____________
_____________
_____________
_____________
_____________
_____________
_____________
_____________
_____________
_____________
_____________
_____________
01=
02=
03=
04=
05=
Agriculture
Forestry
Aquaculture
Sea salt production
Industry(excluding sea salt production)
06=
07=
08=
09=
Construction
Trade
Transportation
Other services activities
12.1 by which activities of working mode in last 12 months of
[Member] ? (Fill a suitable code in the box)
1=
2=
Self-employed
Employee
13. Which is the secondary activities of work of [Member] in last
12 months? (BRIEF DESCRIPTION OF WORK, CODE REFERS TO QUESTION 11, IF THEY DON’T HAVE THE SECONDARY
WORK THEN FILL CODE 10 IN THE BOX)
_____________
_____________ _____________ _____________ _____________ _____________
14. Who decided on the economic activities of households? ?
(Person who manage, control and give decision for almost economics activities in your family) Ethinic group: _____________________
NO. HOUSEHOLD
III. INCOME AND TYPE OF HOUSEHOLD (Mark one x in suitable box)
34
15. Main source of HHs income
(Excluding costs) in the last 12
months is from
1 AGRICULTURE, FORESTRY AND FISHERY 15.2. . Was the main source of HHs income in the
last 12 months from sea salt production?
1 YES
2 NO 2 INDUSTRY, CONSTRUCTION (>>QUES.15.2)
3 TRADE, TRANSPORTATION, OTHER SERVICE ACTIVITIES (>>QUES. 16)
4 OTHER SOURCES (NOT FROM BUSINESS) (>>QUES. 16)
15.1. Was the main source of HHs
income in the last 12 months from
agriculture, forestry, fishery?
1 AGRICULTURE (>>QUES. 16) 16. TYPE OF HOUSEHOLD?
(Enumerator base on results of
question 11, 12, 13, 15 to
identify)
1 AGRICULTURE
2 FORESTRY
3 FISHERY
4 SEA SALT PRODUCTION
5 INDUSTRY(EXCLUDE SEA SALT PRODUCTION)
6 CONSTRUCTION
7 TRADE
8 TRANSPORTATION
9 OTHER SERVICE ACTIVITIES
10 OTHER SOURCES
2 FORESTRY (>>QUES. 16)
3 FISHERY (>>QUES. 16)
PART III. AREA OF AGRICULTURE LAND, FORESTRY LAND, WATER SURFACE FOR AQUACULTURE AND LAND FOR SEA SALT PRODUCTION
TYPE OF LAND
17 Land area used by household on 1st July 2016?
(Include Land rented, borrowed, contracted by household, exclude land leased out, lent out) 18. Fallow land of household in
last 12 months? 17.1.
Number
of parcels
17.2. Total area (m2)
Of which
17.2.1. Land have assigned to
household (m2)
17.2.2. Land rented, borrowed,
contracted by household (m2)
1. Annual crop land
1.1. In which: Paddy field
2. Perennial crop land
3. Land for livestock
4. Forestry land x
In which:
- Area of standard planted forest
x
35
- Area of newly planted forest being
exploitated
x
5. Water surface for aquaculture
6. Land for sea salt production
PART IV. AREA FOR ANIMAL HUSBANDARY, PLANTATION AND FISHERIES
19. Have household ever cultivated any of the following agriculture species, trees in the past 12 month?
I. Annual crop (area counted for each havested season)
II. Perennial crop (on 1st July 2016)
a. Plants b. Planted area (m2) a. Plants b. Concentrating planted area
above 100 m2 (m2)
b1. In which: Productive area
(m2)
c. Scattered trees with
products ( trees)
1. Summer-Autumn paddy
2015
1. Mango
2. Autumn –Winter paddy 2015 2. Pineapple
3. Winter paddy 2015 3. Dragon fruit
4. Winter-Spring paddy 2016 4. Jack-Fruit
5. Maize, corn 5. Orange
6. Sweet potato 6. Grapefruit
7. Casava 7. Longan
8. Sugar cane 8. Coconut
9. Soybeans 9. Cashew
10.Peanut 10. Pepper
11. Vegetables 11. Rubber
12. __________ 12. Coffee
36
13. __________ 13. Green tea
14. __________ 14.__________
15. __________ 15. __________
16. Other annual crop 16. Other perrenial crop X
37
NO. HOUSEHOLD:
20. Does household raise any animal on 1st July 2016 ?
a. Species b. Quantity (unit) a. Species b. Quantity (unit)
1. Buffalo 6. Chicken
1.1. Buffalos for plowing 6.1. Broiler
2. Cattle In which: 6.1a. Industrial chickens
2.1. Cattle for plowing 6.2. Hen
2.2. Milk cows In which: 6.2a. Industrial chickens
In which: 2.2a. Dairy cows 7. Duck
3. Goat In which: 7a. Ducks for eggs
4. Sheep 8. Geese
5. Pig (Exclude sucking pigs)
(5=5.1+5.2+5.3)
9. Quail
5.1. Sow 10. Bee
In which: 5.1a. Farrowing sow 11. Rabbit
5.2. Breeding pig 12...
5.3. Porker 13...
21. Have household culture any spieces of fishery in last 12 months? (Mark one x in suitable box) 1 YES 2 NO>> Question 24
22. Aquaculture area in the last 12 months (excluding cage, raft aquaculture area) (m2)
culture area (m2)
Of which In which
Salt water Brackish water Fresh water Intensive and semi-intensive
1. Fish
38
2. Shrimp
3. Other fishery
4. Breeding
aquaculture
x
23. Cage and raft aquaculture (m3)
a. Total salt water brackish water fresh water
I. Cage
1. Fish
2. Shrimp
3. Others
II. Raft
1. Fish
2. Shrimp
3. Other fishery
4. Breeding aquaculture
PART V. MAJOR EQUIPMENT AND MACHINERY OF THE HOUSEHOLD ( as of 1ST July 2016)
24. Does household has any tractors, ploughs?
1 yes 2 no >> question 25
25. Does household has any motorized boats, ships ? (exclude boats and ships for fishery activities)
1 yes 2 no >> question 26
Capacity (CV) Type of boats, ships Quantity (Unit) Capacity (CV)
1. Tractor, plough 1 Total boats, ships
2. Tractor, plough 2 In which: 1. For agriculture and forestry activities
39
26. Does household has any motorized boats and ships for Aquaculture services? 1 yes 2 no >> question 26
a. Capacity (CV)
b. Major catching method
(Fill a suitable code in the box)*
c. Main Fishing territory
(Fill a suitable code in the box)*)**
d. Number of
labor
(person)
* Major catching method code:
1= Double-pulling net;
02=Single-pulling net;
03= suface-turning net;
04 = deep- turning net;
05= Encircling net in sunshire;
06= Encircling net with lamps;
07= Hand line fishing
08= Fishing squid;
09= Demersal and pelagic fishes;
10= Fishing tuna;
11= Câu vàng Tuna;
12= Catching Tuna;
13= Hook;
14= Deep sea fising;
15= Other method
Ship, boat 1
Ship, boat 2
Ship, boat 3 ** Main Fishing territory code:
1= River;
2= Lake;
3= coastal lagoon
4= Coastal
5= Inshore;
6= Offshore;
7= Sea;
8= Foreign Sea
9= Other
Ship, boat 4
Ship, boat 5
3. Tractor, plough 3 2. For fishery
4. Tractor, plough 4 3. For aquaculture logistic
40
27. Other equipment and machinery NO. HOUSEHOLD:
Kind of equipment and machinery Quantity (Unit)
Kind of equipment and machinery Quantity (Unit)
1. Car 13. Animal food processing machines (pulverizor, mixer, divider...)
In which: car used for agriculture, forestry and fishery activities 14. Aquaculture food processing machines (pulverizor, mixer,...)
2. Forced generator using electrical engines 15. Equipment to creat pure air, mix water for aquaculture production
3. Forced generator using petrol, diesel engines 16. Areator for aquaculture production
4. Generator 17. Water pumps for agriculture, forestry, aquaculture production
4.1. In which: Generator used for agriculture, forestry and fishery activities 18. Motorized Insecticide sprayers
5. Sowing machine 19. Non-motorized fishing boats
6. Harvesters Combine rice mowing machine
20. Devices
20.1. Seamless plows/ plow
7. Other harvesters 20.2. Seamless harrow/ hoeing
8. Rice mowing machines with engines 20.3. Slitting line
9. Corn peeling machine 20.4. Make seedbeds
10. Peanut shelling machine 21. Icubators
11. Coffee hulling machine 22. Milking machine
12. Agriculture, forestry, aquaculture product dryers, ovens 23. Other machinery (specify.......................................................)
PART VI. LIVING ENVIRONMENT AND MAIN FACILITIES OF HOUSEHOLD (Mark one x in suitable box)
28. Is electricity used by
the household ?
1 Yes
2 No >> Question 30
31. Is a purifying
system or chemical
used to treat cooking
and drinking water ?
1 Yes
2 No
29. What is the electricity
used by the household ?
1 National electricity cable
2 Other source (specify .... )
30. What is the main
source of Fresh water
1 Tap-water in house
2 Public tap-water
7 Protected Spring water 32. What is the main
source of Fresh
water used by
1 Tap-water in house
2 Public tap-water
7 Protected Spring water
8 Un-protected Spring water
41
used for cooking and
drinking by household?
3 Purchased water (tank, jar, bottle,…)
4 Drilled well water
5 Protected well water
6 Un-protected well water
8 Un-protected Spring water 9
River, lake, pond water
10 Rain water
11 Other sources
(specify________________)
household? (exclude
for cooking and
drinking) ?
3 Purchased water (tank, jar, bottle,…)
4 Drilled well water
5 Protected well water
6 Un-protected well wate
9 River, lake, pond water
10 Rain water
11 Other sources
(specify________________)
33 What is the main fuel
used for cooking ?
1 Wood
2 Coal
3 Industrial gas
4 BIOGAs
5 electricity
6 Other source specify________)
36. Which type of the main
waste treatment is used of
household ?
1 Collected by staffs
2 Transported to common waste-damp
3 Buried, burned
4 Do not leave
Other (specify________________________)
34. What type of bathroom
is being used?
1 Contructed bathroom
2 Other bathroom
3 No bathroom
37. What is the main sewage
drainage system from house to
outside ?
1 Gutter with cover
2 Gutter without cover
3 Other
4 Not have system >> question 39
35. What type of latrine is
being used?
1 Septic/semi-septic latrines in house
2 Septic/semi-septic latrines outside house
3 Suilabh
4 Ventilated imprived latrine
5 Double vault latrine
6 Other latrine
7 No latrine
38. Does the household’s
sewage drainage system
connect with public sewage
drainage system ?
1 yes
2 no
39. Does household use
internet?
1 yes
2 no
40. Does household has anything of the following as of 1st July 2016?
42
Date of intervew: …….. July, 2016
SIGNATURE
Interviewee Enumerator Team leader
Signature
Full name
Phone number xxx
Item Quantity
Item Quantity
Item Quantity
1. Car (not for business) 6. Desk phone 10. Refrigerator, fridge
2. Motorbike 7. Cell phone 11. Water heater tank
3. Electric bicycle and motorbike 7a. Number of people using cell phone 12. Computer
4. TV 8. Wahing machine 12a. In which : Number of computers have internet connecting
5. Radio, cassettes, Audio system 9. . Air conditioner 13. ...