Improving Methods for Estimating Livestock Production and...
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Improving Methods for Estimating Livestock Production and Productivity Gap Analysis Report November 2016 Working Paper No. 12
Transcript of Improving Methods for Estimating Livestock Production and...
Improving Methods for
Estimating Livestock Production
and Productivity
Gap Analysis Report
Gap Analysis Report
November 2016
Working Paper No. 12
Global Strategy Working Papers
Global Strategy Working Papers present intermediary research outputs (e.g.
literature reviews, gap analyses etc.) that contribute to the development of
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intermediary research products. They are reviewed by the Scientific Advisory
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expert meetings.
The opinions expressed and the arguments employed herein do not necessarily
reflect the official views of Global Strategy, but represent the author’s view at
this intermediate stage. The publication of this document has been authorized
by the Global Office. Comments are welcome and may be sent to
Improving Methods for Estimating
Livestock Production and
Productivity
Gap Analysis Report
Table of Contents Acronyms and Abbreviations……………………………………………………………………………. 7 List of Concepts and Definitions.......................................................................... 9 Executive Summary............................................................................................. 10 1. Introduction………………………………………………………………………………………………….. 13 1.1. The project………………………………………………………………………………………………. 13 1.2. Gap analysis and related terms..................................................................... 13 1.3. Gap identifiers............................................................................................... 14 1.4. Goals of this study……………………………………………………………………………………. 15 1.5. Approach taken.............................................................................................. 15 2. Method…………………………………………………………………………………………………………. 16 2.1. The role of consultation................................................................................. 16 2.2. Questionnaire................................................................................................ 16 2.3. Literature review and its contributions to the GAP analysis......................... 16 3. Pilot Countries................................................................................................. 20 3.1. Livestock information and the countries statistical system.......................... 21 4. Gaps Identified in the Literature Review......................................................... 24 4.1. Relevant issues............................................................................................... 24 4.2. Key messages for testing of livestock data collection methods and tools............................................................................................................... 29 5. Workshops...................................................................................................... 31 5.1. Local Level Workshops Structure................................................................... 31 5.2. Workshop attendance................................................................................... 32 6. Workshop Results........................................................................................... 34 6.1. Most important............................................................................................. 34 6.2. Comments on existing measurement systems.............................................. 37 6.3. Most important information for participant’s organization.......................... 39 6.4. Most important information for livestock producers.................................... 41 6.5. Most important information for livestock traders......................................... 43 6.6. Most important information for government............................................... 45 6.7. Most important information for the livestock industry................................. 47 6.8. GAP identification.......................................................................................... 49 6.9. Suggestions to improve data quality, collection and integration.................. 56 6.10. Suggestions for tests of new indicators, methods and equipment............. 58 7. Conclusions..................................................................................................... 61
7.1. Participants.................................................................................................... 61 7.2. Perception of the importance of livestock indicators................................... 61 7.3. Identification and ratings of GAPS................................................................. 63 7.4. Suggestions to improve data quality and collection methods...................... 65 7.5. Suggestions for tests of indicators, methods and equipment...................... 65 7.6. Proposed tests in each country..................................................................... 66 References.......................................................................................................... 68 Appendix A: Workshop Forms............................................................................. 73
List of Figures
Figure 6.1 Most Important Indicators Tanzania........................................................ 35 Figure 6.2 Most Important Indicators Botswana ...................................................... 36 Figure 6.3 Most Important Indicators Indonesia ...................................................... 36 Figure 6.4 Participant's Comments – Tanzania......................................................... 38
Figure 6.5 Participant's Comments – Botswana....................................................... 38 Figure 6.6 Participant's Comments – Indonesia....................................................... 38 Figure 6.7 Most Important Indicators for Participants Organisations – Tanzania.... 40 Figure 6.8 Most Important Indicators for Participants Organisations – Botswana...
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Figure 6.9 Most Important Indicators for Participants Organisations – Indonesia...
41 Figure 6.10 Most Important Indicators for Producers – Tanzania............................ 42 Figure 6.11 Most Important Indicators for Producers – Botswana........................... 42 Figure 6.12 Most Important Indicators for Producers – Indonesia........................... 43 Figure 6.13 Most Important Indicators for Traders – Tanzania................................ 44 Figure 6.14 Most Important Indicators for Traders – Botswana............................... 44 Figure 6.15 Most Important Indicators for Traders – Indonesia............................... 45 Figure 6.16 Most Important Indicators for Government – Tanzania........................
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Figure 6.17 Most Important Indicators for Government – Botswana....................... 46 Figure 6.18 Most Important Indicators for Government – Indonesia....................... 47 Figure 6.19 Most Important Indicators for the Livestock Industry – Tanzania.........
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Figure 6.20 Most Important Indicators for the Livestock Industry – Botswana........
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Figure 6.21 Most Important Indicators for the Livestock Industry – Indonesia........
49
Figure 6.22 Suggestions for tests of new indicators................................................. 58
Figure 6.23 Suggestions for testing of new data collection methods.......................
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Figure 6.24 Suggestions for new equipment to be tested or equipment required for suggested methods........................................................................... 60
List of Tables
Table 5.1 Workshop Location and Number of Participants...................................... 32
Table 5.2 Role and Position Held by Participants......................................................
33
Table 5.3 Organisations Involved in Workshop Process............................................ 33
Table 6.1 Livestock Data Quality Assessment - All Countries.................................... 51
Table 6.2 Livestock Data Quality Assessment -Tanzania........................................... 52
Table 6.3 Livestock Data Quality Assessment - Botswana......................................... 53
Table 6.4 Livestock Data Quality Assessment - Indonesia (without Sumbawa)........
54
Table 6.5 Table 6.4 Livestock Data Quality Assessment - Sumbawa......................... 55
Table 6.6 Suggestions for Most Common Indicators................................................. 57 Table 7.1 Proposed tests........................................................................................... 67
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Acronyms and Abbreviations
ABARES Australian Bureau of Agricultural and Resource Economics
and Sciences
ABS Australian Bureau of Statistics
AfDB African Development Bank
AMIS Agricultural Market Information System
BPS Badan Pusat Statistik, Indonesia
CBS Central Bureau of Statistics, Indonesia
CPI Consumer Price Index
CSA Central Statistical Agency, Ethiopia
CSO Central Statistics Office, Botswana
DAHP Department of Animal Health and Production, Botswana
DCIAS Data Center and Agricultural Information System, Indonesia
DEFRA Department of Environment, Food and Rural Affairs, United
Kingdom
DGLAH Directorate General of Livestock and Animal Health, Indonesia
EAs Enumeration Areas
EER Establishment and Enterprise Register
EU European Union
FAO Food and Agriculture Organization of the United Nations
FEAST Feed Assessment Tool
GDP Gross Domestic Product
GPS Global Positioning Service
IAEA International Atomic Energy Agency, Austria
ICR Intelligent Character Recognition
IDEAL Infectious Diseases of East African Livestock
iSIKHNAS Integrated Sistem Informasi Kesehatan Hewan Nasional, Indonesia
LDIA Livestock Data Innovation in Africa
LSMS Living Standards Measurement Studies
MAFF Ministry of Agriculture, Forestry and Fisheries, Japan
MLA Meat and Livestock Australia
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NASS National Agricultural Statistics Service, USA
NBS National Bureau of Statistics, Tanzania
NDVI Normalised Difference Vegetation Index
NSS National Statistical Service, Australia
OCGS Office of Chief Government Statistician, Tanzania
ODK Open Data Kit
OECD Organisation for Economic Co-operation and Development
PMO-RALG Prime Minister's Office - Regional Administration and Local
Governments, Tanzania
PSU Primary Sampling Units
SMS Small Message Service
TGLP Tribal Grazing Land Plot
USAID United States Agency for International Development
USDA United States Department of Agriculture
VAT Value -Added Tax
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List of Concepts and
Definitions
Core livestock indicators These are livestock indicators that a country requires on a regular
basis to properly fulfil their mandated reporting requirements
(Sserugga et al., 2013) such as those required to generate GDP and
CPI estimates.
Impact indicator Relates to indicators which provide a measure of the effects of an
outcome wider than the direct and immediate results (Pica-Ciamarra
et al., 2014, p. 28). These include both positive and negative effects
(Halberg et al., 2005) and encompasses indicators such as those that
capture the impact of different production systems on the livelihood
of the poor (Dorward et al., 2004).
Input indicator Are measures of the quantity (and sometimes the quality) of
resources input into a system.
Master sampling frame Is a list of area units that cover an entire country from which
samples of the population can be derived. For each unit,
information is included on whether the area unit is classified as
rural or urban and include information on the spatial boundaries of
the unit. Other information such as number of households per unit,
number of males and females, information on accessibility, which
district and province the unit belongs to may also be included in this
list ensuring an appropriate sample can be derived. The most
common type of master sampling frame is one with enumeration
areas as the basic frame units (Petterson, 2005). Master sample
frame should be the source for all samples derived for surveys of
agricultural holdings, farm households and rural non-farm
households.
Multi-frame approach This technique employs several list frames to ensure an adequate
sample of a commodity is captured where item would otherwise not
be fully captured by a single sampling frame list.
Outcome indicator Are measures of the broader quantity and quality of results achieved
as a result of the provision of different goods and services. An
example could be how well a certain livestock disease has
decreased because of an intervention.
Sample area frame A sampling framework which lists areas into groups of
homogenous groups of land-use stratification or vegetation (Davies,
2009).
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Executive Summary
The project Improving Methods for Estimating Livestock Production and
Productivity seeks methods of improving the quality of livestock data across a
range of species. It supports the Global Strategy on Agricultural and Rural
Statistics, and focuses on production-level livestock: specifically the
measurement of production and productivity at household level.
Gap analysis entails the quantification and comparison of desired and existing
states. This study attempts this for three case study countries (Botswana,
Tanzania and Indonesia). It measures functionality gaps by way of direct
questioning of stakeholders with regard to the systems delivering the data, it
canvasses stakeholders’ ideas about ways in which livestock data may be
improved so as to fill these gaps; and provides, where possible, commentary
regarding capability gaps and likely performance gaps with regard to new and
improved livestock data systems. Informed by the project’s literature review,
this report’s emphasis is on systematic issues of sampling and collection and
approaches to data integration, the quantity of data being collected and its
quality. Commentary and assessment surrounding quality of data follows the
Global Strategy’s proscribed criteria of relevance, accuracy and reliability,
timeliness and punctuality, coherence and comparability, and accessibility and
clarity.
This study employed a questionnaire which was administered in a workshop
format. Workshops were held either in livestock administrative regions or in
capital cities, and spanned a range of stakeholder interests, primarily
government providers and users of livestock data. After identifying up to six
pieces of information that they need in their occupation, participants were asked
to provide information on data availability, familiarity, relevance, accessibility,
accuracy, timeliness and other quality measures. Options were also provided for
stakeholders to make recommendations on how to improve the quality,
collection methods and usefulness of this data. Some 14 workshops were held
in the three countries where 171 stakeholders provided completed
questionnaires.
Workshop participants’ perception of livestock productivity and production
indicators is broad in scope. They nominated a wide range of indicators as
being the most important for livestock production and productivity that they
used in their work. These spanned productivity and production, as well as
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number of animals and income-related indicators. Within each country the
results where relatively homogenous, but between countries the views of
importance differs strongly. Productivity measures and reproductive efficiency
were widely represented as important.
Commentary on existing measurement of livestock production and productivity
was consistent across countries. There were widespread calls for improved
transport, communications and infrastructure to assist these processes. Training
in on-farm record-keeping was strongly advocated for farmers, which indicates
that farmers are perceived to be responsible for some aspects of collection of
livestock data. Botswana’s participants emphasised the importance of
information on grazing, and on growth and meat quality. Across the entire
sample, numbers of animals, feed availability and feed intake featured strongly.
Participants later gave their opinions on the most important indicators for
livestock producers and market availability, the price of livestock and feed
information were the most popularly cited. Disease information was listed in
the two African countries but was not significant in Indonesia. Better
information on the Number of Animals was the most frequently listed by every
occupation. Milk production, market prices, feed and disease information was
considered valuable information in all three countries. Farmers present
nominated the availability of veterinary drugs.
The two livestock production and productivity indicators that were identified as
most important by most of the workshop participants were numbers of animals
and disease information. These were identified as being “available” by some
88% of participants in each case. Surprisingly, the claimed availability of some
important indicators was reported as being available by rather few people. For
example only 35% of participants have access to information on animal deaths
and 45% indicated the availability of information on reproductive efficiency.
Information on Feed Intake and Growth Rates was similarly poor and only
available to a few respondents.
On a scale of 0 to 5 for each of the five quality criteria, the average scores for
all indicators were near the mid-point with Relevance being the highest scoring
at 3.37. Accuracy averaged 2.64, Timeliness 2.41, Coherence 2.58 and
Accessibility scored 2.59. For the most cited indicators, quality problems are
seen to be associated with punctuality, comparability and accessibility. Across
all indicators, Timeliness and Punctuality was the lowest-scored criterion (1.85)
from 45 respondents. Animal Deaths scored 2.0, Breed 2.07 and both Carcass
Weight and Infrastructure for Animal Husbandry 2.13. Infrastructure for
Animal Husbandry also scored poorly for Coherence at 2.25 as did Animal
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Deaths 2.19, Pasture Availability 2.16 and Reproduction Efficiency 1.97.
Growth rate was considered the least available information for participants at
1.25. Liveweight averaged 1.77 for coherence despite receiving the highest
score for relevance (4.22)
For Tanzania, with the exception of Carcass weight and meat production the
availability of needed information was generally high. As a whole, Botswana’s
data Relevance averaged slightly higher (3.98) than the 3-country average.
Reproduction efficiency was the most cited response and was listed 34 times.
While this indicator is rated very high for relevance at 4.04 it scored very
poorly for Timeliness 1.84 and below average for Coherence 2.13 and
Accessibility 2.17. Indonesian assessments of Relevance were slightly below
the 3-country average at 3.19. For Indonesia, carcass weight and meat
production was the lowest scored and received average ratings rated below 2 for
accuracy, timeliness, coherence and accessibility.
Workshop participants provided substantial information and suggestions to
improve both the quality of the data collected and collection methods for
various indicators. These suggestions centred on providing farmer training on
improved record keeping incorporating systems such as record books and
journals, improving the training and number of data collectors and increasing
the frequency and uniformity of counting animals.
From the information gathered through this process the country targets for
methodological improvements are:
Tanzania - milk production, egg production.
Botswana – feed demand, availability and surplus, sheep and goat
numbers, sales channels, weights and growth rates.
Indonesia – Beef growth rates, sheep and goats numbers, sales channels,
weights and growth rates, milk production.
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1 Introduction
1.1. THE PROJECT
The project Improving Methods for Estimating Livestock Production and
Productivity seeks methods of improving the quality of livestock data across a
range of species. It supports the Global Strategy on Agricultural and Rural
Statistics, and focuses on production-level livestock: specifically the
measurement of production and productivity at household level. This Gap
Analysis builds on an earlier Literature Review and preliminary fieldwork to
identify promising tasks for the Field Testing phase of the project.
1.2. GAP ANALYSIS AND RELATED TERMS
Gap Analysis entails a comparison of current performance or state with a
desired or future level. The analysis itself requires identification and
measurement of the current and future (in this case, an envisioning is needed)
states. Inference from the gap analysis is generally applied in such exercises as
needs assessment. Related concepts include:
- “usage gap” which refers to a gap between existing and potential market
penetration by a product or service;
- “product gap”, which entails exclusion of an organisation from a market
or environment due to its lacking a specific product;
- “process gap”, which is a diagnosis of the process-based causes of gaps
as above;
- “capability gap”, identifying target capabilities that do not yet exist and
comparing them with existing capabilities. Such gaps may relate to
systems as much as to teams or individuals;
- “functional gap” is a capability gap solely addressing systems and their
actual and target functionalities; and
- “performance gap” is an intermediate concept which identifies how far
an individual or firm has progressed toward a specified target.
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1.3. GAP IDENTIFIERS
Key identifiers of gaps that are addressed in this GAP analysis are listed below
according to a loose categorisation.
1.3.1. QUANTITY GAPS
Representation of livestock in GDP estimates
Determining populations
Determining herd structures
Accounting for alternate use for livestock (transport, cultivation)
Livestock disease status
Utilisation of communal grazing
Allocating labour hours to enterprises
Absence of adequate on-farm records
Quantifying manure production
1.3.2. QUALITY GAPS
Timeliness of collection and reporting process
Accuracy of data recalled and collected
Transparency of collection methodologies
Avoiding data collection duplication through lack of institutional coordination
Differentiation between types of livestock holders
Collection timed to reflect reproductive cycles
Recall in surveys as a means of measurement
Standardisation of variable definitions
1.3.3. CAPACITY GAPS
Management of the integration of data collection and use
Lack of resources for data collection – funding, training, transport, tools
1.3.4. USAGE GAPS
Evaluation of productivity
Identification of barriers to productivity improvement
Utilisation of indicators referring to herd demographics and dynamics
1.3.5. FUNCTIONAL GAPS
Frequent, consistent, appropriate and cost-effective collection methods
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Accounting for short term variations and their effect on production and
productivity
1.4. GOALS OF THIS STUDY
This gap analysis:
1) identifies both current and desired future levels of quantity and quality
of livestock production and productivity data in three case study
countries (Botswana, Tanzania and Indonesia), and so identifies gaps;
2) measures functionality gaps by way of direct questioning of
stakeholders with regard to the systems delivering the data;
3) canvasses stakeholders’ ideas about ways in which livestock data may
be improved so as to fil these gaps; and
4) provides where possible, commentary regarding capability gaps and
likely performance gaps with regard to new and improved livestock data
systems.
1.5. APPROACH TAKEN
Based on the Literature Review conducted in this project, it is anticipated that
likely gaps in measurement of production and productivity include:
“Fundamentals”: uses of livestock by households; herd dynamics
between and within seasons; feed use and availability and water use and
availability; home consumption, exchange and gifting; ; animal diseases;
interactions between crops, livestock and other productive
activities,(such as the production of dung or the use of animals as a
mean of transport).
“Applied issues” relevant to productivity, such as access to and
utilisation of inputs, markets, capital, technologies and reasons for their
adoption/non-adoption.
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2 Method
2.1. THE ROLE OF CONSULTATION
The consultative approach used here emphasises stakeholder consultation and
the need for assessing and rationalising their objectives, and thence proceeding
to decisions on priorities for indicators. The focus of the gap analysis was on
collection methods, rather than selection of core data and key indicators.
Stakeholders’ views on priority and importance of particular information and
indicators were collected in tandem with their ratings of quantity and quality. In
addition stakeholder’s ideas for change and improvement were also recorded.
Project counterparts in the case study countries were canvassed as to locations
and groupings of livestock data stakeholders, and a questionnaire formulated to
establish a gap analysis framework as described above. Meetings were held
with stakeholders such as statistical authorities, livestock and agriculture
ministries, data collectors and farmers. At these meetings the project’s
components were laid out, and particular emphasis was placed on the need to
define and design trials of alternative methods.
2.2. QUESTIONNAIRE
The questionnaire is attached as appendix A.
2.3. LITERATURE REVIEW AND ITS CONTRIBUTIONS TO
THE GAP ANALYSIS
Messages drawn from the project’s literature review which can inform this
GAP analysis fall largely into three categories, treated separately here:
Systematic issues of sampling, collection and approaches to integration
The quantity of data being collected
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The quality of data being collected
2.3.1. SYSTEMATIC ISSUES OF SAMPLING, COLLECTION AND
APPROACHES TO INTEGRATION
Most advocacies for improved livestock data calls for systematic and
standardised approaches across regions and countries, across indicators, and
across institutional boundaries particularly those of different organs of
government. Particularly in performance contexts such as productivity, this
seems a reasonable goal to pursue in improving livestock data collection.
The literature review identifies limitations in existing methods for counting
animals, and these appear at their most extreme when herd and flock
demographics and dynamics are being assessed. Although other factors
influence satisfaction with these indicators, a systematic approach to the
information and its collection is clearly lacking and appears to be a priority. It
is notable that such indicators would not necessarily be needed regularly, but
rather could be recorded every few years to allow revisions.
Sampling issues emerging from the literature review comprise both the
resource-based issue of insufficient sample size with the lack of suitable sample
frames that refer to livestock. One focus is on the farm holding as the primary
statistical identifier in most agricultural surveys, for which correspondence to
the rural household (the main focus of statistics for economic and planning
purposes) may be weak. The GAP analysis then includes an investigation of
support and strength of ideas for data integration from which issues of sampling
may emerge. This conveniently ties the issue of sampling to that of data (and
more particularly sample) integration. Within the livestock sector, tying input
to output indicators and variables will enhance productivity measures. More
important, however, are the productivity measures which require linkages that
must be developed between sectors (e.g. linking animal services to crop
production or crop production to feed), or generalise farm-level resource use
such as water and labour.
Multiple-stage sampling regimes are discussed in the literature and generally
feature a household or holding based first stage (centred on selecting livestock-
keeping households) and then a resource or asset based stage (such as herbage
microclimate or proximity to water or markets). Such staging of sampling
predisposes to stratification on one hand and integration on another.
Stratification is advocated in the literature where households fall into distinct
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fundamental categories with regard to commercialisation and subsistence.
Integration finds favour where the second-stage sampling exercise refers to
factors affecting productivity. The Global Strategy’s integrated framework is
not sufficiently known amongst livestock data stakeholders to be employed in
the GAP analysis, but those stakeholders’ proposals for integration initiatives
will be able to be used to identify linkages to the global strategy when the
project’s test activities are defined.
2.3.2. THE QUANTITY OF DATA BEING COLLECTED
Pursuit of an idealised “optimal set” of data is easily confused with a
conceptually-appealing “maximum set” of data. The apparent documentation
of an insufficient quantity of data is not particularly clear, and specification of
“core” data and ideal sets of indicators are not particularly demanding. The
GAP analysis then provides scope for canvassing views on the variables and
indicators that that they are using, and the importance assigned to them. These
can be expected to vary by user type and the GAP analysis attempts to capture
this.
Problematic subjects cited in the literature include the capture of data from
transhumant populations (of people and animals), inability to identify individual
animals, and logistic- and technological-type problems of data recording and
transmission within reasonable time frames. These topics refer primarily to
national level data, but a supporting set of difficulties are identified in the
literature review which refer to farm and enterprise level data and to efforts by
various government entities to establish measures of performance and
associated benchmarks for use in extension and related work. At the heart of
this issue is the desire for farmers and small businesses to themselves collect
and maintain records of data. This less conventional view of data quantity
problems is aired in the GAP analysis, and inevitably refers to capacities at
several levels of data collection, as well as a fundamentally changed
relationship between farmer and data collector.
Capacities are a frequently-cited constraint on quantities of livestock data
available. This refers to a lack of training at the level of extension and other
local officers, as well as to limited institutional capacities in organisational units
not well suited to (or insufficiently resourced for) data collection. Such
capacity constraints are likely to be keenly felt with regard to data integration,
particularly in referring to input-output relationships and the required
systemisation discussed in the previous section.
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Livestock data is usually portrayed as a resource-poor activity. Hence,
elimination of the widely-reported duplication of collection should be a priority.
The duplication is however unlikely to be precise, which adds the further
problem of competition between measures and data collection initiatives. This
suggests that the standardisation and systemisation referred to above offers an
opportunity for a rationalisation which will eliminate duplication for the
purposes both of enhanced clarity and accuracy, and fiscal responsibility.
2.3.3. THE QUALITY OF DATA BEING COLLECTED
The many criticisms of the quality of available livestock data will not be
repeated here as contributing methodological factors have been given the most
emphasis. These include, but are not limited to reliance on faulty recall, choice
of survey respondent within households, lack of training of enumerators, lack of
standardisation of methods and formats, and outdated technical coefficients.
The GAP analysis seeks ideas on areas for data quality improvement and these
topics will be aired.
The global strategy identified criteria for assessing data quality which are
directly evaluated in the GAP analysis by stakeholders for each key livestock
variable or indicator as nominated by the stakeholder. These criteria are:
1. Relevance
2. Accuracy and reliability
3. Timeliness and punctuality
4. Coherence and comparability
5. Accessibility and clarity
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3 Pilot Countries The livestock sector plays an important role in the economic growth of many
countries and in some developing countries contributes to over a third of the
agricultural value of the country. Generally in these cases individual
households and smallholder farmers dominate the livestock industry where
animals are used as both as a primary source of household food and income
from by-products. The prevalence of many small farms and subsistence based
agriculture creates significant issues and concerns for the acquisition, quality
and reliability of livestock information.
This project considered three pilot countries in which to examine the existing
livestock data information systems. Tanzania, Indonesia and Botswana were
chosen due to large degree of household and small holder farming and to
provide an opportunity for comparison and contrast between countries to with a
different geographical and economic scope. In each case a gap analysis was
performed to analyse the difference between currently available information
and the information needs of stakeholders.
Country Region Income
Tanzania East Africa Low
Indonesia South East Asia Low-Middle
Botswana Southern Africa Middle
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3.1. LIVESTOCK INFORMATION AND THE COUNTRIES
STATISTICAL SYSTEM
A range of different livestock data and indicators are collected in the pilot
countries at a level of different frequencies and different sample sizes. A brief
summary follows with further information being available in the literature
review.
3.1.1. TANZANIA
Tanzania is currently developing an integrated national statistical system that
aims to deliver reliable and timely statistics in accordance with international
standards and best practice. At present, the Tanzanian statistical system uses a
decentralised approach with data collection officers being under the control of
local government authorities. In 2007, a peer review of Tanzania’s national
statistical found that while the quality of statistics collection had been
improving, areas such as the timeliness of some surveys, lack of an inventory of
data series available from public agencies, coordination and processing of
routine data systems and availability of a centralized dissemination system
needed further development.
Primary sources of livestock-production data collected in Tanzania include:
National census of agriculture (NBS, 2011) - undertaken every five
years;
National panel survey (NBS, 2014) – undertaken every two years;
Large-scale farmer reports (Holland, 2012) – compiled annually;
Administrative data collection (Holland, 2012) – collected monthly,
quarter, annually; and
Livestock market price reporting (Mapunda et al., 2011) – weekly.
3.1.2. INDONESIA
The national statistical system in Indonesia is an integrated system managed by
the Badan Pusat Statistik (BPS). Statistics are grouped into three types under
the national system and include (1) basic statistics, (2) sector statistics and (3)
special statistics. Basic statistics are collected by BPS, sector statistics are
collected by the different ministries, sometimes with input from BPS and
special statistics are usually conducted by private research agencies. Hardjo
(2014) found that all global minimum key variables and global items at the
national and provincial level for agricultural production were satisfactorily
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collected, compiled and published. Local level discussion during this stage of
the project indentified a number of stakholder concerns over the duplication of
data collection and differing methodologies between BPS and the Department
of Agriculture.
The primary sources of livestock-production data collected in Indonesia, along
with the frequency of their collection are:
Agricultural census (DGLAH, 2012; Hardjo, 2012) – undertaken every
ten years;
Livestock census (Margono, 2013) – undertaken on an ad-hoc basis
(last conducted in 2013);
Poultry business report (BPS, 2009b, p. 88) – conducted annually on
poultry businesses maintaining more than 2,500 laying hens or
producing over 19,500 broiler chickens per year;
Dairy cattle business report (BPS, 2009b, p. 89) – undertaken annually
by direct interview on all businesses with more than ten adult dairy
cows;
Large and small livestock company report (BPS, 2009b, p. 90)–
conducted annually on registered livestock business using direct
interiew techniques;
National livestock sample survey (BPS, 2009b, p. 436) – undertaken on
a systematic sample of households identified in household census as
holding livestock; and
Number of animals slaughtered and meat production (FAO, 2010) –
collected monthly from secondary industry and government sources
who report to the central statistics regency.
3.2.3. BOTSWANA
The Botswana Central Statistics Office is the principal body in the official
statistical system of the country and most statistical operations fall under the
mandate of this organization. In a report on African statistical systems by the
AfDB (2014), Botswana was rated as having one of the highest resources in
Africa allocated to collect agricultural statistics as part of their national system.
Some of the recognised challenges and limitations with the current collection
and compilation of statistics include a lack of coordination in data collection as
well as non-standardized codes, concepts and definitions across the country.
These limitations of the current system could be reduced by moving towards an
integrated survey framework.
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The primary sources of livestock production data collected in Botswana,
including the frequency of collection are:
Agricultural census (Statistics Botswana, 2015) – conducted every ten
years;
Agricultural survey report (Statistics Botswana, 2014) – conducted annually on
samples that are grouped into two sectors; the traditional sector comprised of
subsistence farmers on communal land and the commercial sector operating on
freehold, leasehold or Tribal Grazing Land Policy farms or ranches solely for
commercial purposes. The traditional sector is administered by enumerators
using face-to-face interviews and the commercial sector data is collected by
paper-based mail-out questionnaires.
24
4 Gaps Identified in the
Literature Review
4.1. RELEVANT ISSUES
There are three broad issues relevant to the methods of collecting and
measuring livestock production and productivity in the pilot countries. These
include problems associated with sampling, quantity of data available and
quality of the data collected.
4.1.1. SAMPLING
Sampling is a major issue when official livestock statistics are generated from
sample surveys. This is particularly important in countries where the spatial
distribution of animals is not strongly correlated with the distribution of the
sampling units such as rural households or farm holdings as occurs in countries
with large tracts of arid or semi-arid areas (Pica-Ciamarra et al., 2014, p. 40).
Holland (2012) points out that sample sizes in Tanzania are often too small to
provide accurate representations of the complexities in livestock systems.
Metaferia et al. (2011) describes the collection of livestock statistics in many
African countries as still being in a rudimentary stage. They note that, despite
large proportions of livestock being located in lowland and pastoral areas,
collection of livestock statistics to date have often been restricted to the
sedentary farming regions. Leta and Mesele (2014), state that a significant
spatial variation in livestock production and markets occurs in Africa due to
access to markets, availability of feed and water, herd characteristics and
topographical features. Therefore, the sampling frame used when designing
surveys needs to ensure these regions and households with livestock are
captured.
25
Indonesia’s livestock production is largely comprised of numerous small
holders. Due to this structure of livestock holdings, production is highly
fragmented with an irregular distribution across the entire country (Stanton,
2010). Therefore, selection of a satisfactory sample can be problematic and
surveys designed need to ensure that smallholder-based production is captured.
The majority of cows in Indonesia are owned by individual smallholder
farmers. Although restricted geographically due to climatic conditions small
hold farmers in Indonesia also account for an estimated 90 per cent of
Indonesia’s milk production (Wright & Meylinah, 2013). However, production
from corporate dairy farmers is increasing at a greater rate than individual
farmers (Morey, 2011) and any survey needs to be designed to ensure both
corporate and individual landholders are adequately represented in the sampling
process.
4.1.2. QUANTITY OF DATA
There has been a decline in the quantity of livestock data and indicators
collected that are essential to measure production and productivity (World
Bank, 2012). In a survey of livestock data users, Pica-Ciamarra and Baker
(2011) found that more than 90 per cent of respondents didn’t agree that there
was sufficient availability of livestock data. In addition to the unavailability of
some livestock indicators, a problem often highlighted by livestock
stakeholders is the fact that most surveys target farm-level and consumption-
related issues and rarely capture information on other factors along the input
and output value chains (Pica-Ciamarra et al., 2014, p. 40). While data from
traditional surveys such as agricultural/livestock sample surveys and integrated
household surveys can provide sufficient details to generate descriptive
statistics on livestock ownership and production, Pica-Ciamarra et al. (2014)
found that limited or missing information on husbandry practices, inputs and
outputs severely restricted the overall understanding of the livestock sector.
While the current data collection methods of the pilot countries shown provide
estimates of the total production outputs for direct-consumption outputs such as
meat, milk and eggs, it appears that some of the data may be missing due to the
collection technique. For example, the monthly production reports in Tanzania
when capturing milk-production data does not take into account the milk
consumed by households and the weight of livestock slaughtered is not
recorded.
Often a gap in production and productivity data collected which occurs in
developing countries is accounting for the value of livestock where there is a
26
heavy reliance on animals to plough land for crop production. Metaferia et al.
(2011, p. 7) argue that there is a wide gap in the knowledge of capturing the full
value of services the livestock sector provides to the cropping sector through
both the outputs of manure and animal traction. For example, the pilot countries
of this project do not regularly capture manure production and only collect
limited data on animal draught power.
From the summary of surveys undertaken and livestock indicators collected in
the pilot countries it can be seen that, with the exception of vaccinations and
disease treatments, there is a general deficiency in the collection of livestock
input indicators. Inputs of livestock feed, breeding practices, access to water,
livestock housing and labour are all either rarely captured or missing from the
current data collected. These inputs are required to calculate some of the key
livestock productivity indicators. While data on the total livestock herd is
generally captured, herd composition and demographic data is often only
partially collected. For example, Indonesia has eight main beef-cattle varieties;
however, when collecting population data no information is collected to
distinguish between the different breeds (East Java Provincial Livestock
Services, 2011, p. 13).
Another gap in data appears to be the capture of statistics related to informal
livestock exports from African countries which avoids export licences and the
VAT required by formal livestock exports and avoidance of livestock
quarantine requirements. USAID (2013b, p. 35) provides a summary of the
estimates of informal livestock exports from a range of different sources
including the FAO, Ministry of Foreign Trade, The World Bank and their own
estimates. These estimates include a significant number of livestock including
approximately 260,000 head of cattle and 930,000 head of shoats per annum
informaly exported from one African country.
Finally, while most issues relate to insufficient quantity of livestock data in the
pilot countries, inefficiencies in the duplication of data collection also occur.
For example in 2013 both an agricultural census which included livestock and
an ad hoc livestock census were conducted in Indonesia.
4.1.3. QUALITY OF DATA
In addition to the quantity of data available, where data is collected, the
reliability of this data is often questionable, especially in developing countries
(Catley, 2006; Pica-Ciamarra & Baker, 2011). There is a general dissatisfaction
with the quality of routine livestock data collected in African countries (Pica-
27
Ciamarra et al., 2014, p. 37). Disparity exists between the methods employed
by local administrative organisations and the local extension officers collecting
data. In an examination of the quality of livestock data from administrative
records, Okello et al. (2013b) concluded that several factors contribute to the
resulting poor quality of this data. These include the limited collection of data
electronically (with the majority of collection using paper-based forms);
discontentment of the extension officers that collect the data due to insufficient
resources; insufficient time to collect required data; use of extension officers
who are generally not trained in data collection and handling and who collect
information during their daily activities; lack of formal procedures when
collecting data and scattered direct observations.
Based on surveys of African nations general agricultural statistics system
capability, AfDB (2014) concluded that Tanzania has a strong data availability
but a significant lack of resources for agricultural statistics. These resource
constraints primarily relate to a lack of funding, lack of adequately trained staff,
problems with the reliability of vehicles for transportation of enumerators to
survey livestock holders and a lack of data collection tools (Holland, 2012).
Other key issues for the collection of livestock indicators in Tanzania outlined
by Holland (2012) include the request for too much information and
inadequately designed and defined surveys, the use of data parameters for
livestock projections, limited public dissemination of data and a lack of
institutional coordination resulting in duplication of collection efforts and use of
different methodologies which result in inconsistent results. These factors all
reduce the integrity of the livestock data and inferences made based on this
data.
In a paper assessing the value of core livestock technical conversion factors in
Tanzania, Nsiima et al. (2013a) found that many of the important livestock
technical conversion factors used by the Federal Government dated back to the
late 1980s and were often adopted from neighbouring countries. Retention of
dated technical conversion factors, especially where production systems are
dynamic or have changed will reduce the quality of the data produced.
The estimation of livestock’s contribution to Indonesia’s GDP is calculated
using data on livestock population as well as egg, meat and milk production
(DCAIS, 2013, p. 8). The cost data is based on the cost of production from the
National Livestock Census conducted at least eight years prior. Other issues
include the estimation of feed-use which is an important input for determining
livestock productivity. However, a report by AMIS (2014) found that
measurement of livestock feed-use was particularly problematic in Indonesia.
28
This was particularly so for maize estimates, the main ingredient in livestock
feed. They found that Indonesia’s central statistical organization estimated
maize production levels at more than twice of those reported by estimates by
the USDA due to different estimation techniques. Therefore, the use of
appropriate estimation techniques are also important for ensuring the collection
of quality data.
A large proportion of livestock holders in each of the pilot countries consume a
significant proportion of the outputs produced. In Tanzania, the local
administrative data collection only captures information on the milk sold at
markets and does not capture the quantity of milk consumed by the livestock
owners (PMO-RALG, 2015). If this routine data is solely used to determine
milk production, this will lead to a severe underestimation of the amount of
milk produced.
In many countries there is a significant duplication of data collection on
livestock production indicators between different collection agencies. This can
be attributed to the lack of institutional coordination of stakeholders and limited
access of readily available access to data. This replication of indicators is an
inefficient use of limited resources. A clearer delineation of which agencies are
responsible for the collection of what livestock-production indicator data, at
what frequency, across what sample is required from each country’s central
statistics office.
Moving towards an integrated framework will reduce human and financial
resource burdens of collecting data to produce livestock production indicators.
Issues of under-resourced and not appropriately trained staff collecting
statistical data are still likely to occur, especially in the collection of
administrative data by regional extension officers. However, specialised
training of extension officers in data collection and the administration of
questionnaires and the provision of common and consistent collection and
reporting formats this will help progress towards an integrated framework that
collects high quality livestock data.
Not all data needs to be collected in the form of traditional surveys. Numerous
additional livestock data collection methods have be developed which can
supplement data collected from these traditional surveys. This literature review
found that items such as cattle herd population and demographics data is not
even collected via tradition surveys in some developed countries since the
introduction of comprehensive tagging and administrative registers. Therefore,
29
the most effective data collection needs to be considered when moving towards
an integrated framework.
Data that will not change frequently or become obsolete quickly should be
collected in periodic censuses or surveys. However, variables and indicators
that change often or are used in the generation of indicators with high
variability should be collected on a more frequent, at least annual basis. Also,
any technical conversion factors should be updated regularly. Special care
needs to be exhibited when using dated parameters to estimate or project
livestock productivity.
There is often limited dissemination of livestock data to the public, therefore an
integrated framework with a focus on livestock production needs to ensure the
timely and wide-spread release of statistical data collected. In addition, there is
often limited transparency to the survey methodologies used to collect livestock
data. As pointed out in USAID (2013a), to progress towards an integrated
framework, full transparency of survey and questionnaire methodologies are
required.
4.2. KEY MESSAGES FOR TESTING OF LIVESTOCK
DATA COLLECTION METHODS AND TOOLS
The first step is to compile a record of all current livestock data-collection
methods by all stakeholders to ensure variables and indicators are not
unnecessarily double-counted. However, some doubling-up of data collection
from multiple sources is not necessarily a bad thing as it provides a check and
balance on the data assumptions being used (if they are adequately integrated
together to cross check against each other). It could, however, lead to problems
of ‘which is the most accurate’ in situations where significant disparity between
the collected indicators occurs.
When testing the livestock collection methods, the timing of the collection
methods must be considered and varied according to livestock type with
consideration for the different reproductive cycles and generational changes.
For example, while the collection of annual data on cattle populations may be
sufficient due to limited generational change, this timing will be insufficient for
poultry populations where several generations will have cycled through during
this period of time.
High sampling errors can occur when using sample survey data rather than
more comprehensive survey techniques (FAO, 2005). When assessing livestock
30
data-collection methods, the sample size needs to be large enough to ensure the
desired level of precision, detail and level of aggregation. These methods also
need to be designed to collect enough statistically valid responses to cover the
main livestock production methods in a country.
Livestock collection methods need to have clarity and global consistency in
their definitions of variables. This is particularly important when coordinating
the collection and integrating data from different sources. The use of consistent
terminology is particularly important in the collection of administrative data
where the collection agent is often not trained in data collection.
Methods of generating livestock data based on conversion technologies need to
be validated regularly and be modelled on the country/region and production
system for which data is collected. Often in developing countries, livestock
production variables and indicators are produced which are based on out-dated
and/or technical coefficients which have been adapted from neighbouring
countries (Okello et al., 2013a).
Most surveys are based on the premise of the household head/agricultural
manager being the sole or major decision maker. In reality, the decision making
process of agricultural holdings are far more complex. Different household
members may hold different responsibilities and make decisions at different
levels of the strategy ladder (FAO, 2005, p. 24). For example, women in
Ethiopia and Botswana play a minimal role in cattle production (Oladele &
Monkhei, 2008; USAID, 2013b, p. 44). On the other hand, in Indonesia, in
general women are the primary owners and caretakers of village and backyard
extensive poultry production (Oparinde & Birol, 2008, p. 2). Therefore, when
measuring livestock productivity through interview-based survey methods,
correct identification and targeting of surveys towards household members in
charge of the target livestock’s production improve estimation accuracy.
31
5 Workshops
5.1. LOCAL LEVEL WORKSHOPS STRUCTURE
To identify the issues involved with the collection and use of livestock
production and productivity information meetings were held with relevant
stakeholders such as statistical authorities, livestock ministries and data
collectors. These local level workshops were held at a number of regional
centres in each country and involved discussion and the completion of
questionnaire (Appendix A). This process helped identify key aspects and
measures of quality (e.g. variable definition, precision of measurement,
accuracy of collection, format of delivery, etc.) and the means by which these
can be improved for each location and country. These workshops also targeted
survey and/or measurement tools and their application with various livestock
data stakeholders in a standard manner across all pilot countries, but retaining
specificity to each location and country.
Participants were asked to identify in regards to livestock productivity and
production, the four most important data indicators needed by themselves in
their professional role. They were also asked for their opinion on the data needs
of their organisations, the livestock industry, traders, producers and their
national government. They were also invited to provide general comments
about the measurement of livestock production and productivity information.
After identifying up to six pieces of information that they need in their
occupation, participants were asked to provide information on data availability,
familiarity, relevance, accessibility, accuracy, timeliness and other quality
measures. Options were also provided for stakeholders to make
recommendations on how to improve the quality, collection methods and
usefulness of this data.
32
5.2. WORKSHOP ATTENDANCE
In total fourteen workshops were held in regional areas across the three
countries. The location for each workshop was determined by the in-country
project staff to provide a representative sample of livestock information
stakeholders in each country. The number of participants varied in each location
due to differing populations in each area, livestock prevalence and other
location specific factors.
In total 171 stakeholders attended the workshops with the location and number
of participants for each being shown in Table 5.1.
Table 5.1. Workshop Location and Number of Participants
Country Region Participants
Tanzania Mvomero 10
Morogoro 10
Kibaha 10
Bagomoyo 10
Botswana Gaborone 21
Romatswa 21
Serowe 18
Ghanzi 8
Indonesia Lampung Selatan 12
Lampung Timur 11
Kab Blitar 9
Kab Malang 11
Kab Sumbawa 10
Kab Bima 10
171
Table 5.2 lists the role or employment position of the workshop participants for
the three countries. In this instance, the majority of stakeholders, data collectors
and users of livestock information are employed in agricultural extension roles.
Head of section classification included supervisory positions such as team
leaders through to local and regional or heads of department. With this level of
variation approximately 50% of participants in Indonesia were classed as head
of section. Local level statisticians were predominately involved in Tanzania
due to the decentralised structure of the department of statistics. Workshops in
Botswana included a number of farmers and more diversity in stakeholders.
33
Table 5.2. Role and Position Held by Participants
Position Participants
Extension Officer 75
Farmer 15
Scientist 3
Statistician 7
Livestock Producer 21
Secretary 1
Head of Section 38
Government Staff 3
Technician 7
Vet 1
Table 5.3 shows the organisations represented by the workshop process.
Classification issues occur when combining and comparing information from
all three countries. For example agriculture extension workers are employed by
the Ministry of Agriculture in Tanzania and Indonesia but by the local council
in Botswana, similarly respondents may list their employer as the district office
rather than ministry of agriculture. The ambiguity in responses has thus reduced
the analysis and conclusions that have been drawn from organisation data.
As the workshops in Botswana had a greater range of workshop participants,
there is a corresponding increased range of organisations including five people
employed in Academia or research centres and eleven classified as self-
employed.
Table 5.3. Organisations Involved in Workshop Process
Organisation Participants
Botswana Meat Commission 2
District Office/Council 93
Department of Veterinary Services 2
Ministry of Agriculture 49
Self Employed 11
Research Centres / Academia 5
No Response 7
Statistics Department 2
34
6 Workshop Results
6.1. MOST IMPORTANT
Workshop participants were asked to nominate the most important indicators of
production and productivity that they used in their work. For each country
results were then processed to provide the “top 10” which are the 10 most
frequently-cited such indicators. With a smaller number of participants the
number of most frequently cited indicators have been reduced and are also
shown in Figures 6.1 to 6.3.
The results of this question provoke several comments. Firstly, indicators cited
may be divided between productivity (e.g. milk per cow, carcass weight,
growth rate), production (e.g. milk production, reproductive efficiency), count
variables (numbers of animals) and income indicators (number of animals sold,
number slaughtered). Hence, workshop participants’ perception of livestock
productivity and production indicators is broad in scope.
A second comment is that in while within in each country the results where
relatively homogenous, between countries the view of importance differs in a
thematic way that goes beyond differences in livestock species and production
or market context. Some 20% of Botswana’s workshop participants cited
“deaths” as a most important indicator, but only there were only two responses
for this category in Tanzania. In Botswana 24% of the responses cited
Reproduction Efficiency as one of the most important indicators while only a
single participant in Tanzania considered this to be of importance. 8% of the
responses in Indonesia considered Breed and Id of the Animal Owner as most
important whereas neither indicator appears in the Tanzanian workshop data
and only marginally in Botswana. Similarly 16 participants (15%) in Tanzania
listed Number of eggs produced or collected whereas it was only mentioned by
2 people in Botswana or Indonesia.
35
When considered by occupation, the common responses for extension workers
where milk production (30% of responses, predominately in Tanzania) and
Carcass weight 28%. 24% of these participants stated Number of Animals and
the Number of Eggs produced. Reproduction Efficiency and Animal Deaths
were both also cited by more than 20% of responsdants predominately in
Botswana. For the Head of Section classification which is strongly represented
in Indonesia, there were 42 responses citing number of animals (irrespective of
species) and 12 cited reproduction efficiency. Animal deaths and ID of animal
owner were each listed 8 times and Breed and Infrastructure for animal
husbandry 7 times.
Figure 6.1. Most Important Indicators Tanzania
36
Figure 6.2. Most Important Indicators Botswana
Figure 6.3. Most Important Indicators Indonesia
37
6.2. COMMENTS ON EXISTING MEASUREMENT
SYSTEMS
Commentary on existing measurement of livestock production and productivity
variables and indicators drew somewhat consistent comment across the
countries. Farmer training on record-keeping, the facilitation of transport and
equipment and other education of farmers as well as improved measurement
infrastructure were seen as vital methodological improvements. In Botswana
the response to this question was poor, however surveys of grazing (a key
input) and meat quality and value (sales outputs) were advocated. Several
related indicators might be associated with increased use of information
technology in Tanzania and Indonesia where a number of participants
commented of the need for improved communication and co-ordination
between farmers and collectors of data.
In addition to the similarities of responses between countries, within each area
comments were also relatively homogenous. The exception to this the area of
Kab Sumbawa in Indonesia where comments only included market reporting,
use of improved IT and facilitation of transport and equipment. This area has a
government controlled regional animal identification for cattle, horses and
buffalo where extension workers meet with farmers to identify animals, provide
education and other services and based on responses it appears that this system
fulfils the needs of local participants. The most common results for each
country are shown in Figures 6.4 to 6.6.
Extension Workers responses were varied but they predominately made
comments on education and the interactions and communication between
themselves and farmers. The need for farmer training on record keeping was the
most common response (15 responses), the need to facilitate transport and
equipment (9 responses), Communication and co-ordination between farmers
and collectors of data (6 responses) and education on grazing management (4
responses). In addition to these thematic comments the need for improved IT
drew 8 responses. Heads of Section classification had similar results with the
addition of Market Reporting and Uniform reporting across districts.
38
Figure 6.4. Participant's Comments – Tanzania
Figure 6.5. Participant's Comments – Botswana
Figure 6.6. Participant's Comments – Indonesia
Make physical collection easier…
Education on improved breeds
Prices to be recorded by kg
Use of improved IT
Farmer training on record keeping,…
0 1 2 3 4 5 6 7 8
Participants Comments - Tanzania
Make physical collection easier…
Regular surveys of grazing land
Meat inspection data
Quantify local consumption/home…
Facilitate Transport and equipment
Farmer training on record keeping,…
0 1 2 3 4 5 6 7 8
Participants Comments - Botswana
Make physical collection easier…
Success of animal health treatments
More regular and structured…
Communication and co-ordination…
Use of improved IT
Farmer training on record keeping,…
0 5 10 15 20
Participants Comments - Indonesia
39
6.3. MOST IMPORTANT INFORMATION FOR
PARTECIPANT’S ORGANISATION
Participant’s assessment of the most important pieces of livestock information
for their organisation centred on the number of animals as well as animal
disease and aspects of its control. Across the entire sample, feed availability
and intake also feature, as do pure productivity (yield) indicators. Animal
losses are given weight in Botswana. In each country participants placed some
emphasis on sales and slaughter numbers, and prices received. The most
common responses are shown Figures in 6.7 to 6.9.
In Gaborone, the workshop was held with relatively high level staff and was
significantly constrained in the amount time that could be spent. It was
therefore decided to focus as much as possible on what was felt to be the most
critical aspects of the workshop. As such a page was removed from the
questionnaire with this question and the following four not answered in this
location.
When the results are considered by occupation there were 46 responses by
extension workers for Number of Animals, 28 cited Disease in live animals and
13 cited Milk Production. Reproduction efficiency and Disease control
measures were each listed 11 times and Breed and Number of Animals sold 10
times. Heads of Sections’ listed the same responses in the same order with the
exception of Milk Production which was replaced by Number Slaughtered.
Eleven farmers completed this question with the strongest responses being Feed
availability, Water availability and Disease in Live Animals (3 times each) and
the Number of Animals, Feed Intake and Price of livestock sold, 2 times each.
40
Figure 6.7. Most Important Indicators for Participants Organisations - Tanzania
Figure 6.8. Most Important Indicators for Participants Organisations – Botswana
41
Figure 6.9. Most Important Indicators for Participants Organisations - Indonesia
6.4. MOST IMPORTANT INFORMATION FOR LIVESTOCK
PRODUCERS
Participant’s where asked to list what indicators in their opinion are the most
important four pieces of information needed by livestock producers. In each
country market availability and price of livestock featured in the responses as
was feed related information. Disease information was listed in the two African
countries but not significantly in Indonesia.
Most regions in Tanzania had similar responses however the availability of
drugs was only mentioned in Mvomero and milk production was only listed in
Bagamoyo where it was the most common response. Indonesia had the greatest
variety in responses although still focussed throughout on pricing and feed.
Reproduction efficiency was only significantly mentioned in Botswana and six
of the nine pasture availability responses in this country where made during the
workshop in Ramotswa. Results by locations are shown in Figures 6.10-6.12.
42
Extension workers across the three countries displayed similar responses. These
concentrated on market availability (20), prices of livestock sold (18), breed
(13) and feed availability (13). Disease in Live Animals and Drugs Available
were each cited 12 times. The Head of Section category listed Prices of
Livestock as the most needed (20 responses), the Number of Animals (12),
Feed Availability (7) and ID of Animal Owner (4).
Figure 6.10. Most Important Indicators for Producers - Tanzania
Figure 6.11. Most Important Indicators for Producers – Botswana
43
Figure 6.12. Most Important Indicators for Producers – Indonesia
6.5. MOST IMPORTANT INFORMATION FOR LIVESTOCK
TRADERS
In each workshop a question asked participants to list the four most important
pieces of information needed by livestock traders. The responses were similar
for each country and concentrated on market availability, weight, the price of
livestock and disease in live animals. Responses in Tanzania included milk and
egg production but not animal movement which was listed in Botswana and
Indonesia. Infrastructure for animal husbandry was listed six times in Indonesia
but only once in either of the other countries and growth rate only appeared in
Botswana. Results are shown in Figures 6.13-6.15.
As in other questions for some occupations the number of responses was too
broad for the small number of people surveyed and it was not possible to
determine statistically relevant and credible conclusions. However, Extension
workers commonly listed Price of Livestock sold (36), Market Availability
(16), Number of Animals Sold (12) and Number of Animals (11) as what they
believed is needed by livestock traders. Heads of Section listed Number of
44
Animals (26) and Prices of Livestock (22) as the two common pieces of
information needed.
Figure 6.13. Most Important Indicators for Traders – Tanzania
Figure 6.14. Most Important Indicators for Traders – Botswana
45
Figure 6.15. Most Important Indicators for Traders – Indonesia
6.6. MOST IMPORTANT INFORMATION FOR
GOVERNMENT
In each workshop a question asked participants to list the four most important
pieces of information needed by their national government. In each case the
number of animals, prevalence of disease and infrastructure for animal
husbandry were considered key pieces of information needed by the
government. Carcass weight, Reproductive efficiency and animal deaths were
listed in Botswana and Indonesia but not in Tanzania where respondents listed
water, drug and pasture availability. Results are shown in Figures 6.16-6.18
Number of Animals was the highest listed by every occupation. Extension
workers cited it 31 times, Heads of Section 23 times, Statisticians 8 times, Staff
7 and Farmers 5. Diseases in Live Animals were the second most cited by
extension workers (25 times) and third for Heads of Section (5 times) behind
Infrastructure for animal husbandry.
46
Figure 6.16. Most Important Indicators for Government – Tanzania
Figure 6.17. Most Important Indicators for Government – Botswana
47
Figure 6.18. Most Important Indicators for Government – Indonesia
6.7. MOST IMPORTANT INFORMATION FOR THE
LIVESTOCK INDUSTRY
Participants were asked to list the four most important pieces of information
needed by the livestock industry as a whole. Milk production, market prices,
feed and disease information was considered valuable information in all three
countries. Tanzanian participants emphasised the number and quality of skins.
When asked about skins in Indonesia during discussion, participants felt that
these were a by-product and not important. Results are shown in Figures 6.19-
6.21.
Extension workers listed Milk Production 18 times, Number of Animals 15
times, Prices of Livestock 13 times, Number of Skins 11 times and Carcass
weight 7 times. Heads of Section cited Number of Animals 19 times, Feed
Availability 10 times and Prices of Livestock 6 times. Drugs Available was the
most common response for Farmers being cited 4 times.
48
Figure 6.19. Most Important Indicators for the Livestock Industry – Tanzania
Figure 6.20. Most Important Indicators for the Livestock Industry – Botswana
49
Figure 6.21. Most Important Indicators for the Livestock Industry – Indonesia
6.8. GAP IDENTIFICATION
In each country, workshop respondents were asked to identify and assess the
availability and quality of 6 pieces of livestock production and productivity data
that is important to them. The most frequently cited are listed below in Table
6.1.
Participants were initially asked whether the information is available to them in
their current professional role. The most commonly cited indicators had an
availability of over 85%, however Animal deaths and Prices of Livestock in the
Market were between 61% and 63%. Breed was listed 18 times but had an
availability of 39%, similarly Feed Intake and Growth Rate were only
availability to around 40% of those respondents who considered this
information of key importance.
For each indicator respondents were asked if they familiar with how the data
that they are using is collected and whether they are involved in the collection
of that information. Familiarity was generally between 50 - 70% with Animal
Treatments and Outcomes, Number of Skins, Milk Production and Number
50
Slaughtered being approximately 90%. Total consumption of livestock
products, Infrastructure for Animal Husbandry, Growth Rate and Drugs
Available were the least familiar with responses of 40% or less. On average
participants where involved with 34% of the data they deemed most important.
This score ranged from 83% of the six respondents who listed Animal treatment
outcomes through to 13% for ID of animal owner and price of feed.
Respondents scored each of their listed data against five criteria using a scale of
0 to 5 with 0 being not available or not useable through to 5 being perfect. The
criteria and how it was explained to participants was;
Relevance: How close is the data you currently to what you really need?
Accuracy and Reliability: How accurate and reliable is the information?
Timeliness and Punctuality: Is it available when you need it and is it up
to date?
Coherence and comparability: Can you understand it properly? Can it be
compared?
Accessibility and Clarity: How difficult is it to get? Is it the format you
want?
On average for all indicators in all countries scores were near the mid-point
with relevance being the highest scoring 3.37. Accuracy averaged 2.64,
Timeliness 2.41, Coherence 2.58 and Accessibility scored 2.59. For the most
cited indicators, quality problems are seen to be associated with punctuality,
comparability and accessibility. Particular relevance problems are identified for
Infrastructure for Animal Husbandry, Total consumption of livestock products
and number of skins. Identifiers with the lowest score for Accuracy were
Infrastructure for Animal Husbandry, Reproduction efficiency and Breed.
Timeliness and Punctuality was the lowest scored criterion with Reproduction
Efficiency averaging 1.85 from 45 respondents. Animal Deaths scored 2.0,
Breed 2.07 and both Carcass Weight and Infrastructure for Animal Husbandry
2.13. Infrastructure for Animal Husbandry also scored poorly for coherence at
2.25 as did Animal Deaths 2.19, Pasture Availability 2.16 and Reproduction
Efficiency 1.97. Growth rate was considered the least available information for
participants at 1.25. Liveweight averaged 1.77 despite receiving the highest
score for relevance (4.22)
51
Table 6.1. Livestock Data Quality Assessment - All Countries
Des
crip
tio
n
Res
po
nse
s
Av
aila
ble
%
Fam
ilia
r
%
Inv
olv
ed
%
Rel
evan
ce
Acc
ura
cy
Tim
elin
ess
Co
her
ence
Acc
essi
bil
ity
Y N Y N Y N
Number of animals 134 88 10 68 31 31 68 3.60 2.97 2.73 3.05 3.17
Diseases in live
animals 50 88 10 66 28 48 50 3.55 2.94 2.79 2.79 2.83
Reproduction
efficiency 45 49 49 69 20 27 64 3.23 2.23 1.85 1.97 1.86
Animal deaths 35 63 34 69 23 40 49 3.68 2.41 2.00 2.19 2.16
Prices of livestock
sold in market 33 61 33 55 45 24 76 3.12 2.45 2.45 2.48 2.67
Number slaughtered 32 88 13 91 9 63 34 3.88 3.69 3.28 3.38 3.78
Milk production 27 89 4 89 7 56 37 3.13 2.83 2.67 2.50 2.92
Animals treated and
outcomes 27 85 11 93 4 67 26 3.85 3.38 3.15 2.88 3.15
Feed availability 22 73 23 55 45 27 73 3.19 2.81 2.86 2.71 2.86
Liveweight 20 60 40 60 20 30 50 4.22 2.47 2.41 2.31 1.77
Disease control
measures 20 85 15 80 20 60 35 3.80 3.65 3.25 3.40 3.50
Number of animals
sold 19 68 32 74 11 37 47 3.81 3.13 2.56 2.63 3.00
Pasture availability 19 74 21 63 37 32 68 3.16 2.42 2.42 2.16 2.32
Breed 18 39 56 50 33 22 56 3.13 2.27 2.07 2.20 2.20
Carcass weight and
meat production 18 50 33 72 28 28 61 3.44 2.31 2.13 2.31 2.19
Number of eggs
produced/collected 16 75 19 63 31 31 63 3.00 2.47 2.40 2.33 2.67
ID of animal owner 15 93 7 67 33 13 87 3.67 3.13 2.93 3.07 2.93
Animal movement 15 67 33 67 33 20 80 3.33 2.73 2.33 2.60 2.67
Feed intake 12 42 42 58 17 58 17 4.00 2.75 2.38 2.75 2.50
Numbers of skins 9 78 0 89 11 67 22 2.88 2.63 2.88 2.63 2.75
Infrastructure for
animal husbandry 8 25 75 38 63 25 75 2.63 1.75 2.13 2.25 2.38
Price of feed 8 75 25 75 25 13 88 3.50 3.13 2.88 3.38 3.25
Animal treatment
outcomes 6 83 17 83 17 83 17 3.33 3.00 2.83 3.00 3.17
Total consumption of
livestock products of
each commodity
6 83 17 33 67 33 67 2.67 2.67 2.33 2.50 2.33
Growth rate 5 40 60 40 40 20 60 3.25 2.50 2.25 2.00 1.25
Drugs available 5 60 40 40 60 40 60 3.40 2.60 2.20 2.20 2.60
Market availability
of/for animals 5 80 20 60 20 40 60 4.00 3.40 2.80 3.00 3.20
52
When examining the most cited Indicators for workshops held in Tanzania
(Table 6.2) with the exception of Carcass weight and meat production the
availability of needed information was generally high. Similarly respondents
were very familiar the data they are using all scores being in the range of 87-
100%. Whether participants were Involved in data collection ranged from 20%
for feed availability through to 100% for pasture availability.
The average score for each criterion was slightly higher than the all countries
average with Relevance averaging 3.62, Accuracy 3.16, Timeliness 2.87,
Coherence 3.07 and Accessibility 3.28. Although Pasture Availability had the
highest average score (3.92) being ranked at 4 for timeliness and accuracy and
4.2 for relevance information about Feed availability was considered the worst
with scores below 3 for each criterion.
Table 6.2. Livestock Data Quality Assessment – Tanzania
Table 6.3 shows the data quality assessment for Botswana. As a whole
relevance averaged slightly higher (3.98) than the three country average while
each of the other criterion scored on average slightly lower with accuracy at
2.55, timeliness 2.07, coherence 2.32 and accessibility 2.25.
Reproduction efficiency was the most cited response and was listed 34 times.
While this indicator is considered very high for relevance at 4.04 it scored very
poorly for timeliness 1.84 and below average for coherence 2.13 and
accessibility 2.17. Similarly information about animal deaths and diseases in
Des
crip
tio
n
Res
po
nse
s
Av
aila
ble
%
Fam
ilia
r %
Inv
olv
ed %
Rel
evan
ce
Acc
ura
cy
Tim
elin
ess
Co
her
ence
Acc
essi
bil
ity
Y N Y N Y N
Number of animals 30 90 3 87 7 47 50 3.83 3.17 2.90 3.24 3.38
Animals treated and outcomes 19 89 5 95 5 68 26 3.56 3.06 3.17 3.33 3.17
Milk production 18 89 0 89 6 67 28 2.85 3.15 2.92 3.00 3.31
Diseases in live animals 17 94 0 88 0 71 29 2.93 3.00 3.07 2.86 2.86
Number slaughtered 11 100 0 100 0 73 18 3.75 3.38 3.00 3.63 3.75
Feed availability 10 80 10 70 30 20 80 2.63 2.50 2.88 2.88 2.75
Prices of livestock sold in market 9 78 11 100 0 44 56 3.29 3.00 2.57 2.71 2.71
Numbers of skins 9 78 0 89 11 67 22 3.43 3.00 3.00 2.71 2.86
Number of eggs produced/collected 8 88 0 88 0 50 38 3.00 3.00 2.20 2.40 2.80
Disease control measures 6 100 0 100 0 67 17 3.67 3.00 3.00 2.67 3.33
Carcass weight and meat production 6 33 17 100 0 33 67 3.00 2.50 2.50 2.50 2.25
Pasture availability 5 80 0 100 0 100 0 4.20 4.00 4.00 3.60 3.80
53
live animals scored very low (below 2) for timeliness, coherence and
accessibility. Prices of livestock sold in the market was considered inaccessible
(rating 1) by the 7 respondents. Alternatively the 5 participants interested in
information on Milk Production scored each criterion at the maximum of 5 with
the exception of accessibility.
Table 6.3. Livestock Data Quality Assessment – Botswana
Des
crip
tio
n
Res
po
nse
s
Av
aila
ble
%
Fam
ilia
r %
Inv
olv
ed %
Rel
evan
ce
Acc
ura
cy
Tim
elin
ess
Co
her
ence
Acc
essi
bil
ity
Y N Y N Y N
Reproduction efficiency 34 47 50 74 12 32 56 4.04 2.46 1.84 2.13 2.17
Number of animals 26 92 8 85 15 38 62 4.14 2.85 2.36 2.57 3.00
Animal deaths 25 72 24 68 20 44 40 3.09 2.09 1.55 1.91 1.82
Liveweight 20 60 40 60 20 30 50 3.70 2.40 1.90 2.40 2.00
Number of animals sold 14 64 36 71 7 21 57 3.80 2.20 2.00 2.00 2.00
Diseases in live animals 12 67 33 58 33 42 50 3.83 2.33 1.83 1.33 1.33
Feed intake 10 40 40 70 0 70 0 3.75 3.00 2.75 3.00 3.50
Breed 8 13 88 50 13 13 50 3.00 3.00 2.00 3.50 4.50
Prices of livestock sold
in market 7 57 29 0 100 0 100 4.00 2.50 2.00 2.00 1.00
Animals treated and
outcomes 7 86 14 86 0 57 29 4.50 2.50 2.25 2.00 2.00
Milk production 5 100 0 100 0 60 20 5.00 5.00 5.00 5.00 3.00
The area of Kab Sumbawa has a government controlled animal identification
system that operated throughout the 24 sub districts of the region. This is a card
based system used for animal identification (based on hair) of cattle, buffalo
and horses and records. Regulations and controls are in place so that only
recorded animals can be sold or slaughtered etc. The process of recording the
information about the animal is also used for education and dissemination of
information. Overall this provides staff of this area with a large amount of
livestock information. Examination of the data quality assessment results for
this area was found to be significantly different to the rest of Indonesia and
affected the average ratings for the rest of the country. Table 6-4 shows the
results of the quality assessment for Indonesia without the Sumbawa region.
The Sumbawa region is shown separately in Table 6.5.
For the country as a whole relevance was slightly below the three country
average at 3.19 while the averages for the other criterion were all very slightly
54
above average with Accuracy 2.72, Timeliness 2.65, Coherence 2.85 and
Accessibility 2.92.
Although the averages where slightly high, the deviation was reasonably low
and no indicators scored particularly well with the highest score for any criteria
of the most cited indicators was 3.6 for the relevance of the prices of livestock
sold in the market. At the other end of the spectrum, Carcass weight and meat
production was the lowest scored and received average ratings rated below 2 for
accuracy, timeliness, coherence and accessibility. Animal Deaths also did not
score well for these same criteria.
Table 6.4. Livestock Data Quality Assessment - Indonesia (without Sumbawa)
Des
crip
tio
n
Res
po
nse
s
Av
aila
ble
%
Fam
ilia
r %
Inv
olv
ed %
Rel
evan
ce
Acc
ura
cy
Tim
elin
ess
Co
her
ence
Acc
essi
bil
ity
Y N Y N Y N
Number of animals 68 90 10 53 47 19 81 3.21 2.54 2.43 2.85 2.90
Diseases in live
animals 19 100 0 53 47 32 68 3.13 2.53 2.20 2.73 2.67
Number
slaughtered 12 83 17 92 8 42 58 3.20 2.90 2.60 2.80 3.20
Prices of livestock
sold in market 13 46 54 54 46 15 85 3.60 2.20 2.10 2.40 2.60
ID of animal owner 13 92 8 62 38 8 92 3.11 3.11 2.89 3.22 3.22
Disease control
measures 6 67 33 67 33 33 67 3.33 2.67 3.00 3.00 3.00
Pasture availability 12 83 17 50 50 0 100 2.92 2.50 2.50 2.67 2.75
Reproduction
efficiency 9 44 56 44 56 0 100 2.25 2.50 2.13 2.38 2.38
Feed availability 8 75 25 38 63 25 75 2.63 2.13 2.00 2.00 2.13
Animal movement 10 60 40 50 50 0 100 2.13 2.13 2.13 2.38 2.38
Animal deaths 7 29 71 57 43 14 86 3.00 2.14 2.00 1.86 2.14
Breed 7 57 43 29 71 14 86 2.86 2.57 2.00 2.43 2.29
Price of feed 7 71 29 71 29 14 86 2.60 2.40 2.40 2.60 2.60
Number of eggs
produced/collected 7 57 43 29 71 0 100 2.33 2.17 1.83 2.00 2.00
Carcass weight and
meat production 6 50 50 50 50 0 100 2.80 1.80 1.60 1.80 1.80
Infrastructure for
animal husbandry 6 0 100 17 83 17 83 2.17 2.17 2.17 2.17 2.50
55
The response from Sumbawa has been listed In Table 6-5 including indicators
with only a single response to show that most criteria were rated by participants
at 4 or above. The worst scored indicator for the area was prices of livestock in
the market which averaged 3.95 and would be considered high in other area.
Reproduction Efficiency was rated to have an average of 4.4.and while cited by
only 2 participants this indicator has been one of the lowest scored in most
other locations.
Table 6.5. Livestock Data Quality Assessment – Sumbawa
Des
crip
tio
n
Res
po
nse
s
Av
aila
ble
%
Fam
ilia
r %
Inv
olv
ed %
Rel
evan
ce
Acc
ura
cy
Tim
elin
ess
Co
her
ence
Acc
essi
bil
ity
Y N Y N Y N
Number of animals 10 60 40 70 30 50 50 4.70 4.80 3.80 4.30 4.50
Diseases in live
animals 2 50 50 50 50 50 50 4.50 4.00 3.50 4.00 4.50
Number
slaughtered 6 67 33 67 33 67 33 4.80 4.80 4.00 4.60 4.80
Prices of livestock
sold in market 4 75 25 50 50 50 50 4.50 3.75 3.75 3.75 4.00
ID of animal owner 2 100 0 100 0 50 50 4.00 4.00 4.00 4.00 4.00
Disease control
measures 7 86 14 71 29 71 29 4.50 4.00 3.50 3.75 4.25
Reproduction
efficiency 2 100 0 100 0 50 50 5.00 4.00 4.00 4.00 5.00
Feed availability 3 67 33 67 33 67 33 4.33 4.33 4.33 4.33 4.33
Animal movement 1 100 0 100 0 0 100 5.00 5.00 4.00 4.00 4.00
Animal deaths 1 100 0 100 0 100 0 5.00 5.00 5.00 5.00 5.00
Price of feed 1 100 0 100 0 0 100 4.00 4.00 4.00 4.00 4.00
Labour 3 67 33 67 33 67 33 4.00 4.00 4.00 4.00 4.00
Drugs available 1 100 0 100 0 100 0 5.00 5.00 3.00 5.00 5.00
Feed intake 1 100 0 0 100 0 100 3.00 5.00 4.00 4.00 4.00
Animal treatment
outcomes 1 100 0 100 0 100 0 5.00 5.00 3.00 4.00 5.00
Milk processed
(amount/quality) 1 100 0 0 100 0 100 4.00 5.00 4.00 4.00 4.00
56
6.9. SUGGESTIONS TO IMPROVE DATA QUALITY,
COLLECTION AND INTEGRATION
For each of the indicators rated for availability and quality, respondents
provided substantial information about suggested changes. Taking the indicator
‘Number of Animals’ as an example, a number of suggestions are made for
quality improvement such as providing farmer training on (and presumably
encouragement toward) record keeping; improved training of data collectors; a
registry book for farms (in some respects duplicating the first answer); and
enhanced frequency and uniformity of counting animals.
Suggestions to improve collection again focused on training of both farmers
and enumerators, farm-level recording (in “registry books”) and enhanced
facilitation by way of IT and infrastructure. A very wide variety of indicators
were proposed for integration with measurement of animal numbers, including
technical performance measures, prices, and details of the production system
such as breed, environment and feed.
Across a range of indicators, common themes include a proposed greater role
for farmers in data collection which is associated with on-farm recording, a
greater role for IT, and improvements in infrastructure. Institutional elements
such as enhanced communication between branches of government are
frequently advocated across all the key indicators. Suggestions for integration
of indicators with other data were also consistent in both input and output
indicators were listed, particularly those affecting feed and key cost items. This
is to say that improved measurement of efficiency and productivity are clear
goals of workshop participants.
Suggestions for the most common indicators are shown in Table 6.6 with the
total number of responses provided for each indicator and each suggestion.
57
Table 6.6. Suggestions for Most Common Indicators
Suggestion
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Qua
lity
Colle
ctio
n
Improved training of data collectors 29 16 1 3 2 1 2 1 0 2 1 5 1 0 2 3 2 1 0 0 2 3 2 1 3 0 1 0 3 1 4 4 55 41
Frequent counting of livestock 22 9 2 2 1 0 0 0 0 0 0 0 0 0 2 3 2 1 1 1 3 0 0 0 1 1 1 0 2 1 2 0 39 18
Use of improved IT 16 19 2 2 0 0 3 2 0 3 1 0 0 2 3 6 1 3 0 5 3 6 2 0 1 0 1 4 2 0 1 3 36 55
Farmer training on record keeping, including sales records 15 6 12 9 1 0 1 0 11 3 6 4 4 1 7 3 1 0 1 0 3 2 1 2 2 1 1 0 1 1 1 1 68 33
Facilitate Transport and equipment 11 4 2 0 1 1 2 0 1 1 2 0 1 0 7 2 0 0 0 0 3 3 2 1 4 1 0 1 0 0 0 3 36 17
More regular and structured collection 10 3 0 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 3 0 1 0 0 0 0 0 1 0 1 1 0 0 19 6
Co-ordination between farmers and collectors of data 9 11 2 0 2 1 2 1 0 1 0 0 0 0 4 2 1 3 4 1 2 3 0 1 1 3 1 1 2 3 2 0 32 31
Registry book on farms and unique farmer identifier 7 4 0 0 0 0 0 0 4 0 0 1 1 0 1 1 1 0 0 0 5 0 4 3 1 2 0 0 0 0 0 0 24 11
Uniform reporting across districts 6 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 1 11 2
Animal Identification and Tracing 5 3 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 8 5
Quality Control of data 4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 8 0
Make collection easier (transport and infrastructure) 2 2 0 1 0 0 0 0 0 2 2 0 0 0 2 1 0 1 0 1 1 2 1 1 0 0 1 0 0 1 0 1 9 13
Co-ordination between local govt and data collectors 2 2 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 2 0 1 0 0 0 0 0 0 1 0 5 9
Market Reporting 1 1 1 0 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 2 1 0 0 2 1 0 0 10 4
Improved training of LS marketing officers 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 3 2
Education on grazing management 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1
Education on improved breeds 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
Better reporting and more access to reports 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 1 1 0 0 0 2 0 1 0 2 1 13 2
Use of data from other departments 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0
Improving work environment, motivation of data collectors 0 1 0 0 1 1 1 0 0 0 1 0 0 0 2 1 0 0 0 0 1 0 1 0 0 0 0 0 0 2 0 0 7 5
Data to be collected from slaughter slab and abattoir 0 1 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 10 1 16 3
Information and reporting on diseases and treatments 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 5 1 1 2 0 0 0 0 0 0 0 0 7 3
Timetable or better organisation for dipping and spraying 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 2 2
Success of animal health treatments 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 2 1
Special forms (for collection of data on feeds, milk etc.) 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 4 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 5 3
Prices to be recorded by kg 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 2
Data at different stages of milk production 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Regular surveys of grazing land 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 3
Meat inspection data 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2
Identification on cause of death 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Total Responses for Indicator 144 82 25 18 11 6 13 5 21 16 13 11 12 6 36 24 16 13 13 11 38 23 18 13 18 11 10 6 16 13 23 18
Ani
mal
mov
emen
t
Pric
es o
f
lives
tock
sol
d
in m
arke
t
Num
ber
slau
ghte
red
Ani
mal
dea
ths
Tota
l
Res
pons
es f
or
Sugg
esti
on
Rep
rodu
ctio
n
effi
cien
cy
Feed
avai
labi
lity
Past
ure
avai
labi
lity
Dis
ease
s in
live
anim
als
Ani
mal
s
trea
ted
and
outc
omes
Dis
ease
cont
rol
mea
sure
s
Num
ber
of
anim
als
Bre
ed
ID o
f an
imal
own
er
Milk
prod
ucti
on
Live
we
ight
Carc
ase
we
ight
and
mea
t
58
6.10. SUGGESTIONS FOR TESTS OF NEW INDICATORS,
METHODS AND EQUIPMENT
6.10.1. NEW INDICATORS
Workshop participants advocated a very wide range of indicators and variables
for use in testing new data collection methods. Some indicators such as number
of animals, disease in animals and liveweight where well supported but over
50% of the proposed indicators were advocated by just one or two participants.
The most common (3 or more advocates) are shown in Figure 6.22.
Figure 6.22. Suggestions for tests of new indicators
0 20 40 60 80 100
ID of animal owner
Grades of livestock and carcases…
Feed composition
Disease control measures
Drugs available
Feed crops grown
Growth rate
Rangeland Quality
Sex of animal
Number slaughtered
Breed
Feed availability
Milk processed (amount/quality)
Reproduction efficiency
Liveweight
Number of animals
All
Tanzania
Botswana
Indonesia
59
6.10.2. NEW DATA COLLECTION METHODS
As for the indicators advocated, suggested new collection methods varied
between countries. There is support in each country for survey methods and
interviews, and for methods specific to particular contexts. Roles are also seen
for existing activities such as post-mortem inspection, and new ones such as
self-reporting by farmers. Despite the enthusiasm for measurement of inputs
(such as feeds), few methods are proposed to achieve this besides rangeland
assessment. Alternatives to labour intensive methods (such as weighing) are
proposed (such as condition scoring). The most commonly suggested methods
are shown in Figure 6.23.
Figure 6.23.Suggestions for testing of new data collection methods
0 20 40 60 80 100 120
Workshops
Alcohol Test
Faecal Sample
Post Mortem Autopsy
Meat Inspection
Checklists
Strip Cup Test
Cooperate with University
Communication and coordination…
Involve Head of village or head of…
Rangeland Assessment
Self Reporting
Condition Scoring
Do Census
Direct Visit to the Farmers
Specific Forms
Livestock ID/LITS
Blood test
Weighing (of animals/products)
Dedicated data collection officers
Personal Observation by Field…
Interview
Surveys
All
Tanzania
Botswana
Indonesia
60
6.10.3. NEW EQUIPMENT
Participants were asked to suggest new equipment that they feel should be
tested to improve livestock productivity and production data or would be
required for the methods they suggested in the previous question. Twenty eight
different equipment items were suggested ranging from the more numerous
suggestions of scales, stationary and access to transport through to a singular
suggestion for storage tanks. The results are shown figure 6.24.
Figure 6.24.Suggestions for new equipment to be tested or equipment required for
suggested methods
0 20 40
Storage Tanks
Protective gear
Ultrasound
Remote Sensing
Strip Cup
Audio Master Quality…
Branding Iron
Themometer
Measuring Cylinders
Tape Measures
Milking Machine
Camera
Syringes
Stock Cards
Feed Sampling Tools
Lactometer
Database
Chemical & Reagents
LIVESTOCK ID (Ear Tags, CHIP)
Record Books
Microscope
Computers
GPS
Slides
Mobile Electronics
Transport
Stationary
Scales
All
Tanzania
Botswana
Indonesia
61
7 Conclusions
7.1. PARTICIPANTS
This GAP analysis is a consultative procedure seeking stakeholder views on
their livestock data needs, perceptions of others’ needs, and assessment of
quality gaps between the current and an ideal system. Based on conclusions
drawn from a review of literature on the subject of livestock data in developing
countries, a questionnaire was drawn up and administered at 14 workshops in
Tanzania, Botswana and Indonesia. In total 171 livestock data stakeholders
participated in the GAP analysis in some 4-6 locations in each country. The
stakeholders were primarily employees of Ministries of Agriculture and
livestock, livestock extension workers and employees of local government.
Results presented here feature the contributions of small numbers of
participants across large numbers of topics. This means that statistical
interpretation of results is generally limited, but nonetheless there are clear
tendencies toward agreement and disagreement on the main subjects of degree
of satisfaction with exciting livestock data, the importance attached to it, and
the rating of gaps in terms of quality when using FAO-nominated quality
criteria.
7.2. PERCEPTION OF THE IMPORTANCE OF LIVESTOCK
INDICATORS
Workshop participants’ perception of livestock productivity and production
indicators is broad in scope. Workshop participants nominated a wide range of
indicators as being the most important for livestock production and productivity
that they used in their work. Nominated indicators spanned productivity and
production, as well as numbers of animals and income-related indicators.
Within each country the results where relatively homogenous, but between
countries the views of importance differs strongly. When the perceptions of
importance were examined across employment categories, patterns were
62
difficult to identify but productivity measures and reproductive efficiency were
widely recognised.
Commentary on existing measurement of livestock production and productivity
was consistent across countries. There were widespread calls for improved
transport, communications and infrastructure to assist these processes. Notably,
however, training in on-farm record-keeping was advocated for farmers, and
this indicates that farmers are perceived to be responsible for some aspects of
collection of livestock data. Botswana’s participants emphasised the
importance of information on grazing, and on meat quality. Although responses
in this regard were somewhat homogeneous between and within countries, an
exception was Kab Sumbawa in Indonesia where advocacy mostly concerned
market reporting because of the animal identification programme in place there.
When commentary on existing measurement of livestock production and
productivity indicators is examined by professional category, extension workers
tended to comment on education, and interactions between themselves and
farmers: and this is where the strongest call for farmer involvement in record
keeping was made. In particular, communication with farmers on the details of
high quality data collection was advocated.
When asked to nominate the most important pieces of livestock information for
their own organisations, answers centred on the number of animals, as well as
disease and aspects of its control. Across the entire sample, feed availability
and intake also featured. Across occupations, there was little variation and
Number of Animals, Disease in live animals, Milk Production and
Reproduction efficiency dominated considerations. Participating farmers led
with Feed availability, Water availability and Disease in Live Animals.
Notably, participants later gave their opinions on the most important indicators
for livestock producers, and market availability, the price of livestock and feed
information were the most popularly cited. Disease information was listed in
the two African countries but was not significant in Indonesia. Participants’
views of livestock traders’ livestock data needs centred on market availability,
weight, prices and disease in live animals.
Views on the most important pieces of information needed by national
governments were somewhat consistent across countries but differed in ways
explicable in terms of countries’ likely priorities. For all countries, indicators
listed included number of animals, prevalence of disease and infrastructure for
animal husbandry. Botswana favoured carcass weight, reproductive efficiency
and animal deaths while in Tanzania water, veterinary drugs and pasture
63
availability were nominated. The Number of Animals was the most frequently
listed by every occupation.
Participants were asked to list the four most important pieces of information
needed by the livestock industry as a whole. Milk production, market prices,
feed and disease information was considered valuable information in all three
countries. Tanzanian participants emphasised the number and quality of skins
but when discussed during the workshops in Indonesia, participants felt that
these were a by-product and not important.
Assessment of the importance of indicators for extension workers evoked
nomination of a wide range of indicators including Milk Production, Number of
Animals, Prices of Livestock, Number of Skins produced and feed availability.
Farmers present nominated the availability of veterinary drugs.
7.3. IDENTIFICATION AND RATINGS OF GAPS
The two livestock production and productivity indicators that were identified as
most important by most of the workshop participants were numbers of animals
and disease information. These were identified as being “available” by some
88% of participants in each case. Surprisingly, the claimed availability of some
important indicators was reported as being available by rather few: animal
deaths at just 35% and reproductive efficiency by 45% of participants for
example. Feed Intake and Growth Rate were only availability to around 40% of
those respondents who considered this information of key importance.
Familiarity with data was reported at around 50 - 70% of participants with
exceptions being Milk Production and the Number Slaughtered at around 90%.
Total consumption of livestock products, Infrastructure for Animal Husbandry,
Growth Rate and Drugs Available were the least familiar with responses of
40% or less. It should be noted that on average, participants were involved with
34% of the data they deemed most important.
Using a scale of 0 to 5 for each of five criterions the average scores for all
indicators and all indicators were near the mid-point with relevance being the
highest scoring 3.37. Accuracy averaged 2.64, Timeliness 2.41, Coherence 2.58
and Accessibility scored 2.59. For the most cited indicators, quality problems
are seen to be associated with punctuality, comparability and accessibility.
Particular relevance problems are identified for Infrastructure for Animal
Husbandry, Total consumption of livestock products and number of skins.
Identifiers with the lowest score for Accuracy were Infrastructure for Animal
Husbandry, Reproduction efficiency and Breed.
64
Across all indicators, Timeliness and Punctuality was the lowest-scored
criterion with that attribute of Reproduction Efficiency indicators averaging just
1.85 from 45 respondents. Animal Deaths scored 2.0, Breed 2.07 and both
Carcass Weight and Infrastructure for Animal Husbandry 2.13. Infrastructure
for Animal Husbandry also scored poorly for coherence at 2.25 as did Animal
Deaths 2.19, Pasture Availability 2.16 and Reproduction Efficiency 1.97.
Growth rate was considered the least available information for participants at
1.25. Liveweight averaged 1.77 for coherence despite receiving the highest
score for relevance (4.22).
For Tanzania, with the exception of Carcass weight and meat production the
availability of needed information was generally high. Similarly respondents
were very familiar the data they are using all scores being in the range of 87-
100%. Involvement in data collection varied: 20% for feed availability through
to 100% for pasture availability. The average score for each criterion was
slightly higher than the all countries average with Relevance averaging 3.62,
Accuracy 3.16, Timeliness 2.87, Coherence 3.07 and Accessibility 3.28.
Although Pasture Availability had the highest average score (3.92) being
ranked at 4 for timeliness and accuracy and 4.2 for relevance, information about
Feed availability was considered the worst with scores below 3 on all criteria.
As a whole, Botswana’s data relevance averaged slightly higher (3.98) than the
three country average while each of the other criterion scored on average
slightly lower. Reproduction efficiency was the most cited response and was
listed 34 times. While this indicator is rated very high for relevance at 4.04 it
scored very poorly for timeliness 1.84 and below average for coherence 2.13
and accessibility 2.17. Similarly information about animal deaths and diseases
in live animals scored very low (below 2) for timeliness, coherence and
accessibility. Prices of livestock sold in the market was considered inaccessible
(rating 1) by the 7 respondents citing this indicator as important.
Indonesian assessments of relevance were slightly below the three country
average at 3.19 while the averages for the other criterion were all slightly above
with Accuracy at 2.72, Timeliness 2.65, Coherence 2.85 and Accessibility 2.92.
Variance around these values was low and no indicators were seen to score
particularly well with the highest score for any criteria of the most cited
indicators was 3.6 for the relevance of the prices of livestock sold in the market.
At the other end of the spectrum, Carcass weight and meat production was the
lowest scored and received average ratings rated below 2 for accuracy,
timeliness, coherence and accessibility. Animal Deaths also did not score well
for these same criteria. The area of Kab Sumbawa stood out in the Indonesian
65
data, due to the government-controlled animal identification system that
operates throughout the 24 sub districts of the region. Widespread enthusiasm
for this system was apparent in the data with very high scores recorded.
7.4. SUGGESTIONS TO IMPROVE DATA QUALITY AND
COLLECTION METHODS
Workshop participants provided substantial information and suggestions to
improve both the quality of the data collected and collection methods for
various indicators. These suggestions centred on providing farmer training on
improved record keeping incorporating systems such as record books and
journals, improving the training and number of data collectors and increasing
the frequency and uniformity of counting animals.
Facilitation of transport, IT and other infrastructure were also commonly cited
and Institutional elements such as enhanced communication between branches
of government are frequently advocated across all the key indicators.
Suggestions for integration of indicators with other data were also consistent in
both input and output indicators were listed, particularly those affecting feed
and key cost items. This is to say that improved measurement of efficiency and
productivity are clear goals of workshop participants.
7.5. SUGGESTIONS FOR TESTS OF INDICATORS,
METHODS AND EQUIPMENT
Workshop participants advocated a very wide range of indicators that they
would interested in or suggest as test cases for the later stages of the project.
The most popular were number of animals (particularly in Indonesia), disease in
animals and liveweight as well as prices of livestock sold in the market and
reproduction efficiency. Collection methods suggested for either trial or to
improve data quality concentrated on survey methods, interviews and personal
observation. Roles are also seen for existing activities such as post-mortem
inspection, and new ones such as self-reporting by farmers. Despite the
enthusiasm for measurement of inputs through the earlier questions of the
workshop few methods were proposed to achieve this besides rangeland
assessment.
When provided the opportunity to suggest equipment needed or be trialled to
improve livestock productivity and production data participants gave a diverse
response with 28 suggestions. Most commonly these were for scales, stationary
66
(improved record keeping) and access to transport. After these three responses
suggestions concentrated on IT and general technological items such as
computers, GPS and mobile devices. Livestock specific equipment generally
rated between 1 and 5 response for all countries.
7.6. PROPOSED TESTS IN EACH COUNTRY
Based on proposals and discussions arising from the circulation of the draft
GAP Analysis Report in April 2015, candidates for new collection
methodologies have been identified and tests outlined (see Table 7-1). At the
time of writing, and prior to the expert meeting (scheduled for July 2015 in
Accra, Ghana) the list is still considered preliminary, and it is recognised that
just 1-2 tests will be conducted in each country.
67
Table 7.1. Proposed tests
Product, indicator Existing method (E) Gold Standard (GS) Alternative (A) Sampling Countries, notes
Milk
Production per head per lactation Survey, recall
Measure milk production
every day for 3 months
Measure one day’s milk
production and fit to
lactation curve
3 cows on farm (L, M, H
productivity)
* 20 farms @ 2 locations
= 120 cows on 40 farms
Tanzania
E+GS+A all on the same farms
Eggs
Indigenous production per clutch Survey, recall
Count eggs laid per day for 3
months
Count hens every day
Count hens just once
Count eggs laid just once
Farmer reports length of
clutch period
20 farms @ 2 locations
= 40 farms Tanzania, Botswana
E+GS+A all on same farms
Poultry
Chicken numbers
Growth rates
Weight at sale
Survey, recall (numbers
only)
Weights, growth rates not
measured
Weigh twice approx. 3 weeks
apart
Weigh at sale
Counting every week (using
diary)
Farmer diary on numbers
Farmer weighs chickens
twice approx. 3 weeks apart
Farmer weighs chickens at
sale
20 farms
+ 20 farms @ 2 locations
= 60 farms
Tanzania, Botswana
20 farms for GS + E
40 farms for A + E
Beef cattle
Herd profile and numbers
Growth rates
Weight at sale
Sales channel
Survey, recall (numbers
and sales channels only)
Weights, growth rates not
measured
Weigh twice approx. 2
months apart (scales and
brisket tape)
Weigh at sale (scales and
brisket tape)
Counting every month on all
inward/outward movements
(using diary)
Uses animal ID system in
Sombuara in NTB province)
Farmer uses diary to record
animal numbers
Farmer uses brisket tape to
estimate weight twice
approx. 2 months apart
Farmer uses brisket tape at
sale
20 farms
+ 20 farms @ 3 locations
= 80 farms
Indonesia
20 farms for GS + E (Sombuara)
60 farms for A + E
(NTT, SS and EJ provinces)
Sheep and goats
Herd profile and numbers
Growth rates
Weight at sale
Sales channel
Survey, recall (numbers
and sales channels only)
Weights, growth rates not
measured
Weigh twice approx. 2
months apart (scales)
Weigh at sale (scales)
Counting every month on all
inward/outward movements
(using diary)
Farmer uses diary to record
animal numbers and sales
channel
Farmer weighs using scales
twice approx. 2 months apart
Farmer uses scales to weigh
at sale
20 farms
+ 20 farms @ 2 locations
= 60 farms
Indonesia (goats only),
Tanzania, Botswana
20 farms for GS + E
40 farms for A + E
Feed
surplus or deficit
Feed supply measured by
areas planted + access to
pasture
Feed requirement not
measured
Monthly estimate of animal numbers disaggregated by
physiological state
Calculation from extraneous data on monthly feed
requirements
Calculation of feed supply based on extraneous data on
monthly feed supply and availability
20 farms @ 2 locations
= 40 farms
Indonesia, Tanzania, Botswana
40 farms for GS + E
(no explicit A)
68
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73
GAP ANALYSIS WORKSHOP SURVEY
DATE
BACKGROUND
1
2
33.1
YES/NO
0.2LOCATION 0.1 DETAILS 0.3
3.1
3.2
3.3
3.4
3.5
Do you have any general
comments about the
measurement of livestock
production and productivity
if YES, which are the most
important indicators of
production and productivity
that you use?
Do you use information on
livestock production or
productivity?
Which organisation do you
work for?
What is your job title?1.1
2.1
Appendix A: Workshop Forms
74
IMPORTANCE What are the four most important pieces of information about livestock needed for:
4YOUR
ORGANISATION
5THE LIVESTOCK
INDUSTRY
6THE LIVESTOCK
TRADERS
7THE LIVESTOCK
PRODUCERS
8 THE GOVERNMENT
8.1
8.2
8.3
8.4
5.1
5.2
5.3
5.4
7.1
7.2
7.3
7.4
4.1
4.2
4.3
4.4
6.1
6.2
6.3
6.4
75
QUALITY RATING Drawing on the discussion so far, let's focus on 6 pieces of information about livestock that you consider the most important overall:.1 .2 .3 .4 .5 .6 .7 .8 .9 .10 .11 .12
SIX pieces of information that
YOU need:
Is it
availabl
e to
you?
What is/are
the sources
of this
information?
Are you
familiar
with how
this data
is
collected?
Are you
involved
in
collecting
this
data?
Rele
vanc
e
Accu
racy
and
relia
bilit
y
Tim
elin
ess a
nd
punc
tual
ity
Cohe
renc
e an
d
com
para
bilit
y
Acce
ssib
ility
and
clarit
y
What do you suggest to help
improve quality of the data
delivered?
What do you suggest to help
improve collection methods
or procedures?
Which data would you like to
see this information combined
or integrated with?
9.1 YES/NO YES/NO YES/NO
9.2 YES/NO YES/NO YES/NO
9.3 YES/NO YES/NO YES/NO
9.4 YES/NO YES/NO YES/NO
9.5 YES/NO YES/NO YES/NO
9.6 YES/NO YES/NO YES/NO
QUALITY CRITERIA 0 = not at all, 1 =
very poor; 2 = poor, 3 = average, 4
= good, 5 = excellent
76
INFORMATION TO BE COLLECTED AS PART OF A TEST METHODS FOR COLLECTION TO BE TESTED EQUIPMENT TO BE TESTED10 10.2 10.3
FUTURE: This project has resources to test new methods of measuring
and collecting data about livestock production and productivity.
Please write anything here that provides advice or an opinion about
which information or which collection methods should be the subject of
those tests, or any pieces of measurement or collection equipment
which could be tested:
