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GCP/GLO/208/BMG - CountrySTAT for Sub-
Saharan Africa
FIRST PANORAMA REPORT
TANZANIA
Onasimbo A. Ntikha (Mphil)
National Food Security Division, Ministry of Agriculture
Food Security and Cooperatives
Consultant in National Information System and Agricultural Statistics
Dar es Salaam 2009
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TABLE OF CONTENTS
TABLE OF CONTENTS …………………………………………...……….…………………… 2 LIST OF ACRONYMS …………………………………………………………………………….. 4 0.0 Overview …………………………………………..……………………….………………. 6 1. The National Statistics System………………………………………………………… 6
1.1 Legal framework and Statistical Advisory Bodies ………………. 6 1.2 Structure of the National Statistics System ……………………….. 6 1.3 National Strategy for Development of Statistics (NSDS) …… 7
2.0 Reference Situation for Agricultural Statistics System ………………… 7 2.1 Legal Framework and Food and Agriculture Statistical Advisory Bodies ………………………………………………………………………….. 7 2.2 Structure of the Food and Agriculture Statistics System ….. 7 2.3 National Strategy for Food and Agriculture Statistics ……….. 9 2.4 Human Resources available ……………………………………………….. 9 2.5 Non-Human Resources available ……………………………………….. 9 2.6 Data Dissemination Policy for Food and Agriculture Statistics ….10
2.7 Modalities of promoting User-Producer Dialogue ……………. 10 2.8 Existing Databases and Data Dissemination Tools and Platforms ….10 2.9 Regional Integration and International Technical Assistance received …………………………………………………………………………………… 11
� An evaluation of available agricultural and food information in Tanzania: … 11
� Results and products of the information system and agricultural statistics…12
3. Outputs, Data Sources, and Metadata of the Food and Agriculture Statistics …………………………………………………………………………………………… 12
3.1 MAFC/NBS Annual Publications ………………………………………. 12 3.2-4 Crop, Fishery, Forestry, Water Resources, Land use (ha. cropland; irrigated cropland - ha., ownership, legal tenured; share of agriculture in urban/peri-urban/rural area) and Livestock statistics ………………………………………………………… 13 3.5 Consumption, Trade, and other relevant Indicators for the Agriculture Sector …………………………………………………………………… 14
� Evaluation of results and products of the information system and
agricultural statistics. ……………………………………………………………… 14 3.6 Limitations of the available Food and Agriculture Statistics … 15
3.7 Food availability for human consumption, External Trade, Population (Total/male/female/rural/urban/agricultural/non agricultural) including farm household and rural household and other relevant indicators for the agriculture sector such as: ….….….….….….….….….….….….….….….….….….….…. 15
FS/N Information Systems ….…….….…….…….…….…….……. 16
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3.8. Description of national/ sub-national commodities codes system 18
3.9 National classification/nomenclatures and links to international classifications (if existing) ………………………………… 22 3.10. Consumption, Trade and other relevant Indicators for the Agriculture sector …………………………………………………………………… 22
� 3.11. Limitations of the available Food and Agriculture Statistics 23
� Assessment of needs on agricultural statistical data ………… 24 4. Overview of User Needs for Food and Agriculture Statistics in Tanzania ... 26
4.1. The Demand for Agricultural Statistics ………………… 26
4.2. Public Sector Decision Makers ……………………………… 28
4.3. Private Sector Decision Makers ……………………………… 28 5. Expectations from CountrySTAT and Synergies with on-going Initiatives in Tanzania ……………………………………………………………………… 29 6. Important Factors for the Success of the CountrySTAT project in Tanzania ………………………………………………………………………………………… 31 7. Summary and OUR Expectations ………………………………………………… 32
List of Tables: Table 3.8.1: Crop Groups, Names, Tanzanian and FAO Codes ………………… 19
Table 3.8.2: Livestock Groups, Names, Tanzanian and FAO Codes …………… 22
List of Annexes:
Annex 1: FSQ1 Questionnaire for Forecasting Food Crop Production ………… 34 Annex 2: Monthly Routine Reporting System ……………………………………………… 35 Annex 3: WRS1-2 Weekly Retrieval System for Crop Target and Crop Phenology … 36 Annex 4: WRS3-5 Weekly Retrieval System for Crop Pests, Market availability and Rainfall status ………………………………………………………………………………………. 37 Annex 5: AGSTATS for Food Security - A typical output of Tanzania NEWS ……… 38 Annex 6: The Crop Forecasting Methodology used in Tanzania …..…… 44
Annex 7: Crop Groups, Names, Tanzanian and FAO Codes ………………….…… 46
Annex 8: Livestock Groups, Names, Tanzanian and FAO Codes ………………… 49
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LIST OF ACRONYMS
AGSASU Agricultural Sample Survey
AGSTATS Agricultural Statistics
ASDP Agricultural Sector Development Programme
ASLMs Agricultural Sector Lead Ministries
ASMP Agricultural Sector Management Project
BOT Bank of Tanzania
CASS Current Agricultural Sample Survey
CBS Central Bureau of Statistics
CMEW Crop Monitoring and Early Warning
CPI Consumer Price Index
DALDO District Agricultural and Livestock Development Officer
EAC East African Community
EEM Eye Estimation Methods
EU European Union
FAO Food and Agriculture Organization of the United Nations
FAS Food and Agriculture Statistics
FCPFS Food Crop Production Forecasting Survey
FEWSNET Famine Early Warning System Network
FS/N Food Security and Nutrition
FSIT Food Security Information System
FSQ1 Food Security Questionnaire 1
GDP Gross Domestic Product
HBS Household Budget Survey
LGA Local Government Authority
MAB Ministerial Advisory Board
MAFC Ministry of Agriculture Food Security and Cooperatives
MDA Ministries, Departments and Agencies
MDGs Millennium Development Goals
MITM Ministry of Industries, Trade and Marketing
MKUKUTA Mkakati wa Kupunguza Umasikini na Kuinua Uchumi Tanzania
MOA Ministry of Agriculture
MTUHA Mfumo wa Taarifa za Uendeshaji wa Huduma za Afya
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(Kiswahili for HMIS – Health Management Information System)
NBS National Bureau of Statistics
NEWS National Early Warning System
NFBS National Food Balance Sheet
NGO Non Government Organization
NSDS National Statistical Development Strategy
NSS National Statistical Strategy
OCGS Office of the Chief Government Statistician
PFM Projective-forecasting
PMO-RALG Prime Minister’s Office-Regional Administration and Local Government
RALG Regional Administration and Local Government
RRS1 Routine Reporting System
SADC Southern African Development Community
SC-UK Save the Children –United Kingdom
SSR Self sufficiency Ratio
TA Technical Assistance
TASCDA Tanzania Agriculture Sample Census Dissemination Application
TISD Tanzania Integrated Statistical Database
TMA Tanzania Meteorological Agency
TOR Terms of Reference
TRA Tanzania Revenue Authority
TSED Tanzania Socio-economic Authority
TSMP Tanzania Statistical Master Plan
TZA Tanzania
UN United Nations
UNDP United Nations Development Programme
USAID United States Agency on International Development
WAEO Ward Agricultural Extension Officer
WRS1 Weekly Retrieval System - 1
WRS2 Weekly Retrieval System - 2
WRS3 Weekly Retrieval System - 3
WRS4 Weekly Retrieval System - 4
WRS5 Weekly Retrieval System - 5
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0.0 Overview
In preparing this report, available literature at the Ministry of Agriculture, Food Security and Cooperatives (MAFC), National Bureau of Statistics (NBS) as well as Agriculture Sector Lead Ministries (ASLMs) and other stakeholder institutions was reviewed. In the process, this assignment involved reviewing national statistical systems in brief and agricultural statistical systems in relative detail. Terms of reference from FAO (Appendix 10) guided the way this task was handled until eventually the FIRST PANORAMA REPORT for Tanzania was in place. 1. The National Statistics System
1.1 Legal framework and Statistical Advisory Bodies
In Tanzania, the Statistics Act of 2002 gives the mandate to the National Bureau of Statistics (NBS) to carry out and co-ordinate statistical activities in Tanzania Mainland. As for Zanzibar the statistical information dissemination is mandated to the Statistics Act no 9 of 1999 of Zanzibar (OCGS). The Act does not explicitly refer to the National Statistical System. In Tanzania Mainland the Ministerial Advisory Board (MAB-Ministry of Finance and Economy) is the legal Statistical Advisory Body for the NBS. In this evaluation we focus to broad national statistics system and later we focus to agricultural statistics system.
1.2 Structure of the National Statistics System
Tanzania’s National Statistical System (NSS) comprises Data Producers (Households, Establishments, Institutions, and others), Data Users (Government, MDA’s, NGO, etc), Data Collectors (NBS, OCGS, MDAs, Public Sector, NGOs, Researchers) and Research Centres (Research Centres,
Universities and other high learning institutions). In Tanzania Mainland the Coordinating institution responsible for statistical issues by law is the National Bureau of Statistics (NBS) while in Zanzibar it is the Office of the Chief Government Statistician (OCGS) which is the coordinating institution.
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1.3 National Strategy for Development of Statistics (NSDS)
The NSDS, which is currently being developed due to greater emphasis and demand both by developing countries and by the international community on need of evidence based Policy-making including the need to focus development efforts on measurable results. In Tanzania, the NSDS initiative is represented by the Tanzania Statistical Master Plan (TSMP). It was launched on 6th October 2006 in Dar-es-Salaam by His Excellency Dr Ali Mohamed Shein who is currently the Vice President of the United Republic of Tanzania. Since then, the NBS has been supervising the TSMP development process in collaboration with the OCGS Zanzibar and Development Partners on behalf of the Government.
2.0 Reference Situation for Agricultural Statistics System
With reference to same situations we now focus to agricultural statistics system.
2.1 Legal Framework and Food and Agriculture Statistical Advisory Bodies
The legal/mandate to collect and disseminate official statistics for Tanzania including agricultural statistics is given to NBS. The NBS is required to consult OCGS which is mandated by the Statistics Act No 9 of 1999 of Zanzibar to be the authoritative source of all official statistics for Tanzania Zanzibar on statistical matters for Zanzibar. The advisory services for technical issues on agriculture Statistics is rendered by FAO, universities, etc.
2.2 Structure of the Food and Agriculture Statistics System
The agricultural statistics system in Tanzania is decentralized with several agencies and institutions getting involved in collecting various aspects of Food and Agricultural Statistics. The main ones are: i) The National Bureau of Statistics (NBS) (formerly Central Bureau of Statistics – CBS) ii) Ministry of Agriculture, Food Security and Cooperatives (MAFC) iii) Bank of Tanzania (BOT) iv) PMO-RALG Ministry of Local Government v) Ministry of Industries, Trade and Marketing
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vi) Tanzania Meteorological Agency (TMA) vii) Crop Boards viii) International Organizations such as UN-agencies, FEWSNET, USAID, EU and SC-UK. ix) Food Security Information Team (FSIT) is a joint actor body for clarifying issues of food security by sharing resources and technicalities among food security stakeholders for Government and donor community. (x) Although there are many different producers of agricultural related data in the country, these data collection efforts are not coordinated and the agricultural statistics system as a whole is “fragile, vulnerable and unsustainable”. (xi) The quality of the data that are available is questioned by users and is largely not used. (xii) There are many data gaps. (xiii) With such anomalies, the system has often been unable to meet growing demand for food and agricultural statistics.
The history of the development of agricultural statistics in Tanzania is not very different from other developing countries in SSA. It has been marked by a number of initiatives at different times to build capacity and to establish regular data collection and compilation programs, but, for a variety of reasons. Unfortunately more often than otherwise, it has not been possible to sustain such initiatives when direct support provided by different donors has come to an end. Agricultural related indicators such as price and quantity indicators are routinely reported by several authorities including MITM, MAFC & NBS. In order to complement and/or to check/control the quality of routine administrative data, benchmark data is made available through surveys by NBS in collaboration with Agriculture Sector Ministries. The most recent survey conducted in Tanzania is the Agricultural Census/Survey of 2002/03, so far, the largest in Sub-Saharan Africa. A follow-up survey was conducted for agricultural year 2008/09 implemented in May/June 2009. Likewise a PANEL survey was conducted in 2008 to give estimates at National Level. The national early warning system (NEWS) continues to collect data on routine basis (weekly, bi-weekly, monthly and seasonally). The data normally collected from Local Government Authority (LGAs)1 and other stakeholders is analysed for Food Security and
1 Use is made of WRS1-5, RRS1 and FSQ1 early warning tools to respectively collect basic data at weekly, monthly and seasonally
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immediately made available through AGSTATS for Food Security the data normally produced for early warning purposes. 2.3 National Strategy for Food and Agriculture Statistics
The strategy for development of Agricultural Statistics in Tanzania is to carry out agriculture survey and census after every five years and produce routine data on quarterly basis. The NEWS based at Ministry of Agriculture Food Security and Cooperatives conducts regular surveys and assessments to forecast food availability status and give appropriate early warnings for food security purposes. The outcomes are disseminated as preliminary forecast, final forecast and post harvest view through AGSTATS for Food Security reports (See Annexes). The Agricultural Statistics system has been integrated into the TSMP strategic work Programme as strategy for development of the overall National Statistical system. This will ensure that data producers and data users are brought together in a coordinated manner, so as to institute a smooth agricultural data collection process ensuring timeliness, cost effectiveness, quality and integrity of agricultural statistics data
2.4 Human Resources available
At the National, Regional and District Levels there is a considerable number of human resources equipped with the necessary training skills. At Ward and Village levels there is limited human resources with limited skills. In general, this limited human resources and skills compromises the quality of statistics produced
2.5 Non-Human Resources available
Generally there is a shortage of working equipment such as computers, vehicles, communication tools and financial resources for agriculture data production at all levels. This shortage to some extent compromises the timing and quality of statistics available
frequencies over the crop growing season and marketing/consumption year.
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2.6 Data Dissemination Policy for Food and Agriculture
Statistics
The National policy for dissemination of Food and Agriculture Statistics is not yet in place. Agricultural Statistical information is disseminated individually by producers at the time when the information is available. This will be streamlined when the TSMP becomes operational ie users and producers are brought together in a more coordinated manner.
2.7 Modalities of promoting User-Producer Dialogue
During the planning process of Agricultural Surveys and Censuses the National Bureau of Statistics in collaboration with agriculture Sectoral Ministries holds user producer workshops. The main purpose of these workshops is to share ideas on variables to be covered, levels of precision, costs and timings of the surveys/censuses. These workshops are repeated at the end of the survey/census so as to give feedback to the users on the general achievement of the exercise, and deliberate on dissemination issues.
2.8 Existing Databases and Data Dissemination Tools and Platforms
The Tanzania Agriculture Sample Census Dissemination Application (TASCDA) is under development. Two Staff (one from the IT department and one from Statistics Unit) attended the countrySTAT training in Rome in 2005 together with the PC axis reference group workshop which was held in Zanzibar in 2006. The back end design of the dissemination application is complete, while the front end (Meta data) is not yet completed. This is the only dissemination application for agricultural information available in Tanzania.
Other databases also exist like the Tanzania Social Economic Database (TSED) and the Tanzania Integrated Statistical Database (TISD) which are both managed by the National Bureau of Statistics. These are general databases comprised of a range of social-economic indicators. The Food Security Database developed for the purpose of managing food security and guiding decision making in food security issues was developed between 2003 and 2005 by the assistance of UNDP and needs to be concretised and finalized.
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2.9 Regional Integration and International Technical Assistance received
Regional Integration exists as SADC and EAC. SADC has been the main source of International technical assistance under Regional Integration. Otherwise, FAO, UNDP etc have provided international technical assistance under international organizations.
Currently, there is an initiative by the East African Community Secretariat on harmonization of methodologies, concepts and definitions in conducting censuses of agriculture starting with the current programme for the Censuses of Agriculture 2010.
Both NBS and MAFC have jointly received technical assistance and training in the current programme for the Tanzania National Census of Agriculture from FAO, as well as World Bank and other doners through ASDP. There has also been training by FAO on the CountrySTAT programme.
An evaluation of available agricultural and food information in
Tanzania:
In Tanzania a number of Agricultural and Food information systems exist. In this report we concentrate on the main systems which include NBS, MAFC, NEWS, FEWSNET, and FSIT with a focus to coverage, data types, quality, standardization, management and distribution.
NBS MAFC NEWS FEWSNET FSIT
Coverage 21 regions 21 regions 21 regions ? Variable
Data types Primary/
Secondary
Primary/
Secondary
Primary/
Secondary
Primary/
Secondary
Primary/
Secondary
Quality Good Good Fair Fair Fair
Standardization High Fair Fair OK OK
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Management analysis, storage, databases, reporting, dissemination
analysis, storage, databases, reporting, dissemination
analysis, storage, databases, reporting, dissemination
analysis, reporting, dissemination
analysis, reporting, dissemination
Distribution Yes Yes Yes on request Yes/on request
Yes on request
Timeliness Fair Fair Good Depends Fair/Depends
� Results and products of the information system and agricultural statistics.
3. Outputs, Data Sources, and Metadata of the Food and
Agriculture Statistics
3.1 MAFC/NBS Annual Publications
The Food and Agriculture Statistics data are published regularly by different institutions. Harmonization of data is the most notable challenge mainly associated with lack of a common reporting format, inconsistencies in data produced and conflicts among data particularly in MAFC. Even when harmonized the data is of no use if it is not reported or disseminated properly. In some offices there are no regular reports apart from supplying data to the writing of minister’s speeches to parliament. In others the reports produced find no way back to those who supplied the original data yet in others the reports are stored in filing cabinets making it difficult for others to access. Some of the common format items like publishing dates, reference periods, data collection methods, metadata to variables, limitations etc. are not provided
Data on current agricultural statistics concerning area under crop, crop production, livestock production and diseases, among others are collected by VAEOs and/or WAEOs. The data are from a single source, which is the village. The data collecting institutions have often been requested to supply data and information using different formats making the work difficult. This has also led presence of non – comparable statistics. The
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harmonization processes can run smoothly and faster if these inconsistencies are eliminated.
Structural data such as land use, holders characteristics, farming practices and technology, farm implements, livestock numbers etc often rely on censuses/sample surveys at specified frequencies (every five or ten years). Harmonization processes for such data has started to be smoothly implemented through ASMP. The harmonization process has also involved bringing together different departments of MAFC to work on modalities to eliminate inconsistencies and conflicts among data systems and indicate an appropriate set of crop statistics that can be disseminated for different purposes.
Use of secondary data has been the real area where the harmonization processes and coordination must be handled carefully. Secondary data are produced with the primary objective of addressing internal issues but other stakeholders use such data to address their concerns as well. Foreign exchange and cow’s milk are examples of such data. BOT has the responsibility of controlling foreign exchange but TRA is using exchange rates to convert, say British Pound (£) to Tanzania Shillings (Tshs), or MLDF is assessing the cow’s milk productivity while the NBS is using it to estimate value added (GDP) of milk through projected number of cows.
3.2-4 Crop, Fishery, Forestry, Water Resources, Land use (ha. cropland; irrigated cropland - ha., ownership, legal tenured;
share of agriculture in urban/peri-urban/rural area) and Livestock statistics
Two sources of this information exist:
(i) Routine collection (on quarterly basis by NBS), MAFC, MITM and
(ii) Surveys and censuses (conducted after every five-ten years by NBS in collaboration with sector Ministries).
The National Bureau of Statistics normally collects data and information for the purpose the computation of GDP and Price indices.
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3.5 Consumption, Trade, and other relevant Indicators for the Agriculture Sector
Consumption Statistics is available through MAFC Crop Forecast Surveys and the NBS Household Budget Survey (HBS). Trade statistics also collected by the Ministry of Industries, Trade and Marketing as well as the Bank of Tanzania and the Tanzania Revenue Authority.
The NEWS provides the following outputs based on basic data sources from LGA and TMA:
AGSTATS for Food Security: Preliminary forecast which includes production and analysis of food supply status … Vuli+Msimu data in May and produced in June
AGSTATS for Food Security: Final forecast which includes production and analysis of food supply status …… Msimu+ Masika+Vuli
AGSTATS for Food Security: Post-Harvest View which includes an analysis of food supply availability status, pest threats and market forces
The high frequency forecasting approach providing 5 products in a season viz. Likely Production Forecast, Season’s Onset forecast, Early Forecast, Preliminary Forecast and Final Forecast attempted since 2003 has taped expert desires for revising food status from all districts with a reasonable field feel and ground truth. Although the need for improving non-response rates remains a real challenge the approach continues to be the most plausible means of retrieving field level information and make a snapshot food security status for decision making as and when required.
� Evaluation of results and products of the information
system and agricultural statistics.
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The results and products of the existing information systems and agricultural statistics are institution-specific and are summarized by NBS the overall coordinator. Without a careful scrutiny NBS may be bogged down by what could seem as unharmonized. For example, when MAFC produces Production as preliminary figures and later as final forecasts and finally as post-harvest estimates these are not three different results of data production process but the same figures only evolving from the former to the latter. The difference is only told by time of release and the time matters in relation to the effect of the causative agent and the purpose of the products. When interest is early warning then timeliness becomes paramount.
The Preliminary Forecast and Final Forecast reports produced through AGSTATS for Food Security have been quite instrumental in achieving field feel of management decisions made by authorities at national and regional level headquarters towards harvest. The interaction between national level organizers and field level agro-extension experts during data retrieval meetings at regional headquarters are considered the real gear towards the success of crop forecast in producing food security reports with real world reflection. The main challenge with this approach is the high non-response rates which have necessitated field visit to interactively share the trouble encountered in field data collection and cross-checking with field staff. This interaction has enhanced the national level reliability of crop production forecasts to as high as 90% of accuracy and timeliness.
3.6 Limitations of the available Food and Agriculture Statistics
Quality issues
Lack of necessary training skills for District and village extension officers compromises the quality routine Food and Agriculture Statistics
Lack of necessary equipment like motorbikes, bicycles to facilitate data collection and compilation at all levels also compromises timeliness and quality of the information collected
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3.7 Food availability for human consumption, External
Trade, Population (Total/male/female/rural/urban/ agricultural/non agricultural) including farm household
and rural household and other relevant indicators for the agriculture sector such as: - Food security - Inputs (fertilizers, pesticides, machinery, labour and cost of inputs)
- (Rural) Infrastructure (storage facilities, roads, electrification, mobile phone coverage) - Credit (access to, amount, source and geographical distribution) - Economic (Agriculture Value Added and its disaggregation level; income distribution (Gini); rural/urban income)
Food Security and Nutrition (FS/N) Analysis Systems
Livelihoods-based Food Security and Nutrition analytical methods available in Tanzania are not systematic. There is no formal livelihoods-based food security and nutrition framework that defines what data and information need to be collected, how they should be analyzed, how outputs should be produced, and what decisions can come from these outputs (though some NGOs conduct livelihoods-based analysis in small project areas). superficial understanding on how to conduct livelihoods-based FS/N analysis does exist, but it is not formally structured or systematically practiced.
Potential users of FS/N information are numerous. Often, they are very clear on sector-specific data sets (market prices, rainfall, disease and pests) and how these can be useful. What seems lacking is how FS/N analysts bring the data together for appropriate analysis. Users of FS/N information for livelihoods-based analysis are not clearly defining what data they need or how to better utilize the data they have. Many key informants themselves ask what are the most important indicators and how should they be analyzed.
The three most predominant national systems for FS/N analysis are: the Food Security Information Team via the Rapid Vulnerability Assessments, the Ministry of Agriculture and Food Security and Cooperatives Routine District Reports & Village Sample Surveys (described earlier above), and the Famine Early Warning Systems (FEWS NET). All of these systems use secondary data for their analysis. The MAFC systems focus mainly on cereal production to generate statements about national food insecurity. The results of which define when and
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where a detailed assessment is required via RVA. The RVA process has introduced tools for a more cross-sectoral livelihoods analysis but these are relatively new. The RVAs are conducted each season or when there is a need, but they are not systematic and the tools continue to change. FEWS NET gathers data and information from other agencies, sometimes directly from districts, for its analysis. However, major challenges exist such as late arrival of data, coverage gaps, and poor quality, result in less reliable and less frequent reporting (reporting used to be monthly but is now quarterly).
Generally speaking, there is an over-reliance on food availability (mostly cereal production estimates) to determine food insecurity. Poor nutrition indicators and the lack of data from other sectors results in a narrow view of food insecurity.
Practically no comprehensive sub-national FS/N analysis occurs. What does exist is an informal early warning system to alert regional, zonal, and national levels of local pests, diseases, and other shocks. But these are based on telephone, fax, and other informal modes of communication which are effective communicating the problem but do not add to an information base to enable systematic analysis.
NGO’s, Projects, and Research Institutes, have programPspecific monitoring and evaluation systems which are isolated to certain areas of operation. These systems rarely feed into a harmonized center even within the agencies themselves so there is little hope they can be shared with other partners.
Other data sets, from the National Bureau of Statistics, Regional Administration for Local Government, or other partners, can contain useful and often accurate information for socioeconomic
and poverty analysis but are not routine (most assessments are conducted annually).
FS/N Information Systems
A large challenge for FS/N analysts is to receive data and information from multiple sectors in order to conduct cross-sectoral analysis. Although sector-specific information systems
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do exist, they are not well integrated into a common system for FS/N analysis largely due to the fact that a livelihoods-based analytical framework is not well established or appropriately articulated to the relevant partners.
Some of the sector-specific information systems work well, but most face profound challenges mainly revolving around resources and capacity. Lack of systematic data management processes (collection, processing, analysis, output, dissemination) in most of the information systems means that there is little in the way of reliable data and information to share at higher levels.
Much information is very late in arriving at Ministries headquarters. Delays of 2-5 months for rainfall data are common. Local Government Authority monthly reports are aggregated at each level (ward and district) and thus lose great detail which is otherwise useful for a more profound geographic understanding. These reports seldom reach regional authorities, not to mention national levels. Key data included in these reports, such as livestock production and prices, are seldom seen by the Ministry of Livestock Development in Dar es Salaam.
Though it is difficult to pinpoint exactly where data and information are bottlenecking, there does appear to be a large delay between district and higher levels. A possible reason for this is that much of the LGA data are used for planning purposes (not situation analysis). Once the data arrive at district level planning offices, they survey their purpose.
Other information systems, such as the Health Management Information System, are commendable. The MTUHA system produces outputs at each level, including the health facility itself. Providing summary output at health centers and dispensaries-at the point of data collection—enables local level understanding.
3.8. Description of national/ sub-national commodities codes system
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The Crop Groups, Names and Codes used by NBS and the respective FAO code are shown in Table 3.8.1 while the Livestock codes are as per Table 3.8.2 below.
Table 3.8.1: Crop Groups, Names, Tanzanian and FAO Codes
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Crop
Group
Crop
name
TZA
Code
FAO
Code
Seasonal
Crops Maize
(Green) 11
56
(446)
Rice 12 27
Sorghum 13 83
Bulrush
millet 14 79
Finger
millet 15 79
Wheat 16 15
Barley 17 44
Sweet
Potatoes 22 122
Irish
potatoes 23 116
Yams 24 137
Cocoyams 25 0136
Onions 26 402/403
Ginger 27 0720
Crop
Group
Crop name TZA
Code
FAO
Code
Permanent
Crops Palm oil 44 0257
Coconut 45 0249
Cashewnut 46 217
Sisal 53 0789
Coffee 54 656
Tea 55 667
Cocoa 56 661
Rubber 57 0836
Wattle 58
Kapok 59 0778
Sugar cane 60 157
Cardamom 61 0702
Tamarin 63 0619
Crop
Group
Crop
name
TZA
Code
FAO
Code
Permanent
Crops
(Contd) Cinamon 64 693
Nutmeg 65 0702
Clove 66 0698
Black
Pepper 18 0687
Pigeon
pea 34 0417
Cassava 21 125
Pineapple 75 574
Passion
Fruit 70 0603
Banana
(Food/Pla
ntn) 71 489
Avocado 72 572
Mango 73 571
Pawpaw 74 600
Orange 76 490
Grapefrui
t 77 507
Grapes 78 0620
Mandarin 79 495
Guava 80 0620
Plums 81 536
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Apples 82 515
Crop
Group
Crop
name
TZA
Code
FAO
Code
Permanent
Crops
(Contd) Pears 83 0521
Peaches 84 521
Lime/Le
mon 85 0497
Pomelo 68 497
Jackfruit 69 0603
Durian 97 0603
Bilimbi 98 603
Rambuta
n 99 0603
Bread
fruit 67 603
Malay
apple 38 603
Star fruit 39 603
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Table 3.8.2: Livestock Groups, Names, Tanzanian and FAO Codes
Livestock Group Livestock name TZA
Code
FAO Code
Cattle 01
0866
Goat 02
1016
Sheep 03
0976
Pig 04
1034
Chicken 05
1057
Donkey 06
1110
Duck 07
1068
Turkey 08
1079
Rabbit 09
1140
Guinea pig 11
1150
3.9 National classification/nomenclatures and links to
international classifications (if existing)
The Crop Codes given in Table 3.8.1 and used in Tanzania are based on the International Classifications and are therefore comparable. The livestock codes in Table 3.8.2 are however available only for Tanzania. In any case, these are only used in the data collection and analysis by NBS and MAFC.
3.10. Consumption, Trade and other relevant Indicators for the Agriculture sector
Data for consumption is obtained from MAFC Crop Forecast Surveys and NBS Household Budget Surveys; Import/Export Statistics are compiled by MAFC and the Tanzania Revenue Authority (TRA) complimented by data from the Ministry of Industry, Trade and Marketing as well as parastatals. Consumer Price Indices (CPI) from NBS includes food crops in the basket of goods.
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3.11. Limitations of the available Food and Agriculture
Statistics
It is clear from the inventory that there are many producers who are not coordinated; the existing data are not harmonized or consistent between sources and are scattered among institutions collecting them. There are also many data gaps. It could therefore be concluded that, currently there is very little basic current agricultural statistics being collected namely; crop area, yield, production, livestock inventory and products at the holding/farm level. Through TSMP the two main responsible institutions – MAFC and NBS – are only planning to put in place systems to collect these data.
According to the Agricultural Sector Strategic Plan for Statistics document, the historical background of data collection in MAFC is divided into three phases. Phase I (pre – 1983) was characterized by inadequate data production using official reporting system of the then Ministry of Agriculture (MOA). Phase II (the period 1983 – 1992) was characterized by introduction of objective methods in data collection. Starting in 1983/84 the MOA conducted Current Agricultural Sample Survey (CASS) which was used to collect agricultural statistics, taking a few regions at a given year, using objective methods. From 1986/87 the then Central Bureau of Statistics (CBS) conducted nationally representative annual Agricultural Sample Survey (AGSASU) which collected Agricultural data on crops and livestock. Phase III (the period 1993 onwards) marked the consummation of the coordination arrangements between the MOA and the CBS and further improvements in the methodology for collecting crop forecast data. The Statistics Unit in MOA and the CBS collaborated in the planning and implementation of the National Sample Census of Agriculture 1994/95 and the subsequent agricultural sample surveys.
Further, there has been the crop forecasting methodology developed over the years in Tanzania. This methodology essentially combines three methods namely; Eye Estimation, Projective-Forecasting and the Food Crop Production Forecasting Sample Survey (FCPFS) with the major aim of minimizing subjectivity and maximizing objectivity while making best use of available routine reports for optimal quality and timely release of crop forecast. In the process of analyzing Self Sufficiency Ratios
24
(SSRs) and National Food Balances (NFBs), the process extends to the calculation of food production in grain equivalent terms.
The Eye Estimation method are largely used to produce DALDO estimates which are normally subjective and often introduce errors in the analytical processes essential for calculating SSR, preparing NFB sheets and detecting areas with vulnerable population. Projective-forecasting method partly borrows from CASS, which by employing crop cutting experiment and area measurement surveys produces a correction factor or trend estimates, which may be separately or jointly used to reduce subjectivity from DALDO estimates and increase objectivity in the resultant forecasts. In effect, the DALDO estimates coming from WAEOs information are improved and become more objective and less subjective.
Noting the need for timely availability of production data for early warning purposes, the FCPFS methodology was devised in 1992/93 and has been operational since. It involves use of a simple questionnaire that is used to collect crop data from sample villages with intention to estimate expected food availability status at sub-national level. Due to the differences in terms of frequency of retrieval the other tools for which different variables are retrieved include Weekly retrieval System and the Monthly routine reporting system.
The Eye estimation method involves a conventional real world observation whereby area under crop is estimated during planting or during harvest and used in conjunction with yield forecasts based on crop-water balance model to compute DALDO production estimates. Unfortunately due to the rough application of real world observation it is not free from errors especially those associated with subjectivity.
Projective-forecasting method relate to an attempt to lessen errors associated with subjectivity observation. Projective forecasting methods are employed combining current year DALDO estimates and CASS series developed over a certain period of years. In effect a tend model arises that is used to produce production estimates that are less subjective and more objective. This method is very useful in curbing subjectivity but
25
with time the power to do so declines and hence the need for periodic revitalization.
Food crop production forecasting survey method involves a sample survey that seeks to examine the direction of change in area cultivated vis-à-vis previous season. In conjunction with yield estimates from DALDOs trend values, as well as Crop Monitoring and Early Warning series or Plant-water balance model production is computed.
It should be noted that the outputs of the ASMP project included a series of annual agricultural surveys, including a livestock component. The project engaged international consultancies to work on cost effective methodologies to ensure sustainability after the project gets phased out. One of the outcomes was relying on selected farmer’s assessment on area planted as well as yield, from the traditional crop cutting experiments. Another output from the project was the entrenchment of institutional working relationship between the agricultural sector ministries on the one hand and the national statistics office on the other. This continues to the current day. The advantage has been that there is optimal utilisation of the scare human resource. Further, while under such an arrangement statistical standards are ensured form the national statistics office side, the subject matter relevancy is ensured by experts from those from the sector. A mutual advantage that has shown up is that the experts from the sector working in the whole survey process go back after analysis and report writing with a product which they own.
The FAOSTAT and CountrySTAT framework currently at advanced stage of development will prove to be a major development as it picks from the gain achieved under the ASMP as its implementation, by design, is conditioned to take into account the existing national system of food and agricultural statistics. The framework is based on FAO data standard structure, concepts and definitions used in FAOSTAT and it considers each member country specific data needs, that is:
• Generates harmonized information • Facilitates data exchange (sharing)
26
• Presents country specific data with corresponding Metadata at decentralized and disaggregated level.
� Assessment of needs on agricultural statistical data.
4. 0 Overview of User Needs for Food and Agriculture Statistics in Tanzania
In general data is required by a variety of users both in public and private sector decision making machinery. Whether in public or in private end, the data should preferable be disaggregated by gender and should be of relatively high quality with Timeliness, Reliability, Availability, Accessibility and Plausibility criteria being well considered.
The importance of agriculture in the economy of Tanzania need no overstress. It has been so recognized since independence and continues to be widely accepted that it remains the back bone of the national economy. The sector as a whole accounts for as much as 26 per cent of Gross Domestic Product, provides for approximately 80 percent of total employment and is the major source of livelihoods for more than 70 percent of the country’s total population. At the same time, poverty in Tanzania is very largely a rural phenomenon and most of the poor rely directly on agriculture for much of their income. It is clear that sustained economic growth and poverty reduction can only happen if there are both, growth in total agricultural output and substantial increases in agricultural productivity.
4.1. The Demand for Agricultural Statistics
While challenges for data supply have been well expressed and strategies for dealing with them are relatively developed, the data demand situation is not adequately understood.
It is not only important to clearly identify the needs for data in the existing development frameworks, but also necessary to recognise that new initiatives will require additional data. The issues of adequacy and timeliness of data must be addressed to
27
ensure effective formulation, monitoring and evaluation of policies and development outcomes.
The demand for agricultural statistics derives from a number of sources and processes and it is important that the ASDP not only addresses current needs, but also provides sufficient flexibility to address future needs, which are not yet identified. Key requirements for food and agricultural statistics include the following;
(i) To monitor progress with high level national strategies, especially with the MKUKUTA. Agricultural indicators to monitor development targets include: the growth rates of agriculture value added; the proportion of total agricultural output that is marketed; the proportion of total value added of agriculture that is exported, the proportion of households with land titles of agricultural production; and the proportion of household with general land titles;
(ii) To monitor indicators and progress with sectoral strategies, especially Rural Development Initiative;
(iii) To report on progress towards international agreements and goals that Tanzania is committed to, especially the Millennium Development Goals (MDGs);
(iv) To support the development of appropriate agricultural policies to promote growth, job creation and poverty reduction;
(v) To support the day-to-day implementation of agricultural policies, programs and projects;
(vi) To provide information to farmers and other participants in different agricultural related markets, to support effective investment decisions;
(vii) To monitor food security and to provide the information to support interventions where required;
(viii) To provide information generally to allow the public at large and other organizations to participate more effectively in the development debate.
There have been several reviews of food and agricultural data needs, gaps and priorities in Tanzania. These include: ….
While we cannot for certain, claim authenticity of the methods used to determine the data needs and priorities in all these reviews, but we are sure they give some guidance. The studies
28
and assessments are however, all in the past and future data needs will have to be identified for the monitoring and evaluation of the food and agriculture sector and other national development plans and policies.
4.2. Public Sector Decision Makers
Data is required by the following:
i) Government Ministries, Departments and Agencies – eg. MAFC
ii) Non-Government Institutions and Policy implementers,
iii) Academic Institutions - Universities
iv) LGAs Regions, Districts and lower administrative units
v) Development partners and international agencies
4.3. Private Sector Decision Makers
Data is required by the following:
i) Manufacturers, Traders, Farmers, Banking Institutions;
ii) Media;
iii) Individual Researchers and Development Practitioners.
In general the private sector needs statistics to make sound investment decisions. The private sector also produces data and is an important source of statistical information especially from surveys of establishments and enterprises. The Media help in raising the profile of statistics by using data to support their reports and by publishing statistics regularly. In general, Civil Society in performing their functions, produce and use data and statistics to monitor programmes and for advocacy and awareness purposes, thus helping raising the profile of statistics in the country.
5. Expectations from CountrySTAT and Synergies with on-
going Initiatives in Tanzania
29
As described above Tanzania has an abundant amount of data ranging from social, economic as well as food and agriculture. CountrySTAT is an Ideal platform for storage and dissemination of this information for access by users. Based on the available Tanzania agriculture census and food security data, the system is flexible and friendly as it allows individuals to perform own secondary manipulation of data as desired. It is believed that the system will increase the value and accessibility of the information to users at all levels and it can also be linked with the available databases so as to make data easily accessible to users.
It is expected, that CountrySTAT will provide technical assistance to support collaborative efforts in the National Statistical System with special emphasis on Food and Agricultural Statistics. There are three main areas of activity in regard to Data Management, where the basic objective is to make best use of the data that has already been collected.
The first desirable requirement is to undertake a formal inventory of the different data systems that are taking part in the collection and compilation of food and agricultural data. The inventory survey should also ascertain, as far as possible the quality of the data and the extent to which they can be used for different purposes. Consistently, a detailed description of the methods and procedures used – what is often referred to as metadata - will need to be compiled. This initial exercise was started as part of the preparation of the “Tanzania Food Security and Nutrition Information Systems” report, which was submitted early in December, 2008.
The second requirement will include revitalization of objective data set through regular current agricultural sample surveys covering 12 popular food crops in Tanzania. This will greatly lend towards the pressing need of strengthening crop forecasting capability through adopting high frequency forecasting approach.
The third requirement is in support of weekly, bi-weekly and monthly retrieval systems for effective forecasting and provision of snapshot food security status. Although the need for improving non-response rates remains a real challenge, the
30
approach continues to be the most plausible means of retrieving field level information and make a snapshot food security status for decision making as and when required.
The fourth area is to support ongoing data harmonization initiative that brings different departments of the MAFC under a TWG for Data Harmonization purtposes. Currently the MAFC is carrying out a comprehensive data harmonization process whereby different actors in food and agricultural statistics is for reliable data for planning and decision making needs in both local and foreign ends. The data harmonization initiative which is in its middle stage is quickly advancing having started revising production data series. The exercise will continue until all crop production aspect parameter primarily area, yield and production and other areas are covered. Support to ongoing data harmonization initiative is imperative as the process will eventually designate specific indicators and statistics as official statistics and hence a reliable input to the CountrySTAT for local foreign access by all www users.
The fifth area is that MAFC will also need some technical assistance to support the completion of work on food security database for analysing and archiving food security data and information. Allowance of technical assistance for three years should be included in the ASDP.
Finally the sixth requirement, once the inventory has been completed and data sources have been identified and data harmonization is fully institutionalized the step is to develop an integrated food and agricultural database. The need for an integrated database, that would provide access to data on different aspects of food security and agriculture, has already been identified. The main aim is to create a single point of access to the data for users, both inside and outside government. The establishment of such a database requires careful design and implementation. Rather than having to design the structure from scratch, clearly Tanzania will benefit from work already carried out by FAO under CountrySTAT. The advantages of using the FAO process include being able to use an existing structure that is consistent with both international statistics and neighbouring countries. The software is supported
31
by FAO, is available free of charge and it should be possible to access technical support and assistance as required.
Unfortunately, there is currently very little current FAS being collected by the two main institutions – MAFC and NBS. Secondly, data from the various sources are not directly comparable. Therefore some adjustments will be required. Thirdly, the primary data from the past censuses and surveys is largely not readily available. All these greatly reduce the value of the database except for the future.
6. Important Factors for the Success of the CountrySTAT
project in Tanzania
Training of Competent and skilled personnel and Development of Institutional Capacity
Advocacy and Publicity of the system – low level to high level policy makers
Allocation of adequate funds for funding TA and operational Activities
Provision of Equipment eg computers, network facilities etc.
Coordinated approach in the implementation of CountrySTAT among MAFC, NBS and other stakeholders in agriculture and food statistics.
Discussion of the development of institutional capacity in different agencies involved in the food and agricultural statistics system will depend on a number of things. Together with implementation of other proposals this includes the possibility of establishing a single cadre for professional statisticians working in the public service in Tanzania. In particular it is recommended that plans for human resource development should include training for data producers at both the professional and sub-professional levels; further, training and support for the data users. For the latter it may be useful to distinguish between general training in statistical literacy for a large number of users and higher level technical training for specialized analysts and planners.
32
In developing proposals for capacity building, it will also be important to ensure that the comparative advantage of different agencies is both, identified and utilized. It is expected, for example, that NBS should continue to develop, manage and implement large-scale household and other surveys. It will not make sense for this capacity to be duplicated elsewhere. The challenge here will continue to be specificity of data needs, frequency and timeliness and this has to be carefully scrutinized and addressed towards putting in place a noble system enforcing credible principles of validity, reliability and comparability as well as institutional mechanism for harmonisation.
Funding for capacity building will be required for NBS, MAFC and the other agencies concerned with FAS. Secondly, it has been assumed that some capacity building will be available under the general NBS and MAFC. Thirdly, data entry and analysis will be done in NBS for the first two years as capacity is built in MAFC. The training and support for data users will be carried out during the sensitization and publication of reports for the various surveys and censuses.
7. Summary and OUR Expectations
♦ Presentation of this report at a meeting that will form an ideal platform for the outreach of the CountrySTAT system to ASLM’s in Tanzania;
♦ An elaboration of the CountrySTAT program and steps of implementation;
♦ Creation of advocacy and awareness for a comprehensive adoption of the countrySTAT system by the ASLM’s, providing a one shopping center for Food and Agriculture Statistics;
♦ Agreement on sustainability conscious technical and financial support between Government and Development partners with a gradual take-over through MTEF budgets expected in the implementation of CountrySTAT.
♦ Sharing experiences from other countries and regional blocs within the African continent;
33
34
Annex 1: FSQ1 Questionnaire for Forecasting Food Crop Production
35
Annex 2: Monthly Routine Reporting System
36
Annex 3: WRS1-2 Weekly Retrieval System for Crop Target and Crop
Phenology
37
Annex 4: WRS3-5 Weekly Retrieval System for Crop Pests, Market
availability and Rainfall status
38
AGSTATS FOR FOOD SECURITY
0000
2222
4444
6666
8888
10101010
MzMzMzMz CrCrCrCr NcNcNcNc TotTotTotTot
VOLUME 1: The 2008/09
Preliminary Food Crop Production Forecast for 2009/10
Food Security
EXECUTIVE SUMMARY
Crop Monitoring and Early Warning
Ministry of Agriculture Food Security and Cooperatives
P.O. Box 9192,
Tel 2865950, Fax 2865951, 2866752
E-Mail: [email protected]
Dar es Salaam, Tanzania
Dar es Salaam June, 2009
United Republic of Tanzania
Ministry of Agriculture Food Security and Cooperatives
Tanzan ia Pr el im inar y Food Cr op Pr oduct ion For ecast f or
20 0 8 / 0 9 (Wi t h Cr op wise Pr op or t ional Cont r ib ut ion)
Potatoes
14%
Bananas
10%
Cassava
18%
Pulses
10%
Wheat
1%
Rice
8%
Millets
2%
Sorghum
6%
Maize
31%
10,921,536
Tonnes
Annex 5: AGSTATS for Food Security - A typical output of Tanzania NEWS
39
Main Highlights
♦ The 2008/09 Preliminary Food Crop Production Forecast amounts 10,921,536
tonnes grain equivalent and requirement for 2009/10 amounts 10,578,196 tonnes.
In terms of cereals and non-cereals, a deficit status of 80% implying a shortage
amounting 1,313,199 tonnes and a surplus status of 141% implying a surplus
amounting 1,656,539 tonnes are expected respectively. In effect a net food
situation involving the 2008/09 production is such that a self sufficiency ratio of
103% or a little surplus of 343,340 tonnes is attainable.
♦ Compared to previous season, production decline has been observed in sorghum
(20%), maize (4%), rice (4%) and pulses (4%), due to poor rains in respect of late
onset, inappropriate distribution and early cessation, dismal rains in some bimodal
areas, soil degradation and pest and vermin prevalence. In general, a marginal
increase of approximately 7% is notable in non-cereals (10% in potatoes and 10%
in cassava and 9% in Bananas).
♦ An analysis of carry-over stocks (COS) shows that about 462,870 tonnes was
available from three different sources viz. Private stocks (121,560 tonnes), Public
stocks/SGR (109,876 tonnes) and Farm retention (231,435 tonnes). Adding the
COS to the above mentioned production surplus a total of 806,210 tonnes will be
available over and above the amount satisfying national requirement for year
2009/10.
♦ A caution is however inevitable as the forecast is sensitive to vuli performance.
Recent experience has witnessed a rickety vuli shaking off about 26% of normal
vuli production. Based on this Preliminary forecast, the 2008 Vuli season
contributed 13% only of total production (vs normal 18% national perspective) or
24% only (vs 32% bimodal area perspective)2, showing a possible draw-down
impact of about 518,359 tonnes!
♦ At sub-national level SSR analysis warns of 10 deficit regions. Furthermore,
though self sufficient or surplus, further warnings are focused to 8 additional
regions bearing pockets of food shortage. Excluding Dar es Salaam (which is
largely non-agricultural), a total of 17 regions bear vulnerable population in 61
districts. The identified vulnerable areas will need to be subjected to an in-depth
vulnerability assessment for necessary intervention.
♦ Observant to current global food crisis manifested through soaring food prices, the
produced food during 2008/09 should be prudently managed to avoid adverse food
shortage normally encountered due to excessive trading and ‘misuse’ for non food
purposes. Measures should be taken to improve utilization potential of a broader
basket of foods including sorghum, bulrush millet, cassava, banana and sweet
potatoes to enhance food availability. Innovative practices such as blending should
2 Worked out from the 2007/08 Final forecast and taken as a proxy to recent experience
40
be pursued to include such crops.
♦ Regional integration is an area that may be adversely or favorably exploited by
member countries and this should be explored for the interest of Tanzania. Blanket
policy statements of non-exportation protectionism have not always shown a
positive impact. A serious study should be carried out in this area to find out what
is taking place on the ground regarding food without borders. A syndicate of food
exporters seems to deny the country of either Government revenue or physical food
outlets through unofficial exit points (arising through ever increasing border
porosity).
Table 1: The 2008/09 National Level Preliminary Food Crop Production
versus Requirement and gap/surplus analysis for 2009/10
Sorghum&
Cereals Maize millets Rice Wheat Cereals
Production 3,424,984 898,869 843,556 97,901 5,265,309
Requirement 4,131,782 1,531,816 710,754 204,156 6,578,508
Gap(-)/Surplus(+) -706,797 -632,947 132,802 -106,256 -1,313,199
Non-cereals Pulses Bananas Cassava Potatoes Non-cereals
Production 1,085,448 1,071,923 1,982,718 1,516,139 5,656,227
Requirement 654,840 733,176 1,830,567 781,106 3,999,688
Gap(-)/Surplus(+) 430,608 338,747 152,151 735,033 1,656,539
TOTAL Cereals Non-cereals
TOTAL
Production 5,265,309 5,656,227 10,921,536
Requirement 6,578,508 3,999,688 10,578,196
41
Gap(-)/Surplus(+) -1,313,199 1,656,539 343,340
Figure 1: Carry-Over Stocks Analysis 31st May, 2009 (Tonnes)
Private,
121,560
Farmret,
231,435
Public,
109,876
462,870
Tonnes
42
ArushaKilimanjaro
Singida
Tanga
Dodoma
Pemba
Unguja
Morogoro
Iringa
Lindi
Ruvuma Mtwara
Kagera
Mara
Mwanza
Shinyanga
Kigoma
Tabora
Rukwa
Mbeya
Legend
Deficit (For SSR <100%)
Self Sufficient (For 100%<=SSR <120%)
Surplus (For SSR =>120%)
Total Food Supply Forecast for the 2009/2010 Marketing Year
(Based on 2008/09 Preliminary Food Crop Production Forecasts)
Coast
Dar es Salaam
Coast
In summary:
Based on 2008/09 Preliminary Food Crop Production Forecast Survey,
Tanzania, during 2009/10, will be marginally self sufficient. Specifically, there
is evidence to indicate that: 9 regions will be definitely deficit, 4 regions will be
definitely self-sufficient and 7 regions will be definitely surplus. Here and there,
pockets of vulnerable areas are signalled in 61 districts in 17 regions.
Arusha (Deficit)
Manyara (Deficit)
43
44
Annex 6: The Crop Forecasting Methodology used in Tanzania
A snapshot story by O. A. Ntikha3
The methodology of crop forecasting essentially combines 3 methods
namely, Eye estimation (EEM), Projective-forecasting (PFM) and the Food
crop production forecasting sample survey (FCPFS). In the process of
analyzing Self sufficiency ratios and National Food Balances, the process
extends to the calculation of food production in grain equivalent terms.
The Eye Estimation method (EEM) are largely used to produce DALDO
estimates which are normally subjective and often introduce errors in the
analytical processes essential for calculating self-sufficiency ratios,
preparing national food balance sheets and detecting areas with vulnerable
population. Projective-forecasting method partly borrows from Current
Agricultural Sample Survey methods (CASS), which by employing crop
cutting experiment and area measurement surveys produces a correction
factor or trend estimates, which may be separately or jointly used to reduce
subjectivity from DALDO estimates and increase objectivity in the resultant
forecasts. In-effect, the DALDO estimates are improved and become more
objective and less subjective.
Noting the need for timely availability of production data for early warning
purposes, the FCPFS methodology was devised in 1992/93 and has been
operational since. It involves use of a simple questionnaire (FSQ1) that is
used to collect crop data form sample villages with intention to estimate
expected food availability status at sub-national level.
Noting the differences in terms of frequency of retrieval the other tools for
which different variables are retrieved include Weekly retrieval System
(WR1-5) and the Monthly routine reporting system (RRS1)
Eye estimation method
The Eye estimation method involves a conventional real world observation
whereby area under crop is estimated during planting or during harvest and
used in conjunction with yield forecasts based on crop-water balance model to
compute DALDO production estimates. Unfortunately due to the rough
3 The author is in charge of food statistics in the National Food Security Division.
45
application of real world observation it is not free from errors esp. associated
with subjectivity.
Projective-forecasting method
In an attempt to lessen errors associated with subjectivity observation,
projective forecasting methods are employed combining current year
DALDO estimates and CASS series developed over a certain period of
years. In effect a tend model arises that is used to produce production
estimates that are less subjective and more objective. This method is very
useful in curbing subjectivity but with time the power to do so declines and
hence the need for periodic revitalization.
Food crop production forecasting survey method
This is a sample survey that seeks to examine the direction of change in area
cultivated vis-à-vis previous season and in conjunction with yield estimates
from DALDOs trend values, CMEW series or Plant-water balance model
production is computed.
Results and Outputs of these include:
Preliminary forecast which includes production and analysis of food supply
status … Vuli+Msimu data in May and produced in June
Final forecast which includes production and analysis of food supply status
…… Msimu+ Masika+Vuli
Post-Harvest View which includes an analysis of food supply availability
status, pest threats and market forces
Since its inception in 2003 the approach has been quite instrumental in
achieving national level reliability of upto 90% and high level timeliness.
Also detection of vulnerable areas has been widely acceptable.
Opportunities and Challenges
Opportunities
46
� The most outstanding opportunity is the high frequency retrieval
system that is able to monitor and send out signals of food shortage or
surplus on at least a monthly basis.
� There is a strong collaboration between survey teams from
headquarters management and field extension authorities, which
provides a very reliable mechanism to check on consistency and
ground truth. This is enhanced by frequency of retrieval and joint
assessments during preliminary and final forecasting survey exercises
as well as post harvest food availability surveys.
Challenges
� The main challenge is that the methodology could best be
demonstrated, explained and illustrated with examples and case
studies through seminars or workshops. Extra effort is required to
develop this justification among decision makers.
� There is need to conduct Current Agricultural Sample Surveys on 5
year basis to revitalize CMEW Series for facilitating projective
forecasting.
Annex 7: Crop Groups, Names, Tanzanian and FAO Codes
47
Crop
Group
Crop name TZA
Code
FAO
Code
Seasona
l Crops Maize
(Green) 11
56
(446)
Rice 12 27
Sorghum 13 83
Bulrush
millet 14 79
Finger
millet 15 79
Wheat 16 15
Barley 17 44
Sweet
Potatoes 22 122
Irish
potatoes 23 116
Yams 24 137
Cocoyams 25 0136
Onions 26 402/403
Ginger 27 0720
Crop
Group
Crop name TZA
Code
FAO
Code
Permanent
Crops Palm oil 44 0257
Coconut 45 0249
Cashewnut 46 217
Sisal 53 0789
Coffee 54 656
Tea 55 667
Cocoa 56 661
Rubber 57 0836
Kapok 59 0778
Sugar cane 60 157
Cardamom 61 0702
Tamarin 63 0619
Crop
Group
Crop
name
TZA
Code
FAO
Code
Permanen
t Crops
(Contd)
Cinamon 64 693
Nutmeg 65 0702
Clove 66 0698
Black
Pepper 18 0687
Pegion
pea 34 0197
Cassava 21 125
Pineappl
e 75 574
Passion
Fruit 70 0603
Banana
(Food/Pl
antn) 71 489
48
Avocado 72
57
2
Mango 73 571
Pawpaw 74 600
Orange 76 490
Grapefru
it 77 507
Grapes 78 0620
Mandari
n 79 495
Guava 80 0603
Plums 81 536
Apples 82 515
Crop
Group
Crop
name
TZA
Code
FAO
Code
Permanen
t Crops
(Contd)
Pears 83 0521
Peaches 84 521
Lime/Le
mon 85 0497
Pomelo 68 0507
Jackfruit 69 0603
Durian 97 0603
Bilimbi 98 603
Rambuta
n 99 0603
Bread
fruit 67 603
Malay
apple 38 603
Star fruit 39 603
49
Annex 8: Livestock Groups, Names, Tanzanian and FAO Codes
Livestock Group Livestock name TZA
Code
FAO Code
Cattle 01
0866
Goat 02
1016
Sheep 03
0976
Pig 04
1034
Chicken 05
1057
Donkey 06
1110
Duck 07
1068
Turkey 08
1079
Rabbit 09
1140
Guinea pig 11
1150
