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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [F104]
Agricultural-Census

Agriculture Sample Census Survey 2002-2003

United Republic of Tanzania, 2004
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Reference ID
TZA_2002-2003_ASCS_v01_EN_M_v01_A_OCS
Producer(s)
National Bureau of Statistics, Office of Chief Government Statistician-Zanzibar
Collections
Agricultural Censuses
Metadata
Documentation in PDF DDI/XML JSON
Created on
Oct 24, 2019
Last modified
Oct 24, 2019
Page views
114230
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  • Study Description
  • Data Dictionary
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  • Data files
  • annual crop and
    vegetable
    production-long
    rainy
    seasonR072
  • annual crop and
    vegetable
    production-short
    rainy
    seasonR071
  • Cattle diseases
    R185
  • Cattle intake
    R183
  • Cattle offtake
    R184
  • Cattle
    population,
    intake and
    offtake R182
  • crop extension
    messages R152
  • crop extension
    services R151
  • crop storage
    R092
  • permanentperennial
    crops and fruit
    tree production
    R073
  • Goat diseases
    R195
  • Goat intake
    during 20022003
    R193
  • Goat offtake
    R194
  • Goat
    population,
    intake and
    offtake R192
  • Goat
    population,
    intake and
    offtake R092_1
  • Goat
    population,
    intake and
    offtakeR092_1
  • Livestock
    extension R291
  • Livestock
    extension
    service
    providers R292
  • Livestock Pest
    and Parasite
    control R220
  • Livestock
    product R250
  • marketing
    problem R102
  • marketing
    problem R103
  • Milk production
    R186
  • Milk production
    R196
  • access and use
    of resources
    R061
  • access to farm
    inputs and
    implements R121
  • access to
    functional
    Livestock
    StructuresaccessoriesR270
  • activities of
    the
    householdR022
  • Agroprocessing
    and
    by-productsR082
  • Animal
    contribution to
    crop production
    R171
  • Chicken
    diseases R240
  • credit for
    Agriculture
    purposesR13_1
  • erosion
    controlwater
    harvesting
    facilities R112
  • farm implements
    and assets used
    and owned by
    the household
    during
    2002-2003 R122
  • Fish farming
    R282
  • household
    information
    R031
  • Household
    information,
    Agriculture
    activities,
    Livestock
    informationR00
  • labour use R311
  • land
    accessownershiptenure
    R041
  • land use R051
  • livelihood
    constraints
    R162
  • livelihood
    constraintsR161
  • other Livestock
    R230
  • outlets for the
    sale of
    Livestock R260
  • related to
    respondent and
    household head
    R122_00
  • tree
    farmingagroforestry
    R142
  • use of
    secondary
    products R076
  • Pig
    diseasespestsconditions
    R215
  • Pig increase
    R213
  • Pig offtake
    R214
  • Pig population
    and production
    R212
  • Sheep diseases
    R205
  • Sheep intake
    R203
  • Sheep offtake
    R204
  • Sheep
    population,
    intake and
    offtake R202
  • R321
  • R331
  • R332
  • use of credit
    for agriculture
    purposes R130
  • use of credit
    for agriculture
    purposes R131
  • use of credit
    for agriculture
    purposes R132

District (District)

Data file: other Livestock R230

Overview

Valid: 434796
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 5400
1.2%
012 Mpwapwa 3637
0.8%
013 Kongwa 3629
0.8%
014 Dodoma Rural 4034
0.9%
015 Dodoma Urban 3620
0.8%
021 Monduli 3645
0.8%
022 Arumeru 4042
0.9%
023 Arusha 540
0.1%
024 Karatu 3637
0.8%
025 Ngorongoro 3645
0.8%
031 Rombo 3645
0.8%
032 Mwanga 3637
0.8%
033 Same 3645
0.8%
034 Moshi Rural 5378
1.2%
035 Hai 3637
0.8%
036 Moshi Urban 0
0%
041 Lushoto 5400
1.2%
042 Korogwe 4033
0.9%
043 Muheza 5384
1.2%
044 Tanga 3203
0.7%
045 Pangani 3223
0.7%
046 Handeni 4050
0.9%
047 Kilindi 3636
0.8%
051 Kilosa 5392
1.2%
052 Morogoro Rural 4049
0.9%
053 Kilombero 3645
0.8%
054 Ulanga 3645
0.8%
055 Morogoro Urban 3367
0.8%
056 Mvomero 3645
0.8%
061 Bagamoyo 3645
0.8%
062 Kibaha 3645
0.8%
063 Kisarawe 3645
0.8%
064 Mkuranga 4050
0.9%
065 Rufiji 3645
0.8%
066 Mafia 2682
0.6%
071 Kinondoni 2700
0.6%
072 Ilala 1350
0.3%
073 Temeke 3105
0.7%
081 Kilwa 3636
0.8%
082 Lindi Rural 4042
0.9%
083 Nachingwea 3628
0.8%
084 Liwale 3597
0.8%
085 Ruangwa 3645
0.8%
086 Lindi Urban 810
0.2%
091 Mtwara Rural 4042
0.9%
092 Newala 4033
0.9%
093 Masasi 5400
1.2%
094 Tandahimba 4050
0.9%
095 Mtwara Urban 810
0.2%
101 Tunduru 4050
0.9%
102 Songea Rural 3645
0.8%
103 Mbinga 5400
1.2%
104 Songea Urban 2295
0.5%
105 Namtumbo 3645
0.8%
111 Iringa Rural 4050
0.9%
112 Mufindi 4050
0.9%
113 Makete 5400
1.2%
114 Njombe 3645
0.8%
115 Ludewa 3645
0.8%
116 Iringa Urban 810
0.2%
117 Kilolo 3645
0.8%
121 Chunya 3637
0.8%
122 Mbeya Rural 4050
0.9%
123 Kyela 3645
0.8%
124 Rungwe 5392
1.2%
125 Ileje 3645
0.8%
126 Mbozi' 5400
1.2%
127 Mbarali 3645
0.8%
128 Mbeya Urban 3240
0.7%
131 Iramba 4050
0.9%
132 Singida Rural 5400
1.2%
133 Manyoni 3645
0.8%
134 Singida Urban 2430
0.6%
141 Nzega 4050
0.9%
142 Igunga 3645
0.8%
143 Uyui 3645
0.8%
144 Urambo 4050
0.9%
145 Sikonge 3627
0.8%
146 Tabora Urban 3637
0.8%
151 Mpanda 4050
0.9%
152 Sumbawanga Rural 5400
1.2%
153 Nkansi 3645
0.8%
154 Sumbawanga Urban 3510
0.8%
161 Kibondo 3645
0.8%
162 Kasulu 3645
0.8%
163 Kigoma Rural 3645
0.8%
164 Kigoma Urban 675
0.2%
171 Bariadi 4050
0.9%
172 Maswa 3645
0.8%
173 Shinyanga Rural 4050
0.9%
174 Kahama 5400
1.2%
175 Bukombe 4050
0.9%
176 Meatu 3645
0.8%
177 Shinyanga Urban 3105
0.7%
178 Kishapu 4050
0.9%
181 Karagwe 4050
0.9%
182 Bukoba Rural 5400
1.2%
183 Muleba 4050
0.9%
184 Biharamulo 4050
0.9%
185 Ngara 3645
0.8%
186 Bukoba Urban 1080
0.2%
191 Ukerewe 3645
0.8%
192 Magu 4050
0.9%
193 Nyamagana 0
0%
194 Kwimba 4050
0.9%
195 Sengerema 4050
0.9%
196 Geita 5400
1.2%
197 Misungwi 3645
0.8%
198 Ilemela 2430
0.6%
201 Tarime 5400
1.2%
202 Serengeti 3645
0.8%
203 Musoma Rural 4050
0.9%
204 Bunda 3645
0.8%
205 Musoma Urban 405
0.1%
211 Babati 3600
0.8%
212 Hanang 3645
0.8%
213 Mbulu 3645
0.8%
214 Simanjiro 3645
0.8%
215 Kiteto 3645
0.8%
Warning: these figures indicate the number of cases found in the data file. They cannot be interpreted as summary statistics of the population of interest.
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