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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [V1051]
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
125728
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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: marketing problem R102

Overview

Valid: 522462
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 7117
1.4%
012 Mpwapwa 4160
0.8%
013 Kongwa 3344
0.6%
014 Dodoma Rural 3888
0.7%
015 Dodoma Urban 2592
0.5%
021 Monduli 2022
0.4%
022 Arumeru 4989
1%
023 Arusha 576
0.1%
024 Karatu 3711
0.7%
025 Ngorongoro 1504
0.3%
031 Rombo 5760
1.1%
032 Mwanga 4005
0.8%
033 Same 4192
0.8%
034 Moshi Rural 7936
1.5%
035 Hai 4478
0.9%
036 Moshi Urban 0
0%
041 Lushoto 7712
1.5%
042 Korogwe 4600
0.9%
043 Muheza 7958
1.5%
044 Tanga 3760
0.7%
045 Pangani 4078
0.8%
046 Handeni 4592
0.9%
047 Kilindi 4832
0.9%
051 Kilosa 5712
1.1%
052 Morogoro Rural 4907
0.9%
053 Kilombero 5631
1.1%
054 Ulanga 4784
0.9%
055 Morogoro Urban 4524
0.9%
056 Mvomero 4384
0.8%
061 Bagamoyo 2057
0.4%
062 Kibaha 3072
0.6%
063 Kisarawe 3968
0.8%
064 Mkuranga 5200
1%
065 Rufiji 3967
0.8%
066 Mafia 3744
0.7%
071 Kinondoni 2416
0.5%
072 Ilala 1232
0.2%
073 Temeke 4608
0.9%
081 Kilwa 4800
0.9%
082 Lindi Rural 4640
0.9%
083 Nachingwea 4816
0.9%
084 Liwale 5584
1.1%
085 Ruangwa 4400
0.8%
086 Lindi Urban 640
0.1%
091 Mtwara Rural 4720
0.9%
092 Newala 5040
1%
093 Masasi 5856
1.1%
094 Tandahimba 5008
1%
095 Mtwara Urban 656
0.1%
101 Tunduru 6912
1.3%
102 Songea Rural 5918
1.1%
103 Mbinga 8732
1.7%
104 Songea Urban 3360
0.6%
105 Namtumbo 6108
1.2%
111 Iringa Rural 3664
0.7%
112 Mufindi 3983
0.8%
113 Makete 7853
1.5%
114 Njombe 5196
1%
115 Ludewa 4368
0.8%
116 Iringa Urban 480
0.1%
117 Kilolo 5068
1%
121 Chunya 2768
0.5%
122 Mbeya Rural 6079
1.2%
123 Kyela 5488
1.1%
124 Rungwe 8624
1.7%
125 Ileje 5423
1%
126 Mbozi' 8432
1.6%
127 Mbarali 3440
0.7%
128 Mbeya Urban 4368
0.8%
131 Iramba 2864
0.5%
132 Singida Rural 4832
0.9%
133 Manyoni 3392
0.6%
134 Singida Urban 1104
0.2%
141 Nzega 3022
0.6%
142 Igunga 3120
0.6%
143 Uyui 3408
0.7%
144 Urambo 5776
1.1%
145 Sikonge 3996
0.8%
146 Tabora Urban 2960
0.6%
151 Mpanda 5584
1.1%
152 Sumbawanga Rural 8304
1.6%
153 Nkansi 5136
1%
154 Sumbawanga Urban 5072
1%
161 Kibondo 4240
0.8%
162 Kasulu 6096
1.2%
163 Kigoma Rural 5936
1.1%
164 Kigoma Urban 1040
0.2%
171 Bariadi 4768
0.9%
172 Maswa 5120
1%
173 Shinyanga Rural 3152
0.6%
174 Kahama 5616
1.1%
175 Bukombe 3867
0.7%
176 Meatu 3824
0.7%
177 Shinyanga Urban 400
0.1%
178 Kishapu 3648
0.7%
181 Karagwe 6656
1.3%
182 Bukoba Rural 8864
1.7%
183 Muleba 6164
1.2%
184 Biharamulo 6224
1.2%
185 Ngara 5296
1%
186 Bukoba Urban 1152
0.2%
191 Ukerewe 4539
0.9%
192 Magu 5383
1%
193 Nyamagana 0
0%
194 Kwimba 4448
0.9%
195 Sengerema 5584
1.1%
196 Geita 6896
1.3%
197 Misungwi 4320
0.8%
198 Ilemela 2463
0.5%
201 Tarime 6544
1.3%
202 Serengeti 5008
1%
203 Musoma Rural 4896
0.9%
204 Bunda 4352
0.8%
205 Musoma Urban 160
0%
211 Babati 5152
1%
212 Hanang 3360
0.6%
213 Mbulu 3088
0.6%
214 Simanjiro 1632
0.3%
215 Kiteto 3568
0.7%
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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