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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [F83]
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
115690
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  • Study Description
  • Data Dictionary
  • Downloads
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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 R103

Overview

Valid: 163310
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 2225
1.4%
012 Mpwapwa 1300
0.8%
013 Kongwa 1045
0.6%
014 Dodoma Rural 1215
0.7%
015 Dodoma Urban 810
0.5%
021 Monduli 635
0.4%
022 Arumeru 1560
1%
023 Arusha 180
0.1%
024 Karatu 1160
0.7%
025 Ngorongoro 470
0.3%
031 Rombo 1800
1.1%
032 Mwanga 1255
0.8%
033 Same 1310
0.8%
034 Moshi Rural 2480
1.5%
035 Hai 1400
0.9%
036 Moshi Urban 0
0%
041 Lushoto 2410
1.5%
042 Korogwe 1440
0.9%
043 Muheza 2490
1.5%
044 Tanga 1175
0.7%
045 Pangani 1275
0.8%
046 Handeni 1435
0.9%
047 Kilindi 1510
0.9%
051 Kilosa 1785
1.1%
052 Morogoro Rural 1535
0.9%
053 Kilombero 1760
1.1%
054 Ulanga 1495
0.9%
055 Morogoro Urban 1415
0.9%
056 Mvomero 1370
0.8%
061 Bagamoyo 645
0.4%
062 Kibaha 960
0.6%
063 Kisarawe 1240
0.8%
064 Mkuranga 1625
1%
065 Rufiji 1240
0.8%
066 Mafia 1170
0.7%
071 Kinondoni 755
0.5%
072 Ilala 385
0.2%
073 Temeke 1440
0.9%
081 Kilwa 1500
0.9%
082 Lindi Rural 1450
0.9%
083 Nachingwea 1505
0.9%
084 Liwale 1745
1.1%
085 Ruangwa 1375
0.8%
086 Lindi Urban 200
0.1%
091 Mtwara Rural 1475
0.9%
092 Newala 1575
1%
093 Masasi 1830
1.1%
094 Tandahimba 1565
1%
095 Mtwara Urban 205
0.1%
101 Tunduru 2160
1.3%
102 Songea Rural 1850
1.1%
103 Mbinga 2730
1.7%
104 Songea Urban 1050
0.6%
105 Namtumbo 1910
1.2%
111 Iringa Rural 1145
0.7%
112 Mufindi 1245
0.8%
113 Makete 2455
1.5%
114 Njombe 1625
1%
115 Ludewa 1365
0.8%
116 Iringa Urban 150
0.1%
117 Kilolo 1585
1%
121 Chunya 865
0.5%
122 Mbeya Rural 1900
1.2%
123 Kyela 1715
1.1%
124 Rungwe 2695
1.7%
125 Ileje 1695
1%
126 Mbozi' 2635
1.6%
127 Mbarali 1075
0.7%
128 Mbeya Urban 1365
0.8%
131 Iramba 895
0.5%
132 Singida Rural 1510
0.9%
133 Manyoni 1060
0.6%
134 Singida Urban 345
0.2%
141 Nzega 945
0.6%
142 Igunga 975
0.6%
143 Uyui 1065
0.7%
144 Urambo 1805
1.1%
145 Sikonge 1250
0.8%
146 Tabora Urban 925
0.6%
151 Mpanda 1745
1.1%
152 Sumbawanga Rural 2595
1.6%
153 Nkansi 1605
1%
154 Sumbawanga Urban 1585
1%
161 Kibondo 1325
0.8%
162 Kasulu 1905
1.2%
163 Kigoma Rural 1855
1.1%
164 Kigoma Urban 325
0.2%
171 Bariadi 1490
0.9%
172 Maswa 1600
1%
173 Shinyanga Rural 985
0.6%
174 Kahama 1755
1.1%
175 Bukombe 1210
0.7%
176 Meatu 1195
0.7%
177 Shinyanga Urban 125
0.1%
178 Kishapu 1140
0.7%
181 Karagwe 2080
1.3%
182 Bukoba Rural 2770
1.7%
183 Muleba 1930
1.2%
184 Biharamulo 1945
1.2%
185 Ngara 1655
1%
186 Bukoba Urban 360
0.2%
191 Ukerewe 1420
0.9%
192 Magu 1685
1%
193 Nyamagana 0
0%
194 Kwimba 1390
0.9%
195 Sengerema 1745
1.1%
196 Geita 2155
1.3%
197 Misungwi 1350
0.8%
198 Ilemela 770
0.5%
201 Tarime 2045
1.3%
202 Serengeti 1565
1%
203 Musoma Rural 1530
0.9%
204 Bunda 1360
0.8%
205 Musoma Urban 50
0%
211 Babati 1610
1%
212 Hanang 1050
0.6%
213 Mbulu 965
0.6%
214 Simanjiro 510
0.3%
215 Kiteto 1115
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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