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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [V819]
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
115992
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  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • 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: annual crop and vegetable production-long rainy seasonR072

Overview

Valid: 90008
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 1372
1.5%
012 Mpwapwa 810
0.9%
013 Kongwa 737
0.8%
014 Dodoma Rural 1157
1.3%
015 Dodoma Urban 1149
1.3%
021 Monduli 493
0.5%
022 Arumeru 712
0.8%
023 Arusha 113
0.1%
024 Karatu 708
0.8%
025 Ngorongoro 454
0.5%
031 Rombo 638
0.7%
032 Mwanga 633
0.7%
033 Same 405
0.4%
034 Moshi Rural 780
0.9%
035 Hai 700
0.8%
036 Moshi Urban 0
0%
041 Lushoto 891
1%
042 Korogwe 613
0.7%
043 Muheza 697
0.8%
044 Tanga 308
0.3%
045 Pangani 442
0.5%
046 Handeni 545
0.6%
047 Kilindi 551
0.6%
051 Kilosa 1065
1.2%
052 Morogoro Rural 724
0.8%
053 Kilombero 481
0.5%
054 Ulanga 601
0.7%
055 Morogoro Urban 757
0.8%
056 Mvomero 534
0.6%
061 Bagamoyo 570
0.6%
062 Kibaha 457
0.5%
063 Kisarawe 232
0.3%
064 Mkuranga 154
0.2%
065 Rufiji 183
0.2%
066 Mafia 178
0.2%
071 Kinondoni 429
0.5%
072 Ilala 134
0.1%
073 Temeke 468
0.5%
081 Kilwa 867
1%
082 Lindi Rural 1282
1.4%
083 Nachingwea 1172
1.3%
084 Liwale 1150
1.3%
085 Ruangwa 974
1.1%
086 Lindi Urban 215
0.2%
091 Mtwara Rural 917
1%
092 Newala 1431
1.6%
093 Masasi 1506
1.7%
094 Tandahimba 1056
1.2%
095 Mtwara Urban 176
0.2%
101 Tunduru 1366
1.5%
102 Songea Rural 1405
1.6%
103 Mbinga 1526
1.7%
104 Songea Urban 906
1%
105 Namtumbo 1689
1.9%
111 Iringa Rural 837
0.9%
112 Mufindi 993
1.1%
113 Makete 1654
1.8%
114 Njombe 1016
1.1%
115 Ludewa 1195
1.3%
116 Iringa Urban 207
0.2%
117 Kilolo 892
1%
121 Chunya 866
1%
122 Mbeya Rural 946
1.1%
123 Kyela 696
0.8%
124 Rungwe 781
0.9%
125 Ileje 1325
1.5%
126 Mbozi' 1560
1.7%
127 Mbarali 779
0.9%
128 Mbeya Urban 765
0.8%
131 Iramba 985
1.1%
132 Singida Rural 1303
1.4%
133 Manyoni 965
1.1%
134 Singida Urban 527
0.6%
141 Nzega 1166
1.3%
142 Igunga 1140
1.3%
143 Uyui 1146
1.3%
144 Urambo 1359
1.5%
145 Sikonge 1135
1.3%
146 Tabora Urban 1291
1.4%
151 Mpanda 1195
1.3%
152 Sumbawanga Rural 1511
1.7%
153 Nkansi 977
1.1%
154 Sumbawanga Urban 957
1.1%
161 Kibondo 166
0.2%
162 Kasulu 336
0.4%
163 Kigoma Rural 410
0.5%
164 Kigoma Urban 86
0.1%
171 Bariadi 1064
1.2%
172 Maswa 1313
1.5%
173 Shinyanga Rural 1502
1.7%
174 Kahama 1792
2%
175 Bukombe 1178
1.3%
176 Meatu 1052
1.2%
177 Shinyanga Urban 826
0.9%
178 Kishapu 1455
1.6%
181 Karagwe 454
0.5%
182 Bukoba Rural 409
0.5%
183 Muleba 148
0.2%
184 Biharamulo 189
0.2%
185 Ngara 495
0.5%
186 Bukoba Urban 32
0%
191 Ukerewe 292
0.3%
192 Magu 137
0.2%
193 Nyamagana 0
0%
194 Kwimba 233
0.3%
195 Sengerema 259
0.3%
196 Geita 171
0.2%
197 Misungwi 272
0.3%
198 Ilemela 253
0.3%
201 Tarime 755
0.8%
202 Serengeti 747
0.8%
203 Musoma Rural 504
0.6%
204 Bunda 283
0.3%
205 Musoma Urban 34
0%
211 Babati 685
0.8%
212 Hanang 853
0.9%
213 Mbulu 928
1%
214 Simanjiro 498
0.6%
215 Kiteto 515
0.6%
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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