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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [V927]
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
114780
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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: crop storage R092

Overview

Valid: 382252
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 4348
1.1%
012 Mpwapwa 2880
0.8%
013 Kongwa 2370
0.6%
014 Dodoma Rural 2211
0.6%
015 Dodoma Urban 3079
0.8%
021 Monduli 1800
0.5%
022 Arumeru 3490
0.9%
023 Arusha 550
0.1%
024 Karatu 3540
0.9%
025 Ngorongoro 2322
0.6%
031 Rombo 2910
0.8%
032 Mwanga 2330
0.6%
033 Same 2420
0.6%
034 Moshi Rural 3840
1%
035 Hai 2829
0.7%
036 Moshi Urban 0
0%
041 Lushoto 5700
1.5%
042 Korogwe 3550
0.9%
043 Muheza 5000
1.3%
044 Tanga 1790
0.5%
045 Pangani 2500
0.7%
046 Handeni 3860
1%
047 Kilindi 3680
1%
051 Kilosa 4870
1.3%
052 Morogoro Rural 3533
0.9%
053 Kilombero 3860
1%
054 Ulanga 3570
0.9%
055 Morogoro Urban 2861
0.7%
056 Mvomero 3330
0.9%
061 Bagamoyo 1609
0.4%
062 Kibaha 930
0.2%
063 Kisarawe 2000
0.5%
064 Mkuranga 580
0.2%
065 Rufiji 1220
0.3%
066 Mafia 1270
0.3%
071 Kinondoni 1390
0.4%
072 Ilala 400
0.1%
073 Temeke 1770
0.5%
081 Kilwa 1270
0.3%
082 Lindi Rural 3190
0.8%
083 Nachingwea 3640
1%
084 Liwale 2830
0.7%
085 Ruangwa 2620
0.7%
086 Lindi Urban 190
0%
091 Mtwara Rural 2100
0.5%
092 Newala 3980
1%
093 Masasi 5120
1.3%
094 Tandahimba 2620
0.7%
095 Mtwara Urban 360
0.1%
101 Tunduru 4370
1.1%
102 Songea Rural 4000
1%
103 Mbinga 5560
1.5%
104 Songea Urban 2480
0.6%
105 Namtumbo 3890
1%
111 Iringa Rural 3980
1%
112 Mufindi 4170
1.1%
113 Makete 5849
1.5%
114 Njombe 3600
0.9%
115 Ludewa 3720
1%
116 Iringa Urban 700
0.2%
117 Kilolo 3840
1%
121 Chunya 3330
0.9%
122 Mbeya Rural 3870
1%
123 Kyela 3740
1%
124 Rungwe 5600
1.5%
125 Ileje 3940
1%
126 Mbozi' 5860
1.5%
127 Mbarali 3030
0.8%
128 Mbeya Urban 3510
0.9%
131 Iramba 3831
1%
132 Singida Rural 5700
1.5%
133 Manyoni 3720
1%
134 Singida Urban 2350
0.6%
141 Nzega 4270
1.1%
142 Igunga 3300
0.9%
143 Uyui 3729
1%
144 Urambo 4330
1.1%
145 Sikonge 3979
1%
146 Tabora Urban 3820
1%
151 Mpanda 4430
1.2%
152 Sumbawanga Rural 5610
1.5%
153 Nkansi 3680
1%
154 Sumbawanga Urban 3770
1%
161 Kibondo 3960
1%
162 Kasulu 3950
1%
163 Kigoma Rural 3710
1%
164 Kigoma Urban 670
0.2%
171 Bariadi 3790
1%
172 Maswa 2410
0.6%
173 Shinyanga Rural 4150
1.1%
174 Kahama 5770
1.5%
175 Bukombe 4040
1.1%
176 Meatu 3240
0.8%
177 Shinyanga Urban 2160
0.6%
178 Kishapu 3310
0.9%
181 Karagwe 4100
1.1%
182 Bukoba Rural 5490
1.4%
183 Muleba 4150
1.1%
184 Biharamulo 3810
1%
185 Ngara 3680
1%
186 Bukoba Urban 1060
0.3%
191 Ukerewe 1490
0.4%
192 Magu 3959
1%
193 Nyamagana 0
0%
194 Kwimba 3950
1%
195 Sengerema 4350
1.1%
196 Geita 5800
1.5%
197 Misungwi 3780
1%
198 Ilemela 2452
0.6%
201 Tarime 4270
1.1%
202 Serengeti 3651
1%
203 Musoma Rural 2570
0.7%
204 Bunda 3430
0.9%
205 Musoma Urban 140
0%
211 Babati 3670
1%
212 Hanang 3130
0.8%
213 Mbulu 3170
0.8%
214 Simanjiro 1700
0.4%
215 Kiteto 3620
0.9%
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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