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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-CENSUS / TZA_2002-2003_ASCS_V01_EN_M_V01_A_OCS / variable [F80]
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
114069
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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: Livestock Pest and Parasite control R220

Overview

Valid: 48345
Invalid: 0
Type: Discrete
Width: 3
Range: -
Format: character

Questions and instructions

Categories
Value Category Cases
011 Kondoa 600
1.2%
012 Mpwapwa 405
0.8%
013 Kongwa 405
0.8%
014 Dodoma Rur 450
0.9%
015 Dodoma Urb 404
0.8%
021 Monduli 405
0.8%
022 Arumeru 450
0.9%
023 Arusha 60
0.1%
024 Karatu 405
0.8%
025 Ngorongoro 405
0.8%
031 Rombo 405
0.8%
032 Mwanga 405
0.8%
033 Same 405
0.8%
034 Moshi Rur 600
1.2%
035 Hai 405
0.8%
041 Lushoto 600
1.2%
042 Korogwe 449
0.9%
043 Muheza 600
1.2%
044 Tanga 360
0.7%
045 Pangani 360
0.7%
046 Handeni 450
0.9%
047 Kilindi 404
0.8%
051 Kilosa 600
1.2%
052 Morogoro 450
0.9%
053 Kilombero 405
0.8%
054 Ulanga 405
0.8%
055 Morogoro Urb 375
0.8%
056 Mvomero 405
0.8%
061 Bagamoyo 405
0.8%
062 Kibaha 405
0.8%
063 Kisarawe 405
0.8%
064 Mkuranga 450
0.9%
065 Rufiji 405
0.8%
066 Mafia 298
0.6%
071 Kinondoni 300
0.6%
072 Ilala 150
0.3%
073 Temeke 345
0.7%
081 Kilwa 404
0.8%
082 Lindi Rur 450
0.9%
083 Nachingwea 404
0.8%
084 Liwale 405
0.8%
085 Ruangwa 405
0.8%
086 Lindi Urb 90
0.2%
091 Mtwara Rur 450
0.9%
092 Newala 449
0.9%
093 Masasi 600
1.2%
094 Tandahimba 450
0.9%
095 Mtwara Urb 90
0.2%
101 Tunduru 450
0.9%
102 Songea Rur 405
0.8%
103 Mbinga 600
1.2%
104 Songea Urb 255
0.5%
105 Namtumbo 405
0.8%
111 Iringa Rur 450
0.9%
112 Mufindi 450
0.9%
113 Njombe 600
1.2%
114 Ludewa 405
0.8%
115 Makete 405
0.8%
116 Iringa Urb 90
0.2%
117 Kilolo 405
0.8%
121 Chunya 405
0.8%
122 Mbeya Rur 450
0.9%
123 Kyela 405
0.8%
124 Rungwe 600
1.2%
125 Ileje 405
0.8%
126 Mbozi 600
1.2%
127 Mbarali 405
0.8%
128 Mbeya Urb 360
0.7%
131 Iramba 450
0.9%
132 Singida Rur 600
1.2%
133 Manyoni 405
0.8%
134 Singida Urb 270
0.6%
141 Nzega 450
0.9%
142 Igunga 405
0.8%
143 Uyui 405
0.8%
144 Urambo 450
0.9%
145 Sikonge 403
0.8%
146 Tabora Urb 405
0.8%
151 Mpanda 450
0.9%
152 Sumbawanga Rur 600
1.2%
153 Nkasi 405
0.8%
154 Sumbawanga Urb 390
0.8%
161 Kibondo 405
0.8%
162 Kasulu 405
0.8%
163 Kigoma Rur 405
0.8%
164 Kigoma Urb 75
0.2%
171 Bariadi 450
0.9%
172 Maswa 405
0.8%
173 Shinyanga Rur 450
0.9%
174 Kahama 600
1.2%
175 Bukombe 450
0.9%
176 Meatu 405
0.8%
177 Shinyanga Urb 345
0.7%
178 Kishapu 450
0.9%
181 Karagwe 450
0.9%
182 Bukoba Rur 600
1.2%
183 Muleba 450
0.9%
184 Biharamulo 450
0.9%
185 Ngara 405
0.8%
186 Bukoba Urb 120
0.2%
191 Ukerewe 405
0.8%
192 Magu 450
0.9%
194 Kwimba 450
0.9%
195 Sengerema 450
0.9%
196 Geita 600
1.2%
197 Missungwi 405
0.8%
198 Ilemela 270
0.6%
201 Tarime 600
1.2%
202 Serengeti 405
0.8%
203 Musoma Rur 450
0.9%
204 Bunda 405
0.8%
205 Musoma Urb 45
0.1%
211 Babati 400
0.8%
212 Hanang 405
0.8%
213 Mbulu 405
0.8%
214 Simanjiro 405
0.8%
215 Kiteto 405
0.8%
511 Kaskazini-A 0
0%
512 Kaskazini-B 0
0%
521 Kati 0
0%
522 Kusini 0
0%
531 Magharibi 0
0%
541 Wete 0
0%
542 Micheweni 0
0%
551 Chake 0
0%
552 Mkoani 0
0%
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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