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
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Metadata
Created on
Oct 24, 2019
Last modified
Oct 24, 2019
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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: livelihood constraintsR161
Overview
Valid:
241725
Invalid:
0
Type:
Discrete
Width:
3
Range:
-
Format:
character
Questions and instructions
Categories
| Value | Category | Cases | |
|---|---|---|---|
| 011 | Kondoa | 3000 |
1.2%
|
| 012 | Mpwapwa | 2025 |
0.8%
|
| 013 | Kongwa | 2025 |
0.8%
|
| 014 | Dodoma Rural | 2250 |
0.9%
|
| 015 | Dodoma Urban | 2020 |
0.8%
|
| 021 | Monduli | 2025 |
0.8%
|
| 022 | Arumeru | 2250 |
0.9%
|
| 023 | Arusha | 300 |
0.1%
|
| 024 | Karatu | 2025 |
0.8%
|
| 025 | Ngorongoro | 2025 |
0.8%
|
| 031 | Rombo | 2025 |
0.8%
|
| 032 | Mwanga | 2025 |
0.8%
|
| 033 | Same | 2025 |
0.8%
|
| 034 | Moshi Rural | 3000 |
1.2%
|
| 035 | Hai | 2025 |
0.8%
|
| 036 | Moshi Urban | 0 |
0%
|
| 041 | Lushoto | 3000 |
1.2%
|
| 042 | Korogwe | 2245 |
0.9%
|
| 043 | Muheza | 3000 |
1.2%
|
| 044 | Tanga | 1800 |
0.7%
|
| 045 | Pangani | 1800 |
0.7%
|
| 046 | Handeni | 2250 |
0.9%
|
| 047 | Kilindi | 2020 |
0.8%
|
| 051 | Kilosa | 3000 |
1.2%
|
| 052 | Morogoro Rural | 2250 |
0.9%
|
| 053 | Kilombero | 2025 |
0.8%
|
| 054 | Ulanga | 2025 |
0.8%
|
| 055 | Morogoro Urban | 1875 |
0.8%
|
| 056 | Mvomero | 2025 |
0.8%
|
| 061 | Bagamoyo | 2025 |
0.8%
|
| 062 | Kibaha | 2025 |
0.8%
|
| 063 | Kisarawe | 2025 |
0.8%
|
| 064 | Mkuranga | 2250 |
0.9%
|
| 065 | Rufiji | 2025 |
0.8%
|
| 066 | Mafia | 1490 |
0.6%
|
| 071 | Kinondoni | 1500 |
0.6%
|
| 072 | Ilala | 750 |
0.3%
|
| 073 | Temeke | 1725 |
0.7%
|
| 081 | Kilwa | 2020 |
0.8%
|
| 082 | Lindi Rural | 2250 |
0.9%
|
| 083 | Nachingwea | 2020 |
0.8%
|
| 084 | Liwale | 2025 |
0.8%
|
| 085 | Ruangwa | 2025 |
0.8%
|
| 086 | Lindi Urban | 450 |
0.2%
|
| 091 | Mtwara Rural | 2250 |
0.9%
|
| 092 | Newala | 2245 |
0.9%
|
| 093 | Masasi | 3000 |
1.2%
|
| 094 | Tandahimba | 2250 |
0.9%
|
| 095 | Mtwara Urban | 450 |
0.2%
|
| 101 | Tunduru | 2250 |
0.9%
|
| 102 | Songea Rural | 2025 |
0.8%
|
| 103 | Mbinga | 3000 |
1.2%
|
| 104 | Songea Urban | 1275 |
0.5%
|
| 105 | Namtumbo | 2025 |
0.8%
|
| 111 | Iringa Rural | 2250 |
0.9%
|
| 112 | Mufindi | 2250 |
0.9%
|
| 113 | Makete | 3000 |
1.2%
|
| 114 | Njombe | 2025 |
0.8%
|
| 115 | Ludewa | 2025 |
0.8%
|
| 116 | Iringa Urban | 450 |
0.2%
|
| 117 | Kilolo | 2025 |
0.8%
|
| 121 | Chunya | 2025 |
0.8%
|
| 122 | Mbeya Rural | 2250 |
0.9%
|
| 123 | Kyela | 2025 |
0.8%
|
| 124 | Rungwe | 3000 |
1.2%
|
| 125 | Ileje | 2025 |
0.8%
|
| 126 | Mbozi' | 3000 |
1.2%
|
| 127 | Mbarali | 2025 |
0.8%
|
| 128 | Mbeya Urban | 1800 |
0.7%
|
| 131 | Iramba | 2250 |
0.9%
|
| 132 | Singida Rural | 3000 |
1.2%
|
| 133 | Manyoni | 2025 |
0.8%
|
| 134 | Singida Urban | 1350 |
0.6%
|
| 141 | Nzega | 2250 |
0.9%
|
| 142 | Igunga | 2025 |
0.8%
|
| 143 | Uyui | 2025 |
0.8%
|
| 144 | Urambo | 2250 |
0.9%
|
| 145 | Sikonge | 2015 |
0.8%
|
| 146 | Tabora Urban | 2025 |
0.8%
|
| 151 | Mpanda | 2250 |
0.9%
|
| 152 | Sumbawanga Rural | 3000 |
1.2%
|
| 153 | Nkansi | 2025 |
0.8%
|
| 154 | Sumbawanga Urban | 1950 |
0.8%
|
| 161 | Kibondo | 2025 |
0.8%
|
| 162 | Kasulu | 2025 |
0.8%
|
| 163 | Kigoma Rural | 2025 |
0.8%
|
| 164 | Kigoma Urban | 375 |
0.2%
|
| 171 | Bariadi | 2250 |
0.9%
|
| 172 | Maswa | 2025 |
0.8%
|
| 173 | Shinyanga Rural | 2250 |
0.9%
|
| 174 | Kahama | 3000 |
1.2%
|
| 175 | Bukombe | 2250 |
0.9%
|
| 176 | Meatu | 2025 |
0.8%
|
| 177 | Shinyanga Urban | 1725 |
0.7%
|
| 178 | Kishapu | 2250 |
0.9%
|
| 181 | Karagwe | 2250 |
0.9%
|
| 182 | Bukoba Rural | 3000 |
1.2%
|
| 183 | Muleba | 2250 |
0.9%
|
| 184 | Biharamulo | 2250 |
0.9%
|
| 185 | Ngara | 2025 |
0.8%
|
| 186 | Bukoba Urban | 600 |
0.2%
|
| 191 | Ukerewe | 2025 |
0.8%
|
| 192 | Magu | 2250 |
0.9%
|
| 193 | Nyamagana | 0 |
0%
|
| 194 | Kwimba | 2250 |
0.9%
|
| 195 | Sengerema | 2250 |
0.9%
|
| 196 | Geita | 3000 |
1.2%
|
| 197 | Misungwi | 2025 |
0.8%
|
| 198 | Ilemela | 1350 |
0.6%
|
| 201 | Tarime | 3000 |
1.2%
|
| 202 | Serengeti | 2025 |
0.8%
|
| 203 | Musoma Rural | 2250 |
0.9%
|
| 204 | Bunda | 2025 |
0.8%
|
| 205 | Musoma Urban | 225 |
0.1%
|
| 211 | Babati | 2000 |
0.8%
|
| 212 | Hanang | 2025 |
0.8%
|
| 213 | Mbulu | 2025 |
0.8%
|
| 214 | Simanjiro | 2025 |
0.8%
|
| 215 | Kiteto | 2025 |
0.8%
|
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.