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    Home / Central Data Catalog / LSMS / VNM_1997_VLSS_V01_M / variable [V1004]
lsms

Living Standards Survey 1997-1998

Viet Nam, 1997 - 1998
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Reference ID
VNM_1997_VLSS_v01_M
Producer(s)
General Statistical Office (GSO)
Collection(s)
Living Standards Measurement Study (LSMS)
Metadata
Documentation in PDF DDI/XML JSON
Created on
Oct 13, 2016
Last modified
Oct 26, 2023
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975096
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  • Study Description
  • Data Description
  • Documentation
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  • Data files
  • SCR00A
  • SCR00B
  • SCR00C
  • SCR01A1
  • SCR01A2
  • SCR01B
  • SCR01C1
  • SCR01C2
  • SCR01D
  • SCR02A
  • SCR02B
  • SCR02C
  • SCR02D
  • SCR02E
  • SCR04A
  • SCR04B1
  • SCR04B2
  • SCR04B3
  • SCR04B4
  • SCR04C
  • SCR04D1
  • SCR04D2
  • SCR04E
  • SCR04F1
  • SCR04F2
  • SCR04F3
  • SCR04G
  • SCR04H
  • SCR05
  • SCR06A
  • SCR06B1
  • SCR06B2
  • SCR06C
  • SCR07
  • SCR08A1
  • SCR08A2
  • SCR08A3
  • SCR08B
  • SCR09A11
  • SCR09A12
  • SCR09A21
  • SCR09A22
  • SCR09A31
  • SCR09A32
  • SCR09A41
  • SCR09A42
  • SCR09A51
  • SCR09A52
  • SCR09A53
  • SCR09B1
  • SCR09B2
  • SCR09B3
  • SCR09B4
  • SCR09B5
  • SCR09B6
  • SCR09C1
  • SCR09C2
  • SCR09D01
  • SCR09D02
  • SCR09D03
  • SCR09D04
  • SCR09D05
  • SCR09D06
  • SCR09D07
  • SCR09D08
  • SCR09D09
  • SCR09D10
  • SCR09D11
  • SCR09D12
  • SCR09D13
  • SCR09E1
  • SCR09E2
  • SCR09F1
  • SCR09F2
  • SCR09G1
  • SCR09G2
  • SCR09H1
  • SCR09H2
  • SCR09J
  • SCR09K
  • SCR09L
  • SCR10A
  • SCR10B
  • SCR10C
  • SCR10D
  • SCR11A
  • SCR11B
  • SCR12A
  • SCR12B
  • SCR12C
  • SCR12D1
  • SCR12D2
  • SCR13A
  • SCR13B
  • SCR14A1
  • SCR14A2
  • SCR14A3
  • SCR14A4
  • SCR14B1
  • SCR14B2
  • SCR14B3
  • SCR14B4
  • SCR14C
  • SCR15
  • SCR031
  • SCR032
  • SCR033
  • SCR034
  • SCR035
  • SCR036
  • SCR100
  • SCR110
  • SCR120
  • SCR130
  • HHEXP98N
  • CMT00A
  • CMT00B
  • CMT00C
  • CMT05
  • CMT011
  • CMT012
  • CMT021
  • CMT21
  • CMT022
  • CMT22
  • CMT23
  • CMT24
  • CMT25
  • CMT30
  • CMT031
  • CMT31
  • CMT032
  • CMT32
  • CMT033
  • CMT33
  • CMT034
  • CMT34
  • CMT035
  • CMT061
  • CMT062
  • CMT063
  • CMT071
  • CMT072
  • CMT073
  • CMT081
  • CMT082
  • CMT083
  • CMT084
  • CMT091
  • CMT092
  • CMT101
  • CMT102
  • CMT103
  • CMT104
  • CMT105
  • CMT106
  • CMT107
  • CMT108
  • CMT109
  • CMT110
  • CMT111
  • CMT112
  • CMT0400
  • CMT0401
  • CMT0402
  • CMT0403
  • CMT0404
  • CMT0405
  • CMT0406
  • CMT0407
  • CMT0408
  • CMT0409
  • CMT0410
  • CMT0411

9. For which crops? (s9dq9cc)

Data file: SCR09D03

Overview

Valid: 12729
Invalid: 1
Minimum: 1
Maximum: 62
Type: Continuous
Decimal: 0
Start: 10
End: 11
Width: 2
Range: 1 - 62
Format: Numeric

Questions and instructions

Categories
Value Category Cases
1 winter rice 2625
20.6%
2 autumn rice 1205
9.5%
3 winter rice 2340
18.4%
4 swidden rice 9
0.1%
5 annual rice 272
2.1%
6 glutinous rice 677
5.3%
7 specialty rice 67
0.5%
8 corn/maize 862
6.8%
9 sweet potatoes 600
4.7%
10 cassava/manioc 216
1.7%
11 other staple crops 18
0.1%
12 potatoes 112
0.9%
13 kohlrabi, cabbage, cauliflower 316
2.5%
14 other leafy greens 208
1.6%
15 tomatoes 46
0.4%
16 water morning glory 374
2.9%
17 fresh legumes (beans) 40
0.3%
18 dried legumes (beans) 133
1%
19 herbs and spices 169
1.3%
20 other vegetables, tubers and fruits 354
2.8%
21 soy beans 95
0.7%
22 peanuts 556
4.4%
23 sesame seeds 27
0.2%
24 sugar cane 249
2%
25 tobacco 81
0.6%
26 cotton 5
0%
27 jute, ramie 9
0.1%
28 rush (for making mats) 1
0%
29 other annual industrial crops 1
0%
30 other annual crops 25
0.2%
31 tea 54
0.4%
32 coffee 292
2.3%
33 rubber 27
0.2%
34 black pepper 54
0.4%
35 coconut (for oil, copra) 24
0.2%
36 mulberry 9
0.1%
37 cashew 24
0.2%
38 other perennial industrial crops 0
0%
39 oranges, limes and mandarins 101
0.8%
40 pineapple 7
0.1%
41 bananas 59
0.5%
42 mango 27
0.2%
43 apple 24
0.2%
44 grapesplum 0
0%
45 papaya 5
0%
46 litchi, longan, rambutan 12
0.1%
47 sapodilla 182
1.4%
48 custard apple 14
0.1%
49 custard apple 36
0.3%
50 jackfruit, durian 16
0.1%
51 mangosteen 1
0%
52 other fruit trees 65
0.5%
53 Mu oil tree 0
0%
54 cinnamon tree 0
0%
55 anise tree 0
0%
56 pine tree 0
0%
57 varnish tree 0
0%
58 tree for wood 0
0%
59 bamboo 1
0%
60 fan palm tree 0
0%
61 water coconut palm 0
0%
62 other silviculture tree 3
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.
Interviewer instructions
List the crops with their codes.. List all crops before going to 3-7

These questions request recording in detail the quantity, and the value of
chemical fertilizer used in the last 12 months, including urea-nitrogen, phosphate, potassium.
NPK and other chemical fertilisers. Urea is calculated as the standard Urea, 2 kg Sulphate urea =
1 kg Urea. When interviewing regarding the quantity used for each kind of plant, in fact many
households can’t remember how much has been used and for which plants, even if they only
respond about the amount of Urea, potassium.. which has been bought in the last 12 months...If
the interviewer faces this case, s/he has to calculate the amount used for each plant according to
the rule: Based on the proportion of the area planted to each plant. For example: in the last 12
months Mr Nam’s household has bought 75 KGs Urea to fertilise 600 m2
of rice, 100 m2
of sweet
potato and 50 m2
of cabbage. If we follow the above rule, then the total area fertilised with Urea
is 750 m2
in which rice area accounts for 80%, sweet potato makes up 13% and cabbage area
accounts for 7%. Calculation of urea for each plant is as follows:
- Urea for rice = 60kg (75 kg x 0,8)
- Urea for sweet potato = 9,8 kg (75 kg x 0,13)
- Urea for cabbage = 5,2 kg (75 kg x 0.07).
This method is also applied for other fertilisers.
If the interviewer faces difficulties in the above calculation method, the interviewer can
record the total fertilizer and values of each that is used the most.
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