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ASIA AND PACIFIC COMMISSION ON
AGRICULTURAL STATISTICS
TWENTY-SEVENTH SESSION
Nadi, Fiji, 19 – 23 March 2018
Agenda Item 9.2
Master Sampling Frame 2016
Contributed by: Birendra Kumar Kayasth, Director
Central Bureau of Statistics
Nepal
APCAS/18/9.2.2P
Master Sampling Frame 2016Introduction:
• The Global Strategy (GS) to Improve Agriculture and Rural Statistics,
adopted by the United Nations Statistical Commission (UNSC) in
2010.
• The Statistics Division of the Food and Agriculture Organization
(FAO/UN) is responsible to coordinate and implement the Global
Strategy.
• The Master Sampling Frame for Agricultural Survey has been
identified as the priority research topics.
• Three countries Brazil, Rwanda, and Nepal were selected for field
test on building Master Sampling Frame.
• This project “Nepal Master Frame Test 2016” was undertaken by
CBS under the technical and financial support of the FAO.
• The MFT was applied in two districts as a pilot test: Kavre (Central
Hill) and Chitwan (Central Tarai).
Master Sampling Frame 2016
Objectives:
� To calculate the estimations of area and production using
data from the field, and analyze them in order to evaluate
the efficiency of the sampling design
� To compare the results of the two surveys: point
observations and farm interviews
� To compare cost and reliability of data between MSF and
Agriculture census 2011.
Master Sampling Frame 2016
Major Activities:
• Identification of land coverage on satellite imageries through
Google Earth
• Finalization of number of strata prior to photo interpretation
• Finalization of data items to be included in Point and farm
survey
• Printing of survey maps
• Adaptation and customizing of the data entry interface
• Preparation of survey instruction manual and training package
• Training of enumerators by FAO consultant
• Survey operation
• Data processing and analysis with the FAO consultant
Master Sampling Frame 2016
Software tools used:
• FAO Collect Earth software(Openforis).
• Google Earth Pro
• Google earth Engine
• Imagery from Bing maps and
• OpenForis Collect and Collect Mobile used for data entry
• STAT-AGRI (2017) database for data cleaning & analysis
Master Sampling Frame 2016Sampling Frame Construction:
• On sample districts, a regular grid of 500m (25 Ha.) distance
was overlaid on Google Map.
• Total 5563 segments was in Kavre where as 8969 segments
was in Chitwan District.
Master Sampling Frame 2016In each segment four points were overlaid such a way that the
distance between two point was 150m.
Master Sampling Frame 2016
Points were photo-interpreted and classified into 9 strata:
• AGRI1 (one Agriculture land),
• AGRI2 (Two Agriculture land),
• AGRI34 (Three or four Agriculture land),
• F (forest),
• W (water),
• BL (Bare Land),
• S (urban),
• 0 (other),
• and DOUBT.
Master Sampling Frame 2016
Master Sampling Frame 2016
Bing Image
Master Sampling Frame 2016For sampling propose, stratification was done into four strata of
agricultural intensity, one non-agricultural stratum
• AGRI1
• AGRI2
• AGRI34
• Others (Bare Land, urban, other and DOUBT).
• Forest and water strata are not sampled at all considering that
their photo interpretation accuracy should be very high andno agriculture should be found at the points location.
• Segments(forest or water)reducing the area of interest by 35
percent in Kavre and 66 percent in Chitawan.
• From population, stratified samples of 200 segments were
selected in each district, aiming at getting the following
sampling fraction:
fAGRI34 = 2*fAGRI2 = 4*fAGRI1 =8*fOTHER.
Master Sampling Frame 2016
Master Sampling Frame 2016
Training
• Four day training program for surveyor team was organized with close involvement of International consultant.
• The training comprised of classroom session and practical field demonstration.
Field Operation:
• Three groups of enumerators were deployed in each district to conduct the field work.
• Each group comprised of two enumerators and each enumerator to complete 1.7 segments per day, or 6.8 points.
• With the help of handheld GPS, the entire route is beingtracked as well as the waypoints of the observation.
• Field survey consisted of two parts:• First, the land cover was observed and recorded at the 800
sample points per district;
Master Sampling Frame 2016
• Second, if a point was located on arable land or permanentcropland, corresponding field of farmer was identified and aninterview was conducted with him or her, gathering whole-farm-level information.
• Collected Farm level information was:
• Crops: area planted, production and yield for wheat, maize, rice, millet, potatoes, oilseeds, vegetables and banana
• Agricultural inputs: use of nitrogen fertilizer and water use (area irrigated and cost of irrigation)
• Livestock and poultry: type and number of animals at the time of survey
• Survey data was directly entered into the Tablet and send to the Dropbox via internet from field.
• Field operation of 800 sample points in each district was completed in 20 days.
Master Sampling Frame 2016
Master Sampling Frame 2016
Master Sampling Frame 2016
Population and sample sizes after final data cleaning
District Stratum
Number
of
segments
(populati
on)
Number
of
segments
(sample)
Number
of
segments
(sample)
visited
Number
of points
visited
Number
of farm
interviews
AGRI1 1262 40 37 142 36
AGRI2 992 60 58 212 80
AGRI34 768 90 88 347 191
F+W 1935 0 0 0 0
BL+S+O+D 606 10 9 36 0
Total 5563 200 192 737 307
AGRI1 544 15 13 52 14
AGRI2 554 30 30 118 49
AGRI34 1568 145 145 566 410
F+W 5891 0 0 0 0
BL+S+O+D 412 10 10 40 0
Total 8969 200 198 776 473
Kavre
Chitawan
Master Sampling Frame 2016
Reasons for not observing a point and recording the land cover
• Too far to see: 36 points, 26.9 %
• Too difficult to identify point: 30 points, 22.4 %
• View blocked by vegetation, wall, etc.: 53 points, 39.6 %
• National park: 15 points, 11.2 %
Master Sampling Frame 2016
Missing farm interviews:
• A total of 874 of the observed sample points were located in
arable or permanent cropland and a total of 780 farmer
interviews were conducted.
• In 39 cases (4.5 percent), the farmer had already been
selected by another point.
• For the 42 cases in which the interview could not be
conducted,
– only in 19 cases (2.2 percent) the farmer could not be
identified.
– In the other 23 cases, the farmer was absent at the time or
lived far from his field.
Master Sampling Frame 2016
Estimation method:
At stratum level, with segments of 25 ha and four points per
segment, the formula for estimation in the two-stage design is
�� = (� ∗ 25)/(4 ∗ �) ����
���Where,
nci is the number of points in segment i where the crop c is
observed
n is the number of sampled segments and
N is the population number of segments.
Master Sampling Frame 2016
For variance estimation:
���(��) = (1 − �)/(��/�(� − 1))∑ (������� − ��)�with Yci = (25/4)* nci and f = n/N
At district level, the estimated crop area is the sum of the crop
areas per stratum; the same holds for the variance.
Master Sampling Frame 2016• Results of Point, Farm Survey and Agri. census of Kavre District
Point Survey Farm Survey Agriculture Census 2011
Data Item Area (ha)Std Err
(ha)CV (%) Data Item Area (ha)
Std Err
(ha)CV (%) Data Item Area (ha) Std Err
CV (%)
Ri ce - Mai n
paddy 5,485.1740.8 13.5 Rice Summer 6,711.9 552.4 8.2 Paddy 10,119.6 421.3 4.2
Wheat 1,409.6 399.4 28.3 Wheat Winter 3,151.9 477.8 15.2 Wheat 4,891.6 301.0 6.2
Maize -
Spring/wi nter536.1 215.8 40.2 Maize Wi nter 398.6 117.1 29.4 Maize 19,706.5 1,029.0 5.2
Maize -
Summer7,308.6 912.2 12.5 Maize Summer 18,448.1 920.0 5.0
Mi l l et 1,283.8 383.8 29.9 Mi l l et Summer 2,418.2 452.2 18.7
Potato -
Summer2,420.1 572.7 23.7 Potato-Winter 2,111.3 391.9 18.6 Potato 4,458.5 445.3 10.0
Potato - Wi nter 455.6 250.4 54.9 Potato-Summer 3,073.2 428.2 13.9
Mustard seeds 5,047.2 707.3 14.0Mustard seeds
winter5,565.8 601.7 10.8
Banana 56.9 53.5 94.1Banana
Summer8.1 3.7 45.7
Hycinth bean 1,232.7 532.1 43.2Irrigated
land11,214.7 444.2 4.0
Total Area 27,703.2 937.2 3.4 Total Area 39,707.3 2,166.6 5.5
Master Sampling Frame 2016• Results of Point, Farm Survey and Agri. census of Chitwan District
Point Survey Farm Survey Agriculture Census 2011
Data Item Area (ha)Std Err
(ha)CV (%) Data Item Area (ha)
Std Err
(ha)CV (%) Data Item Area (ha) Std Err
Relative
Std Err %
Rice - Ma i n
paddy22,391.3 1,221.7 5.5 Rice Summer 26,686.9 1,157.4 4.3 Paddy 36588.4 2155.0 5.9
Wheat 137.3 92.1 67.1 Wheat Winter 2,927.2 451.7 15.4 Wheat 5063.6 285.6 5.6
Maize -
Spring/winter1,036.7 365.5 35.3 Maize Winter 2,395.4 451.3 18.8 Maize 20390.5 1201.7 5.9
Maize-Summer 1,814.8 511.5 28.2Maize Summer
15,032.8 925.3 6.2
Mi l l et 657.4 314.3 47.8Millet Summer
1,029.5 270.8 26.3
Potato-Winter 13.7 13.1 95.3Potato-Winter
1,008.3 167.7 16.6 Potato 1093.2 72.1 6.6
Potato-Summer 70.6 67.2 95.3Potato-Summer
144.4 89.6 62.1
Mustard seeds 5,466.8 816.6 14.9Mustard seeds
winter11,073.5 947.0 8.6
Banana 1,870.3 453.7 24.3Banana
Summer1,169.9 326.4 27.9
Hyacinth bean 643.1 318.9 49.6I irrigated
land31015.1 1447.5 4.7
Total Area37,808.6 941.0 2.5 Tota l Area 40631.6 1580.3 3.9
Master Sampling Frame 2016
District Estimate: Kavre
Livestock
Farm Survey Agriculture
Census 2011
Number stderr (num) CV (%) Number
Cattle
93,174
7,343.2
7.9
71,598
Buffalo
78,114
6,707.1
8.6
66,252
Goat
323,391
26,750.5
8.3
280,022
Chicken
680,672
212,150.4
31.2
719,929
Master Sampling Frame 2016
District Estimate: Chitwan
Livestock
Farm Survey Agriculture
Census 2011
Number stderr (num) CV (%) Number
Cattle
126,300
13,866.2
11.0
71,864
Buffalo
59,668
5,197.7
8.7
77,045
Goat
277,854
23,522.6
8.5
238,373
Chicken
957,931
281,310.8
29.4
2,810,327
Duck
86,039
17,261.9
20.1
47,604
Master Sampling Frame 2016Cost comparison of Point Frame survey and List Frame survey
(Agriculture Census):Expenditure Details
S.NO. EXPENSES US$ REMARKS
1 Training expenses 3561
2 Field work expenses 6686
Chitwan 3255
Kavre 3431
3 Supervision expenses 1934
4 Fuel and transportation expenses 860
5 Miscellaneous (sim card, recharge card, battery charger
etc) expenses
190
6 Logistic expenses 9,356
Samsung galaxy tab 3280
Garmin etrex 20x gps 2438
Epson colour printer 2438
Enumerators packs 1200
7 Consultant expenses 18,100
Data processing (international consultant) 7350 Including air fare
Software and training (international consultant) 7350 Including air fare
Desk simulation (local consultant) 2400
Photo interpretation 1000
8 Central bureau of statistics expenses 6,462
CBS training expenses 1981
One month salary of central staff (5 persons) 1,651
One month salary of field staff (12 persons) 2,830
Total cost 47,149
Enumeration Cost for Kavre and Chitwan District in National Sample Census of Agricultural 2011/12, Nepal
NPR US$* NPR US$*
1 Equipments 50000.00 500.00 50000.00 500.00 Equipment for central office for census work
2
Consultancy
and other
services
30000.00 300.00 30000.00 300.00 At central level
3 Frame building 12000.00 120.00 40000.00 400.00
Data collection for the frame was done at the time
of the household listing operation during the
2011population census. This cost is only for Desk
work.
4 Printing 72000.00 720.00 75000.00 750.00
Questionnaire forms, manuals, control forms,
administration forms, financial administration
forms and other materials
5 Logistics 75000.00 750.00 70000.00 700.00Enumerators' bags, clip boards, calculators, torch
lights, back packs, black and red dot pens
6 Training 180000.00 1800.00 172000.00 1720.00
7 Transportation 10000.00 100.00 20000.00 200.00
8Media
campaigning33000.00 330.00 33000.00 330.00
AM/FM Radio, television, newspapers, posters,
pamphlet, leaflet, folders, banners, etc.
9 Field operation 1360000.00 13600.00 1309000.00 13090.00 Including central level supervision
10Data
processing85000.00 850.00 89000.00 890.00
11 Miscellaneous 30000.00 300.00 30000.00 300.00
12
Salary of
regular Central
Staff
80000.00 800.00 80000.00 800.00
2017000.00 20170.00 1998000.00 19980.00
79.00 83.00
1975.00 2075.00
25532.00 255.32 24072.00 240.72
Total number of EA
enumerated
Total number of
Holdings enumerated
Cost Per EA
enumeration
S. No. CostBudget of Kavre Budget of Chitwan
Remarks
Total Cost
Master Sampling Frame 2016Field problem:
� More time should have been given for training on the use ofTablet and GPS. Many reported difficulty in using theseinstruments in the early stage of the field work.
� The time given (1.7 segment/day) was rather insufficient for
field work.
� Finding farmers was time consuming. For this reason alsomore time is required.
� Farmers reported area in approximation (rounding off tothe largest unit).
� The maps provided was rather dim and of low quality � Data Entry Tool software could have been made more friendly
and easy to use had it been developed in collaboration withCBS staff
� Recent maps should have been used.
Master Sampling Frame 2016Way Forward:
• Nepal should move towards adopting new technology inthe field of statistics. One such avenue has been opened upwith the recently conducted NMFT in collaboration with theFAO.
• Though this NMF Test was conducted for a differentpurpose, the results of the point and farm survey isencouraging due to cost & time.
• The ongoing statistical system design implemented toprovide annual statistics on crops and livestock by theMinistry of Agriculture Development will be improvedprogressively by adopting Master Sampling Frame.
• The NMFT used new data archiving and transmissiontechnology of data collected during the field survey. Thistechnique would certainly maintain the data quality andreduce the time to process them.
• Sharing of knowledge and experience among the countrieswhich have undertake the Master Sampling Frame Testwould be very useful.
Dot sampling� Conducted in Tarai Region comprises of 20 district as a
pilot test in 2014.
� Total Budget = US$ 10,000
� Main objectives:
� To estimate different category of Land used Area.
� Especially comparison of temporary crops area
between Agriculture census 2011 & Dot sampling.
� To learn more about Dot Sampling.
� Comparison of Land use Area with other sources
(Government Agencies).
Dot sampling
� Nepal 147181 km²
� Tarai 34019 km² (Around 23%)
� Total sample dots = 5499
� One dot represent (Area of Tarai/5499) = 6.186
km²
� Distance Between two dots = 2.487 km
� Total field visited 339 sample points which is
about 6 % of Total dots.
Sample Dots on Google Earth
Identification of dot category
Pond=0.36
Road=1.76
Mountain/ Rock=0.29
Unused Land=1.87
Findings From Dot sampling in Tarai Region
Calculation of Land use Area
Type of Land use No. of dot point % Share Total Area km²
Forest/National park 2257 41.04 13962.699
Planted/Cultivated 1999 36.35 12366.609
River/stream/canal 278 5.06 1719.819
Temporary fallow 174 3.16 1076.433
Scatter tree 155 2.82 958.892
Meadow/Pasture 149 2.71 921.773
Dwelling/ Resident 142 2.58 878.468
Permanent crop 109 1.98 674.317
Unused Land 103 1.87 637.199
Road 97 1.76 600.081
Pond 20 0.36 123.728
Mountain/ Rock 16 0.29 98.982
Total 5499 100.00 34019
Confidence interval for Land use category
Lower Limit
km²
Upper Limit
km²
1 Planted/Cultivated 1999 0.36 0.006 1.78 0.013 12367 157 12209 12524
2 Forest 2257 0.41 0.007 1.62 0.013 13963 182 13781 14144
3 Permanent crop 109 0.02 0.002 9.48 0.004 674 2 672 677
4 Scatter tree 155 0.03 0.002 7.92 0.004 959 4 955 963
5 Temporary fallow 174 0.03 0.002 7.46 0.005 1076 5 1071 1081
6 Meadow/Pasture 149 0.03 0.002 8.08 0.004 922 4 918 926
7 Pond 20 0.00 0.001 22.32 0.002 124 0 124 124
8 Road 97 0.02 0.002 10.06 0.003 600 2 598 602
9 River/stream/canal 278 0.05 0.003 5.84 0.006 1720 10 1710 1730
10 Mountain/ Rock 16 0.00 0.001 24.96 0.001 99 0 99 99
11 Dwelling/ Resident 142 0.03 0.002 8.28 0.004 878 4 875 882
12 Unused Land 103 0.02 0.002 9.76 0.004 637 2 635 639
Confidence Interval
at 95%Sn. Category of Land use
No. of dot
point
Proporti
on
Standard
Error
Coefficien
t of
Variance
(CV)
Confidence
Interval at
95%
Total
Area km²
Confiden
ce
Interval
km²
Area comparison with
different Sources
Temp. Planted/Cultivated Area(Km²)
Sources Area (Km²) Difference Remarks
Dot sampling 12367
Agriculture Census
2011
12195 -172 (1.4%) *Not included Institutional Temp.
crop, Floriculture, Herbs
*Confidence lower limit=12209
*land out of Agri. Definition 2011.
Survey-
Department
(compiled in 2011)
16398(Cultiv
ated + Temp
fallow)
(12367+
1076=13443)
= +2955(23.9%)
*Unavailable of Proper Definition.
Area comparison with different
Sources
River/stream/canal Area(Km²)
Sources Area (Km²) Difference Remarks
Dot sampling 1720
Survey-Department
(compiled in 2011)
1706 -14(0.81%) Nearly same
(water + sand)
Area comparison with different
Sources
Forest Area(Km²)
Sources Area (Km²) Difference Remarks
Dot sampling 13963
Agriculture Census
2011
44 *Private Forest(Under the
definition of Agri. Census)
Survey-
Department
(compiled in 2011)
13581 -382(2.7%)
State of Nepal’s
Forests
2015(Department
of Forest)
13995 +32(0.23%)
Findings/ Recommendations� Estimation of Land use areas from dot sampling method
are seems to be reliable.
� In this survey, one dot represents 6.2 Km², Which is
significantly large.
� For field identification of sample dot, Hard copy of Map
should be provided to Enumerator in order to find exact
location according to Geo coordinate of Google Earth Pro .
� In order to increase precision level for rare event, we need
to increase the number of sample dots, however cost does
not effect in large extent.
� This method should be used in order to estimate District
level land use area by increasing sufficient number of
sample points.
� Dot sampling method can be used for crop production by
selection some dot points.
Thank you