ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS Master Sampling Frame 2016 ... · 2018-05-28 · ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-SEVENTH SESSION

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

[email protected]

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).


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.


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


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.


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.



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.



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;


• 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.




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


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 %


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.


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.


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


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


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)


Dot sampling 1720

Survey-Department

(compiled in 2011)

1706 -14(0.81%) Nearly same

(water + sand)

Area comparison with different

Sources

Forest Area(Km²)


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

Documents

ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS Master Sampling Frame 2016 ... · 2018-05-28 · ASIA AND PACIFIC COMMISSION ON AGRICULTURAL STATISTICS TWENTY-SEVENTH SESSION