Package of
Agricultural Production Survey
25-29 March 2013
Inception Meeting on Improving Food Security Information in Africa
Africa Rice, Cotonou, Benin
KAMIKURA, Kenji
Senior Statistician
Statistics Department
Ministry of Agriculture, Forestry and Fisheries
Japan
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Contents
1 Experience of Japan Concerning the Technical Cooperation
1.1 Features of Japan’s Technical Cooperation
1.2 Reporting System
1.3 Production Survey
1.3.1 Yield Survey
1.3.2 Area Survey
1.3.3 Establishment of Package for Production Survey
2 Package of Agricultural Production Survey
2.1 Method of Estimating Rice Production
2.2 Yield Survey Using the Crop Cutting
2.2.1 Crop Cutting
2.2.2 Procedure of Crop Cutting
2.2.2.1 To Decide the Number of Samples
2.2.2.2 To Select the Villages and Farm Households
2.2.2.3 To Select the Spots for Reaping Rice
2.2.2.4 To Reap, Thresh, Dry and Weigh Rice
2.2.2.5 To Estimate the Average Yield
2.2.3 In Case of Using the Dot Sampling Method
2.2.4 Reference
2.3 Planted Area Survey Using the Dot Sampling Method
2.3.1 The Dot Sampling Method
2.3.2 Background
2.3.3 Characteristics of the Dot Sampling Method
2.3.3.1 Simplicity
2.3.3.2 Efficiency
2.3.3.3 Reliability
2.3.3.4 No Sample Frame
2.3.4 Procedure of a Planted Area Survey, and Example
2.3.4.1 1st Stage: Preparatory
2.3.4.2 2nd Stage: Field
2.3.4.3 3rd Stage: Estimation
2.3.5 Technical Note on the Way to Put Sample Dots on Google Earth
2.3.5.1 To Decide the Number of Samples
2.3.5.2 To Create a Table - Latitude and Longitude Coordinates for Sample Dots
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2.3.5.3 To Put Sample Dots on Google Earth
2.3.6 Supplementary Explanations
2.3.6.1 Why Get Information on the Total Area of the Target Region
2.3.6.2 How to Increase Precision
2.3.6.3 How to Develop Organization of Conducting Survey
2.3.7 Reference
3 Standardization of Statistical Survey Methods
3.1 Statistical Issues of Reporting System
3.1.1 No Standard for Data Entry
3.1.2 Intuitive Reporting
3.1.3 Reporting Depending on the Views of the Reporters
3.1.4 Confusion of Structural Statistics and Production Statistics
3.1.5 Unclear Definition of items
3.1.6 Lack of Data on Farmer’s Side
3.1.7 Tendency to Under-report on Farmer’s Side
3.2 Statistical Issues on Improved Method
3.2.1 Lack of Actual Measurement
3.2.2 Lack of Aimed Precision
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1
1 Experience of Japan Concerning the Technical Cooperation
1.1 Features of Japan’s Technical Cooperation
In relation to agricultural statistics, Japan has experience in technical
cooperation in a variety of fields. With the recognition that statistical techniques in
Japan cannot be directly applied to developing countries, Japan has conducted
technical cooperation considering approaches that suit each country.
The features of Japan’s technical cooperation are to first place capacity
building in developing countries, and to then introduce methods which are feasible
and sustainable for each country. More specifically, as can be seen in the case of
technical cooperation in Lao PDR, Japan has introduced methods whereby surveys
can be made possible even if populations are not developed as well as you don’t need
any materials such as GPS, distance meter and moisture meter which cannot be
repaired or replenished and use only general-purpose computer software such as
Excel.
1.2 Reporting System
In terms of statistical methods, Reporting System is an attractive method for
developing countries. There are many drawbacks, but as for the data such as
production of the certain crops which does not need to be extremely accurate or can
be easily collected by questioning, you could improve current Reporting System,
through the standardization of data definitions and data collection methods, while
continuing reporting system.
However, with regard to production for important crops, actual measurement
sample survey is indispensable and this is the reason why technical cooperation in
production survey is required.
1.3 Production Survey
As for the production survey, it is possible for Japan to ascertain its merits
and disadvantages since the current production survey in Japan is based on solid
statistical survey methodology.
In addition, Japan’s survey techniques for developing countries have
progressed greatly through long experience in technical cooperation for many
countries as well as in conducting JICA training courses in Japan.
1.3.1 Yield Survey
Regarding a yield survey, the method have basically been established in Lao
PDR through a JICA project over a three-year period from 2007.
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1.3.2 Area Survey
On the other hand, there were no suitable area survey methods which were
able to be applied to developing countries, so a method of interviewing sample
farmers has been introduced through technical cooperation for developing countries
which are unable to implement area survey in the same way as developed countries.
The introduction of a method of interviewing sample farmers was a great
improvement, but there are still issues with reliability, and this has been a major
obstacle in technical cooperation with developing countries.
Under these circumstances, at JICA group training course in 2011, an
instructor who is a former staff member of the Ministry of Agriculture, Forestry and
Fisheries and has been involved for many years in technical cooperation relating to
production statistics in developing countries, introduced a new survey method to
make it easy for any country to conduct area survey. This new survey method is
called the Dot Sampling Method, which will be described in this paper. Also, with
the usefulness of this new method confirmed through practice, the prospects for the
technical cooperation have been opened.
1.3.3 Establishment of Package for Production Survey
Through the development of the method of collecting production data by
conducting area survey using the Dot Sampling Method and yield survey using crop
cutting method, a package for technical cooperation on production survey has been
established. This makes it possible to ensure the reliability of timely, accurate, and
transparent agricultural statistics in developing countries.
Note: “Yield” is used as “production per unit area” in this paper.
2 Package of Agricultural Production Survey
The package of agricultural production survey consists of three components.
The content of a specific package will be described below, using rice as an example.
2.1 Method of Estimating Rice Production
The first component of the package is the method of estimating rice
Production.
The method of estimating production is based on the calculating formula that
“the average yield times the planted area”, as opposed to estimates obtained from
interviews with the heads of villages or farmers.
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the average yield of rice
in the target region
×
the planted area of rice
in the target region
For example, in order to estimate the amount of rice production in a province,
the average yield of rice in the province is multiplied by the planted area of rice in
the province.
Note: Target region is an estimation unit and is usually nation or its administrative
subdivision.
Note: Planted area is almost as same as cultivated area, when you check planted area at
the time of harvesting.
Note: In this package, area does not include dyke. So the yield must be calculated without
dyke.
Therefore if area includes dyke, you cannot follow this package, as in that case the
yield must be calculated with dyke.
2.2 Yield Survey Using the Crop Cutting
The second component of the package is yield survey.
2.2.1 Crop Cutting
As for yield survey, the average yield is estimated by actual measurement
weighing the rice reaped from a sample lots selected by probability proportional to
area during harvest season.
2.2.2 Procedure of Crop Cutting
The spots for the crop cutting are selected by a pre-determined method so as
to avoid any bias of enumerators.
2.2.2.1 To Decide the Number of Samples
First, you determine the number of samples in the target region, considering
precision aimed as well as availability of personnel and budget.
For example, when the coefficient of variation of yield is known to be around
32% and aimed precision is 10%, you need to select 10 sample villages in the target
region.
Note: Target region is an estimation unit and is usually nation or its administrative
subdivision.
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Note: necessary number of samples = (coefficient of variation )²/ (aimed precision)²
= 32²/10²≒ 10
2.2.2.2 To Select the Villages and Farm Households
The sampling method is “Two stage sampling”.
You select 10 villages at first and then 1 or 2 farm households from the
selected villages. To select villages, the selection is made using probability
proportional sampling to size based on the area of the rice field, so you need to get
the information of area of the rice field in each village from existing documents
beforehand. You are recommended to select 2 sample farm households using
systematic random sampling from the list of rice farm households in the selected
villages.
2.2.2.3 To Select the Spots for Reaping Rice
When the fields in the selected farmer are separated into divisions, you select
a division at random, and then a specific rice field at random from the division, and
the spots for crop cutting are then selected at random from the rice field. When
selecting the spots for crop cutting, it is best for you to select the spots using a
certain number of steps rather than using a measuring tape, so that a single
enumerator can select the spots.
2.2.2.4 To Reap, Thresh, Dry and Weigh Rice
You conduct crop cutting, thresh and dry. These tasks are carried out in the
way usually conducted by farmers. Finally, you weigh the dried rice.
2.2.2.5 To Estimate the Average Yield
From the results of all of the samples, you estimate the average yield of the
target region.
2.2.3 In Case of Using the Dot Sampling Method
You can also select the spots for crop cutting using the Dot Sampling Method
as well. In case of using the Dot Sampling Method, each dot is selected by
probability proportional to size. Therefore, you select the spots surrounding the dots,
without the need for selecting villages, farm house holds, etc.
2.2.4 Reference
“2007/2008 Rice Crop Cutting Survey” by Mr. Issei Jinguji, December 2007
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2.3 Planted Area Survey Using the Dot Sampling Method
The third component of the package is planted area survey.
2.3.1 The Dot Sampling Method
The Dot Sampling Method is used for estimating planted area of rice.
You multiply rice’s share by the total area of the target region using rice’s
share of the total number of sample dots which you put in the target region.
the total area of the target region
×
the rice’s share of the total number of sample dots
in the target region
As shown below, what you have to do is you put sample dots in the target
region on a Web-based map such as Google Earth, and check the land usages at
sample dots.
2.3.2 Background
The method of estimating area based on the number of dots has been used for
a long time. Traditionally, by placing a grid of dots over a map, you have estimated
the area based on the number of dots which fall on the region of interest. It has been
well known that you can estimate area more precisely, if you use a finer grid.
The Dot Sampling Method is an application of this traditional method, but
no one has ever applied it to an area survey. The reason why the Method has not
been applied to an area survey is that you had neither been able to put sample dots
on a map nor identified the land usage at each sample dot on a map. But you know
now that you can make it with web mapping service, i.e. Google Earth.
With the advent of Google Earth, the Dot Sampling Method can now be easily
applied. Google Earth makes it possible to freely place dots on the map, and Google
Earth also makes it possible to identify the land usages of a considerable number of
dots.
2.3.3 Characteristics of the Dot Sampling Method
2.3.3.1 Simplicity
The Method is a simple method, easy to conduct and calculate precision. Even
the unskilled can follow the technique.
2.3.3.2 Efficiency
The Method is an efficient method. Given that you can examine a significant
amount of the land usages at sample dots on Google Earth at desk beforehand, the
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method has the advantage of significantly reducing the number of sample dots
which need to conduct field surveys. You can save time, cost, and human resources
at each stage of statistical activities, from preparation to estimation. Even countries
advanced in a statistical system would benefit greatly from the Method.
2.3.3.3 Reliability
The Method is a reliable method based on a statistical theory. Non-sampling
errors hardly happen, as what you conduct at the time of field survey is to check
only a crop at a spot of sample dot. You don’t need to get data from farmers, as you
get them from sample dots.
2.3.3.4 No Sample Frame
With the Method, you are able to conduct an area survey without sample
frame, as the Method doesn’t require population or a sample frame to extract
samples. It has the major advantage of eliminating the great effort required to
maintain such a population.
2.3.4 Procedure of a Planted Area Survey, and Example
Planted area survey consists of three stages, i.e., preparatory stage, field
stage and estimation stage.
At the last stage of the survey, you calculate the rice’s share of sample dots.
Then you multiply the rice’s share by the total area of the target region to estimate
the planted area of rice.
2.3.4.1 1st Stage: Preparatory
At first you put sample dots in the target region on Google Earth using
systematic random sampling (Fig.1).
Fig.1 Placemarks for Sample Dots on Google Earth
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Next, you check the category of land usages at sample dots on Google Earth
(Fig.2) and count the number of dots for each category.
For example, provided you put 10,000 sample dots and the number of dots for
each category is as follows.
The number of dots
Actually planted land in cultivated land 1300
Dyke in cultivated land 160
Tree/Rock in cultivated land 40
Non-cultivated land 8000
Land usage unidentified 500
Total 10000
Fig2. Checking the category of land usage at a sample dot
2.3.4.2 2nd Stage: Field
You visit the spots at sample dots to identify whether rice is planted or not.
You don’t need to visit non-cultivated land as there are no possibilities of rice
planting.
In the case of the above example, you are to visit 1,800 dots out of 10,000
sample dots, i.e. 1,300 dots of planted land and 500 dots of land usage unidentified.
You don’t need to visit other 8,200 dots.
The result is, for example, as follows:
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Rice 1000
Other crops 400
Non-cultivated area 400
Total 1800
2.3.4.3 3rd Stage: Estimation
To estimate the planted area of rice is the last stage of the survey.
At first, you calculate the rice’s share of sample dots. Then you multiply the
rice’s share by the total area of the target region.
In the case of an example above, suppose total area of the target region is
250,000 ha, planted area of rice is as follows.
Note: You have to get information on the total area of the target region beforehand.
Number Share (%) Area (ha)
Rice 1000 10 25000
Other crops 400 4 10000
Dyke in cultivated area 160 1.6 4000
Tree/rock in cultivated area 40 0.4 1000
Non-cultivated area 8400 84 210000
Total 10000 100.0
2.3.5 Technical Note on the Way to Put Sample Dots on Google Earth
2.3.5.1 To Decide the Number of Samples
The number of sample dots is determined based on the rice’s proportion of the
target area obtained from existing information as well as on the aimed precision
considering the policy requirement and available resources such as labor force.
For example, when the rice’s proportion of the target area is 10% and the
aimed precision is 3%, 10,000 sample dots will be required (Table below).
Note: The calculation of precision is as follows.
Coefficient of variation = standard error of sample means/p
= /p
= /0.1
= 0.03
The answer is n = 10000
Where p = the rice’s proportion of the target area
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q = 1 – p
n = number of sample dots
Table Relationship between sample size, precision
and rice’s proportion of the target area
(unit:%)
Rice’s
Proportion
the number of samples
100dots 200dots 500dots 1000dots 5000dots 10000dots
1 99.5 70.4 44.5 31.5 14.1 9.9
2 70.0 49.5 31.3 22.1 9.9 7.0
3 56.9 40.2 25.4 18.0 8.0 5.7
4 49.0 34.6 21.9 15.5 6.9 4.9
5 43.6 30.8 19.5 13.8 6.2 4.4
6 39.6 28.0 17.7 12.5 5.6 4.0
7 36.4 25.8 16.3 11.5 5.2 3.6
8 33.9 24.0 15.2 10.7 4.8 3.4
9 31.8 22.5 14.2 10.1 4.5 3.2
10 30.0 21.2 13.4 9.5 4.2 3.0
15 23.8 16.8 10.6 7.5 3.4 2.4
20 20.0 14.1 8.9 6.3 2.8 2.0
30 15.3 10.8 6.8 4.8 2.2 1.5
40 12.2 8.7 5.5 3.9 1.7 1.2
50 10.0 7.1 4.5 3.2 1.4 1.0
60 8.2 5.8 3.7 2.6 1.2 0.8
70 6.5 4.6 2.9 2.1 0.9 0.7
80 5.0 3.5 2.2 1.6 0.7 0.5
90 3.3 2.4 1.5 1.1 0.5 0.3
2.3.5.2 To Create a Table - Latitude and Longitude Coordinates for Sample Dots
In order to put sample dots on Google Earth using systematic random
sampling, you create a series of longitudes and latitudes coordinates for sample dots
in an Excel worksheet (see Fig.3). You can make the table for longitudes and
latitudes coordinates easily, if you use the program which has been developed
already. Without the program, you will follow the procedure described below.
At first you decide a starting point which is more northern than the most
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northern place in the target region and more western than the most western place
in the target region. You obtain area of the target area from existing materials and
calculate interval or distance between sample dots calculated by the square root of
the value obtained by dividing the target area by the required number of samples,
and then convert the distance into an interval of longitudes and that of latitudes to
confirm the dot locations.
Precondition is as follows.
Area of the target region = 945,087 ㎢
Sample size = 10,000
Approximate latitude of the center of the region = - 6 degrees
Latitude of starting point = - 0.95 degrees
Longitude of starting point = 29.3 degree
Difference of latitude in 1 km = 0.0090133734198 degrees
Difference of longitude in 1 km = 0.00903263456 degrees
Note: Difference of longitude in 1 km depends on the latitude of a sample dot.
But in this calculation, approximate latitude of the center of the region
is regarded as the latitude of all sample dots for simplification.
Calculation is as follows.
Area/10000 = 94.5087 ㎢
Interval = = 9.721558517km
Interval of latitude = 9.721558517 x 0.0090133734198 = 0.08762403714 degrees
Interval of longitude = 9.721.558517 x 0.00903263456 = 0.08781128544 degrees
Then you create a table of latitude and longitude coordinates for sample dots
in Excel.
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Fig.3 Latitude and Longitude Coordinates for sample dots
2.3.5.3 To Put Sample Dots on Google Earth
You put sample dots on Google Earth. You can display locations of sample
dots on an Excel worksheet all at once on Google Earth, if you use the program
which has been developed already.
Without the program, you have to enter the latitudes and longitudes
coordinates one by one in the Google Earth search box to create placemarks on the
map. You may put only around 60-100 dots in an hour, if you are not experienced in
this kind of task.
2.3.6 Supplementary Explanations
2.3.6.1 Why Get Information on the Total Area of the Target Region
One of the secrets of the reliability of the Method is to take advantage of the
existing total area of the target region. With the Method, you divide the total area
into each category of land usage. On the other hand, in prevailing surveys, you add
fragments of area of each category together to make the total area, which may not
be harmony with the existing value.
The information of the total area of the target region is a key point to conduct
area survey using the Method. You can get the information of the total area from
other sources. And also you could get it from Google Earth. In the case of using
Google Earth, you make a rectangle on Google Earth which is almost as large as the
target region and you calculate the area of the rectangle and put dots in the
rectangle on Google Earth, and then you calculate the area of the target region.
2.3.6.2 How to Increase Precision
In order to increase precision, you need to increase the number of sample
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dots.
Please note that you have to consider available manpower and budget before
deciding a number of sample dots.
2.3.6.3 How to Develop Organization of Conducting Survey
Usually you need to develop capacity of organization and human resources to
conduct area survey. But as for the survey with the Method, you don’t need any
special preparation, as it is easy to conduct and requires neither any special
equipment nor a sample frame. What you need is an environment to use Google
Earth and short term training for staff as well as budget to put guide maps into
print for enumerators.
Central/Local government staff is supposed to put sample dots on Google
Earth and make guide maps, each of which shows you the spot of a sample dot to
conduct field survey.
Central/Local government staff delivers guide maps to enumerators for them
to check the category of a land usage at a sample dot.
Central/Local government staff estimates the area of each land category.
2.3.7 Reference
“How to Develop Master Sampling Frames using Dot Sampling Method and
Google Earth” by Mr. Issei Jinguji, 5 December 2012
(http://www.fao.org/fileadmin/templates/ess/global_strategy/PPTs/MSF_PPTs/5.MS
F_Dot_sampling_method_on_Google_Earth_Jinguji.pdf)
“Dot Sampling Method and Master Sampling Frames using Google Earth” by
Mr. Issei Jinguji, Project for Capacity Development for the ASDP Monitoring and
Evaluation System Phase 2, Dar es Salaam, Tanzania, 26 November 2012
“Package of Technical Cooperation on Agricultural Production Survey” by Mr.
Kenji Kamikura, 7 February 2013 (http://cars.adb.org)
“Estimation of planted area using the dot sampling method” by Mr. Kenji
Kamikura, 8-12 October 2012
(http://www.fao.org/fileadmin/templates/ess/ess_test_folder/Workshops_Events/APC
AS_24/Paper_after/APCAS-12-21-_Planted_Area_using_Dot_Sampling.pdf)
3 Standardization of Statistical Survey Methods
Surprisingly, the agricultural statistical methods used by various countries
vary greatly. This is an understandable phenomenon because the statistical
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methods employed vary based on factors such as the policy objectives of the country,
the state of agriculture, and financial conditions. But if you do not have an uniform
method of one survey within the country, you will be faced with big problems.
3.1 Statistical Issues of Reporting System
The data collection methods observed in many countries are based on
Reporting System. Under the System, a questionnaire is created by the central
government, and is then distributed to villages, to be filled in by the head of the
village as a means of reporting to the central government through local
governments. The System is convenient, because all you have to do is distribute the
questionnaires, and then you receive reports on figures from local governments.
This System doesn’t require lots of human resources or budget.
However, the problem with this approach is that you cannot interpret the
results. It is impossible to verify whether the results are correct or incorrect.
3.1.1 No Standard for Data Entry
Since the filling in of data for the Reporting System is not standardized, it is
impossible to know how the figures have been calculated. Therefore, it is impossible
to know how trustworthy the results are, resulting in the problem of the survey
results being impossible to analyze.
3.1.2 Intuitive Reporting
The process of reporting data in stages from villages to the central
government may result in more intuitive reporting of figures. In this event, the
report tends to show the same production volume year after year, or else show
growth rates at convenient levels such as 10% or 20%.
3.1.3 Reporting Depending on the Views of the Reporters
The reports can be changed at each stage, depending on the views of the
reporters.
For example, a phenomenon often seen in countries with former planned
economies is the reporting of production volumes at target levels, to show the
achievement of targets. In this case, in order to match excessive supply with the
country’s planned supply and demand levels, consumption and inventory have been
adjusted by large amounts (Fig. 4). There have even been cases where yields have
increased in spite of massive damage due to cold weather (Fig. 5).
In contrast, there have also been cases where production volumes have been
under-reported due to concerns at the place of production over a decline in prices for
certain agricultural products.
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3.1.4 Confusion of Structural Statistics and Production Statistics
Questionnaires often mix structural statistics and production statistics, so
that you understand everything in a single survey.
In the case of structural statistics such as the number of farmers, number of
households, and number of livestock etc., many of these items are answered as of
the time of conduction the survey, and collected data may be close to the true values
as long as definitions are firmly in place. But in the case of production statistics
survey, each question is difficult to be answered immediately at the time of the
survey, resulting in most numerical values deviating largely from the true values.
3.1.5 Unclear Definition of items
As items in questionnaires are not often clearly defined, the survey results
depend on the enumerator who conducts the survey.
For example, as the definition of farm household or farmer is not clear, the
number of farm households or farmers differs depending on enumerators.
The handling of dyke in yield survey and area survey is a significant issue,
but since the definitions are usually not clear, the same area can differ by as much
as 20 or 30% depending on the enumerator or respondent. When the production is
calculated on the basis of “yield multiplied by area”, yield must be defined with
including dyke in case area includes dyke, but in many cases there are no
definitions made on how to handle this issue.
One of the major causes of unclear definition is a lack of clear survey
objectives or scope of the survey.
3.1.6 Lack of Data on Farmer’s Side
It is often the case with farmers that they don’t know the precise figure of
production or planted area.
Many farmers often do not know the production precisely, because they don’t
need to measure the production of crops, as in many cases they are non commercial
farmers. Indeed, in many cases they don’t even have a scale.
Common method of estimating area is estimation from the volume of seeds
used. This method is, for example, as follows.
60 kg usage of seeds in the field = 1 ha
3.1.7 Tendency to Under-report on Farmer’s Side
There may be a tendency to under-report on farmer’s side in order to
maintain consistency for tax audits, even if they know the production volume to
some extent.
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3.2 Statistical Issues on Improved Method
3.2.1 Lack of Actual Measurement
Recognizing the number of problems for collecting production data through
questionnaires, some attempts have been made for improved method. For example,
one of best methods is to estimate the amount of total production by multiplying the
total planted area in the target region by the average yield in the target region. In
this calculation, the yield must be estimated by actually reaping certain spots of the
field for weighing.
However, even in this case, yield can end up being estimated by the personal
knowledge and experience of enumerators, or from interviews, or by the reverse
calculation, i.e. production divided by area, on the contrary to the actual
measurement.
Also, in some cases the area is the same every year based on the belief that
the crop is planted every year in the same places.
3.2.2 Lack of Aimed Precision
Introducing sample survey with actual measurement is the best way to
conduct a production survey.
However, aimed precision of the survey is not often decided. Therefore you
cannot use or interpret the results of the survey.
You should make the aimed precision clear based on the survey objectives.
