Crops Production Survey 2016
Philippines, 2016
Reference ID
PHL-PSA-CrPS-2016-v1.0
Producer(s)
Philipine Statistics Authority
Collection(s)
Metadata
Created on
Sep 23, 2021
Last modified
Sep 23, 2021
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Identification
Crops Production Survey 2016
Name | Abbreviation |
---|---|
Philippines | PHL |
PHL-PSA-CrPS-2016-v1.0
The CrPS is conducted quarterly to generate production estimates for crops other than palay and corn at the national, regional and provincial levels disaggregation.The survey aims to support the data needs of planners, policy and decision makers and other stakeholders in the agricultural sector, and to provide periodic updates on crop related developments. The survey adopts two-stage sampling with the municipality as the primary sampling unit and the households as the secondary sampling unit.
Of the 282 crops covered, the individual estimates of the 19 crops highlighted in the quarterly Performance of Agriculture Report are released at the national level, while the rest were lumped as Others. Provincial level estimates are available on an annual basis.
The survey aims to support the data needs of planners, policy and decision makers and other stakeholders in the agricultural sector, and to provide periodic updates on crop related developments.
The survey adopts two-stage sampling with the municipality as the primary sampling unit and the households as the secondary sampling unit.
Of the 282 crops covered, the individual estimates of the 19 crops highlighted in the quarterly Performance of Agriculture Report are released at the national level, while the rest were lumped as Others. Provincial level estimates are available on an annual basis.
The survey aims to support the data needs of planners, policy and decision makers and other stakeholders in the agricultural sector, and to provide periodic updates on crop related developments.
The survey adopts two-stage sampling with the municipality as the primary sampling unit and the households as the secondary sampling unit.
Sample survey data [ssd]
An agricultural production-related survey with a farmer-respondent questionnaire which would have provincial unit of analysis.
Version
version 1.0 Division edits for preliminary estimates computation (raw, first edit)
2017-06-30
v0 is the unedited household-level raw data
v1.0 is the household level raw data edited at the provincial, not anonymized, for internal use.
v2.0 the household level raw data edited at Central Office, not anonymized, for internal use.
v1.0 is the household level raw data edited at the provincial, not anonymized, for internal use.
v2.0 the household level raw data edited at Central Office, not anonymized, for internal use.
Scope
Topic | Vocabulary | URI |
---|---|---|
Agriculture, forestry, fisheries | Philippine Statistics Authority |
Coverage
National Provinces in Regions (National Capital Region not included)
The lowest level of geographic disaggregation is the municipality.
An agricultural production-related survey with a farmer-respondent questionnaire which would have provincial unit of analysis.
The survey covers all small and large farms producers of all agricultural crops, other than palay and corn, nationwide .
Producers and sponsors
Name | Affiliation | Role |
---|---|---|
Sugar Regulatory Administration | DA | data collection and validation for canes milled for centrifugal sugar |
Philippine Coconut Authority | Office of the President | data collection and validation for coconut |
Name | Abbreviation | Role |
---|---|---|
Government of the Philippines | GOP | Full funding |
Sampling
The survey employs two-stage sampling design with municipality as the primary sampling unit (psu) and farmer-producer as the secondary sampling unit (ssu).
Farms are classified as small and large farms. For small farms, crops are classified based on coverage of the Farm Price Survey, e.i. Farm Price Survey and non-Farm Price Survey. For crops under Farm Price Survey, the top five producing municipalities based on the volume of production were chosen as psus. In each municipality, five sample farmer-producers were enumerated as ssus.
For small farms of all other crops not covered under Farm Price Survey, top two to three producing municipalities were chosen as primary sampling units (psus) . In each municipality, three sample farmer-producers as were enumerated as ssu .
This scheme is applied to each of the crops being covered every survey round. It is possible for a farmer-producer to be a respondent for several crops which he plants and/or harvests during the reference quarter.
Classification for large farms is based on the cut-off on area planted. Each survey round covers a maximum of 5 large farms by crop.
The above scheme was adopted since 2005 to date.
Farms are classified as small and large farms. For small farms, crops are classified based on coverage of the Farm Price Survey, e.i. Farm Price Survey and non-Farm Price Survey. For crops under Farm Price Survey, the top five producing municipalities based on the volume of production were chosen as psus. In each municipality, five sample farmer-producers were enumerated as ssus.
For small farms of all other crops not covered under Farm Price Survey, top two to three producing municipalities were chosen as primary sampling units (psus) . In each municipality, three sample farmer-producers as were enumerated as ssu .
This scheme is applied to each of the crops being covered every survey round. It is possible for a farmer-producer to be a respondent for several crops which he plants and/or harvests during the reference quarter.
Classification for large farms is based on the cut-off on area planted. Each survey round covers a maximum of 5 large farms by crop.
The above scheme was adopted since 2005 to date.
Not available
Responses on actual levels from the respondents are summarized and the overall change at the provincial level is estimated for each crop separately for large and for small farms. The overall percent change for the province accounts for both large and small farms and are computed based on their relative contributions of area planted in the province. These levels of contribution are discussed, reviewed and validated by the Provincial Statistical Officers (PSOs) and their staff based on their best judgment and assessment.
Data Collection
Start | End | Cycle |
---|---|---|
2016-02-18 | 2016-02-27 | Quarter 1 |
2016-05-20 | 2016-05-29 | Quarter 2 |
2016-08-21 | 2016-08-31 | Quarter 3 |
2016-11-18 | 2016-11-29 | Quarter 4 |
Start | End | Cycle |
---|---|---|
2016-01-01 | 2016-03-31 | Quarter 1 (preliminary) |
2016-03-31 | 2016-04-01 | Quarter 1 (final) |
2016-06-30 | 2016-06-30 | Quarter 2 (preliminary) |
2016-07-01 | 2016-09-30 | Quarter 3 (preliminary) |
2016-09-30 | 2016-10-01 | Quarter 3 (final) |
2016-12-31 | 2016-12-31 | Quarter 4 (preliminary) |
Face-to-face [f2f]
Field supervision is undertaken by the Provincial Statistical Offices staff in their respective municipalities of assignments. The Provincial Statistics Officer (PSO) serves as overall supervisor in the province, while the Regional Direcor (RD) is the overall supervisor in the region. The Central Office technical staff also make visits in some provinces to observe the field operations.
Among the responsibilities of the supervisor are to conduct training for Statistical Researchers (SR) prior to data collection, make spotchecking and backchecking activities during and after data collection, edit completed returns, address problems encountered by the SRs under his/her supervision and report to Central Office the significant finds that may contribute to the analysis of the survey results.
Among the responsibilities of the supervisor are to conduct training for Statistical Researchers (SR) prior to data collection, make spotchecking and backchecking activities during and after data collection, edit completed returns, address problems encountered by the SRs under his/her supervision and report to Central Office the significant finds that may contribute to the analysis of the survey results.
The collection forms is in the English language. This captures production, area, and bearing trees for the current quarter and same period of the current year. A remarks column is also provided for the explanation on the changes this year against last year. It also serves as summary worksheet for the small and large farms and provincial summary.The instrument is a one-page collection form which could accommodate as many as five crops. The number of sheets may vary depending on the number of crops covered in the province.
Name | Abbreviation | Affiliation |
---|---|---|
Philippine Statistics Authority | PSA | National Economic and Development Authority |
Sugar Regulatory Administration | SRA | Department of Agriculture |
Philippine Coconut Authority | PCA | Office of the President |
Data Processing
Editing is done in four stages during the data review. The initial stage is at the collection point while with the respondent. This starts with the completeness and correctness of the entries in the collection form. The yield per unit area or kilograms per bearing tree and bearing tree per hectare were computed and verified with the respondents when these are out of range. The range varies by crop and reference period. Also, the farmer-producer as respondents are asked on the climatic condition during the previous quarter up to the current quarter, and explanations on the change in the level against the same period a year ago.
During the Provincial Data Review, Regional Data Review and National Data Review, data editing is done after encoding and data transfer from one form or system to another during the generation of estimates.
During the Provincial Data Review, Regional Data Review and National Data Review, data editing is done after encoding and data transfer from one form or system to another during the generation of estimates.
Data Appraisal
To ensure the quality of its statistical services, the PSA has mainstreamed in its statistical system for generating production statistics, a quarterly data review and validation process. This is undertaken at the provincial, regional and national levels to incorporate the impact of events not captured in the survey.
The data review process starts at the data collection stage and continues up to the processing and tabulation of results. However, data examination is formalized during the provincial data review since it is at this stage where the data at the province-level is analyzed as a whole. The process involves analyzing the survey data in terms of completeness, consistency among variables, trend and concentration of the data and presence of extreme observations.
Across validation levels, a set of parameters is being used as guideposts and the available data from other agencies. The existing indicators also accounts for the situation in the province. At the RDR, the data is assessed to reflect the situation of the region and the levels in comparison between and among the provinces in the region. At the NDR, the data are validated in comparison to national level data and the data between and among the regions.
To some extent and for valid reasons, this involves adjustment of the levels of the data generated.
The data review process starts at the data collection stage and continues up to the processing and tabulation of results. However, data examination is formalized during the provincial data review since it is at this stage where the data at the province-level is analyzed as a whole. The process involves analyzing the survey data in terms of completeness, consistency among variables, trend and concentration of the data and presence of extreme observations.
Across validation levels, a set of parameters is being used as guideposts and the available data from other agencies. The existing indicators also accounts for the situation in the province. At the RDR, the data is assessed to reflect the situation of the region and the levels in comparison between and among the provinces in the region. At the NDR, the data are validated in comparison to national level data and the data between and among the regions.
To some extent and for valid reasons, this involves adjustment of the levels of the data generated.
Data access
Name | Affiliation | URI | |
---|---|---|---|
Lisa Grace S. Bersales | Philippine Statistics Authority | info@psa.gov.ph | www.psa.gov.ph |
contacts
Name | Affiliation | URI | |
---|---|---|---|
National Statistician | Philippine Statistics Authority | info@psa.gov.ph | www.psa.gov.ph |