Good Growth Plan 2014-2019
Indonesia, 2014 - 2019
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IDN_2014-2019_GGP-P_v01_M_v01_A_OCS
Good Growth Plan 2014-2019
| Name | Country code |
|---|---|
| Indonesia | IDN |
Agricultural Survey [ag/oth]
Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.
Sample survey data [ssd]
Agricultural holdings
Scope
Data was collected on the usage of inputs, such as crop protection products, chemical fertilizer, seeding rates, labor hours, machinery usage hours, and marketable crop yield on a per hectare basis.
| Topic | Vocabulary |
|---|---|
| Agriculture & Rural Development | FAO |
| Environment | FAO |
| Agricultural input efficiency | FAO |
Coverage
National coverage
Producers and sponsors
| Name |
|---|
| Syngenta |
| Name | Role |
|---|---|
| Kynetec | Technical assistance |
Sampling
A. Sample design
Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.
B. Sample size
Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).
C. Selection procedure
The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.
BF Screened from Indonesia were selected based on the following criterion:
(a) Corn growers in East Java
- Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit
(b) Rice growers in West and East Java
- Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology)
- A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others)
- mid-tier (sub-optimal CP/SE use)
- may need longer credit
Survey instrument
Data collection tool for 2019 covered the following information:
(A) PRE- HARVEST INFORMATION
PART I: Screening
PART II: Contact Information
PART III: Farm Characteristics
a. Biodiversity conservation
b. Soil conservation
c. Soil erosion
d. Description of growing area
e. Training on crop cultivation and safety measures
PART IV: Farming Practices - Before Harvest
a. Planting and fruit development - Field crops
b. Planting and fruit development - Tree crops
c. Planting and fruit development - Sugarcane
d. Planting and fruit development - Cauliflower
e. Seed treatment
(B) HARVEST INFORMATION
PART V: Farming Practices - After Harvest
a. Fertilizer usage
b. Crop protection products
c. Harvest timing & quality per crop - Field crops
d. Harvest timing & quality per crop - Tree crops
e. Harvest timing & quality per crop - Sugarcane
f. Harvest timing & quality per crop - Banana
g. After harvest
PART VI - Other inputs - After Harvest
a. Input costs
b. Abiotic stress
c. Irrigation
See all questionnaires in external materials tab
Data collection
| Start | End |
|---|---|
| 2014 | 2019 |
- Face-to-face [f2f]
Data processing
Data processing:
Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.
Quality assurance
Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.
• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.
• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.
• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.
• Cross-validation of the answers:
o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size)
o Kynetec cross validates the answers of the growers in three different ways:
- Within the grower (check if growers respond consistently during the interview)
- Across years (check if growers respond consistently throughout the years)
- Within cluster (compare a grower's responses with those of others in the group)
o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.
• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.
• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.
• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.
Data appraisal
Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:
For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.
Data Access
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO |
Micro datasets disseminated by FAO shall only be allowed for research and statistical purposes. Users requesting access to any datasets must agree to the following minimal conditions:
- The micro dataset will only be used for statistical and/or research purposes;
- Any results derived from the micro dataset will be used solely for reporting aggregated information, and not for any specific individual entities or data subjects;
- The users shall not take any action with the purpose of identifying any individual entity (i.e. person, household, enterprise, etc.) in the micro dataset(s). If such a disclosure is made inadvertently, no use will be made of the information, and it will be reported immediately to FAO;
- The micro dataset cannot be re-disseminated by users or shared with anyone other than the individuals that are granted access to the micro dataset by FAO.
The Good Growth Plan Progress Data - Productivity 2019
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| The Good Growth Plan team | Syngenta | goodgrowthplan.data@syngenta.com | https://www.syngenta.com/en/sustainability/good-growth-plan |
Metadata production
DDI_IDN_2014-2019_GGP-P_v01_M_v01_A_OCS
| Name | Affiliation | Role |
|---|---|---|
| Office of Chief Statistician | Food and Agriculture Organization | Metadata producer |
| Development Economics Data Group | The World Bank | Metadata adapted for World Bank Microdata Library |
2023-01-26
Metadata version
Version 01 (January 2023): This metadata was downloaded from the FAO website (https://microdata.fao.org/index.php/catalog) and it is identical to FAO version (IDN_2014-2019_GGP-P_v01_EN_M_A_OCS). The following two metadata fields were edited - Document ID and Survey ID.