Honduras

  • Agile Approaches
  • Methodology
  • Overview
  • Findings
  • Research Indicators

Glossary

Agile Data Technologies

  • Computer Assisted Personal Interviews (CAPI)

    Computer Assisted Personal Interviews (CAPI) is a survey method where interviewers conduct face-to-face interviews using a handheld computer or tablet to ask questions and record responses. This technique enhances data accuracy and efficiency by allowing for real-time data entry and the use of complex question formats, making it effective for collecting detailed information in various research settings.
  • Computer Assisted Telephone Interviews (CATI)

    Computer Assisted Telephone Interviews (CATI) is a survey method where interviewers conduct telephone interviews using a computer system to manage questions and responses. This approach improves data accuracy and efficiency by reducing human error and allowing for immediate data entry. CATI is widely used in market research and public opinion polling to collect quantitative data from various respondents.

Agile Data Indicators

Time

  • Survey Duration

    The mean amount of time it takes for a respondent to complete a survey from start to finish. It is calculated by taking the total time spent by all respondents to complete the survey and dividing it by the number of respondents. This metric is important for assessing the survey’s efficiency.
  • Number of call attempts

    Number of calls made before receiving an answer.
  • Response Rate

    The percentage of individuals that respond to the survey out of the total number of people invited to participate.

    It is a key indicator of the survey’s success and representativeness, reflecting how well the target population was reached and how engaged they were.

Cost

  • Data Collection Cost

    The reduction in expenses achieved during the data collection phase of a research project compared to the standard costs.

    It reflects the overall financial benefit of optimizing the data collection process.
  • Sample Size Reached

    Refers to the percentage of individuals that responded to the survey out of the total number of people in the sample.
  • Survey Cost

    The amount of money saved on each individual survey compared to the traditional survey mode Computer Assisted Personal Interviews (CAPI) cost per survey.

    This metric helps assess the financial efficiency of data collection on a per-unit basis, reflecting how much less it costs to collect data from each respondent.

Validity

  • Data Accuracy

    Inaccuracies or deviations between the recorded values and the true values of the variables being measured in a study.
    Sources of measurement errors include:

    • Respondent errors (e.g., misunderstanding questions, providing inaccurate responses).
    • Interviewer errors (e.g., inconsistent question delivery, leading the respondent).
    • Instrument errors (e.g., faulty survey tools, miscalibrated equipment).
    • Environmental factors (e.g., distractions during data collection).

    Minimizing measurement errors improves data quality and ensures that the findings of a study are more accurate and reliable.
  • Replacement Rate

    The percentage of individuals who were successfully reached by using phone numbers from a secondary or replacement list over the total number of realized interviews.

Methodology

The European Union has embraced a regulation (EU Deforestation Regulation-EUDR) to restrain the negative impact of the EU market on global deforestation and the degradation of forests worldwide.

COSA and AidData were partnering to support the Central American Early Action Initiatives (EAIs) learning community to assess the effectiveness of EUDR (European Union Deforestation Regulation) leading solutions being deployed by these initiatives. This investment aimed to support the development of a standardized benchmark to study the accuracy, cost-effectiveness, and inclusion of using Earth Observation (EO) data for EUDR compliance, focusing on Early Action Initiatives (EAIs) led by coffee producers, traders and governments. Considering that the majority of initiatives within the Central America EAI learning community are based in Honduras, with some of them incorporating cutting-edge tools for earth observation, our focus was directed towards activities within Honduras, however the learnings are applicable globally.

We used the Honduras data under the above initiative to calculate the bias existing between farmer estimates of land size versus estimates obtained through full plot boundaries, and investigate the presence of inverse relationship between land size and productivity.

Survey

Types of information collected from farmers during the survey includes:

  • Basic socio-demographic characteristics such as gender, age, number of household members, level of education, productive and durable assets owned, and experience in coffee farming.
  • Farm characteristics such as total and coffee land size, land ownership.
  • Description of selected plot land use such as type of cultivated coffee, coverage of trees/vegetation vs coffee plants, steepness of the plot; and coffee plot characteristics such as  estimated number of coffee trees in plot, distance between trees, coffee trees density, age of coffee trees, type of pruning.
  • Production of coffee in the selected plot and total coffee production.
  • Exposure to shocks and stressors in the last 5 years, and capacity to cope with shocks.
  • Deforestation and land use change measures such as trees added, removed, or lost over the past 5 years; and land and coffee land added and/or removed from cultivation in the last 5 years.

Sampling
A total sample of 1500 households was selected covering 12 out of the 15 coffee departments in Honduras. This sample was proportionally divided into 750 households as EAIs households and 750 non-EAI households randomly selected from IHCAFE 2022/2023 coffee registry. The rationale of having two groups of farmers (EAI versus non-EAI) was determined by the  need to understand the representativeness of the EAI farmer’s sample at national level.

For the EAIs households, COSA and AidData drew a sub-sample of farmers from each EAI’s farmers list using a stratified random sampling to estimate a proportion, accounting for gender and land size (i.e. strata). For the non-EAI households, COSA and AidData used the IHCAFE national coffee registry to create this non-EAI sample.

 

Technology

Polygon measurements were taken using the EOS ARROW 100 GNSS receiver, while all the ground-truthing data was collected through Computer Assisted in Person Interviews (CAPI).

Overview

Risk, Resilience and Climate

Productivity

Service Delivery

Climate Smart and Regenerative Agriculture

Indicators

The right data drives clear insights
COSA indicators are recognized for their practicality, precision, and ease of adoption, providing managers and researchers with clarity while minimizing data costs. Proven globally, these credible indicators are tested across thousands of applications, delivering science-based data on sustainability.