Skip to main content
SearchLoginLogin or Signup

Farmer versus Researcher data collection methodologies: Understanding variations and associated trade-offs (lay summary)

This is a lay summary of the article published under the DOI: 10.31730/

Published onJun 20, 2023
Farmer versus Researcher data collection methodologies: Understanding variations and associated trade-offs (lay summary)

Farmers and researchers assessed bean varieties differently in Kenya

This study reported that farmers and researchers drew similar conclusions from a study on bean varieties, even though individual farmer responses differed. This means that if farmers collect information for research purposes, the information may not be reliable, but could be used just to make general conclusions.

Sometimes, instead of researchers collecting information themselves in studies, they involve people not trained in research to do it. They call this approach ‘crowdsourcing’. The approach, if used among farmers for example, can allow scientists to get ideas of different farmer conditions. However, information collected by farmers may not always be reliable.

This study examined differences in information collected by farmers and researchers. The study also compared conclusions drawn from both sets of information to determine which approach was better.

The researchers compared their own observations regarding different bean varieties in different seasons with the observations of farmers. Both groups had to assess how fast the beans emerged, their tolerance to pests and diseases, how long they took to mature, and how much yield they produced.

The researchers also asked the farmers how exactly they determine how well a crop performs.

The study found that farmers did a lot of reading to educate themselves. The results also showed that farmers who had been participating in similar research activities were now able to do better evaluations than others, even though the traits they looked for, and reasons behind those, were just the same.

The researchers reported that farmers choose bean varieties based on different characteristics, and this varied from one farmer to another. Farmers’ observations and assessments were mainly based on how much they preferred certain characteristics, and on appearance. Farmers liked varieties that emerged fast, grew upright and could withstand drought, diseases and pests.

This study showed that farmers preferred crops that matured fast if they needed food more urgently, or those that matured slowly if they needed more food produce. Farmers could sometimes choose crops just because they liked them, or because they bring in more money. In most cases, farmers compared new varieties with varieties they already knew.

On the other hand, results showed that researchers’ observations were based on measurements. For example, measuring height, weight, temperatures, rainfall amount, or an exact number of days.

In general, their results showed that although farmers and researchers recorded their observations differently, they did draw similar general conclusions.

The study reported the limitations when either researchers or people not trained in research collect information for research purposes.

However, the researchers cautioned that measurements such as grain yield could have some errors because they were estimated, and this might have affected their results.

This study was done in Kenya. 


The number of non-experts (such as farmers) participating in research activities has increased over the years, with the aim of them addressing their heterogeneous conditions. The situation has resulted in them being engaged in data collection through a process called crowdsourcing. The study examined the level of variation between data sets and the conclusions drawn from data collected using researcher (expert) and farmer (non-expert) methodologies, and also determined the associated trade-offs for using either methodology. The results showed a low convergence between individual observations of the methodologies on most variables with coefficients ranging from |0.39| to |0.60|. However, there was stronger convergence in the conclusions drawn when the results were aggregated (r>|0.80|) for all the variables tested in this study. Therefore, expert and non-expert data were equivalent for average results. However, data may not be comparable for understanding variations in technology performance due to lack of precision in the subjective assessments of farmers relative to the objective measurements of the researcher.


This summary is a free resource intended to make African research and research that affects Africa, more accessible to non-expert global audiences. It was compiled by ScienceLink's team of professional African science communicators as part of the Masakhane MT: Decolonise Science project. ScienceLink has taken every precaution possible during the writing, editing, and fact-checking process to ensure that this summary is easy to read and understand, while accurately reporting on the facts presented in the original research paper. Note, however, that this summary has not been fact-checked or approved by the authors of the original research paper, so this summary should be used as a secondary resource. Therefore, before using, citing or republishing this summary, please verify the information presented with the original authors of the research paper, or email [email protected] for more information.

No comments here
Why not start the discussion?