The accuracy of farmer-generated data in an agricultural citizen science methodology
Over the last decades, participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory tr...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2017
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/83478 |
| _version_ | 1855520641217724416 |
|---|---|
| author | Steinke, J. Etten, Jacob van Mejia Zelan, P. |
| author_browse | Etten, Jacob van Mejia Zelan, P. Steinke, J. |
| author_facet | Steinke, J. Etten, Jacob van Mejia Zelan, P. |
| author_sort | Steinke, J. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Over the last decades, participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory trials, building on recent advances in citizen science and crowdsourcing methodologies, involve large numbers of participants and little researcher supervision. Reduced supervision may affect data quality, but the “Wisdom of Crowds” principle implies that many independent observations from a diverse group of people often lead to highly accurate results when taken together. In this study, we test whether farmer-generated data in agricultural citizen science are good enough to generate valid statements about the research topic. We experimentally assess the accuracy of farmer observations in trials of crowdsourced crop variety selection that use triadic comparisons of technologies (tricot). At five sites in Honduras, 35 farmers (women and men) participated in tricot experiments. They ranked three varieties of common bean (Phaseolus vulgaris L.) for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Furthermore, with a simulation approach using the empirical data, we did an order-of-magnitude estimation of
the sample size of participants needed to produce relevant results. Reliability of farmers’ experimental observations was generally low (Kendall’s W0.174 to 0.676). But aggregated observations contained information and had sufficient validity (Kendall’s tau coefficient 0.33 to
0.76) to identify the correct ranking orders of varieties by fitting Mallows-Bradley-Terry models to the data. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers to generate statistically meaningful results, demonstrating the usefulness of the Wisdom of Crowds principle in agricultural research. In this first study on data quality from a farmer citizen science methodology, we show that realistic numbers of less than 200 participants can
produce meaningful results for agricultural research by tricot-style trials. |
| format | Journal Article |
| id | CGSpace83478 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace834782025-12-08T09:54:28Z The accuracy of farmer-generated data in an agricultural citizen science methodology Steinke, J. Etten, Jacob van Mejia Zelan, P. participatory approaches citizen participation kidney beans methodology agricultural research Over the last decades, participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory trials, building on recent advances in citizen science and crowdsourcing methodologies, involve large numbers of participants and little researcher supervision. Reduced supervision may affect data quality, but the “Wisdom of Crowds” principle implies that many independent observations from a diverse group of people often lead to highly accurate results when taken together. In this study, we test whether farmer-generated data in agricultural citizen science are good enough to generate valid statements about the research topic. We experimentally assess the accuracy of farmer observations in trials of crowdsourced crop variety selection that use triadic comparisons of technologies (tricot). At five sites in Honduras, 35 farmers (women and men) participated in tricot experiments. They ranked three varieties of common bean (Phaseolus vulgaris L.) for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Furthermore, with a simulation approach using the empirical data, we did an order-of-magnitude estimation of the sample size of participants needed to produce relevant results. Reliability of farmers’ experimental observations was generally low (Kendall’s W0.174 to 0.676). But aggregated observations contained information and had sufficient validity (Kendall’s tau coefficient 0.33 to 0.76) to identify the correct ranking orders of varieties by fitting Mallows-Bradley-Terry models to the data. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers to generate statistically meaningful results, demonstrating the usefulness of the Wisdom of Crowds principle in agricultural research. In this first study on data quality from a farmer citizen science methodology, we show that realistic numbers of less than 200 participants can produce meaningful results for agricultural research by tricot-style trials. 2017-08 2017-09-12T10:17:24Z 2017-09-12T10:17:24Z Journal Article https://hdl.handle.net/10568/83478 en Open Access application/pdf Springer Steinke, J.; van Etten, J.; Mejia Zelan, P. (2017) The accuracy of farmer-generated data in an agricultural citizen science methodology. Agronomy for Sustainable Development 37(32) ISSN: 1774-0746 |
| spellingShingle | participatory approaches citizen participation kidney beans methodology agricultural research Steinke, J. Etten, Jacob van Mejia Zelan, P. The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title | The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title_full | The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title_fullStr | The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title_full_unstemmed | The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title_short | The accuracy of farmer-generated data in an agricultural citizen science methodology |
| title_sort | accuracy of farmer generated data in an agricultural citizen science methodology |
| topic | participatory approaches citizen participation kidney beans methodology agricultural research |
| url | https://hdl.handle.net/10568/83478 |
| work_keys_str_mv | AT steinkej theaccuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology AT ettenjacobvan theaccuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology AT mejiazelanp theaccuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology AT steinkej accuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology AT ettenjacobvan accuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology AT mejiazelanp accuracyoffarmergenerateddatainanagriculturalcitizensciencemethodology |