Crowdsourcing Crop Improvement in Sub-Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security

Etten, Jacob van

doi:10.1111/j.1759-5436.2011.00240.x

Cited by 51 publications

(27 citation statements)

References 19 publications

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“…Indeed, these fields may be considered as mother trials in which farmers identified the most desirable wheat genotypes. The top ranking varieties identified in this experiment, including several landraces topping MVs, are being redistributed and grown in fields autonomously managed by smallholder farmers with a crowd-sourcing approach 44 . By 2016, more than 900 individual households were reached in 24 villages.…”

Section: Resultsmentioning

confidence: 99%

Joining smallholder farmers’ traditional knowledge with metric traits to select better varieties of Ethiopian wheat

Mancini

Kidane

Mengistu

et al. 2017

Sci Rep

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Smallholder farming communities face highly variable climatic conditions that threaten locally adapted, low-input agriculture. The benefits of modern crop breeding may fail to reach their fields when broadly adapted genetic materials do not address local requirements. To date, participatory methods only scratched the surface of the exploitability of farmers’ traditional knowledge in breeding. In this study, 30 smallholder farmers in each of two locations in Ethiopia provided quantitative evaluations of earliness, spike morphology, tillering capacity and overall quality on 400 wheat genotypes, mostly traditional varieties, yielding altogether 192,000 data points. Metric measurements of ten agronomic traits were simultaneously collected, allowing to systematically break down farmers’ preferences on quantitative phenotypes. Results showed that the relative importance of wheat traits differed by gender and location. Farmer traits were variously contributed by metric traits, and could only partially be explained by them. Eventually, farmer trait values were used to produce a ranking of the 400 wheat varieties identifying the trait combinations most desired by farmers. The study scale and methods lead to a better understanding of the quantitative basis of Ethiopian smallholder farmer preference in wheat, broadening the discussion for the future of local, sustainable breeding efforts accommodating farmers’ knowledge.

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Section: Resultsmentioning

confidence: 99%

Joining smallholder farmers’ traditional knowledge with metric traits to select better varieties of Ethiopian wheat

Mancini

Kidane

Mengistu

et al. 2017

Sci Rep

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“…Here, we study the large-N citizen science approach proposed by Van Etten (2011) in an application to the detection of phenotypic differences between varieties of common bean, Phaseolus vulgaris L. (Sect. 2.1).…”

Section: Introductionmentioning

confidence: 99%

The accuracy of farmer-generated data in an agricultural citizen science methodology

Steinke

Etten

Zelan

2017

Agron. Sustain. Dev.

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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 farmergenerated 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 orderof-magnitude estimation of the sample size of participants needed to produce relevant results. Reliability of farmers' experimental observations was generally low (Kendall's W 0.174 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.

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“…The main idea of the initiative is: if farmers have the opportunity to access better information and different varieties, they are better able to choose what is appropriate for their conditions and cope with unpredictable weather [27, 28]. The Seeds for Needs initiative addresses the issue of access to information and seed variety by exposing farmers to more crop varieties and increasing their knowledge about different traits.…”

Section: Methodsmentioning

confidence: 99%

What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers

et al. 2017

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As the sustainability of agricultural citizen science projects depends on volunteer farmers who contribute their time, energy and skills, understanding their motivation is important to attract and retain participants in citizen science projects. The objectives of this study were to assess 1) farmers’ motivations to participate as citizen scientists and 2) farmers’ mobile telephone usage. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. The questionnaire was applied in three communities of farmers, in countries from different continents, participating as citizen scientists. We used statistical tests to compare motivational factors within and among the three countries. In addition, the relations between motivational factors and farmers characteristics were assessed. Lastly, Principal Component Analysis (PCA) was used to group farmers based on their motivations. Although there was an overlap between the types of motivations, for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. While fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Two major groups of farmers were distinguished: one motivated by sharing information (egoistic intrinsic), helping (altruism) and contribute to scientific research (collectivistic) and one motivated by egoistic extrinsic factors (expectation, expert interaction and community interaction). Country and education level were the two most important farmers’ characteristics that explain around 20% of the variation in farmers motivations. For educated farmers, contributing to scientific research was a more important motivation to participate as citizen scientists compared to less educated farmers. We conclude that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. Citizen science does have high potential, but easy to use mechanisms are needed. Moreover, gamification may increase the egoistic intrinsic motivation of farmers.

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Crowdsourcing Crop Improvement in Sub-Saharan Africa: A Proposal for a Scalable and Inclusive Approach to Food Security

Cited by 51 publications

References 19 publications

Joining smallholder farmers’ traditional knowledge with metric traits to select better varieties of Ethiopian wheat

Joining smallholder farmers’ traditional knowledge with metric traits to select better varieties of Ethiopian wheat

The accuracy of farmer-generated data in an agricultural citizen science methodology

What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers

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