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Translational Research or Community Science?

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By Thushara Gunda, recent Vanderbilt University PhD graduate

A reflection on my dissertation journey as part of an interdisciplinary research project with community partners.

Over the last few months, I have been thinking a fair bit about whether my interdisciplinary, dissertation research journey reflects the principles of translational research or those of community science. In many ways, these two principles are complementary: translational research is defined as research that applies findings from basic science to enhance human health and well-being whereas community science is defined as ‘any partnership between researchers and decision-makers that leverages the expertise of both groups to design, conduct, apply, and share the research.’ But there are also differences between them – translational research does not explicitly require coproduction of knowledge whereas community science does not explicitly consider interdisciplinary methods. Although I was not aware of either of these terms when I started my PhD program, I used interdisciplinary methods and collaborated closely with community partners. So what principle does my PhD research more closely reflect? Let me step back and share my journey with you first.

My PhD research focused on improving our understanding of how water was used for food production in the island nation of Sri Lanka. This work was part of a larger, interdisciplinary project that looked at farmer adaptation to drought in the island nation. In addition to hydrology, our team members had members with backgrounds in social psychology, behavioral economics, ethnography, and modeling. As a physical scientist, my analysis began with the physical system – mainly I wanted to know, were the rain patterns indeed shifting as some of the farmers indicated? So I constructed a couple of drought metrics of meteorological data (dating back to 1880s) and looked at changes in the patterns of drought over time and in different parts of the country. Our results confirmed that both the wet and dry parts of the country were indeed getting drier. The dry parts of country is home to a lot of rice production, a water-intensive crop that is also a dietary staple and culturally-important in Sri Lanka.

When we shared these findings with our institutional partners (mainly government officials from the meteorological, agriculture, and irrigation departments), the troublesome-nature of the results prompted a lot of discussion. Currently, the country is more than self-sufficient in its rice production and there were new reservoirs being planned but there was also potential for behavioral shifts, by planting less-water intensive crops for example. One of the irrigation managers invited us to do outreach that highlighted different water requirements of crops and hopefully, help farmers in his region shift from planting rice.

After discussing different options, our team decided to do the outreach in the form of a game-based lesson plan that reflected the local context as much as possible. This approach could help increase the likelihood that any lessons learned from the game would be translated to actual farming decisions. So we selected three crops that farmers have planted in the region (rice, soybeans, and onions) as options the farmers could select to plant during the game. These crops also had different water requirements (rice the most and onions the least) needed for the underlying lesson. We wanted to reward farmers when they made water-efficient decisions (e.g., if they planted rice in a wet season or onions in a dry season); water availability would be simulated using a ”wheel of rain” reflecting seasonal forecasts developed by the Meteorological Department. The most obvious reward would be to use some form of currency.

As we started looking at agricultural statistics, we realized that the local economic conditions were so complex that we could not predict how many money chips we should prepare for the lesson; onions were subject to market fluctuations, soybeans’ price depended on contracts made with local purchasers, and the return for rice was relatively fixed due to government policies. This revelation highlighted a key missing piece in our knowledge to date, specifically how farmers balanced concurrent uncertainties associated with weather and market. So we decided to collect the crop selection decisions made by the farmers during the outreach to increase our own understanding of their responses to different combinations of weather and market conditions. So what ended up starting as an research-informed outreach project quickly evolved into an outreach-informed research project as well. We shared the farmers’ reflections from the game as well as findings from a system dynamics model that was informed by the game with our institutional partners.

In many ways, our project aimed to apply basic findings (e.g., about shifting climate patterns) to gain more valuable insight to local dynamics, notably regarding factors influencing farmers’ adaptations to drought. We hoped that sharing our findings with our institutional and community partners would pave the way for more informed decisions. Throughout this process, we engaged local stakeholders, from government officials to farmers in the field, as part of the research effort. We shared our findings and consulted them during critical aspects of the work, including survey designs. These practices align well with the definitions of translational research (aimed at enhancing human health and well-being) as well as coproduction of expertise necessary for community science.

But in some ways, did we also fall short of both? For instance, although there were discussions prompted by our research findings (including the value of open data and economic dilemmas of farmers), we were not actively involved in any follow-ups nor tracked the decisions made as a result. Furthermore, while decision-makers were involved in our research, they were not involved in actively defining the specific research questions being asked. This was, in part, intentional because the needs expressed to us were often narrow and explicit by some of the decision-makers (e.g., what size should a new reservoir be?) without necessarily being transparent about why a new reservoir was considered necessary in the first place.

In many ways, our team and community partners had good intentions of working together, but we hadn’t quite figured out what this actually entailed. Our project interactions ended up providing us the perfect opportunities to build these relations, however. In research meetings, we tried our best to consider our community partners’ perspectives as well as shared our own, such as the importance of collaboration and interdisciplinary methods in research analyses. At the project debrief, our partners expressed an appreciation for the insights we shared and we walked away with a much deeper appreciation of the local context.

So maybe my dissertation journey does not quite reflect translational research or community science, but it sure seems to fall somewhere along the spectrum. What do you think?

Sarah Wilkins subscriber

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