Defining my research question Part II

My big project of this semester is writing my prospectus, which is a full-length research proposal that I will later present and defend in front of my committee. I'm also working on my NSF GRFP proposal, which I got an honorable mention for last year and am really working on right now. So I'm working on the "big picture" prospectus, and then cramming it all into a 2-page (with detailed methodology, of course) research proposal for the NSF. Today I gave a presentation about my research, and was highly encouraged to look not only at public research organizations, but private as well. They looked at my figure (above) and asked the glaring question: where would a company like Monsanto be? I think we're onto something, so here goes...

Question 
How do crop varieties that are developed for short-term weather variability become promoted as a long-term climate adaptation strategy? What is the role of, and interaction between, international public and private research organizations in developing and promoting these varieties?

Motivating context
My research question revolves specifically around technological innovations in plant genetics, which are often promoted as a solution to climate change adaptation in agriculture. Drought-resistant, flood-tolerant, salt-tolerant, and heat-tolerant varieties can improve plant responses to weather variability, which is expected to increase under climate change. My research will examine how climate change is addressed in plant genetic research in the agricultural innovation system, and some of the farm-level implications of these technologies.

‘Agricultural innovation systems’ are typically viewed as the research pipeline from public international, to national, to local research and extension systems. The international research centers provide a centralized hub of knowledge production and, critically, innovations in plant genetics. Plant genetic improvement—such as “modern” (high-yielding) crop varieties, hybrids, and transgenics—has guided agricultural innovation systems over the past century. This concept has captured the imagination of scientists, policy-makers, and the public alike since the Green Revolution.

However, today’s agricultural innovation system is much more complex than the linear research pipeline. Farmers now participate in plant breeding research, and non-governmental organizations and private seed companies work in parallel with the public, Green Revolution-style research and extension infrastructure. Notably, the introduction of patents and intellectual property rights on genes and plant varieties frustrates the public-good-oriented public agricultural research, while providing an economic incentive for private agricultural research. The result is not a bifurcation of research goals, but rather a collaboration of public, private, and other agricultural organizations woven together in a “triple-helix” model of innovation, rather than the linear model. For example, this article shows the interactions between public and private research and funding:

Monsanto and BASF, for instance, are working with the International Maize and Wheat Improvement Center and national agricultural research programs in Kenya, Uganda, Tanzania, and South Africa to develop drought-tolerant corn. The program is supported by a $47 million grant from the Bill and Melinda Gates Foundation. In March this year, the African Agricultural Technology Foundation announced that Monsanto and BASF have agreed to donate royalty-free drought-tolerant transgenes to the African researchers.

Innovation theory
The Hayami-Ruttan “Induced Innovation Hypothesis” seeks to explain how “supply” and “demand” factors influence the development of new agriculturally technologies. On the “supply” side is scientific agricultural research. On the “demand” side is farmers’ willingness to adopt new innovations. “Climate,” and other environmental forces, also affects the “demands” of agriculture, imposing new conditions that limit or provide opportunities for innovations. Can Hayami-Ruttan’s hypothesis provide insight into where we expect innovations to happen in the research pipeline, in light of the new organizational and institutional arrangements?

So what?
We imagine futures based on current technologies and past trajectories, thus certain innovations get “locked-in” and others “locked-out” of research and development. While climate is a relevant variable in the future of agriculture, it is not the only variable, especially in light of farmer livelihoods and the complexities of climate change adaptation and the overall resilience of agro-ecological systems. How does climate change influence farmers’ adoption of new crops, and facilitate or hamper longer-term climate adaptation strategies?


Further reading:
Parayil, G. (2003). Mapping technological trajectories of the Green Revolution and the Gene Revolution from modernization to globalization. Research Policy, 32, 971-990.

The importance of innovation: stories of sugar beets and soybeans

Sugar beets: not the prettiest sight. Image source.

In my last post I highlighted the "myth" of the linear model of science policy, and how this impedes progress in energy policy and ultimately making climate models applicable to local settings (i.e. science for decision-making). An alternative to the linear model is a more nuanced view of innovation. The "innovation approach" is a possible solution to the policy gridlock over climate change and energy. Innovation has been historically important to economic growth in the U.S., and is a more politically palatable solution (investing in clean energy technologies) than setting limits on greenhouse gas emissions. The Breakthrough Institute has some great scholarship on this topic, so check out them and their blog.

So how does innovation actually work? I've already implied that it doesn't follow the linear model of basic to applied research. Interestingly, on Tuesday I had the pleasure of attending a U.S. Senate Agricultural Committee Field hearing at MSU's campus. Many of the speakers called for renewed investment in "basic research," especially at the university. There is certainly a place for basic research at universities, because they often take on more risky research projects than the private sector. For example, I learned that MSU is the only place that researches sugar beet genetics. Sugar beets are an economically important crop to Michigan farmers, and MSU research, coupled with outreach by MSU Extension, is an important asset for improving the productivity of sugar beets.

As you can see from this video (here's the related article), private and public partnerships can yield "sweet success" for farmers. Involving end-users, such as farmers, can improve the social outcomes of scientific research through what Dan Sarewitz and Roger Pielke, Jr. call "reconciling the supply and demand of science." Download their article, which overviews many of the issues I've discussed on this blog, here.

Innovations aren't just serendipitous discoveries in the lab. They are often discovered and shaped by user-needs and preferences, by available technology, and market prices (such as energy, raw materials, market demand, and financing options). Scholars are now investigating the role of climate in inducing technological innovations in agriculture. One of the best examples of this is a study published in 2001 by John Smithers and Alison Blay-Palmer (download here).

These authors aim to open the "black box" of climate-induced technological innovation in the Canadian soybean industry. They link several innovations in soybeans to climate-related factors since the 1970s. Improvements in technology helped farmers manage the risk of normal climate variation (not necessarily related to climate change) and of adapting soybeans to new climates while the growing region expanded. One of the most important innovations in soybeans is the development improved crop varieties from plant breeding, for example, cold-tolerant crops.

Contrary to much of the technological optimism in agriculture towards climate change, the authors list some biological and economic constraints to future climate-induced innovations, specifically the limits of biotechnology and plant breeding. Plant breeding for new crops takes several years, and it can be difficult to predict future local climate conditions. They also list the narrow focus on crop yields as a possible constraint to innovation, as new varieties of crops for future climates may not have higher yields, but rather will help farmers adapt to new conditions. This is why it's important to involve farmers in the research decision process; because it is ultimately up to them whether to adopt a new crop.

The authors also discuss the prospects of public and private research (and the need for alliances), patents and intellectual property rights, and changing markets. In the past, public-private research partnerships had lower transaction costs, but these have risen because of gene patents that are often held by private companies. Addressing these barriers is crucial to future agricultural innovations for a changing climate.

They conclude with the provocative question, “Which adaptations seem likely given the current scientific limits and institutional constraints on innovation, and the competing influence of various other innovation needs in agriculture and society?” (Smithers & Blay-Palmer, 2001, p. 193). Climate change adaptation in agriculture is embedded in a complex social, political, economic, and technological system in which researchers, extension educators, and farmers must make decisions.

Source: Smithers, John and Alison Blay-Palmer. Technology innovation as a strategy for climate adaptation in agriculture. Applied Geography 21 (2001): 175–197.

The linear model: science to policy

Image source: FY 2012 NIH Budget Roll-out, PowerPoint Presentation, February 15, 2011.

In my last post, I brought up the "linear model" of basic to applied research. This is pervasive at the highest levels of U.S. federal science agencies: for example, the image above is from a presentation by Francis Collins, the director of the National Institutes of Health (NIH). The NIH has recently been under attack for not producing breakthroughs in biomedicine that can be applied to society. Looking at the statistics, the United States spends the most on health care (per GDP and per capita) than any other developed country, yet we rank 24/30 for life expectancy of these developed countries (source: Crow, 2011). And we spent 26.6 billion on NIH-funded scientific research in 2010. There's not simply a "gap" in the pipeline that links science with society; there's a fundamental mismatch of research funding and goals, and with health outcomes. Many of the health-related outcomes we strive for do not require more basic research, but rather changes in social, behavioral, and economic factors (access to cheap, nutritious food, preventative medicine, cessation of smoking, etc.).

Another version of the linear model as a research "pipeline."

A recent report by a medical advocacy group promotes the linear model that investment in the NIH has led to positive economic outcomes, such as creating public and private jobs. That's great, but that still doesn't answer the question about health outcomes. For example, what's the difference between creating medical jobs, and simply endowing the arts and creating more jobs for artists? A good answer is that we use science for more than just finding cures; we also use it for guiding policy decisions and making politics more transparent through a common language of science.

STS scholars like Yaron Ezrahi have written extensively on how science is necessary to democratic politics because we can require politicians to justify their actions. "Seeing is believing" has been a mantra of science since the 1600s, and science can be used to "see" things like environmental and health impacts. But most of the time, science is not so easy to translate into politics. The case of climate change, and other environmental debates, are a good example of this.

This brings me to the second type of the mythical "linear model": the science-to-policy model. Roger Pielke, Jr. writes about this in The Honest Broker, which I will once again recommend. Like the NIH, the Intergovernmental Panel on Climate Change (IPCC) is part of a scientific authority that believes that more science=good policy outcomes. For quite a few years now, the assumption has been that science tells us climate change is bad, therefore we need policy to stop carbon emissions. In this model, the scientific experts appear to be removed from the politics (the "Mertonian ideal") However, climate change is more complicated than just carbon emissions, and this linear model limits how we can deal with the impacts of climate change that we cannot stop. Dr. Silke Beck is a German social scientist who writes about this in an article called, "Moving beyond the linear model of expertise? IPCC and the test of adaptation," published in the scientific journal Regional Environmental Change in 2010.

According to the linear model, humans cause carbon emissions, carbon emissions cause climate change, and climate change has impacts that we must adapt to. If we are unsure of any of these steps, policy-making becomes a gridlocked debate over the science (which is full of inherent uncertainties, even when nearly all scientists agree that climate change is happening because of humans). Beck's analysis explains why more science has not led to better policies. In the linear model, solutions to climate change are restricted to limiting emissions. But there are other options: policies to promote overall adaptive capacity, and win-win improvements to infrastructure and technological innovation.

Beck's alternatives to the linear science policy model include promoting useful science that will aid decision-makers in addressing climate change impacts. She also calls for bottom-up involvement of local stakeholders (like farmers). This analysis relates not only to my previous post on "science for decision-making," but also future posts where I will discuss public participation in science. As a final thought, Dan Sarewitz and Roger Pielke wrote a great article in 2007 about reconciling the "supply" of science with the "demand" of social outcomes. They write,

"The resulting picture is complex and yields no single, straightforward model for how knowledge and application interact; yet one feature that invariably characterizes successful innovation is ongoing communication between the producers and users of knowledge." (Sarewitz & Pielke, 2007, p. 7)

Sources:

Beck, Silke (2010). "Moving beyond the linear model of expertise? IPCC and the test of adaptation." Regional Environmental Change. DOI 10.1007/s10113-010-0136-2

Crow, Michael (31 March 2011). "Time to rethink the NIH." Nature 471, 569-571.

Sarewitz, D. & Pielke, R. Jr. (2007). The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science and Policy, 10, 5-16.

1) A new science for decision-making

"Science" means many different things to many people. Science is an action, a set of methods for hypothesis testing; science is a result, a way of illuminating the world around us. By "science," I mean the combination of the practice and use of organized, institutional science. We typically think of science as something that happens in the laboratory. Maybe a spark of discovery leads to a new cancer-fighting drug. But we now know that science happens all around us. Citizens can collect air samples from their neighborhood and test the level of toxins. "Basic" research discoveries don't always lead to cures for devastating diseases. That's why I'm calling this post "A new science for decision-making."

This summer I'm working with Michigan State University Extension on a project about climate change and agriculture. Our guiding idea is of communicating climate change science and policy as not just a top-down, one-way street, but as a participatory process for scientists, policymakers, and stakeholders (farmers and anyone involved and affected by climate change). During my first year of graduate school at Arizona State University, I discovered that other people actually write about this! Thus, I am collecting resources that I hope you will find interesting and helpful. I have started from the perspective of Science and Technology Studies (STS, also related to the broader “social studies of science”). STS tends to take a critical look at the scientific processes of research and development and science and technology policy. The intended audience of my resource is interdisciplinary academics and science policy practitioners who are interested in the broader theoretical and practical relevance of their work. Special note: the articles are not in order of author, but rather the order in which I think they should be read for each set. Also, I've included a glossary of words that are critical concepts but may be unfamiliar to most people. Work in progress, May 2011. Comments appreciated!

Pielke, Roger, Jr. (2007). The Honest Broker: Making Sense of Science in Policy and Politics. Cambridge: Cambridge University Press. Available at Amazon.com

• This short book is an excellent guide to some of the principles of science policy, and particularly the role of scientists in political debates. If you’re looking to get started in learning about science policy and why things like the climate change debate have gotten so complex and confusing, this is the place to start. Also check out Roger's blog!

Kunseler, Eva (2007). “Towards a new paradigm of science in scientific policy advising.” http://www.nusap.net/downloads/KunselerEssay2007.pdf

• This short essay gives a good overview of the distinction between “normal” and “post-normal” science. Kunseler uses the framework of a “paradigm shift” which is the change from one set of widely accepted views about science to another. As referenced, Funtowicz and Ravetz (1993) are best known for introducing “post-normal” science, where they provide a framework for a new type of science needed for today’s uncertain and complex environmental problems. Like Jasanoff (next article), they call for more dialogue with non-scientific stakeholder communities.

Jasanoff, Sheila (2003). “Technologies of Humility: Citizen Participation in Governing Science.” Miverva 41. http://sciencepolicy.colorado.edu/students/envs_5100/jasanoff2003.pdf

• This article is a great starting point for some of the main issues that confront contemporary science policy and public participation in these political decisions. Jasanoff is a leading STS scholar and often focuses how science is used in federal regulatory decisions. Her proposed “technologies of humility” turn traditional scientific norms on their head.

Sarewitz, Daniel (2000). “Science and Environmental Policy: An Excess of Objectivity.” http://www.cspo.org/products/articles/excess.objectivity.html

• Sarewitz is another well-known science policy scholar. This book chapter is an insightful commentary on how science can actually impede the political process. Political debates focus on disputable and uncertain facts while ignoring underlying value conflicts in highly politicized environmental issues. The “excess of objectivity” refers to the incompatibility of multiple fields of science, and how while each field claims objectivity, they drive controversy and muddy the political waters.
• See also “How science makes environmental controversies worse.” By Daniel Sarewitz, 2004. http://www.cspo.org/_old_ourlibrary/documents/environ_controv.pdf

McNie, Elizabeth. (2007). “Reconciling the supply of scientific information with user demands: An analysis of the problem and review of the literature.” Environmental Science & Policy 10. http://sciencepolicy.colorado.edu/admin/publication_files/resource-2486-2007.03.pdf

• This final paper is a great tie-in between this section and the next (coming soon!). McNie takes a case-based, fairly non-theoretical approach to the “problem of linking science to decision-making” (p. 18). She outlines models of public participation in shaping science for decision-making.

Vocab:

Extended peer community/review: the proposal that scientific practice should be reviewed by not just scientists, but stakeholders. For example, breast cancer patients and activists would be involved in selecting what research projects to fund.

Linear model: the idea that “basic research” leads to fundamental breakthroughs that can be applied for economic growth. Basic research=lab/bench research, supposedly removed from society. Applied research=scientific discoveries applied to societal problems or for technology. Many science policy scholars now question this model, since many “discoveries” come from non-science, and science and technology are involved in a complex and iterative relationship. See Donald Stokes' Pastuer's Quadrant.

Mertonian ideal: science characterized by “universalism, communism, disinterestedness, and organized skepticism (Merton, 1973)” (McNie, 2007, p. 23)

Post-normal/"Mode 2" science: the evolution of scientific practice (or knowledge production) in a world of highly complex, uncertain, and interdisciplinary problems.

Social contract of science: the implicit agreement between scientists and the government/public that science should be funded based on peer review/merit, and without intervention. Based upon the “linear model” promoted by Vannevar Bush.

Well-ordered science: a proposal for aligning scientific research goals with societal goals. Based on Philip Kitcher’s argument in Science, Truth, and Democracy.