Entries tagged with “agriculture”.


Man, has there ever been a less enticing blog post title?  But it pays to be direct – so there it is.  I have funding for a Ph.D. student, starting in January, to help me on my USAID-funded work on climate services for development.  So, without further ado, the ad:

Graduate Student Opportunity for January 2013

University of South Carolina, Department of Geography

Ed Carr is seeking a Ph.D. student to support ongoing work on climate services for development in sub-Saharan Africa and develop an independent research program in this broad area of inquiry.  The funding for this position is attached to USAID’s Climate Change Resilient Development (CCRD) program, and the candidate will have specific responsibilities supporting the the development of field methods and the analysis of preliminary data, as well as conducting extensive fieldwork in one or more Malian communities in May-July 2013 as part of the project “An Assessment of Mali Meteorological Service’s Agrometeorological Program.”

Qualifications:

  • Candidates will have to be admitted to the geography graduate program at the University of South Carolina
  • Candidates should be from a country in which USAID operates. Preference will be given to candidates from West Africa, then other parts of sub-Saharan Africa, as this is the current target region for the project.
  • Candidates should have experience in one or more of the following: climate change adaptation, rural/community development, rural agriculture, climate science
    • The bulk of initial project work will focus on community-level information needs, and therefore preference will be given to those candidates with experience conducting qualitative research in rural settings.
  • Candidates should hold a Masters degree in Geography, Anthropology, Planning or another closely related field
  • Excellent written and spoken English.  French language ability is preferred.

The duration of funding is January-July 2013, with likely continuation through July 2014.  The candidate will receive tuition, a living stipend, and salary/research support for work to be conducted in May-July 2013.  Candidates who meet departmental expectations of progress and excellence will be eligible for additional semesters of support to complete their degrees.

Please note the very short lead time for this opportunity – viable candidates will likely have to have a visa in hand if they are to start in January 2013.  Candidates who cannot make this deadline, or who are not selected in this round, should stay tuned – I am hoping to open up a few more slots in the fall.

Prospective candidates are encouraged to contact Ed Carr at carr@sc.edu.  Applications are due on 1 November, 2012 via the instructions on the departmental web page: http://artsandsciences.sc.edu/geog/academics/admissions.html

 

 

I was on a panel at the Organic Trade Association‘s research series at the Natural Products Expo East in Baltimore last Friday, discussing the issue of organic farming and the need to feed the world.  As I heard over and over from proponents of organic agriculture, the argument “you can’t feed the world on organic” is something thrown at them all the time.  As I argued, though, this is a production-based argument: that is, organic farming often has somewhat lower levels of productivity than industrial farming (though there are several cases where this does not seem to hold, and a number of confounding factors that make it entirely possible that the productivity difference is actually quite small).  Well, that would be a relevant argument if we were already using our food resources carefully.  Except we aren’t.  Consider:

  • We still produce more than enough food globally to feed everyone a very healthy number of calories, and probably enough that those calories could be accompanied by adequate nutrients.  The current problems of food insecurity are primarily about distribution, not production.
  • Anywhere between 20% and 40% of all food grown globally spoils before it reaches market.  The figures are lower for grains (which tend to travel well) and much higher for vegetables.
  • In the US, we throw away roughly 30% of all food we purchase.
  • Consider those two numbers together: In the US, we probably lose a lot less of the crop between farm and purchase at market, but then throw 30% of it away.  In other places, the food that reaches the table is nearly completely eaten, but we could lose up to 40% of that food before it reaches market.  In other words, no matter where you go on Earth, there is a hell of a lot of waste in the food system.
  • Finally, consider that 33% of all farmland is used for animal feed, one of the less efficient ways of getting calories out of the environment.  It is unclear to me if this 33% includes biofuel crops, but in any case biofuels would only add a few percentage points to this at most.

In short, we have distribution problems and an astonishing amount of waste in our food systems, but it seems that a lot of the food security debate in policy circles is driven by production arguments.  Enhancing production is not a low hanging fruit.  Enhancing production is often used as an excuse for ignoring local knowledge and capacity in favor of reworking entire agroecological systems (which usually ends badly).  Those of us working in development would be well-served to consider all the ways we might address hunger, including waste and distribution, rather than focus myopically on one cause for what might be a phantom problem.  Welcome to another central theme of Delivering Development: misunderstanding/misidentifying the development challenge, and then trying to solve the wrong thing.

One caveat: there are places in the world in absolute production crises – that is, they lack market access to facilitate the movement of needed food, and their agricultural systems are no longer resilient in the face of current challenges.  In these places, waste may be less of an issue, and distribution solutions may be years in the future (good infrastructure and markets require good governance, which is no easy fix), and therefore the application of new agricultural technologies might become the low hanging fruit solution for the time being, until the other challenges can be met. It’s about finding the right tool for the job (and knowing exactly what the job is, too).



OK, ok, you say: I get it, global environmental change matters to development/aid/relief.  But aside from thinking about project-specific intersections between the environment and development/aid/relief, what sort of overarching challenges does global environmental change pose to the development community?  Simply put, I think that the inevitability of various forms of environmental change (a level of climate change cannot be stopped now, certain fisheries are probably beyond recovery, etc.) over the next 50 or so years forces the field of development to start thinking very differently about the design and evaluation of policies, programs, and projects . . . and this, in turn, calls into question the value of things like randomized control trials for development.

In aid/development we tend to be oriented to relatively short funding windows in which we are supposed to accomplish particular tasks (which we measure through output indicators, like the number of judges trained) that, ideally, change the world in some constructive manner (outcome indicators, like a better-functioning judicial system).  Outputs are easier to deliver and measure than outcomes, and they tend to operate on much shorter timescales – which makes them perfect for end-of-project reporting even though they often bear little on the achievement of the desired outcomes that motivated the project in the first place (does training X judges actually result in a better functioning judicial system?  What if the judges were not the problem?).  While there is a serious push in the development community to move past outputs to outcomes (which I generally see as a very positive trend), I do not see a serious conversation about the different timescales on which these two sorts of indicators operate.  Outputs are very short-term.  Outcomes can take generations.  Obviously this presents significant practical challenges to those who do development work, and must justify their expenditures on an annual basis.

This has tremendous implications, I think, for development practice in the here and now – especially in development research.  For example, I think this pressure to move to outcomes but deliver them on the same timescale as outputs has contributed to the popularity of the randomized control trials for development (RCT4D) movement.  RCT4D work gathers data in a very rigorous manner, and subjects it to interesting forms of quantitative analysis to determine the impact of a particular intervention on a particular population.  As my colleague Marc Bellemare says, RCTs establish “whether something works, not how it works.”

The vast majority of RCT4D studies are conducted across a few months to years, directly after the project is implemented.  Thus, the results seem to move past outputs to impacts without forcing everyone to wait a very long time to see how things played out.  This, to me, is both a strength and a weakness of the approach . . . though I never hear anyone talking about it as a weakness.  The RCT4D approach seems to suggest that the evaluation of project outcomes can be effectively done almost immediately, without need for long-term follow-up.  This sense implicitly rests on the forms of interpretation and explanation that undergird the RCT4D approach – basically, what I see as an appallingly thin approach to the interpretation of otherwise interesting and rigorously gathered data. My sense of this interpretation is best captured by Andrew Gelman’s (quoting Fung) use of the term “story time”, which he defines as a “pivot from the quantitative finding to the speculative explanation.” It seems that many practitioners of RCT4D seem to think that story time is unavoidable . . . which to me reflects a deep ignorance of the concerns for rigor and validity that have existed in the qualitative research community for decades.  Feel free to check the methods section of any of my empirically-based articles (i.e. here and here): they address who I interviewed, why I interviewed them, how I developed interview questions, and how I knew that my sample size had grown large enough to feel confident that it was representative of the various phenomena I was trying to understand.  Toward the end of my most recent work in Ghana, I even ran focus groups where I offered my interpretations of what was going on back to various sets of community members, and worked with them to strengthen what I had right and correct what I had wrong.  As a result, I have what I believe is a rigorous, highly nuanced understanding of the social causes of the livelihoods decisions and outcomes that I can measure in various ways, qualitative and quantitative, but I do not have a “story time” moment in there.

The point here is that “story time”, as a form of explanation, rests on uncritical assumptions about the motivations for human behavior that can make particular decisions or behaviors appear intelligible but leave the door open for significant misinterpretations of events on the ground.  Further, the very framing of what “works” in the RCT4D approach is externally defined by the person doing the evaluation/designing the project, and is rarely revised in the face of field realities . . . principally because when a particular intervention does not achieve some externally-defined outcome, it is deemed “not to have worked.”  That really tends to shut down continued exploration of alternative outcomes that “worked” in perhaps unpredictable ways for unexpected beneficiaries.  In short, the RCT4D approach tends to reinforce the idea that development is really about delivering apolitical, technical interventions to people to address particular material needs.

The challenge global environmental change poses to the RCT4D randomista crowd is that of the “through ball” metaphor I raised in my previous post.  Simply put, identifying “what works” without rigorously establishing why it worked is broadly useful if you make two pretty gigantic assumptions: First, you have to assume that the causal factors that led to something “working” are aspects of universal biophysical and social processes that are translatable across contexts.  If this is not true, an RCT only gives you what works for a particular group of people in a particular place . . . which is not really that much more useful than just going and reading good qualitative ethnographies.  If RCTs are nothing more than highly quantified case studies, they suffer from the same problem as ethnography – they are hard to aggregate into anything meaningful at a broader scale.  And yes, there are really rigorous qualitative ethnographies out there . . .

Second, you have to assume that the current context of the trial is going to hold pretty much constant going forward.  Except, of course, global environmental change more or less chucks that idea for the entire planet.  In part, this is because global environmental change portends large, inevitable biophysical changes in the world.  Just because something works for improving rain-fed agricultural outputs today does not mean that the same intervention will work when the enabling environmental conditions, such as rainfall and temperature, change over the next few decades.  More importantly, though, these biophysical changes will play out in particular social contexts to create particular impacts on populations, who will in turn develop efforts to address those impacts. Simply put, when we introduce a new crop today and it is taken up and boosts yields, we know that it “worked” by the usual standards of agricultural development and extension.  But the take-up of new crops is not a function of agricultural ecology – there are many things that will grow in many places, but various social factors ranging from the historical (what crops were introduced via colonialism) to gender (who grows what crops and why) are what lead to particular farm compositions.  For example, while tree crops (oil palm, coconut, various citrus, acacia for charcoal) are common on farms around the villages in which I have worked in Ghana, almost none of these trees are found on women’s farms.  The reasons for this are complex, and link land tenure, gender roles, and household power relations into livelihoods strategies that balance material needs with social imperatives (for extended discussions, see here and here, or read my book).

Unless we know why that crop was taken up, we cannot understand if the conditions of success now will exist in the future . . . we cannot tell if what we are doing will have a durable impact.  Thus, under the most reliable current scenario for climate change in my Ghanaian research context, we might expect the gradual decline in annual precipitation, and the loss of the minor rainy season, to make tree crops (which tend to be quite resilient in the face of fluctuating precipitation) more and more attractive.  However, tree crops challenge the local communal land tenure system by taking land out of clan-level recirculation, and allowing women to plant them would further challenge land tenure by granting them direct control over access to land (which they currently lack).  Altering the land tenure system would, without question, set off a cascade of unpredictable social changes that would be seen in everything from gender roles to the composition of farms.  There is no way to be sure that any development intervention that is appropriate to the current context will be even functional in that future context.  Yet any intervention we put into place today should be helping to catalyze long-term changes . . .

Simply put: Global environmental change makes clear the limitations of our current thinking on aid/development (of which RCT4D is merely symptomatic).   Just like RCTs, our general framing of development does not move us any closer to understanding the long-term impact of our interventions.  Further, the results of RCTs are not generalizable past the local context (which most good randomistas already know), limiting their ability to help us transform how we do development.  In a world of global environmental change, our current approaches to development just replicate our existing challenges: they don’t really tell us if what we are doing will be of any lasting benefit, or even teach us general lessons about how to deliver short-term benefits in a rigorous manner.

 

Next up: The Final Chapter – Fixing It