I wrote this article for the blog of the International Food Policy Research Institute (IFPRI), celebrating its 40th anniversary. The goal was to present a key research topic through the years from the personal perspective of the researcher. This article appeared May 4, 2015. Below, I present a few key paragraphs, and a link to the full article. — Tim Thomas
Long before I joined IFPRI, I served in the U.S. Navy. One of the first lessons I learned in my naval training was how to avoid running into − or being run into by − another ship. You would think it would be pretty simple to avoid, but the trouble is that ships don’t turn very fast nor do they stop instantly. What’s more, on the ocean there are no lane markings to keep ships separated (except for buoys in channels). It turns out that without a simple rule of thumb, keeping two large vessels on different trajectories from running into each other isn’t always straightforward. Fortunately for ships, such a rule exists: if the other ship is on a constant bearing relative to your position and the range is decreasing, you’re going to collide unless somebody changes course or speed − and generally speaking, the sooner the better. Keeping your focus in the right place helps you figure this out.
It’s similar in regard to determining how climate change will affect the world food system. Many things are moving in different directions and speeds at the same time and, if you focus on the wrong thing, you’re going to draw the wrong conclusion. For example, we use crop models together with climate models to determine how much production is going to be hindered by the changing climate, all other things being equal. We generally find that the direct climate impact is negative and sometimes quite large. For example, some of the models suggest that climate change will reduce the productivity of corn in the U.S. by up to 40 percent by 2050. Since the U.S. is the leading corn producer, and since other major producers should be similarly affected, this is a big deal. Focusing on this number, however, as some people do, would lead to a very frightening conclusion. Add on top of that an expanding global population that will be a third larger in 2050 than it is today, and you would think that global food security is in an even more dire condition.
Yet what has been overlooked in all of this is the fact that maize yields have grown globally around 2 percent per year during the past forty years, without a sign of slowing down — and this is all despite the impacts of climate change already being felt. Furthermore, maize production in the last 20 years has grown even faster − at 3 percent per year − because expanding yields also have coincided with expanded harvested area. So while climate change creates a tremendous drag on what could have been − the yields we would experience without climate change − agricultural productivity gains due to technological innovations such as high yield maize varieties and nitrogen-based fertilizers together with expanding production areas have compensated. They will most certainly continue to compensate in the future, though probably with less effectiveness as the intensity of climate change grows.
Continue reading at the IFPRI blog
The Bangladesh Centre for Advanced Studies (BCAS) and the International Food Policy Research Institute (IFPRI), with the support of the United States Agency for International Development (USAID), held a final workshop on Low Emissions Development Strategies (LEDS) for the agriculture, forestry, and land use change (AFOLU) sectors at the BRAC Inn Centre in Dhaka, Bangladesh, on September 25 and 26. The workshop brought together more than 60 experts in their fields to discuss ways to reduce greenhouse gas (GHG) emissions without seriously hurting agricultural production.
Attendees of LEDS AFOLU workshop in Dhaka.
One of the features of the event was putting together 4 working groups that covered soils and crops; livestock and fisheries; forestry; and biofuels and cookstoves. Each expert chose the working group that best suited their area of expertise. Each of these working groups developed concrete policies in their particular focus topic that could be implemented to deliver “smart mitigation” — reduction of GHG emissions per unit of output without hurting overall production.
I was part of a workshop in Dhaka, Bangladesh on June 6, during which preliminary results were presented regarding the work of the International Food Policy Research Institute (IFPRI) and the Bangladesh Centre for Advanced Studies (BCAS) under a USAID-funded research project on Low Emissions Development Strategies (LEDS) in the agriculture, forestry, and land use change (AFOLU) sectors. The workshop brought together senior people from several ministries, national agricultural research institutes, and universities.
(From left) Tim Thomas (IFPRI), Khandaker Mainuddin (BCAS), Atiq Rahman (BCAS), Alex De Pinto (IFPRI)
As the bar graph on the right shows (which is from data in the Second National Communications to the UNFCCC), greenhouse gas (GHG) emissions from agriculture and land use change make up a significant part of total emissions in Bangladesh. Furthermore, the CO2 emissions from biomass burning, which does not count in the official figures, presents some opportunities for additional savings.
Some possible areas of reduction are in the following list, which is only meant to suggest possible areas to evaluate for their cost-effectiveness, not that they have been tested and recommended. All are looking for win-win scenarios, in which profitability or productivity can rise while reducing GHG emissions. One of the aims of the project is to evaluate the price of the trade-off in the cases that are not win-win.
Ester Boserup is an economist who wrote two books which influenced me greatly. I read both of these books around 15 years ago while in grad school, and I can’t find my copies at home, so I can’t refer to them more precisely. I bet they are both at work. The first book, published in 1965, is “The Conditions of Agricultural Growth: The Economics of Agrarian Change under Population Pressure”, which I was delighted today to find online for free. This book describes how agricultural intensification results from increased population density. I think the underlying story is one of production technologies changing based on land abundance and scarcity. The second, published in 1981, is “Population and Technological Change: A Study of Long Term Trends”, was broader, looking at specialization throughout the economy. It is the second one that I mostly think about in this post.
What I am trying to get at is what may cause a country to get stuck in a high population, low GDP situation, and what might be done to help it get unstuck. My thoughts, based on Boserup’s books, began in regard to the issues of intensification and specialization. The first thing that came to mind was that once a country was more market-oriented or integrated into the global system, what drives development is investments in improving the productivity of labor and capital, since returns to land (owned by someone) and returns to labor are paid at the rate of the value of the marginal product of labor.
We have been using nested logits and multinomial logits (MNL) to model land use and land use change for modeling greenhouse gas (GHG) emissions from the agriculture, forestry, and land use (AFOLU) sector. The Vietnamese government makes 10-year plans for agriculture, making targets for land dedicated to various crops and other uses. While the targets are included in the plan, the policy instruments used to meet those targets are less clear. Farmers may be pressured to change their own plans to achieve those goals, but the methods used are not very certain.
The underlying household model on which the MNL land use models are based is a random utility model (RUM). A RUM computes utility for various land uses or crop choices. Because these utilities are known with uncertainty (stochastically), the probability of a given piece of land to be put to a given use is computed by comparing the utility of that use to the utility of each of the other choices.
The easiest utility to think about is profit per hectare from cultivating different crops. To effect a reduction in area devoted to one crop, a market-solution would be to raise the cost of cultivating that crop (or equivalently, reduce the profitability of the crop, perhaps by reducing the price). Non-market methods of influencing that decision are difficult or impossible to model directly, but within a model, they might be thought of as a shadow price that could be estimated. If the shadow price is a per unit cost, it can be modeled as a constant. If the shadow price is a per unit of output cost, we can model it as a multiplier on output price.