David Schimmelpfennig

Irreversibility, Uncertainty, and Learning: Portraits of Adaptation to Long-Term Climate Change

The usefulness of adaptation strategies to changing climate depends on the characteristics of the system that must adapt. Divergent views on whether climate change will seriously affect society and what society can do about it can be traced, in part, to divergent views on these characteristics of systems. Issues of scale and how impacts are measured are also important. We identify a set of fundamental characteristics of natural systems and social systems that help to make underlying assumptions in climate change adaptation studies explicit. These are: Short-run autonomous flexibility; short-run non-autonomous flexibility; knowledge and capacity to undertake short-run actions; long-run autonomous flexibility; long-run non-autonomous flexibility; and knowledge and capacity to plan for and undertake adaptations that require changes in long-lived assets. Applications to crop agriculture and ecosystems illustrate how these portraits can be used. We find that if empirical research is to resolve questions of adaptability, more careful specification of the exact measure of impact and far richer models of the process of adaptation, able to test implicit assumptions in much of the existing empirical research, are needed.

A GM subsistence crop in Africa: the case of Bt white maize in South Africa

The Republic of South Africa (RSA) is the first developing country to plant genetically modified staple food - Bt white maize. The following paper describes the development and spread of Bt maize in RSA that started in 1998. After that, based on surveys of 33 large commercial Bt maize farmers and 368 smallholders in 2001/2, it shows that Bt maize gives higher yields for both groups and reduces pesticide use particularly for the large commercial farmers. The paper concludes with a discussion of policy options which would make the Bt maize more accessible to more small holders.

Research Investments and Market Structure in the Food Processing, Agricultural Input, and Biofuel Industries Worldwide

Meeting growing global demand for food, fiber, and biofuel requires robust investment in agricultural research and development (R&D) from both public and private sectors. This study examines global R&D spending by private industry in seven agricultural input sectors, food manufacturing, and biofuel and describes the changing structure of these industries. In 2007 (the latest year for which comprehensive estimates are available), the private sector spent

The impact of seed industry concentration on innovation: a study of US biotech market leaders

19.7 billion on food and agricultural research (56 percent in food manufacturing and 44 percent in agricultural input sectors) and accounted for about half of total public and private spending on food and agricultural R&D in high-income countries. In R&D related to biofuel, annual private-sector investments are estimated to have reached

Induced Innovation in United States Agriculture, 1880–1990: Time Series Tests and an Error Correction Model

1.47 billion worldwide by 2009. Incentives to invest in R&D are influenced by market structure and other factors. Agricultural input industries have undergone significant structural change over the past two decades, with industry concentration on the rise. A relatively small number of large, multinational firms with global R&D and marketing networks account for most R&D in each input industry. Rising market concentration has not generally been associated with increased R&D investment as a percentage of industry sales.

Uncertainty in economic models of climate-change impacts

Agricultural research drives increases in agricultural productivity, and the number of private agricultural input firms has been declining. The empirical relationship between the number of firms doing applied biotechnology crop research and the amount of research output they produce is investigated in a research profit function model. Increases in seed industry concentration have reduced biotech research intensity in the United States in the 1990s. Concentration and research are simultaneously determined and are influenced by the appropriability of research results and the state of technological opportunity.

What is the Value of Agricultural Economics Research

An error correction model (ECM) of induced innovation, based on the two-stage CES production function allows direct tests of the inducement hypothesis, which are applied to U.S. data for 1880–1990. The time series properties of the variables include a structural break in 1920, cointegration is established and an ECM constructed, which allows factor substitution to be separated from technological change. Causality tests show that the factor-price ratios and R&D are Granger-prior to the factor-saving biases of technological change. The inducement hypothesis is corroborated, and identified as one factor in the complex development of U.S. agriculture. Copyright 2002, Oxford University Press.

The Contribution of Private Industry to Agricultural Innovation

Uncertainty is poorly represented in existing studies of climate-change impacts. Methods that have been used to characterize uncertainty in the literature are described and the limitations of each discussed. It is found that two broad characterizations are useful. A large number of studies are based on several specific scenarios or attempt to randomize selected variables in their deterministic economic models. Other studies describe individual or collective reaction to risk. The first category falls short of an adequate representation of uncertainty by focusing primarily on a few values of variables included to capture variability. The second group of studies tend to focus more on behavior than impacts. What is needed are Monte Carlo type simulations where randomness is apparent in a series of independent draws from a distribution suitably adjusted for climate change. Some of the benefits of improvements in the characterization of uncertainty are discussed.

The rise and fall of public sector plant breeding in the United Kingdom: a causal chain model of basic and applied research and diffusion

Methods are presented for evaluating the economic benefits of agricultural economics research (AER). Decision theory and economic surplus analysis are used to evaluate returns to five representative types of AER. Benefits stem from effects of AER on economic efficiency through reduced uncertainty about optimal resource allocation or improved institutional design.

Quantifying Structural Change in U.S. Agriculture: The Case of Research and Productivity

Led by seed biotechnology, private-sector spending in agricultural R&D grew 43% from 1994 to 2010. Most of the increase in global agricultural production over the past half-century has come from raising crop and livestock yields rather than through area expansion. This growth in productivity is attributed largely to investments in research and innovation (1). Since around 1990, there has been a decline in the rate of growth in yield per area harvested for several important crops (2). In parallel, the rate of growth in public spending on agricultural research and development (R&D) has also fallen, which may account for declining crop yield growth and may be contributing to rising food prices (3).