Forecasting the Adoption of GM Oilseed Rape: Evidence from a Discrete Choice Experiment in Germany

This paper explores farmers’ willingness to adopt genetically modified (GM) oilseed rape prior to its commercial release and estimates the ‘demand’ for the new technology. The analysis is based upon choice experiments with 202 German arable farmers. A multinomial probit estimation reveals that GM attributes such as gross margin, expected liability from cross pollination, or flexibility in returning to conventional oilseed rape significantly affect the likelihood of adoption. Neighbouring farmers’ attitudes towards GM cropping and a number of farmer and farm characteristics were also found to be significant determinants of prospective adoption. Demand simulations suggest that adoption rates are very sensitive to the profit difference between GM and non‐GM rape varieties. A monopolistic seed price would substantially reduce demand for the new technology. A monopolistic seed supplier would reap between 45% and 80% of the GM rent, and the deadweight loss of the monopoly would range between 15% and 30% of that rent. The remaining rent for farmers may be too small to outweigh possible producer price discounts resulting from the costs of segregating GM and non‐GM oilseed rape along the supply chain.     

Agent‐based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand

This study uses an agent‐based model for ex ante assessment of agricultural innovations. The model builds on whole farm mathematical programming (MP) and extends the methodology with a spatial representation of the system, the heterogeneity of farm households and landscapes, and the interaction between farm households. We apply the model in a northern Thai watershed to study the potential of four innovations to increase the profitability of litchi orchards. Cost‐benefit analysis shows that each innovation would increase the profitability of litchi growing; however, the results of the agent‐based model show that at current price levels these innovations alone would not be enough to stem the decline in the area under litchis. The model was validated and the sensitivity of the results tested for variations in the irrigated water supply and liquidity. We report on how farmers responded to these results and discuss the implications for other areas in northern Thailand.     

Why do global long‐term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison

Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long‐term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of “real world commodity prices” differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between ?0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro‐economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross‐fertilize analyses at different scales.     

Biofuels and developing economies: is the timing right?

In this article, we discuss some key aspects of biofuel production in developing countries, and where constraints and tradeoffs are likely to occur. We use the example of two countries, Senegal and India, to highlight some of the issues of cost competitiveness, problems with ensuring stable supply of feedstock as well as critical missing links in the biofuels value chain that pose problems to scaling up national programs in these countries. We discuss the particularly problematic nature of jatropha, as a biodiesel feedstock, and underline some helpful guiding principles that can help country‐level policy. We point to an underlying “duality” between a well‐functioning food system and favorable conditions for agribusiness enterprises, such as biofuels, and the relevance it has to achieving food security goals.