Miguel Carriquiry

Reputations, Market Structure, and the Choice of Quality Assurance Systems in the Food Industry

Many food traits desired by consumers are costly to provide and difficult to verify. A complicating factor is that delivered quality can only be affected stochastically by producers and imperfectly observed by consumers. Markets for these goods will emerge only if supplying firms can be trusted. We develop a repeated purchases model to explore how quality discoverability, market structure, nature of reputations, market premiums, and discount factors drive firm choice about the stringency of quality assurance systems designed to gain consumer trust. Reputation protection is key incentive for firms to invest in high-quality goods and quality assurance systems.

Managing hydroclimatological risk to water supply with option contracts and reservoir index insurance

[1] This paper explores the performance of a system of economic instruments designed to facilitate the reduction of hydroclimatologic variability-induced impacts on stakeholders of shared water supply. The system is composed of bulk water option contracts between urban water suppliers and agricultural users and insurance indexed on reservoir inflows. The insurance is designed to cover the financial needs of the water supplier in situations where the option is likely to be exercised. Insurance provides the irregularly needed funds for exercising the water options. The combined option contract – reservoir index insurance system creates risk sharing between sectors that is currently lacking in many shared water situations. Contracts are designed for a shared agriculture – urban water system in Metro Manila, Philippines, using optimization and Monte Carlo analysis. Observed reservoir inflows are used to simulate contract performance. Results indicate the option – insurance design effectively smooths water supply costs of hydrologic variability for both agriculture and urban water.

Index Insurance, Probabilistic Climate Forecasts, and Production

Index insurance and probabilistic seasonal forecasts are becoming available in developing countries to help farmers manage climate risks in production. Although these tools are intimately related, work has not been done to formalize the connections between them. We investigate the relationship between the tools through a model of input choice under uncertainty, forecasts, and insurance. While it is possible for forecasts to undermine insurance, we find that when contracts are appropriately designed, there are important synergies between forecasts, insurance, and effective input use. Used together, these tools overcome barriers preventing the use of imperfect information in production decision making.

Integrating Seasonal Forecasts and Insurance for Adaptation Among Subsistence Farmers: The Case of Malawi

Climate variability poses a severe threat to subsistence farmers in southern Africa. Two different approaches have emerged in recent years to address these threats: the use of seasonal precipitation forecasts for risk reduction (for example, choosing seed varieties that can perform well for expected rainfall conditions), and the use of innovative financial instruments for risk sharing (for example, index-based weather insurance bundled to microcredit for agricultural inputs). So far these two approaches have remained entirely separated. This paper explores the integration of seasonal forecasts into an ongoing pilot insurance scheme for smallholder farmers in Malawi. The authors propose a model that adjusts the amount of high-yield agricultural inputs given to farmers to favorable or unfavorable rainfall conditions expected for the season. Simulation results - combining climatic, agricultural, and financial models - indicate that this approach substantially increases production in La Nina years (when droughts are very unlikely for the study area), and reduces losses in El Nino years (when insufficient rainfall often damages crops). Cumulative gross revenues are more than twice as large for the proposed scheme, given modeling assumptions. The resulting accumulation of wealth can reduce long-term vulnerability to drought for participating farmers. Conclusions highlight the potential of this approach for adaptation to climate variability and change in southern Africa.

Biofuel Expansion, Fertilizer Use, and GHG Emissions: Unintended Consequences of Mitigation Policies

Increased biofuel production has been associated with direct and indirect land-use change, changes in land management practices, and increased application of fertilizers and pesticides. This has resulted in negative environmental consequences in terms of increased carbon emissions, water quality, pollution, and sediment loads, which may offset the pursued environmental benefits of biofuels. This study analyzes two distinct policies aimed at mitigating the negative environmental impacts of increased agricultural production due to biofuel expansion. The first scenario is a fertilizer tax, which results in an increase in the US nitrogen fertilizer price, and the second is a policy-driven reversion of US cropland into forestland (afforestation). Results show that taxing fertilizer reduces US production of nitrogen-intensive crops, but this is partially offset by higher fertilizer use in other countries responding to higher crop prices. In the afforestation scenario, crop production shifts from high-yielding land in the United States to low-yielding land in the rest of the world. Important policy implications are that domestic policy changes implemented by a large producer like the United States can have fairly significant impacts on the aggregate world commodity markets. Also, the law of unintended consequences results in an inadvertent increase in global greenhouse gas emissions.

Land-Use Change And Greenhouse Gas Emissions In The Fapri-Card Model System: Addressing Bias And Uncertainty

Even with a normalized and standardized biofuel shock, the wide range of land-use change estimates and their associated greenhouse gas (GHG) emissions have raised concern on the adequacy of existing agricultural models in this new area of analysis. In particular, reducing bias and improving precision of impact estimates are of primary concern to policy makers. This paper provides a detailed overview of the FAPRI-CARD agricultural modeling system, with particular emphasis on the modifications recently introduced to reduce bias in the results. We illustrate the impact of these new model features using the example of the new yield specification that now includes updated trend parameter, intensification and extensification effects, and a spatially disaggregated Brazil specification. The paper also provides a taxonomy of the many types of uncertainty surrounding any analysis, including parameter-coefficient uncertainty and exogenous variable uncertainty, identifying where specific types of uncertainty originate, and how they interact. Finally, FAPRI-CARDs long experience in using stochastic analysis is presented as a viable approach in addressing uncertainty in the analysis of changes in the agricultural sector, associated land-use change, and impacts on GHG emissions.

The effects of potential changes in United States beef production on global grazing systems and greenhouse gas emissions

We couple a global agricultural production and trade model with a greenhouse gas model to assess leakage associated with modified beef production in the United States. The effects on emissions from agricultural production (i.e., methane and nitrous oxide emissions from livestock and crop management) as well as from land-use change, especially grazing system, are assessed. We find that a reduction of US beef production induces net carbon emissions from global land-use change ranging from 37 to 85 kg CO2-equivalent per kg of beef annualized over 20 years. The increase in emissions is caused by an inelastic domestic demand as well as more land-intensive cattle production systems internationally. Changes in livestock production systems such as increasing stocking rate could partially offset emission increases from pasture expansion. In addition, net emissions from enteric fermentation increase because methane emissions per kilogram of beef tend to be higher globally.

Production Costs of Biofuels

The production cost of biofuels is one of the key determinants of the commercial viability of biofuels and its social costs of promoting through fiscal stimuli and regulations. Estimates of production costs for different types of biofuels vary widely and are evolving over time (see Fig. 3.1). The sources of variability depend on the category/feedstock/production technology. The costs of first generation biofuels, whose production technologies are matured with commercial production, are influenced mostly by costs of feedstock. In the case of corn-based ethanol, for example, feedstock accounts for about 70 % of the total production costs. For biodiesel, the share of feedstock in total costs of production is even higher, reaching 85–90 %. The recent price volatility in agricultural commodities further contributed to the higher costs of biofuels. In the case of second generation biofuels, much less is known in terms of both process technologies and costs, as there is little experience on commercial production. The available costs are ex-ante estimates with assumptions changing in each estimate (Klein-Marcuschamer et al. 2012). Also, technology pathways for converting cellulosic biomass into biofuels are associated with technical and cost uncertainties.

Empirical Findings from Agricultural Expansion and Land Use Change in Brazil

Agricultural production will play an increasing role in the development of renewable energy sources. Modeling land use changes due to agricultural conversion continues to be a challenge. This chapter contributes by demonstrating an improved methodology to empirically determine elasticities used to compute land use change. It presents an updated land use section of the Brazilian Land Use Model, using secondary economic data, remote sensing information, and improved allocation methodology, especially with extensive margin effects, to better capture multicropping systems, livestock dynamics, and land use dynamics. It shows improved ways for economic and geospatial models to interface with each other, a necessary step in quantifying impacts of dynamic processes. The results of the simulations confirm that agriculture will continue to convert native vegetation, but sugarcane has very little direct impact on natural vegetation due to demand being met through pasture intensification. Also, deforestation rates will decrease compared to those in the past. Finally, policies to ensure compliance with agricultural intensification and guarantees from farmers of compliance with Brazilian forest regulations will be necessary to complement market forces and technological advances in biofuel crop production.