International trade in meat: the tip of the pork chop

This paper provides an original account of global land, water, and nitrogen use in support of industrialized livestock production and trade, with emphasis on two of the fastest-growing sectors, pork and poultry. Our analysis focuses on trade in feed and animal products, using a new model that calculates the amount of "virtual" nitrogen, water, and land used in production but not embedded in the product. We show how key meat-importing countries, such as Japan, benefit from "virtual" trade in land, water, and nitrogen, and how key meat-exporting countries, such as Brazil, provide these resources without accounting for their true environmental cost. Results show that Japan's pig and chicken meat imports embody the virtual equivalent of 50% of Japan's total arable land, and half of Japan's virtual nitrogen total is lost in the US. Trade links with China are responsible for 15% of the virtual nitrogen left behind in Brazil due to feed and meat exports, and 20% of Brazil's area is used to grow soybean exports. The complexity of trade in meat, feed, water, and nitrogen is illustrated by the dual roles of the US and The Netherlands as both importers and exporters of meat. Mitigation of environmental damage from industrialized livestock production and trade depends on a combination of direct-pricing strategies, regulatory approaches, and use of best management practices. Our analysis indicates that increased water- and nitrogen-use efficiency and land conservation resulting from these measures could significantly reduce resource costs.     

A Global Model Tracking Water, Nitrogen, and Land Inputs and Virtual Transfers from Industrialized Meat Production and Trade

Rising populations and incomes throughout the world have boosted meat demand by over 75% in the last 20years, intensifying pressures on production systems and the natural resources to which they are linked. As a growing proportion of global meat production is traded, the environmental impacts of production become increasingly separated from where the meat is consumed. In this paper, we quantify the use of three important resources associated with industrial livestock production and trade—water, land, and nitrogen—using a country-specific model that combines trade, agronomic, biogeochemical, and hydrological data. Our model focuses on pigs and chickens, as these animals are raised predominantly in intensive systems using concentrated, compound feeds. The results describe the geographical patterns of environmental resource use due to meat production, trade, and consumption. We show that US feed, animal, and meat destined for export require almost as much nitrogen and land, and 20% more water, than products destined for domestic consumption. Model results also demonstrate that among various production factors, improvements in crop yields and animal feed conversion efficiencies result in the most significant reductions in environmental harm. By explicitly tracking the externalities of meat production, we hope to bolster suppliers’ accountability and provide better information to meat consumers.

Space-time modeling for the Missouri Turkey Hunting Survey

The Missouri Turkey Hunting Survey (MTHS) is a postseason mail survey conducted by the Missouri Department of Conservation. The 1996 MTHS provides information concerning the number of turkeys harvested by hunters on each day and the total number of trips made to the counties by these hunters on each day of the hunting season. The success rates are then found from this information. Small sample sizes produce large standard errors for the estimates at the county level. We use a Bayesian hierarchical generalized linear model to estimate daily hunting success rates at the county level. The model includes an autoregressive process for the days of the hunting season and spatially correlated random geographic effects. The computations are performed using Gibbs sampling and adaptive rejection sampling techniques. Results show that there are significant spatial corelations between counties and correlations between days of the hunting season. The estimates are close to the frequency estimates at the state level and much more stable at the county level.     

Spatiotemporal modeling of irregularly spaced aerosol optical depth data

Many advancements have been introduced to tackle spatial and temporal structures in data. When the spatial and/or temporal domains are relatively large, assumptions must be made to account for the sheer size of the data. The large data size, coupled with realities that come with observational data, make it difficult for all of these assumptions to be met. In particular, air quality data are very sparse across geographic space and time, due to a limited air pollution monitoring network. These “missing” values make it difficult to incorporate most dimension reduction techniques developed for high-dimensional spatiotemporal data. This article examines aerosol optical depth (AOD), an indirect measure of radiative forcing, and air quality. The spatiotemporal distribution of AOD can be influenced by both natural (e.g., meteorological conditions) and anthropogenic factors (e.g., emission from industries and transport). After accounting for natural factors influencing AOD, we examine the spatiotemporal relationship in the remaining human influenced portion of AOD. The presented data cover a portion of India surrounding New Delhi from 2000–2006. The proposed method is demonstrated showing how it can handle the large spatiotemporal structure containing so much missing data for both meteorologic conditions and AOD over time and space.