For the 1980–2003 period, we analyzed the relationship between crop yield and three climatic variables (minimum temperature, maximum temperature, and precipitation) for 12 major Californian crops: wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios. The months and climatic variables of greatest importance to each crop were used to develop regressions relating yield to climatic conditions. For most crops, fairly simple equations using only 2–3 variables explained more than two-thirds of observed yield variance. The types of variables and months identified suggest that relatively poorly understood processes such as crop infection, pollination, and dormancy may be important mechanisms by which climate influences crop yield. Recent climatic trends have had mixed effects on crop yields, with orange and walnut yields aided, avocado yields hurt, and most crops little affected by recent climatic trends. Yieldclimate relationships can provide a foundation for forecasting crop production within a year and for projecting the impact of future climate changes.

Trends in recent temperature observations and model projections of the future are characterized by greater warming of daily minimum (tmin) relative to maximum (tmax) temperatures. To aid understanding of how tmin and tmax differentially affect crop yields, we analyzed variations of regional spring wheat yields and temperatures for three irrigated sites in western North America that were characterized by low correlations between tmin and tmax. The crop model CERES-Wheat v3.5 was evaluated in each site and used to project future response to temperature changes. Tmin and tmax exhibited distinct historical correlations with yields, with CERES successfully capturing the observed relationships in each region. In the Yaqui Valley of Mexico, historical yields were strongly correlated with tmin but not tmax. However, CERES projections of response to increased tmin or tmax (holding other variables constant) were similar (6% °C^-1), indicating that the apparent historical importance of tmin mainly results from covariation between temperatures and solar radiation and not greater direct effects of tmin on yields. In the San Luis-Mexicali Valley of Mexico and in the Imperial Valley of California, the opposite was observed: historical yield correlations with tmin and tmax were similar, but projected responses to tmax were roughly three times larger than tmin. The latter is explained by opposing effects of tmin and tmax on grain filling rates in CERES, with higher tmin increasing harvest indices. This model mechanism was not clearly supported by historical data and remains an area of uncertainty for projecting yield responses to climate change.

Several impacts of climate change may depend more on changes in mean daily minimum (Tmin) or maximum (Tmax) temperatures than daily averages. To evaluate uncertainties in these variables, we compared projections of Tmin and Tmax changes by 2046-2065 for 12 climate models under an A2 emission scenario. Average modeled changes in Tmin were similar to those for Tmax, with slightly greater increases in Tmin consistent with historical trends exhibiting a reduction in diurnal temperature ranges. In contrast, the inter-model variability of Tmin and Tmax projections exhibited substantial differences. For example, inter-model standard deviations of June-August Tmax changes were more than 50% greater than for Tmin throughout much of North America, Europe, and Asia. Model differences in cloud changes, which exert relatively greater influence on Tmax during summer and Tmin during winter, were identified as the main source of uncertainty disparities. These results highlight the importance of considering separately projections for Tmax and Tmin when assessing climate change impacts, even in cases where average projected changes are similar. In addition, impacts that are most sensitive to summertime Tmin or wintertime Tmax may be more predictable than suggested by analyses using only projections of daily average temperatures.

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. Mitigating 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.

Climate change, as an environmental hazard operating at the global scale, poses a unique and "involuntary exposure" to many societies, and therefore represents possibly the largest health inequity of our time. According to statistics from the World Health Organization (WHO), regions or populations already experiencing the most increase in diseases attributable to temperature rise in the past 30 years ironically contain those populations least responsible for causing greenhouse gas warming of the planet. Average global carbon emissions approximate one metric ton per year (tC/yr) per person. In 2004, United States per capita emissions neared 6 tC/yr (with Canada and Australia not far behind), and Japan and Western European countries range from 2 to 5 tC/yr per capita. Yet developing countries' per capita emissions approximate 0.6 tC/yr, and more than 50 countries are below 0.2 tC/yr (or 30-fold less than an average American). This imbalance between populations suffering from an increase in climate-sensitive diseases versus those nations producing greenhouse gases that cause global warming can be quantified using a "natural debt" index, which is the cumulative depleted CO2 emissions per capita. This is a better representation of the responsibility for current warming than a single year's emissions. By this measure, for example, the relative responsibilities of the U.S. in relation to those of India or China is nearly double that using an index of current emissions, although it does not greatly change the relationship between India and China. Rich countries like the U.S. have caused much more of today's warming than poor ones, which have not been emitting at significant levels for many years yet, no matter what current emissions indicate. Along with taking necessary measures to reduce the extent of global warming and the associated impacts, society also needs to pursue equitable solutions that first protect the most vulnerable population groups; be they defined by demographics, income, or location. For example, according to the WHO, 88% of the disease burden attributable to climate change afflicts children under age 5 (obviously an innocent and "nonconsenting" segment of the population), presenting another major axis of inequity. Not only is the health burden from climate change itself greatest among the world's poor, but some of the major mitigation approaches to reduce the degree of warming may produce negative side effects disproportionately among the poor, for example, competition for land from biofuels creating pressure on food prices. Of course, in today's globalized world, eventually all nations will share some risk, but underserved populations will suffer first and most strongly from climate change. Moreover, growing recognition that society faces a nonlinear and potentially irreversible threat has deep ethical implications about humanity's stewardship of the planet that affect both rich and poor.

The integration of the agricultural and energy sectors caused by rapid growth in the biofuels market signals a new era in food policy and sustainable development. For the first time in decades, agricultural commodity markets could experience a sustained increase in prices, breaking the long-term price decline that has benefited food consumers worldwide. Whether this transition occurs, and how it will affect global hunger and poverty, remain to be seen. Will food markets begin to track the volatile energy market in terms of price and availability? Will changes in agricultural commodity markets benefit net food producers and raise farm incomes in poor countries? How will biofuels-induced changes in agricultural commodity markets affect net consumers of food? At risk are over 800 million food-insecure people, mostly in rural areas and dependant to some extent on agriculture for incomes, who live on less than $1 per day and spend the majority of their incomes on food. An additional 2 to 2.5 billion people living on $1 to $2 per day are also at risk, as rising commodity prices could pull them swiftly into a food-insecure state.