The recent upheavals in staple food prices, financial markets, and the global economy raise questions about the state of food insecurity, the nature of price variability, and the appropriate strategies for international agricultural development. For decades preceding this turmoil, agriculture had received waning attention from the global development community as real food prices declined on trend. Analysts who worried about food insecurity focused on the fate of poor producers. The dramatic upswing in prices in 2007-08 turned attention toward poor consumers as many countries struggled with food riots, mounting malnutrition, and the adoption of grain self-sufficiency policies. New debates have been spurred over whether real agricultural prices will resume their long downward decline or whether there has been a more general reversal in the real price of food.

Brazil has developed a large-scale commercial agricultural system, recognized worldwide for its role in domestic economic growth and expanding exports. However, the success of this sector has been associated with widespread destruction of Brazilian ecosystems, especially the Cerrado and the Brazilian Amazon rainforest, as well as environmental degradation. Brazil's agricultural development has also led to land consolidation, aggravating a historical land distribution inequality. This pattern of agricultural growth has reinforced Brazil's status as one of the world's most inequitable countries in terms of income distribution, making it difficult to assert that the nation is pursuing a sustainable development path. In order to achieve sustainable development Brazil must reconcile its increasingly productive, modern tropical agricultural system with environmental preservation, social equity, and poverty alleviation in rural and urban areas. Although a daunting task, Brazil has the opportunity to lead tropical countries in combining modernized agriculture with highly diverse and functional ecosystems. Continued improvement in socioeconomic conditions is equally important and will require stronger efforts to decrease inequalities in income and land distribution in the rural sector.

Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha-1 vs. ∼63 tons·ha-1) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha-1·y-1 vs. 3.84 tons·ha-1·y-1). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2◦C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models' predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change.

Global demand for agricultural products such as food, feed, and fuel is now a major driver of cropland and pasture expansion across much of the developing world. Whether these new agricultural lands replace forests, degraded forests, or grasslands greatly influences the environmental consequences of expansion. Although the general pattern is known, there still is no definitive quantification of these land-cover changes. Here we analyze the rich, pan-tropical database of classified Landsat scenes created by the Food and Agricultural Organization of the United Nations to examine pathways of agricultural expansion across the major tropical forest regions in the 1980s and 1990s and use this information to highlight the future land conversions that probably will be needed to meet mounting demand for agricultural products. Across the tropics, we find that between 1980 and 2000 more than 55% of new agricultural land came at the expense of intact forests, and another 28% came from disturbed forests. This study underscores the potential consequences of unabated agricultural expansion for forest conservation and carbon emissions.