Suchergebnisse

Introduction / David Lobell, Marshall Burke -- Climate effects on food security : an overview / Marshall Burke, David Lobell -- Climate models and their projections of future changes / Claudia Tebaldi, Reto Knutti -- Crop response to climate : ecophysiological models / Jeffrey W. White, Gerrit Hoogenboom -- Crop responses to climate : time-series models / David Lobell -- Crop responses to climate and weather : cross-section and panel models / Wolfram Schlenker -- Direct effects of rising atmospheric carbon dioxide and ozone on crop yields / Elizabeth A. Ainsworth, Justin M. McGrath -- Food security and adaptation to climate change : what do we know? / Marshall Burke, David Lobell -- Breeding strategies to adapt crops to a changing climate / R.M. Trethowan, M.A. Turner, T.M. Chattha -- Global and regional assessments / David Lobell, Marshall Burke -- Where do we go from here? / David Lobell, Marshall Burke

Crop-yield forecasts provide useful information to growers, marketers, government agencies and other users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, although official forecasts do not exist for many crops. Because broad-scale crop yields depend largely on the weather, measurements from existing meteorological stations have the potential to provide reliable, timely and cost-effective predictions. We developed weather-based models of statewide yields for 12 major California crops and tested their accuracy using cross-validation from 1980 to 2003. Many of the weather-based forecasts were highly accurate, as judged by the percentage of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, in many cases allowing longer lead times than existing procedures.

Armed conflict within nations has had disastrous humanitarian consequences throughout much of the world. Here we undertake the first comprehensive examination of the potential impact of global climate change on armed conflict in sub-Saharan Africa. We find strong historical linkages between civil war and temperature in Africa, with warmer years leading to significant increases in the likelihood of war. When combined with climate model projections of future temperature trends, this historical response to temperature suggests a roughly 54% increase in armed conflict incidence by 2030, or an additional 393,000 battle deaths if future wars are as deadly as recent wars. Our results suggest an urgent need to reform African governments' and foreign aid donors' policies to deal with rising temperatures.

Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate—a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh ([Formula: see text] 18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 [Formula: see text] g/ [Formula: see text] of [Formula: see text] (particulate matter of a diameter less than 2.5 [Formula: see text] m) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry.