Journal articleIFPRI3; ISI; HarvestChoice; CRP2EPTD; PIMPRCGIAR Research Program on Policies, Institutions, and Markets (PIM

Agricultural droughts affect whole societies, leading to higher food costs, threatened economies, and even famine. In order to mitigate such effects, researchers must first be able to monitor them, and then predict them; however no book currently focuses on accurate monitoring or prediction of these devastating kinds of droughts. To fill this void, the editors of Monitoring and Predicting Agricultural Drought have assembled a team of expert contributors from all continents to make a global study, describing biometeorological models and monitoring methods for agricultural droughts. These models and methods note the relationships between precipitation, soil moisture, and crop yields, using data gathered from conventional and remote sensing techniques. The coverage of the book includes probabilistic models and techniques used in America, Europe and the former USSR, Africa, Asia, and Australia, and it concludes with coverage of climate change and resultant shifts in agricultural productivity, drought early warning systems, and famine mitigation. This will be an essential collection for those who must advise governments or international organizations on the current scope, likelihood, and impact of agricultural droughts. Sponsored by the World Meterological Organization

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

Model simulation is an important way to study the effects of climate change on agriculture. Such assessment is subject to a range of uncertainties because of either incomplete knowledge or model technical uncertainties, impeding effective decision-making to climate change. On the basis of uncertainties in the impact assessment at different levels, this article systematically summarizes the sources and propagation of uncertainty in the assessment of the effect of climate change on agriculture in terms of the climate projection, the assessment process, and the crop models linking to climate models. Meanwhile, techniques and methods focusing on different levels and sources of uncertainty and uncertainty propagation are introduced, and shortcomings and insufficiencies in uncertainty processing are pointed out. Finally, in terms of how to accurately assess the effect of climate change on agriculture, improvements to further decrease potential uncertainty are suggested.climate change, agriculture, impact assessment, uncertainty, model simulation Citation:Yao F M, Qin P C, Zhang J H, et al. Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods.

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