Investments aimed at improving agricultural adaptation to climate change inevitably favor some crops and regions over others. An analysis of climate risks for crops in 12 food-insecure regions was conducted to identify adaptation priorities, based on statistical crop models and climate projections for 2030 from 20 general circulation models. Results indicate South Asia and Southern Africa as two regions that, without sufficient adaptation measures, will likely suffer negative impacts on several crops that are important to large food-insecure human populations. We also find that uncertainties vary widely by crop, and therefore priorities will depend on the risk attitudes of investment institutions.

El Niñ o events typically lead to delayed rainfall and decreased rice planting in Indonesia's main rice-growing regions, thus prolonging the hungry season and increasing the risk of annual rice deficits. Here we use a risk assessment framework to examine the potential impact of El Niñ o events and natural variability on rice agriculture in 2050 under conditions of climate change, with a focus on two main rice-producing areas: Java and Bali. We select a 30-day delay in monsoon onset as a threshold beyond which significant impact on the country's rice economy is likely to occur. To project the future probability of monsoon delay and changes in the annual cycle of rainfall, we use output from the Intergovernmental Panel on Climate Change AR4 suite of climate models, forced by increasing greenhouse gases, and scale it to the regional level by using empirical downscaling models. Our results reveal a marked increase in the probability of a 30-day delay in monsoon onset in 2050, as a result of changes in the mean climate, from 9 -18% today (depending on the region) to 30 -40% at the upper tail of the distribution. Predictions of the annual cycle of precipitation suggest an increase in precipitation later in the crop year (April-June) of Ϸ10% but a substantial decrease (up to 75% at the tail) in precipitation later in the dry season (July-September). These results indicate a need for adaptation strategies in Indonesian rice agriculture, including increased investments in water storage, drought-tolerant crops, crop diversification, and early warning systems.empirical downscaling models ͉ risk assessment A gricultural production in Indonesia is strongly influenced by annual and interannual variations in precipitation, caused by the Austral-Asia monsoon and El Niño-Southern Oscillation (ENSO) dynamics. Indonesia consistently experiences dry climatic conditions and droughts during the warm phase of the ENSO cycle (El Niño), with significant consequences for agricultural output, rural incomes, and staple food prices (1, 2). The year-to-year dynamics of ENSO and precipitation over the archipelago have been well studied (3-6), as have various links between ENSO, crop production, and famines in different parts of the country (7-10). Over the longer run, rising concentrations of greenhouse gases will likely create additional climate impacts on Indonesian agriculture. The combined forces of climate variability and climate change could have a dramatic effect on agricultural production in Indonesia and other tropical countries.Here, we present a framework for assessing the risks of climate change for Indonesian rice agriculture, drawing on the observational record of interannual variability in precipitation and production and on projections of climate change. We focus on precipitation, rather than temperature, because the links between precipitation and production in Indonesia are significant and well documented. Our earlier work (1) showed that ENSO has been the primary determinant of year-to-year variation in Indonesian rice outp...

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Despite the impact of El Niño-Southern Oscillation (ENSO) events on climate in the Indo-Pacific region, models linking ENSO-based climate variability to Indonesian cereal production are not well developed. This study measures connections among sea-surface temperature anomalies (SSTAs), rainfall, and Indonesian rice and corn production from 1971 to 1998. Year-to-year August SSTA fluctuations explain about half the interannual variance in paddy production during the main (wet) season. These effects are cumulative for rice: during strong El Niño years, wet season production shortfalls are not made up subsequently. For corn, the cumulative area sown is actually higher in El Niño years than La Niña years. Indonesia's paddy production varies on average by 1.4 million tons for every 1°C change in August SSTAs. The paper illustrates how an SSTA model might assist policy makers with budgetary processes, and private sector cereal traders with framing production expectations.

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