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INDONESIAN FOOD SECURITY AND CLIMATE
CHANGE:AGRICULTURE FUTURES
Endah Murniningtyas, Nono Rusono, Setyawati, and Jarot Indarto, National DevelopmentPlanning Agency
Gerald C. Nelson, Daniel Mason-DCroz, and Amanda Palazzo, International Food PolicyResearch Institute
October 2011
DRAFT VERSION, NOT READY FOR CITATION OR DISTRIBUTION
ContentsIntroduction .................................................................................................................................................. 1
Regional impacts of climate change ......................................................................................................... 1
Agriculture, Food Security and Indonesian Development ............................................................................ 5
Review of the Current Situation ................................................................................................................... 5
Population ................................................................................................................................................ 5
Income ...................................................................................................................................................... 7
Vulnerability ............................................................................................................................................. 8
Review of Land Use and Agriculture ........................................................................................................... 10
Land Use Overview ................................................................................................................................. 10
Agriculture Overview .............................................................................................................................. 12
Impacts of Climate Change: Scenarios for Adaptation ............................................................................... 20
Biophysical Scenarios ............................................................................................................................. 20
Climate Scenarios ............................................................................................................................... 20
Crop Physiological Response to Climate Change ............................................................................... 23
From biophysical scenarios to socioeconomic consequences: The IMPACT Model .......................... 30
Income and Demographic Scenarios ...................................................................................................... 31
Agricultural Vulnerability Scenarios (Crop-specific) ............................................................................... 33
Human Vulnerability Scenarios .............................................................................................................. 40
Agriculture and Greenhouse Gas Mitigation .............................................................................................. 41
Agricultural Emissions History ................................................................................................................ 41
Technical potential for agricultural mitigation ....................................................................................... 42
Economic potential for agricultural mitigation ...................................................................................... 42
Technical potential for agricultural adaptation ...................................................................................... 43
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Adaptation and mitigation synergies ..................................................................................................... 43
Conclusions ................................................................................................................................................. 43
References .................................................................................................................................................. 44
Table of TablesTable 1.Population Growth Rates, 1960-2008 (%)........................................................................................ 6
Table 2.Education and labor statistics ........................................................................................................ 10
Table 3.Harvest area of leading agricultural commodities, average of 2006-2008 .................................... 12
Table 4.Value of production for leading agricultural commodities, average of 2006-2008 ....................... 13
Table 5.Consumption of leading food commodities, average of 2003-2006 ............................................. 13
Table 6.GDP and population choices for the three overall scenarios......................................................... 31
Table 7.Average scenario per capita GDP growth rates (percent per year) ............................................... 32
Table 8.Indonesia and U.S. Per Capita Income Scenario Outcomes for 2010, 2030, and 2050 (2000US$
per person) .................................................................................................................................................. 33
Table of FiguresFigure 1.Changes in mean annual precipitation between 2000 and 2050 using the A1B scenario (mm per
year). ............................................................................................................................................................. 3
Figure 2.Changes in annual maximum temperature between 2000 and 2050 using the A1B scenario (C) 4
Figure 3.Population Trends: Total Population, Rural Population, and Percent Urban, 1960-2008 ............. 5
Figure 4.Population distribution (persons per square kilometer) ................................................................ 6
Figure 5.Population scenarios for 2010 to 2050 ........................................................................................ 7Figure 6.Per capita GDP (constant 2000 US$) and share of GDP from agriculture .................................... 8Figure 7.Poverty trend in Indonesia (using the national poverty line) ......................................................... 9
Figure 8.Poverty (percent below US$2 per day) ........................................................................................... 9
Figure 9.Well-Being Indicators: Life Expectancy at Birth and under 5 Mortality Rate ............................... 10
Figure 10.Land cover, 2000 ......................................................................................................................... 11
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Figure 11.Protected areas ........................................................................................................................... 11
Figure 12.2000 Yield and harvest area density for main crops: irrigated rice ............................................ 14
Figure 13.2000 Yield and harvest area density for main crops: rainfed maize .......................................... 16
Figure 14.2000 Yield and harvest area density for main crops: rainfed cassava ........................................ 17
Figure 15.2000 Yield and harvest area density for main crops: rainfed soybeans ..................................... 18
Figure 16.2000 Yield and harvest area density for main crops: rainfed sugarcane.................................... 19
Figure 17.Changes in mean annual precipitation for Indonesia between 2000 and 2050 using the A1B
scenario (millimeters) ................................................................................................................................. 21
Figure 18.Changes in normal annual maximum temperature for Indonesia between 2000 and 2050 using
the A1B scenario (C) .................................................................................................................................. 22
Figure 19.Yield change map under climate change scenarios: irrigated rice ............................................. 24
Figure 20.Yield change map under climate change scenarios: rainfed rice ............................................... 25
Figure 21.Yield change map under climate change scenarios: irrigated maize .......................................... 26
Figure 22.Yield change map under climate change scenarios: rainfed maize ............................................ 27
Figure 23.Yield change map under climate change scenarios: irrigated soybeans .................................... 28
Figure 24.Yield change map under climate change scenarios: rainfed soybeans ...................................... 29
Figure 25.The IMPACT modeling framework .............................................................................................. 30
Figure 26.The 281 FPUs in the IMPACT model ........................................................................................... 31
Figure 27.GDP Per Capita Scenarios ........................................................................................................... 32
Figure 28.Scenario outcomes for rice area, yield, production, net exports, and prices ............................. 34
Figure 29.Scenario outcomes for maize area, yield, production, net exports, and prices ......................... 35
Figure 30.Scenario outcomes for cassava area, yield, production, net exports, and prices ...................... 36
Figure 31.Scenario outcomes for groundnuts area, yield, production, net exports, and prices ................ 37
Figure 32.Scenario outcomes for soybeans area, yield, production, net exports, and prices.................... 38
Figure 33.Scenario outcomes for sugarcane area, yield, production, net exports, and prices .................. 39
Figure 34.Average daily kilocalories availability under multiple income and climate scenarios (kilocalories
per person per day) ..................................................................................................................................... 40
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Figure 35.Number of malnourished children under 5 years of age under multiple income and climate
scenarios ..................................................................................................................................................... 41
Figure 36.GHG Emissions (CO2, CH4, N2O, PFCs, HFCs, SF6) in Indonesia by Sector ................................. 42
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IntroductionNatural resources and environment plays an important role in economic development and life support
system. Agriculture also has been recognized as playing a vital role not only in economic development
but also in food security. However, in recent years there has been a serious concern on natural
resources degradation and environmental deterioration that may have impacts on the sustainability ofthese roles. The continued growth of world population along with changes in individual economic
behavior are leading to increased demand on natural resources, with impacts on food supplies.
Climate change also adds pressure on food security because it is expected to reduce agricultural food
production. Changes in precipitation and temperature additionally may increase the frequency of
floods and droughts in cultivated areas, further affecting food production.
In the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Working Group
1 reports that climate is often defined as 'average weather'. Climate is usually described in terms of
the mean and variability of temperature, precipitation and wind over a period of time, ranging from
months to millions of years (the classical period is 30 years) (Le Treut et al., 2007, pg.96)).
The unimpeded growth of greenhouse gas emissions is raising average temperatures. The
consequences include changes in precipitation patterns, more and more extreme weather events, and
shifting seasons. The accelerating pace of climate change, combined with global population andincome growth, threatens food security everywhere.
Agriculture is vulnerable to climate change in a number of dimensions. Higher temperatures
eventually reduce yields of desirable crops and tend to encourage weed and pest proliferation. Greater
variations in precipitation patterns increase the likelihood of short-run crop failures and long-run
production declines. Although there might be gains in some crops in some regions of the world, the
overall impacts of climate change on agriculture are expected to be negative, threatening global food
security. The impacts are
Direct, on crops and livestock productivity domestically Indirect, on availability/prices of food domestically and in international markets Indirect, on income from agricultural production both at the farm and country levels
This report provides an assessment of challenges to Indonesian food security through 2050. Thisincludes an analysis on agricultural production, price and trade futures incorporating with scenarios of
economic development, demography and climate change. It also assesses climate change effects on
human being using indicators per calorie consumption and child malnutrition numbers.
The first part of this paper is an overview of the current food security situation, the underlying
natural resources available in Indonesia and the drivers that lead to the current state, focusing on
income and population growth. The second part reviews the Indonesia-specific outcomes of a set of
scenarios for the future of food security in the context of climate change. These country-specific
outcomes are based on IMPACT model runs from July 2011.
Regional impacts of climate changeWhile the general consequences of climate change are becoming increasingly well known, great
uncertainty remains about how climate change effects will play out in specific locations 1.Figure 1
1To understand the significant uncertainty in how these effects play out over the surface of the earth it is useful to describe
briefly the process by which the results depicted in the figures are derived. They start with global (or general) circulation
models (GCMs) that model the physics and chemistry of the atmosphere and its interactions with oceans and the land surface.
Several GCMs have been developed independently around the world. Next, integrated assessment models (IAMs) simulate the
interactions between humans and their surroundings, including industrial activities, transportation, agriculture and other land
uses and estimate the emissions of the various greenhouse gasses (carbon dioxide, methane and nitrous oxide are the most
important). Several independent IAMs exist as well. The emissions simulation results of the IAMs are made available to the
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shows changes in average precipitation globally between 2000 and 2050 for four General Circulation
Models (GCMs), each using the A1B scenario. Figure 2shows the change in average maximum
temperature. In each set of figures, the legend colors are identical; a specific color represents the
same change in temperature or precipitation across the models.
A quick glance at these figures shows that substantial differences exist. For example, in Figure 1 the
CNRM GCM predicts that Southeast Asia will be much drier, while the ECHAM model has the same
region getting wetter. In South Asia, the MIROC GCM has an increase in precipitation, especially in the
northeast, while the CSIRO GCM has a drier South Asia. In northeast Brazil, the CNRM GCM shows
significant drying while the MIROC scenario has a sizeable increase in precipitation. In Figure 2, we see
that the MIROC and ECHAM GCMs predict very big temperature increases for northeast South Asia, but
they differ on whether northwest South Asia will also experience such a severe temperature increase.
Despite of the differences, the figures show that all models project all part of the world will
experience higher temperature in 2050.
These figures illustrate qualitatively the range of potential climate outcomes using current modeling
capabilities and provide an indication of the uncertainty in climate-change impacts. The differences
across models are why policy makers must avoid seeking specific solutions for specific locations unless
there is significant agreement across models. Rather, it is important to note general trends and to
consider policies that are helpful and robust across the range of climate outcomes.
GCM models as inputs that alter atmospheric chemistry. The end result is a set of estimates of precipitation and temperature
values around the globe often at 2 degree intervals (about 200 km at the equator) for most models. Periodically, the
Intergovernmental Panel on Climate Change (IPCC) issues assessment reports on the state of our understanding of climate
science and interactions with the oceans, land and human activities.
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Figure 1.Changes in mean annual precipitation between 2000 and 2050 using the A1B scenario (mm per year).
CNRM-CM3 GCM CSIRO-MK3 GCM
Change in annual
precipitation(millimeter
ECHAM5 GCM MIROC3.2 medium resolution GCM
Source: IFPRI calculations based on downscaled climate data available athttp://ccafs-climate.org.
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Figure 2.Changes in annual maximum temperature between 2000 and 2050 using the A1B scenario (C)
CNRM-CM3 GCM CSIRO-MK3 GCM
Change in annual maximum
temperature (C)
ECHAM5 GCM MIROC3.2 medium resolution GCM
Source: IFPRI calculations based on downscaled climate data available athttp://ccafs-climate.org/.
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Agriculture, Food Security and Indonesian DevelopmentThe agricultural sector still plays a vital role for economic development in Indonesia. This sector
contributes to the national income, employment and the provision of food. In 2009, agriculture
(including forestry and fisheries) contributed 15.6 percent of gross domestic product. The agriculture
sector also has contributed to national exports, primarily through the export of palm oil, cocoa, coffee,
and coconuts. On the labor side, agriculture remains a major employer in Indonesia. National statisticshows that about 41 percent or 43 million people are employed in agricultural sector. Based on
Indonesians experiences during economic crises, agriculture is the last resort for labor force migrating
from non-agriculture sectors.
Providing food is the main contribution of agriculture sector to the nation. Since 2008, Indonesia has
achieved self-sufficiency in rice. By pursuing policies that promote food security, Indonesia is able to
develop a strong foundation for nation building. Agriculture not only has an economic effect, but also
has social and environmental impacts for Indonesias society. Indonesians society has primarily evolved
from agricultural culture. Nowadays, Indonesian is still an agrarian community. This sector also has a
great impact on environmental conditions. Rural landscapes are one of Indonesias largest tourist
attractions. Clearly, agricultural land-use has a function in maintaining our life-support system.
Review of the Current Situation
PopulationThe Indonesian population in 2010 was 239 million with about 1 percent growth per year. Out of the
total population around 58 percent people live in rural areas. Figure 3 shows total and rural population
and counts (left axis) and the share of urban population (right axis). It shows that there is a significant
increase in the percentage of urban population.
Figure 3.Population Trends: Total Population, Rural Population, and Percent Urban, 1960-2008
Source: World Development Indicators (World Bank, 2009).
Table 1 provides additional information on rates of population growth. The table shows urban
population growth rate during the period of 1970 to 2008 is higher than the rural growth rate.
Urban migration may play roles on the higher urban growth rate.
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Table 1.Population Growth Rates, 1960-2008 (%)Decade Total
Growth
Rate
Rural Growth
Rate
Urban Growth Rate
1960-1969 0.02 0.02 0.02
1970-1979 0.02 0.02 0.051980-1989 0.02 0.01 0.05
1990-1999 0.01 0.00 0.00
2000-2008 0.01 -0.01 0.04Source: IFPRI calculations, based on World Development Indicators (World Bank, 2009)
Related with density, persons per square kilometers of land, is variable across Indonesia. The
western part of Indonesia especially Java Island is the most densely populated area in Indonesia.
At average, the Indonesian population density was 125 per people per square kilometers in 2007.
Figure 4 shows the geographic distribution of population within Indonesia.
Figure 4.
Population distribution (persons per square kilometer)
Source: IFPRI estimates from GRUMP for 2000.(Center for International Earth Science Information Network Columbia University2004)
Recently United Nations has issued population projections. The projections suggest that world
population will reach 8.9 billion by 2050, while the Indonesian population is projected to grow
from 211 million in 2000 to 293 million by 2050 in the median scenario. Figure 5 shows Indonesian
population projections by the UN Population office through 2050.
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Figure 5.Population scenarios for 2010 to 2050
Source: UN Population Projections (United Nations 2008).
IncomeThe income available to an individual is the single best indicator of their resilience to stresses.
Figure 6 shows trends in GDP per capita and proportion of GDP from agriculture. The agricultural
share is included both because its vulnerability to climate change impacts as well as an indicator
of the level of development of the country. As development increases, the importance of
agriculture in GDP tends to decline. This figure shows Indonesias GDP per capita has increased
since 1960s while the contribution of agriculture to GDP has decreased to less than 20 percent in
2000s.
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Figure 6.Per capita GDP (constant 2000 US$) and share of GDP from agriculture
Source: World Development Indicators (World Bank 2009).
VulnerabilityVulnerability is the lack of ability to recover from a stress. Poor people are vulnerable to many
different kinds of stresses because they lack the financial resources to respond. In agriculture,
poor people are particularly vulnerable to the stresses of an uncertain climate. In this report the
focus is on income, both level and sources. At the national level, vulnerability arises in the
interactions among population and income growth and the availability of natural and manufactured
resources. National per capita income statistics reported above show averages but potentially
conceal large variations across sectors or regions.Indonesia has defined the national poverty line at approximately USD 1.50 (PPP). Using the
national poverty line, the incidence of poverty generally has decreased from 24.2 percent in 1998
to 13.3 percent in 2010. Although the percentage of population living below the national poverty
line has been reduced, the total number of poor people is still high, about 31 million of people in
2010. Figure 7 illustrates this trend in declining poverty in both percentage living below the
poverty line, and the number of people living in poverty.
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Figure 7.Poverty trend in Indonesia (using the national poverty line)
Source: BPS, several years
Figure 8 shows the distribution of the proportion of the population living on less than $2.00 perday. The regional disparities become apparent whereby in some areas the incidence of poverty is
still very high. Reduction of the regional disparities becomes a challenge to reduce poverty in
Indonesia.
Figure 8.Poverty (percent below US$2 per day)
Source: Wood et al. (2010) available atlabs.harvestchoice.org/2010/08/poverty-maps
Related with the disparities, there is also a significant difference between the poverty rate inurban and rural areas. The percentage of population below the national poverty line in rural areas
is still higher than in urban areas. In 2010 the poverty rate in Indonesias rural areas was 16.6
percent while in urban areas it was only 9.9 percent.
Besides income, other indicators related to human resources are important. Table 2 provides
some data on additional indicators of vulnerability and resiliency to economic shocks: the
education level of the population, literacy, and concentration of labor in poorer or less dynamic
sectors.
National Poverty
Line (percentage),
1998, 24.2
National Poverty
Line (percentage),
1999, 23.4
National Poverty
Line (percentage),
2000, 19.1
National Poverty
Line (percentage),
2001, 18.4
National Poverty
Line (percentage),
2002, 18.2
National Poverty
Line (percentage),
2003, 17.4
National Poverty
Line (percentage),
2004, 16.7
National Poverty
Line (percentage),
2005, 16.0
National Poverty
Line (percentage),
2006, 17.8
National Poverty
Line (percentage),
2007, 16.6
National Poverty
Line (percentage),
2008, 15.4
National Poverty
Line (percentage),
2009, 14.2
National Poverty
Line (percentage),
2010, 13.3
Number of poor
population
(millions), 1998,
49.5
Number of poor
population
(millions), 1999,
48.0
Number of poor
population(millions), 2000,
38.7
Number of poor
population
(millions), 2001,
37.9
Number of poor
population
(millions), 2002,
38.4
Number of poor
population
(millions), 2003,
37.3
Number of poorpopulation
(millions), 2004,
36.1
Number of poorpopulation
(millions), 2005,
35.1
Number of poor
population(millions), 2006,
39.3
Number of poor
population
(millions), 2007,
37.2
Number of poorpopulation
(millions), 2008,
35.0
Number of poor
population
(millions), 2009,
32.5
Number of poor
population
(millions), 2010,
31.0
National Poverty Line (percentage) Number of poor population (millions)
http://labs.harvestchoice.org/2010/08/poverty-maps/http://labs.harvestchoice.org/2010/08/poverty-maps/http://labs.harvestchoice.org/2010/08/poverty-maps/http://labs.harvestchoice.org/2010/08/poverty-maps/ -
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Table 2.Education and labor statisticsIndicator Year Value
Primary school enrollment: Percent gross (3-year average) 2007 117.3
Secondary school enrollment: Percent gross (3-year average) 2007 73.5
Adult literacy rate 2006 92Percent employed in agriculture 2006 44.5
Under-5 malnutrition (weight for age) 2005 24.4Source: World Development Indicators (World Bank 2009).
The outcomes of significant vulnerability include low life expectancy and high infant mortality.
Figure 9 shows two non-economic correlates of poverty, life expectancy at birth and under-5
mortality. From 1960 to 2007, the life expectancy at birth and the under-5 mortality show positive
progress. During the period, the life expectancy at birth has increased and mortality of children
under five has decreased gradually. The World development Indicators shows that in 1960 the life
expectancy at birth in Indonesia was about 40 years then it increased to about 70 year in 2007. The
under-5 mortality rate was more than 200 deaths per 1000 live births in 1960 and it had declined
to about 30 deaths per 1000 live births in 2007.
Figure 9.Well-Being Indicators: Life Expectancy at Birth and under 5 Mortality Rate
Source: World Development Indicators (World Bank, 2009)
Review of Land Use and Agriculture
Agricultural production is dependent on the availability of land that has sufficient water, soilresources and an adequate growing season.
Land Use OverviewIndonesias land areas occupy 1.9 million square kilometers.Figure 10 shows land cover as of 2000.
A large portion of areas in eastern part of Indonesia is tree cover, while in western part is
cropland.
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Figure 10.Land cover, 2000
Source: Source: GLC2000 (JRC 2000).
Figure 11 shows the locations of protected areas, including parks and reserves. These locations
provide important protection for fragile environmental areas, which may also be important for the
tourism industry. Indonesia has a large number of protected/conservation areas. According to
Indonesias report on Millennium Development Goals, the ratio of protected forest areas to the
total land area of Indonesia was 26.4 percent in 2008. For marine areas, the ratio of marine
protected areas was 4.35 percent of the national territorial waters in 2009.
Figure 11.Protected areas
Source: World Database on Protected Areas (UNEP 2009). Water is from Global Lakes and Wetlands Database (WWF) (Lehner andDll 2004).
Policy makers also need to keep in mind the importance of transport costs when considering
potential for agricultural expansion; that is, if fertile but unused land is far from markets, it
represents potential land for expansion only if transportation infrastructure is put in place, and if
the land does not conflict with preservation priorities seen in Figure 11.
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Agriculture OverviewTables 3 to 5 show key agricultural commodities in terms of area harvested, value of the harvest,
and food for people (this last item was ranked by weight) for the period centered around 2006-
2008. Rice, maize, and cassava are by far the most important food crops in terms of area
harvested. These three crops use about 50 percent of total harvested area. Rice, maize and
cassava retain the top three positions in terms of value of production of food crop.
Table 3.Harvest area of leading agricultural commodities, average of 2006-2008Rank Crop % of total Area harvested
(000 hectares)
1 Rice, paddy 34.50% 12,081
2 Oil palm fruit 13.00% 4,550
3 Maize 10.50% 3,660
4 Coconuts 8.10% 2,833
5 Natural rubber 8.00% 2,800
6 Cassava 3.40% 1,207
7 Coffee, green 2.80% 9768 Cocoa beans 2.70% 940
9 Groundnuts, with shell 1.90% 668
10 Soybeans 1.60% 544
Total 100.00% 35,021Source: FAOSTAT (FAO 2010)
Rice is the most important agricultural commodities in Indonesia. Based on national statistics, the
paddy production and harvested area have increased gradually. The production of paddy increased
from 51.9 million tons in 2000 to 64.4 million tons in 2009. The data of harvested areas also show a
similar trend with paddy area increasing from 11.8 million hectares in 2000 to 12.9 million
hectares in 2009.
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Table 4.Value of production for leading agricultural commodities, average of 2006-2008Rank Crop % of total Value of Production
(million US$)
1 Rice, paddy 35.30% 13,261.10
2 Oil palm fruit 11.50% 4,322.10
3 Coconuts 5.90% 2,208.804 Maize 5.40% 2,034.60
5 Natural rubber 4.30% 1,631.30
6 Cassava 3.70% 1,400.70
7 Oranges 3.10% 1,154.30
8 Bananas 2.80% 1,055.40
9 Chilies and peppers,
green
2.70% 1,009.90
10 Fruit, tropical fresh nes 2.60% 980.6
Total 100.00% 37,550.80Source: FAOSTAT (FAO, 2010)
Rice and cassava are important food crops in terms of consumption. About 40 percent of
consumption is from these two crops. Table 5 shows the average consumption of leading food
commodities during the period of 2003-2006.
Table 5.Consumption of leading food commodities, average of 2003-2006Rank Crop % of total Food consumption
(000 mt)
1 Rice (Milled Equivalent) 30.70% 27,889
2 Cassava 10.00% 9,056
3 Vegetables, Other 7.10% 6,469
4 Fruits, Other 6.90% 6,2555 Maize 6.60% 6,044
6 Coconuts - Incl Copra 6.00% 5,466
7 Wheat 4.80% 4,340
8 Bananas 4.70% 4,233
9 Sugar (Raw Equivalent) 3.70% 3,355
Total 100.00% 90,893Source: FAOSTAT (FAO, 2010)
Figure 12 to Figure 16 show the estimated yield and growing areas for key crops, irrigated rice,
rainfed rice, rainfed maize, rainfed cassava, rainfed soybeans, and rainfed sugarcane. These
figures are based on the SPAM data set (Liangzhi You, Wood, and Wood-Sichra 2009), a plausible
allocation of national and subnational data on crop area and yields. As general observation,Indonesian agriculture is concentrated in the western half of the country, especially in Java and
Sumatra islands.
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Figure 12.2000 Yield and harvest area density for main crops: irrigated rice
Yield Harvest area density
Yield legend
Harvest area
density legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Figure 13.2000 Yield and harvest area density for main crops: rainfed rice
Yield Harvest area density
Yield legend
Harvest area
density legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Figure 13.2000 Yield and harvest area density for main crops: rainfed maize
Yield Harvest area density
Yield legend
Harvest areadensity legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Figure 14.2000 Yield and harvest area density for main crops: rainfed cassava
Yield Harvest area density
Yield legend
Harvest area
density legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Figure 15.2000 Yield and harvest area density for main crops: rainfed soybeans
Yield Harvest area density
Yield legend
Harvest areadensity legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Figure 16.2000 Yield and harvest area density for main crops: rainfed sugarcane
Yield Harvest area density
Yield legend
Harvest area
density legend
Source: SPAM Dataset (Liangzhi You, Wood, and Wood-Sichra 2009)
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Impacts of Climate Change: Scenarios for AdaptationIn this section, the current status of the country with respect to vulnerability is reviewed. This
includes a brief overview of current population trends, per capita income growth and its
distribution, and the state of agriculture. This also includes the biophysical effects on crop yields,
the impacts on agricultural production and prices, and the impacts on per capita calorie
consumption and child malnutrition.To better understand the possible vulnerability to climate change, it is necessary to develop
plausible scenarios. The Millennium Ecosystem Assessment's Ecosystems and Human Well-being:
Scenarios, Volume 2, Chapter 2 provides a useful definition: Scenarios are plausible, challenging,
and relevant stories about how the future might unfold, which can be told in both words and
numbers. Scenarios are not forecasts, projections, predictions, or recommendations. They are
about envisioning future pathways and accounting for critical uncertainties (Raskin et al. 2005).
For this report, combinations of economic and demographic drivers have been selected that
collectively result in three pathways a baseline scenario that is middle of the road, a
pessimistic scenario that chooses driver combinations that, while plausible, are likely to result in
more negative outcomes for human well-being, and an optimistic scenario that is likely to result in
improved outcomes relative to the baseline. These three overall scenarios are further qualified by
four climate scenarios: plausible changes in climate conditions based on scenarios of greenhouse
gas emissions.
Biophysical ScenariosThis section presents the climate scenarios used in the analysis and the crop physiological response
to the changes in climate between 2000 and 2050.
Climate Scenarios
As mentioned in the introduction, we used downscaled results from 2 GCMs with 2 SRES scenarios
for each GCM. Figure 17 shows precipitation changes for Indonesia under 4 downscaled climate
models with the A1B scenario. Figure 18 shows changes in annual maximum temperature for
Indonesia between 2000 and 2050 using the A1B scenario.Figure 18 shows that substantial differences exist across climate models in projecting
precipitation changes in Indonesia. For example, the MIROC model results in greater increases in
precipitation in most areas of Indonesia. Meanwhile the CNRM scenario shows a drier future
especially in western Indonesia. Figure 18 shows all models project higher temperatures in
Indonesia in 2050.CNRM scenarios predict 1.5 to 2 degree Celsius of change in annual maximum
temperature, while CSIRO models project the change of annual maximum temperature is 1 to 1.5
degree of celcius.
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Figure 17.Changes in mean annual precipitation for Indonesia between 2000 and 2050 using the A1B scenario (millimeters)
CNRM-CM3 GCM CSIRO-MK3 GCM
Change in annual
precipitation
(millimeters )
ECHAM5 GCM MIROC3.2 medium resolution GCM
Source: IFPRI calculations based on downscaled climate data available athttp://ccafs-climate.org/
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Figure 18.Changes in normal annual maximum temperature for Indonesia between 2000 and 2050 using the A1B scenario (C)
CNRM-CM3 GCM CSIRO-MK3 GCM
Change in
annual
maximum
temperature (C)
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data available athttp://ccafs-climate.org/
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Crop Physiological Response to Climate Change
In this section, the effects of climate change scenarios on agricultural yields are assessed using
DSSAT model for irrigated and rainfed crop. The DSSAT crop modeling system(Jones et al. 2003) is
used to simulate responses of five important crops (rice, wheat, maize, soybeans, and groundnuts)
to climate, soil, and nutrient availability, at current locations based on the SPAM dataset of crop
location and management techniques (Liang You and Wood 2006). In addition to temperature and
precipitation, we also input soil data, assumptions about fertilizer use and planting month, and
additional climate data such as days of sunlight each month.
We then repeated the exercise for each of the 4 future scenarios for the year 2050. For all locations,
locations, variety, soil and management practices were held constant. We then compared the
future yield results from DSSAT to the current or baseline yield results from DSSAT. The output for
key crops is mapped in Figure 19 to
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Figure 24. The comparison is between the crop yields for 2050 with climate change compared
to the yields with 2000 climate.
Those figures show that in Indonesia yield decreases for most crops, with irrigated rice, rainfed
rice and rainfed maize especially hit. The figures also reflect that the yield of rainfed soybeans
will increase slightly.
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Figure 19.Yield change map under climate change scenarios: irrigated rice
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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Figure 20.Yield change map under climate change scenarios: rainfed rice
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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Figure 21.Yield change map under climate change scenarios: irrigated maize
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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Figure 22.Yield change map under climate change scenarios: rainfed maize
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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Figure 23.Yield change map under climate change scenarios: irrigated soybeans
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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Figure 24.Yield change map under climate change scenarios: rainfed soybeans
CNRM-CM3 GCM CSIRO-MK3 GCM
Legend for yield change
figures
ECHAM5 GCM MIROC3.2 medium resolution GCMSource: IFPRI calculations based on downscaled climate data and DSSAT model runs
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From biophysical scenarios to socioeconomic consequences: The IMPACT Model
This section assesses population trends, per capita income growth, the state of agriculture in terms of its
production, yield and prices, and per capita calorie consumption and child malnutrition numbers, based on IMPACT
model.
Figure 25 provides a diagram of the links among the three models used in this analysis : IFPRIs IMPACT model
(Cline 2008), a partial equilibrium agriculture model that emphasizes policy simulations; a hydrology model and an
associated water-supply demand model incorporated into IMPACT; and the DSSAT crop modeling suite (Jones et al.
2003) that estimates yields of selected crops under varying management systems and climate change scenarios.
The modeling methodology reconciles the limited spatial resolution of macro-level economic models that operate
through equilibrium-driven relationships at a national level with detailed models of biophysical processes at high
spatial resolution. The DSSAT system is used to simulate responses of five important crops (rice, wheat, maize,
soybeans, and groundnuts) to climate, soil, and nutrient availability, at current locations based on the SPAM
dataset of crop location and management techniques. This analysis is done at a spatial resolution of 15 arc
minutes, or about 30 km at the equator. These results are aggregated up to the IMPACT models 281 spatial units,
called food production units (FPUs) (see Figure 26). The FPUs are defined by political boundaries and major river
basins. (See the Appendix for location of the Indonesian FPUs.)
Figure 25.
The IMPACT modeling framework
Source: Nelson, et al, 2010.
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Figure 26.The 281 FPUs in the IMPACT model
Source: Nelson et al. 2010
Income and Demographic ScenariosIFPRIs IMPACT model has a wide variety of options for exploring plausible scenarios. The drivers used for
simulations include: population, GDP, climate scenarios, rainfed and irrigated exogenous productivity and area
growth rates (by crop), and irrigation efficiency. In all cases except climate, the country-specific (or more
disaggregated) values can be adjusted individually. Differences in GDP and population growth define the overall
scenarios analyzed here, with all other driver values remaining the same across the three scenarios. Table 6
documents the GDP and population growth choices for the three overall scenarios for this analysis.
Table 6.GDP and population choices for the three overall scenariosCategory Pessimistic Baseline Optimistic
GDP, constant
2000 US$
Lowest of the four GDP growth
rate scenarios from the
Millennium Ecosystem
Assessment GDP scenarios
(Millennium Ecosystem
Assessment 2005)andthe rate
used in the baseline (next
column)
Based on rates from
World Bank EACC
study (Margulis
2010), updated for
Sub-Saharan Africa
and South Asian
countries
Highest of the four GDP
growth rates from the
Millennium Ecosystem
Assessment GDP
scenarios andthe rate
used in the baseline
(previous column)
Population UN High variant, 2008 revision UN medium variant,
2008 revision
UN low variant, 2008
revision
Source: Based on analysis conducted for Nelson et al. 2010.
The IMPACT modeling suite was run with four climate model and scenario combinations; the CSIRO and the MIROC GCMs with theGCMs with the A1B and the B1 scenarios. Those four outputs were used with each of the three GDP per capita scenarios.
Table 7shows the annual growth rates for different regional groupings as well as for Indonesia. Figure 27 illustrates
the path of per-capita income growth for Indonesia under these scenarios. In all scenarios, Indonesia s income
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growth exceeds those of the developed group of countries and most developing countries, although it is expected
to slow from the current rapid pace.
Table 7.Average scenario per capita GDP growth rates (percent per year)
Category 19902000
20102050
Pessimistic Baseline Optimistic
Indonesia 3.76 3.41 4.74 5.93
Developed 2.7 0.74 2.17 2.56
Developing 3.9 2.09 3.86 5.00
Low-income developing 4.7 2.60 3.60 4.94
Middle-income developing 3.8 2.21 4.01 5.11
World 2.9 0.86 2.49 3.22
Source: World Development Indicators for 19902000 and authors calculations for 20102050.
Figure 27 graphs the three GDP per capita scenario pathways, the result of combining the three GDP projections
with the three population projections of Figure 5from the United Nations Population office. The "optimistic
scenario" combines high GDP with low population. The "baseline scenario" combines the medium GDP projection
with the medium population projection. Finally, the "pessimistic scenario" combines the low GDP projection with
the high population projection.
Figure 27.GDP Per Capita Scenarios
Source: Based on IMPACT results of July 2011, computed from World Bank and United Nations population estimates (2008 revision).
Note that the scenarios used apply to all countries; that is, in the optimistic scenario, every country in the world is
assumed to experience high GDP growth and low population growth. As expected, in optimistic scenario, the
Indonesian GDP per capita will have a significant increase by 2050.
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The GDP per capita scenario results for Indonesia and the U.S. can be seen in Table 8. In the pessimistic
scenario, U.S. per capita income increases less than 2 times while in the optimistic scenario, it almost triples
between 2010 and 2050. The Indonesian per capita income increases four times in the pessimistic scenario and
increases almost 12 times in the optimistic scenario. However, despite Indonesias much more rapid growth than in
the U.S. its per capita income in 2050 is still only one-fifth of that in the U.S.
Table 8.Indonesia and U.S. Per Capita Income Scenario Outcomes for 2010, 2030, and 2050 (2000US$ per person)2010 2030 2050
Pessimistic
Indonesia 1,541 2,891 6,241
U.S. 37,504 51,132 58,291
Baseline
Indonesia 1,532 4,099 10,694
U.S. 37,723 56,517 88,841
Optimistic
Indonesia 1,920 6,906 21,822
U.S. 39,218 67,531 101,853
Agricultural Vulnerability Scenarios (Crop-specific)Figure 28 to Figure 33 show simulation results from the IMPACT model for rice, maize, cassava, groundnuts,
soybeans and sugarcanes. Each crop has five graphs: one each showing production, yield, area, net exports, and
world price.
The following figures are box and whisker plots that present the effects of the climate change scenarios in the
context of each of the economic and demographic scenarios. Each box has 3 lines. The top line represents the 75th
percentile, the middle line is the median, and the bottom line is the 25th percentile.2
Under all climate scenarios, Indonesia rice production and yield will have a slight increase from 2010 to 2050,
while the cultivated area will decrease slightly. While rice price is projected to increase during the period 2000
until 2050, the imports are expected to increase until around 2025 when it will begin to decrease until 2050.
2These graphs were generated using Stata with Tukey's(Tukey 1977) formula for setting the whisker values. If the interquartile range (IQR) is
defined as the difference between the 75th and 25th percentiles, the top whisker is equal to the 75th percentile plus 1.5 times the IQR. The bottom
whisker is equal to the 25th percentile minus 1.5 times the IQR (StataCorp 2009).
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Figure 28.Scenario outcomes for rice area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
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The production, yield, area and price of maize are projected to increase under all scenarios. However, for net-
export it tends to decrease with a big range of possible outcomes under different climate scenarios.
Figure 29.Scenario outcomes for maize area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
Production of cassava is projected to increase slightly. The yield and price are expected to increase as well, while
the area is projected to decline under all climate change scenarios. The scenarios show declining trends in net
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export during 2010 to 2030, then after 2030 the scenarios show an increasing trend of cassava net exports
particularly in the optimistic scenario.
Figure 30.Scenario outcomes for cassava area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
The groundnuts production is projected to increase, with a slight increase in yield. Area, net exports and price are
also expected to increase under all climate change scenarios.
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Figure 31.Scenario outcomes for groundnuts area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
The soybean production is projected to increase slightly until 2030, after that it will decrease slightly. Net export
of soybeans is projected to decrease, while the price is expected to increase under all scenarios.
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Figure 32.Scenario outcomes for soybeans area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
Under all scenarios the sugarcane production is projected to increase dramatically. The yield and area are also
expected to increase. In the net-export graph, even though it shows a declined trend under all scenarios, the
graph also shows that there is a variation of net-export outcomes between optimistic scenarios with other
scenarios.
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Figure 33.Scenario outcomes for sugarcane area, yield, production, net exports, and prices
Production Yield
Area Net Exports
PricesSource: Based on IMPACT results of July 2011.
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Human Vulnerability ScenariosThis section presents scenario outcomes for calorie availability and projections of under-5 malnourished children
number. Figure 34 shows scenario outcomes for the average daily kilocalories per capita and Figure 35 the number
of malnourished children under five. The story is much the same in both figures in qualitative terms. The
optimistic scenarios show substantial increases in calorie availability; the baseline and pessimistic scenario has a
slight increase in 2050. Climate change has relatively little effect within an overall scenario.
Figure 34.Average daily kilocalories availability under multiple income and climate scenarios (kilocalories per person per day)
Source: Based on IMPACT results of July 2011.
As expected, the baseline and optimistic scenarios do best in reducing malnourished children. In the optimistic
scenario the count drops close to 2 million children, while with the baseline it falls from about 5.3 million children
in 2010 to about 3 million in 2050. The pessimistic scenario is also the least desirable from the perspective of
reducing malnourished children. After a decline to just below 5.3 millionby the mid-2020s, the decline stops andthe number increases slightly.
As the box and whiskers plots indicate, within a particular overall scenarios climate change has relatively little
impact on the number of malnourished children. The range in 2050 from the different climate scenarios is typically
less than 1 million children malnourished. The reason, as we discuss below, is the ability of Indonesia to import
and/or export depending on how climate change affects production domestically and abroad.
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Figure 35.Number of malnourished children under 5 years of age under multiple income and climate scenarios
Source: Based on IMPACT results of July 2011.
Agriculture and Greenhouse Gas Mitigation
Agricultural Emissions HistoryClimate change is widely considered to give impacts on agriculture. However, agricultural sector also contributes to
the increase of greenhouse gases emission. For example, inappropriate rice cultivation, manure management, and
fertilizer utilization may increase methane and nitrous emissions.
In Indonesia, the current greenhouse emissions are dominated by land-use change emissions, including forest
degradation and peat fires. Energy and agriculture emissions also contribute substantially to the total emissions. In
cumulative number, GHG emission from agricultural sector is 117 million tones CO2.
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Figure 36.GHG Emissions (CO2, CH4, N2O, PFCs, HFCs, SF6) in Indonesia by Sector
Source: Climate Analysis Indicators Tool (CAIT) Version 8.0. (World Resource Institute 2011)
Technical potential for agricultural mitigationTo mitigate the negative impacts of climate change, it is necessary to develop and implement appropriate
measures to reduce greenhouse gas emission and enhance sinks. In agricultural sector, several technical potentials
for mitigation are:
1. Introducing new crop varieties with low emission. This includes increasing research and technology todevelop the varieties.
2. Prevent land burning3. Improving fertilizer application techniques to reduce emission, such as utilizing organic fertilizer4. Improving crop land management to increase soil carbon storageEconomic potential for agricultural mitigationSeveral economic potential for agricultural mitigation should also be identified. The main agricultural mitigation
that has important roles in reducing emission is preventing land burning. Therefore, it is essential to reduce
deforestation and land burning as well as manage peat land.
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Increasing investment for agricultural sector may also be one of important policies for agricultural mitigation.
Greater investment in agricultural research and technology are needed to cope with climate change.
Technical potential for agricultural adaptationThere are also several adaptation measures that can be taken in agricultural sector to cope with climate change.
The measures include:
1. Increasing the production and productivity of main food and promoting diversification on commodityconsumption.
2. Developing new varieties that resist to a range of environment such as drought and heat3. Developing adaptive agriculture technology, including developing soil management technology4. Improving water management including irrigation system to reduce water usage and water leakage5. Improving management of crop residue6. Building farmers and authorities capacity7. Developing crop weather insurance for farmers to increase farmers resilience from climate change
effects
Adaptation and mitigation synergiesTo cope with climate change there is a need to integrate mitigation and adaptation responses into development
policies. Therefore, several national development plans and policies have been formulated to build national
consensus on the climate change. The documents address commitment from all stakeholders to reduce emissionand take an action for adaptation measures.
1. Mainstreaming climate change into National Medium term Development Plan (RPJMN) 2010-2014. It reflectsall ministries and agencies of Government of Indonesias commitment to take an active role in program of
adaptation and mitigation.
2. Formulating Indonesia Climate Change Sectoral Roadmap (ICCSR) in 2010. The ICCSR outlines strategic visionthat emphasizes on climate change effects on several sectors. It elaborates prioritized actions and climate
change responses.
3. Formulating Presidential Regulation on National Action Plan on Greenhouse Gas Emission Reduction in 2011.This document elaborates Indonesias commitment and action plan to reduce carbon emission by 26 percent
from business as usual by 2020.
4. Establishing Indonesia Climate Change Trust Fund (ICCTF). The ICCTF manages funding for adaptation andmitigation measures. It consists of 3 windows: land based activities; energy; and adaptation and resilience.
5. Formulating an adaptation plan (on progress).ConclusionsBased on the analysis, climate change puts stresses on agricultural state and effect food security. The results show
that Indonesias crop yield, production and prices will be affected.
Consequently, it is essential to develop policies that can minimize the negative impacts of climate change and
contribute to the reduction of GHG emissions. It suggests following adaptation and mitigation policy
recommendation:
1. Promote integration and coordination among stakeholders to consistently implement the national climatechange policies.
2. Build synergies of mitigation and adaptation measures into sustainable development plans.3. Increase research, technology, infrastructure investment on agriculture to meet the future demand and
maintain food security.
4. Build human resources capacity to deal with climate change.The paper was presented at the International Conference on Climate Change and Food Security (ICCCFS, Beijing, China, November 6-8), jointly hosted bythe International Food Policy Research Institute (IFPRI) and the Chinese Academy of Agricultural Sciences (CAAS). The authors would like to acknowledgefinancial support from CCAFS. Any errors and omissions are the responsibility of the authors. Any opinions expressed in this paper are those of the authorsand are not necessarily endorsed by IFPRI or CAAS. The boundaries and names shown and the designations used on the map(s) herein do not imply official
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