Indonesia Report Final Draft 11.18

8/3/2019 Indonesia Report Final Draft 11.18

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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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2

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.
http://ccafs-climate.org/http://ccafs-climate.org/http://ccafs-climate.org/http://ccafs-climate.org/


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Figure 2.Changes in annual maximum temperature between 2000 and 2050 using the A1B scenario (C)


Change in annual maximum

temperature (C)


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


(millions), 2005,

35.1

Number of poor

population(millions), 2006,

39.3

Number of poor

population

(millions), 2007,

37.2


(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 legend

Harvest area

density legend



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Figure 13.2000 Yield and harvest area density for main crops: rainfed maize


Yield legend

Harvest areadensity legend



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Figure 14.2000 Yield and harvest area density for main crops: rainfed cassava


Yield legend

Harvest area

density legend



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Figure 15.2000 Yield and harvest area density for main crops: rainfed soybeans


Yield legend

Harvest areadensity legend



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Figure 16.2000 Yield and harvest area density for main crops: rainfed sugarcane


Yield legend

Harvest area

density legend



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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)


Change in annual

precipitation

(millimeters )


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)


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


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



figures



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Figure 21.Yield change map under climate change scenarios: irrigated maize



figures



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Figure 22.Yield change map under climate change scenarios: rainfed maize



figures



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Figure 23.Yield change map under climate change scenarios: irrigated soybeans



figures



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Figure 24.Yield change map under climate change scenarios: rainfed soybeans



figures



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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


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


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


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


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



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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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