Vulnerability of Paddy and Maize to Climate Variability in Indonesia and

Vulnerability Map of paddy to Climate Change in Bali

Dr. Edvin Aldrian, Dr. Takeshi Takama, Alifi Maria Ulfa, Pudji Setyani, Risya Okta and Mamenun

Center for Climate Change and Air Quality Meteorological Climatological and Geophysical 

Agency (BMKG)Email: edvin.aldrian@bmkg.go.id

The First International Workshop of Climatic Changes and their Effects on Agriculture in Asian Monsoon Region, 3-4 March 2012, Bangkok, Thailand

Presentation Outline

1.Background of regional climate phenomena

2.ENSO phases and impacts3.Vunerability index of harvest area

from paddy and maize4.Development of Vulnerability Map

of Paddy for Bali

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Background of regional climate phenomena

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Yearly rainfallBMKG

Climatology of regional rainfall► Mainly monsoonal► Three distinct rainfall climate regions

monsoonal

Semi-monsoonal Anti-monsoonal

Aldrian and Susanto, 2003, Intl J Climatol.

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Monsoonal climatology of Southeast AsiaBMKG

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Colour indicate dekads (ten days)

Area with rainfall amount > 50mm/dekad (Non ZOM)

Area with rainfall amount < 50mm/dekad (Non Monsoon)

Dry Season Onset (data TRMM 1998‐2010)

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Wet Season Onset (data TRMM 1998‐2010)

Colour indicate dekads (ten days)

Area with rainfall amount > 50mm/dekad (Non ZOM)

Area with rainfall amount < 50mm/dekad (Non Monsoon)

ENSO phases and impacts

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Background

Regional climate phenomena

Rainfall data

Harvest area changes

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Record1970-2009

http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

El Nino La Nina

Weak Mod Strong Weak Mod Strong

76‐77 86‐87 72‐73 71‐72 70‐71 73‐74

77‐78 87‐88 82‐83 74‐75 98‐99 75‐76

04‐05 94‐95 91‐92 84‐85 99‐00 88‐89

06‐07 02‐03 97‐98 95‐96 07‐08

00‐01

Weak El Nino4 (10%) Moderate 

El Nino4 (10%)

Strong El Nino4 (10%)

Neutral15 (39%)

Weak La Nina5 (13%)

Moderate La Nina4 (10%)

Strong La Nina3 (8%)

ENSO EpisodesBMKG

BMKGEL NINO / LA NINA

INDONESIA SST

A S I A

A S I A

1

2

3DIPOLE MODE POSITIVE/ DIPOLE MODE NEGATIVE1

2

1

3

2

Dipole Mode (0C)

SST Indonesia 

(0C)

0.4 positive > 0.5 warm

‐0.4 negative

<‐0.5 cool

El Nino            La Nina

0.5 – 1 weak

‐1 – ‐0.5 weak

1 – 2 Moderate

‐2 – ‐1Moderate

> 2 strong <‐2 strong

1963197219821997

3

EL NINO, DIPOLE MODE

LA NINA, DIPOLE MODE

12

‐ Oceanic  Nino Index (ONI) selected from NOAA   3‐monthly moving average for SST anomaly in Nino 3.4 region (5oN‐5oS, 120o‐170oW)

5 Consecutive months :• Anomaly  ≥ +0.50C  El Nino• Anomaly ≤ ‐0.50C  La Nina

Threshold  for at least 3 consecutive months :•Weak El Nino  0.5 ‐ 0.9 0C•Medium El Nino  1.0 ‐ 1.4 0C•Strong El Nino  ≥ 1.5 0C

ENSO phasesBMKG

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Drought

Drought IndexJune-July-August 1997

Rainfall Deficit

El Nino Impactin Indonesia

Source: GPCC

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El Niño impact episode

Ensemble El Niño events during 1960-1991 against climatology

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La Niña impact episode

Ensemble La Niña events during 1960-1991 against climatology

Harvest Failure related to drought during El Nino and flood during La Nina

Source : Surmaini dan Susanti (2009)

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Event record 1971-2008

Harvest area changes of paddy and maize related to

El Nino

Source : Indonesia Bureau of Statistics= years of strong El Nino

DryPaddy Wet Paddy

MaizeBMKG

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Data: Indonesia Bureau of Statistics

Event record 1971 - 2008

Wet paddy field

La Nina Netral El Nino

Dry paddy field

La Nina Netral El Nino

Maize

La Nina Netral El Nino

Tend toward harvest area decreaseduring El Nino, neutral and La Nina 

condition

ENSO Impacts to Precipitation (Droughts) in Indonesia

1% Significance Level5% Significance Level10% Significance LevelNot Significant

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Vunerability index of harvest area from paddy and maize

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Changes on harvest area in parts of Sumatra and West Java is mainly influenced by Dipole Mode phenomena that came with ENSO in the same time.

10% 1%

weak strong

Drought Influences due to El Nino on HarvestArea Anomalies for Wet Paddy, Dry Paddy, and Maize

on 1%, 5%, and 10% Level of Significance

Wet Paddy (1%)Dry Paddy (1%)Dry Paddy (10%)Maize (1%)Maize (5%)Wet Paddy (1%), Dry Paddy (5%)Wet Paddy (5%), Maize (5%)Wet Paddy (10%), Maize (10%)Wet Paddy (5%). Maize (10%)Wet Paddy (5%), Maize (5%)Wet Paddy (1%). Dry Paddy (5%), Maize (5%)Not Significant

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Provinces with Changes on Average Harvest Area overthe Period of 1971 – 2008 with 1% and 5% Significance Levels

Event Wet paddy field Dry paddy field Maize fieldEl Nino Bengkulu (‐), Sumsel (‐), 

Lampung (‐), Jatim (‐), Kalsel (‐), Kaltim (+), Sultenggara (‐), Sulsel (‐)

Sumbar (+), Jabar (‐), Kaltim (+), Sulteng (‐), Sulsel (‐)

Jambi (‐), Sumsel (‐), Lampung (‐), Jabar (‐), Jateng (‐), Jatim (‐), NTB (‐),  Sulsel (‐)

Netral Bengkulu (+) ‐ ‐

La Nina DKI Jakarta (‐), Bali (‐), Sultenggara (+)

Sulteng (+), Sulsel (+) Jambi (+), DKI Jakarta (+), Jabar (+), Jateng (+), Sultenggara (+), Sulsel (+)

(‐)  = harvest area decreased;   (+) = harvest area increased

Droughts due to El Nino cause reduced harvest areas of paddy and maize, while La Nina tends to increase harvest area in some provinces.

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National harvest pattern

Maize Harvest Pattern

Wet Paddy Harvest Pattern Dry Paddy Harvest Pattern

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Percentage of Provinces with Decreased Harvest Area during Strong El Nino

About 54 – 85% of provinces in Indonesia experienced varies paddy and maize harvest area loss during strong El Nino.

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BMKG Percentage of Paddy and Maize Harvest Area Losses in Indonesia During Strong El Nino episodes

Strong El Nino occurred in 1997‐1998 but the greatest impacts was felt in 1972‐1973 and 1982‐1983.

Vulnerability Index

Vulnerability :Level in which climate condition can destroy or perturb asystem; not only depends on system sensitivities, but alsodepends on capability of this system to adapt new climatecondition (IPCC in Gomes, 1998 as cited by Irawan, 2002).

A region is vulnerable to El Nino (strong to weak) when this areais having a significant production and/or harvest area losses asthe result of significant rainfall reduction.

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…Vulnerability Index

Expected production loss :Percentage of production loss during El Nino years  toward 3‐year moving average of production.

(IPCC 2001 in Irawan, 2002)

Rainfall reduction :Percentage of rainfall during El Nino Years toward average rainfall in a certain period. 

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Vulnerability Index of Wet Paddy Production During Strong El Nino(1972/1973, 1982/1983, 1991/1992, 1997/1998)

Vulnerability Index of Wet Paddy Production During Weak El Nino(1976/1977, 1977/1978, 2004/2005, 2006/2007)

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Vulnerability Index of Dry Paddy Production During Strong El Nino(1972/1973, 1982/1983, 1991/1992, 1997/1998)

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Vulnerability Index of Dry Paddy Production During Strong El Nino(1972/1973, 1982/1983, 1991/1992, 1997/1998)

Vulnerability Index of Maize Production During Strong El Nino(1972/1973, 1982/1983, 1991/1992, 1997/1998)

Vulnerability Index of Maize Production During Weak El Nino(1976/1976, 1977/1978, 2004/2005, 2006/2007)

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No ProvinceStrong El Nino Weak El Nino

Wet Paddy

Dry Paddy

Maize Wet Paddy

Dry Paddy

Maize

1 Lampung 0.00 0.40 0.60 0.19 0.10 0.49

2 Jawa Barat 0.10 0.20 0.60 0.10 0.24 0.67

3 Jawa Tengah 0.00 0.20 0.50 0.07 0.21 0.78

4 DIY 0.10 0.00 0.60 0.02 0.00 0.49

5 Jawa Timur 0.00 0.10 0.20 0.02 0.12 0.35

6 Sultenggara 0.20 0.10 0.20 0.27 0.12 0.13

7 Sulsel 0.20 0.20 0.50 0.26 0.43 0.63

Vulnerability IndexProvinces with Rainfall Reduction and Production Loss During Strong and Weak El Nino

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Maize, which is sensitive to water availability, tends to experience productionlosses when water availability is low during El Nino and experience productiongain during La Nina.

Source : Dorenboos dan Kassam (1979) in Subagyono, etc. (2004)

Beside using rainfall as water source, wet paddy can use irrigation whereasdry paddy is commonly planted on wet season.

Type  Water Necessity (Fenology Phase) Total

Paddy 50 mm (10 days)

320 mm (60 days)

80 mm (15 days)

85 mm (20 days)

65 mm (15 days)

600 mm (120 days)

Maize  56 mm (20 days)

167 mm (30 days)

115 mm (15 days)

250 mm (40 days)

62 mm (15 days)

650 mm (120 days)

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Development of Vulnerability Map of Paddy for Bali

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1979 1990 2000 2010

Tempe

rature [d

egree Celcius

]

23

24

25

26

27

28

29

30

31

Long term trends – avg. land temperature

Top: Pondok Betung – Jakarta ; Bottom: Darmaga – Bogor 

1984 1990 2000 2008

Tempe

rature [d

egree Celcius

]

23

23.5

24

24.5

25

25.5

26

26.5

27

27.5

28

28.5

Trend maps – average temperature

Other (major) islands are in progress …  

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Indonesia … largest and widest archipelago …

Bali … rise in population, economy, etc.

In Sep 2010,Capacity development project for Climate Change Strategies

Sub-pro: Vulnerability assessment

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Exposure

Impact

Sensitivity

Adaptive Capacity

Vulnerability to climate change

Outcome Vulnerability Contextual Vulnerabilityhazard

Important to identify issues, context, purpose, system, etc

Vulnerability concepts

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1. Identify range of issues

2. Identify and select issues of concern

3. Define purpose of indicator

4. Define system for

5. Define harm in context

6. Identify and select variables

Literature review

assessmentStatistical assessment

assessment

Stakeholder/ policy

assessment

interviewsFarmer

interviews

Assessment frame work

1. Identify range of issues~Climate change in Bali~

Downscaled Monthly Daily Max Temp. Projections

Downscaled Monthly Daily Min Temp. Projections

~2℃Climate change in Bali(SRES A2 2046‐65)

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e.g.  “Develop drought early warning system” in National Action Plan 2007 

e.g.  “impact study of climate change to the agriculture” in ICCSR 2009 & National Action 

Plan 2007 

1. Identify range of issues2. Identify and select issues of concern

~ Vulnerability in Indonesia ~

Based on 10 policy reports incl. RPJMN, ICCSR, NARASI

23 adaptation measures are found in agriculture sector!

2. Identify and select issues of concern3. Define purpose of indicator

Stakeholder meetings

Scaling the impact on exposure units3 = significant impact on the exposure unit2 = medium impact on the exposure unit1 = low impact on the exposure unit0 = no impact on the exposure unit

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Farming land

Fishery

Water

Drought

Heavy rainFloodsBMKG

3. Define purpose of indicator4. Define system forPresidential Decree No.5.2011“for paddy sustainability”

1st: … coordinated and integrated by tasks, functions … production of paddy/rice … to face extreme climate conditions 2nd: BMKG analyses and 

disseminate climate change for Ministry of Agriculture and related agencies 

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Food security (Rice)

Production

Consumption

Distribution

Presidential decree No. 5

Self-sustainability in Bali

4. Define system for (conceptual framework)

Food security concepts

Climate change

Population change

Land use change

Pest outbreaks

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5. Define harm in context

Land use (ha)

paddy non‐paddy

non‐firm

2000 85,776 267,794 210,0972001 84,860 268,258 210,5512002 83,562 268,948 211,1552003 82,644 269,473 211,5492004 82,089 269,411 212,1342005 81,207 273,332 209,1272006 80,997 273,386 209,2832007 81,235 271,704 210,7272008 81,482 274,755 207,429

Year

Paddy Harvested area (ha)

Production rate (quintal/ha)

Production (ton)

2000 155,049 53.33  826,8382001 147,942 53.35  789,2322002 148,025 54.70  809,6882003 145,294 54.60  793,2602004 142,663 55.00  788,3612005 141,577 55.00  785,4812006 150,557 56.00  840,8912007 145,030 58.00  839,7752008 143,999 58.37  840,465

5% 7%Not sure

Productivity assessment

Paddy area  X productivity = ?

2.5% 8%

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5. Define harm in context

Soil‐climate suitability for paddy20 %

Land Suitability  by 20% in 20 years= productivity?

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“we never fail  rice for 20 years”

“Not enough water, only 1 harvesting” “20% became housing”

“100% of farmers 

changed from rice to 

plantation ”

“Downstream does not have 

water, so changed to cacao”

“always have enough 

water”

“not enough water , so need to rotate rice and crop ”

6. Identify and select variablesPreliminary fieldworks

BMKG Potential decision affectingStrong decision factors

Types of land use (decision output)

“rice intensive” subak”

(2/3 rice)

“rice & crop balanced” subak”

(1/2 – 1/3 rice)

“plantation” subak”

(0% rice)

“land converted” “land converted” subak”

(0% rice)

Prices of

crops

Prices of

crops

OpportOpportunity

Prices

of rice

Prices

of rice

Subak support

policies

Subak support

policies

Pest/r

ain

Pest/r

ain

Strict subak

rule

Strict subak

ruleof land

Prices

of landlevels

Water

levels

6. Identify and select variables

1. Identify range of issues

2. Identify and select issues of concern

3. Define purpose of indicator

4. Define system for

5. Define harm in context

6. Identify and select variables

reviewLiterature

review

t

Statistical assessmen

t

assessment

Stakeholder/ policy

assessment

interviews

Farmer

interviews

Presidential decree No.5

Initial VA for food security

Climate Change

Food security

Development 

Water level

Temprature Prices  Policy

Floods Drought Land use change

Paddy Water Crops 

Ready for vulnerability assessment!

“Issues, context, purpose, system were identified!”BMKG

BMKGExposure Sensitivity Adaptive 

capacityVulnerabil

ity

Wet & dry spells

Precipitation

Max & min temperatures

Farm density

Farmer population density

Availability/CP ratio (0534)

Poverty/BPL (0.598)

Road (0.771)

Electricity (0.746)

Life expectation (0.802)

Underweight (0.783)

Female Illiteracy (0.911)

Water (0.759)

Health (0.604) 

An example:Vulnerability index

SensitivityExcept north and east regions, all kabupatens are sensitive

Demo: Sensitivity (Farming area density)

Adaptive CapacityKaragasam, Klungkung, & Bujeleng has the bottom 3 adaptive capacity in Bali

Demo: Adaptive Capacity (WFP)

Adaptive CapacityKaragasam has low adaptive capacityin Indonesian standard

Demo: Adaptive Capacity (WFP)

Bali Climate exposure, Dry spell

Bali Climate exposure, Wet spell

Bali Climate exposure, Extreme rainfall

Conclusions

1.The droughts due to El Nino cause reduced harvest areas of paddy and maize, while La Nina tends to increase harvest area

2.Maize receive the most significant impact from El Nino due to non irrigating nature while wet paddy receive the least due to the water management nature of the irrigation system

3.South Sulawesi is the most prone area against El Nino impact.

4.Bali has a unique adaptatif capacity in food security that implicate to the development of vulnerability map for paddy

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THANK YOU