Farmer Adaptive Capacity to Climate Change in Strengthening Rice Sufficiency in Sumedang Regency, West Java Province

FARMER ADAPTIVE CAPACITY TO CLIMATE CHANGE

IN STRENGTHENING RICE SUFFICIENCY

IN SUMEDANG REGENCY, WEST JAVA PROVINCE

ADE CANDRADIJAYA

POSTGRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

Declaration

I, the undersigned below, hereby declare that the content of the dissertation

entitled “Farmer Adaptive Capacity to Climate Change in Strengthening Rice

Sufficiency in Sumedang Regency, West Java Province” is the result of my own work and only with the use of cited sources as listed in the reference. I also declare that this dissertation has not been submitted to any other university.

I herewith transfer the copyright of this paper to Bogor Agricultural University.

Bogor, January 2015

Ade Candradijaya

RINGKASAN

ADE CANDRADIJAYA. Kapasitas Adaptasi Petani terhadap Perubahan Iklim dalam Penguatan Ketahanan Pangan di Kabupaten Sumedang, Provinsi Jawa Barat. Dibimbing oleh CECEP KUSMANA, LAILAN SYAUFINA, YUSMAN SYAUKAT, dan AKHMAD FAQIH.

Emisi gas rumah kaca menyebabkan pemanasan global yang melandasi permasalahan ketahanan pangan saat ini. Peningkatan suhu global sekitar 4°C atau lebih diatas suhu pada akhir abad ke-20, dan peningkatan permintaan pangan, menjadi ancaman utama terhadap ketahanan pangan, secara global maupun regional. Sejumlah fakta menunjukkan bahwa peningkatan suhu 1°C saja menimbulkan dampak negatif terhadap sejumlah produk pangan utama seperti beras dan jagung. Selain itu, laju pemanasan global diproyeksikan terus meningkat dalam beberapa dekade kedepan, dibandingkan laju pemanasan pada beberapa dekade terakhir.

Petani kecil, dimana penghidupan mereka sangat bergantung pada sektor pertanian, merupakan kelompok yang paling terkena dampak buruk perubahan iklim. Sejumlah hasil kajian menunjukkan bahwa petani kecil telah melakukan tindakan adaptasi dalam praktek pertanian mereka, seperti penyesuaian kalender tanam dan investasi dalam penyediaan infrastruktur. Namun demikian, tindakan adaptasi tersebut dilaporkan belum cukup memadai, dan masih menyisakan residu dampak yang cukup besar.

Penelitian ini bertujuan untuk mempelajari kapasitas adaptasi petani terhadap dampak perubahan ikim dan mencari alternatif penguatan kapasitas adaptasi kedepan dalam rangka penguatan ketahanan pangan pada tingkat rumah tangga. Secara spesifik, tujuan penelitian meliputi: (1) menunjukkan bahwa perubahan iklim telah terjadi di lokasi kajian dan membuat estimasi dampak perubhan iklim terhadap produktivitas beras berdasarkan keragaman praktek pertanian saat ini, (2) membuat estimasi dampak kondisi iklim saat ini dan masa datang terhadap kecukupan beras tingkat rumah tangga berdasarkan keragaman praktek adaptasi saat ini, (3) membuat estimasi tingkat kerentanan rumah tangga petani terhadap ancaman kekurangan beras pada tingkat rumah tangga sebagai dampak perubahan kondisi iklim saat ini dan masa datang, dan (4) melakukan identifikasi faktor-faktor penentu keragaman praktek adaptasi petani.

Hasil penilaian kondisi iklim di lokasi kajian menunjukkan adanya kecenderungan peningkatan rata-rata suhu minimum dan maksimum serta penurunan rata-rata curah hujan tahunan selama periode 1981-2010. Hasil proyeksi kondisi iklim menggunakan 17 General Circulation Models (GCMs)

dengan skenario Representative Concentration Pathways (RCP)4.5 juga

menunjukkan penurunan rata-rata curah hujan sebesar 6,34% untuk periode 2011-2040 dan 7,34% untuk periode 2041-2070. Sementara itu, rata-rata suhu minimum dan maksimum diproyeksikan meningkat sebesar 0.65oC dan 0.69oC untuk periode 2011-2040, dan 1.23oC dan 1.28oC untuk periode 2041-2070. Hasil proyeksi dengan skenario RCP8.5 menunjukkan kecenderungan serupa dengan tingkat perubahan yang sedikit lebih tinggi.

Perubahan kondisi iklim berdampak pada produktivitas padi petani di lokasi kajian. Hasil simulasi menggunakan model CROPWAT menunjukkan terjadinya

penurunan produktivitas padi sebesar 32% dan 31% untuk periode 2011-2040 dengan skenario RCP8.5 dan RCP4.5, dibandingkan periode baseline (1981-2010). Hal serupa juga terjadi untuk periode 2041-2070, dengan tingkat penurunan sedikit lebih tinggi. Produktivitas padi sangat ditentukan oleh keragaman praktek pertanian saat ini. Pergeseran waktu tanam menyesuaikan pola curah hujan saat ini dapat menekan penurunan hasil sebesar 12,95% untuk sawah tadah hujan dan 14,07% untuk sawah irigasi. Sementara itu, perbaikan pola pengairan irigasi menekan penurunan hasil sebesar 16,16%.

Penilaian kecukupan beras tingkat rumah tangga berdasarkan kondisi iklim saat ini menunjukkan bahwa rata-rata tingkat kecukupan beras rumah tangga (HRSL) adalah sebesar 62.89% ± 8.93%, masih jauh dibawah ambang batas kecukupan sebesar 90%. Hal ini berarti kekurangan ketersediaan beras pada level rumah tangga petani telah terjadi di lokasi kajian. Analisis terhadap praktek adaptasi petani saat ini menunjukkan bahwa praktek adaptasi berhasil meningkatkan HRSL, namun belum cukup memadai untuk menjamin kecukupan beras pada level rumah tangga petani. Rata-rata HRSL untuk rumah tangga petani yang telah melakukan adaptasi on-farm adalah sebesar 64,98% dan untuk adaptasi off-farm adalah sebesar 66,27%, masih jauh dibawah ambang batas kecukupan (90%). Bahkan untuk petani yang telah melakukan praktek adaptasi terbaik, yaitu kombinasi antara on-farm dan off-farm, rata-rata HRSL-nya juga masih dibawah ambang batas (67,59%). Terbatasnya kepemilikan lahan petani, yang hanya berkisar rata-rata 0,03 ha hingga 0,06 ha, merupakan faktor penentu rendahnya tingkat kecukupan beras rumah tangga petani di lokasi kajian.

Hasil kajian juga menunjukkan bahwa rumah tangga petani yang melakukan praktek adaptasi memiliki tingkat resiliensi yang lebih baik terhadap dampak perubahan iklim pada masa datang, sebagaimana terlihat pada HRSL mereka yang cenderung lebih stabil untuk periode 2011-2040 dan 2041-2070 relatif terhadap level baseline, dibandingkan rumah tangga petani yang belum melakukan praktek adaptasi. Lebih jauh, hasil kajian menunjukkan bahwa walaupun praktek adaptasi on-farm menghasilkan HRSL yang lebih rendah untuk kondisi iklim saat ini dan masa datang, praktek adaptasi ini mampu mempertahankan HRSL yang lebih stabil untuk periode 2011-2040 dan 2041-2070, dibandingkan praktek adaptasi off-farm. Hasil tersebut menunjukkan potensi efek saling melengkapi dari kedua praktek adaptasi dimaksud.

Hasil penilaian tingkat kerentanan rumah tangga petani (Composite-HVI) terhadap dampak perubahan iklim menunjukkan bahwa rumah tangga petani dengan status kecukupan beras “cukup” memiliki nilai HVI yang lebih rendah (0,39) dibandingkan rumah tangga petani dengan status “kurang” (0,46) dan “sangat kurang” (0,54). Hal ini mengindikasikan dampak perubahan iklim terhadap tingkat kecukupan beras suatu rumah tangga petani tidak semata-mata ditentukan oleh derajat penurunan hasil sawah milik rumah tangga tersebut, tetapi juga ditentukan oleh tingkat kerentanan sosial petani dimaksud terhadap dampak perubahan iklim. Selain itu, hasil kajian menunjukkan bahwa rumah tangga petani yang telah melakukan praktek adaptasi memiliki IPCC-HVI yang lebih rendah dibandingkan petani yang belum melakukan praktek adaptasi. Nilai IPCC-HVI untuk adaptasi on-farm adalah sebesar -0,11, lebih rendah dibandingkan adaptasi off-farm (-0,03) dan non-adaptasi (+0,11). Rumah tangga petani yang melakukan kombinasi adaptasi on-farm dan off-farm memiliki nilai IPCC-HVI yang paling

rendah (-0,12). Hasil ini menunjukkan bahwa rumah tangga petani dengan tingkat kerentanan yang lebih rendah cenderung melakukan praktek adaptasi yang lebih baik.

Hasil analisis faktor penentu praktek adaptasi menunjukkan bahwa dari 12 faktor penentu yang diuji, terdapat 6 faktor yang menunjukkan pengaruh nyata terhadap pilihan petani untuk tidak mengadopsi praktek adaptasi terbaik, yaitu kombinasi praktek adaptasi on-farm dan off-farm. Hasil ini menunjukkan bahwa rumah tangga petani dengan kepala rumah tangga berpendidikan lebih rendah, berusia lebih tua, berjenis kelamin perempuan, tidak memiliki akses terhadap kredit, kepemilikan lahan sawah lebih kecil, dan kepemilikan lahan sawah dengan lokasi lebih dekat ke sumber air cenderung mengadopsi kelompok praktek adaptasi di luar praktek adaptasi terbaik.

SUMMARY

ADE CANDRADIJAYA. Farmer Adaptive Capacity to Climate Change in Strengthening Rice Sufficiency in Sumedang Regency, West Java Province. Supervised by CECEP KUSMANA, LAILAN SYAUFINA, YUSMAN SYAUKAT, and AKHMAD FAQIH.

Greenhouse gas emission leads to global warming, which is fundamental to the current food security problem. An increase in global temperature of ~4°C or more above late-20th-century level, combined with increasing food demand, would pose large risks to food security globally and regionally. There are growing evidences that even at just 1°C of warming there are negative impacts for major crops like rice and corn. It is also indicated that the rate of global warming in the next decades is projected to be substantially higher than that in the last decades.

Smallholder farmers, whose livelihood relies to the greatest extent on agriculture, have been reported worldwide to be one of those most affected by the adverse implication of climate change. Previous studies indicated that smallholder farmers have assumed some adaptation measures in their existing farming practices, which range from adjustment on planting calendar to investment on input and infrastructure. Their existing adaptations, however, have been reported to be inadequate and still leave substantial residual impacts untapped.

The study aimed to examine existing farmer adaptive capacity to climate change and explore its future strengthening alternatives to improve the rice sufficiency at household level. Specifically, the objectives of the research were:(1) To verify the change of climatic condition in the study area and estimate its impact on rice yield under various types of existing farming practices, (2) To assess the effect of current and future climatic condition on farmers’ household rice sufficiency under the existing adaptation practices, (3) To assess farmers’ household vulnerability to the impact of current and future climatic condition on their household rice sufficiency, and (4) To identify the factors determining farmers’ existing adaptation practices.

The assessment of current climate indicated an increasing trend in the annual average of minimum and maximum temperature (TMin and TMax) during 1981 – 2010, while the annual rainfall showed a decreasing trend during the same period. Furthermore, climate projection for near (2011 – 2040) and far-future (2041 – 2070) period generated by the 17 General Circulation Models (GCMs) under climate change scenario of Representative Concentration Pathways (RCP)4.5 also indicated that average annual rainfall has been projected to decrease by 6.81% and 7.34%, respectively. Meanwhile, the TMin and TMax were projected to increase by 0.65oC and 0.69oC for near-future, and then further increased by 1.23oC and 1.28oC for far-future, respectively. Similar changes in climatic condition were indicated to slightly higher extent under RCP8.5 for near- and far-future periods.

The assessment of rice yield under current climate suggested that during 1990 – 2010, rice yield in the study area has been fluctuating. Furthermore, the simulated rice yield generated by CROPWAT indicated that a reduction in rice yield was projected to occur in the near- and far-future. Rice yield has been projected to decrease by 32.00% and 31.81%, in comparison to baseline, for near-future under RCP8.5 and RCP4.5, respectively. The reduction extended, with a slightly higher degree, to the far-future. The reduction was sensitive to variation in

the existing farming practices. The shifting of planting time to better match rainfall pattern reduced the rice yield reduction by 12.95% for rainfed farming and 14.07% for the irrigated. Meanwhile, improved irrigation scheduling reduced the yield reduction by 16.16%.

The assessment of household rice sufficiency under current climate indicated that the average Household Rice Sufficiency Level (HRSL) of the sample households fell below 90%, with an average of 62.89% ± 8.93%, suggesting a condition of household-level rice insufficiency has been occurring in the study area. Analysis of the adequacy of existing adaptations suggested that though they have managed to generate higher HRSL for the adapted households, the existing adaptations have yet to be adequate to ensure rice-sufficient status for the farmers. The average HRSL of the on-farm and off-farm adapted households were 64.98% and 66.27%, respectively, still far below the threshold for rice-sufficient status. Even under the empirically identified existing best adaptation practices, i.e. the combined on- and off-farm adaptation, the average HRSL of the adapted households were still below the threshold (67.59%). The farmers’ limited ownership of land, only 0.03 – 0.06 ha on average, was identified as the underlying factor for the occurrence of rice insufficiency in the study area.

The result of the study suggested that the adapted households were more resilient to the impact of future climate, as indicated by their more stable HRSL to the near- and far-future, relative to the baseline than that of the non-adapted. Furthermore, though it has generated lower HRSL under current and future climate, on-farm adaptation maintained the HRSL relatively more stable to the near- and far-future than the off-farm adaptation did. This finding signified the complementary nature between the on-farm and off-farm adaptations.

The assessment of household vulnerability indicated that rice-sufficient households have lower Household Vulnerability Index (HVI) (0.39) than the insufficient (0.46) and severely-insufficient (0.54). The finding suggested that the impact of climate change on rice sufficiency of a particular farmer was not determined exclusively by the magnitude of climate-induced yield reduction of his rice farm plot, but also by his household vulnerability level. The study also suggested that adapted households typically have smaller Intergovernmental Panel on Climate Change (IPCC)-HVI than the non-adapted did. The IPCC-HVI of the on-farm adapted was recorded at -0.11, lower than either the non-adapted (+0.11) or the off-farm adapted (-0.03). The combination of the on- and off-farm adaptation linked to the lowest IPCC-HVI, which was recorded at -0.12. This finding suggested that less vulnerable farmers were more likely to adopt better adaptation practices.

Analysis of factors that underlie the farmers’ existing adaptations indicated that among the 12 factors tested, there were 6 factors that showed significant influence on households’ decision for not adopting the empirically identified current best adaptation practice observed in the study area, i.e. the combined on- and off-farm adaptations. The result suggested that smallholder farmers who have been lower educated-, older age-, and female-headed; having no-access to credit; having smaller farm size; and having farm plot with shorter distance to water reservoir tended to adopt types of existing adaptation practices other than the combined on- and off-farm adaptation.

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FARMER ADAPTIVE CAPACITY TO CLIMATE CHANGE

IN STRENGTHENING RICE SUFFICIENCY

IN SUMEDANG REGENCY, WEST JAVA PROVINCE

ADE CANDRADIJAYA

Dissertation

Submitted in fulfillment of the requirement for a Doctorate Degree in

the Study Program of Natural Resource and Environmental Management

POSTGRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

Examiners for Closed Defense:

1. Dr. Ir. Hajrial Aswidinnoor, M.Sc.

Department of Agronomy and Horticulture, Faculty of Agriculture, Bogor Agricultural University

2. Dr. Perdinan, S.Si., M.Nat.Res.Econ.

Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University

Examiners for Public Defense:

1. Dr. Ir. Lala M. Kolopaking, MS

Department of Community Development, Faculty of Human Ecology,

Bogor Agricultural University 2. Dr. Ir. Surachman, MP

Director, Center for Agricultural Training,

DG of Agricultural Extension and Human Resource Development Ministry of Agriculture

Title : Farmer Adaptive Capacity to Climate Change in Strengthening Rice Sufficiency in Sumedang Regency, West Java Province

Name : Ade Candradijaya

Student ID No. : P062100354/PSL

Certified By:

Supervising Committee

Prof. Dr. Ir. Cecep Kusmana, MS

Promotor

Dr. Ir. Lailan Syaufina, MSc Dr. Ir. Yusman Syaukat, MEc Dr. Akhmad Faqih, SSi

Co-promotor Co-promotor Co-promotor

Acknowledged By:

Head,

Study Program of Natural Resource and Environmental Management

Dean,

Postgraduate School

Prof. Dr. Ir. Cecep Kusmana, MS Prof. Dr. Ir. Marimin, MSc

FOREWORD

Praise be to Allah, the Glorious, who endowed me with the graces of health, patience, strength and perseverance; and made available to me those people who advised and guided me to complete this dissertation entitled “Farmer Adaptive Capacity to Climate Change in Strengthening Rice Sufficiency in Sumedang Regency, West Java Province”.

I would like to take this opportunity to convey immense and sincere thanks and appreciation, and endless and massive gratitude to my supervisors Prof. Dr. Cecep Kusmana, MS, Dr. Ir. Lailan Syaufina, MSc, Dr. Ir. Yusman Syaukat, MEc, and Dr. Akhmad Faqih, SSi, whom I have known honorable mentors.

My special thank also goes to all colleagues in the Agricultural Offices of Sumedang District and Ujungjaya District, for providing me all the required data and information. A sincere gratitude are also due to farm households in Ujungjaya District for their time and patience in filling out the questionnaire. Without their kind support, this research would not be possible.

I do not want to miss this opportunity to thank all colleagues in the Center for International Cooperation and Bureau of Planning, Ministry of Agriculture and Directorate of Food and Agriculture, Agency for National Planning and Development, for their encouragement and friendship.

Last but not least, I must thank to all my beloved families for their moral support who accompanied me during my long journey in completing this doctorate study. Their understanding and support during the entire period of my study was essential.

Bogor, January 2015

TABLE OF CONTENTS

Page

LIST OF TABLE xv

LIST OF FIGURE xvi

LIST OF ATTACHMENT xviii

GLOSSARY xix

1 GENERAL INTRODUCTION

General Background 1

Problem Statement 2

Research Objectives 3

Novelty of the Study 3

Outline of the Dissertation 5

2 RESEARCH METHODS

Study Area 7

Conceptual Framework 10

Sources of Data 12

Sampling 13

Analytical Framework 13

3 CLIMATE CHANGE IMPACT ON RICE YIELD AND ADAPTATION RESPONSE OF THE FARMERS

Introduction 16

Analytical Framework 18

Material and Methods 18

Results and Discussion 22

Conclusion 37

4 HOUSEHOLD RICE SUFFICIENCY UNDER CLIMATE CHANGE AND ADAPTATION RESPONSE OF FARMERS

Introduction 38

Analytical Framework 40

Materials and Methods 41

Results and Discussion 43

Conclusion 51

5 FARMER HOUSEHOLD VULNERABILITY AND

ADAPTATION TO CLIMATE CHANGE IMPACT ON RICE SUFFICIENCY

Introduction 53

Analytical Framework 55

Materials and Methods 56

Results and Discussion 60

Conclusion 66

6 FACTORS DETERMINING FARMERS’ ADAPTATION TO

CLIMATE CHANGE IMPACT ON HOUSEHOLD RICE SUFFICIENCY

Introduction 69

Analytical Framework 70

Materials and Methods 71

Results and Discussion 73

Conclusion 84

7 GENERAL DISCUSSION AND CONCLUSION

General Discussion 85

General Conclusion and Recommendation 93

REFFERENCES 95

LIST OF TABLE

Page

1 Characteristics of rice varieties grown in the study area 8

2 GCM Models used in the study 21

3 Irrigation interval under different planting time 22

4 Adjustment of crop data to match the local condition 22

5 Adjustment of soil data to match the local condition 22

6 Description of smallholder current farming practices in the study area 23

7 Irrigation interval under different planting time 43

8 Projection of annual rainfall, temperature minimum and maximum, and rice yield generated by 17 GCMs under RCP4.5

47

9 Projection of annual rainfall, temperature minimum and maximum, and rice yield generated by 17 GCMs under RCP8.5

47

10 The components of household composite-vulnerability 59

11 The scoring of IPCC-vulnerability components 60

12 Description of smallholder current farming practices in the study area 74

13 Projected changes of climatic condition (average of 17 GCMs) 76

14 Climate-induced rice yield reduction under current farming practices 77

15 HRSL and current adaptations under changing climate 78

16 Summary of descriptive statistics of adaptation determinants 80

LIST OF FIGURE

Page

1 Map of Study Area 7

2 Planting calendar of irrigated (a) and rain-fed farm (b), and rainfall pattern (c)

9

3 Conceptual Framework for the Study 11

4 Steps of Analysis 15

5 Analytical framework of the study 19

6 Trend of annual rainfall for the period of 1981 – 2010 25

7 Decadal monthly rainfall for the period of 1981 – 2010 25

8 Trend of minimum temperature for the period of 1981 – 2010 26

8 Trend of maximum temperature for the period of 1981 – 2010 26

9 Decadal Monthly minimum temperature for the period of 1981-2010 27

10 Decadal Monthly maximum temperature for the period of 1981-2010 27

11 The range of changes in monthly precipitation (%) relative to baseline projected by 17 GCMs under RCP4.5 and RCP8.5 for near- and far-future

28

12 The range of changes in monthly minimum temperature projected by 17 GCMs under RCP4.5 and RCP8.5 for near- and far-future

30

13 The range of changes in monthly maximum temperature projected by 17 GCM under RCP4.5 and RCP8.5 for near- and far-future

31

14 Trend of annual average rice yield for the period of 1990 – 2010 32

15 The range of changes in rain-fed rice yield estimated by 17 GCMs under RCP 4.5 and RCP 8.5 for near- and far-future

33

16 The range of changes in irrigated rice yield estimated by 17 GCMs under RCP4.5 and RCP8.5 for near- and far-future

35

17 Analytical framework of the study 40

18 The current Rice Sufficiency Level of sample households under different types of adaptation practices

47

19 The HRSL of smallholder farmers for near- and far-future projection by 17 GCMs under RCP8.5(a) and RCP4.5(b)

48

20 The HRSL of smallholder farmers under various types of existing adaptations for near- and far-future projection by 17 GCMs under RCP8.5(a) and RCP4.5(b).

50

21 Analytical framework of the study 55

sufficiency status, under different types of adaptations

23 Spider diagram indicating the link between the five major components of composite-vulnerability and the household rice sufficiency status

63

24 Triangle diagram indicating the link between the three components of IPCC-vulnerability (exposure, sensitivity, and adaptive capacity) and different types of adaptation practices

65

LIST OF ATTACHMENT

Page

1 Questionnaire 105

GLOSSARY

Adaptive Capacity: The potential, capability, or ability of a system to adapt to climate change stimuli or their effects or impacts. In this thesis, the term adaptive capacity represents the potential, capability, or ability of smallholder farm households to adapt to climate change induced rice yield reduction of their farm plot that threaten their household-level rice sufficiency.

Adaptation: The process of adjustment to actual or expected climate and its effects. In human systems, adaptation seeks to moderate or avoid harm or exploit beneficial opportunities. In some natural systems, human intervention may facilitate adjustment to expected climate and its effects. In this thesis, the terms of adaptation at on-farm level is used to indicate the process of adjustment to the existing farming practices in the study area in order to moderate the climate change-induced rice yield reduction of different farm plots under different farming practices. Meanwhile, at off-farm level the terms of adaptation refers to farm households’ diversified livelihoods to moderate the impact of climate change induced rice yield reduction of their farm plots on their household-level rice sufficiency.

Climate change: Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcing or to persistent anthropogenic changes in the composition of the atmosphere or in land use systems.

Climate scenarios: Plausible and often simplified representations of the future climate based on an internally consistent set of climatological relationships, that have been constructed for explicit use in investigating the potential consequences of anthropogenic climate change. The difference between a climate scenario and the current climate provides a climate change scenario.

Crop growth simulation: Simplified representation of the complex relation between crop growth and environmental factors (climate, soil and management).

Emission scenarios / Representative concentration pathways: Plausible representation of the future development of emissions of greenhouse gas concentrations based on coherent and internally consistent set of assumptions about driving forces (such as demographic and socio-economic development, technological change) and their likely relationships.

Exposure: The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected.

coastal communities will have higher exposure to sea level rise and cyclones, while communities in semi-arid areas may be most exposed to drought. In this thesis, the term of exposure refers to the presence of rice farm plots, and smallholder farm households who relies their livelihoods, to the greatest extent, on those farm plots, in locations that could be adversely affected by climate change. In this case, since the main limiting factors to the rice yield of a particular farm plot is sufficient supply of irrigation water, the level of exposure is highly determined by a relative distance of a farm plot from irrigation water reservoir. The longer the distance of a farm plot from the reservoir, the higher the level of exposure to climate change-induced rice yield reduction of the farm plot.

Food security: A condition whereby all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life.

Food security is defined as the existence of the necessary conditions for human beings to have physical and economic access, in socially acceptable ways, to food that is safe, nutritious and in keeping with their cultural preferences, so as to meet their dietary needs and live productive and healthy lives. Those conditions include:(1) The physical availability of food in sufficient quantities and of sufficient quality produced in and imported into the country(including food aid), (2) Access of all people to food because they have the economic and other resources needed to acquire sufficient nutritious and safe food, (3) Reaching a level of nutritional well-being where all physiological needs are met, thanks to an adequate diet, availability of and access to clean water, sanitation and health care (importance of non-food inputs), and (4) Stable access to foods at all times, without the risk of running out of food as a result of unexpected political, economic or climatic crises or cyclical events (seasonal food insecurity), which includes both stable availability and access.

Food security is also defined by Food Law No. 18/2012 of the Republic of Indonesia as the condition of the need for food being fulfilled as reflected in the availability of food which is both quantitatively and qualitatively sufficient, safe, diverse, nutritious, and evenly distributed to enable one to continuously have a healthy, active, and productive life. The factors influencing household food security are sufficiency in food availability, stability in food availability, food accessibility or affordance, and food quality or safety. The Food Law No.18/2012 enriches the definition of food security by emphasizing the need to ensure food security until individual level and the requirement to meet the religious and cultural norms of the nation.

Impacts: Effects on natural and human systems. In this thesis, the term impacts on natural system is used primarily to refer to the effects of climate change on the rice yield of different types of farm plots under the diversity of existing farming practices identified in the study area. Meanwhile, the term impacts on human system is defined as the impacts of the climate change-induced rice yield reduction on the rice sufficiency level of different groups of farm households under existing adaptation responses identified in the study area.

Resilience: The capacity of social, economic, and environmental systems to cope with a hazardous event or trend or disturbance, responding or reorganizing in ways that maintain their essential function, identity, and structure, while also maintaining the capacity for adaptation, learning, and transformation.

Sensitivity: The degree to which a given community or ecosystem is affected by climatic stresses. For example, a community dependent on rain-fed agriculture is much more sensitive to changing rainfall patterns than one where mining is the dominant livelihood. Likewise, a fragile, arid or semi-arid ecosystem will be more sensitive than a tropical one to a decrease in rainfall, due to the subsequent impact on water flows. In this thesis, the terms of sensitivity at area-level is used to indicate that the yield of rain-fed farm plots is more sensitive to changing in rainfall and temperature than the irrigated farm plots. Meanwhile, the terms of sensitivity at farm household-level refers to the degree to which the rice sufficiency level of a particular smallholder farm household is affected by climate change-induced rice yield reduction of the household’s farm plot.

Uncertainty: An expression of the degree to which a value (e.g., the future state of the climate system or its impact) is unknown.

Vulnerability: The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity (IPCC, 2007). According to Fussel and Klein (2006), the definition covers internal and external dimension of a system under study. The external dimension is represented by the exposure of the system to climate variation, while the internal dimension is represented by its sensitivity and its adaptive capacity to these stressors.

1

GENERAL INTRODUCTION

General Background

Greenhouse gas emission leads to global warming that is fundamental to the current food security problem. According to Dutta (2014) an increase in global temperature of ~4°C or more above late-20th-century levels, combined with increasing food demand, would pose large risks to food security globally and regionally. The report also found that even at just 1°C of warming there are negative impacts for major crops like wheat, rice and corn. Other reports indicated that the rate of global warming in the next decades is projected to be substantially higher than that in the last decades (IPCC, 2013; Maarten et al., 2007). Unless the global efforts manage to control the current trend of increasing global temperature, and its adverse implication on global food crop production and hence food security, the pessimism views such as doomsday theory or the Malthusian pessimism is likely to come on the rise.

While climate change is a global phenomenon, the impact is largely site-specific for different countries, regions, economic sectors, and social groups, depending on their level of vulnerability (Tazeze, Haji, and Ketema, 2012; Lane and Jarvis, 2007). The differences in country- or regional-level impact are due partly to the fact that the changes in climate and the distribution of resources and wealth occur unevenly, depending on geographical position of the country or region (Breisinger et al., 2011; Ruth, Coulho, and Karetnikov, 2007; Alam et al., 2011).

Climate change affects all economic sectors, directly or indirectly, to the extent highly variable, and agriculture has been among the sectors most affected (Mertz et al., 2010; Slater et al., 2007; McCarthy et al., 2007; Sharma et al., 2006; Eitzinger and Kubu, 2009; Schlenker et al., 2007). The impacts of global warming are expected to reduce agricultural production and put further pressure on food security challenges already confronting smallholder farm households in low latitude developing countries. Since the livelihoods of the smallholders rely to the greatest extent on agriculture, any small reduction in crop yield is likely to worsen the already limited access of the smallholders to sufficient food (Hertel and Rosch, 2010). Furthermore, the smallholders’ limited resources and capacity to cope with the shocks made them highly vulnerable, and accordingly forced most of them to shift in and out of a state of undernourishment (Capaldo et al., 2010).

Smallholder farm households have been reported worldwide to be one of those most affected by changes in climatic condition (AGRA, 2014; Morton, 2007; Harvey et al., 2014). Though there is no unanimously agreed estimate on the portion of smallholders in the world’s farms, most likely due to the lack of standardized definitions of the terms (Morton, 2007), all estimates worldwide suggested a huge portion, which ranged from 50% (Jazairy, Alamgir, and Pannuccio, 1992) to 85% (Nagayet, 2005). In Indonesia, the smallholders constitute equally substantial portion, where the latest Farm Household Census

reported a figure of around 55.95%, defined as those whose farmland size is less that 0.5 ha (BPS, 2013a). Taking the problem of huge portion into account, the impact of changing climatic condition on smallholder farmers has been a focus of attention worldwide.

The smallholders seem to have no alternative to address the adverse impact of climate change on their food condition but to adapt their livelihood systems to the changing climate (Ngigi, 2009). Previous studies indicated that smallholder farmers have assumed some adaptation measures in their farming management practices, which range from adjustment on planting calendar to investment on input and infrastructure (Komba and Muchapondwa, 2012; Kalinda, 2011). Referring back to their highly limited resources and hence capacity, the existing adaptation measures of the smallholders have yet to be optimal and still leave substantial residual impacts untapped (IPCC, 2014). This has been a key challenge for decision makers, policy makers, and development partners to understand the current adaptation measures of the smallholders in their efforts to address the adverse implication of climate change on their household-level food security condition.

Regardless the already substantial empirical evidences indicating the locally-specific characters of climate change impact, until recently most adaptation efforts have paid only very little attention on the local communities’ existing coping mechanism with changes in their environment. Current adaptation interventions are designed mostly based on top-down approach, through downscaling of global-level impact and simulation of quasi-adaptation responses into local societal and environmental context, and neglecting the significance of locally specific focuses of the bottom-up approach. The study combines the bottom-up and top-down approach, where empirical data generated from household survey are integrated into bio-physical impact model to examine the adequacy of existing adaptation practices of smallholder farmers and explore some future adaptation-strengthening options.

Problem Statement

Previous studies generated sufficient evidences to claim that climate change has been occurring at both global and regional level and the smallholder farmers have been those most affected by the changes. For smallholder farmers whose livelihood depended to the greatest extent on agriculture, there seemed to be no alternative but to adapt their livelihood systems to the changing climate. Given the locally specific impact of climate change on agriculture and hence the smallholders’ food security, effective adaptation measures could not be manifested, unless all stakeholders managed to master the locally specific context of the impacted system. Therefore, this research focused to address the following research questions:

1. What is the current and future condition of climate (rainfall, minimum temperature, and maximum temperature) in the study area? And how it affects rice yield under various types of existing farming practices?

2. How the current and future climatic condition affects rice production and household-level rice sufficiency of farmers under various types of existing adaptation practices?

3. What is the current household vulnerability of farmers in the study area to the impact of current and future climatic condition on their household rice sufficiency?

4. What factors determine farmers’ existing adaptation practices?

Research Objectives

The main objective of the research is to examine existing farmer adaptive capacity to climate change in strengthening rice sufficiency and explore future alternatives for the strengthening of household-level adaptive capacity. Specifically, the objectives of the research are:

1. To verify the change of climatic condition in the study area and estimate its impact on rice yield under various types of existing farming practices.

2. To assess the effect of current and future climatic condition on rice productivity and farmers’ household rice sufficiency under the existing adaptation practices.

3. To assess farmers’ household vulnerability to the impact of current and future climatic condition on their household rice sufficiency.

4. To identify the factors determining farmers’ existing adaptation practices.

Novelty of the Study

State of the Art

The development of Climate Change Impact, Adaptation, and Vulnerability (CCIAV) approach has been surprisingly fast, but tends to be partial. Impact study, on the one hand, culminated in “top-down” approach (Dessai and Hulme, 2004) or “scenario-based” approach (Carter and Makinen, 2011). On the other hand, vulnerability study led to bottom-up approach (Dessai and Hulme, 2004). Meanwhile, in its recent development, adaptation has been incorporated into the top-down approach to assess its adequacy in addressing the biophysical impact of climate change (Obeng et al, 2010; Syaukat, 2011) and into the bottom-up approach to address the vulnerability of a system to climate change (Hahn, Riederer, and Foster, 2009; Vincent and Cull, 2010). Important critics pertinent to this approach have been dealing with integration of the top-down and bottom-up. Though at conceptual level, it has been adequately addressed by the fast development of integrated approach (Fussel, 2007), at methodological level, however, the progress has been rather limited.

The food security implications of climate change, to date, have been explored largely in relation to climatic condition-related changes in yields of food crops

and hence, changes in food production (Chijioke, Haile, and Waschkeit, 2011; Rowhani et al, 2011). While food production has been identified as the main determinant of food availability (Gregory Ingram, and Brklacich, 2005), to single it out as the only indicator is not adequate to provide sufficient indication on the extent to which climate change has affected food security. To date, studies using indicators that better represent the food insecurity impact of climate change have been evolving, at macro and meso, but not at micro level. For example, Butt et al

(2005) measured the national level food insecurity implication of climate change in Mali using Risk of Hunger (ROH), which indicated the percentage of the population whose daily calorie intake falls below requirements for a healthy life. Other examples are Ye et al (2014) used daily calorie availability as an overall indicator of climate change impact on national level food security in China and Syaukat (2011) used food balance to indicate the national food security impact of climate change in Indonesia.

Adaptation can greatly reduce vulnerability to climate change by making rural communities better able to adjust to climate change and variability, moderating potential damages, and helping them cope with adverse consequences (IPCC, 2001). According to Gregory, Ingram, and Brklacich (2005), adaptation of food system to climate change may occur in relation to agronomic aspects regarding food production, government-set price and income concerning access to food, and changes in societal values concerning food utilization. Sophisticated approach has been growing to simulate the adequacy of adaptation options in addressing the impact of changing climate, by integrating the GCMs and various crop growth simulations, such as APSIM (Obeng et al, 2011; Maccarthy and Vlek, 2012), DSSAT (Felkner, Tazhibayeva, and Townsend, 2009; Basak et al, 2010), and CROPWAT (Bana et al, 2013; Mimi and Jamous, 2010). This integrated approach has been applied largely at macro and meso level studies to simulate the extent to which sets of prescriptive adaptations have been effective in addressing the impact of climate change on Risk of Hunger in Mali (Butt et al, 2005), daily calorie intake in China (Ye et al, 2014), and food balance in Indonesia (Syaukat, 2011). However, study that applied the approach to assess the adequacy of existing adaptation practices at micro (households) level is still limited.

Significance of the Study

The study contributes to body of knowledge on enhanced approach for climate change impact, adaptation, and vulnerability assessment, on the one side and practical solutions to adverse implication of climate change on household-level rice sufficiency of smallholder farmers, on the other side. With regards to its practical solution contribution, the research generates recommendation for locally-relevant and technologically appropriate adaptation-strengthening interventions for those most affected among the smallholder farm households in the area under study.

The contribution of the research to the body of knowledge involved the followings:

1. This research combines the bottom-up and top-down approach, where empirical data collected through household survey are integrated into bio-physical impact model to examine the adequacy of existing adaptation

practices of smallholder farmers and explore some future adaptation-strengthening options.

2. For the impact study, this research provides at least 2 contributions:

- A two-step approach for micro-level climate change impact assessment, where the household-level impact is mediated by the impact on the area, on which the households are residing. It describes how a uniform intensity of climate change impact throughout the area under study generates a highly variable household-level impact depending on the household’s existing adaptation responses and current vulnerability conditions.

- A new indicator for food insecurity implication of climate change at household level, named Household Rice Sufficiency Level (HRSL).

3. For the vulnerability study, this research provides an alternative set of variables for the assessment household-level vulnerability of smallholder farmers to the adverse implication of climate change on their household-level food security. 4. For Adaptation study, this research introduces an alternative approach to study

a causal link among impact, vulnerability, and adaptation. The approach enables this research to describe how household-level rice sufficiency implication of climate change varies among impacted households depending on their type of adaptations and their vulnerability level. It also explains how a household’s particular type of adaptation could deviates from the empirically identified current best adaptation practice.

Outline of the Thesis

The thesis is structured in 7 chapters, where the first chapter elaborates the background of the research that leads to the formulation of problem statement and research objectives. The research method is elaborated in Chapter 2, which covers description of the study area, conceptual framework, the sources of data required for the analysis, sampling procedure, and analytical framework. Afterward, the thesis addresses its objectives in four complementary chapters (Chapter 3 to 6), which are then followed by general discussion and conclusion (Chapter 7).

Chapter 3 presents an analysis of climate change in the study area and the extent to which changes in rainfall and minimum and maximum temperature affect the local rice yield. The chapter also identifies the diversity in local existing farming practices and how the climate change induced rice yield reduction level varies among the existing farming practices. Afterward, the household-level rice sufficiency implication of the locally potential climate change-induced rice yield reduction is elaborated on Chapter 4. The average household rice sufficiency level (HRSL) of 156 sample households is presented for different types of households’ existing adaptation practices to provide an indication on the extent to which each adaptation has been adequate to address the impact of climate change. Chapter 5 elaborates the causal link among climate change impact, vulnerability and adaptation, where the analysis is contextualized into the local condition of the study area, by using the 156 sample households. Finally, the analysis ends-up with

identification of factors that determine the local smallholder farm households’ current adoption of a particular type of adaptation, as presented on Chapter 6. In this chapter twelve potential adaptation determinants are analyzed to explain the shifting of individual farm household along the continuum of adaptation options identified in the study area, which ranges from the “non-adapted” to the “combined on- and off-farm adapted”.

Chapter 7 synthesizes the key findings from all the above chapters. This chapter highlights all the newly introduced methodological approaches for climate change vulnerability, impact, and adaptation assessment. The chapter also gives a summary of the main findings of the study. Finally, it ends up with recommendation for locally-relevant and technologically appropriate adaptation interventions for those most affected among the smallholder farm households in the area under study.

2

RESEARCH METHODS

Study Area

The study was conducted in Ujungjaya District, Sumedang Regency, West Java Province, Indonesia (Figure 2.1.). The location assumes the typical characteristics of most smallholder rice farming systems in Indonesia, whose yield level is determined to the greatest extent by local climatic conditions. It is therefore, the study areas could be a model case and typical example, representing most Indonesia’s smallholder rice farming area.

Figure 2.1. Map of Study Area

The location of the study lies approximately between longitudes 107°54' - 108°52' E and latitude 6°54' - 7°54' S, with the altitude of 50 m above sea level, indicating areas with the lowest position. Ujung Jaya District has a total population of around 9,726 households (BPS, 2013b). According to the recent database of the National Social Protection Program, around 37.44% of those households fall into poor household category (TNP2K, 2013), whose livelihoods mostly (72.68%) rely on smallholder farming (BPS, 2013b).

Sumedang Regency covers an area of 8,122 ha, where agriculture occupies 2,637 ha or around 32.47%. According to its water supply, farming is divided into rain-fed (828 ha) whose water supply is exclusively derived from rainfall and irrigated (1,709 ha) whose water supply is supplemented and/or regulated by irrigation infrastructure (BPS, 2013). In the study area, however irrigation infrastructure mostly, if not all, has no sufficient capacity to maintain stable water supply for farming all year around. This is because irrigation infrastructure is not equipped with well-constructed water storage facilities to accumulate water from

rainfall during rainy season and release it during the dry season. The main variety of rice commonly grown by the local farmers is Ciherang. There are also other rice varieties grown, but still very limited, i.e. Mekongga, Inpari 4, and Inpari 10. The characteristic of those varieties is presented on Table 2.1.

Table 2.1. Characteristics of rice varieties grown in the study area

Varieties Life Time (days)

Height (Cm)

Potential Yields (Ton/Ha GKG)

Ciherang 116-125 107-115 5.0-7.0

Mekongga 116-125 91-106 8.40

Inpari 4 115 95 6.04

Inpari 10 108-116 100-120 4.80

Source: Indonesian Center for Food Crops R&D (ICFORD, 2013)

Regardless the above characteristic, local farmers believe that Ciherang is still the best variety to grow. Among the very limited farmers who grow rice varieties other than Ciherang mentioned that they grew the variety simply as a trial and they treat it relatively equal to Ciherang. According to the local farmers, the average growing period of those varieties is relatively similar, that is around 120 days, from seed sowing to harvesting. The average annual productivity of irrigated rice was recorded at 6.28 ton/ha and that of the rainfed was 4.20 ton/ha (BPS, 2013).

Farming calendar generally follows the pattern of rainfall. In the rain-fed areas, planting is generally made twice a year. The first planting links to the onset of rainy season (mostly in November or December), while the second starts immediately after the first harvesting. The second planting time is highly critical in relation to the pattern of rainfall, where the risk of failure resulting from limited water supply is critically high. Farmers are fully aware of the risks but for most of them little they can do due to their limited resources. They just rely on their fortune, hoping that enough rain will still occur until harvesting. The average annual rainfall of the area is around 2,597 mm during the last 5 years, the lowest in comparison to that in other districts of Sumedang Regency.

In irrigated areas, planting time is relatively more flexible, made possible by supplementary water supply from irrigation. Planting occurs at almost every month, though the general pattern still follows that of the rainfall (Figure 2.2). Delay in planting time is relatively common in the study area, in relation to the onset of rainy season. There are at least two main reasons for the delay in planting. The first is limited labor. The phenomenon of decreasing interest of the youth on farming is already observed in the study area. The youths generally move to urban areas for off-farm employments, leaving the old to grapple with farming. The second links to water availability at farm plots level, determined mainly by access to water reservoir. Land preparation for planting requires large amount of water. For those farmers whose farm plots are close to reservoir or those who own enough resources to make better access to water supply, land preparation can be done immediately at the onset of rainy season. Meanwhile, farmers with limited resources or those whose farm plots are far off the reservoir should wait until the

level of reservoir high enough to flow, or until water from rainfall is sufficiently accumulated in their farm plots.

Figure 2.2. Planting calendar of irrigated (a) and rain-fed farm (b), and rainfall pattern (c). Source: Annual report of planting area for 2012 (ADO, 2013)

According to the local practices, planting rice generally starts with raising the seedlings in a nursery and later transplanting them in the main field. Small numbers of farmers also do direct planting, where seeds are drilled directly to zero tilled land, but this practice is only limited to dry-season planting. The main motivations of farmers to do zero-tilled direct planting are to save water and at the

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same time shorten the growing period, so that they can gain early harvest, giving them more flexibility for the next planting.

Irrigation is generally applied on a rotational-based, with an application interval of 3 days during the earlier stages of rice growth and 7 days during the later stages. However, when water is not adequately available (usually during dry season), the application interval was prolonged until 7 or 10 days during the earlier stages and often until 14 days during the later stages. The depth of water irrigation in each application is set relatively constant, generally at a level of no more than 20 mm. The frequency of irrigation application varies for different locations of farm plots, depending on their access to water reservoir. For those farmers whose farm plot has limited access to water reservoir (e.g. rain-fed or farm plots with irrigation canals but located far-off the reservoir), irrigation might be supplemented with water pumps. But, this is only possible for famers who own adequate resources, while those who cannot afford the pumps just rely exclusively on rainfall. These variations in the onset of planting time and irrigation schedule among farmers reflect their existing responses to changes in local climate, which is highly determined by their capital.

Conceptual Framework

The conceptual framework of the study refers to the IPCC-concept of Climate Change Impact, Adaptation, and Vulnerability (CCIAV). Under this framework (Figure 2.3), impact assessment is made in 2 steps, which covers impact on the area under study and that on the households within the area under study. The magnitude of climate change impact at the study area-level refers to the extent to which the rice yield of a farm plot located in a particular location within the study area is affected by the changes of climatic condition of the area under study. The magnitude of impact is a function of exposure, i.e. the magnitude of changes in precipitation, and minimum and maximum temperature; and sensitivity, i.e. the characteristic of the location on which the farm plot under study is located. The climatic condition of the whole study area is considered uniform, because the research used only one set of observed climatic data, derived from one climate station. In this case, this research does not allow for spatial comparison of impact under different climatic condition. It is therefore the variation of area-level impact is made according to the characteristics of the location on which the farm plot under study is located, such as comparison of impact on irrigated and rain-fed farm plot.

At household level, impact is defined as the magnitude of rice sufficiency implication of the climatic condition-related changes in rice yield of a particular farm plot that belongs to smallholder farmers under study. Household-level impact is a function of household-level exposure and sensitivity. The magnitude of the household-level exposure is highly determined by the distance of households’ farm plot to irrigation reservoir and methods used by the households to access the reservoir. Meanwhile, the magnitude of households’ sensitivity is determined by the portion of households’ rice requirement derived from the

households’ own farm plots and the length of households’ facing food scarcity. The combination of a household’s exposure and sensitivity, then leads to a household’s level initial impact, i.e. impact at household level with the absence of household-level existing adaptation. In this research, the magnitude of initial impact is represented by average household rice sufficiency level (HRSL) of the non-adapted households.

Figure 2.3. Conceptual Framework for the Study

AREA UNDER STUDY-LEVEL ANALYSIS Exposure

(Magnitude of ΔP, ΔTMin, ΔTMax)

(Simulated data extracted from 17 GCMs under climate change scenarios of RCP4.5 & RCP.8.5

Sensitivity (Characteristics of Locations )

Area-Level Bio-physical Impact (Climatic Condition-related Changes in Rice Yield (YR) YR for Different Existing Farming Practices, e.g. Rainfed Vs. Irrigated) Local Climate Condition:

Precipitation (P) & Temperature (T)

FARM HOUSEHOLDS UNDER STUDY-LEVEL ANALYSIS HH’s Sensitivity

- Percent of HH’s Rice from Own Prod. - Length of HH’s Facing Food Scarcity

HH’s Exposure

(Magnitude of Rice Yield Reduction)

- Distance of HH’s Farm to Reservoir - HH’s Methods to Access the Reservoir

HH-Level Initial Impact

(Average HRSL of Non-Adapted Households)

HH-Level Residual Impact (HRSL of Adapted – HRSL of Non-Adapted)

Different Types of HH’s Existing Adaptations

HH’s Adaptive Capacity - HH Head Gender, Education, Age - HH Members

- HH’s Access to Credit - HH’s Involvement in Group - HH’s Livelihood Diversification - HH’s Ownership of Land - HH’s Ownership of Livestock

Climate Change

Adaptation Strengthening Interventions

Responding to the findings of previous studies, which indicate that ignoring adaptations often lead to an overestimate of the climate change impact, this research then includes a step of identification of smallholder farm households existing adaptation practices. Different types of households’ adaptation responses identified in the study area are then entered into impact assessment model. This enables the research to calculate the average HRSL of different types of adaptations to indicate the adequacy of each type of adaptation response in addressing the rice insufficiency implication of the climate induced rice yield reduction. The difference between the HRSL of the adapted households and that of the non-adapted represents the level of residual impact that has yet to be addressed adequately by the existing adaptation responses. In other words, the level of residual impact suggests an opportunity for adaptation-strengthening interventions.

Literatures (UNFPA, 2011; Nutters, 2012) suggest that the formulation of an effective adaptation intervention requires a thorough understanding on the socio-economic, demographic, and environmental background of the nature and community of a system under study. Various factors that potentially determine the capacity of smallholder farm households to address the current impact and anticipate the future potential impact need to be identified. Then determinant analysis using various analytical tools needs to be applied. Based on which sound policy recommendation could be formulated to promote sets of locally-relevant and technologically appropriate adaptation strengthening interventions.

Sources of Data

Data collection was conducted from November to December 2013. It was just after planting for some households and during planting or land preparation for some others. Data was collected using questionnaire through interview with the housewife together with the head of sample households. Interview was made at the house of sample households, upon a prior appointment for most convenient time to the respondents. Two couples of experienced interviewers were recruited and short training to familiarize the questionnaire was made prior to the data collection. In order to verify the data generated from the interview, a triangulation was made through Focus Group Discussion (FGD) and interview with key informants, field observation, and secondary data collected from related local offices.

Data collected included: (i) household’s rice production system that involved current farm management practices, current yield, and current allocation of its production; (ii) household’s consumption pattern assessed by weekly-based household food consumption through interview with the housewife to generate data on the portion of total household calorie requirement derived from rice; and (iii) household’s socio-economic characteristics to explore factors determining the household vulnerability and adaptation practices.

In addition, observed climate data of precipitation and minimum and maximum temperature was also collected for 30 years (1981 – 2010) from local climate station located closest to the study area, which is Jatiwangi Climate Station. The observed climate data was used to analyze the current climatic condition and assess the extent to which the climatic condition has been changing in the study area. Moreover, the observed data also served as basis for extracting simulated data from Coupled Model Intercomparison Project Phase 5 (CMIP5) to generate climate projection for near-future (2011 - 2040) and far-future (2041 – 2070). The study applied new climate change scenarios of Representative Concentration Pathways (RCPs) used by IPCC in its latest Fifth Assessment Report (IPCC AR5) (Bernie, 2010; Jubb, Canadell, and Dix, 2010).

Sampling

Sample households were calculated using the following formula:

Where:

n = Number of minimum sample required

α = Confidence interval (95%)

p = Proportion of climate change-induced food-insecure households (estimated based on the percentage of farm plots suffering from planting/harvesting failure to the total farm plots affected by drought, flood, and pest/diseases infestation. Using the Sumedang District Agricultural Office (ADO) data, the proportion was estimated at 0.32) d = Limit error or absolute precision (0,05)

N = Total Population, i.e. all households in the study area whose welfare fall within the lowest fourth deciles, which according to the 2011 Data Collection for Social Protection Programs (PPLS) conducted by Statistics Indonesia (BPS), the total number was around 3.641 households (TNP2K, 2013).

Based the above formula, it is found out that the required number of sample for this study was 156 households. The sample was selected randomly from the “by-name and by-address” data of the 3.641 households, which has been released officially by PPLS.

Analytical Framework

The study adopted the new IPCC concept of impact, adaptation, and vulnerability assessment. The analysis started with climate change assessment to

simulate the extent to which climatic condition has been changing for baseline (1981 – 2010), near-future (2011 - 2040) and far-future (2041 - 2070) periods (Step 1). At the same time, analysis was also made to set of empirical data generated from household survey to gain an insight into existing farming practices and adaptation responses of smallholder farmers in the study area.

The output of the above analysis then entered into CROPWAT simulation model to assess the current climatic condition-related changes in rice yield and its future likelihood, based on which the individual household’s annual rice production could be calculated for baseline and near- and far-future projections (Step 2). Afterward, the individual farm household rice sufficiency level was calculated as a ratio of a household’s annual rice requirement to its actual annual rice availability (Step 3). Then, in order to estimate the extent to which farm households’ current adaptation responses have been adequate to address the impact of climate change in the study area, the average HRSL for different types of adaptations were calculated for baseline, near-future, and far-future periods (Step 4). The household-level vulnerability of smallholder farmers to the impact of climatic condition-related changes in rice yield on their household-level rice sufficiency was assessed by using 2 vulnerability indexes, i.e., composite vulnerability index and IPCC-vulnerability index (Step 5). Finally, in order to gain a thorough understanding on factors that underlie a farm household’s not adopting the empirically identified best adaptation measure identified in the study area, an ordered logistic regression analysis was applied (Step 6). The steps of analysis applied in this study are presented on Figure 2.4.

Figure 2.4. Steps of Analysis - Assessment of

current and future climatic condition - Identification of existing farming practices - Identification of existing adaptation practices Assessment of climate change impact on rice yield under different existing farming practices Assessment of current and future climate-induced rice yield reduction impact on HRSL under existing adaptation practices Assessment of household vulnerability under existing adaptation practices Analysis of factors determining household existing adaptation practices STEP I STEP II STEP III STEP IV STEP V

- P, TMin, TMax (local station) - 17 GCMs

CMIP5 - RCP4.5 and

RCP8.5 1981-2010 2011-∆P ∆TMin ∆TMax Interview FGD Observatio n CROPWAT

∆YR for Diff. Farming

Farming Practices

Adaptation Practices

Socio-economic & Demographic Data

HRSL (Ratio of Availability to HRSL for different Farming Practices Vulnerabilit y Index Logistic Regression Composite-& IPCC-HVI for different Adaptation practices Diterminants of existing adaptation practices

3

CLIMATE CHANGE IMPACT ON RICE YIELD AND

ADAPTATION RESPONSE OF THE FARMERS

Abstract

Despite the well-documented model-simulated adverse impact of climate change on rice yields reported elsewhere, interventions to address the issue seem still limited, particularly at local level. This links to the uncertainty that entails to climate projection and its likely future impact, which varies across regions and climate models. The study analyzes climate change impact on rice yield and the adequacy of existing adaptations to cope with a large range of impact under various climate models. Seventeen General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) climate change scenarios of RCP8.5 and RCP4.5, combined with CROPWAT model are used to generate projection for near-future (2011-2040) and far-future (2041-2070). The output confirms rice yield reduction to occur in the near-future, to the extent variable across the GCMs. At the highest estimation, rice yield decreases by 32.00% and 31.81%, in comparison to baseline, for near-future under RCP8.5 and RCP4.5, respectively. The reduction extends, with a slightly higher degree, to the far-future. The reduction is sensitive to variation in the existing farming practices, in particular that in planting time and irrigation scheduling. The shifting of planting time to better match rainfall pattern reduces the rice yield reduction by 12.95% for rainfed farming and 14.07% for the irrigated. Meanwhile, improved irrigation scheduling reduces the yield reduction by 16.16%. The findings provide valuable inputs for relevant authorities to understand the whole continuum of climate change impact on rice yield, based on which sets of planned interventions locally specific for the areas can be developed, accordingly.

Key Words: Climate change, Rice yields, adaptation, planting time, Irrigation

Introduction

Most Indonesian population (94%) relies to the greatest extent on rice as their staple food. Taking into account its huge population of 237.64 millions (BPS, 2010), it is understandably that Indonesia cannot rely on the still “thin and volatile” international rice market to fulfill its domestic demand. The Indonesian new Food Law No. 18/2012 explicitly states that food security in Indonesia has to be based on domestic food availability and food sovereignty (Republic of Indonesia, 2012). In this connection, self-sufficiency on staple food, particularly rice, has been one solution, to which most government programs are directed in order to ensure national food security.

In Indonesia, producing more rice for the future is a growing challenge, particularly under the adverse impacts of climate change. According to (Dasgupta, 2013), climate change affects rice yield through movements of climatic variables such as temperature and precipitation. Temperature affects evapotranspiration and determines the length of crop growing season, while rainfall controls irrigation water supply to meet the crop water requirement (FAO, 2009).

Studies confirmed that changes in climate have been observed in Indonesia, to a level substantially variable among regions. Temperature increased at about 0.3oC over the last decade, and annual precipitation has decreased by 2-3%. The pattern of rainfall has changed. In the southern regions, the rainfall was declined, while in the northern part an increase was observed. A change has also been observed in the seasonality of precipitation, where an increase in the wet-season rainfall was recorded in the southern and a decrease in the dry-season rainfall in the northern region (Hulme and Sheard, 1999; Boer and Faqih, 2004). In terms of future climate projection, it is estimated that warming in Indonesia will occur at a rate highly variable across regions, ranging from the lowest of 1.16oC to the highest of 1.58oC until 2070, where the highest temperature potentially occurring in the Island of Kalimantan. In Java, the warming ranges from 1.30oC in the west part to 1.36oC in the east (Susandi, 2007). With respect to future projection of rainfall, it is estimated that the trend in future precipitation is highly variable across regions, where majority of Indonesian Island show an increasing trend, while the rest indicates a trend of reduction, particularly in the southern parts. In Java Island, rainfall is estimated to decrease around 30% until 2080 (Hulme and Sheard, 1999).

Efforts have also been made by numbers of studies to quantify the impact of climate change on crop yields. It is reported that a reduction in crop yields will occur in some parts of Asia at a level of 2.5-10% until 2020 and 5-30% until 2050 (Lasco et al., 2011). Studies in Indonesia estimated that climate change will likely decrease rice yield by 4% per year, soybean by 10%, and maize by 50% (World Bank, 2007). Furthermore, it is reported that until 2050 crop yield reduction will be at a level of 20.3-27.1% for rice, 13.6% for corn, and 12.4% for soybean (Bappenas, 2011).

Despite the model-simulated adverse impact of climate change on crop yields, farmers are likely still able to survive. This has been made possible by a large number of adaptations in farming management practices assumed to occur autonomously, ranging from adjustment on planting calendar to large investment on input and infrastructure. However, the uncertainty that entails to the climate projection and its likely future impact on yields, which varies substantially across crops, regions, and climate models, posted high risks of reduced or inadequate adaptation. This links to the facts that uncertainty in climate change and its likely future impact tend to discourage relevant authorities to develop adequate adaptation measures, particularly those that require substantial investments. In this regards, any climate impact study should address the potential of inadequate or reduced adaptation by assuming a wider assumptions or scenarios to capture the whole possible range of climate impacts.

This study provides an analysis of the impact of climate change on rice yields and the adequacy of the existing adaptation practices of smallholder

farmers to cope with the large range of current and future climate change impact using 17 GCMs under RCP8.5 and RCP4.5. In specific, the study aims to analyze (i) the current climatic condition and its future likelihood using 17 GCMs under RCP8.5 and RCP4.5, (ii) the extent to which the current and future climatic condition affects rice yield, and (iii) the adequacy of existing smallholder farmers’ adaptation practices to address the impact of current and future climatic condition on rice yield.

Analytical Framework

The analytical framework is presented on Figure 3.1. The analysis started with climate change assessment to simulate the extent to which climatic condition has been changing for baseline (1981 – 2010), near-future (2011 - 2040) and far-future (2041 - 2070) periods. At the same time, analysis was also made to set of empirical data generated from household survey to gain an insight into existing farming practices of smallholder farmers in the study area. The output then entered into CROPWAT simulation model to assess the impact of current and future climatic condition on rice yield for different existing farming practices.

Materials and Methods

Analysis of Local Climatic Condition

The analysis of local climatic condition was made to assess the current and future climatic condition in the study area. Changes in current climatic condition was assessed by using a 30-year time series observed data (1981 - 2010) of precipitation and the minimum and maximum temperature collected from a local climate station located closest to the study area, that is Jatiwangi Climate Station. The 30 year period was chosen considering that this is the minimum period needed to define a climate (WMO, 2014). The total annual rainfall and average annual maximum and minimum temperature was then plotted to have an indication on general trend of rainfall and temperature in the study area during the last 30 years. Afterward, a plot of decadal monthly average rainfall and temperature was also made to assess a decadal shifting in the mean of monthly rainfall and temperature.

GCMs were used to project changes in average monthly rainfall and monthly minimum and maximum temperature for two time slices, these are 2011 – 2040 (near-future) and 2041 – 2070 (far-future). The simulated rainfall data was extracted from GCM-CMIP5 and then corrected to the local observed climate data by using CCROM_RainPro_v01 Software (Faqih, 2013). Meanwhile, the simulated minimum and maximum temperature was corrected manually. The corrected baseline for rainfall and temperature was made using the following formula:

Figure 3.1. Analytical framework of the study

Where:

CF : Correction Factor

Obs : Observed data for baseline period

Model (baseline) : GCM-simulated data for baseline RCorrected : Corrected rainfall for baseline RModel : GCM-simulated rainfall for baseline TCorrected : Corrected temperature for baseline

TModel : GCM-simulated temperature for baseline

... (Equation 3.1) ... (Equation 3.2)

... (Equation 3.3)

Analysis of Current Climate

Plotting annual and monthly-decadal average of observed data

ObservedData P&T Corrected Baseline GCM

Projection of P Projection of T

Simulated Monthly Average of P&T

Near-Future Far-Future

Baseline

GCM-simulated Data for Rainfall (P) & Temperature(T)

(CMIP5 under RCP8.5 & RCP4.5 for the longitudes of 107°54' - 108°52' E& the latitude of 6°54' - 7°54' S

CROPWAT MODEL

Diversity in Current Farming Practices

(Planting Time, Irrigation Scheduling, Rice Varieties, Land Preparation)

Yield Reduction (YR) for Different Farming

Practices

Changes in Annual Rice Yield

Near-Future Far-Future Baseline

9. Jelaskan alokasi penggunaan hasil produksi dari lahan garapan anda!

No

Komo-ditas

Produksi (Kg)

Alokasi Penggunaan Upah

Panen (Kg)

Bibit (Kg)

Jual (Kg)

Sewa (Kg)

Lain-Lain (Kg)

Hasil Panen MT1

1 Padi

2 Jagung

3 Singkong

4 Ubi

5 Lainnya:

-

Hasil Panen MT2

1 Padi

2 Jagung

3 Singkong

4 Ubi

5 Lainnya:

-

Hasil Panen MT3

1 Padi

2 Jagung

3 Singkong

4 Ubi

5 Lainnya:

-

10. Apakah anda bekerja sebagai buruh tani?

1. Ya 2. Tidak

Jika Ya, jawab pertanyaan 11 s/d 12 Jika Tidak, lanjut ke pertanyaan 13

11. Berapa orang anggota keluarga anda bekerja sebagai buruh tani? ...

12. Bagaimana sistem pengupahan yang anda terima?

No Kegiatan Pertanian Sistem Pengupahan (bentuk upah yang diterima dan cara perhitungan

upah) 1 Penyiapan lahan

2 Penanaman

3 Perawatan

4 Pemanenan

5 Lainnya:

- -

1. Ya 2. Tidak Jika Ya, Sebutkan...

No Jenis Ternak Jumlah Kepemilikan*)

1 Ayam

2 Kambing

3 Sapi

4 Kerbau

5 Lainnya (sebutkan): -

- -

*)

Keterangan: 1. Milik Sendiri 2. Merawat milik orang lain 3. Lainya (Sebutkan)

III. DAMPAK IKLIM TERHADAP PERTANIAN

14. Apakah pertanian anda pernah mengalami gangguan akibat kekeringan/ banjir/serangan hama dalam 5 tahun terakhir?

Jenis Gangguan

Waktu Kejadian (Bulan & Tahun)

Luas Areal Terkena

Luas Areal Puso Kekeringan

Banjir

Hama/Penyakit

15. Apakah yang anda lakukan untuk mencegah dan mengatasi gangguan akibat kekeringan/banjir/serangan hama?

Jenis Gangguan Pencegahan (Sebelum Terkena)

Penanganan (Setelah Terkena)

Kekeringan - -

- -

- -

Banjir - -

- -

- -

Hama/Penyakit - -

- -

IV. KETERSEDIAAN PANGAN

16. Apakah keluarga anda mengalami kekurangan pangan dalam 1 tahun terakhir? 1. Ya 2. Tidak

Jika Ya, sebutkan pada bulan apa

saja ... ... ...

17. Apakah anda pernah mendapatkan bantuan dalam 1 tahun terakhir?

1. Ya 2. Tidak

Jika Ya, Sebutkan...

No Bentuk Bantuan Jumlah Sumber Waktu

1 Uang (Rp)

2 Barang:

- Raskin (Kg)

- Input produksi (Kg)

- - -

*)

Sumber:1. Kredit Bank 2. Bantuan Pemerintah 3. Bantuan Kelompok 4. Lainnya, sebutkan.

18. Apakah anda menjadi anggota kelompok tertentu?

1. Ya 2. Tidak

Jika Ya, Sebutkan...

No Jenis Kelompok Posisi Dalam Kelompok *)

Manfaat Yang Didapat

1 Kelompok Tani

2 Koperasi 3 Arisan

4 Lainnya (sebutkan): -

- -

*)

Keterangan: 1. Ketua 2. Pengurus 3. Anggota 4. Lainnya, sebutkan

V. KONSUMSI MAKANAN DALAM SEMINGGU TERAKHIR

No Rincian Unit Produk

si Sendiri

Beli Bantuan Pemerinta

h

Pinjam Lain-Lain

Padi-Padian 1 Beras 2 Beras Ketan 3 Tepung Beras 4 Lainnya (Sebutkan):

- Jagung

5 Jagung Muda 6 Jagung Pipil

8 Lainnya (Sebutkan): -

Umbi-Umbian

9 Singkong

10 Tepung Singkong 11 Gaplek

12 Tiwul 13 Ubi Jalar 14 Talas

15 Lainnya (Sebutkan): -

Kacang-Kacangan 16 Kacang Tanah Kulit 17 Kacang Tanah Non

Kulit

18 Kacang Kedele 19 Kacang Hijau 20 Kacang Mete

21 Tahu

22 Tempe

23 Oncom

24 Tauco

25 Lainnya (Sebutkan): -

Sayuran 26 Kangkung 27 Kacang Panjang

28 Timun

29 Daun Singkong 30 Daun Pepaya

31 Bayam

32 Terung

33 Labu

34 Sawi

35 Buncis 36 Wortel 37 Cabe Merah 38 Cabe Hijau 39 Cabe Rawit 40 Bawang Merah 41 Bawang Putih 42 Lainnya (Sebutkan):

- Buah-Buahan 43 Semangka

44 Mangga

45 Pepaya 46 Pisang 47 Tomat 48 Rambutan 49 Alpokat

50 Sawo

52 Lainnya (Sebutkan): -

Daging

53 Daging Sapi 54 Daging Kambing 55 Daging Kerbau 56 Daging Ayam 57 Lainnya (Sebutkan):

- Ikan

58 Ikan Laut Segar 59 Ikan Tawar Segar 60 Ikan Asin

61 Lainnya (Sebutkan): -

Telur

62 Telur Ayam 63 Telur Bebek 64 Telur Puyuh 61 Lainnya (Sebutkan):

-

Minyak dan Lemak 62 Minyak Goreng 63 Minyak Kelapa 64 Margarin

65 Lainnya (Sebutkan): -

Bahan Minuman 66 Gula Pair 67 Gula Merah 68 Sirup

69 Kopi

70 Teh

71 Lainnya (Sebutkan): -

Makanan Jadi 72 Roti 73 Kue Basah 74 Gorengan 75 Bubur Kacang 76 Mie Instan 77 Mie Bakso 78 Lainnya (Sebutkan):

-

Makanan & Minuman Lainnya

79 80

CURRICULUM VITAE

Ade Candradijaya was born on June 3rd 1969 in Sumedang District, West Java Province, Indonesia. He obtained his B.Sc. (S1) degree in Food Technology from Bogor Agricultural University in 1993. In 1995, he continued his master degree in Community Nutrition at SEAMEO-TROPMED, University of Indonesia, and graduated in 1997.

Since 1999, he has been working with Center for International Cooperation, Ministry of Agriculture. In 2002, he obtained a Master Scholarship from STUNED, the Netherlands Embassy to study Rural Development at Larenstein University, the Netherlands and Imperial College, London niversity, UK, and graduated in 2004.

In 2010, he enrolled for Doctorate Program in Natural Resource and Environmental Management at Bogor Agricultural University. During his doctorate study, he produced the following publications:

1. Candradijaya, A., C. Kusmana, Y. Syaukat, L. Syaufina, and A. Faqih. 2014. Climate change impact on rice yield and adaptation response of local farmers in Sumedang District, West Java Province, Indonesia. International Journal of Ecosystem, Vol. 4 (5): 212-223.

2. Candradijaya, A., C. Kusmana, Y. Syaukat, L. Syaufina, and A. Faqih. 2014. Smallholder farm households’ vulnerability and adaptation to climate-induced foodinsecurity. British Journal of Applied Science and Technology, Vol. 4 (36): 4974-4991.

In addition, he has prepared several manuscripts, which have been accepted or under review for publication by national or international scientific journals, as follows:

1. Candradijaya, A., C. Kusmana, Y. Syaukat, L. Syaufina, and A. Faqih. 2014. Pemanfaatan model proyeksi iklim and simulasi tanaman dalam penguatan adaptasi sistem pertanian padi terhadap penurunan produktivitas akibat perubahan iklim. Accepted to be published at Jurnal Informatika Pertanian Vol. 23, No. 2 (December 2014).

2. Candradijaya, A., C. Kusmana, Y. Syaukat, L. Syaufina, and A. Faqih. 2014. Determinants of Smallholder Farm Households’ Adaptation to Climate Change-Induced Food Insecurity. Asian Journal of Agricultural Research.Initially accepted for publication in Asian Journal of Agricultural Research.

3. Candradijaya, A., C. Kusmana, Y. Syaukat, L. Syaufina, and A. Faqih. 2014. Food Security under Climate Change and Adaptation Response of Smallholder Farm Households in Sumedang District, West Java Province, Indonesia. Under review by Journal of Climatic Change.