Nov 20, 2018 - ... van de landbouwgezinnen in een regressie opgenomen om hun impact ...... SPSS. The SPSS version 22, 23 and 24 are used for the data ...
Cover_Aung.pdf
1
20/11/18
10:18
Characterizing farm households’ vulnerability and adaptation to climate change in Myanmar
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M
Y
CM
MY
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CMY
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Characterizing farm households’ vulnerability and adaptation to climate change in Myanmar Aung Tun Oo
ISBN 978-9-4635714-7-0
789463
571470
2018
9
Aung Tun Oo
i
Promoters Prof. dr. ir. Stijn Speelman Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium
Prof. dr. ir. Guido Van Huylenbroeck Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium
Dean Rector
Prof. dr. ir. Marc Van Meirvenne Prof. dr. ir. Rik Van de Walle
ii
Characterizing farm households’ vulnerability and adaptation to climate change in Myanmar
Aung Tun Oo
Thesis submitted in fulfillment of the requirements for the degree of Doctor (PhD) in Applied Biological Sciences
iii
Dutch translation of the title: Klimaatsverandering in Myanmar: aanpassing en kwetsbaarheid van landbouwgezinnen
Suggested way of citation: Tun Oo, A. (2018). Characterizing farm households’ vulnerability and adaptation to climate change in Myanmar. Doctoral dissertation, Ghent University, Belgium.
ISBN: 978-94-6357-147-0
Copyright The author and the promoters give the authorization to consult and copy parts of this work for personal use only. Since every other use, such as reproduce, distribute or transmit in any form or by any means, is subject to the copyright laws, permission to reproduce or distribute any material contained in this work should be obtained from the author.
Cover image by author
iv
Members of the examination board Prof. dr. ir. Peter Bossier (Chairperson) Department of Animal Sciences and Aquatic Ecology Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Pieter De Frenne (Secretary) Department of Environment Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Marijke D'Haese Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. Brent Bleys Department of General Economics Faculty of Economics and Business Administration Ghent University, Belgium Prof. dr. Myo Kywe Rector (retired), Yezin Agricultural University, Myanmar Presently: Chairman at National Education Policy Commission Prof. dr. ir. Guido Van Huylenbroeck (Promoter) Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Stijn Speelman (Promoter) Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium
v
Thousands of candles can be lighted from a single candle, and the life of the candle will not be shortened. Happiness never decreases by being shared. (Buddha)
Never think of knowledge and wisdom as little. Seek it and store it in the mind. Note that anthills are built with small particles of dust, incessantly falling rain-drops, when collected, can fill a big pot. (Lakaniti Dhamma Literature, Pandita issue, Line 4)
vi
Dutch translation of the title: Klimaatsverandering in Myanmar: aanpassing en kwetsbaarheid van landbouwgezinnen
Suggested way of citation: Tun Oo, A. (2018). Characterizing farm households’ vulnerability and adaptation to climate change in Myanmar. Doctoral dissertation, Ghent University, Belgium.
ISBN: 978-94-6357-147-0
Copyright The author and the promoters give the authorization to consult and copy parts of this work for personal use only. Since every other use, such as reproduce, distribute or transmit in any form or by any means, is subject to the copyright laws, permission to reproduce or distribute any material contained in this work should be obtained from the author.
Cover image by author
iv
Members of the examination board Prof. dr. ir. Peter Bossier (Chairperson) Department of Animal Sciences and Aquatic Ecology Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Pieter De Frenne (Secretary) Department of Environment Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Marijke D'Haese Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. Brent Bleys Department of General Economics Faculty of Economics and Business Administration Ghent University, Belgium Prof. dr. Myo Kywe Rector (retired), Yezin Agricultural University, Myanmar Presently: Chairman at National Education Policy Commission Prof. dr. ir. Guido Van Huylenbroeck (Promoter) Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium Prof. dr. ir. Stijn Speelman (Promoter) Department of Agricultural Economics Faculty of Bioscience Engineering Ghent University, Belgium
v
Summary The main objective of this dissertation is to evaluate farm households’ vulnerability to the adverse effects of climate change and examine farmers’ uptake of climate change adaptation strategies in Myanmar. Farm households are vulnerable to the adverse impacts of climate change because agriculture is directly dependent on the climate and is sensitive to different hydro-climatic conditions. Several reports have called for Myanmar to tackle the negative effects of climate change on agriculture because it is ranked second in the global list of countries most at risk from climate-related hazards. However, Myanmar is still lagging behind in the process of climate change adaptation, vulnerability assessment and disaster prevention and preparedness (NAPA 2012, Slagle 2014, and MoSWRR 2017). Therefore, understanding climate change impacts and the vulnerability of societies or communities, and assessing climate change adaptation measures, are becoming essential for national planning and policy intervention. Given the national interest in the assessment of climate change vulnerability and adaptation, and the negative impact of climate change on the agricultural sector, this dissertation aims to evaluate farm households’ vulnerability and adaptation to climate change in Myanmar. Firstly, a case study was conducted in the delta areas of Myanmar for the purpose of evaluating the climate change vulnerability of farm households to saltwater intrusion and flooding. In addition, two case studies were carried out in the Central Dry Zone (CDZ) of Myanmar to evaluate farmers’ choice of climate change adaptation strategies and the economic impacts of climate change on agriculture. Farm households in the delta areas of Myanmar are increasingly impacted by the negative effects of climate change. They are increasingly vulnerable to the impacts of flooding and saltwater intrusion onto farmland and the associated threat to their livelihoods and socioeconomic conditions. In principal, vulnerability cannot be measured directly. The issue is, therefore, how to measure the climate change vulnerability of farm households. According to the literature, climate change vulnerability depends on three parameters: exposure, sensitivity and adaptive capacity (IPCC 2007). Indicator-based approaches are a common method for assessing the climate vulnerability of a system or society. They are used for assessment at all scales, aggregating data into vulnerability indices (Hinkel 2011). This study adopted two indicator approaches (the Livelihood Vulnerability Index (LVI) and the Socioeconomic Vulnerability Index (SeVI)) to assess the climate change vulnerability of farm households in Myanmar. Several studies have used a single index (either LVI or SeVI) to assess climate change vulnerability of a society or community, while a few studies have made a comparison. In our study, we have combined two indicator approaches (both LVI and SeVI) to obtain a complete picture of the vulnerability of farm household communities to saltwater intrusion and flooding. This analysis demonstrates that farm households are increasingly vulnerable to the impacts of saltwater intrusion and flooding. Among the vulnerable households, farm households with higher adaptive capacity indices are less vulnerable compared to farm households with lower adaptive capacity indices, even though the climate change exposure and
ix
sensitivity indices are more or less similar. Therefore, this study indicates that empowering the adaptive capacity of farm households in the delta areas of Myanmar could reduce their climate change vulnerability. There are many factors affecting the climate change vulnerability of farm households. These include social and economic characteristics and institutional factors. We have used a Principal Component Analysis (PCA) to evaluate these factors. Hinkel (2011) denoted that there are three kinds of substantial arguments (deductive, inductive and normative argumentations) for developing vulnerability indicators, while sometimes non-substantial arguments are also used. To determine the vulnerability of farm households to climate change-induced saltwater intrusion and flooding, this study has adopted a mixture of approaches, for example for the Household Vulnerability Index (HVI), the selection of the indicators is based on normative arguments because value judgement are used, but also non-substantial arguments, like data availability play a role. The results show that many socio-economic and institutional factors affect farm households’ vulnerability. Smallholder farmers are less likely to adopt farm-level climate change adaptation strategies compared to large landholding farmers. Large landholding farmers are less vulnerable to climate change than smallholders. Adoption of climate change adaptation at farm level helps farmers to reduce the negative impacts of climate change on agriculture. Farm households have already experienced the adverse effects of climate change, especially in the dry zone of Myanmar, and have taken a number of adaptation measures to reduce the effects at farm level. Therefore, we have chosen a case study approach focusing on Magwe region in the dry zone of Myanmar to examine farmers’ choice of climate change adaptation strategies. At farm level, there are two types of decision making before farmers take up climate change adaptation strategies. These are farmers’ perceptions of climate change and the adoption of climate change adaptation strategies. Therefore, in this study, we first assessed farmers’ perceptions of climate change phenomena, such as changes in rainfall and temperature. Secondly, we collected information about the farmers’ choice of climate change adaptation strategies. Then, we developed a Multinomial Regression Analysis to determine the factors affecting farmers’ choice of climate change adaptation measures. Finally, the MLR model showed that information from radio, access to seeds and extension services affect the choice of adaptation strategies. Farmers’ choice to adopt adaptation strategies for climate change is constrained by not only socio-economic and institutional factors but also practical availability or barrier at farm-level. Therefore, we also investigate barriers to the uptake of climate change adaptation strategies at farm level. In the light of climate change, farm households face uncertainty about their income or profit from farming. Therefore, additional assessment has been carried out to evaluate the economic impacts of climate change and to project the likely impact on crop revenue for farm households in the Central Dry Zone (CDZ) of Myanmar. We conducted a survey covering 16 townships in the CDZ. To assess the biophysical and economic impacts of climate change, a Ricardian analysis was chosen and applied using primary farm household survey data, such as the socioeconomic characteristics of farm households and
x
their application of climate change adaptation strategies, and secondary demographic and climate change data. We undertook two analyses with the available data. Firstly, we calculated the net revenue of the farm households. Secondly, we regressed the factors affecting net crop revenues. In our regression analysis, we first checked for the marginal effects of exogenous climate variables, such as temperature and precipitation on net crop revenues. Furthermore, we regressed the marginal effects of exogenous climate change parameters and socioeconomic characteristics of farm households on net crop revenues. 1
Three climate change projection models (PCM, CGCM3 and CSIRO ) are used for the projected impact of climate change on agriculture in the CDZ. The estimated marginal impacts suggest that global warming will affect crop productivity in the region. Predictions from the three global circulation models confirm that global warming will have a substantial impact on net crop revenues in the CDZ of Myanmar. After carefully characterizing climate change vulnerability and the adaptation strategies of farm households in the dry zone and delta areas of Myanmar, we are able to generate several key insights to address how to stimulate adoption of climate change adaptation measures and how to reduce farm households’ vulnerability. We formulate four policy recommendations for development planners and policy makers. 1) Enhance the adaptive capacity of farm households through formal and informal training programs and regular extension services. 2) Develop the rural road infrastructure, improving accessibility of marketplaces (This would give farmers access to inputs such as seeds, and may enhance the exchange of goods, services, various technologies and information with other farmers). 3) Develop or improve early warning information systems and climate change information-sharing networks, so that farmers can obtain knowledge on climate change adaptation processes and reduce the climate change vulnerability of agriculture in the face of changing climate conditions in Myanmar. 4) Prevention is more effective than treatment. Since the negative consequences of climate change and variability on farming have been observed, policy makers and development planners should articulate the implications of climate change and implement the necessary adaptation measures and mitigation options to reduce the negative impacts of climate change on agriculture. For instance, investment in agricultural research and rural development projects such as integrated water management projects, sustainable agriculture and organic farming projects, is strongly encouraged so that the adaptive capacity of farmers can be increased and climate change vulnerability reduced. We additionally formulate three key suggestions for farm households in developing countries for the purpose of reducing climate change vulnerability. 1
PCM=Parallel Climate Model, CGCM3 = Coupled Global Climate Model, CSIRO = Commonwealth Scientific and Industrial Research Organization Climate Model
xi
1) This study confirms that farm households who fail to adopt strategies for adaptation to climate change are more vulnerable than households who have made adaptations. Farm households should take into account the importance of climate change adaptation measures in the face of a changing climate. Therefore, this study encourages farmers to take up the climate change adaptation measures that are available at farm level. 2) This study points out that the level of vulnerability decreases with increased access to extension services, non-farm income and access to seeds. In addition, the inclusion of cash crops in cropping patterns ensures income for the households. Therefore, farmers are required to make contact with agricultural extension services in their regions and should consider the potential for cash crops in their cropping patterns. 3) This study also confirms that farmer-to-farmer extension activities, as a proxy of social capital, increase the farmers’ uptake of climate change adaptation strategies. Also, information-sharing and extension services by peer farmers and different stakeholders for the welfare of whole communities should be carried out in the region, so that the adaptive capacity of each individual farmer can be improved and the uptake of adaptation practices stimulated.
xii
Samenvatting De kwetsbaarheid van landbouwgezinnen in Myanmar voor de nadelige effecten van klimaatverandering staat centraal in dit doctoraatsonderzoek. De hoofddoelstelling is om de kwetsbaarheid van deze gezinnen te evalueren en te onderzoeken of landbouwers bepaalde aanpassingsstrategieën toepassen tegen klimaatverandering. Landbouwgezinnen zijn erg kwetsbaar voor de nadelige effecten van klimaatverandering omdat hun inkomen direct afhankelijk is van het klimaat en gevoelig is voor wijzigende klimatologische omstandigheden. Aangezien het land tweede gerangschikt is in termen van risico op negatieve effecten van klimaatverandering hebben verscheidene rapporten Myanmar aangespoord om maatregelen te nemen. Toch blijft Myanmar achter op het vlak van aanpassing aan klimaatverandering, inschatting van de kwetsbaarheid, en rampenpreventie en –paraatheid (NAPA 2012; Slagle 2014; MoSWRR 2017). Om deze kloof te dichten is eerst en vooral een beter begrip nodig van de gevolgen van klimaatverandering en van de kwetsbaarheid van gemeenschappen, alsook een evaluatie van de aanpassingsmaatregelen. Dit doctoraatsonderzoek levert hiertoe een belangrijke bijdrage met een grondige evaluatie van de kwetsbaarheid voor de klimaatverandering en van aanpassingsstrategieën die landbouwgezinnen in Myanmar toepassen. Ten eerste werd een gevalstudie uitgevoerd in het deltagebied van Myanmar met als doel het evalueren van de kwetsbaarheid, meer bepaald voor zoutwaterindringing en overstroming. Daarnaast werden twee bijkomende gevalstudies uitgevoerd in het centrale gedeelte van Myanmar, dat vooral met droogte kampt, om de keuze van landbouwers inzake hun aanpassingsstrategie na te gaan en om de economische gevolgen van klimaatverandering op de landbouw te berekenen. Landbouwgezinnen in de deltagebieden van Myanmar hebben in toenemende mate te kampen met de negatieve gevolgen van de klimaatopwarming. Ze zijn steeds meer kwetsbaar voor de impact van overstromingen
en
zoutwaterintrusie.
Dit
heeft
een
negatieve
impact
of
hun
inkomen
en
sociaaleconomische omstandigheden. In principe kan kwetsbaarheid niet gemeten worden op een directe manier, de vraag die hieruit voortvloeit is hoe kwetsbaarheid voor klimaatverandering van een landbouwgezin dan wel bepaald kan worden. In de literatuur worden hiervoor drie aspecten naar voren geschoven: de blootstelling, gevoeligheid en het aanpassingsvermogen (IPCC 2007). Benaderingen gebaseerd op indicatoren zijn een vaak gebruikte methode om de kwetsbaarheid voor klimaatverandering van een systeem of maatschappij te bepalen. Deze studie maakt gebruikt van twee indicator benaderingen om de kwetsbaarheid te bepalen, namelijk de Livelihood Vulnerability Index (LVI) en de Socioeconomic Vulnerability Index (SeVI). Terwijl verscheidene studies slechts één benadering gebruiken combineren we de twee benaderingen (zowel LVI en SeVI) om een zo volledig mogelijk beeld te schetsen van de kwetsbaarheid. Onze analyse toont aan dat landbouwgezinnen zeer kwetsbaar zijn voor de impact van zoutwaterintrusie en overstroming. Wanneer we de scores vergelijken merken we dat vooral
xiii
een hoger aanpassingsvermogen de gezinnen minder kwetsbaar maakt, terwijl de indices voor blootstelling en gevoeligheid min of meer gelijk zijn. De studie geeft dus aan dat het versterken van het aanpassingsvermogen van landbouwgezinnen de kwetsbaarheid voor klimaatverandering in de deltagebieden van Myanmar kan verminderen. Hinkel (2011) bespreekt drie substantiële argumenten voor het ontwikkelen van indicatoren om kwetsbaarheid te bepalen, namelijk deductieve, inductieve en normatieve argumenten. Daarnaast zijn er ook niet-substantiële argumenten. In deze studie is de ontwikkeling van een Household Vulnerability Index (HVI) gebaseerd op normatieve argumenten waarbij uitgegaan wordt van een waardeoordeel met betrekking tot de geselecteerde indicatoren, maar worden ook niet-substantiële argumenten gebruikt om via een Principal Component Analysis (PCA) te kijken of het aantal indicatoren kan gereduceerd worden. De resultaten tonen aan dat zowel sociaaleconomische als institutionele factoren de kwetsbaarheid van een
landbouwgezin
beïnvloeden.
Kleinschalige
landbouwers
zijn
minder
geneigd
om
aanpassingsstrategieën toe te passen op bedrijfsniveau dan grootschalige landbouwers. Grootschalige landbouwers zijn ook minder kwetsbaar voor klimaatverandering dan kleinschalige landbouwers. Het toepassen van aanpassingsstrategieën helpt landbouwers om de negatieve impact te verminderen. In de centrale droge regio in Myanmar ondervinden landbouwgezinnen momenteel ook de nadelige gevolgen van klimaatverandering. Vaak hebben zij reeds een aantal aanpassingsmaatregelen getroffen om de effecten te verminderen. Bijgevolg hebben we geopteerd om voor de Magwe regio in de droge zone van Myanmar de keuze van landbouwers te onderzoeken inzake aanpassingsstrategieën voor klimaatverandering. Deze beslissing is afhankelijk van hun perceptie met betrekking tot klimaatverandering en hun evaluatie van de aanpassingsstrategieën. Daarom analyseerden we eerst hun perceptie over klimaatverandering fenomenen, zoals veranderingen in neerslag en temperatuur, en daarna verzamelden we informatie over de landbouwers hun keuze voor aanpassingsstrategieën. Met behulp van een multinomiale regressieanalyse zijn we nagegaan welke factoren een invloed hebben op de keuze voor aanpassingsmaatregelen. De resultaten van het regressiemodel tonen aan dat de keuze bepaald
wordt
door
informatievoorziening
per
radio
en
door
toegang
tot
zaaigoed
en
voorlichtingsdiensten. De keuze van landbouwers om aanpassingsstrategieën voor klimaatverandering toe te passen wordt niet enkel beperkt door sociaal-economische en institutionele factoren, maar ook door de praktische beschikbaarheid of belemmering op landbouwbedrijfsniveau. Bijgevolg, onderzochten we ook welke belemmeringen aanwezig zijn voor het toepassen van aanpassingsstrategieën op landbouwbedrijfsniveau. Veel
landbouwgezinnen
verkeren
in
onzekerheid
over
hun
inkomen
of
winst
uit
landbouwactiviteiten wegens de klimaatverandering. Daarom werd een bijkomende analyse gedaan om
xiv
Summary The main objective of this dissertation is to evaluate farm households’ vulnerability to the adverse effects of climate change and examine farmers’ uptake of climate change adaptation strategies in Myanmar. Farm households are vulnerable to the adverse impacts of climate change because agriculture is directly dependent on the climate and is sensitive to different hydro-climatic conditions. Several reports have called for Myanmar to tackle the negative effects of climate change on agriculture because it is ranked second in the global list of countries most at risk from climate-related hazards. However, Myanmar is still lagging behind in the process of climate change adaptation, vulnerability assessment and disaster prevention and preparedness (NAPA 2012, Slagle 2014, and MoSWRR 2017). Therefore, understanding climate change impacts and the vulnerability of societies or communities, and assessing climate change adaptation measures, are becoming essential for national planning and policy intervention. Given the national interest in the assessment of climate change vulnerability and adaptation, and the negative impact of climate change on the agricultural sector, this dissertation aims to evaluate farm households’ vulnerability and adaptation to climate change in Myanmar. Firstly, a case study was conducted in the delta areas of Myanmar for the purpose of evaluating the climate change vulnerability of farm households to saltwater intrusion and flooding. In addition, two case studies were carried out in the Central Dry Zone (CDZ) of Myanmar to evaluate farmers’ choice of climate change adaptation strategies and the economic impacts of climate change on agriculture. Farm households in the delta areas of Myanmar are increasingly impacted by the negative effects of climate change. They are increasingly vulnerable to the impacts of flooding and saltwater intrusion onto farmland and the associated threat to their livelihoods and socioeconomic conditions. In principal, vulnerability cannot be measured directly. The issue is, therefore, how to measure the climate change vulnerability of farm households. According to the literature, climate change vulnerability depends on three parameters: exposure, sensitivity and adaptive capacity (IPCC 2007). Indicator-based approaches are a common method for assessing the climate vulnerability of a system or society. They are used for assessment at all scales, aggregating data into vulnerability indices (Hinkel 2011). This study adopted two indicator approaches (the Livelihood Vulnerability Index (LVI) and the Socioeconomic Vulnerability Index (SeVI)) to assess the climate change vulnerability of farm households in Myanmar. Several studies have used a single index (either LVI or SeVI) to assess climate change vulnerability of a society or community, while a few studies have made a comparison. In our study, we have combined two indicator approaches (both LVI and SeVI) to obtain a complete picture of the vulnerability of farm household communities to saltwater intrusion and flooding. This analysis demonstrates that farm households are increasingly vulnerable to the impacts of saltwater intrusion and flooding. Among the vulnerable households, farm households with higher adaptive capacity indices are less vulnerable compared to farm households with lower adaptive capacity indices, even though the climate change exposure and
ix
sensitivity indices are more or less similar. Therefore, this study indicates that empowering the adaptive capacity of farm households in the delta areas of Myanmar could reduce their climate change vulnerability. There are many factors affecting the climate change vulnerability of farm households. These include social and economic characteristics and institutional factors. We have used a Principal Component Analysis (PCA) to evaluate these factors. Hinkel (2011) denoted that there are three kinds of substantial arguments (deductive, inductive and normative argumentations) for developing vulnerability indicators, while sometimes non-substantial arguments are also used. To determine the vulnerability of farm households to climate change-induced saltwater intrusion and flooding, this study has adopted a mixture of approaches, for example for the Household Vulnerability Index (HVI), the selection of the indicators is based on normative arguments because value judgement are used, but also non-substantial arguments, like data availability play a role. The results show that many socio-economic and institutional factors affect farm households’ vulnerability. Smallholder farmers are less likely to adopt farm-level climate change adaptation strategies compared to large landholding farmers. Large landholding farmers are less vulnerable to climate change than smallholders. Adoption of climate change adaptation at farm level helps farmers to reduce the negative impacts of climate change on agriculture. Farm households have already experienced the adverse effects of climate change, especially in the dry zone of Myanmar, and have taken a number of adaptation measures to reduce the effects at farm level. Therefore, we have chosen a case study approach focusing on Magwe region in the dry zone of Myanmar to examine farmers’ choice of climate change adaptation strategies. At farm level, there are two types of decision making before farmers take up climate change adaptation strategies. These are farmers’ perceptions of climate change and the adoption of climate change adaptation strategies. Therefore, in this study, we first assessed farmers’ perceptions of climate change phenomena, such as changes in rainfall and temperature. Secondly, we collected information about the farmers’ choice of climate change adaptation strategies. Then, we developed a Multinomial Regression Analysis to determine the factors affecting farmers’ choice of climate change adaptation measures. Finally, the MLR model showed that information from radio, access to seeds and extension services affect the choice of adaptation strategies. Farmers’ choice to adopt adaptation strategies for climate change is constrained by not only socio-economic and institutional factors but also practical availability or barrier at farm-level. Therefore, we also investigate barriers to the uptake of climate change adaptation strategies at farm level. In the light of climate change, farm households face uncertainty about their income or profit from farming. Therefore, additional assessment has been carried out to evaluate the economic impacts of climate change and to project the likely impact on crop revenue for farm households in the Central Dry Zone (CDZ) of Myanmar. We conducted a survey covering 16 townships in the CDZ. To assess the biophysical and economic impacts of climate change, a Ricardian analysis was chosen and applied using primary farm household survey data, such as the socioeconomic characteristics of farm households and
x
their application of climate change adaptation strategies, and secondary demographic and climate change data. We undertook two analyses with the available data. Firstly, we calculated the net revenue of the farm households. Secondly, we regressed the factors affecting net crop revenues. In our regression analysis, we first checked for the marginal effects of exogenous climate variables, such as temperature and precipitation on net crop revenues. Furthermore, we regressed the marginal effects of exogenous climate change parameters and socioeconomic characteristics of farm households on net crop revenues. 1
Three climate change projection models (PCM, CGCM3 and CSIRO ) are used for the projected impact of climate change on agriculture in the CDZ. The estimated marginal impacts suggest that global warming will affect crop productivity in the region. Predictions from the three global circulation models confirm that global warming will have a substantial impact on net crop revenues in the CDZ of Myanmar. After carefully characterizing climate change vulnerability and the adaptation strategies of farm households in the dry zone and delta areas of Myanmar, we are able to generate several key insights to address how to stimulate adoption of climate change adaptation measures and how to reduce farm households’ vulnerability. We formulate four policy recommendations for development planners and policy makers. 1) Enhance the adaptive capacity of farm households through formal and informal training programs and regular extension services. 2) Develop the rural road infrastructure, improving accessibility of marketplaces (This would give farmers access to inputs such as seeds, and may enhance the exchange of goods, services, various technologies and information with other farmers). 3) Develop or improve early warning information systems and climate change information-sharing networks, so that farmers can obtain knowledge on climate change adaptation processes and reduce the climate change vulnerability of agriculture in the face of changing climate conditions in Myanmar. 4) Prevention is more effective than treatment. Since the negative consequences of climate change and variability on farming have been observed, policy makers and development planners should articulate the implications of climate change and implement the necessary adaptation measures and mitigation options to reduce the negative impacts of climate change on agriculture. For instance, investment in agricultural research and rural development projects such as integrated water management projects, sustainable agriculture and organic farming projects, is strongly encouraged so that the adaptive capacity of farmers can be increased and climate change vulnerability reduced. We additionally formulate three key suggestions for farm households in developing countries for the purpose of reducing climate change vulnerability. 1
PCM=Parallel Climate Model, CGCM3 = Coupled Global Climate Model, CSIRO = Commonwealth Scientific and Industrial Research Organization Climate Model
xi
1) This study confirms that farm households who fail to adopt strategies for adaptation to climate change are more vulnerable than households who have made adaptations. Farm households should take into account the importance of climate change adaptation measures in the face of a changing climate. Therefore, this study encourages farmers to take up the climate change adaptation measures that are available at farm level. 2) This study points out that the level of vulnerability decreases with increased access to extension services, non-farm income and access to seeds. In addition, the inclusion of cash crops in cropping patterns ensures income for the households. Therefore, farmers are required to make contact with agricultural extension services in their regions and should consider the potential for cash crops in their cropping patterns. 3) This study also confirms that farmer-to-farmer extension activities, as a proxy of social capital, increase the farmers’ uptake of climate change adaptation strategies. Also, information-sharing and extension services by peer farmers and different stakeholders for the welfare of whole communities should be carried out in the region, so that the adaptive capacity of each individual farmer can be improved and the uptake of adaptation practices stimulated.
xii
Samenvatting De kwetsbaarheid van landbouwgezinnen in Myanmar voor de nadelige effecten van klimaatverandering staat centraal in dit doctoraatsonderzoek. De hoofddoelstelling is om de kwetsbaarheid van deze gezinnen te evalueren en te onderzoeken of landbouwers bepaalde aanpassingsstrategieën toepassen tegen klimaatverandering. Landbouwgezinnen zijn erg kwetsbaar voor de nadelige effecten van klimaatverandering omdat hun inkomen direct afhankelijk is van het klimaat en gevoelig is voor wijzigende klimatologische omstandigheden. Aangezien het land tweede gerangschikt is in termen van risico op negatieve effecten van klimaatverandering hebben verscheidene rapporten Myanmar aangespoord om maatregelen te nemen. Toch blijft Myanmar achter op het vlak van aanpassing aan klimaatverandering, inschatting van de kwetsbaarheid, en rampenpreventie en –paraatheid (NAPA 2012; Slagle 2014; MoSWRR 2017). Om deze kloof te dichten is eerst en vooral een beter begrip nodig van de gevolgen van klimaatverandering en van de kwetsbaarheid van gemeenschappen, alsook een evaluatie van de aanpassingsmaatregelen. Dit doctoraatsonderzoek levert hiertoe een belangrijke bijdrage met een grondige evaluatie van de kwetsbaarheid voor de klimaatverandering en van aanpassingsstrategieën die landbouwgezinnen in Myanmar toepassen. Ten eerste werd een gevalstudie uitgevoerd in het deltagebied van Myanmar met als doel het evalueren van de kwetsbaarheid, meer bepaald voor zoutwaterindringing en overstroming. Daarnaast werden twee bijkomende gevalstudies uitgevoerd in het centrale gedeelte van Myanmar, dat vooral met droogte kampt, om de keuze van landbouwers inzake hun aanpassingsstrategie na te gaan en om de economische gevolgen van klimaatverandering op de landbouw te berekenen. Landbouwgezinnen in de deltagebieden van Myanmar hebben in toenemende mate te kampen met de negatieve gevolgen van de klimaatopwarming. Ze zijn steeds meer kwetsbaar voor de impact van overstromingen
en
zoutwaterintrusie.
Dit
heeft
een
negatieve
impact
of
hun
inkomen
en
sociaaleconomische omstandigheden. In principe kan kwetsbaarheid niet gemeten worden op een directe manier, de vraag die hieruit voortvloeit is hoe kwetsbaarheid voor klimaatverandering van een landbouwgezin dan wel bepaald kan worden. In de literatuur worden hiervoor drie aspecten naar voren geschoven: de blootstelling, gevoeligheid en het aanpassingsvermogen (IPCC 2007). Benaderingen gebaseerd op indicatoren zijn een vaak gebruikte methode om de kwetsbaarheid voor klimaatverandering van een systeem of maatschappij te bepalen. Deze studie maakt gebruikt van twee indicator benaderingen om de kwetsbaarheid te bepalen, namelijk de Livelihood Vulnerability Index (LVI) en de Socioeconomic Vulnerability Index (SeVI). Terwijl verscheidene studies slechts één benadering gebruiken combineren we de twee benaderingen (zowel LVI en SeVI) om een zo volledig mogelijk beeld te schetsen van de kwetsbaarheid. Onze analyse toont aan dat landbouwgezinnen zeer kwetsbaar zijn voor de impact van zoutwaterintrusie en overstroming. Wanneer we de scores vergelijken merken we dat vooral
xiii
een hoger aanpassingsvermogen de gezinnen minder kwetsbaar maakt, terwijl de indices voor blootstelling en gevoeligheid min of meer gelijk zijn. De studie geeft dus aan dat het versterken van het aanpassingsvermogen van landbouwgezinnen de kwetsbaarheid voor klimaatverandering in de deltagebieden van Myanmar kan verminderen. Hinkel (2011) bespreekt drie substantiële argumenten voor het ontwikkelen van indicatoren om kwetsbaarheid te bepalen, namelijk deductieve, inductieve en normatieve argumenten. Daarnaast zijn er ook niet-substantiële argumenten. In deze studie is de ontwikkeling van een Household Vulnerability Index (HVI) gebaseerd op normatieve argumenten waarbij uitgegaan wordt van een waardeoordeel met betrekking tot de geselecteerde indicatoren, maar worden ook niet-substantiële argumenten gebruikt om via een Principal Component Analysis (PCA) te kijken of het aantal indicatoren kan gereduceerd worden. De resultaten tonen aan dat zowel sociaaleconomische als institutionele factoren de kwetsbaarheid van een
landbouwgezin
beïnvloeden.
Kleinschalige
landbouwers
zijn
minder
geneigd
om
aanpassingsstrategieën toe te passen op bedrijfsniveau dan grootschalige landbouwers. Grootschalige landbouwers zijn ook minder kwetsbaar voor klimaatverandering dan kleinschalige landbouwers. Het toepassen van aanpassingsstrategieën helpt landbouwers om de negatieve impact te verminderen. In de centrale droge regio in Myanmar ondervinden landbouwgezinnen momenteel ook de nadelige gevolgen van klimaatverandering. Vaak hebben zij reeds een aantal aanpassingsmaatregelen getroffen om de effecten te verminderen. Bijgevolg hebben we geopteerd om voor de Magwe regio in de droge zone van Myanmar de keuze van landbouwers te onderzoeken inzake aanpassingsstrategieën voor klimaatverandering. Deze beslissing is afhankelijk van hun perceptie met betrekking tot klimaatverandering en hun evaluatie van de aanpassingsstrategieën. Daarom analyseerden we eerst hun perceptie over klimaatverandering fenomenen, zoals veranderingen in neerslag en temperatuur, en daarna verzamelden we informatie over de landbouwers hun keuze voor aanpassingsstrategieën. Met behulp van een multinomiale regressieanalyse zijn we nagegaan welke factoren een invloed hebben op de keuze voor aanpassingsmaatregelen. De resultaten van het regressiemodel tonen aan dat de keuze bepaald
wordt
door
informatievoorziening
per
radio
en
door
toegang
tot
zaaigoed
en
voorlichtingsdiensten. De keuze van landbouwers om aanpassingsstrategieën voor klimaatverandering toe te passen wordt niet enkel beperkt door sociaal-economische en institutionele factoren, maar ook door de praktische beschikbaarheid of belemmering op landbouwbedrijfsniveau. Bijgevolg, onderzochten we ook welke belemmeringen aanwezig zijn voor het toepassen van aanpassingsstrategieën op landbouwbedrijfsniveau. Veel
landbouwgezinnen
verkeren
in
onzekerheid
over
hun
inkomen
of
winst
uit
landbouwactiviteiten wegens de klimaatverandering. Daarom werd een bijkomende analyse gedaan om
xiv
de economische impact van klimaatverandering in centraal Myanmar te bepalen aan de hand van een projectie van de waarschijnlijke impact op opbrengsten. We hebben een bevraging gedaan in 16 dorpen in de centrale droge zone. Op basis van de verzamelde primaire gegevens van landbouwgezinnen en de klimaatprojecties werd een Ricardian model geschat om de biofysische en economisch impact van klimaatverandering te berekenen. Deze gebruikte gegevens omvatten de socio-economsiche kenmerken van landbouwgezinnen en het gebruik van aanpassingsstrategieën voor klimaatverandering, alsook secondaire demografische kenmerken en gegevens over de klimaatverandering. Al deze gegevens werden gebruikt in twee analyses. Ten eerste hebben we de netto-opbrengst van landbouwgezinnen berekend en de factoren die die netto-opbrengst kunnen beïnvloeden in een regressievergelijking opgenomen. In de regressieanalyse, hebben we eerst gekeken naar de marginale effecten van exogene klimaat variabelen, zoals temperatuur en neerslaghoeveelheid, op netto-opbrengsten van de oogst. Vervolgens hebben we deze marginale effecten van exogene klimaatverandering parameters samen met socio-economische kenmerken van de landbouwgezinnen in een regressie opgenomen om hun impact op netto-opbrengsten te bepalen. Drie prognosemodellen voor klimaatverandering (PCM, CGCM3 en 2
CSIRO ) zijn gebruikt om de verwachte impact van klimaatverandering op landbouw in centraal Myanmar te bepalen. De resultaten tonen de geschatte marginale effecten en suggereren dat klimaatopwarming de gewasproductiviteit in de regio zal aantasten. De voorspellingen van de drie globale circulatie modellen bevestigen dat de opwarming van de aarde een aanzienlijke impact zal hebben op nettooogstopbrengsten in Myanmar. Na een grondige beschrijving van de kwetsbaarheid en de aanpassingsstrategieën van landbouwgezinnen in de droge regio en deltagebieden in Myanmar, kunnen we enkele algemene inzichten geven over hoe de opname van aanpassingsmaatregelen voor klimaatveranderingen kan worden gestimuleerd en hoe de kwetsbaarheid van landbouwgezinnen kan worden beperkt. De vier onderstaande beleidsaanbevelingen zijn gericht op ontwikkelingsplanners en beleidsmakers. 1) Het verhogen van het aanpassingsvermogen van landbouwgezinnen door middel van formele en informele scholingsprogramma’s en advies- en voorlichtingsdiensten. 2) Het verbeteren van weginfrastructuur op het platteland en toegang tot de markt. Dit kan landbouwers een betere toegang verlenen tot productiemiddelen zoals zaaigoed, en kan de uitwisseling van goederen, diensten en verschillende technologieën en informatie bevorderen. 3) Het ontwikkelen en verbeteren van systemen voor vroegtijdige waarschuwing en netwerken voor informatie-uitwisseling voor klimaatverandering, zodat landbouwers kennis kunnen vergaren over 2
PCM=Parallel Climate Model, CGCM3 = Coupled Global Climate Model, CSIRO = Commonwealth Scientific en Industrial Research Organization Climate Model
xv
aanpassingsprocessen voor klimaatverandering en de kwetsbaarheid van landbouw als gevolg van wijzigende klimaatomstandigheden in Myanmar verminderd kan worden. 4) Preventie is meer doeltreffend dan behandeling. Vermits verschillende negatieve gevolgen van klimaatverandering en schommelingen in de landbouw worden waargenomen, dienen beleidsmakers en ontwikkelingsplanners de gevolgen van klimaatverandering te verduidelijken en de noodzakelijke aanpassingsmaatregelen en risico verlagende opties uit te voeren om de negatieve impact van klimaatverandering op landbouw in te perken. Dit kan bijvoorbeeld door investeringen aan te moedigen in landbouwonderzoek en rurale ontwikkelingsprojecten toegespitst op bv. geïntegreerd waterbeheer, duurzame
landbouwpraktijken
en
biologische
productiemanieren,
zodat
de
landbouwers
hun
aanpassingsvermogen kunnen versterken en minder kwetsbaar worden voor klimaatverandering. Ten
slotte
hebben
we
nog
drie
belangrijke
suggesties
voor
landbouwgezinnen
in
ontwikkelingslanden om hun kwetsbaarheid voor klimaatverandering te verminderen. 1)
Deze
studie
bevestigt
dat
landbouwgezinnen
die
geen
aanpassingsstrategieën
voor
klimaatverandering toepassen meer kwetsbaar zijn dan gezinnen die aanpassingen hebben gedaan. Daarom spoort deze studie landbouwers aan om in te zetten op aanpassingsmaatregelen . 2) Deze studie wijst erop dat het niveau van kwetsbaarheid daalt met betere toegang tot voorlichtingsdiensten, niet-agrarische inkomensbronnen en toegang tot zaaigoed. Daarnaast zorgt het opnemen van commerciële gewassen in teeltpatronen ook voor een inkomen voor gezinnen.. 3) Deze studie stelt ook vast dat voorlichtingsactiviteiten tussen de landbouwers onderling, als proxy voor sociaal kapitaal, tot een toename leidt in de toepassing van aanpassingsstrategieën voor klimaatverandering. Informatie-uitwisseling en voorlichtingsdiensten door medelandbouwers en andere stakeholders zouden moeten worden georganiseerd, zodat het aanpassingsvermogen van elke landbouwer kan worden versterkt en de toepassing van aanpassingsstrategieën kan worden gestimuleerd.
xvi
Table of Contents Members of the examination board ........................................................................................ v Acknowledgement ..................................................................................................................vii Summary ..................................................................................................................................ix Samenvatting .........................................................................................................................xiii Table of Contents ..................................................................................................................xvii List of the figures ....................................................................................................................xx List of the tables ....................................................................................................................xxi List of Abbreviations ............................................................................................................xxii Chapter 1 General Introduction: Background and Problem Statement ............................... 1 1.1 General Introduction ......................................................................................................... 2 1.2 Background ...................................................................................................................... 3 1.2.1 The occurrence of climate change in Myanmar .......................................................... 3 1.2.2 Climate change impacts on agriculture....................................................................... 8 1.2.3 Impacts of climate change on Myanmar’s agriculture ................................................. 9 1.2.4 Review of literature on climate change vulnerability assessment ..............................10 1.2.5 Review of literature on climate change adaptation in agriculture ...............................13 1.2.6 Methods for assessing the impacts of climate change on agriculture ........................15 1.3 Objectives, scope and methodology ................................................................................17 1.3.1 Thesis objectives.......................................................................................................17 1.3.2 Site selection.............................................................................................................17 1.3.3 Data collection method ..............................................................................................19 1.3.4 Approaches and methods of the study ......................................................................19 1.3.5 Limitations of the research ........................................................................................20 1.3.6 Outline and organization of the thesis .......................................................................21 Chapter 2 Assessment of Climate Change Vulnerability of Farm Households in Pyapon District, a Delta Region in Myanmar ......................................................................................23 2.1 Background .....................................................................................................................25 2.1.1 Theoretical conceptualization ....................................................................................26 2.2 Method ............................................................................................................................28 2.2.1 Profile of the study region..........................................................................................28
xvii
2.2.2 Data collection ..........................................................................................................30 2.2.3 Empirical model and index specification ....................................................................31 2.3. Results and Discussions.................................................................................................36 2.3.1 Domain wise vulnerability: SeVI ................................................................................37 2.3.2 Components based vulnerability: LVI ........................................................................40 2.4 IPCC defined vulnerability assessment based on the LVI and SeVI.................................45 2.5 Conclusion and Recommendations .................................................................................47 Chapter 3 Characterizing Households’ Vulnerability to Climate Change in Pyapon District in the Delta Region of Myanmar .............................................................................................50 3.1 Introduction......................................................................................................................52 3.1.1 Theoretical conceptualization ....................................................................................53 3.2 Materials and Methods ....................................................................................................55 3.2.1 Study area.................................................................................................................55 3.2.2 Data collection ..........................................................................................................55 3.2.3 Data analysis ............................................................................................................56 3.3 Results and Discussions..................................................................................................58 3.4 Conclusion and Outlook...................................................................................................65 Chapter 4 Determining Factors for the Application of Climate Change Adaptation Strategies among Farmers in Magwe District, Dry Zone Region of Myanmar ....................67 4.1 Background .....................................................................................................................69 4.2 Methodology ....................................................................................................................71 4.2.1 Study areas, sampling procedure, and data collection ..............................................71 4.2.2 Climatic information ...................................................................................................73 4.2.3 Empirical specification of the model variables ...........................................................74 4.2.4 Analytical framework and empirical specification of multinomial logistic regression model .................................................................................................................................77 4.3 Results and Discussion ...................................................................................................79 4.3.1 Farm characteristics and cropping patterns ...............................................................79 4.3.2 Perception about climate change ..............................................................................81 4.3.3 Comparison of past and current climate change adaptation strategies ......................82 4.3.4 Model Results and Discussions.................................................................................83 4.3.5 Barrier to climate change adaptation .........................................................................86
xviii
4.4 Conclusions and policy recommendation .........................................................................87 Chapter 5 The Economic Impact of Climate Change on Crop production in the Dry Zone of Myanmar: A Ricardian Approach ......................................................................................90 5.1 Introduction......................................................................................................................92 5.2 Observed climate change patterns and an overview of agriculture in CDZ ......................92 5.3 Agro-ecological features of the sampled districts/ regions ...............................................93 5.4 Research Methodology ....................................................................................................94 5.4.1 Data ..........................................................................................................................94 5.5 Theory: Ricardian analysis ..............................................................................................97 5.6 The Regression Results ..................................................................................................99 5.7 Predications of forecasted climate scenario on net farm revenue ($ per ha) ..................103 5.8 Conclusions and Recommendations..............................................................................106 Chapter 6 Summary, Conclusions, Implications and Limitations of the study ................108 6.1 Research objectives and conclusions ............................................................................109 6.2 Limitations of the study and further research .................................................................112 6.2.1 Challenge 1: Risk theory and the nature of risk avoidance ......................................113 6.2.2 Challenge 2: Validity of indicators in the future and the application of climate change adaptation strategies........................................................................................................113 6.2.3 Challenge 3: Macro-level assessment tool (Ricardian model) looks at the micro-level ........................................................................................................................................114 6.2.4 Challenge 4: location specific but need to assess with more rigorous data .............115 6.2.5 Challenge 5: Noticing institutional barriers but how can implementation be achieved? ........................................................................................................................................115 6.3 Policy recommendations................................................................................................116 6.4 Concluding remarks.......................................................................................................120 References: .........................................................................................................................121 Annex ..................................................................................................................................137 Annex Table A: Major components and sub-components in LVI assessment ...................137 Annex Table B: Major domains and sub-domains in SeVI assessment ............................140 Annex Table C: Rotated Components Matrix Scores for SHF (n=96) and LHF (n=82) .....142 Questionnaire ......................................................................................................................143 Scientific Curriculum Vitae ...................................................................................................154
xix
List of the figures Figure 1. Vulnerability of areas and Regions/states to climate change-related increases in intensity and severity of extreme weather events ....................................................................... 4 Figure 2. Average annual rainfall and temperature on some selected meteorological stations in CDZ and Ayeyarwaddy regions, Myanmar (1995-2015) ............................................................. 7 Figure 3. IPCC framework for assessing vulnerability to climate change ...................................11 Figure 4. Map of Myanmar ........................................................................................................18 Figure 5. Scope of the thesis.....................................................................................................22 Figure 6. Maps showing the location of Pyapon District ............................................................29 Figure 7. Major components (spider diagram) and contributing factors (triangular diagram) of LVI (a and b) and SeVI (c and d) in Pyapon District, Myanmar..................................................44 Figure 8. Overall vulnerability index scores and IPCC-dimensions scores for LVI (a) and SeVI (b) for Dedaye, Pyapon, and Bogale Townships in Pyapon District ...........................................46 Figure 9. Histogram showing the landholing of the sampled farmers in Pyapon district, Ayeyarwaddy delta of Myanmar ................................................................................................56 Figure 10. Scree plot of Eigen values and number of principal components (SHF n=96) ..........59 Figure 11. Scree plot of Eigen values and number of principal components (LHF n=82) ...........59 Figure 12. Decomposition of the household vulnerability index (HVI) by landholding ................63 Figure 13. Map showing the study areas, Magwe District, Dry Zone region of Myanmar ...........72 Figure 14. Average Monthly Rainfall and Temperature (A: 1990-2012) and Mean Temperature and rainfall (B: 2004-2013) of Magwe District, Dry Zone Area of Myanmar ...............................74 Figure 15. Comparison of crop production areas of major crops (total acreage) for the period of 2010 and 2014 in the study areas .............................................................................................79 Figure 16. Farmers’ perceptions about the impact of climate change ........................................81 Figure 17. Comparison of current (A) and past (B) climate change adaptation strategies (The Yaxis shows the different adaptation strategies taken by farmers) ..............................................83 Figure 18. Barrier to climate change adaptation (The Y-axis shows the different barriers to climate change adaptation faced by farmers) ............................................................................87 Figure 19. Map of the Dry Zone region in Central Myanmar ......................................................95 Figure 20. Farm revenue of crops ($) per hectare .....................................................................96
xx
List of the tables Table 1. Key differences between the LVI and SeVI index approaches.....................................27 Table 2. Rice Production Area under Salt Intrusion and flooding conditions (2014)...................30 Table 3. Sub-component values and minimum and maximum sub-component values of SeVI for Dadeye, Pyapon and Bogale Township, in the Pyapon district, Myanmar .................................33 Table 4. Sub-component values and minimum and maximum sub-component values of LVI for Dadeye, Pyapon and Bogale Township, in the Pyapon district, Myanmar .................................35 Table 5. Indexed sub-components, major components and overall SeVI scores for Dedaye, Pyapon and Bogale Township, in the Pyapon district, Myanmar ...............................................38 Table 6. Indexed sub-components, major components and overall LVI scores for Dedaye, Pyapon and Bogale Township, in the Pyapon district, Myanmar ...............................................42 Table 7. Comparison of the degree of LVI and SeVI vulnerability in Pyapon district, Myanmar .47 Table 8. Principal Component Analysis for SHF (n=96) and Total Variance Explained .............60 Table 9. Principal Component Analysis for LHF (n=82) and Total Variance Explained ..............61 Table 10. Comparison of the degree of HVI of farm households in Pyapon district, Myanmar ...62 Table 11. Adoption of Climate Adaptation Strategies by farm households in Pyapon District, Myanmar ...................................................................................................................................65 Table 12. Description of the Independent variables ...................................................................75 Table 13. Cropping patterns of farmers in the study areas (10 years ago and currently) ...........80 Table 14. Parameter estimates of the Multinomial Logit Regression Model for climate change adaptation decisions (n= 212) ...................................................................................................85 Table 15. Descriptive statistics: Variables for net revenue regression model ..........................100 Table 16. Determinants of net farm revenue per hectare ($ ha-1) ............................................101 Table 17. Projected changes in temperature and rainfall by three climate models ..................104 Table 18. Marginal effect of temperature and precipitation on net revenue per hectare ($ ha -1) ...............................................................................................................................................105 Table 19. Climate predictions of SRES models from 2020-2100 years ...................................105
xxi
Therefore, in the following section, the differences in climate change effects in these two focus zones are explicitly presented. 1.2.1.1 Delta areas: The occurrence of sea-level rise, salt water intrusion, and flooding Myanmar lies along the border of the Bay of Bengal and the Andaman Sea. It is prone to multiple natural hazards, and disasters. During the period between 1887 and 2005, 1,248 tropical storms were reported in the Bay of Bengal, of which 80 hit the coast of Myanmar. Before 2000, cyclones reached Myanmar’s coast once every three years, but since 2000, storms have become more frequent, such as once a year (HPM 2009, RIMES 2011). Examples of cyclones that have recently hit are Cyclone Mala (2006), Nargis (2008), Giri (2010), Viyaru and Phailin (2013) Komen (2015), Roanu, Dianmu, and Kyant (2016) and Maarutha and Mora (2017)5. The most devastating cyclone was Cyclone Nargis, which hit the Ayeyarwaddy Delta region and led to the loss of more than hundred thousand human lives (the estimated death toll was 184,000), damaged 9.88 million acres of rice (57 percent of the total annual rice production areas) and caused a total estimated loss of 10 billion USD6 (HPM 2009, MoSWRR 2017). The Ayeyarwaddy delta is one of the most vulnerable regions to flooding and saltwater intrusion (Driel and Nauta 2013, Boutry et al. 2017, Tun Oo et al. 2018). In recent decades, the duration of daily and monthly tidal influences has increased, making fields more saline and prone to pest infestation. This has resulted in yield reductions and increases in farmers’ debt (Hallegatte et al. 2016). Measures have been taken over recent decades to protect the rice farms from salt water intrusion through the construction of polders provided with embankments, sluice gates, and drainage systems. Much of this infrastructure was damaged by cyclone Nargis in 2008. Even a decade after the disaster the recovery is still far from complete (World Bank 2014a). The damage to embankments and streams are the limiting factors that delay the rehabilitation process and affect farmers in the delta areas of Myanmar. MOAI (2015) reported that many of the damaged rice areas still remain prone to saltwater intrusion, even in the monsoon season. It is important that the government takes adequate measures to reduce the vulnerability of people living in the coastal regions of Myanmar and to safeguard their livelihoods. The delta zone in Myanmar has often suffered from excessive water during the monsoon season, with massive rainfall and sea level rise which create the problem of “stagnant flooding” (ADB 2015). This leads to the loss of agricultural productivity and land (Slagle 2014) and also threatens the livelihoods of people living near the coast and alongside estuaries (Driel and Nauta 2013, Wong et al. 2014). According to UNDP (2007), 5 6
https://en.wikipedia.org/wiki/List_of_tropical_cyclones_that_affected_Myanmar United States Dollar ($)
5
the baseline risks of cyclones, sea level rise and floods on the Myanmar economy are all high and it is projected to be much higher in the future. According to Weiss (2009), Myanmar is highly vulnerable to climate change events because climate exposure is high, while adaptive capacity is quite low. Along the entire coast of Myanmar, the sea levels are projected to rise between 5-13 cm in the 2020s, increasing to 20-41 cm in the 2050s, and 37-83 cm in the 2080s (Horton et al. 2017). Therefore, several reports highlighted that the delta areas of Myanmar will become more vulnerable in the future, with rising sea levels affecting the coastline causing flooding and saltwater intrusion. Several studies have looked assessed flood recurrences and looked at impacts on socioeconomic characteristics of societies or communities (HPM 2009, RIMES 2011, NAPA 2012, Horton et al. 2017). 1.2.1.2 Central Dry Zone: The occurrence of drought and extreme day temperature Myanmar differs geographically from south to north. In the center of Myanmar, there is a huge valley, which is approximately 600 km7 long and 110 km wide and is situated about 600-750 km from the coast. This valley is named the central dry zone (CDZ). The area receives most of its rainfall from the southwest monsoon and lies in the rain-shadow areas of the Bago and Yakhaing Yoma mountains, which block much of the precipitation (HPM 2009). This valley has a semi-arid climate, with an average rainfall ranging from 508 to 1016 mm per annum, but with high variability and uneven distribution. The area is semi-desert and is dominated by thorny trees and shrubs. The CDZ covers three regions: Sagaing, Mandalay and Magwe. The CDZ occupies two thirds of the agricultural lands in the country (approximately 54,390 square kilometers) and represents about 10% of the country’s total land area (JICA 2013, MIP 2015). The average mean temperature is about 270C and in the summer period, the temperature often rises to about 430C (HPM 2009). The rainfall pattern is bimodal, with a dry spell occurring in July during the rain-fed crop growing season. The monsoon onset is in May (early wet season or pre-monsoon) and higher rainfall is observed from August to October (late wet season or post-monsoon). This rainfall pattern has allowed farm households to practice a double crop system, meaning that farmers can grow crops twice on the same plot each year (MOAI 2015). For the dry zone, the potential hazard level for drought and extreme day temperatures was classified as high (NAPA 2012, CEDMHA 2014, NAPA 2012). The recorded historical rainfall data show that the onset of the monsoon has withdrawn since the 1970s, while the duration has reduced by three weeks in northern Myanmar and one week in other parts of the country (Lwin 2002, HPM 2009). In the central dry zone (Sagaing, Magwe and Mandalay regions), the average temperature (max) is higher than in the rest of the country and in the Ayeyarwaddy delta. 7
Kilometer
6
However, the CDZ receives a lower amount of rainfall than the rest of the country (See figure 2).
Temperature C-Mean max.
Temperature C-Mean min.
Average rainfall (mm)
3500
40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
3000 2500 2000 1500 1000 500 0 Sagaing (Monywa)
Magwe (Magwe)
Mandalay (Nyaung Oo)
Ayeyarwaddy (Pathein)
Temperature (0C)
Annual Rainfall in Millimeter
National average (30 stations)
Some selected meteorological stations in CDZ and Ayeyarwaddy regions, Myanmar Figure 2. Average annual rainfall and temperature on some selected meteorological stations in CDZ and 8 Ayeyarwaddy regions, Myanmar (1995-2015)
The CDZ region is typically dry and household water availability remains uncertain. The majority of the households in the dry zone utilize rain water for domestic and household consumption. Moreover, water resources from reservoirs, rivers and ground water are also consumed by the people in the region (Slagle 2014). Hallegatte et al. (2016) highlighted that in the dry zone, the water availability is related to the amount of rainfall received but is determined by seasonal cycles, and evaporation rates and other hydrological systems. During the 1980s-1990s, the frequent occurrence of drought years of moderate intensity was observed, with extended dry seasons and increased temperatures in Myanmar. Between 1990 and 2002, droughts were more severe and occurred more frequently (NAPA 2012). Recently, in 2010, severe drought diminished village water resources throughout the country and impacted on agricultural yields of several crops, most noticeably in CDZ (Yi et al. 2012, RIMES 2011, MOAI 2015). Slagle (2014) said that, in the dry zone of Myanmar, the increasing occurrence of droughts is brought about by a shorter monsoon season, and an El Nino year which is likely to exacerbate the negative effects. Seasonal water scarcity, decreasing rainfall and increased occurrence of droughts are the major risk factors affecting agricultural livelihoods, farm households and food security for the people in the CDZ (WFP 2011). A clear trend in rising temperatures has been observed in Myanmar (Drakenberg and Wolf 2013). Over the past three decades, groundwater irrigation and river pumping schemes 8
Source: Department of Meterology and Hydrology, Yangon, Myanmar
7
seasons (Tun 2015). Increasing frequencies of extreme events are reported in Myanmar (IPCC 2014b). Climate change projections show that Myanmar is likely to face temperature increases between 10C and 40C by the end of the century, though the projected outcomes may vary throughout the year and spatially across the country. In addition, the average rainfall will increase by around 10% over the coming decades, mostly in the monsoon season (RIMES 2011, World Bank 2012, NAPA 2012). In Myanmar, there are 14 regions and states (MIP 2015). In figure 1, the vulnerability of areas and regions/states to climate change related events and natural hazards is presented. Bascially, the lower part of the country is vulnerable to sea level rise, floods and cyclones, while the upper part of the country and the central dry zone are vulnerable to increasing occurance of droughts, and extreme day temperature (HPM 2009, RIMES 2011, NAPA 2012).
Figure 1. Vulnerability of areas and Regions/states to climate change-related increases in intensity 4 and severity of extreme weather events
The following section describes the most noticeable climate change impacts in Myanmar. This thesis focusses on two distinct geographical zones in Myanmar, the dry zone and delta zones. While, the dry zone is facing recurrent droughts, the coastal areas are facing more cyclones, sea level rise and thus flooding with all possible consequences.
4
Source: NAPA 2012 (Edited by author)
4
appear especially vulnerable to increased food insecurity (FAO, IFAD and WFP, 2015, Rosenzweig et al. 2014, FAO 2016, Elbehri et al. 2015, IPCC 2014). The negative impacts of climate change on the productivity of crops, livestock, fisheries and forestry will be more severe in all regions after 2030 (IPCC 2007, IPCC 2014, Hallegatte et al. 2016, FAO 2016, FAO, IFAD, UNICEF, WFP and WHO, 2017). In addition, the projected world population is estimated to increase to 9 billion in 2050 (Lutz et al. 2014, IFAD 2014). The ambition to feed the entire world population is therefore challenged by the projected population increases and the increasing impacts of climate change on food production systems (IPCC 2014, FAO, IFAD, and WFP, 2015, Elbehri et al. 2015). In most developing countries, agriculture is the mainstay livelihood and source of household income. The adverse effects of climate change on agricultural productivity will first reduce farm earnings (lower returns over investment generate lower profits). In addition, climate change will indirectly affect rural non-farm households’ earnings, for instance, through the labour markets. In cases where climate change affects agricultural productivity and prices, rural wages, or market wages, will be affected and the earnings of non-farm households will be affected indirectly by climate change (Hertel and Bosch 2010, IPCC 2014). Overall, climate change is a challenging issue for farm households in most developing countries. 1.2.3 Impacts of climate change on Myanmar’s agriculture According to the World Bank (2012), Myanmar’s agriculture is highly likely to experience hydro-climatic extremes due to climate change. In the central dry zone of Myanmar, the main cause of crop failure is the unpredictability of rainfall and the frequency of droughts, which has led local inhabitants to increase their resilience and apply climate change adapation measures (Boutry et al. 2017). In the LIFT Dry Zone livelihood study, farmers reported that extreme weather events were shocks for agricultural crop production in the region (LIFT 2013). Again, in a household survey of food security assessment in Rakhine and the Central Dry Zones, climate change and extreme events, such as flash floods, landslides, and droughts are considered by farmers to be the main agricultural constraints (WFP 2011, LIFT 2012). The impacts of changes in rainfall and temperature are highest for subsistence farming households with rain-fed agriculture. In Myanmar, 80 percent of rice production is under rain-fed farming. The increasing occurrence of drought and change in rainfall patterns affect rice production in Myanmar. Not only shortages of food crops but also animal feed shortages have occurred due to the prevalence of prolonged droughts or flash floods (NAPA 2012).
9
In the delta area, farming systems are dominated by paddy (rice) production. The land productivity in the delta areas is determined by the agro-ecological conditions and by water salinity. In the Ayeyarwaddy delta, prior to the passing of Cyclone Nargis, there were about 318 flood protection works, which protected a total of 2.96 million acres of cultivable land. During Cyclone Nargis in 2008, many of these structures were badly damaged, leaving much of the most productive parts vulnerable to flooding and saltwater intrusion (Driel and Nauta 2013). Therefore, large areas of the delta can now only produce monsoon paddy, as summer paddy became impossible to cultivate (Boutry et al. 2017). In addition, compared to the rest of the country, the average production cost of monsoon paddy in the delta area is higher in term of labor requirements and the post-harvest losses are larger due to climatic and flooding accidents (Driel and Nauta 2013, Boutry et al. 2017). The impact of flooding and saltwater intrusion on agriculture in the Ayeyarwaddy delta region is already apparent, and these trends will continue because of due to the projected sea level rise, saltwater intrusion and flooding in the delta areas of Myanmar is expected to aggravate (Horton et al. 2017). Therefore, high vulnerability to climate change and natural hazards is one of the main causes of rural poverty in Myanmar (IFAD 2014, FAO 2013, MOAI 2015). Myanmar should aim to be better prepared to tackle the negative effects of climate change on agriculture and livelihoods by adopting climate change adaptation strategies and policies as well as introducing climate-smart agricultural practices. McKinley et al. (2015) suggested that adaptation strategies should be formulated based on the experience to deal with climate variability and extreme climate events, as well as being flexible, innovative and context-specific with the provision for contingency in Myanmar. Therefore, empirical research studies should be carried out in the dry zone and delta areas of Myanmar to increase understanding of farm households’ vulnerability to climate change and their coping strategies to reduce its negative effects at farm level. 1.2.4 Review of literature on climate change vulnerability assessment In the climate change literature, the concept of vulnerability has been abundantly used, and defined. IPCC (2007) defines vulnerability as the degree to which a system is susceptible to, and unable to cope with, the adverse effects of climate change. In the IPCC report, vulnerability is defined as the propensity or predisposition to be adversely affected and describes exposure and vulnerability as the determinants of risk (IPCC 2012). From a more simplistic view, vulnerability is a function of exposure, sensitivity and adaptive capacity (IPCC 2007, Hahn et al. 2009). In the IPCC fifth assessment report, vulnerability is redefined as the propensity or predisposition to be adversely affected, and it encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt (IPCC 2014). This study adopted the definition of vulnerability used in the 10
IPCC assessment report, and the adaptive capacity of a system or community is taken into account in the climate change vulnerability assessment (see figure 3). According to Füssel (2009), the relationship between the three dimensions of climate vulnerability is not specified adequately by the IPCC definition on climate vulnerability. In terms of conceptual definitions, a common increase in exposure and sensitivity would increase the vulnerability of an element, but a decrease would reduce vulnerability. However, adaptive capacity has the opposite effect on vulnerability, which means that an increase in adaptive capacity would reduce vulnerability and vice versa (Diouf and Gaye 2015). Hence, adaptive capacity does not change the combined effects produced by exposure and sensitivity, but rather lessens their impact (Diouf and Gaye 2015).
Sensitivity
Vulnerability
Potential impacts
Exposure
Adaptive capacity Figure 3. IPCC framework for assessing vulnerability to climate change
9
Therefore, the vulnerability assessment is a process of assessing, measuring and/or characterizing the exposure, sensitivity and adaptive capacity of a natural or human system to disturbance (Deressa et al. 2009). Climate change vulnerability has been assessed in local-scale studies (Deressa et al. 2009, Pandey and Jha 2012) and global-scale studies (Yohe et al. 2006, Allison et al. 2009), while other studies have applied it at the national or subnational scale (O’Brien et al. 2004, Malone and Brenkert 2008). There are many methods available for assessing the climate change vulnerability of a specific community or society. In the assessment of climate change vulnerability, the most common analyses are econometric and indicators approaches (Deressa 2010).
9
Source: Johnson and Welch 2010 (Edited by author)
11
The main idea of the econometric methods is to estimate the probability of a household or vulnerability of a person cuased by given climatic shocks. This method can be used with primary household data and socioeconomic characteristics of household survey data to analyse the vulnerability level of different social groups or communities. This method can be further categorized into three: vulnerablity as low expected utility, vulnerability as expected poverty and vulnerability as uninsured exposure to risk (Hoddinott and Quisumbing 2003). More detail explanation of the individual method can be found at Deressa (2010) and Hoddinott and Quisumbing (2003). The main idea of indicator approach is based on selecting some indicators from a set of potential indicators, in which the selected indicators are systematically combined to point out the level of vulnerability (Deressa 2010). Indicator systems are simple and developed to measure adaptive capacity and identify entry points for enhancing it (Adger et al. 2009, ASC 2011, Swanson et al. 2012). Therefore, indicator-based approaches are commonly used to assess the climate vulnerability of a system or society and are used for assessment at all scales to aggregate data into vulnerability indices (Hinkel 2011). However, the indices or selected variables are very site-specific and vary between regions. By using indicator approach, climate change vulnerability has been analyzed under different contexts, and can be assessed exclusively from a climate perspective at local (Adger 1999); national (O’Brien et al. 2004, Diouf and Gaye 2015); regional (Vincent 2004) and global scales (Brooks et al. 2005). Most vulnerability assessment indicators are used to indicate how vulnerable a system or community is and generally it is a single measurement of characteristics (Hinkel 2008, Deressa 2010, Hinkel 2011). Many researchers have developed indices or index approaches to better quantify and assess vulnerability at regional or global scale. Yohe et al. (2006) pointed out that assumptions for the concepts of climate vulnerability are critical and, thus, indices could vary with different assumptions regarding climate sensitivity, development of adaptive capacity and other calibration parameters. Thus, in the assessment of climate vulnerability, questions have been asked concerning the validity of the index, or the formulation of the index (Hinkel 2011). For calculating the level of vulnerabilitiy using this method, there are two options. The first option is assuming that all indicators of vulnerability have equal importance and thus gives them equal weights (Cutter et al. 2003, Sullivan et al. 2002, Hahn et al. 2009). The second option is assigning different weights given the diversity of indicators used (Ahsan and Warner 2014). In the case of unequally weighted indicators, many methodological approaches can be used (Deressa 2010). Methodological approaches in a number of studies include the use of expert judgment (Kaly and Pratt 2000), principal component analysis (Cutter et al. 2003) or correlation with past disaster events (Brooks et al. 2005). Vulnerability assessments using indicator or index methods have some limitations. Diouf and 12
Gaye (2015) pointed out that there are three main limitations regarding the formulation of indices. The first limitation is the potential inappropriate nature of the relationship posed by the indices, while the seond limitation is related to the incumbent relationship of these indices upon aggregation. The third limitation is related to the local specificities in the formulation of national indices. 1.2.5 Review of literature on climate change adaptation in agriculture IPCC (2014a) defines resilience as the capacity of social, economic and environmental systems to cope with a hazardous event, trend or disturbance, by responding or recognizing ways that maintain their essential function, identity and structure, while also maintaining the capacity for adaptation, learning and transformation. Climate change adaptation is the response (adaptation) of a community or society to climate change in order to reduce the climate change vulnerability (IPCC 2014, IPCC 2014a). When communities or societies are affected by the impacts of climate change, they have adjusted or coped with the negative effects of climate change, climate variability, and extreme events as well as finding adaptation options for their communities. Agronomic adaptation is important for farm households as it could reduce the negative effects of climate change on crop production. When compared to the situation without adaptation, a meta-analysis of crop simulation under several climate scenarios found that improvements in agricultural management would increase crop yields (wheat, rice and maize) by about 7-15 percent on average (Challinor et al. 2014). According to IPCC (2014), about 15-18 percent of current crop yields of wheat, rice and maize could be improved by agronomic adaptation or farm-level adaptation. The success of adaptation measures to reduce the negative effects of climate change on agriculture is determined by many factors, and by their implementation processes. Adaption actions can be preceded by peer processes from individuals to governments, but adaption planning can be enhanced through complementary actions across levels (IPCC 2014a). At the farm level, the perceptions of farmers on climate change are translated into agricultural decisions (Bryant et al. 2000). However, Maddison (2007) argued that if farmers learn gradually about climate change, they might also learn gradually about the best techniques and adaptation options available. Nhemachena and Hassan (2007) said that a better understanding of farmers’ perceptions regarding long-term climate changes, and climate change adaptation strategies is necessary for successful adaptation in the agriculture sector. Maddison (2007) and Deressa et al. (2010) mentioned that there are two key components of adaptation: perceptions of climate change and adoption of climate change adaptation strategies. Perceptions of farmers about climate change shape the application of 13
adaptation strategies (Mertz et al. 2009, Nhemachena and Hassan 2007). Thus, Bryan et al. (2009) stated that perceiving climate change is the most important factor in the adoption of climate change adaptation strategies. In addition, the likelihood of adopting adaptation strategies is determined by the level of perceived risk associated with the capacity to adapt to climate change (Hisali et al. 2011). Therefore, it is necessary to understand perceptions in the context of climate change for the successful implementation of climate change initiatives (Byg and Salick 2009). The success of climate change adaptation is also becoming important in terms of investment in adaptation. The very first important point to climate change adaptation is societal recognition, understanding what to do and grappling with the complexities of adaptation (WICCI 2011). The chances of success can be increased through adequate consideration of a range of factors, when deciding upon adaptation strategies (Bowyer et al. 2014). The best way for climate change adaptation to be successful is through the implementation of various different options (Bowyer et al. 2014). However, the benefits of adaptation measures can be observed in the short or long term. WICCI (2011) stated that climate change adaptation strategies will provide few direct benefits to those who undertake them, but they mostly have indirect benefits for the future. Because it is assumed that climate change is a problem in the future and climate adaptation is of benefit in the future, the benefits of climate change adaptation actions are not likely to be observed immediately, especially in the mega-project adaptation processes. Berger and Troost (2013) highlighted that micro-level assessments should be carried out, because the assessment could take into account heterogeneity and interactions between smallholder farmers so that the full distribution of constraints, opportunities and responses for smallholder agriculture can be captured. There are many models available for assessing climate change adaptation effects on agriculture. The most common approaches are agronomic-economic models, and discrete choice models (Deressa 2010). The agronomic-economic models allow incorporation of the effects of farm management (adaptation) options in the analysis between crop yields and climatic factors such as temperature and rainfall. The advantages of these models include understanding farmers’ adaptation responses to changing climate, and the physical and biological responses. However, the main limitation of the agronomic-economic models is the very high cost associated with experimentation (Cheng and Long 2007, Deressa 2010). In climate change adaptation research, the use of discrete choice models is relatively new (Deressa 2010), but it benefits from computational simplicity in calculating the farmers’ choices of climate change adaptation possibilities (Hassan and Nhemachena 2008, Bryan et al. 2009). This study adopts the use of discrete choice models to assess climate change adaptation strategies of farm households in Myanmar. The discrete choice models are quite 14
similar to agricultural technology adoption models, or decision making models, where the basic concept is whether to adopt or not to adopt the introduced technology (Deressa 2010). In these models, the user’s profit maximizing or utility behaviors are central and the user adopts a technology only when their perceived utility from using it increases (Pryanishnikov and Katarina 2003, Deressa 2010). The most common models for analyzing such decision data are probit and logit models. When the number of choices available are two (whether or not to adopt), binary probit or logit models are applied, while multinomial logit and probit models are employed when the number of choices available is more than two (Deressa 2010). In the climate change adaptation assessment, both multivariate models are often applied and the advantages of using these models are the models’ capabilities for selfselection and interactions between alternatives and for exploring both factors, conditioning specific choices or combinations of choices (Deressa 2010, Nhemachena and Hassan 2007, Seo et al. 2009, Bryan et al. 2009). 1.2.6 Methods for assessing the impacts of climate change on agriculture According to Hertel and Rosch (2010) and Rötter and Höhn (2015) there are three main approaches for assessing the impacts of climate change on crop yield and agriculture. These are crop growth simulation models, statistical crop weather models (statistical studies) and cross-sectional models, or the Ricardian approaches. Crop growth simulation models assess the impacts of climate change on agricultural productivity. They are typical process-based models with extensive data requirements. The approaches have not yet been validated on a global basis, but are mostly simulated with a highly calibrated, field-based approach (Deressa 2010, Challinor and Wheeler 2008). However, they have increasingly been applied to evaluate climate change scenarios (Challinor et al. 2014). The users can simulate the impact of climate change on agricultural productivity with varying temperature and precipitation inputs. Crop growth rates are separately calculated, while management factors are also considered, and the resulting simulated crop growth and management factors are included in the model. The main strengths of crop simulation modeling are the possibility of using daily recorded temperature data, as crop growth is simulated by crop growth stage, and its simplicity for users to specify crop varieties, and fertilizer applications and irrigation availability, as well as other farm-level adaptation strategies (Hertel and Rosch 2010). In statistical approaches, the relationship between crop yields, on the one hand, and temperature and precipitation, on the other, are estimated. These models are not process based. However, many statistical models have predictors and a structure that is informed by process understanding (Lobell and Asseng 2017). They rely on predicting future responses based on past relationships (Schlenker and Lobell 2010). The approach does not require 15
large datasets and can be used with cross-sectional data or time series data. It can also be implemented at a global scale or for large geographic areas, such as nations or continents, because the model does not take into account changes in varieties grown and other agroecological choices (White et al. 2011, Lobell and Asseng 2017). One of the limitations of this approach, when cross sectional data are used, is that omitted variables might cause biased parameter estimates. However, time-series data are not available in most regions of the world. In addition, the model does not fully calibrate for the climate change parameters, as it is only estimates the short-term impact of climate change (McCarl et al. 2007, Hertel and Rosch 2010). Finally, the greatest limitation of this approach is that it does not consider the adaptation responses. In climate change impact studies, the most common approaches for assessing climate change impacts on agriculture are cross-sectional models. For example, the Ricardian cross-sectional approach explores the relationship between agricultural capacity (measured by land revenue or crop net revenue) and climate variables (usually temperature and precipitation) on the basis of statistical estimates from farm survey or country-level data (Mendelsohn et al. 1994, Zhai et al. 2009). The Ricardian model is one of the cross-sectional models in the climate change adaptation assessment, but it is also used for climate change impact assessment in agriculture. It has been widely applied by numerous researchers to assess the impact of climate change by incorporating climate change adaptation measures, using cross-sectional data analysis under different climatic conditions (Zhai et al. 2009). The Ricardian model was spearheaded by Mendelsohn et al. (1994). Mendelsohn et al. (1994) assumed that farmers will vary their mix of activities to achieve the highest possible yield or return from agriculture on any given parcel of land. This model examines the relationship between the value of land, or net revenue, over the given amount of land and the agro-climatic factors (Kurulasuriya and Mendelsohn 2008, Seo and Mendelsohn 2008, Deressa et al. 2009, Deressa 2010, Seo et al. 2009). The model is also useful for assessing the long term economic value of climate on any given parcel of land, by associating climate variation in the cross-sectional data. The advantages of the Ricardian model are its cost-effectiveness in carrying out analysis using secondary data on cross-sectional sites and its ability to incorporate private adaptations. (Mendelsohn et al. 1994, Mendelsohn et al. 2007, Deressa 2010). Moreover, adaptation responses can be embedded within the context of profit maximizing responses on the part of representative farmers (Zhai et al. 2009, Hertel et al. 2010). In addition, this approach assumes that there is long term equilibrium in factor markets (especially land) but that there is no adjustment costs, so that land rents fully reflect the value of climate at any given location. In most cases, the value of land is not available, especially in most developing countries. In this case, the total return or crop yield over the 16
given price can be used as a proxy for the value of land (Mendelsohn et al. 2007). This model has been applied at a global scale, and at an individual level, with farm data and with district-level data. Again, the approach has some limitations. In cases where cross-sectional data is used, the approach might also suffer from omitted variable bias (Hertel and Rosch 2010). In addition, this approach does not account for price changes and any impacts of other variables that might affect farm incomes in the future (Cline 1996, Mendelsohn et al. 2007, Zhai et al. 2009). Nevertheless, this approach has been widely used in the assessment of climate change impacts on agriculture, not only in terms of macro-level assessment, but also micro-level assessment (Mendelsohn et al. 2007, Deressa 2010, Hertel and Rosch 2010).
1.3 Objectives, scope and methodology 1.3.1 Thesis objectives The main objective of this study is to examine farm households’ vulnerability to climate change and to understand their perceptions concerning the impacts of climate change and to investigate climate change adaptation strategies at farm level in Myanmar. The study includes the following intermediate objectives. 1) To evaluate the livelihoods and socioeconomic conditions of farm households in delta areas of Myanmar 2) To evaluate the factors influencing farm households’ vulnerability and their climate change adaptation strategies 3) To investigate farmers’ choices of climate change adaptation strategies and the determinant factors in the dry zone of Myanmar 4) To assess the economic impacts of climate change on agriculture in the dry zone 1.3.2 Site selection This section is about the selection of research survey areas and the idea behind it. The study was performed in two different regions of Myanmar: the Central Dry Zone and the delta areas. These areas were objectively selected as they are known to be climate change hot-spot regions in Myanmar (see Figure 4). Myanmar has a wide range of topography and climate and is abundantly rich in natural resources. The main objective for selecting these areas is prior knowledge of the areas. In addition, these areas are also populated by farmers, as this study has been objectively implemented using farm households and interviewed farmers. Also, about 43% of the country’s population inhabits the dry zone (Sagaing, Mandalay and Magwe regions) and Ayeyarwaddy regions (MIP 2015).
17
The main idea of the econometric methods is to estimate the probability of a household or vulnerability of a person cuased by given climatic shocks. This method can be used with primary household data and socioeconomic characteristics of household survey data to analyse the vulnerability level of different social groups or communities. This method can be further categorized into three: vulnerablity as low expected utility, vulnerability as expected poverty and vulnerability as uninsured exposure to risk (Hoddinott and Quisumbing 2003). More detail explanation of the individual method can be found at Deressa (2010) and Hoddinott and Quisumbing (2003). The main idea of indicator approach is based on selecting some indicators from a set of potential indicators, in which the selected indicators are systematically combined to point out the level of vulnerability (Deressa 2010). Indicator systems are simple and developed to measure adaptive capacity and identify entry points for enhancing it (Adger et al. 2009, ASC 2011, Swanson et al. 2012). Therefore, indicator-based approaches are commonly used to assess the climate vulnerability of a system or society and are used for assessment at all scales to aggregate data into vulnerability indices (Hinkel 2011). However, the indices or selected variables are very site-specific and vary between regions. By using indicator approach, climate change vulnerability has been analyzed under different contexts, and can be assessed exclusively from a climate perspective at local (Adger 1999); national (O’Brien et al. 2004, Diouf and Gaye 2015); regional (Vincent 2004) and global scales (Brooks et al. 2005). Most vulnerability assessment indicators are used to indicate how vulnerable a system or community is and generally it is a single measurement of characteristics (Hinkel 2008, Deressa 2010, Hinkel 2011). Many researchers have developed indices or index approaches to better quantify and assess vulnerability at regional or global scale. Yohe et al. (2006) pointed out that assumptions for the concepts of climate vulnerability are critical and, thus, indices could vary with different assumptions regarding climate sensitivity, development of adaptive capacity and other calibration parameters. Thus, in the assessment of climate vulnerability, questions have been asked concerning the validity of the index, or the formulation of the index (Hinkel 2011). For calculating the level of vulnerabilitiy using this method, there are two options. The first option is assuming that all indicators of vulnerability have equal importance and thus gives them equal weights (Cutter et al. 2003, Sullivan et al. 2002, Hahn et al. 2009). The second option is assigning different weights given the diversity of indicators used (Ahsan and Warner 2014). In the case of unequally weighted indicators, many methodological approaches can be used (Deressa 2010). Methodological approaches in a number of studies include the use of expert judgment (Kaly and Pratt 2000), principal component analysis (Cutter et al. 2003) or correlation with past disaster events (Brooks et al. 2005). Vulnerability assessments using indicator or index methods have some limitations. Diouf and 12
adaptation strategies (Mertz et al. 2009, Nhemachena and Hassan 2007). Thus, Bryan et al. (2009) stated that perceiving climate change is the most important factor in the adoption of climate change adaptation strategies. In addition, the likelihood of adopting adaptation strategies is determined by the level of perceived risk associated with the capacity to adapt to climate change (Hisali et al. 2011). Therefore, it is necessary to understand perceptions in the context of climate change for the successful implementation of climate change initiatives (Byg and Salick 2009). The success of climate change adaptation is also becoming important in terms of investment in adaptation. The very first important point to climate change adaptation is societal recognition, understanding what to do and grappling with the complexities of adaptation (WICCI 2011). The chances of success can be increased through adequate consideration of a range of factors, when deciding upon adaptation strategies (Bowyer et al. 2014). The best way for climate change adaptation to be successful is through the implementation of various different options (Bowyer et al. 2014). However, the benefits of adaptation measures can be observed in the short or long term. WICCI (2011) stated that climate change adaptation strategies will provide few direct benefits to those who undertake them, but they mostly have indirect benefits for the future. Because it is assumed that climate change is a problem in the future and climate adaptation is of benefit in the future, the benefits of climate change adaptation actions are not likely to be observed immediately, especially in the mega-project adaptation processes. Berger and Troost (2013) highlighted that micro-level assessments should be carried out, because the assessment could take into account heterogeneity and interactions between smallholder farmers so that the full distribution of constraints, opportunities and responses for smallholder agriculture can be captured. There are many models available for assessing climate change adaptation effects on agriculture. The most common approaches are agronomic-economic models, and discrete choice models (Deressa 2010). The agronomic-economic models allow incorporation of the effects of farm management (adaptation) options in the analysis between crop yields and climatic factors such as temperature and rainfall. The advantages of these models include understanding farmers’ adaptation responses to changing climate, and the physical and biological responses. However, the main limitation of the agronomic-economic models is the very high cost associated with experimentation (Cheng and Long 2007, Deressa 2010). In climate change adaptation research, the use of discrete choice models is relatively new (Deressa 2010), but it benefits from computational simplicity in calculating the farmers’ choices of climate change adaptation possibilities (Hassan and Nhemachena 2008, Bryan et al. 2009). This study adopts the use of discrete choice models to assess climate change adaptation strategies of farm households in Myanmar. The discrete choice models are quite 14
similar to agricultural technology adoption models, or decision making models, where the basic concept is whether to adopt or not to adopt the introduced technology (Deressa 2010). In these models, the user’s profit maximizing or utility behaviors are central and the user adopts a technology only when their perceived utility from using it increases (Pryanishnikov and Katarina 2003, Deressa 2010). The most common models for analyzing such decision data are probit and logit models. When the number of choices available are two (whether or not to adopt), binary probit or logit models are applied, while multinomial logit and probit models are employed when the number of choices available is more than two (Deressa 2010). In the climate change adaptation assessment, both multivariate models are often applied and the advantages of using these models are the models’ capabilities for selfselection and interactions between alternatives and for exploring both factors, conditioning specific choices or combinations of choices (Deressa 2010, Nhemachena and Hassan 2007, Seo et al. 2009, Bryan et al. 2009). 1.2.6 Methods for assessing the impacts of climate change on agriculture According to Hertel and Rosch (2010) and Rötter and Höhn (2015) there are three main approaches for assessing the impacts of climate change on crop yield and agriculture. These are crop growth simulation models, statistical crop weather models (statistical studies) and cross-sectional models, or the Ricardian approaches. Crop growth simulation models assess the impacts of climate change on agricultural productivity. They are typical process-based models with extensive data requirements. The approaches have not yet been validated on a global basis, but are mostly simulated with a highly calibrated, field-based approach (Deressa 2010, Challinor and Wheeler 2008). However, they have increasingly been applied to evaluate climate change scenarios (Challinor et al. 2014). The users can simulate the impact of climate change on agricultural productivity with varying temperature and precipitation inputs. Crop growth rates are separately calculated, while management factors are also considered, and the resulting simulated crop growth and management factors are included in the model. The main strengths of crop simulation modeling are the possibility of using daily recorded temperature data, as crop growth is simulated by crop growth stage, and its simplicity for users to specify crop varieties, and fertilizer applications and irrigation availability, as well as other farm-level adaptation strategies (Hertel and Rosch 2010). In statistical approaches, the relationship between crop yields, on the one hand, and temperature and precipitation, on the other, are estimated. These models are not process based. However, many statistical models have predictors and a structure that is informed by process understanding (Lobell and Asseng 2017). They rely on predicting future responses based on past relationships (Schlenker and Lobell 2010). The approach does not require 15
large datasets and can be used with cross-sectional data or time series data. It can also be implemented at a global scale or for large geographic areas, such as nations or continents, because the model does not take into account changes in varieties grown and other agroecological choices (White et al. 2011, Lobell and Asseng 2017). One of the limitations of this approach, when cross sectional data are used, is that omitted variables might cause biased parameter estimates. However, time-series data are not available in most regions of the world. In addition, the model does not fully calibrate for the climate change parameters, as it is only estimates the short-term impact of climate change (McCarl et al. 2007, Hertel and Rosch 2010). Finally, the greatest limitation of this approach is that it does not consider the adaptation responses. In climate change impact studies, the most common approaches for assessing climate change impacts on agriculture are cross-sectional models. For example, the Ricardian cross-sectional approach explores the relationship between agricultural capacity (measured by land revenue or crop net revenue) and climate variables (usually temperature and precipitation) on the basis of statistical estimates from farm survey or country-level data (Mendelsohn et al. 1994, Zhai et al. 2009). The Ricardian model is one of the cross-sectional models in the climate change adaptation assessment, but it is also used for climate change impact assessment in agriculture. It has been widely applied by numerous researchers to assess the impact of climate change by incorporating climate change adaptation measures, using cross-sectional data analysis under different climatic conditions (Zhai et al. 2009). The Ricardian model was spearheaded by Mendelsohn et al. (1994). Mendelsohn et al. (1994) assumed that farmers will vary their mix of activities to achieve the highest possible yield or return from agriculture on any given parcel of land. This model examines the relationship between the value of land, or net revenue, over the given amount of land and the agro-climatic factors (Kurulasuriya and Mendelsohn 2008, Seo and Mendelsohn 2008, Deressa et al. 2009, Deressa 2010, Seo et al. 2009). The model is also useful for assessing the long term economic value of climate on any given parcel of land, by associating climate variation in the cross-sectional data. The advantages of the Ricardian model are its cost-effectiveness in carrying out analysis using secondary data on cross-sectional sites and its ability to incorporate private adaptations. (Mendelsohn et al. 1994, Mendelsohn et al. 2007, Deressa 2010). Moreover, adaptation responses can be embedded within the context of profit maximizing responses on the part of representative farmers (Zhai et al. 2009, Hertel et al. 2010). In addition, this approach assumes that there is long term equilibrium in factor markets (especially land) but that there is no adjustment costs, so that land rents fully reflect the value of climate at any given location. In most cases, the value of land is not available, especially in most developing countries. In this case, the total return or crop yield over the 16
given price can be used as a proxy for the value of land (Mendelsohn et al. 2007). This model has been applied at a global scale, and at an individual level, with farm data and with district-level data. Again, the approach has some limitations. In cases where cross-sectional data is used, the approach might also suffer from omitted variable bias (Hertel and Rosch 2010). In addition, this approach does not account for price changes and any impacts of other variables that might affect farm incomes in the future (Cline 1996, Mendelsohn et al. 2007, Zhai et al. 2009). Nevertheless, this approach has been widely used in the assessment of climate change impacts on agriculture, not only in terms of macro-level assessment, but also micro-level assessment (Mendelsohn et al. 2007, Deressa 2010, Hertel and Rosch 2010).
1.3 Objectives, scope and methodology 1.3.1 Thesis objectives The main objective of this study is to examine farm households’ vulnerability to climate change and to understand their perceptions concerning the impacts of climate change and to investigate climate change adaptation strategies at farm level in Myanmar. The study includes the following intermediate objectives. 1) To evaluate the livelihoods and socioeconomic conditions of farm households in delta areas of Myanmar 2) To evaluate the factors influencing farm households’ vulnerability and their climate change adaptation strategies 3) To investigate farmers’ choices of climate change adaptation strategies and the determinant factors in the dry zone of Myanmar 4) To assess the economic impacts of climate change on agriculture in the dry zone 1.3.2 Site selection This section is about the selection of research survey areas and the idea behind it. The study was performed in two different regions of Myanmar: the Central Dry Zone and the delta areas. These areas were objectively selected as they are known to be climate change hot-spot regions in Myanmar (see Figure 4). Myanmar has a wide range of topography and climate and is abundantly rich in natural resources. The main objective for selecting these areas is prior knowledge of the areas. In addition, these areas are also populated by farmers, as this study has been objectively implemented using farm households and interviewed farmers. Also, about 43% of the country’s population inhabits the dry zone (Sagaing, Mandalay and Magwe regions) and Ayeyarwaddy regions (MIP 2015).
17
The research areas are essentially populated with the Bamars tribe and are fairly stable with no armed conflict. Ayeyarwaddy delta is close to Yangon, the economic capital city, while the central dry zone is situated near to Nay Pyi Taw, the new capital city. In addition, these areas are easily accessible by bus and boat. In Myanmar, the study areas are often affected by the adverse effects of climate change. Farm households in these areas are severely impacted by the negative effects of climate change. Thus, as the country’s climate is projected to shift dramatically in the coming decades, several reports have suggested making many interventions; and not only state-led climate change intervention, but also private-led assessments of climate change impacts on farm households and farmers’ response strategies to cope with increasing stresses (WFP 2011, Driel and Nauta 2013, Boutry et al. 2017, Horton et al. 2017).
Central Dry Zone
Ayeyarwaddy Delta
Figure 4. Map of Myanmar
10
10
Source: U.S. Central Intelligence Agency, http://www.lib.utexas.edu/maps/burma.html
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Abstract Sea level rise causes saltwater intrusion and flooding of agricultural land and ultimately threatens the livelihoods of farm households in the delta region of Myanmar. Empirical research on the effects of climate change on the delta's agriculture and an assessment of the vulnerability are becoming necessary. This study explores the vulnerability of farm households to sea level rise using two methods: the Livelihood Vulnerability Index (LVI), which is comprised of 37 indicators, and the Socioeconomic Vulnerability Index (SeVI), which contains 35 indicators. Interviews with 178 farmers were conducted in Bogale, Pyapon and Dedaye Townships in Pyapon District. In addition, 7 focus group discussions were performed, with at least 2 discussions in each Township. Both index approaches identify Bogale to be the most vulnerable Township, followed by Dedaye and Pyapon Townships. Following the LVI approach, Bogale Township has the highest sensitivity to climate effects and the highest exposure to natural hazards, but also a higher adaptive capacity than the other townships. In contrast using the SeVI approach, Bogale was found to have the highest sensitivity and exposure to natural hazards but the lowest adaptive capacity score. The study found that the climate change adaptation measures taken by the farmers are important to limit vulnerable to the adverse effects of climate change and thus promotion of the adaptive capacity of farmers is important for the delta region of Myanmar. Keywords: Flooding; Saltwater intrusion; Vulnerability; Livelihood and Socioeconomics
This chapter is published as: Tun Oo, A., Van Huylenbroeck, G. and Speelman, S. (2018). ‘Assessment of climate change vulnerability of farm households in Pyapon District, a delta region in Myanmar’, International Journal of Disaster Risk Reduction, Vol. 28, pp. 10–21. https://doi.org/10.1016/j.ijdrr.2018.02.012 24
1.3.3 Data collection method This study used survey techniques and both qualitative and quantitative approaches in order to produce meaningful information. The questionnaires are designed to capture mostly quantitative data, although the qualitative data are also essential to the research. In each empirical chapter, a detailed explanation of the research methods is provided. The combination of using qualitative and quantitation data collection methods was essential in this research. A survey was conducted in two phases during the period. The first survey was conducted in Magwe region in the dry zone and Pyapon district in Ayewarwaddy region during the period December 2014 to February 2015. A total of 212 farm households were interviewed in Magwe district in Central Dry Zone and a total of 178 farm households were interviewed in Pyapon district in Ayeyarwaddy region. The localized study, or survey, was carried out to address the first, second and third study objective. The second survey was a cross-sectional survey and was implemented in Mandalay, Sagaing, and Magwe regions in the dry zone of Myanmar during the period February 2016 to April 2016. This region wide survey was conducted to address the fourth objectives of the study. A total of 425 farm households were interviewed. During the survey, five enumerators were hired due to the large data requirement with a given and limited time frame. The enumerators were trained. After the data collection, all data were considered as raw data, checks were undertaken and responses with missing or incomplete data were discarded. The data was first entered into an Excel spreadsheet in the field, whenever electricity was available. To avoid data mismatches, and omissions in the data entry process, data were verified, coded, classified, cleaned and later entered into SPSS. The SPSS version 22, 23 and 24 are used for the data analysis. This study used multiple data analysis approaches or techniques that are separately presented in the following sections. 1.3.4 Approaches and methods of the study This study adopts several methodologies to study climate change vulnerability and farmers’ choice of climate change adaptation strategies. Firstly, farm households’ vulnerability to climate change-induced saltwater intrusion and flooding is analyzed by comparing
two
Socioeconomic
indicator
(index)
Vulnerability
Index
approaches
(Livelihood
approaches).
Secondly,
Vulnerability the
farm
Index
and
households’
vulnerability is calculated by using Household Vulnerability Index and is further analyzed using a principal component analysis to reduce the number of indicating variables necessary to describe the state of the system whose vulnerability is indicated. Thirdly, a discrete choice model, or multinomial logistic regression model, was used to evaluate the factors influencing farmers’ choice of climate change adaptation strategies. Finally, a Ricardian model is 19
employed to analyze the economic impacts of changing climate on farm households and to forecast the potential impacts of the projected climate change on agriculture in the region. The calculated results and findings are presented in descriptive form with tables and graphs or figures, as well as a tabular and numerical description of the data. Finally, the outputs were used in the interpretations and eventual drawing of conclusions. 1.3.5 Limitations of the research Although this study was carried out in line with the proposed research objectives, which have been developed from a wide range of theoretical and academic literature findings, and the research survey has been conducted using the abovementioned data collection methods, the study has a number of limitations. Given budgetary and time constraints, this study may possess a few shortcomings. The quantitative data did not allow collection of the information necessary to analyze constraints such as labor constraints, and climate change-related constraints. In some cases, the limitations are associated with the specificities of each of two very different contexts (Ayeyarwaddy and Dry Zone) in one single section of the questionnaires, which was encountered especially in the first survey. This study could not be completed within the given time frame unless the enumerators were trained and closely involved. Before the field survey, the enumerators were trained and tested with the set of questionnaires for about a month. However, the level of understanding of the questionnaires by enumerators could be questioned. Because their level of understanding on the concepts of climate change and the types of questionnaires may not be perfect, some limitations may remain. Nevertheless the data quality was checked regularly and ultimately entered into an Excel spreadsheet in the field, and high quality information was gathered. To evaluate farm households’ vulnerability and examine farmers’ uptake of climate change adaptation strategies to the adverse effects of climate change on farming, this study considers a set of exogenous factors such as changes in temperature and precipitation, as well as flooding and saltwater intrusion along with the rise of sea level. However, this study does not take into account other exogenous factors such as extreme natural events such as cyclones, storms, El Nino, forest fires and earthquates, etc. Moreover, this study also does not take into account other global environmental change drivers such as rising CO2 and methane concentration in the atmosphere, changes in land use, grazing, pollution and pollutants deposition in soil and water, etc, although these drivers can all affect agriculture production. Moreover, the study is limited to climate change effects in the agricultural sector and does not take into account the impacts of climate change on ecosystems, biodiversity (fauna and flora), forest, public health and aquatic animals such as fish, and other natural resources. 20
1.3.6 Outline and organization of the thesis This introductory chapter explained the objectives and content of the study and provided an overview of the information collected, which will be used in the subsequent chapters. In addition, this chapter reviews the literature findings on climate change vulnerability and adaptation to climate change, and covers the conceptual framework for assessing climate change vulnerability and adaptation measures. Additionally, a brief overview of the research methodology and a brief presentation of the methodological framework used are presented. This dissertation is a compilation of individual studies in which each chapter provides insight into the above stated research objectives. This thesis is divided into four main chapters and one final chapter, before conclusions are drawn. The remaining five chapters are structured to examine different issues relating to climate change impacts, vulnerability and climate change adaptation strategies by farm households in Myanmar. In this regard, we carefully structured the content and outline of the presentation to have a minimum of repetition. Figure 5 provides a visual summary of this rationale, as it describes how climate change relates to vulnerability and effects on farms and how the adoption of climate change adaptation strategies influence farm households. In Myanmar, climate change negatively affects agriculture which, in turn, poses threats to crop revenues and the socioeconomic conditions of farm households (RIMES 2011, NAPA 2012, Slagle 2014, MoSWRR 2017). The climate change vulnerability of farm households is determined by the impacts of climate change (exposure) on a climate sensitive sector such as agriculture and by the adaptive capacity of farm households (Deressa 2010, Hinkel 2011, Douka et al. 2012). Depending on the level of climate change vulnerability, farm households perceive the negative consequences of climate change and climate change phenomena, such as changes in temperature and rainfall. Then, the farm households make decisions on whether or not to adopt climate change adaptation strategies. In practice, climate change vulnerability itself and the adoption of adaptation strategies are influenced by the socioeconomic characteristics of farm households and by institutional factors (Hassan and Nhemachena 2008, Deressa et al. 2009, Obayelu et al. 2014, Shongwe et al. 2014, Tun Oo et al. 2017). They are, moreover, influenced by the practical availability of adaptation methods and by barriers to their uptake (Deressa et al. 2009, Bryan et al. 2009, Tessema et al. 2013, Tun Oo et al. 2017). The level of climate change vulnerability of farm households and their application of climate change adaptation strategies will determine the impacts of climate change on net crop revenues and will shape the impacts of climate change on the economy of farm households in the future (see Mendelsohn et al. 2007, Kurukalasuriya and Ajwad 2007, Deressa and Hassan 2009).
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Chapters two and three evaluate the climate change vulnerability of farm households in the delta areas of Myanmar. Chapter four consists of empirical findings on the farmers’ uptake of climate change adaptation strategies and their determinants from the research in central Myanmar, while chapter five presents the economic impacts of climate change on crop agriculture for dry zone farmers in central Myanmar. Chapter six concludes, provides some policy recommendations and limitations of the study and further recommendations for future research studies on climate change in Myanmar.
Climate Change (CC) Impacts on Agriculture
Socioeconomic characteristics of farm households (FHs)/ barriers THESIS SCOPE: CHARACTERIZING FARM HOUSEHOLDS’ VULNERABILITY AND ADAPTATION
Adaptive capacity of FHs
Climate change vulnerability assessment
Climate change sensitivity Climate change exposure
Chapter 2 & 3 (Indicator approach) Climate change vulnerability
Climate change adaptation strategies
Climate change perception
Chapter 4 (Discrete choice model) Adoption of CC’ adaptation strategy
Potential impacts (future)?
Biophysical Impact assessment
Chapter 5 (Ricardian approach)
Institutional factors / barriers Figure 5. Scope of the thesis
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Chapter 2 Assessment of Climate Change Vulnerability of Farm Households in Pyapon District, a Delta Region in Myanmar
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Table 2. Rice Production Area under Salt Intrusion and flooding conditions (2014) Rice production area (hectare)
13
Pyapon
Bogale
KyikeLatt
Dedaye
District Total
Fresh Water Area (ha)
19963
33497
55721
22318
131500
Mixed Area (ha)
11429
19606
n.a*
17073
48109
Salt Affected Area (ha)
52828
72682
n.a*
32842
158353
Total (ha)
84222
125786
55721
72234
337964
Pyapon
Bogale
KyikeLatt
Dedaye
District Total
Monsoon rice under Flooding (ha)
73623
113625
50258
69466
306973
Severely flooded rice area (ha)
38526
560
1946
27295
68328
Mildly affected rice area (ha)
8722
169
n.a*
3388
12281
Monsoon rice production areas (ha)
* n.a (data is not available)
2.2.2 Data collection A farmer level survey was conducted in Bogale, Pyapon and Dedaye Townships the areas in Pyapon district most affected by climate-induced saltwater intrusion and flooding. To acquire an understanding of the overall local vulnerability conditions in the sampled areas, interviews were first conducted with six agricultural extension officers and three specialists. In each Township, the flood-affected and saltwater intruded villages were purposively chosen. As vulnerability is assumed to be site-specific, we focused on microlevel analyses and on the village areas to understand the latent vulnerability of farmers. At the village level, 10 households were also randomly selected and interviewed. By selecting 10 respondents in each village, understanding is gained about the impact of saltwater intrusion and flooding at individual farm level. Six villages from each Township were randomly chosen as the sample villages. A total of 18 villages from Pyapon district were selected. Structured questionnaires were used to interview farmers. Each survey took about an hour and 30 minutes. The questionnaires included questions about demographic characteristics, livelihoods, occupation and water management practices, food security, social networks and economic characteristics. The questionnaires were designed to capture the farmers’ vulnerability to natural hazards, and to formulate the indices for the assessment. This was completed with a broad range of literature findings, and information. Moreover, information about the farmers’ adaptation practices to flooding and saltwater intrusion was collected. When conducting the survey, an agricultural extension officer from the Township Department of Agriculture Service (TDAS) formally introduced the author to the farmers. Being introduced by somebody familiar to the farmers increased their active participation in the survey. The author conducted the interviews with 60 farmers from Dedaye, 66 farmers from Pyapon and 60 farmers from Bogale Township and thus a total of 186 respondents 13
Source: Department of Agricultural Service, Pyapon District, Ayeyarwaddy region, Myanmar (29/12/2014
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were interviewed in 2015-2016. Due to missing information, 8 samples were removed from consideration and a total of 178 samples were included in the data. In addition to this, focus group discussions were performed to formulate the relative weights of our concerned indicators, which were used in the socioeconomic vulnerability index approach. Before conducting a focus group discussion, semi-structured questionnaires were prepared. Several stakeholders from different administrative departments, key farmers or leaders, project leaders from private organizations (NGOs, and INGOs) were invited. These discussions looked at the preparedness against disasters, and were used to identify locally applicable and reliable indicators to assess vulnerability. Seven focus group discussions were held, at least two in each township. In addition, secondary data such as social and demographic charateristics, land utilization, and crop production data were acquired from the Township Department of Agriculture and change climate information (temperature and rainfall) was collected from the Township Department of Meteorology. 2.2.3 Empirical model and index specification Vulnerability indexes are based on major components as an aggregate of different sub-components. This study explores the vulnerability of farm households using two indexes: the Livelihood Vulnerability Index, which is comprised of 37 indicators, and the Socioeconomic Vulnerability Index, which contains 35 indicators. As mentioned above, the indicators were formulated to capture the vulnerability of farm households in the community and were specifically selected on the basis of a broad range of literature findings, and information suited to a locally based vulnerability assessment (for detail see Annex Table A and B). In table 3 and 4, we presented the average of each specific indicators reported by the households at Township level. The main reason for comparing climate change vulnerability for the selected Townships is that the climate change vulnerability of the farm households and the different dimensions of climate change vulnerability can be looked at at the Township level. Therefore, this localized index based assessment helps for the regional development planners and Township Department of Agriculture to implement several strategies to reduce the climate change vulnerability of farm household at Township level. It can be used to articulate on what policy dimensions there should be focus on the Township level. Furthermore, climate exposure, sensitivity and adaptive capacity were calculated and depicted with spider and triangular diagrams and were discussed. In the following section, the two indexes used to assess farm households’ vulnerability are presented. 2.2.3.1 The Socioeconomic Vulnerability Index In the vulnerability index assessment applied by, for example Cherni et al. (2007), Urothody and Larsen (2010), Vincent and Cull (2010), Ahsan and Warner (2014), five capital assets, namely - human, natural, financial, social and physical capital are used to examine 31
the vulnerability of farm households. The vulnerability consists of three main dimensions: adaptive capacity, sensitivity and exposure (IPCC 2007, Urothody and Larsen 2010, Vincent and Cull 2010, Ahsan and Warner 2014). The individual indicators are measured at different scales and, thus, it is necessary to standardize each of them. In order to estimate the vulnerability indexes for each dimension, it is relevant to use different indicators (Ahsan and Warner 2014, Pandey and Jha 2012, Hahn et al. 2009). The following approach (see UNDP 2007a) is used to standardize the indicator index value for Township ‘𝒕’.
𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓 𝑰𝒏𝒅𝒆𝒙 𝑺𝒄𝒐𝒓𝒆 (𝑰𝑰𝑺)𝒕 =
𝑿𝒕 𝑿𝒎𝒊𝒏 ------------------------------------------Eq 𝑿𝒎𝒂𝒙 𝑿𝒎𝒊𝒏
(1)
Where, 𝑿𝒕 is the original value of the indicator for Township ‘𝒕’, 𝑿𝒎𝒂𝒙 is the highest value of this indicator and 𝑿𝒎𝒊𝒏 is the lowest value of the indicator. Once the standardized indicator index score was obtained; the relative weight was obtained through a follow-up focus group and this was multiplied with the concerned indicator. In this way, a weighted score for an indicator, as shown by Eq. (2), was determined. The next step is then to combine the different indicators for a specific domain in a domain vulnerability score by aggregating the weighted scores for all indicators within the same domain in (eq. 3) (Hahn et al. 2009, Urothody and Larsen 2010, Vincent and Cull 2010, Ahsan and Warner 2014). 𝐖𝐞𝐢𝐠𝐡𝐭𝐞𝐝 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫 𝐒𝐜𝐨𝐫𝐞 (𝐖𝐈𝐒)𝐭 = (𝐈𝐈𝐒)𝐣𝐭 ∗ 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐖𝐞𝐢𝐠𝐡𝐭 𝐣𝐭 ------------------Eq (2)
𝐃𝐨𝐦𝐚𝐢𝐧 𝐕𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐒𝐜𝐨𝐫𝐞 (𝐃𝐕𝐒)𝐭 =
∑𝐧𝐣 𝟏 (𝐖𝐈𝐒)𝐣𝐭 ∑𝐧𝐣 𝟏(𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐰𝐞𝐢𝐠𝐡𝐭)𝐣𝐭
-------------------Eq (3)
Here, (𝑫𝑽𝑺)𝒕 denotes the domain scores for the vulnerability index for Township ‘𝒕’; ‘𝒋’ is the number of indicators within the domain concerned. When the domain values of vulnerability indexes are obtained, the different dimension values of vulnerability can be deducted as the ratio between the sum of domains under adaptive capacity, sensitivity, and exposure and by the number of different domains involved in the analysis. This is denoted as follows: ∑𝒏 𝒋 𝟏 𝑫𝑽𝑺𝒋𝒕 ------------------------------------------------------------Eq 𝒏
𝑫𝑴𝒌𝒕 =
(4)
Here, ‘𝒌’ denotes the number of domains under adaptive capacity, sensitivity, and exposure to saltwater intrusion and flood occurrence, respectively.
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Table 3. Sub-component values and minimum and maximum sub-component values of SeVI for Dadeye, Pyapon and Bogale Township, in the Pyapon district, Myanmar Domain
Indicator
Demographic
Dependency ratio (child dependency ratio: age
