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MSc Thesis: Ecosystem Services of Tropical Silvopastoral Systems

Economic valuation and analysis of trade-offs

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MSc Thesis: Ecosystem Services of Tropical Silvopastoral Systems

  1. 1. F A C U L T Y O F S C I E N C E U N I V E R S I T Y O F C O P E N H A G E N Master’s thesis Hyeonju Ryu Ecosystem Services of Tropical Silvopastoral Systems Economic Valuation and Trade-Offs Supervised by Palle Madsen, Jens-Peter Barnekow Lillesø and Diego Tobar 8 August 2016
  2. 2. Information Page Name of department: Geosciences and Natural Resource Management MSc programme: Nature Management (Landscape, Biodiversity and Planning) Author: Hyeonju Ryu Student ID: VDH245 Workload: 45 ECTS Title: Ecosystem services of tropical silvopastoral systems – Economic valuation and trade-offs Academic advisor: Palle Madsen, Professor, Forest and Lancscape College Jens-Peter Barnekow Lillesø, Senior researcher, Forest, Nature and Biomass Co-supervisor: Diego Tobar, Center for Teaching and Research on Tropical Agronomy (CATIE) Submitted: 08.08.2016 2
  3. 3. Abstract Ecosystems provide a variety of goods and services to humans such as food pro- vision, climate change mitigation, soil erosion control and watershed protection. In Central America, expansion of cattle production has undermined ecological functions, limiting such environmental goods and services. To solve the problem, Silvopastoral System (SPS) was introduced as an instrument to enhance the envi- ronmental services sustaining agricultural production. In Costa Rica, there have been efforts to promote the SPS, but there are still obstacles in implementing SPS. Lack of information on current status of Ecosystem Services (ES) provided by SPS, furthermore, makes it hard to diagnose the condition of ES from SPS. This study, therefore, aimed to estimate the value of the ecosystem services from SPS and identify trade-offs between the ES in case of Jesus Maria River Watershed in Costa Rica. Provision of food and fiber and regulation of Climate Change were investigated. Combining with analyses on socio-economic factors, the work also examined motivations and challenges in adoption of SPS by the farmers. Results showed that the SPS provides ecosystem services equivalent to $ 3,318.7/ha/year in 2015 International Dollar. Provision of timber and non-timber products was mi- nor accounting for 5% and 10% of the total value respectively. A synergy between carbon regulating service and biodiversity was found, whereas milk production had a negative relation with the carbon regulation and biodiversity. Socio-economic factors including farmers’ dependency of income in livestock production, existence of subsidy, and capacity in SPS management tended to have relations with the adoption of SPS. It was concluded that financial support to the farmers is nec- essary in order to compensate the loss in milk production for higher carbon and biodiversity value in cattle farms. Importance of technical assistance and knowl- edge transfer, was also highlighted in promoting SPS and maximizing the value of ES from the SPS. Despite limitations in the valuation with the scope of ES and uncertainties in estimation, this work provided approximate values of ES in SPS, aggregating multiple services into comparisons. 3
  4. 4. Abbreviations AU Animal Unit BCCR Central Bank of Costa Rica (Banco Central de Cota Rica) CAMBIo Central American Markets for Biodiversity Project CADETI the Advisory Commission on Soil Degradation (Comisi´on Asesora sobre Degradaci´on de Tierras) CATIE Center for Teaching and Research on Tropical Agronomy (Centro Agron´omico Tropical de Investigaci´on y Ense˜nanza) CORFOGA Costa Rican Cattle Corporation (Corporati´on Ganadera) CRC Costa Rican Colones ES Ecosystem Service or Environmental Service FAO Food and Agriculture Organization of the United Nations FONAFIFO National Forest Financing Fund of Costa Rica (Fondo Nacional de Financiamiento Forestal) FONTAGRO Regional Fund for Agricultural Technology GDP Gross Domestic Product GHG Greenhouse Gas IMN National Meteorological Institute (Instituto Meteorol´ogico Nacional) IPCC Intergovernmental Panel on Climate Change ITCR Costa Rican Institute of Technology (Instituto Tecnol´ogico de Costa Rica) MA Millennium Ecosystem Assessment MAG Ministry of Agriculture and Livestock (Ministerio de Agricultura y Ganaderia) 4
  5. 5. MIDEPLAN Ministry of National Planification and Economic Policy (Ministerio de Planificaci´on Nacional y Pol´ıtica Econ´omica) OECD Organisation for Economic Co-operation and Development PES Payment for Ecosystem Service PPP Purchasing Power Party RISEMP Regional Integrated Silvopastoral Ecosystem Management Project SPS Silvopastoral System TEEB The Economics of Ecosystems and Biodiversity UNEP-WCMC The United Nations Environment Programme’s World Conservation Monitoring Centre 5
  6. 6. Contents Information Page 2 Abstract 3 Abbreviations 4 1 Introduction 10 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2 Literature Review 15 2.1 Ecosystem Services . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.1.1 Definition and Categories . . . . . . . . . . . . . . . . . . . 15 2.1.2 Economic Valuation . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Silvopastoral Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.1 Definitions and Types . . . . . . . . . . . . . . . . . . . . . 21 2.2.2 Ecosystem Services of Silvopastoral Systems . . . . . . . . . 22 3 Materials and Method 28 3.1 Study Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Analytical Framework . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3 Selection of Ecosystem Services to Valuate . . . . . . . . . . . . . . 31 3.4 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.5 Valuation of Ecosystem Services . . . . . . . . . . . . . . . . . . . . 35 3.5.1 Provisioning Service Valuation . . . . . . . . . . . . . . . . . 35 3.5.2 Calculation of Carbon Balances . . . . . . . . . . . . . . . . 36 3.5.3 Biodiversity . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Results 41 4.1 Farm Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6
  7. 7. 4.1.1 Spatial Distribution of Farms . . . . . . . . . . . . . . . . . 41 4.1.2 Production Status . . . . . . . . . . . . . . . . . . . . . . . 44 4.1.3 Socio-Economic Status . . . . . . . . . . . . . . . . . . . . . 44 4.1.4 Land Uses and Silvopastoral Systems . . . . . . . . . . . . . 46 4.2 Values of Ecosystem Services . . . . . . . . . . . . . . . . . . . . . . 50 4.2.1 Quantification of Ecosystem Services . . . . . . . . . . . . . 50 4.2.2 Total Economic Value . . . . . . . . . . . . . . . . . . . . . 52 4.3 Synergies and Trade-offs between Ecosystem Services . . . . . . . . 56 4.4 Socio-Economic Factors in Adopting Silvopastoral Systems . . . . . 58 5 Discussions 60 5.1 Values of Ecosystem Services in Silvopastoral Systems . . . . . . . . 60 5.2 Trade-offs Between Ecosystem Services . . . . . . . . . . . . . . . . 63 5.3 Scocio-Economic Factors on Adopting Silvopastoral Systems . . . . 64 5.4 Limitations of the Study . . . . . . . . . . . . . . . . . . . . . . . . 66 6 Conclusion 68 References 69 Appendix. Interview Questions 78 7
  8. 8. List of Tables 2.1 Classification of ecosystem services (Source: Kumar 2010) . . . . . 17 3.1 Key ecosystem services of Silvopastoral Systems analyzed by criteria for indicator selection . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Selected ecosystem services for valuation . . . . . . . . . . . . . . . 34 3.3 Carbon sequestration rates by land uses . . . . . . . . . . . . . . . 37 3.4 Ecological Index for Biodiversity . . . . . . . . . . . . . . . . . . . . 39 4.1 Coverage of districts inside the watershed and desired and actural number of farms by districts . . . . . . . . . . . . . . . . . . . . . . 42 4.2 Land sizes of administrative divisions and the studied farm areas . . 43 4.3 Sizes of the farmlands and the stock . . . . . . . . . . . . . . . . . . 44 4.4 Land uses within farms . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.5 Challenges in adopting or enhancing silvopastoral systems . . . . . 48 4.6 Characteristics of intensification groups . . . . . . . . . . . . . . . . 48 4.7 Quantity of provisioning services . . . . . . . . . . . . . . . . . . . . 50 4.8 The averages of quantified annual production of beef, milk, fruit and timber by intensification group . . . . . . . . . . . . . . . . . . 51 4.9 The average rates of carbon sequestration, emission and net carbon sequestration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.10 The average rates of carbon sequestration, emission and net seques- tration by groups of intensification . . . . . . . . . . . . . . . . . . 52 4.11 Calulated Ecological Index for Biodiversity . . . . . . . . . . . . . . 53 4.12 Estimated values of ecosystem services . . . . . . . . . . . . . . . . 53 4.13 Estimated values of ecosystem services by groups with different level of intensification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 8
  9. 9. List of Figures 2.1 Classification of economic values (Source: Kumar 2010) . . . . . . . 18 3.1 Location of the study site . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Life zones in Jesus Maria River Watershed, Costa Rica . . . . . . . 30 3.3 Land uses in Jesus Maria Watershed, Costa Rica in 2005 . . . . . . 31 3.4 Flowchart of the study . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1 Division of districts and locations of the investigated farms . . . . . 44 4.2 Distribution of farm sizes . . . . . . . . . . . . . . . . . . . . . . . . 45 4.3 Frequency of age of the farmers . . . . . . . . . . . . . . . . . . . . 45 4.4 Education level of the farmers . . . . . . . . . . . . . . . . . . . . . 46 4.5 Income of the farmers . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.6 Classification of the farms by intensification indicators . . . . . . . 49 4.7 Estimated total Ecosystem Service value by farm type . . . . . . . 54 4.8 Relation between farm size and total ES value (2015-International $/ha/year) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.9 Estimated total Ecosystem Service values in 2015 International dol- lars by intensification group . . . . . . . . . . . . . . . . . . . . . . 56 4.10 Relationship between Carbon regulation value and Ecological Index of farm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.11 Relationship between milk provision value and carbon value . . . . 57 4.12 The Ecological Index of production areas by existence of subsidy, economic dependency on cattle farming, existence of capacity on SPS management and frequency of training related to SPS . . . . . 59 9
  10. 10. Chapter 1 Introduction 1.1 Background Natural and semi-natural ecosystems provide a range of goods and services that are important for human well-being and livelihood (De Groot et al. 2012; Kumar 2010; MA 2005). Physical, chemical and biological processes within and between the ecosystems are often beneficial to humans offering food, drinking water, oxygen and mitigating natural hazards. Despite the fundamental importance of the goods and services from the ecosystems, the functions of ecosystems has been drastically degraded mainly caused by anthropogenic activities, such as intensified agricul- tures, deforestation and other unsustainable land uses (De Groot et al. 2012). Beef and dairy industry is one of the major contributors to the degradation of ecosystems. Expansion and intensification of cattle farms have resulted in con- version of forests to pastures, forest fragmentation, degradation of soil and water quality and loss of biodiversity (Edelman 1995; Foley et al. 2005; DeClerck et al. 2010). Livestock production is also attributed to large emission source of green- house gases. Globally the amount of emitted Greenhouse Gases (GHG) from livestock farming accounts for 14.5% of all anthropogenic GHG emissions (Gerber et al. 2013). The loss of carbon sinks in natural ecosystems by expansion of range- lands also contributed to the carbon emission to the atmosphere (Gerber et al. 2013). Central America is one of the regions that have suffered from the environ- mental degradation by livestock production (Edelman 1995; Kaimowitz 1996). In the 1980s, 75 millions hectares of forests was converted mainly to grazing areas (Kaimowitz 1996). Approximately 40% of the land in Central America is covered 10
  11. 11. by pastures (Ibrahim et al. 2001b). Conversion of forests into cattle farms also resulted in loss of habitats for biodiveristy, threatening more than 300 endemic species in Central America (Harvey et al. 2008). Also the expansion of livestock production without appropriate management of pasture lands has caused a se- vere degradation of pasturelands and soils (Calvo-Alvarado et al. 2009; Kaimowitz 1996). The declined ecosystem functions also made the region vulnerable to the Climate Change (Giorgi 2006). The environmental degradation by cattle farms caused a vicious circle, leading to a decrease of animal productivity which requires large areas (Betancourt et al. 2003). Despite the negative impacts of cattle production on the ecosystems and hu- man well-being, the livestock sector is not likely to be scaled down any time soon in Central America (Harvey et al. 2008; Pagiola et al. 2004). The reasons are associated with 1) its long history, 2) influences on economy of agriculture, and 3) increasing demands for cattle products (Harvey et al. 2008; Murgueitio et al. 2011; Pagiola et al. 2004). The cattle ranching in Mesoamerica started five cen- turies ago, integrated closely into the rural livelihoods (Murgueitio et al. 2011). Most of the farms are small-medium scale run by families, supporting their living (MAG-CATIE 2010). In Costa Rica the livestock sector supports 153,000 families directly, and more than 300,000 families indirectly (MAG-CATIE 2010). Cattle production also contributes a large part to the economy in the Central Ameri- can countries. In Costa Rica and Nicaragua, beef and dairy industries contribute 14.7% and 10% of Gross Domestic Product (GDP) (MAG-CATIE 2010). Increase of market demands on animal products has been the major driver of expansion of animal production in Central America. Population growth led to higher demand on food including animal products (Calvo-Alvarado et al. 2009). Especially in the mid 19th century, the cattle industry grew rapidly due to high price of beef and dairy products in the international markets (Kaimowitz 1996). The trend of increasing demands does not seem to change as the population and meat con- sumption per capita continue growing (Pagiola et al. 2004; Murgueitio et al. 2011). With the increasing pressure both on agricultural production and environmen- tal protection, it has become an important issue to balance demands for food production and other environmental services such as climate change mitigation, watershed protection and soil improvement. To deal with the issue, Silvopastoral System (SPS) was introduced as an instrument for enhancing both land produc- tivity and other environmental services (Harvey et al. 2008). The SPS is a system 11
  12. 12. of animal production combined with tree components on pastures such as live fences, forage bank and scattered trees on paddocks (Montagnini 2008; Alonzo and Ibrahim 2000). The involvement of trees in the cattle farms accommodates higher biodiversity and increases animal productivity, improving multiple ecolog- ical functions beneficial to the human welfare (Harvey et al. 2008; Pagiola et al. 2004; Montagnini 2008). Studies have shown that SPS provides more ecosystem services than open pasture lands (Murgueitio et al. 2011). In Central America, trees have been used for shades and materials for post traditionally (Alonzo and Ibrahim 2000). In the 1970s, planting trees on agricul- tural lands for multiple uses was wide-spread, especially for producing fuel woods (Current et al. 1995). In recent years, SPS has been promoted focusing on improv- ing farm efficiency and ecosystem functions. In Costa Rica, various efforts have been made to reduce deforestation and enhance ecosystem qualities by implement- ing agroforestry systems including SPS (Bautista Sol´ıs 2005). Under a National Action Program to combat soil degradation, the Ministry of Agriculture and Live- stock (MAG) and the Advisory Commission on Soil Degradation (CADETI) have been promoting SPS by offering farmers with tree seeds for live fences and forage banks and building farmers capacity on management of SPS (Gumucio et al. 2015). Regional Integrated Silvopastoral Ecosystem Management Project (RISEMP) was also conducted between 2002 and 2007, aiming to improve degraded soils in cattle farms through researches on the profitability and effects on ecosystem services of SPS (Pagiola and Arcenas 2013). Although numerous studies have provided evidences of enhancement in ecosys- tem services and its profitability, implementation of SPS is encountering many limitations. Governmental regulations on harvesting timber on pastures are one of the obstacles. In Costa Rica, it is not allowed to harvest more than three trees per hectare per year outside timber plantations according to the Article 27 of the Forest Law (Plata 2012). Sales of timber harvested in the pastures need to be reported before the action, which involves long and complicated processes (Plata 2012). High initial cost of establishing SPS and high risk of investment is also a drawback that makes farmers hesitate in adopting SPS. In a technical aspect, decrease of grass production due to tree shade and slow growth of timber are also obstacles in implementing SPS (Esquivel 2007; Alonzo and Ibrahim 2000; Plata 2012). 12
  13. 13. To overcome the barriers in implementing SPS and maximizing ecosystem ser- vices through SPS, it is essential to understand the present state of ecosystem services provided by the current silvopastoral systems. In understanding the pro- vision of various ecosystem services, economic valuations have often been used as a tool for visualizing and monitoring those services in other types of ecosystems (UNEP-WCMC 2011; Kumar 2010). Through an economic valuation, ecosystem services are presented in monetary values, enabling comparisons between differ- ent environmental services (De Groot et al. 2012; MA 2005; UNEP-WCMC 2011). Presentation of environmental values in monetary terms can also assist comprehen- sion of relations between the services, such as synergies and trade-offs (Raudsepp- Hearne et al. 2010; Steffan-Dewenter et al. 2007). Many studies examined effects of tropical SPS on certain ecosystem services and relations between them, in which the services included food provisioning, carbon capturing, watershed protection and bird conservation (R´ıos Ram´ırez et al. 2006; Ibrahim et al. 2007; Esquivel 2007; Harvey et al. 2005; Bravo et al. 2012; Giraldo et al. 1995). There is, how- ever, lack of studies that quantified and evaluated those services by tropical SPS in economic terms, integrating multiple ecosystem services. The actual utilization of tree-related products such as timber and fruits at the farm level, is also poorly understood. 1.2 Objectives Silvopastoral system is often promoted for its benefits in maximizing Ecosystem Services (ES). Can we really gain ‘all’ services without any loss? What are syner- gies and trade-offs between the ecosystem services in SPS? Does the SPS maximize values of the ecosystem services in reality? If not, what are the challenges in op- timizing the benefits? To answer the questions above, this study aimed i) to quantify and estimate the values of the Ecosystem Services provided by current conditions of Silvopastoral Systems, ii) to identify synergies and trade-offs between the examined Ecosystem Services and finally iii) to identify socio-economic factors that affect adoption of SPS. The hypothesis are as follows: 13
  14. 14. Objective 1. Ecosystem Service Values • The major contributor to the total ES value will be provisioning of meat and milk due to limited utilization of fruit and timber and greenhouse gas emissions from livestock. • The carbon regulating value will be minor due to compensation between greenhouse gas emission from the animal production and sequestration on tree components on the farms. • Farms with more SPS elements will have higher total ES value because of increased animal productivity and higher carbon sequestration. • Provision of subsidiary products such as timber and fruit will be greater in the farms with more SPS elements due to the higher availability of the products. Objective 2. Synergies and Trade-offs between Ecosystem Services • There will be a positive relation between the provisioning service, the car- bon regulating service and biodiversity due to positive influence of trees on agricultural production, carbon sequestration and habitat supply. Objective 3. Socio-economic factors in SPS Adoption • Farmers whose income is high or who receive subsidies will have more SPS elements on their farms because there will be little financial restriction with investing on establishing and managing SPS. • Education and technical assistance will be positively associated with adop- tion of SPS, related to knowledge on effective farm management and aware- ness of profitability of SPS. 14
  15. 15. Chapter 2 Literature Review 2.1 Ecosystem Services 2.1.1 Definition and Categories Ecosystem Service is defined as “the benefits people obtain from the ecosphere and its ecosystems” (MA 2005). Ecosystems provide good and services useful for human well-being through their physical, chemical and biological processes. For instance, photosynthesis of vegetation provides oxygen and captures carbon dioxide, one of GHGs, from the atmosphere. Complex root systems in natural ecosystems, for another example, control soil erosion and water run-off, preventing floods and landslides. The concept of Ecosystem Services was addressed in the mid-1960’s with a rise of concerns on environmental degradation (De Groot et al. 2002). In those days, environmental problems, such as air pollution, water contam- ination, soil acidification and forest die-back, were highlighted as a limiting factor in the social and economic growth (Alam et al. 2014). With increasing attention on ecosystem services, Millenium Ecosystem Assessment (MA) was launched to quantify and monitor the global ecosystem services in 2005 (De Groot et al. 2012). A global assessment of the Economics of Ecosystems and Biodiversity (TEEB), af- terwards, launched in 2007, continuing monitoring of changes in global ES values (De Groot et al. 2012; Kumar 2010). Kumar (2010) has classified ecosystem services through reviews on previous classification systems. Ecosystem services are categorized into four groups: provi- sioning services, regulating services, habitat services and cultural services (Table 2.1). The provisioning services refer to supply of products people obtain from ecosystems (De Groot et al. 2012). The provisioning services include food like 15
  16. 16. crops and fruits, water for irrigation and drinking, materials such as timber and fuelwood, and medicinal products. The regulating service means“benefits from the regulation of ecosystem processes” (De Groot et al. 2012). For example, ecosystem functions involve air purification, carbon sequestration, disturbance prevention, and soil erosion control. The habitat service means “provision of habitat for mi- gratory species and gene-pool protectors allowing natural selection processes to maintain the vitality of the gene pool.”(Kumar 2010). The habitat services were a subset of ‘Supporting Services’ in the MA classification. It was, however, amended since the supporting services such as nutrient cycling and food-chain dynamics were regarded as ‘ecological processes’ (Kumar 2010). Instead, the services of accommodating fauna and flora and protecting the gene pool were highlighted in the adjusted classification. The cultural service, lastly, is “the non-material benefits obtained through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences”(MA 2005). The cultural services are, for example, scenic beauty, recreation, inspiration for art and spiritual experience. 2.1.2 Economic Valuation Despite the substantial significance of ecosystem services, their values have been often neglected or underestimated in political decisions (Costanza et al. 1997; MA 2005; Kumar 2010; De Groot et al. 2002). In the processes of decision-making on land uses or constructions, it has been difficult to take ES values into account since their benefits and costs could not be measured (Kumar 2010; MA 2005). To make the ES values visible and comparable, valuation of ES in economic terms was suggested (Kumar 2010). Supporters of economic valuation argue that it en- ables to prioritize conservation options by comparing benefits of different programs (Costanza et al. 1997; Schr¨oter et al. 2014). It is also expected to raise public aware- ness on environmental services by providing familiar expression of values through monetization (De Groot et al. 2012; Schr¨oter et al. 2014; MA 2005). Economic Value Types In an economic valuation, the values are categorized into ‘Use Values’ and ‘Non-use Values’ (Kumar 2010) (Fig. 2.1). Use value is goods and services that are directly or indirectly utilized by human (J´onsson and Dav´ıðsd´ottir 2016; Kumar 2010). The use value is divided into three sub-categories: ‘Direct use’, ‘Indirect use’ and ‘Option value’. Direct value is the value directly used by human such as agri- 16
  17. 17. Table 2.1: Classification of ecosystem services (Source: Kumar 2010) Service types Ecosystem services Provisioning Services 1. Food 2. Water 3. Raw materials 4. Genetic resources 5. Medicinal resources 6. Ornamental resources Regulating Services 7. Air quality regulation 8. Climate regulation 9. Moderation of extreme events 10. Regulation of water flows 11. Waste treatment 12. Erosion prevention 13. Maintenance of soil fertility and nutrient cycling 14. Pollination 15. Biological control Habitat Services 16. Maintenance of life cycles of migratory species 17. Maintenance of genetic diversity Cultural and Amenity Services 18. Aesthetic information 19. Opportunities for recreation and tourism 20. Inspiration for culture, art and design 21. Spiritual experience 22. Information for cognitive development cultural products and tourism. Indirect value refers to the societal or functional benefits. The indirect uses are mainly related to regulation services, such as flood prevention (J´onsson and Dav´ıðsd´ottir 2016). Option value means the potential value of being used directly or indirectly in the future. For examples, maintaining plant biodiversity gives potential to discover medicinal products in the future. Non-use value is, meanwhile, the value people assign although it never has been and never will be used (Costanza et al. 1997; J´onsson and Dav´ıðsd´ottir 2016). The non-use value includes bequest value, altruist value and existence value (Kumar 2010). Bequest value means the value placed on the option to reserve the ability of future generations to use the service in the future (Kumar 2010). Designation of protected national parks is one example of reserving the aesthetic and academic 17
  18. 18. values of natural ecosystems for the future generations. Altruist value refers to the value stemming from satisfaction of knowing that the present generation can access to the environmental benefits. For instance, some people in temperate coun- tries value mangroves in tropical countries for their benefits to the local people. Existence value indicates the value that people assign on resource simply knowing that it exists (Kumar 2010). For example, existence of endangered species is ap- preciated. Figure 2.1: Classification of economic values (Source: Kumar 2010) Valuation Methods Plenty of economic valuation methods for ecosystem services have been developed, especially since the global ES estimation by Costanza et al. (1997) (De Groot et al. 2012). The methods can be categorized into three groups: ‘Direct market valuation approaches’, ‘Revealed preference approaches’ and ‘Stated preference approaches’ (Kumar 2010). Direct market valuation is a means of valuing a service using prices transacted in the markets (Kumar 2010; J´onsson and Dav´ıðsd´ottir 2016). The techniques are divided into three approaches: ‘Market price-based approaches’, ‘Cost-based 18
  19. 19. approaches’ and ‘Approaches based on production functions’ (Kumar 2010). The market price-based approaches use the values of ecosystem services that have been traded in markets (De Groot et al. 2002). This method is commonly used for provisioning services (UNEP-WCMC 2011). For example, Godoy et al. (2002) es- timated provisiong services of tropical forests in Bolivia and Honduras, such as provision of timber, games and fruits using their consumer prices in markets. The cost-based approaches include avoided cost method and replacement cost method. Avoided cost method estimates an ecosystem service value by calculating costs that would have been incurred in the absence of the service (De Groot et al. 2002). In New Zealand, the biological control service of organic farms was esti- mated using the avoided cost of pesticides (Sandhu et al. 2008). Replacement cost method calculates costs to replace a service with man-made systems (De Groot et al. 2002). For example, waste treatment service of wetlands can be valuated by the cost of operating a purification plant (Woodward and Wui 2001). Production function-based approaches estimate the ecosystem services linked to enhanced commercial profits (De Groot et al. 2002; Kumar 2010). For example, as a result of pollination service, productivity of crops can increase in adjacent farms. In Costa Rica, the pollination service of ecosystems was valuated by measuring increase of productivity in coffee farms (Ricketts et al. 2004). Using the direct market approaches have several advantages. First of all, they uses actual market data which may represent their value well based on the rela- tions of supply and demand (Kumar 2010). The techniques are also cost-efficient because obtaining existing market data is easy (Kumar 2010). There are, however, also limitations in the methods. Estimations can be misled in case the markets are distorted by subsidies (Kumar 2010). Also the approaches cannot be used to valuate non-use values (Kumar 2010). On the other hand, ‘Revealed preference techniques’ are based on choices of individuals observed in existing markets (Kumar 2010). The main methods in this approches are ‘Hedonic pricing method’ and ‘Travel cost methods’. Hedonic pric- ing method estimates values using prices reflected in prices of associated goods. Using this method, cultural values have been estimated in many studies. For ex- ample, amenity of forests is often estimated with the increased house prices as proximity to the nature is higher. Travel cost method calculates travel expenses 19
  20. 20. to use certain services, especially recreation. Recreational value of the Monteverde Cloud Forest Biological Reserve in Costa Rica, for instance, was valuated by esti- mation of costs that visitors spend to travel to the place (Tobias and Mendelsohn 1991). The revealed preference approaches are useful for estimates of use values that does not have markets. The techniques, however, also have several drawbacks. Like direct market valuation, the approaches are only for use values. The methods are also expensive and time-consuming, requiring complex statistical analysis and large dataset (Kumar 2010). Stated preference techniques are, meanwhile, based on decisions of people made in hypothetical scenarios of changes in service qualities (J´onsson and Dav´ıðsd´ottir 2016). Stated preference tools consist of ‘Choice experiments’, ‘Contingent valua- tion’ and ‘Group valuation. In the choice experiments, people are asked to make choices among bundles of services and prices. In the contingent valuation, the respondents are questioned whether they would pay a specific price for increase of certain services. Group valuation is a combination of stated preference techniques but use deliberative processes instead of surveys. The methods based on stated preference have advantages that they are applica- ble for both use and non-use values (Kumar 2010; J´onsson and Dav´ıðsd´ottir 2016). There are, however, critiques on its hypothetical assumption, questioning if people would really pay the amount as they answered in reality (Kumar 2010). In valuation of ecosystem services in preceding studies, the most common meth- ods for use values were direct market methods, production function-based meth- ods, cost-based methods, travel cost methods and hedonic methods (J´onsson and Dav´ıðsd´ottir 2016; UNEP-WCMC 2011; De Groot et al. 2012). Most frequently- measured services in economic valuations were food and raw materials for provi- sioning services, water quality, climate regulation and erosion regulation in regu- lating services, and recreation for cultural services (UNEP-WCMC 2011). Limits of Economic Valuation of ES Estimating ecosystem services in economic terms has the virtue as a means of providing visible and comparable information on ecosystem services for decision- 20
  21. 21. making, linking ecosystems to human well-being (Kumar 2010; MA 2005). Eco- nomic valuation of ES, yet, has been criticized for ethical issues and uncertainty (Schr¨oter et al. 2014). It is asserted that ES valuations are focused on instrumental values of ecosystems from an anthropocentric view, excluding intrinsic values of ecosystems (Schr¨oter et al. 2014). Opponents to this argument addressed that the main focus of economic valuations is to offer additional information for decision- making, not to estimate intrinsic values of the nature (Schr¨oter et al. 2014). The problem of uncertainty is the most addressed concern in the discourses on economic valuation of ecosystem services. The uncertainties stem from gaps in knowledge about ecosystem dynamics and application of valuation tools (Kumar 2010). Sev- eral ecosystem functions are linked to more than one ecosystem services, which increases interdependence of the services (De Groot et al. 2002). The high com- plexity of ecosystem services can be a cause of double-counting (de Groot et al. 2002; Kumar 2010). Adoption of valuation tools also affects risk of uncertainty. Researchers have not reached a consensus on measurement methods, disputing over their limitations, as discussed earlier. 2.2 Silvopastoral Systems As the conflict between demand for agriculture and environmental protection was highlighted, options of integrated land uses for balancing the demands were dis- cussed (Harvey et al. 2008). As an approach for protecting biodiversity while sus- taining agricultural productivity and rural livelihood, Silvopastoral System (SPS) was devised (Harvey et al. 2008; Murgueitio et al. 2011) 2.2.1 Definitions and Types Silvopastoral System refers to a combination of multipurpose trees with livestock production (Montagnini 2008). There are four categories of SPS according to Pa- giola et al. (2004): pastures with dispersed trees, forage bank, live fence and forest plantation with animal grazing. The pastures with dispersed trees are the systems where trees and/or shrubs are scattered in grazing areas, providing shade and al- iment (Murgueitio et al. 2011; Pagiola et al. 2004). Forage bank is an area for cultivation of forage in which woody or herbaceous plants are grown in a high tree density (Esquivel 2007). Live fence is a line of fast-growing trees and/or shrubs for division of paddocks and/or for windbreak (Murgueitio 2000). The forest planta- tion with animal grazing is a timber or fruit plantation where livestock grazes under 21
  22. 22. the trees (Murgueitio 2000). The cattle grazing in plantations mainly for control- ling invasive plants (Murgueitio 2000). Recently, Intensive Silvopastoral System (ISS) have been developed (Murgueitio et al. 2011; Calle et al. 2012). The sys- tem is an improved pasture integrated with forage bank at high density (>10,000 plants/ha) and lines of timber trees in east-west (Murgueitio et al. 2011). ISS was devised to minimize decrease in pastures by tree shades and maximize protection from winds and production of timber (Murgueitio et al. 2011; Calle et al. 2012). 2.2.2 Ecosystem Services of Silvopastoral Systems A plenty of studies demonstrated mechanisms of ecosystem services provided by SPS, including provisioning, regulating and habitat services. Provisioning Services Increase of provisioning services has been one of the major focuses of the studies on SPS to prove its economic profitability and efficiency. Two major findings of the studies on the provisioning services were i) increased cattle productivity and ii) additional products from tree components. A few studies have shown that in a silvopastoral system, beef and dairy produc- tivity was improved. Restrepo-S´aenz et al. (2004) and Esquivel (2007) observed increase of weight gain of cows in moderate level of SPS where the canopy cover is lower than 30%. In cattle farms in Nicaragua, milk production was higher in SPS than in pastures with no or few trees on pastures (Betancourt et al. 2003). Factors that contribute to the increase of productivity are known as 1) shade of dispersed trees and live fences, 2) diet supplement from forage bank and fo- liage and fruits of dispersed trees, and 3) increased quality of aliment (Pagiola et al. 2004; Restrepo-S´aenz et al. 2004; Esquivel 2007; Alonzo and Ibrahim 2000). Betancourt et al. (2003) demonstrated that production in pasture with moderate tree density (20–32% canopy cover) was 29% higher than in pasture with low tree density (0–7%) due to longer time spent in grazing under the shades. Increase of aliment supply from forage banks and trees on pasture is also linked to the increased productivity. Diet supplement from forage bank increased animal production by 20–30% in sub-humid tropics (Ibrahim et al. 2001a). Holmann et 22
  23. 23. al. (1992), furthermore, showed that stock size increased in the improved pasture combined with legumes due to sufficient aliment from the trees. Some tree species also provide substantial amount of supplement. For example, Gaucimo (Guazuma ulmifolia) produces 50–60 kg (dry weight) of forage annually (Giraldo et al. 1995). Cecnizaro (Samanea saman) and Guanacaste (Enterolobium cyclocarpum), for an- other example, produce 270kg of fruits per tree every year (Durr 2001). Forage banks and tree components in a farm also are known for contributing to increased productivity by producing aliment with high nutrient content. Leaves of nitrogen-fixing plant species such as G. sepium and Erythrina spp. have high protein content (Harvey et al. 2005; Esquivel 2007). It was also demonstrated that fruits of trees were more nutritious than pasture than grasses, increasing daily milk production by 2.2 liters per cow (Esquivel 2007). The nutrition content of pasture under dispersed trees were, moreover, enhanced by shade through adaption to the light-limiting condition in Costa Rica, especially when nitrogen is a limiting fac- tor (Esquivel 2007). Several studies have also demonstrated that increase of B. brizantha productivity at medium tree cover (22% of canopy cover)(Esquivel 2007). Some studies, on the other hand, argued that high tree cover on grazing land decrease grass productivity. Herbage biomass decreased under certain tree species, of which the crowns intercept high proportion of light, such as Enterolobium cyclo- carpum and Guazuma ulmifolia (Esquivel 2007). Esquivel (2007) also simulated that increase of crown cover from 10 to 50% would decrease pasture production by 2.7–51.3% of pasture without trees. Secondly, a silvopastoral system has a potential to provide fodder, timber, fruits and fuelwood as well as livestock products such as meat, milk and cheese. In 1995 when timber supply was limited in Costa Rica, 20% of the domestic tim- ber transacted was produced from pastures, especially scattered trees in paddocks (Murgueitio et al. 2011). From farmers’ perspective, the diversified products bring additional income and reduce risks by natural disasters and market fluctuation (Pagiola et al. 2004; Alonzo and Ibrahim 2000). Harvey et al. (2005) demon- strated that farmers in Costa Rica and Nicaragua chose to establish live fences for supplementary production such as fodder. Opposed to the statement that SPS enhances provisioning services by diver- sification, some studies have argued that there are conflicts between production 23
  24. 24. of different products, especially between timber production and agricultural pro- duction (Current et al. 1995; Harvey et al. 2005). The conflicts were driven from allocation of limited resources such as labor and competition over lights and nutri- ents between trees and pastures (Harvey et al. 2005; Current et al. 1995; Alonzo and Ibrahim 2000; Plata 2012; Esquivel 2007). Regulating Services Preceding studies have shown that SPS provides regulating services including en- hancement of chemical and physical condition of soil, watershed protection, climate change mitigation and adoption and improvement of air quality. It was reported that soil quality under SPS can be improved through its efficient nutrient cycling (Montagnini 2008; Belsky 1994; Esquivel 2007). The efficiency in nutrient use is because trees uptake nutrients from deeper soil than the grass species (McPherson 1997; Scholes & Archer 1997; Nair et al. 2007; Pagiola et al. 2004). The nutrients absorbed by the trees, moreover, return to the top soil in forms of organic matters when a tree sheds foliages, twigs and fruits (Menezes et al. 2002; Pagiola et al. 2004). The plentiful organic matter under the trees, meanwhile, increases the nutrients available for plants (Esquivel 2007). The organic matter facilitates activities of de- composers (Esquivel 2007). In fact, more extractable phosphorus (P), potassium (K) and calcium (Ca) were found in the soils under trees than pastures without trees (Rao et al. 1998). Nitrogen fixing species are also one of the great con- tributers to the enhancement of soil fertility in SPS (Rao et al. 1998; Bryan 1999). Nitrogen is often a limiting factor in terrestrial ecosystems including pasture lands. Therefore, provision of nitrogen through fixation by legume species allows increase pasture productivity without fertilizer input. Tree components on grazing lands also contribute to improvement of physical soil condition. The root systems of the trees prevent compaction of the soil by animals (Ayres et al. 2009). Combination of grasses and trees also controls erosion of the soils by retaining water and soil (Ibrahim et al. 2007; R´ıos Ram´ırez et al. 2006). Trees have higher infiltration rate of the rainfall, which allows lower level of run-off. The reduced run-off on the surface, as a result, decreases loss of soils carried by surface water (Pagiola et al. 2004; R´ıos Ram´ırez et al. 2006). Complex root systems with various trees of different root depth also prevent landslides by physically stabilizing the soils (Pagiola et al. 2004). By reducing soil erosion, the 24
  25. 25. SPS can decrease loss of soil nutrient from the agro-ecosystem. The watershed protection service by SPS is closely related to the improved soil quality. Low soil compaction and high content of organic matter increase the wa- ter holding capacity, hence releasing less water into rivers (Esquivel 2007). Along with the high infiltration rate discussed above in soil quality, the amount of surface run-off substantially decreases, which reduces risk of floods (Pagiola et al. 2004; R´ıos Ram´ırez et al. 2006). Also reduced sediments associated with soil retainment alleviate the risk of floods by maintaining the level of riverbed low (R´ıos Ram´ırez et al. 2006). The SPS can also enhance the water quality including drinking water and fresh- water habitats. Due to the high retention of nutrients and water in the soil, more amount of water penetrate into the water than surface run-off. Majority of nitro- gen and phosphate in the water remains onto the soil particles and is utilized by trees and pastures (Rao et al. 1998). The rest of water surcharges the groundwa- ter, which is extracted later for drinking water or agricultural uses. The reduced run-off also contribute to the high quality of aquatic habitats by preventing leakage of excessive nutrients into the rivers. Regarding climate change, many studies have shown that the SPS contributes to mitigating climate change by 1) capturing carbon into tree biomass and soils, and 2) reducing carbon emissions from livestock (Ibrahim et al. 2007; Ru´ız Garc´ıa 2002; Kim et al. 2016; Reid et al. 2004; Current et al. 1995). Ibrahim et al. (2007) demonstrated higher rates of carbon sequestration in SPS compared to the open pastures. Ru´ız Garc´ıa (2002) also showed that carbon storage increased both in above- and belowground in SPS with high tree density compared to pastures with low tree density. SPS is, meantime, also associated with decrease of emissions from livestock production. The principal greenhouse gases are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). The major emitter of the gases is livestock, espe- cially enteric fermentation of cattle and manure (Reid et al. 2004). Several studies have found that under a SPS, methane emission can be reduced by increased di- gestibility of pasture and forage. Ibrahim et al. (2007) showed that the emission of methane decreased by 20% in pastures with high tree density compared to range- lands with low tree density, resulted from high protein content and low cellulose 25
  26. 26. contain in the forage. Belsky (1992) also proved that the digestibility of pasture under trees was higher than pastures without shade. Other aliment by trees such as foliages and fruits were also shown to have higher digestibility by 54–80% than grasses (Benavides 1999; Esquivel 2007). Enhancement of air quality by SPS was also addressed in a few studies (Current et al. 1995; Scheelje Bravo 2009). Current et al. (1995) argued that SPS has an effect of reducing density of dust in the atmosphere by functioning as windbreaks. Habitat Services SPS offers complex habitats for both the aboveground fauna and flora and the soil biota. A number of researches have proved that SPS has a capacity to facilitate various species by providing shelter, food and seed sources (Pagiola et al. 2004; Murgueitio et al. 2011; Milder et al. 2010). The SPS provides shelter for birds, butterflies, invertebrates and trees (Harvey et al. 2005; Milder et al. 2010; Mur- gueitio et al. 2011; Pagiola et al. 2004). Regarding bird biodiversity, Pagiola et al. (2004) argued that complex structure of vegetation in SPS provides birds with nesting substrates and protection from predators. Milder et al. (2010) observed higher bird diversity in live fences than in pastures without trees. Diverse butterfly species were also found in live fences using the bushes as habitats (Milder et al. 2010). S´aenz et al. (2007), meanwhile, showed that 6.3% of bird species found in SPS were the species whose population has been decreased at the regional level, indicating SPS’s roles in conservations. Food availability for animals in SPS is another factor in increasing biodiversity. Harvey et al. (2005) observed birds in live fences feeding on shrubs, vines, mistletoes and nectars in flowers in tropical SPS. Biodiversity in soils are also enhanced by SPS (Murgueitio et al. 2011). Dennis et al. (1996) showed that there were more species of invertebrates in the systems of pastures with high tree density compared to the pastures without trees. Harvey et al. (2006) also observed increased diversity of dung beetles with increase of tree cover in pastures in Nicaragua. A few studies, meanwhile, have shown that SPS is favorable for natural regener- ation of trees, which increases tree diversity at landscape level (Harvey et al. 2005; Pagiola et al. 2004). Tree components in pastures, such as live fences and scat- tered trees, function as a foci for seed dispersal and plant recruitment (Esquivel 26
  27. 27. 2007). Pagiola et al. (2004) also argued that propagation of native tree species under scattered trees is high. SPS also supports conservation of remnant forests by enhancing ecological connectivity and reducing pressure on the forests. Especially live fences pro- vide structural connectivity between forest patches (Harvey et al. 2005). Har- vey et al. (2005) observed that birds utilize live fences to move between rem- nant forests. Multi-strata fences were, moreover, reported to maintain 56% of the species in nearby forests, increasing connectivity between forest fragments (Tobar and Ibrahim 2010). S´aenz et al. (2007) also found that 33.2% of bird species in SPS were forest-dependent species and 60.5% requires forest patches, which indi- cates that SPS provides stepping stones for birds in forest patches. Connectivity of disturb-tolerant bird species also tended to increase in Honduras (Sanfiorenzo 2008). Not only the birds but bats were also found to move between forest patches using trees in SPS (Medina et al. 2007). Some studies, however, refuted to the positive impacts of SPS on biodiversity. Ram´ırez (2007) argued that only few forest-dependent bird species were observed in SPS, indicating its limited function for conserving biodiversity in remnant forests. Harvey et al. (2006) also found that diversity of dung beetles and butterflies had no relation with tree cover. Cultural Services The cultural services of SPS were barely investigated in tropics. A few studies on cultural values of silvopastoral systems were conducted in Europe. Franco et al. (2003) discussed scenic beauty of traditional agroforestry –a system with inte- gration of crop and trees – in Italy. Ispikoudis and Sioliou (2004) described that silvopastoral systems in Europe have values as a cultural heritage associated with old traditions, and aesthetics of the landscape. In tropics, it was found that farm- ers appreciate the scenic beauty of trees on pastures in Costa Rica (Plata 2012). 27
  28. 28. Chapter 3 Materials and Method 3.1 Study Site The study was conducted in Rio Jesus Maria Watershed (N449893.481 E1106874.654– N423033.769 E1089910.626, WGS84) located in Central Pacific Region (Fig.3.1). This area was chosen because there was accumulated data that can be utilized in estimating the ES values. Several studies described the status of Silvopastoral systems of the region such as the structure of vegetations, which provided general information of the current status of SPS in the region (Bautista Sol´ıs 2005; Plata 2012; Villanueva Najarro et al. 2013). Zamora-L´opez (2006) and Vega (in-press), furthermore, investigated the carbon sequestration rates of biomass on farms and emissions from the cattle production activities, which provide proxy information for estimating carbon balance. FONAFIFO-CATIE (2011) have developed a model of the hydrological system in the region that could be used in water-based ES val- uation. Also Chagoya (2004) and Bravo et al. (2012) have conducted a financial analysis on SPS. For a cost-efficiency, Jesus Maria River watershed was selected maximizing its benefits of data and proxies as suggested by UNEP-WCMC (2011). The study site locates Puntarenas Province and Alajuela Province. The area of Rio Jesus Maria is 352.8 km2 . The watershed administratively consists of districts of Esparza, Montes de Oro of Puntarenas Province, and San Mateo, Orotina and San Ram´on of Alajuela Province. The region is of rainy and dry tropical climate. The annual precipitation is 2200–3300 mm, where the average is 2780 mm (FONAFIFO-CATIE 2011). The region receives 91% of the rainfall between May and November, which is called the rainy season (FONAFIFO-CATIE 2011). Evapotranspiration is from 1,000 28
  29. 29. Figure 3.1: Location of the study site, Jesus Maria River Watershed, Costa Rica. The black line marks the boundary of the watershed. (Source: ITCR 2008; Open- StreetMap 2016) to 12,000 mm (FONAFIFO-CATIE 2011). The altitude ranges between 0 and 1440 meters from the sea level (FONAFIFO-CATIE 2011). In the region the dry season is five month long, and the number of rainy days is 190 days (IMN 2016; FONAFIFO-CATIE 2011). The average temperature is 24.8◦ C, and the average relative humidity is 71.5% (IMN 2016). The dominant soil type is Alfisols (T. Haplustalfs), characterized by a sandy surface layer and increasing clay content in the lower layers (ITCR 2008; Sharma et al. 2005). According to the classification system of life zones of Costa Rica, the watershed includes six types of life zones: 1) Tropical moist forest (10,364ha), 2) Premontane to lower montane wet forest (5,279.5ha), 3) Premontane wet forest (4,359.8, 4) Premontane to lower montane moist forest (3,927.5ha), 5) Tropid- cal moist to perhumid forest (3,137.5ha), and 6) Tropical moist to dry forest (2,293.3ha) (Fig.3.2). The classification system is based on altitude (e.g. mon- tane, premontane, etc.), annual precipitation (e.g. forest, tundra) and humidity (e.g. wet, perhumid, moist, dry) (ITCR 2008; Hartshorn 1983). The tropical moist forest, which covers the largest area in the watershed, is characterized with 29
  30. 30. higher temperature (avg. 24-30◦ C) and premontane forests and more rainfall than dry and moist forests (Hartshorn 1983). The premontane and lower forest wet forests is the second largest zone in the area, which is considered most suitable for cattle production becuase of low temperature and water stress (Hartshorn 1983; FONAFIFO-CATIE 2011). Figure 3.2: Life zones in Jesus Maria River Watershed, Costa Rica In the watershed, there are 11,933 habitants, of which 40% dwell in Orotina (FONAFIFO-CATIE 2011). Emigration has been at a high rate in the region caused by little job opportunity and lack of human development (FONAFIFO- CATIE 2011). There are migrant labors from Nicaragua who work in farms of sugarcane, coffee and fruits temporarily (FONAFIFO-CATIE 2011). The major land-use is ‘Pastures with dispersed trees’, accounting for 37.7% of the territory, followed by secondary forest (22.3%) (FONAFIFO-CATIE 2011) (Fig.3.3). The site is one of the regions where cattle production is the major economic activity in Costa Rica (Ibrahim 2016; FONAFIFO-CATIE 2011). Fruit production and double-purpose cattle production, which produces both beef and dairy products, are the major economic activities in the region (Plata 2012; FONAFIFO-CATIE 2011). Monior activities include coffee production (3.4% of the area) and agricul- tural lands (1.4% of the area) for vegetables and fruits in a small-scale dispersed over the region (FONAFIFO-CATIE 2011). 30
  31. 31. Figure 3.3: Land uses in Jesus Maria Watershed, Costa Rica in 2005. Most of the ’no forests’ are covered by pastures with dispersed trees 3.2 Analytical Framework This study was conducted in four stages (Fig.3.4). The first step was to select key ecosystem services, the indicators to quantify the selected services, and the adequate methods of the valuation. Based on the chosen ecosystem services and indicators, required data was gathered through interviews with farmers and lit- erature review. Collected data was used to quantify each ecosystem service and estimate their economic values. Synergies and trade-offs between the ecosystem services were also examined. Lastly, socio-economic factors related to the adoption of SPS were identified. 3.3 Selection of Ecosystem Services to Valuate UNEP-WCMC (2011) suggested the followings to consider when selecting ES to evaluate and indicators to measure for performing a Ecosystem Service Valuation. • Clear objectives to avoid misinterpretation • Adoption of a small set of specific policy-relevant indicators • Valuation beyond provisioning services 31
  32. 32. Figure 3.4: Flowchart of the study • Utilization of existing data and proxies • Engagement of stakeholders, including mainstreaming ES and collaborating with other sectors • Linkage to national development plans Through the literature review, the important Ecosystem Services of SPS were identified. Provisioning services include ‘Enhanced provision of animal products’ and ‘Additional food and raw material provision from trees (e.g. fruits and tim- ber)’. The major regulating services were ‘Climate change mitigation’, ‘Improved soil quality’, ‘Flood control’ and ‘Water purification’. The habitat services dis- cussed were ‘Supply of habitats’ and ‘Buffer for remaining natural areas’. The Cultural services were rarely studied. Among the services mentioned above, a few ecosystem services were selected for the valuation by the following criteria. • National priorities • Recognition by farmers • Easiness in measuring and monetizing • Data availability To include ecosystem services that are concerned important in the country, the ecosystem services designated in the National Program of Payment for Ecosystem Services (PES) were considered. The services in the PES were watershed con- servation, biodiversity, conservation and social development (Daniels et al. 2010). Since the Costa Rican government targets to achieve National Carbon Neutrality by 2021, the carbon regulating service was included in the research scope (MIDE- PLAN 2014). The ecosystem services recognized important by farmers were also considered to indirectly engage the key stakeholders. Plata (2012) has shown that 32
  33. 33. cattle farmers in the region tend to perceive ecosystem services of shades for an- imal production, timber production, biodiversity, protection of water sources and scenic beauty. Table 3.1: Key ecosystem services of Silvopastoral Systems analyzed by criteria for indicator selection Ecosystem Services of SPS National Priority Recognition by Farmers Easy to Measure Data Availability Enhanced provision of animal products o o o Additional food and raw material provision o o o Climate change mitigation o o o Improved soil quality Water purification Watershed protection o o Supply of habitats o o o Buffer for remaining natural areas o Scenic beauty o Due to the limited time and labor for investigation, services that are difficult to measure and lacks proxy data, such as ‘Buffer for remaining natural areas’ and ‘Scenic beauty’, were excluded in the valuation (Table 3.1). ‘Habitat provising service’ was not included into the services to monetize but was quantified and 33
  34. 34. compared with other services in analyzing trade-offs. ‘Waste purification’ and ‘Watershed protection (run-off)’ was not be able to included even though they were one of the highly prioritized ES, and there was a hydrological model devel- oped in the region, because the permission to the model was not given for this study. ‘Improved soil quality’ was excluded from the valuation to avoid double- counting, as the benefits of the enhanced fertility was considered to be integrated in the provision service of animal and tree products by increasing productivity of the pasture and the trees. Table 3.2: Selected ecosystem services for valuation Category Service Indicator Provisioning Food Annual yield of meat, milk and fruit Raw Material Annual yield of timber Regulating Mitigation of Climate Change Carbon balance In result, the selected services were the provisioning service of food (milk, beef and fruit) and raw materials (timber), and the regulating service of climate change mitigation (Table 3.2). To quantify and monetize the chosen services, indicators that were easy to measure and frequently used in other studies were selected (De Groot et al. 2012; J´onsson and Dav´ıðsd´ottir 2016; UNEP-WCMC 2011). The pro- visioning services were measured by the annual amount of production per area and were monetized with the domestic market price using a direct market valuation approach. To estimate the regulating service, carbon balance (Carbon sequestra- tion in aboveground biomass subtraced by carbon emission in animal production) was calculated and monetized with the domestic carbon price compensated by Na- tional Forest Financing Fund of Costa Rica (FONAFIFO). 3.4 Data Collection Structured interviews were conducted with cattle farmers in Rio Jesus Maria re- gion from 11th to 20th of May in 2016. Beef producers and double-purpose (beef 34
  35. 35. and dairy) producers were targeted since they attribute to 94% of cattle produc- tion in Costa Rica (MAG-CATIE 2010), and there were few milk producers in the region. In the region, 29 farmers participated the interview, including 21 double- purpose farms and eight farms producing only beef. The number of farmers for the interview was allocated through stratification by the land size of each district. The interviewees were randomly chosen within each district. Through the interviews, the farmers’ socio-economic information, productivity and costs of meat, milk, forage, fruit and timber, and amount of auto-consumption of the farm products were examined. The socio-economic information included gender, age group, education level, income group, economic dependency on cattle production, other income sources, and number of family members. During the interviews, boundaries of the farms and land-uses within the farms were marked on maps. Status of live fences such as tree species and reasons for selection of the species was also investigated. Attitudes towards SPS, existence of capacity in SPS management, existence of technical assistance in SPS, and challenges in adopting or enhancing SPS were asked. 3.5 Valuation of Ecosystem Services The ecosystem services in the farms were quantified and valuated in economic terms. The quantification used the data obtained through the interviews. For the economic valuation, the Direct Market Method was used since all the chosen services had actual markets. The average prices of the items in the domestic market were taken in the period between 2015 and 2016. The inflation rate between the year of 2015 and 2016 was neglected since it fluctuated between -1% and 1% (BCCR 2016). The local currency was converted to 2015 International Dollar, which is an adjustment based on Purchasing Power Parties (PPP). This adjustment allows comparisons of prices relative to income, or purchasing power (Costanza et al. 1997; De Groot et al. 2012). In the calculation, 1 2015-International $ was equal to 380.12 Costa Rican Colones (CRC) (OECD 2016). 3.5.1 Provisioning Service Valuation In estimation of the provisioning services of meat, milk, fruit and timber, the val- ues were categorized into direct and indirect values. The direct value means the values transacted in the domestic market while the indirect value refers to the 35
  36. 36. auto-consumption by the farmers. The amount of meat production was calculated by subtracting purchase weight of cattle from sale weight each year. It was assumed that 50% of the live weight is dressed out by slaughter (Beef and Zealand 2016). To estimate the economic value of meat production, the slaughtered weight was multiplied by the average of prices of beef in the domestic market between June 2015 and May 2016 (CORFOGA 2016). To estimate the milk provisioning value, milk productivity (L/cow/day) each in dry and rainy seasons was obtained by the interviews. Assuming that the du- rations of dry and rainy season are 213 days 152 days respectively, the quantity of the annual milk production was calculated. The economic values were estimated using the average of the domestic consumer prices between June 2015 and May 2016, which was $1.51/L (573 CRC). Fruit and timber production were quantified based on the results of the inter- views on annually-harvested species and their amount. For the economic valuation the average of domestic fruit prices between March 2015 and February 2016 was used (System of Agricultural Product Information, 2016). For valuation of timber, the stumpage prices in 2015 were used (Barrantes and Ugalde 2015). 3.5.2 Calculation of Carbon Balances The carbon balances of the farms were calculated by subtracting carbon emissions from the farm activities from carbon sequestration rate of the farms. The carbon emissions consist of methane (CH4) emitted from enteric fermentation of animals and from manure, carbon dioxide (CO2) and nitrus oxide (N2O) emissions from application of fertilizers and herbicides, and CO2 emissions from energy use such as diesel, gasoline and electricity. In the calculation, the Carbon Emission Cal- culater developed by Regional Fund for Agricultural Technology (FONTAGRO) (unpublished) was used. The emission calculator developed by FONTAGRO is a model of carbon emission in livestock sector in Costa Rica. The model is based on an emission model of IPCC (2006) but modified the parameters according to local measurements. The carbon sequestrations by farms were estimated based on preceding re- 36
  37. 37. searches on carbon sequestration rates by land-uses (Table 3.3) (Ibrahim et al. 2007; Zamora-L´opez 2006). The annual carbon sequestrations (tCO2/year) were calculated by the areas of each land-use. The areas of each land use was obtained by asking farmers to mark the land uses on maps. The drawings were analyzed using QGIS (version 2.16.0) and the atlas of Costa Rica (ITCR 2008), through which the areas by land uses were calculated. The criterion to designate ‘high tree density’ and ‘low tree density’ was the tree density of 30 trees per hectare, of which the diameter is larger than 5cm and the height is greater than 2m (Zamora-L´opez 2006). The carbon price of $7.5/tCO2 set by a compensation scheme for reducing carbon emission by FONAFIFO was used to calculate the value of the carbon regulation service of the farms (FONAFIFO 2016). Table 3.3: Carbon sequestration rates by land uses Land Use Carbon Sequestration Rate (tCO2/ha/year) Secondary forest (<20 years) 9.50 Riparian forest 9.50 Secondary shrubby vegetation 10.28 Improved pasture with high tree density 8.55 Forage bank (woody) 5.03 Multi-strata live fence 8.00 Naturalized pasture with high tree density 6.24 Timber plantation (monoculture) 11.78 Naturalized pasture with low tree density 4.95 Improved pasture with low tree density 4.95 Simple live fence 2.61 Naturalized pasture without trees 0.15 Improved pasture without trees 4.77 37
  38. 38. 3.5.3 Biodiversity To estimate the biodiversity in the farms, Ecological Index for Biodiversity devel- oped by the Integrated Silvopastoral Systems for Ecosystem Management project was used. The Ecological Index for Biodiversity is a tool to estimate the level of biodiversity by land use developed by Regional Integrated Silvopastoral Ecosystem Management Project (Pagiola et al. 2004). The index was scaled from 0 as the most biodiversity-poor land use to 1 as the most biodiversity-rich (Table 3.4). The points were assigned by a panel experts, and later the point system was demon- strated by a follow-up project that measured and compared biodiversity by land use in Costa Rica, Colombia and Nicaragua (Pagiola et al. 2004). Ecological Index = Sum of [Index] x [Percentage of each land use] The same land-use data used in the estimation of carbon sequestration was used to calculate the Ecological Index. 3.6 Data Analysis The total values of ecosystem services in the Silvopastoral Systems were compared by farm type (double-purpose and beef production) and groups with different in- tensification level. The intensification refers to compact resource input into the production and productivity per unit area. The level of the intensification is often represented by animal density, land use, input of supplementary aliments, breed, labor input and farm size. Among the variables, most determinant variables were chosen for a hierarchical cluster analysis. When conducting the cluster analysis, the variables were normalized in order to avoid large figures determining the clas- sification. The cluster analysis was done using R Statistics (version 3.3.1.). Trade-offs between the ecosystem services were analyzed by a Spearman’s Cor- relation Analysis, a non-parametric method, using R since most of the dataset did not follow a normal distribution. T-test and the Mann-Whitney U test (so- called Wilcoxon rank sum test), a parametric and non-parametric method each, were used to identify significance of differences between two sets of data (Fay and Proschan 2010). To decide analysis tools between parametric and non-parametric methods, the normalities of the variables were examined by Wilk-Shapiro Normal Test. 38
  39. 39. Table 3.4: Ecological Index for Biodiversity Land-Use Ecological Index Primary forest 1 Secondary forest 0.9 Riparian forest 0.8 Secondary shrubby vegetation 0.6 Improved pasture with high tree density 0.6 Forage bank 0.6 Multi-strata live fence 0.6 Mixed species orchard 0.6 Naturalized pasture with high tree density 0.5 Timber plantation (monoculture) 0.4 Naturalized pasture with low tree density 0.3 Improved pasture with low tree density 0.3 Simple live fence 0.3 Naturalized pasture 0.1 Improved pasture 0.1 Degraded pasture 0 To identify socio-economic factors associated with adoption of SPS, economic factors including existence of subsidy, income, and economic dependency on cattle farming, and social factors such as age, education level, capacity in SPS man- agement, and frequency of capacity building were analyzed. As an indicator of adoption level of SPS, the Ecological Index within production areas was used. The level of SPS was defined as tree density on pastures, existence of forage bank and complexity of live fences. Hence, a farm with a high level of SPS has high tree density on the grazing lands, forage banks and multi-strata live fences, whereas a farm with a low level of SPS has low tree density on the pastures and may have simple live fences without a forage bank. To represent the level of SPS, the Eco- logical Index within production areas excluding forests and natural regenerations was regarded suitable because the index reflects both the level of SPS and areas 39
  40. 40. of those SPS elements. Within a range between 0.1 and 0.6 (due to exclusion of forests and degraded pastures), pastures with high tree density, forage bank and multi-strata live fences have values between 0.5–0.6, while the values of pastures no or little tree density and simple live fences range from 0.1 to 0.3 (Table 3.4). The calculation is also based on the percentage of areas of each land uses. The total Ecological Index on production areas, therefore, is higher in farms with larger application of tree density, forage banks and multi-strata live fences. Based on the indicators mentioned above, correlation analyses and t-test or the Mann-Whitney U test were conducted between the socio-economic factors and the adoption level of SPS. 40
  41. 41. Chapter 4 Results 4.1 Farm Characteristics 4.1.1 Spatial Distribution of Farms The farms of the interviewed 29 farmers were located as displayed in Figure 4.1. Mostly the number of farmers were close to the desired number of farmers as- signed by the percentages of land size of the districts within the Jesus Maria River Watershed (Table 4.1). In some of the regions, however, the actual number of interviewees did not match the allocated sample size due to the land use status and chance to encounter farmers. For example, Orotina was a residential area where cattle farms rarely exist. There were also two farms located outside the watershed in San Jeronimo in Esparza, of which the owners were met in the study area, but the location of the farms were identified later. Since estimation of the water production service was excluded from the valuation, the farms outside were included in the analyses since there is little difference in the socio-economical and ecological context between San Jeronimo and the watershed. Most of the farms were located in the medium-high region of the watershed (Fig. 3.1). The studied farm lands were covering 2.4–7.6% of the district within the watershed except in Hacienda Vieja (Table 4.2). The area of the investigated farms was 30% of the district, caused by a large-scale farm. Only one farm was examined in Hacienda Vieja, but it was the biggest farm in the study covering 236.5ha. 41
  42. 42. Table 4.1: Coverage of districts inside the watershed and desired and actural num- ber of farms by districts (*San Jeronimo is located outside the watershed.) Canton Disctrict Area (%) Desired sample size No. of samples Atenas San Isidro 0 0 0 Atenas Jesus 0 0 0 Esparza Macacona 5 1 1 Esparza San Jeronimo * 0 2 Esparza San Rafael 12 3 5 Esparza San Juan Grande 10 3 0 Orotina Hacienda Vieja 2 1 1 Orotina Mastate 3 1 0 Orotina Orotina 2 1 0 Orotina Coyolar 0 0 0 Orotina Ceiba 15 4 0 Palmares Santiago 0 0 0 San Mateo San Mateo 22 6 6 San Mateo Desmonte 6 2 3 San Mateo Labrador 7 2 3 San Mateo Jesus Maria 6 2 3 San Ramon San Rafael 5 1 0 San Ramon Santiago 6 2 5 42
  43. 43. Table 4.2: Land sizes of administrative divisions, the studied farm areas and their coverage in each distict in Jesus Maria River Watershed (*San Jeronimo is located outside the watershed.) Canton Disctrict Area (ha) Farm Area (ha) Percentage (%) Atenas San Isidro 1.3 0 – Atenas Jesus 1.0 0 – Esparza Macacona 1,467.6 46.0 3.1 Esparza San Jeronimo * 74.2 – Esparza San Rafael 3,424.3 150.2 4.4 Esparza San Juan Grande 2,843.1 0 – Orotina Hacienda Vieja 728.4 236.5 32.5 Orotina Mastate 847.6 0 – Orotina Orotina 638.0 0 – Orotina Coyolar 83.3 0 – Orotina Ceiba 4,267.8 0 – Palmares Santiago 0.4 0 – San Mateo San Mateo 6,322.4 456.6 7.2 San Mateo Desmonte 1,774.5 41.9 2.4 San Mateo Labrador 2,116.7 112.2 5.3 San Mateo Jesus Maria 1,875.2 82.4 4.4 San Ramon San Rafael 1364.9 0 – San Ramon Santiago 1,624.6 122.6 7.6 43
  44. 44. Figure 4.1: Division of districts and locations of the investigated farms (*note that the black line marks the boundary of the Jesus Maria River Watershed, and the circles are the farms with their ID) 4.1.2 Production Status Farm sizes varied from 2.5 ha to 236.5 ha, among which most of the farms were small-medium scale (Table 4.3)(Fig.4.2). The average stock size was 1.8 AU/ha (Table 4.3). Table 4.3: Sizes of the farmlands and the stock Item Mean S.D Median Min. Max. Total Area (ha) 45.3 53.6 28.0 2.5 236.5 Production Area (ha) 32.6 36.2 18.7 2.0 145.0 Animal Stock (AU/ha) 1.8 1.6 1.6 0.3 7.9 4.1.3 Socio-Economic Status Regarding the socio-economic status of the farmers, the major age group was 50– 59 years (Fig.4.3), and the final education of most farmers was ‘Primary school 44
  45. 45. Figure 4.2: Distribution of farm sizes (*note that each box displays the first and third quartiles as the left-end and right-end of the box, median in the band inside the box and outliers as circles) completed’(Fig.4.4). The majority of the farmers earned more than 400,000 CRC (around $1,050) monthly in total, including economic activities outside the farms (Fig.4.5). The farmers’ economic dependency on cattle production was 63% in av- erage, where 28% was fully dedicated in the livestock production. Other economic activities included pig farming, chicken farming, fruit monoculture, pension, house rent and profession such as doctor and professor. Among the farmers, 24% was receiving subsidies in a form of donations of aliment, equipment, seeds of pasture and forage plants from agricultural government bodies such as MAG and COR- FOGA. Figure 4.3: Frequency of age of the farmers 45
  46. 46. Figure 4.4: Education level of the farmers Figure 4.5: Income of the farmers 4.1.4 Land Uses and Silvopastoral Systems The land use analysis showed that the major land cover of the farms was ‘Im- proved pasture with low tree density’ (34%), followed by ‘Improved pasture with high tree density’ (27%) and ‘Riparian forest’ (11%) (Table 4.4). All farms had dispersed trees on their farms, which were mostly naturally regenerated. In a few farms (10% of the farmers), some trees were planted on the pastures for fruit and timber production and for shades for the animals and human. 46
  47. 47. Table 4.4: Land uses within farms Land Use Average (%) S.D. Secondary forest 7.40 11.04 Riparian forest 11.41 12.37 Natural regeneration 1.09 3.99 Improved pasture with high tree density 27.27 20.51 Forage bank 2.15 3.22 Multistrata live fences 8.41 8.50 Natural pasture with high tree density 2.02 6.72 Timber plantation 0.70 2.71 Natural pasture with low tree density 0.08 0.46 Improved pasture with low tree density 34.14 21.12 Simple live fences 1.48 3.30 Improved pasture 3.84 8.90 Silvopastoral systems include pastures with trees of high density, simple and multi-strata live fences and forage banks. Most of the farms (93%) had part of pastures with high tree density (>30 trees/ha), although pasture with low tree density was dominant in most of the farms. The tree species that exist inside the paddocks mentioned by the farmers were Cordia alliodora, Enterolobium cy- clocarpum, Tabebuia rosea, Gliricidia sepium, Cedrela odorata and Diphysa ameri- cana. Use of multi-strata live fences was reported in 86% of the farms, while simple live fences were reported in 28% of the farms. The structure of live fences were mostly multi-strata live fences combined with dead fences. The farmers tended to select fast-growing tree species for the live fences such as Bursera simaruba, Jat- ropha curcans and Gliricidia sepium. Especially Jatropha curcans was preferred by the farmers for low chance of damage by the cattle. Regarding the forage bank, 59% of the farms had a forage bank of gramineous plants such as Pennisetum sp. and sugarcane (Saccharum sp.), among which only 3 farmers had fodder bank of leguminous perennial plants such as Cratylia sp.. The majority of the farmers (69%) were interested in introducing or enhancing a Silvopastoral System on their farms (e.g. establishment or increase of forage bank and live fences, utilization of products from trees). The most common challenge for farmers to adopt or enhance the silvopastoral system was lack of labor (’Labor’ 47
  48. 48. in Table 4.5). The farmers also mentioned other economic limitations such as high cost for establishing forage bank or multi-strata live fences (’Cost’) and reduction of pasture productivity (’Productivity’). Another challenge was the complicated administrative process through which farmers need to get permission to harvest and sell timber (’Process’). Other obstacles reported by the farmers include lack of space within the farm for establishing more forage bank or live fences(’Space’), and steep terrain restricting fruit or timber harvest (’Slope’). Table 4.5: Challenges in adopting or enhancing silvopastoral systems Limits Labor Cost Process Space Productivity Slope No. of farmers 13 6 5 4 3 3 Classification by Level of Intensification For categorization of farms by intensification, the most determinant variables were identified through a cluster analysis as follows: stock size, supplementary aliment, and land use. As indicators of the variables, AU per ha, amount of annual expense on supplementary aliment, and the Ecological Index within production areas (pas- tures, live fences and forage bank) were used. Based on the chosen variables, the farms were classified into Group A, B and C (Fig.4.6). The sizes of the Group A, B and C were 18 farms, five farms and five farms each. One farm was excluded in the classification due to its distinct feature from others. Table 4.6: Characteristics of intensification groups (*note that prices are in 2015- International Dollars, and that the Ecological Index ranges from 0.1 to 0.6.) Group A B C Value Average S.D. Average S.D. Average S.D. Total Area (ha) 27.43 21.33 24.69 20.95 138.57 66.67 Stock Size (AU/ha) 2.55 1.29 1.34 0.71 0.85 0.49 Expense in Aliment ($/AU/year) 160.54 126.95 349.29 205.14 69.96 67.22 Ecological Index in Production Area 0.40 0.09 0.49 0.03 0.50 0.10 48
  49. 49. Figure 4.6: Classification of the farms by intensification indicators Group A was characterized as small-scale, large stock size per area, medium- scale of invest on supplementary aliment and relatively low biodiversity value on the farm (Table 4.6). Group A showed the lower ecological index (0.41) than the other two groups B(0.49) and C (0.50) (p<0.05). Such characteristics indicate that the farms in the Group A use the pasture lands intensively with high density of animal per area and high dependency of feed on grazing, hence fewer trees on the pastures. Group B is also small-scale farms like Group A, but its stock density is lower than the Group A, and the expenditure on supplementary aliment is the largest among the groups. Its Ecological Index of the pastures is higher than Group A. These features show that the livestock feeds more on the supplementary aliment rather than the pasture, resulting low intensity of grazing. Group C, lastly, is characterized as large farm sizes (average of 138.6 ha) and low stock density, low expense on supplementary aliment and high biodiversity value. These features suggest that the farms in the Group C are in an extensive production, with sufficient amount of pastures, hence not depending much on additional feeding. 49
  50. 50. 4.2 Values of Ecosystem Services 4.2.1 Quantification of Ecosystem Services Provisioning Services There were large variances in the amount of production of meat, milk, fruit and timber among the farms. A few farms (n=2) showed negative values in meat production due to large investment in purchasing cattle during the past year. Fruits were collected from the dispersed trees or live fences by 72% of the farmers, among which 52% sold the fruits to markets directly or through an intermediary. The fruits include mango(Mangifera indica), avocado (Persea americana), lemon (Citrus latifolia), orange (Citrus sinensis), mamon (Melicoccus bijugatus), guyaba (Psidium guajava), mara˜non (Anacardium occidentale), cas (Psidium friedrich- sthalium) and coyol (Acrocomia aculeata). It was shown that most of the farmers who harvested fruits from their farms (79% of the farmers) do not manage the fruit trees, not conducting planting, pruning or fertilizing. In terms of timber production, 72% of the farmers were utilizing trees on their farms for timber, mostly for construction in the farms. The major source of the tim- ber was dispersed trees on the pasture. The most commonly utilized species were Gliricidia sepium, Cordia alliodora, Diphysa americana, Cedrela odorata, Tabebuia rosea, Enterolobium cyclocarpum and Teak (Tectona grandis). Among the timber users, only 14% (n=3) had sold timber for extra income and 29% (n=5) performed management activities such as planting (n=1) and pruning (n=5). Table 4.7: Quantity of provisioning services Product Mean S.D Median Min. Max. Beef (kg/ha/year) 81.6 99.7 48.9 -88.3 348.0 Milk (L/ha/year) 1,563.4 1,722.3 1,132.7 0.0 6,413.8 Fruit (kg/ha/year) 209.2 500.9 26.6 0.0 2,014.4 Timber (m3 /ha/year) 1.1 1.8 0.2 0.0 7.6 Among the groups classified by the level of intensification, there was no signifi- cant differences in the amount of meat production although the Group C showed a relatively low beef production per area in average (Table 4.8). Likewise, the fruit 50
  51. 51. production was not significantly different among the groups, but there was a ten- dency that Group A produces more fruits per area. Milk productivity, meanwhile, tended to be high in Group A and B. The difference in milk production between Group A and B was statistically insignificant. There was no difference in timber production between the groups at a significant level (95%). Table 4.8: The averages of quantified annual production of beef, milk, fruit and timber by intensification group Intensification Group A B C Beef (kg/ha/year) 80.3 103.8 10.5 Milk (L/ha/year) 1,972.5 1,331.3 107.1 Fruit (kg/ha/year) 279.0 87.8 121.2 Timber (m3 /ha/year) 1.0 1.3 1.6 Regulating Services The average carbon balance was positive, showing that in general the farms in the region function as a carbon sink (Table 4.9). There were, however, a few farms where the carbon emissions were greater than the sequestration (n=4). Digestion and manure of non-milking cows was the major source of emissions, contributing in average 60% of the total emission. Emission from milking cows and chemical uses (ferilizers and herbicides) accounted for 16% and 15% of the emission respectively. The emission tended to have a positive relation with the expense on additional aliment supply such as cereals and sugarcane (p<0.05, 0.38). Table 4.9: The average rates of carbon sequestration, emission and net carbon sequestration Carbon Flow Mean S.D Median Min. Max. Sequestration (tCO2/ha/year) 6.7 1.5 6.9 1.8 8.9 Emission (tCO2/ha/year) 5.0 6.1 3.2 0.6 32.1 Net Sequestration (tCO2/ha/year) 1.7 6.6 3.9 -25.3 8.3 Between the intensification groups, significant dissimilarities in carbon flows were detected. The carbon sequestration rate was significantly higher in Group C 51
  52. 52. than Group A and B (p<0.05) (Table 4.10). Regarding emissions from the produc- tion activities, the emission in Group C was substantially smaller than the other groups (p<0.05). There was no significant difference in emission rates between Group A and B. Although the average emission rate was higher in Group B (8.6 tCO2/ha/year) than Group A (4.4 tCO2/ha/year), it seemed that the difference was induced by one farm in Group B with an extremely high emission rate. The median of the emission rate of Group B (2.71 tCO2/ha/year) was, in fact, lower than that of Group A (4.37 tCO2/ha/year). In comparison of the net sequestra- tion rates, Group C showed the highest value among the groups. Although the average net rate was lower in Group B with the negative value than that of Group A, it seemed to be derived from the same farm with extremely large emission in Group B. The median was high in Group B (4.7 tCO2/ha/year) compared to that of Group A (1.8 tCO2/ha/year). Table 4.10: The average rates of carbon sequestration, emission and net seques- tration by groups of intensification Intensification Group A B C Sequestration (tCO2/ha/year) 6.3 7.3 7.8 Emission (tCO2/ha/year) 4.4 8.6 1.1 Net Sequestration (tCO2/ha/year) 1.9 -1.3 6.7 Biodiversity The level of biodiversity was quantified using the Ecological Index for Biodiversity. The Ecological Index of entire farms including secondary and riparian forests was 0.52 in average (Table 4.11), indicating that their biodiversity is slightly above the biodiversity in ‘Naturalized pasture with high tree density’. Excluding forests within the farms and calculating the index only for the areas used for production, the Ecological Index was 0.44, closer to the level of biodiversity between ‘Pastures with low tree density’ and ‘Naturalized pasture with low tree density’ (Table 4.11). There was no evidence of differences in biodiversity between farm type (double- purpose and beef) and intensification groups. 4.2.2 Total Economic Value The total ES value from the current SPS was estimated as $3,318.7/ha/year, rang- ing from -$359.6 to $9,791.1/ha/year (Table 4.12). Provisioning service of the milk 52
  53. 53. Table 4.11: Calulated Ecological Index for Biodiversity Item Mean S.D Median Min. Max. Entire farm 0.52 0.11 0.52 0.30 0.71 Production area 0.44 0.10 0.42 0.26 0.60 and meat products was the largest contributer of the total ES value, accounting for 83% of the total value in average (Table 4.12). Fruit and timber production values accounted for 10% and 5% respectively. Carbon value was the most minor value in the total ES value, contributing 2% of the total value. Table 4.12: Estimated values of ecosystem services (2015-International $/ha/year) (*Direct values refer to the values obtained by selling products to the market, whereas indirect values mean the values of products that was consumed in the farms.) Ecosystem Service Mean Median Min. Max. Beef 480.2 288.2 -519.7 2,048.9 Milk 2,356.7 1,707.5 0 9,668.4 Direct 2,309.1 1,682.7 0 9,665.2 Indirect 47.6 19.7 0 316.5 Fruit 340.4 36.4 0 3,186.6 Direct 222.1 0 0 3,091.0 Indirect (Human) 102.9 12.9 0 1,558.8 Indirect (Animal) 15.4 0.9 0 178.8 Timber 128.7 30.9 0 893.3 Direct 36.5 0 0 625.3 Indirect 92.1 20.4 0 593.2 Carbon 12.7 29.1 -189.8 62.6 Total Value 3,318.7 2,855.5 -359.6 9,791.1 Double-purpose (beef and dairy) farms tended to have higher ES values (avg. $4,345/ha/year) than the beef-only producers (avg. $624/ha/year) (p<0.05) (Fig.4.7). 53
  54. 54. Total farm size had a strong negative relationship with the ecosystem service val- ues per hectare (p<0.05, coefficient = -0.73) (Fig. 4.8). Figure 4.7: Estimated total Ecosystem Service value by farm type (*note that each box displays the first and third quartiles as the left-end and right-end of the box, median in the band inside the box and outliers as circles) Figure 4.8: Relation between farm size and total ES value (2015-International $/ha/year) Among the groups of different intensification levels, Group C with extensive 54
  55. 55. pastures had lower ES value per area ($686.7/ha/year) than the other groups (A: $4,033.6/ha/year, B: $2,853.7/ha/year), mainly attributing to the low meat and milk production per area (Table 4.13, Fig.4.9). There was, however, no evidence of difference in the total ES values between the Group A and B. Group C showed relatively higher Carbon value than the other two groups (p<0.05). The ES values were analyzed with the level of SPS represented as the Ecological Index in production areas. There was no significant correlation between the total value and the level of SPS. The fruit provisioning value, however, had a positive relation with the level of SPS (p<0.05, coefficient = 0.71). The level of SPS was also positively related with the carbon value (p<0.05, coefficient = 0.57). Table 4.13: Estimated values of ecosystem services by groups with different level of intensification (2015-International $/ha/year) (*Direct values refer to the values obtained by selling products to the market, whereas indirect values mean the values of products that was consumed in the farms.) Ecosystem Service A B C Beef 473.1 610.9 61.7 Milk 2,973.5 2,006.9 161.5 Direct 2,918.6 1,990.3 160.1 Indirect 54.8 16.6 1.3 Fruit 460.4 98.5 218.5 Direct 304.7 89.6 101.6 Indirect (Human) 132.1 5.5 115.7 Indirect (Animal) 23.6 3.3 1.3 Timber 112.4 147.0 194.7 Direct 24.1 0.0 125.1 Indirect 88.3 147.0 69.7 Carbon 14.2 -9.5 50.3 Total Value 4,033.6 2,853.7 686.7 55
  56. 56. Figure 4.9: Estimated total Ecosystem Service values in 2015 International dollars by intensification group (*note that each box displays the first and third quartiles as the left-end and right-end of the box, median in the band inside the box and outliers as circles) 4.3 Synergies and Trade-offs between Ecosystem Services Correlations between the provisioning service, the carbon regulating service and the Ecological Index were analyzed to identify synergies and trade-offs between the ecosystem services. In terms of synergies, the carbon value was positively related with the biodiversity index of the entire farms (p<0.05, coefficient=0.62) (Fig. 4.10). Meat and milk production in double-purpose farms also tended to have a positive correlation (p=0.07, coefficient=0.45). A trade-off, meanwhile, was found between milk provision and carbon regulation (p<0.01, coefficient=-0.79) (Fig. 4.11). 56
  57. 57. Figure 4.10: Relationship between Carbon regulation value and Ecological Index of farm (*note that the values are in 2015-International dollar) Figure 4.11: Relationship between milk provision value and carbon value (*note that the values are in 2015-International dollar) 57
  58. 58. 4.4 Socio-Economic Factors in Adopting Silvopas- toral Systems The adoption level of SPS, such as forage bank, dispersed trees on the pasture lands and live fences, was shown to have relations with economic dependency on livestock production, existence of subsidy, capacity in the SPS management and frequency of trainings in SPS management. In respect of economic factors, the group of the farmers with subsidies tended to have higher level SPS (0.49 of Ecological Index in production areas) than those without subsidies (0.43) (p=0.09) (Fig. 4.12). The economic dependency on farm activities also showed a significant relation with the level of SPS. The farmers were divided into two groups: high and low economic dependency. Farmers with high economic dependency refers to the producers of which over 80% of income comes from cattle farming. Farmers with low economic dependency means the producers of which less than 80% of their income derives from cattle production. The group of the farmers with high economic dependency showed lower SPS level (0.40 of the Ecological index in the production area) than the farmers with lower economic dependency (0.47) (p<0.05) (Fig. 4.12). Among the social factors, the frequency of training on SPS management showed a positive correlation to the level of SPS (p<0.05, coefficient=0.40) (Fig. 4.12). Farmers who have knowledge in SPS management, mostly on forage bank and live fences, seemed to have slightly higher Ecological index on the production area (0.46) than those without capacity (0.40) (p=0.11) (Fig. 4.12). 58
  59. 59. Figure 4.12: The Ecological Index of production areas by existence of subsidy, economic dependency on cattle farming, existence of capacity on SPS management and frequency of training related to SPS 59
  60. 60. Chapter 5 Discussions 5.1 Values of Ecosystem Services in Silvopastoral Systems The total Ecosystem Service value was $3,318.7/ha/year in average, mainly at- tributing to food provision ($3,177.3/ha/year). The estimated ES value was higher than the world average even though many ecosystem services, such as hydrological services and biodiversity value, were excluded in the evaluation. The global estima- tion of Ecosystem Services in grasslands including rangelands was $3,330/ha/year, where the major contributor was food provision ($1,383/ha/year) (De Groot et al. 2012). The high value can be explained by the intense land use in the region. The average stock size in Costa Rica (0.48–0.75 heads/ha) is relatively larger than other parts of the world (FAO 2015). For example, the herd sizes in the United States, the largest beef producing country in the world, range from 0.097 to 0.24 animals per hectare of production area (FAO 2015). Also the global estimation included natural pastures without grazing, where provisioning services are lower than rangelands, which may lower the average value. In this valuation, the total ES value is not comparable with total ES values of other regions or other ecosystems. It is because the only limited number of ecosys- tem services were included in the estimation. In this study, mainly provisioning services of food and raw materials were evaluated, and other key services such as waste treatment and erosion control were not taken into account. Indirect ES like regulating services often have larger contribution to the total ES value than provisioning services (Kumar 2010; Alam et al. 2014). For example, in temperate agroforestry systems in Canada, indirect values accounted for around 60% of the total value (Alam et al. 2014). Kumar (2010) estimated that at maximum 67% 60
  61. 61. of the total ES values derived from regulating services, while provisioning services accounted only for 23% in global grasslands. Based on the estimation by De Groot et al. (2012), the total ES value with inclusion of waste treatment, erosion control , and habitat services would be $4,865/ha/year at minimum. In this rough calcu- lation, the values of waste treatment, erosion control and habitat was 87, 51, and 1,408 2015-Int’l dollars/ha/year respectively. In the study, it was found that contribution of timber to the total ES value is minor. The timber provision value was mostly incurred by indirect uses, being utilized for constructions on farms. The value of timber provision to the market was only $36.5/ha/year in average. The limited provision is considered to be re- lated to the current regulation of the government in timber extraction. In Costa Rica, to harvest timber and sell to the market outside plantations, producers are not allowed to cut more than three trees per ha annually and need to apply for permission before harvesting (Plata 2012). In the interviews, farmers who do not sell timber (n=26) mentioned that they do not sell timber because the processes for permission is complicated, costly and time-consuming, and the margin is too small for the processes and costs. The results indicate that timber utilization is currently limited in SPS even though timber production is often mentioned as a major benefit in implementing SPS. Supporters of SPS have argued that producing timber in SPS stabilizes and increases farmers income. In the current regulation, however, it seems hard to expect such benefits from SPS. Fruit provision value was, meanwhile, relatively high, compared to timber pro- vision. Unlike timber, many farmers were selling fruits to the markets, which was higher than indirect values such consumption by families and animals. Greater utilization of fruits than timber, seems to be related to absence of regulations and access to the market. Harvest and sale of fruits from farms are not restricted by the government, which reduces transaction costs compared to timber production. Also there are intermediaries who harvest fruits in the farms and sell them to the markets. Existence of intermediaries allow farmers not to spend their labor on fruit production and provide extra income without efforts to search for markets. For the reasons, SPS at the current state in the region showed a substantial amount of fruit provision. In the estimation of fruit value, however, there is a chance of underestimation. It was recognized that farmers tended to provide information of fruit production 61

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