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Case study: Guatemala Vulnerability Assessment of Frozen Vegetable Value-chain to Climate Change Authors: A. Eitzinger, P. Läderach, L. Rizo, A. Quiroga, A. Pantoja, C. Bunn, J. Gordon International Center for Tropical Agriculture (CIAT), Managua, Nicaragua and Cali, Colombia Cali, Colombia, July 2011 1 Table of Contents 1. Summary and main findings ................................................................................................................. 7 2. Background ........................................................................................................................................... 8 2.1 Introduction of the research area .................................................................................................... 8 2.2 Introduction of "the case"................................................................................................................ 9 2.3 General problems of the people in the area .................................................................................... 9 2.4 Introduction of supply chains........................................................................................................... 9 2.5 New Concept i.e. Cooperative model ............................................................................................ 10 3. Challenges ........................................................................................................................................... 10 3.1 Impact of Climate Change on Agriculture ...................................................................................... 10 3.2 Climate Change in Guatemala ........................................................................................................ 10 4. Our methodology ................................................................................................................................ 11 5. Assessment of observed changes and farmer’s perceptions ............................................................. 12 5.1 Results from focal workshops ........................................................................................................ 12 Farmers perception of historical climate .................................................................................... 12 Farmers perception of natural capital ......................................................................................... 12 Farmers perception of physical capital ....................................................................................... 13 Farmers perception of human capital ......................................................................................... 13 5.2 Examples of farmer’s comments.................................................................................................... 13 Farmers Comments from Chimaltenango ................................................................................... 13 Farmer Comment from Sololá ..................................................................................................... 14 6. Climate change predictions for 2030 & 2050 ..................................................................................... 15 6.1 The summary climate characteristics for 2030 and 2050 .............................................................. 15 General climatic characteristics................................................................................................... 15 Extreme conditions...................................................................................................................... 15 Climate Seasonality ..................................................................................................................... 16 Variability between models ......................................................................................................... 16 6.2 Regional changes in the mean annual precipitation (2030) .......................................................... 16 6.3 Regional changes in the mean annual precipitation (2050) .......................................................... 17 6.4 Regional changes in the mean annual temperature (2030) .......................................................... 17 6.5 Regional changes in the mean annual temperature (2050) .......................................................... 18 6.6 Coefficient of variation of climate variables .................................................................................. 18 7. Exposure of most important crops to climate change ....................................................................... 19 7.1 Measure of agreement of models predicted changes ................................................................... 20 7.2 Broccoli........................................................................................................................................... 21 Current suitability ........................................................................................................................ 21 2 Suitability for Broccoli by 2030 .................................................................................................... 22 Suitability for Broccoli by 2050 .................................................................................................... 22 Change in suitability by 2030....................................................................................................... 23 Change in suitability by 2050....................................................................................................... 23 7.3 Sweet pea ....................................................................................................................................... 24 Current suitability ........................................................................................................................ 24 Suitability for Sweet pea by 2030 ................................................................................................ 24 Suitability for Sweet pea by 2050 ................................................................................................ 25 Change in suitability by 2030....................................................................................................... 25 Change in suitability by 2050....................................................................................................... 26 7.4 Corn ................................................................................................................................................ 26 Current suitability ........................................................................................................................ 26 Suitability for Corn by 2030 ......................................................................................................... 27 Suitability for Corn by 2050 ......................................................................................................... 27 Change in suitability by 2030....................................................................................................... 28 Change in suitability by 2050....................................................................................................... 28 8. Availability and restrictions for agricultural production ..................................................................... 29 8.1 Land use ......................................................................................................................................... 29 8.2 Access ............................................................................................................................................. 30 8.3 Protection....................................................................................................................................... 30 8.4 Combined restrictions for agricultural production ........................................................................ 31 9. Vulnerability of farmer’s livelihoods to climate change ..................................................................... 32 9.1 Vulnerability Index ......................................................................................................................... 32 10. Sensitivity and Adaptive Capacity of Guatemalan farmers to climate change ............................... 35 10.1 Capital stock analysis ..................................................................................................................... 35 10.2 Cluster analysis............................................................................................................................... 37 10.3 Site-specific vulnerability ............................................................................................................... 37 11. Estimated Carbon Footprint ........................................................................................................... 38 11.1 Example for Broccoli ...................................................................................................................... 38 12. Strategies to adapt to the changing climate ................................................................................... 39 12.1 Farmer and supply-chain actors adaptation strategies ................................................................. 39 Communities from Sololá ............................................................................................................ 39 Communities from Chimaltenango ............................................................................................. 40 12.2 From research output recommended adaptation strategies ........................................................ 41 Crop production system .............................................................................................................. 41 Community’s vulnerability to climate change ............................................................................. 42 13. Conclusions ..................................................................................................................................... 43 14. References ...................................................................................................................................... 44 3 Table de Figures Figure 1: Research area................................................................................................................................. 8 Figure 2: Analytical framework. .................................................................................................................. 11 Figure 3: Farmers perceptions of historical climate trend.......................................................................... 12 Figure 4: Farmers perceptions on natural resources. ................................................................................. 12 Figure 5: Farmers perceptions on physical capital. .................................................................................... 13 Figure 6: Farmers perceptions on human capital. ...................................................................................... 13 Figure 7: Climate trend summary 2030 and 2050 for Guatemalan Highlands ........................................... 15 Figure 8: Mean annual precipitation change by 2030 for 5 study sites in Guatemala. .............................. 16 Figure 9: Mean annual precipitation change by 2050 for 5 study sites in Guatemala. .............................. 17 Figure 10: Mean annual temperature change by 2030 for 5 study sites in Guatemala. ............................ 17 Figure 11: Mean annual temperature change by 2050 for 5 study sites of Guatemala. ............................ 18 Figure 12: Coefficient of variation for annual precipitation and temperature 2030 and 2050. ................. 18 Figure 13: Measure of agreement of models predicting changes in the same direction as the average of all models at a given location for 2050. ..................................................................................................... 20 Figure 14: Current suitability for broccoli. .................................................................................................. 21 Figure 15: Suitability for broccoli by s2030................................................................................................. 22 Figure 16: Suitability for broccoli by 2050. ................................................................................................. 22 Figure 17: Change in suitability by 2030. .................................................................................................... 23 Figure 18: Change in suitability by 2050. .................................................................................................... 23 Figure 19: Current suitability for sweet pea. .............................................................................................. 24 Figure 20: Suitability for Sweet pea by 2030. ............................................................................................. 24 Figure 21: Suitability for Sweet pea by 2050. ............................................................................................. 25 Figure 22: Change in suitability by 2030. .................................................................................................... 25 Figure 23: Change in suitability by 2050. .................................................................................................... 26 Figure 24: Current suitability for sweet pea. .............................................................................................. 26 Figure 25: Suitability for Corn by 2030. ...................................................................................................... 27 Figure 26: Suitability for Corn by 2050. ...................................................................................................... 27 Figure 27: Change in suitability by 2030. .................................................................................................... 28 Figure 28: Change in suitability by 2050. .................................................................................................... 28 Figure 29: Availability by land-use. ............................................................................................................. 29 Figure 30: Road access in Guatemala (distance-costs) ............................................................................... 30 Figure 31: Protected areas with buffer-zones in Guatemala. ..................................................................... 30 Figure 32: Combined availability of land-use, access & protected areas in Guatemala............................. 31 4 Figure 33: Vulnerability Index for 3 case studies ........................................................................................ 32 Figure 34: Exposure compared between 3 case studies............................................................................. 33 Figure 35: Sensitivity compared between 3 case studies ........................................................................... 33 Figure 36: Adaptive capacitive compared between 3 case studies ............................................................ 33 Figure 37: Expected impact compared between 3 case studies................................................................. 34 Figure 38: Spider diagram of sensitivity and adaptive capacity for all interviewed farmer ....................... 35 Figure 39: Spider diagram of sensitivity and adaptive capacity for Patzún ................................................ 36 Figure 40: Spider diagram of sensitivity and adaptive capacity for Santa Catarina ................................... 36 Figure 41: Site-specific vulnerability by 2030 ............................................................................................. 37 Figure 42: Site-specific vulnerability by 2050 ............................................................................................. 38 Figure 43: Compared carbon footprint of broccoli ..................................................................................... 38 5 Table of Tables Table 1: Table of suitability and suitability change of selected crops. ....................................................... 19 Table 2: Table of climate-suitability versus restrictions of land, numbers in grey are changes in area. .... 31 6 temperatures will increase moderately by 2030 and will continue to increase progressively by 2050. The overall climate will become more seasonal in terms of variation throughout the year with temperature in specific districts increasing by about 1.3 ºC by 2030 and 2.2 ºC by 2050 and more seasonal in precipitation with the maximum number of cumulative dry month decreases from 6 months to 5 months. 1. Summary and main findings Main research findings Temperature increases by about 1.3 ºC by 2030 and 2.2 ºC by 2050 Rainfall decreases over the year but the number of cumulative dry months decreases as well Farmer observed recently increasing climate variability Area is facing a geographical shift of some crops to western areas in Sololá Western areas in Sololá are presently important contributions to carbon sequestration (forests and coffee production) Broccoli remains on its suitability level while sweet pea suitability decreases especially in Chimaltenango areas “ADAM”-farmers are less sensitive in their social and human assets and have a stronger adaptive capacity than farmers of SUMAR supply chain We analyzed the current and future biophysical suitability EXPOSURE of crops in the Guatemalan Highlands under progressive climate change. Results for Broccoli show a geographical shift of suitable areas to the western areas, which correspond to areas of higher altitude. These areas are covered by forest or are located on steep slopes and therefore not available for broccoli production. The prediction for Sweet pea shows a drastic decrease in suitability by 2050. Through participatory workshops and more than 120 questionnaires we assessed the SENSITIVITY and ADAPTIVE CAPACITY of farmers organized in a cooperative and associated with ADAM/Oxfam and loosely organized leadfarmer-groups of the SUMAR supply chain according to their key livelihood assets. The results summarized in spider-diagrams show that “ADAM”-farmers are less sensitive in their social and human assets and have a stronger adaptive capacity. This document reports on results of a consultancy conducted for Oxfam GB to systematically address the challenge of climate change regarding farmers’ livelihoods and supply chains. After analyzing the data and questionnaires we went back to the farmer and supply-chain actor and shared with them the results of potential threats of a changing climate. In a participatory process we jointly developed adaption strategies. The three main strategies that supply chain actors identified to balance the impacts of climate are (i) irrigation to mitigate In Guatemalan highlands the yearly and monthly rainfall will decrease and the yearly and monthly minimum and maximum 7 the risk of droughts, (ii) train producers in alternative practices and (iii) access funds and credits. Based on our research, we recommend that the Guatemalan vegetable supply-chain increase efficiency of production with the aid irrigation systems to produce crops during the dry months (late October to End of April), consider the cultivation of alternative crops, introduce Agroforestry systems and foster low carbon farming. Figure 1: Research area. Climate: The highlands of Guatemala are characterized by varied climatic regime. The dry season is from October to early May and the rainy season is from late May until the end of September with a period of less rain in July and August often named “canícula” by locals. The average annual temperature in the central highlands is 20° C, and in the higher mountains 15° C. During the dry months there are possibilities for frost in the highlands of Guatemala, a strong ice-wind especially in the flat areas sometimes causes crop failure. To reduce the system’s sensitivity, strengthen the adaptive capacity and promote community organizations, it is important to initiate the training and awareness building for climate change. Traditional and local expert knowledge plays an important role to mitigate the vulnerability to climate change. 2. Background Crops: Small-scale agriculture is the main source of livelihood in this area. Farmers plant a wide variety of crops including: vegetables such as broccoli, snow peas, zucchini, carrots, onions, tomatoes, staple such as maize and beans. 2.1 Introduction of the research area Geography: The research zone for this case study is located in the departments of Sololá and Chimaltenango in the Guatemalan highlands. This area is between 2000 to 2500 meters above sea level (masl) and is characterized by very steeply sloping mountains and enclosed valleys in Sololá and a more flat area in Chimaltenango. Numerous river that drain into the pacific, originate in these highlands where farmers use surface water to irrigate their fields. In Figure actual production areas of vegetables, the project area and participating communities are shown. Economy: Guatemala has the largest economy in Central America with a GDP of US$2,507 per capita. Unfortunately, however, approximately 74% of rural households live in poverty (less than US$2 per day per person) in a country that is considered to have the highest degree of inequality in Latin America. The situation looks even more dismal when considering that fact that agriculture contribution to the GDP has been sharply declining from 23% in 2000 to 11 in 2008. Yet, agriculture still absorbs a disproportionate 52.9% of the labour force. With approximately 61% (8,300,000) of the total population people living in rural area, sustainable development of rural livelihood is 8 an extremely import aspect that must be addressed with the utmost urgency.1 soil fertility made worst by increasing fertilizer application, shifting crops to upper lands, agricultural extension to forest stands and the high threat on extreme weather events causing landslides and wind damage to exposed landscape. 2.2 Introduction of "the case" Guatemala Highland Value Chain Development Alliance: In order to improve the livelihood of the farmers, stimulate sustainable rural development and reduce poverty in rural Guatemala. Oxfam in collaboration with several other non-governmental organizations, research and development institutions and agribusiness have initiated the Guatemala Highland Value Chain Development Alliance. This is an initiative aimed at simultaneously developing economically feasible & environmentally sustainable rural livelihoods. It is intended to facilitate a reliable supply chain to agribusiness, while improving the livelihood of farmers via improved transparency of trade relationship between agribusiness and the small farmers. This pilot project is being implemented in the Soloá Department in the Guatemalan highland and will seek to improve the relationship between small-scale farmers and their main clients for the international market access, improve the roles of women in their communities and farming organizations, while adapting to the challenges and opportunities that climate change may present. 2.4 Introduction of supply chains Lead Farmer System: In the Department of Sololá, a local intermediary called SUMAR purchases agriculture produce from farmers, sorts, processes and packages them. The produce is then exported to the US and Europe via Superior Foods and Sysco. The main products currently exported are broccoli, snow peas, sugar snap peas, zucchini and okra. Presently, SUMAR works through a lead farm system to source the products that they require for their overseas clients. Lead farmers are contracted by SUMAR to liaise with over 3000 local farmers on behalf of SUMAR to get the products to processing plants. This system has been highly beneficial for SUMAR and has been refined overtime via trial and error. SUMAR also provides technical assistance, financial assistance and inputs to their farmers. SUMAR presently has several collection points in isolated rural areas, which make it relatively easy for most farmers to get their produce to the plants 2.3 General problems of the people in the area Unfortunately, the system does not keep track of farmers’ livelihood when they move in and out of the chain. Additionally, the pricing system used by lead farmers to pay small farmers is not transparent. Thus, the economic benefit of this relationship to the farmers is not known and in light of the high level of poverty in rural Guatemala, it is essential that efforts be made to address these concerns. In the communities where the research was conducted, several major problems were highlight. These include, farmers relationship/dependence of intermediaries, lack of proper transport, erosion and declining of 1 Facts from draft-document of ongoing Guatemala Highland Value Chain Development Alliance project (Sustainable Food Lab) 9 frequency of flash flooding and soil erosions and increased incidence of pest and disease and consequently a drop in the yield of major food crops by as much as third (Nelson et al., 2009). Unfortunately, these pending catastrophes cannot be avoided in the short term, thus it is imperative that mitigation and adaptation strategies be implemented to cope with these new stresses (Burton et al., 2006). 2.5 New Concept i.e. Cooperative model In order to improve the livelihood of farmers, Oxfam, in collaboration with several other institutions have proposed an alternative system that will seek to provide greater equitability and transparency. This ‘Cooperative model’, among other things, will see small growers having a greater say in contract negotiations and will also entail a infrastructure for training, traceability, certification and service delivery. This is also expected to increase the farmers’ reliability as a source of agricultural products. One of the primary aims of modern agricultural practices is to increase the quantity of food available to the world population. Ironically, however, the methods of production currently utilize is severely compromising the world’s future food security. Recent research has revealed that many agricultural practices have a substantial role to play in global warming. In Latin America and the Caribbean, climate change mitigation is still not considered in mainstream policy (Smith et al., 2007). This cooperative model is an essential component of Guatemalan High Land Value Chain Alliance, being spearheaded by Oxfam. This initiative seeks to develop a holistic approach to development in rural Guatemala by increasing market access, improving the roles of women in their communities and farming organizations, introducing more sustainable agriculture practices and developing adaptation and mitigation strategies to combat the challenges of climate change and take advantage of its’ opportunities. 3.2 Climate Change in Guatemala Guatemala, similar to other Central American countries, is often plagued by hurricanes, and therefore climate change is first called and equated with extreme weather events by farmer. In recent years, the frequency and intensity of climate-related disasters in the region has reported to be increased. For the future, scientists predict those hurricanes will continue to become more frequent and intense as a result of climate change. As a result of climate variability, the cycle of poverty, vulnerability and dependence on external assistance is expected to intensify in Guatemala. Incidentally, a long-term change in temperature and rainfall patterns requires strategies for adapting agriculture and food systems. In Guatemala farmer have to manage the risk associated with climate variability and 3. Challenges 3.1 Impact of Climate Change on Agriculture Agriculture systems around the world are expected to be confronted with a myriad of challenges as a result of the changing climatic conditions. Chief among them are heat stress associated with higher temperature and lower moisture levels, concentration of rainfall episodes into fewer days, resulting in higher 10 at the same time they have to adapt their production systems to a long term changing climate. Overall this paper will highlight how adaptation to climate change can simultaneously achieve more environmentally friendly agricultural practices, reduce GHG emission, improve both long-term and short-term food security, improve the reliability of the supply chain and increase farmers’ economic viability. This will be achieved by proposing policies that are complimentary to each other and able to achieve multiple objectives. Essentially, this paper is intended to influence policy development on issues related to agriculture, food security, sustainable development and climate change. Generally, in Latin America the impact of climate change is magnified by the poor management and abuse of the natural resources. This paper, presented by CIAT, seeks to explore the potential impact that climate change may have on the efforts of these agencies to improve the viability of the frozen vegetable value chain. Specifically, with the aid of high resolution climate models, it will seek to highlight the climatic challenges that farmers have been experiencing and are likely to experience in the future. Potential mitigation and adaptation strategies will also be propose for place-specific and crop-specific cases and the effects of these strategies will also be evaluated. These policies are expected to not only reduce the impact of climate change in the region, but to also reduce the impact of agriculture on climate change and environmental degradation. This is of particular importance, especially in light that even though agriculture is a major emitter of GHGs, research has proven that certain mitigation options has the potential to substantially reduce the amount agricultural emissions by 24-84% (Smith et al., 2007). Additionally, the authors will seek to document the challenges of soil erosion currently being experienced by the farmers and highlight how these are likely to be exacerbated by the changing climate patterns. Potential solutions will be suggested that will seek to combat this issue and an evaluation of the potential impacts that these efforts are likely to have on the farmer’s livelihood will be made. 4. Our methodology Figure 2: Analytical framework. We base our research on the commonly used definition of vulnerability of the third assessment report (IPCC, 2001) as outlined in the Working Group II report (McCarthy et al., 2001) in combination with the sustainable rural livelihood framework of Scoones (1998). Reviewing the state of the art of climate change vulnerability, Hinkel (2011) found that this approach is appropriate to identify vulnerable people, communities and regions when applied to narrowly defined local systems. 11 Read full methodology in chapter 2 of the “Methodology” document. 5. Assessment of observed changes and farmer’s perceptions Figure 3: Farmers perceptions of historical climate trend. 5.1 Results from focal workshops Farmers perception of natural capital To obtain farmer’s perceptions about climate and its changes that they observed during the last decades we conducted participatory workshops. These workshops utilized facilitators to guide the discussion of a group of farmers to unearth the necessary information. The entire discussions took place with the aid of charts and the farmers were asked to use beans and simple signs to indicate the magnitude, volume, frequency or intensity of specific variables. Farmer reported also on the historical calendar, that deforestation is a noticed problem in the community but can hardly be controlled by them (Figure 4). While some communities have started reforestation activities, others keep on extending their agricultural land trough slash and burn techniques. See more details on the procedure in chapter 4 of the “Methodology report”. Farmers perception of historical climate One of the first exercises farmers were asked to illustrate historical climatic change by assessing favorability of rainfall, temperature and wind in recent years. With the aid of beans, the level of favorability ascertained by indicating how “good” or “bad” these climatic events generally affected the production systems. Figure 3 shows high variability between years and highlight how hurricanes, accompanied by heavy rain periods caused crop failure in all communities in the region. Figure 4: Farmers perceptions on natural resources. 12 Furthermore, there was a general consensus amongst all workshops that river pollution and soil erosion was becoming an increasingly serious problem (Figure 4). Farmers perception of physical capital Figure 6: Farmers perceptions on human capital. Families: “There is little migration of residents.” “Many families are not from this community. They live in Patzún, and only rent the land for cultivation.” Figure 5: Farmers perceptions on physical capital. Education: “In 2004 only 30-60% had access to education.” Roads: “The roads from 2004 to 2006 were very bad, there were small trails.” “From 2007-2010, 75-85% had access to primary and secondary education.” “The roads improved from 2007-2010 and now they are stronger, allowing for travel to surrounding municipalities and facilitating the delivery of products to local markets.” Most of the communities saw a positive change in education during the last few years. Their family situation was little better in the past, however in the recent years there has been some slight migration to the urban areas. As these farmer statements shows, the road situation can be seen as an improving part of the physical capital and communities have been able to count on better road access during the last few years (Figure 5). 5.2 Examples of farmer’s comments Farmers perception of human capital During the field work many farmer told us their personal stories and what kind of problems they are facing in their daily business, most of them are climate related. The following comments show facts and situations from different regions. Farmers Comments from Chimaltenango “Rain has become very irregularly, this year we suffered drought followed by heavy rains during Broccoli season” 13 Because local market is not an attractive alternative at the moment, most of the farmers depend on the export market. A lot of them are also renting their lands, depending on inputs and price they get from intermediaries. “We make a lot of efforts to comply with quality and quantity standards of the exporter.” Two small-holder farmers & brothers, Guatemala, Patzún, October, 2010. Farmers normally plan their agricultural calendar based on their long term experience. As it relates to broccoli sowing, they need to rely on the start of rainy season in the first weeks of May. Using improvised manual irrigation systems they can only bridge a very slight period of drought once the crops are sowed. If they could control watering by irrigation systems they could also grow Broccoli in the dry season. On the other hand, when it gets close to the harvesting period rain gets too heavy and a lot the broccoli begins to rot. Producers, Nimayá, Highlands Guatemala, August 2010. The high quality export market means competing against subsidized and highly engineered agricultural production systems. Farmer Comment from Sololá “There is less water available, because of deforestation.” “We know how and we can produce different crops, but we don’t have any markets to sell to” Producer, ASDIC, Highlands Guatemala, August 2010 Broccoli producer, Pacop chiquito, Highlands Guatemala, August 2010. 14 6. Climate change predictions for 2030 & 2050 In order to predict climate change we used historical climate data from www.worldclim.org database (Hijmans et al., 2005) as current climate. Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature. To generate the future climate we downloaded and downscaled Global Circulation Model (GCM) data from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report See detailed description (“current climate”, “Future climate”) in chapter 3 of Methodology-document. 6.1 The summary climate characteristics for 2030 and 2050 Figure 7: Climate trend summary 2030 and 2050 for Guatemalan Highlands Results are based on 19 GCM Models from 4th (2007) IPCC assessment, A2 scenario (business as usual) General climatic characteristics • The rainfall decreases from 1829 millimeters to 1796 millimeters in 2050 passing through 1791 in 2030 • Temperatures increase and the average increase is 2.2 ºC passing through an increment of 1.3 ºC in 2030 • The mean daily temperature range increases from 11 ºC to 11.6 ºC in 2050 • The maximum number of cumulative dry months decreases from 6 months to 5 months Extreme conditions • The maximum temperature of the year increases from 25.4 ºC to 28 ºC while the warmest quarter gets hotter by 2.4 ºC in 2050 • The minimum temperature of the year increases from 10.4 ºC to 12.1 ºC while the coldest quarter gets hotter by 2 ºC in 2050 15 • • The wettest month gets wetter with 382 millimeters instead of 375 millimeters, while the wettest quarter keeps constant in 2050 The driest month gets drier with 6 millimeters instead of 7 millimeters while the driest quarter gets drier by 4 mm in 2050 Climate Seasonality • Overall this climate becomes more seasonal in terms of variability through the year in temperature and more seasonal in precipitation Variability between models • The coefficient of variation of temperature predictions between models is 3.2% • Temperature predictions were uniform between models and thus no outliers were detected • The coefficient of variation of precipitation predictions between models is 9.6% • Precipitation predictions were uniform between models and thus no outliers were detected 6.2 Regional changes in the mean annual precipitation (2030) Figure 8: Mean annual precipitation change by 2030 for 5 study sites in Guatemala. The edges of the boxes indicate the mean maximum and mean minimum values and the ends of the line the maximum and minimum values. The mean maximum and mean minimum values are defined by the mean + or – the standard deviation. The mean annual precipitation decreases in 2030 on average by 38 mm and in 2050 by 33 mm. In 2030 and 2050 Santa Catarina Ixtahuacan will have larger decrease in precipitation than other communities (Figure 8 and Figure 9). The smallest decrease in precipitation for 2030 and 2050 is predicted for Concepción. 16 6.3 Regional changes in the mean annual precipitation (2050) Figure 9: Mean annual precipitation change by 2050 for 5 study sites in Guatemala. 6.4 Regional changes in the mean annual temperature (2030) Figure 10: Mean annual temperature change by 2030 for 5 study sites in Guatemala. The edges of the boxes indicate the mean maximum and mean minimum values and the ends of the line the maximum and minimum values. The mean maximum and mean minimum values are defined by the mean + or – the standard deviation. The mean annual temperature will increase progressively. The increase by 2050 is between 2.1 and 2.3 ºC (Figures 11) and for 2030 between 1.3 and 1.4 ºC (Figures 10). 17 6.5 Regional changes in the mean annual temperature (2050) Figure 11: Mean annual temperature change by 2050 for 5 study sites of Guatemala. 6.6 Coefficient of variation of climate variables CV precipitation 2030 CV precipitation 2050 CV temperature 2030 CV temperature 2050 Figure 12: Coefficient of variation for annual precipitation and temperature 2030 and 2050. The coefficient of variation (CV) for 2030 and 2050 climate variables ranges between 0 and 20%, and may therefore be accepted as reliable (Figure 12). 18 7. Exposure of most important crops to climate change What means exposure to climate change? Exposure to climate change Exposure is the character, magnitude and rate of climate change and variation. To determine Exposure to climate change we used most the most important crops from identified during the focal group workshops and assesed the current and future biophysical suitability of these crops under a changing climate. We use a mechanistic model based called Ecocrop (Hijmans et al., 2005) and the FAO database with the same name (FAO, 1998 available at http://ecocrop.fao.org/ecocrop/ srv/en/home) to spatially predict crop suitability without having prior knowledge or data available. The model essentially uses minimum, maximum, and mean monthly temperatures, and total monthly rainfall to determine a suitability index. We improved the model with data gathered using expert knowledge and evidence data collected in the field. See more detailed description in the Methodology report on chapter 3. Table 1: Table of suitability and suitability change of selected crops. Table 1 shows results of 11 crops and their climate suitability. Models indicate for most crops values of suitability between 80 and 100, which means excellent growing conditions under current climate conditions. For 2030 predictions show suitability values between 60% and 80%. Suitability is still very good and keeps excellent for Broccoli and Onions. For 2050 suitability is predicted with ongoing decline 19 and Potato and Pea end up between 40% and 60%, which indicates at least as suitable. Values below 40% on climate-suitability end up in marginal conditions for crop development and would not be with sufficient productivity. The fourth and fifth columns show change in suitability as de anomaly between future and current crop suitability. Most affected crops are cauliflower, potato and pea with 30% declining climate suitability. 7.1 Measure of agreement of models predicted changes Figure 13: Measure of agreement of models predicting changes in the same direction as the average of all models at a given location for 2050. The Measure of agreement of models predicting changes in the same direction as the average of all models at a given location is generally high (Figure 13). In the following section we present a more the detailed analysis of the three crops of highest interest. Find maps of all crops on data collection disk! 20 7.2 Broccoli Current suitability Today broccoli is excellent suitable to current climate conditions in most of the areas in Guatemalan highlands. Local expert knowledge confirmed the results of the current crop suitability modeling, which was then used as base for the future modeling (Figure 14). Figure 14: Current suitability for broccoli. Although farmer report recently high loss in broccoli yield caused by high variability of rainfall patterns, which means a unusual drought in the mid summer, followed by excessively heavy rain falls after that, there is no significant change in suitability of broccoli predicted for the future (2030 and 2050) in the entire region. 21 Suitability for Broccoli by 2030 For the year 2030 while areas in Sololá, especially in the municipality of Santa Catarina, are predicted to be more suitable, a slight decrease starts in the north-eastern region of the study area (Figure 15). Figure 15: Suitability for broccoli by s2030. Suitability for Broccoli by 2050 Maps of 2050 climate suitability shows that the same trend on extending suitable areas in Sololá is continuing and as well barely suitable areas in the north-east are becoming larger (Figure 16). Figure 16: Suitability for broccoli by 2050. 22 Change in suitability by 2030 We determine exposure of broccoli for 2030 by calculating the anomaly of future and current suitability as suitability change. Green areas are less exposed than red areas because of a positive suitability change. Figure 17: Change in suitability by 2030. Change in suitability by 2050 Figure 18: Change in suitability by 2050. While in the outer zones red areas with high exposure become larger, areas in Sololá are still gaining suitability and broccoli will be still less exposed to climate change in Sololá. 23 7.3 Sweet pea Current suitability Sweet pea is requires lower temperature and less precipitation than broccoli and therefore in the Guatemalan highlands normally grown during the cooler dry months. Figure 19: Current suitability for sweet pea. While all communities have good conditions for sweet pea under current climate conditions (Figure 19), future predictions show for 2030 decreasing suitability for sweet pea in the region (Figure 20). Suitability for Sweet pea by 2030 Figure 20: Suitability for Sweet pea by 2030. 24 Suitability for Sweet pea by 2050 Figure 21: Suitability for Sweet pea by 2050. For 2050 this trend is going on and affecting large areas in Tecpán, Patzún and San Antonio Palopó (Figure 21). Change in suitability by 2030 Figure 22: Change in suitability by 2030. High exposure to climate change can be identified as red indicated areas with negative suitability change (Figure 22 for 2030 and Figure 23 for 2050). 25 Change in suitability by 2050 Figure 23: Change in suitability by 2050. 7.4 Corn Current suitability Traditional maize is best grown in Guatemala in lower altitude than broccoli and sweet pea. Current climate suitable areas are because of that not very extensive in the departments of Sololá and Chimaltenango (Figure 24) Figure 24: Current suitability for sweet pea. 26 Suitability for Corn by 2030 Figure 25: Suitability for Corn by 2030. Because of increasing temperature patterns climate in the Guatemalan highlands is predicted to be more favorable for maize production in 2030 (Figure 25) and even more for 2050 (Figure 26) as it is today. Suitability for Corn by 2050 Figure 26: Suitability for Corn by 2050. 27 Change in suitability by 2030 As shown in Figure 27/ 28 and compared with Figure 23 maize could be a replacement for sweet pea in areas were sweet pea is predicted to lose most of its suitability by 2030 and 2050 in Tecpán and Patzún. Figure 27: Change in suitability by 2030. Change in suitability by 2050 Figure 28: Change in suitability by 2050. 28 8. Availability and restrictions for agricultural production In order to highlight the important role of land availability for agricultural production systems we analyzed as a next step the three main influencing factors for land availability: Land use Access (road distance) Protection As most important factor for availability of land for agricultural production we analyzed land use (Figure 29) and categorized water bodies and populated areas as not available for agriculture. Areas of currently covered by forest or perennial crop systems such as coffee are classified as available but needs a land use change and would be theoretically available. However it is not recommended to clear forest in order to generate cultivating areas. Remaining areas indicated as white areas available and currently occupied even as cropland, pastureland and areas with low vegetation or wasteland. The second factor to determine availability is accessibility or also called distance-costs. We calculated the distance of each geographical location (each pixel on the map) and its distance to the closest road in distanced categories; distance accessible < 500m, inconvenient access 500-1500m, costly access > 1500m (see Figure 30). If the distance to the next road is higher, distance costs are also high. Last we used protected areas as barrier for availability for agricultural extension and calculated areas inside protected areas and within a distance of 2 Kilometers around protected areas (Figure 31). 8.1 Land use Figure 29: Availability by land-use. 29 8.2 Access Figure 30: Road access in Guatemala (distance-costs) 8.3 Protection Figure 31: Protected areas with buffer-zones in Guatemala. 30 8.4 Combined restrictions for agricultural production Combining the three availability factors we obtain weighted restrictions as result map (Figure 32) and can further discuss highly favorable land for agricultural production with positive and negative change in crop suitability as exposed areas to climate change to develop adaptation strategies. Figure 32: Combined availability of land-use, access & protected areas in Guatemala. Table 2: Table of climate-suitability versus restrictions of land, numbers in grey are changes in area. In Table 2 climate suitability and restrictions to land in 1000 hectares are combined. For 2030 and 2050 most of highly favorable land is facing a negative suitability change. Except corn and tomato the rest of crops are having their highest suitability lost up to -48 for 2030 and -68 in 2050 in available areas. The consequence of this fact is that farmer will tend to extent their production areas to higher altitudes, at moment mostly occupied by forests and will therefore not contribute to mitigate further climate change. 31 9. Vulnerability of farmer’s livelihoods to climate change 9.1 Vulnerability Index To compare vulnerability between regions a vulnerability index has been constructed. It is a function of the exposure by the year 2030, the sensitivity and adaptive capacity, and the households’ expected impact of climate change. Figure 33: Vulnerability Index for 3 case studies Vulnerability index = Exposure + Sensitivity + Adaptive Capacity + Expected Impact As can be seen from the box plots overall vulnerability is intermediate in Guatemala when compared to the other focus regions. This result is confirmed as significant by Oneway-Anova and t-Test statistics. While the means point to a ranking of the countries in terms of vulnerability, the whiskers make clear that this is deceptive. Colombia and Jamaica share a similar range of vulnerability. This means that in both countries inequality could be an issue. In Guatemala this range is lower, due to a lack of resilient households. The observed lower inequality therefore is not good news. Furthermore, Guatemala has the most vulnerable household of our study. Mrs. Petrona Carrillo Perechu in Sololá and her family of 9 children and husband face a high impact without expecting it, while their livelihood assets suggest a very low adaptive capacity but a high chance of indirect impacts. These components together describe the abstract concept of vulnerability in a comprehensive way. The data for our index originates from the suitability modeling exercise and our sustainable livelihood assessment. Additionally, we make use of information about the motivation to adapt (“expected impact”) that we derived during our household survey. All 4 variables have equal weights. Data has been transformed to a 1 to 3 ordinal scale, where 3 refers to high resilience and 1 to a high vulnerability. Thus, the index ranges from 4 – high vulnerability – to 12 – high resilience. For details on the methodology, please refer to the accompanying methodology report. See more detailed description Methodology report on chapter 4. in the First, we discuss the accumulated result of our Index, then we present findings on its components. A comparison of the components of our Index yields additional information about the differences between the countries. 32 Figure 34: Exposure compared between 3 case studies Figure 35: Sensitivity compared between 3 case studies For the construction of the vulnerability index the change in suitability has been separated into terciles of equal number of cases. The graph however shows the original values as this provides additional information. The box plots show that Jamaica and Colombia exhibit similar variation in direct climate change impacts, while Guatemala will experience homogenically a low impact (Fig. 34). Differences exist mostly in the means of Colombia and Guatemala and Jamaica. Here, Colombia and Guatemala are clearly better off. This result is confirmed as significant by Oneway-Anova and t-Test statistics. The households in our sample in Guatemala largely cater to the frozen vegetable industry. Their homogenic production behavior results in the homogenic direct impact distribution. Most households do not experience much suitability change due to climate change. In terms of sensitivity all three countries show the same range of probability of indirect impacts. A clear difference only exists in the means. Colombia shows a higher resilience on average. This result is confirmed as significant by Oneway-Anova and t-Test statistics. The difference in sensitivity between Guatemala and Jamaica is not significant. In the chapter that discusses the results of our sustainable livelihood assessment the reasons for this result are discussed in more detail (see next section). Figure 36: Adaptive capacitive compared between 3 case studies 33 has to be seen in relative but not in absolute terms. Thus, one should not conclude that Guatemala is 10% more or less vulnerable than the other countries as our results are on an ordinal scale. The difference to the more vulnerable Jamaica is mostly a result of the homogenically low impact of climate change on crop suitability. This however results from our sampling method due to the interest in supply chains in this region. Furthermore households in Guatemala expect a higher impact of climate change than households in Jamaica. Our data (not shown), however, suggests that this expectation is not based on a sound education about possible effects. Rather, data appears to reflect a generally higher concern about climate variability. Our sustainable livelihood assessment could not find a statistically significant difference in the magnitude of vulnerability between Jamaica and Guatemala. The discussion of the different assets in the respective chapter provides further insight in the individual capital availabilities between the two countries (see next section). Similar to the results of sensitivity the box plots and a comparison of means using Anova and repeated t-Tests show a higher resilience of Colombia, compared to the other two focus regions. Interestingly, the range of adaptive capacity is nearly the same for all three countries, such that the difference can only be observed in the means. No differences exist between the least prepared and best prepared households of the three focus regions. However, weaknesses and strengths result from different livelihood assets. In the chapter that discusses the results of our sustainable livelihood assessment the reasons are discussed in more detail (see next section). Figure 37: Expected impact compared between 3 case studies The data about expected impacts has been transformed onto a 1 to 3 scale such that data falls into terciles of equal size. This results in the odd shape of the box plots. Guatemala has the highest mean with 2.22 compared to Colombia (2.02) and Jamaica (1.76). Differences in means are significant between Jamaica and Guatemala. Also a small effect exists between Colombia and Jamaica. Thus Jamaica has the lowest mean. As the vulnerability index shows Guatemala takes the middle place in our study. This result 34 contrast, for Sensitivity “3” stands for a low sensitivity and “1” for a highly sensitive capital form. 10.Sensitivity and Adaptive Capacity of Guatemalan farmers to climate change What is the sensitivity and adaptive capacity of a System to climate change? Sensitivity to climate change Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate variability or change. The effect may be direct (e.g., a change in crop yield in response to a change in the mean, range or variability of temperature) or indirect (e.g., damages caused by an increase in the frequency of coastal flooding due to sea-level rise). Figure 38: Spider diagram of sensitivity and adaptive capacity for all interviewed farmer Given this ranking the diagram suggests that farmers in Guatemala are generally highly sensitive to climate change. Four of the five capital forms receive a mode of “1”, meaning that the most frequent answer has been such that they fell into the lowest category. The exception is natural capital, which is of low sensitivity. In the case of adaptive capacity the diagram suggests a rather high capacity to adapt to climate change. Natural capital is neither sensitive, nor is the adaptive capacity low, indicating a low vulnerability and a high potential to cope with climate change. For human capital the high sensitivity is countered by a high capacity to adapt. Furthermore, physical capital appears to be highly sensitive but the adaptive capacity seems to be moderate. Most strikingly however, social capital and financial capital are ranked as highly sensitive and of low adaptive capacity. The vulnerability appears to be highest in these cases. Adaptive capacity Adaptive capacity (in relation to climate change impacts), the ability of a system to adjust to climate change (including climate variability and extremes), to moderate potential damages, to take advantage of opportunities, or to cope with the consequences. 10.1 Capital stock analysis The spider diagram of the modes of the different capital forms separated into sensitivity and adaptive capacity suggests a clear result. In this diagram “1” represents a low vulnerability and “3” a high vulnerability; i.e. a value of three for adaptive capacity is a high adaptive capacity, a value of one means a low adaptive capacity. In 35 Figure 39: Spider diagram of sensitivity and adaptive capacity for Patzún Figure 40: Spider diagram of sensitivity and adaptive capacity for Santa Catarina An analysis of the particular indicators shows that the low social capital results from an absence of organizations in parts of the study region, especially the Patzún area. In other parts of the study area organizations are present, such that here the values for sensitivity and adaptive capacity are not as homogenically low. Both indicators are related, as organization membership provides security and thus a lower sensitivity. On the other hand organizations commonly provide activities such as training that raise the adaptive capacity. Thus, the lack of organizations in an area has a deep impact on the vulnerability of households. The high sensitivity of financial capital is a result of the perceived impact of climatic changes on quality. While for example credit design would allow to bridge financial shortages as conditions are mixed to good, most households already experience income losses due to climatic changes. As for adaptive capacity, the low rank results not from a lack of credit access, although this appears to be an issue in certain regions. Rather, a nearly complete lack of access to certification and a stated limited access to alternative technologies are the reasons for the low adaptive capacity. 36 • 10.2 Cluster analysis Applies biological pest control 10.3 Site-specific vulnerability Cluster analysis of questionnaire results yielded two distinct groups for both sensitivity and adaptive capacity. Each group is either characterized by high or low vulnerability. The indicators most prominent in the cluster analysis for sensitivity and adaptive capacity respectively are: For the analysis of site specific vulnerability we employ the IPCC’s standard definition of vulnerability. It is a function of the exposure as crop to climate suitability change by the year 2030 or rather 2050, the sensitivity and adaptive capacity of the farm system. • Sensitivity: – High sensitivity • Insufficient water • No Credit access • Not in an organization • Claims that CC impacts the family‘s nutrition by pests – Low sensitivity • Receive technical assistance of high quality • Training about markets • Member in an organization • Credit in Cash/inputs • Hedgerows against erosion • Flat topography • Crops only little affected by pests • Adaptive Capacity: – Low adaptive capacity • No technical assistance • No organization • No training • Bad water • More than two hours to market • No pest control – High adaptive capacity • Some or specific Certification • One or multiple members in organization • Good support from organization • Good house • Own car • Access to good training Vulnerability = Exposure + Sensitivity – Adaptive Capacity Similar to our Vulnerability Index based analysis we derive proxies for sensitivity, adaptive capacity and exposure based on our household survey data. We mapped results to show which farmer are highly vulnerable to a changing climate. (Please note that in order to map vulnerability we had to change the scale in comparison to previous chapters). Figure 41: Site-specific vulnerability by 2030 On the horizontal axis Exposure is plotted as crop to climate suitability change (1 low and -3 high); the vertical axis shows Sensitivity rated from 0 (low) to 3 (high); the size of the bubbles indicates the Adaptive Capacity; low Adaptive Capacity is classified as big size and high capacity to adapt to a changing climate are shown as small bubbles. The background color 37 of the chart shows the vulnerability in traffic light colors. Red means high vulnerability and green low. Significant attention must be given to those big sample points in the upper left red colored corner; these are those with maximum Vulnerability to predicted climate change. footprint via the Cool Farm Tool. At some point, it must be stated that most vegetable farmer in the Guatemalan highlands are roughly taking records and reliable data directly from the farmer are very difficult to collect. In most cases the interviewer depends on expert knowledge from technicians of export companies, which gave us an average application of fertilizer and pesticides, no information on residue management and farm management. Most farmers in the region don’t have their own transport and do not use energy on the field and for primary processing. Mapped survey sample points show clearly, that for 2030 the vulnerability of Guatemalan frozen vegetable farmers is still moderate (Figure 41), by 2050 more of them move towards the left corner into higher Vulnerability (Figure 42). Broccoli cultivation results vary in a wide range from 0.15 to 0.80 kg CO2e/kg (Figure 42). The calculated value lies with 0.30 kg CO2e/kg in the lower midfield which is still 48% less than the overall average value from all values obtained from literature. The two values from Fuentes and Carlsson-Kanyama (2006) from South America have no consistency in order to compare with the case study result. Figure 42: Site-specific vulnerability by 2050 11. Estimated Carbon Footprint The carbon footprint of a product presents the total sum of all greenhouse gas emissions caused by a product’s supply-chain expressed in kg Co2e per kg product. In this chapter the aim is to calculate a comprehensive carbon dioxide equivalent footprint for broccoli. For more details on the methodology see chapter 6 of the Methodology report. 11.1 Example for Broccoli Figure 43: Compared carbon footprint of broccoli During the field work we collected the necessary data to calculate the on farm carbon 38 A more detailed report, with other case studies and crops compared results can be find in the carbon footprint report from our contributor Soil & More International. more of a problem and they suggested to introducing them to the means on how to make better family planning decisions. Awareness building also has to be done to initiate a more frequent crop rotation. 12.Strategies to adapt to the changing climate Train and encourage The second strategies they worked out was to offer more training to farmers, but training should be accompanied by practical lessons, because most capacity building they got in the past ended up in theoretical workshops without follow up and applying learned content in practical lessons. This training should include soil management, waste recycling and nursing of births. After having analyzed the collected data of the first field work phase we went back to the communities and presented preliminary results of their vulnerability to climate change to farmer and supply-chain actors. In participatory workshop we jointly developed adaptation strategies on community and supply chain level. See detailed approach of conducted workshops in chapter 5 of Methodology report. Create and implement laws Thirdly they complain about the noncompliance to environmental laws. This should include first knowing about existing laws and then meet their targets. If anybody slashes a tree, he should have to plant ten new ones. 12.1 Farmer and supply-chain actors adaptation strategies Communities from Sololá In the first workshop participants from Adicoso, Asdic and Alanel participated and the following strategies were outlined by them: ADICOSO Comments: In general they are missing financial resources to do something and thinking about climate change they are left with a doubt: “Is it that we cannot do anything?” Awareness building ASDIC Participants reported that some farmers continue slashing trees and awareness building is necessary to change their behaviors. Also chemical use is quite common and farmer needs to be convinced to not use chemicals. To protect natural environment farmer have to collect their farm waste; collect empty fertilizer/pesticide bags and reuse organic waste. On the human capital they mentioned that overpopulation is becoming more and Short term strategies Awareness building of farmers (training, workshops, meetings, experience-exchange); diversification and crop rotation especially for the dry season, we have no irrigation system; collection of water and drip irrigation; improve agricultural practices (“now, market demand force us to use chemicals”); more partnerships (agro-export and non-profit organizations). 39 apply minerals; building alliances between communities and organize barter of fresh consumption products Midterm strategies Apply new practices (plot movement, litter control); recovery and treatment of soil (organic inputs and/or recommendations tailored to market demand); improve productive infrastructure (greenhouses, macro-tunnels); more strategic partnerships; irrigation and wells. Sensitive communities on financial and physical livelihoods Credits to buy inputs; training in farm administration and management for a selfsustainable development. Comments: They have to walk three to four hours to get firewood, therefore reforestation have to be an act of commitment. Comments: they have serious problems with water and need to collect water to irrigate in the dry season. Communities from Chimaltenango ALANEL Alanel community structured their adaptation strategies according to climate change threats as follows: In the second workshop participants from Nimayá, Texpán and Chipiacul (all SUMAR supplying producer) participated and the following strategies were outlined by them: Rainfall variability NIMAYA Implementation of rainwater storage systems direct on the plots; building wells. Change crops; adequate soil management. Funds to purchase land and cultivate near water sources; rotate purchases land between families every two years; use organic pesticides; don’t throw empty containers into the river; address the issue of forest fires; comply with forest protection laws. Affected natural resources TEXPAN Enter into governmental discussion through NGOs so that natural resources like forests and rivers become cultural heritage, they need to be protected; install tree nurseries and plant trees from now; include children and youth on national developing programs. Participants from Texpán community recommended as adaptation strategies to plant new trees, start with crop rotation and give credits to producers to purchase land, because most of them do not own their land and are therefore not willing to invest. Once they are owner of their lands, irrigation systems could be installed. Temperature rise Less climate-suitability of crop Diversification and crop rotation on the same land; use natural and organic techniques and CHIPIACUL 40 In order to strengthen awareness to climate change, more information is needed. Farmer should learn to take care of natural resources while using them. Implement more crop rotation to conserve soil because soil fertility already halved during the last thirty years because of only cultivating sweet pea. and beet are also affected by progressive climate change in a long term. On farmer’s perceptions, in recent year’s damages caused by extreme weather events occurred mostly as direct or following consequences of hurricanes and affect their production. To protect crops from this damage Agroforestry Systems could play an important role as alternative to traditional and widespread slash and burn agriculture (CIAT, 2010). For example the Quesungual Slash & Mulch Agroforestry System, coming originally from the southwestern of Honduras includes the principles: No slash and burn; permanent soil cover; Minimal disturbance of soil; efficient use of fertilizer. Application of these Quesungual principals can result in significant benefits for farmer: increased resilience to extreme natural events; increase in productivity by improving soil and water; surpluses of major staple foods; availability of firewood; reduced greenhouse gas emissions and increased carbon sequestration; conservation of local biodiversity. At the final workshop participants from following organizations took part in developing adaptation strategies: Buen Sembrador, Alanel, Asdic, Adicoso, Sumar, Adam 3 main strategies from final workshop Irrigation systems Good Agricultural Practices Financing organizations to improve infrastructure 12.2 From research output recommended adaptation strategies Adaptation strategies for crop production systems Crop production system As main result from analyzing eleven crops on their biophysical suitability to predicted future climate conditions can be stated, that broccoli responds quite good to changing climate and will remain on more or less the same suitability by 2030 and even by 2050. On the other hand sweet pea is exposed by reducing suitability for the future in areas of Chimaltenango department. The two crops are main income source for most of the farmer we interviewed during the field work. Other significant results on crop suitability change that was identified was that cauliflower, carrot, broad bean, potato 41 Irrigation systems to grow Broccoli all year round (more controlled, no water stress) Alternative crops for highly exposed crops to climate change Introduce Agroforestry systems: e.g. Quesungual System Low carbon agriculture to mitigate climate change Community’s vulnerability to climate change Findings in chapter nine shows that a low social capital results from absence of organizations in parts of the study region, especially the Patzún area. Presence of organizations commonly provides activities and raises the adaptive capacity. The low financial capital results from a nearly complete lack of certifications and a limited access to alternative technologies. While for example credits would allow to bridge shortages, most households already experienced income losses due to climate change. Adaptation strategies that positively impact livelihoods Funding and accompanying of community organizations Training and awareness building of communities for climate change. Building alliance along value-chain Develop both, frozen and fresh vegetable market to spread the risk Strengthening of local capacity to countered with adaptation strategies Resuscitate traditional knowledge e.g. natural weather signs Knowledge sharing and best practice learning from climate similar areas. 42 13.Conclusions Checklist for further actions against climate change in Guatemalan highland vegetable production area In Guatemalan highlands the yearly and monthly rainfall will decrease and the yearly and monthly minimum and maximum temperatures will increase by 2030 and will continue to increase progressively by 2050. The implications are that the distribution of suitability within the current vegetable growing areas will change and for some crops quite seriously by 2050. In general Chimaltenango department will be more affected by decreasing suitability currently grown vegetables while having some opportunities to grow tomato and steady suitability for broccoli, bean and staple crops such as corn. The western part of Sololá will benefit from predicted climate change but have to be carefully developed because of the last conserved forest in the region. The vulnerability index shows that Guatemala takes the middle place in our study. There are many possibilities to adapt to the changing climate. The winners are those who are willing to adapt to an evolving climate. 43 Choose the best adaptation strategies against climate change Learn to manage the risk associated with climate variability. Implement and adjust adaptation strategies together with policy makers Start mitigating to reduce the adverse affects of climate change by reducing emissions 14.References Scoones, Ian. 1998. Sustainable Rural Livelihoods: A Framework for Analysis. IDS Working Paper 72. Burton, I., Diringer, E., & Smith, J. (2006). ADAPTATION TO CLIMATE CHANGE: International Policy Options. Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kymar, P., et al. (2007). Agriculture. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 497-540). Cambridge: Cambridge University Press. CIAT. (2009). Confronting Climate Change: Increasing Competitivity and Reducing Ecological Footprint from High Value Crop and Livestock Systems in Central America (Vol. 0, p. 39). Cali. Fuentes, C.; Carlsson-Kanyama, A. (2006): Environmental information in the food supply system. Available online at http://www.fcrn.org.uk/researchLib/ PDFs/carlsson%20kanyama%20et%20al%20food %20service.pdf, checked on 1/03/2011. CIAT, (2010) Quesungual Slash and Burn Agroforestry System: An Eco-Efficient Option for Rural Poor. Cali: International Center for Tropical Agriculture. FAO. 2011. Ecocrop. Food and Agriculture Organization of the United Nations. http://ecocrop.fao.org/ecocrop/srv/en/home. Fuentes, C., A. Carlsson-Kanyama,. 2006. „Environmental information in the food supply system“. Stockholm: Totalförsvarets forskningsinstitut. Hijmans, R. J, S. E Cameron, J. L Parra, P. G Jones, and Andy Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, no. 15: 1965-1978. Hinkel, Jochen. 2011. “Indicators of vulnerability and adaptive capacity”: Towards a clarification of the science-policy interface. Global Environmental Change 21, no. 1 (February): 198-208. doi: 10.1016/j.gloenvcha.2010.08.002. IPCC. 2001. Climate change 2001 : impacts, adaptation, and vulnerability : contribution of Working Group {II} to the third assessment report of the Intergovernmental Panel on Climate Change. Ed. James McCarthy. Cambridge, UK; New York, USA: Cambridge University Press. McCarthy, J. (2001). Climate Change 2001: Impacts, Adaptation, and Vulnerability: Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK; New York, USA: Cambridge University Press. Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T., et al. (2009). Climate Change: Impact on Agriculture and Costs of Adaptation. Food Policy (pp. 1-30). 44