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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
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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
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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
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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
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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
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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
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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
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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
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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)
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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.
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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.
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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.
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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.
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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
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