In order to be able to adapt to climate change, bean producing smallholders in Central America have to know which type of changes and to which extent and ranges these changes will occur. Adaptation is only possible if global climate predictions are broken down on local levels, to give farmers a direction on what to adapt to, but also to provide detailed information about the extent of climate change impact and the exact location of the affected population to local, national, and regional governments and authorities, and the international cooperation/donors in order to coordinate and focus their interventions in the future. There will be people who will be more affected by climate change than others; some might have to leave the agricultural sector while others will have to change their whole operation. But there will be also new opportunities for those who will adapt quickly making them winners of changes in climate. This technical report seeks to assess the expected impact of climate change on bean production in 4 countries in Central America. We downscaled GCM (Global Climate Models) to a local scale, predicted future bean production using a dynamic crop model called DSSAT (Decision Support for Agro-technology Transfer), we identified based on the DSSAT-results 3 types of focus-spots where impact is predicted to be significant and run DSSAT again with the full range of available GCMs to address uncertainty of model predictions. Alongside this analysis we started a field trial using 10 bean varieties in 5 countries to calibrate DSSAT and run it in post-project-stage again in order to make assumptions on determining factors and possible breeding strategies. Outputs of downscaled climate data show that temperature is predicted to increase in the future, while precipitation will slightly reduce. Crop modeling shows that bean yields will decrease high along the dry corridor in Central America and Hot-Spots with more than 50% yield reduce could be identified in the study area. Based on the results we finally made recommendations for adaptation- and mitigation strategies which will be handed over to decision makers afterwards.

1. Central American maize-bean systems and the changing climate
Tortillas on the Roaster
A. Schmidt, A. Eitzinger, K. Sonder, G. Sain, P. Läderach, J. Hellin, B. Rodriguez, M. Fisher, L. Rizo, S. Ocon
Cali, Colombia, October, 2012
Pic by Neil Palmer (CIAT).
Funded by the The Howard G. Buffett Foundation

2. In Central America more than 1 million smallholder families depend on
the cultivation of maize and/or beans for their subsistence.
Frequently there is a high vulnerability to extended drought periods
and extreme weather events such as hurricanes putting the food
security of these smallholder families at risk.
As climate already is changing by getting hotter & dryer, maize-bean
farmers in Central America will be forced to adapt to changes in crop
suitability to maintain food security.
“Tortillas on the Roaster” seeks to predict locally specific changes in
maize bean production systems that people can act and respond to
ongoing climate change by concrete adaptation measures. 2

3. Activity line and main objectives
3

4. Methods: Climate data
Provide local scale
Climate predictions
• For current climate (baseline)
we used historical climate data from WorldClim
Meteorological stations on which WorldClim is based in the study area
www.worldclim.org
• Future climate: 21 global climate models (GCMs) from
IPCC (WCRP CMIP3) - SRES-A2, 2020 & 2050
• Downscaling (CIAT Decision and Policy Analysis Working Paper, no. 1, “delta-method”)
to provide higher-resolution (2.5 arc-minutes ~ 5 kilometer)

5. Methods: daily climate data
Generate daily
climate data for
• Generating characteristic daily DSSAT
weather data with MarkSim*
*MarkSim was developed to generate precipitation data for tropical regions.
• We modified MarkSim for batch-processing.

6. Methods: Simulate Crop growing cycle
Predict impact on
Decision Support System for Agro technology Transfer (DSSAT) production systems
(beans)
Current yield – Future yield (kg pro hectares)
= expected impact on yield (+/-)

7. Methods: Target future interventions from predicted impact
Identify (impact)
Hot-spots
Areas where the production systems of crops can be
adapted
Adaptation-Spots (more than 25% yield loss)
Focus on adaptation of production system
Areas where crop is no longer an option
Hot-Spots (more than 50% yield loss)
Focus on livelihood diversification
New areas where crop production can be established
Pressure-Spots (more than 25% yield gain)
Migration of agriculture – Risk of deforestation!
7

8. Methods: Socio-economic analysis
Quantify socio-
economic
Socio-economic impact on farmers livelihoods consequences
VULNERABILITY to Climate Change (IPCC 2001)
Degree of susceptibility and Exposure
incapability of a system to Degree to which a system is
confront adverse effects of exposed to significant variation
climate change in climate
Sensitivity Adaptive capacity
Degree to which a system is
The ability of a system to adapt
positively or negatively affected
to climate change
by climate related stimulus
• Focal group workshops on selected Hot- & Adaptation-spot-sites
• On Farm data collection by surveying based on livelihood indicators
off 5 assets: human, natural, social, physical, financial

9. results
Results
Pic by Neil Palmer (CIAT).

10. Results: Predicted Climate Change in Central America
RESULTS
19 GCM (IPCC 4th Assessment report CMIP3) – scenario A2 CLIMATE CHANGE
2 30 year mean periods 2010-2039 [2020], 2040-2069 [2050]
For 2020: mean
annual temp.
increase
1 - 1.1 C
For 2050: less
precipitation
( ~ -10%)
mean temp.
increase
2.2 - 2.4°C
hottest day up
to 35.6°C
(+ 2.4 - 2.6°C)
coolest night
up to 18.2°C
(+ 1.6 - 2°C)

11. Results: Ground proofing and similar Climate patterns
RESULTS
Calculate Climate-Cluster from 19 bioclimatic variables to GROUND PROOFING
understand potential beans production areas
Estimate current bean production areas by Kernel
density using point data from *Beans-Atlas
* Common beans atlas of the Americas
Michigan State University
Suitability (crop to climate) analysis with EcoCrop

12. Results
Assessment of beans
Pic by Neil Palmer (CIAT).

13. Methods: Block diagram of impact assessment of beans
Predict impact on
production systems
(beans)

14. Methods: DSSAT Simulation trials
Predict impact on
Decision Support System for Agro technology Transfer (DSSAT) production systems
(beans)
Weather data input:
Current climate
Average of 99 MarkSim
daily outputs
Future climate
Ensemble of 19GCM & 99
MarkSim outputs for 2020
& 2050
Runs: 17,800 points x 3
Planting date: Between 15th of April and 30th of June1 climates x 99 MarkSim-
samples x 8 trials
Variety 1: IB0006 ICTA-Ostua Variety 2: IB0020 BAT1289
Soil 1: IB00000005 (generic medium silty loam) Soil 2: IB00000008 (generic medium sandy loam)
Fertilizer 1: 64 kg / ha 12-30-0 6 to 10 days after germination and 64 kg / ha Urea (46% N) at 22 to
25 days after germination. Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing and 64
kg/ha UREA at 22 to 30 days after germination.

15. Methods: Field trials to calibrate DSSAT
Predict impact on
production systems
Accompanying field trials in 5 countries to calibrate DSSAT (beans)
• For 2 DSSAT-varieties (IB0006 ICTA-Ostua, IB0020 BAT1289
– “INTA Fuerte Sequia”, “INTA Rojo”, and “Tío Canela 75” originating from Nicaragua
– “ICTA Ostua” and “ICTA Ligero” originating from Guatemala
– “BAT 304” originating from Costa Rica
– “SER 16”, SEN 56”, “NCB 226”, and “SXB 412” originating from CIAT, Colombia.
• Sowing on:
– Primera (Beginning of June)
– Postrera (Beginning of September)
• After recollecting data during 2011
results will be used
in a post-project-analysis
to calibrate 2 initial DSSAT varieties
run it again for trial sites and find
spatial and temporal analogues

16. Results: yield change for year 2020 predicted by DSSAT (Primera) RESULTS
IMPACT ON BEANS
(average 8 trials)

17. Results: yield change for year 2020 (Primera) – 8 trials
RESULTS
COMPARE
SIMULATIONS
Trial 3 – high performance / high impact Trial 7 – medium high performance / less impact
Variety 1: ICTA-Ostua Variety 1: ICTA-Ostua
Soil 1: generic medium silty loam Soil 2: generic medium sandy loam
Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing
and 64 kg/ha UREA at 22 to 30 days after germination and 64 kg/ha UREA at 22 to 30 days after germination

18. Results: Specific country results for year 2020 (Primera) RESULTS
BEANS IMPACT
Nicaragua
Highest impact (negative yield change) would
be expected on the dry corridor (Corredor
seco) from Rivas, Granada up to Estelí and
Madriz.
Improved yields are predicted for the Atlantic
region and Chontales which are traditionally
used for Apante-production (December - April)

19. Results: Specific country results for year 2020 (Primera) RESULTS
BEANS IMPACT
Honduras
Dry corridor continues its path up to Honduras
and El Paraiso, Francisco Morazan to Yoro.
Ocotepeque is the only beans producing
department with in average increasing yields.

20. Results: Specific country results for year 2020 (Primera) RESULTS
BEANS IMPACT
El Salvador
Highest reduction in yield is expected to occur
in the South-Eastern region in the departments
Cuscatlan.
Impact in general is less compared to the other
3 countries

21. Results: Specific country results for year 2020 (Primera) RESULTS
BEANS IMPACT
Guatemala
Some departments have high potential for
future bean production regarding to changing
climate and perhaps because of their different
climate zone.
San Marcos (+38%), Totonicapán (+23%) and
Quezaltenango (+31%) are high potentials for
beans production by 2020 (considering only
climate as factor)

22. Address uncertainty of DSSAT simulation
RESULTS
We calculated 4 different outcomes to address UNCERTAINTY
uncertainty of DSSAT simulation
a Relative yield change as
average of 19 GCMs for 2020
b Average of the 1st quartile
of GCMs
c Average of 3rd quartile of
GCMs
d Breadth of GCMs agreeing
in yield change prediction by
DSSAT.
Because of processing constraints we run
DSSAT on a 15 kilometer buffer around
sites selected for socio-economic analysis

23. maiz
Assessment of maize
Pic by Neil Palmer (CIAT).

24. Methods/Results: DSSAT Simulation trials
Predict impact on
production systems
• model runs were divided according to the two (maize)
general soil types selected.
– best and worst (poor soil conditions) case scenarios

25. Methods/Results: DSSAT Simulation trials
Predict impact on
production systems
• Maize yield differences between (maize)
current climate and 2020s predicted
poor soil good soil

26. Results: Specific country results for year 2020 RESULTS
poor soil scenario MAIZE IMPACT
Nicaragua
Impact for Nicaragua for the
2020s and the poor soil scenario
on the country overall is
predicted to be a reduction of
11% implying a production loss
of 51,741 t compared to the
latest production statistics.
good soil scenario
Areas like Masaya (-46%) and
Chinandega (-43%) would face
higher reductions while the
larger production areas like
Jinotega (-9%), Matagalpa (-9%),
Atlantico Sur (-1%) and Norte
(-1%) are predicted to show less
reductions under the poor soil
condition scenario.

27. Results: Specific country results for year 2020 RESULTS
poor soil scenario MAIZE IMPACT
Honduras
Overall losses for Honduras
compared to the 2009-2010
production (available for 7
regions) would amount to
175,598 t of maize (poor soil
conditions) an overall loss of
30%. For the good soil and the
good soil scenario 2020s losses overall are still
considerable with a total of
69,534 t (12%).

28. Results: Specific country results for year 2020 RESULTS
poor soil scenario MAIZE IMPACT
El Salvador
Impact for El Salvador for the
2020s and the poor soil scenario
on the country overall is
predicted to be a reduction of
over 250,000 t of maize based
on the 2009-2010 production
year.
good soil scenario
Areas like La Paz (-74%), La
Union (-44%), San Miguel (-
43%), Usulután (-40%), San
Vicente (-39%), San Salvador (-
35%) and Cabañas (-34%) would
face higher reductions while
areas like Ahuachapan (-11%)
and Chalatenango (-17%)are
predicted to show less
reductions under the poor soil
condition scenario.

29. Results: Specific country results for year 2020 RESULTS
poor soil scenario MAIZE IMPACT
Guatemala
Impact for Guatemala is softened by the
considerable highland areas mainly in the
West of the country while drier areas like
parts of Petén, coastal areas in the South
(Retalhulehu, Escuintla), and the Eastern
border (Chiquimula and Jutiapa) would
face considerable losses. Also the largest
producer in terms of area, Alta Verapaz, is
good soil scenario little affected due to slight increases under
the good soil scenario and only slight
losses under the poor soil condition
scenario. For the 2020s and the poor soil
scenario on the country overall is predicted
to be a reduction of 98,000 t in
comparison with the latest production
statistics.
For the good soil scenario the overall
balance for the country is positive with
4,247 t increase.

30. Socio-economic consequences
Pic by Neil Palmer (CIAT).

31. Results: Hot-spots for maize or beans production areas in Central America
Identify (impact)
Hot-spots
Message 1: We need to pick out where to start working!

32. Results: Selected 16 sites for socio-economic study
Quantify socio-
economic
consequences

33. Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El Salvador Hot-spot
Las Mesas
Altitude: 667 m
(about 2188 feet)
Hot-spot -141 kg/ha
For 2020:
• 35 mm less rain (current 1605mm)
• mean temperature increase 1.1 C
For 2050:
• Beans as most important income (sell 70% of harvest) • 73mm less rain ( -5%)
•
• Climate variability (intense rain, drought), missing labor •
mean temperature increase 2.3 C
hottest day up to 35 C (+ 2.6 C)
& credits, high input costs, … forces them to changes • coolest night up to 17.7 C (+ 1.8 C)
• Increasing livestock displace crops into hillside areas
• Half of farmer rent their land
• Distance to market is far
• Mostly no road access in rainy season
• They buy inputs/sell produce from/to farm-stores
(they call them: Coyotes)
Message 2: Adaptation Strategies must be fine-tuned at each site!
33

34. Result: Sample-site 2 – Valle de Jamastran, Danlí, Honduras Adaptation-spot
Jamastran
Altitude: 783 m
(about 2568 feet)
Adaptation-spot -
115 kg/ha
For 2020:
•
• Active communities with already advanced agronomic •
41 mm less rain (current 1094 mm)
mean temperature increase 1.1 C
management of maize-bean crops For 2050:
• 80 mm less rain ( -7%)
• Favorable soil conditions and management • mean temperature increase 2.4 C
• Long-term technical assistance / training • hottest day up to 34.2 C (+ 2.6 C)
• coolest night up to 17 C (+ 2.1 C)
• Irrigation schemes (e.g. 50 mz of 17 bean producers)
• Diversification options (vegetables, livestock)
• Market channels through processing industries
• Advanced infrastructure (electricity, roads)
• Need to optimize water use efficiency
• Credit problems
Message 3: There can be winners if they adapt quickly!34

35. Socio-economic
Results: Focal groups results Quantify socio-
Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpected economic
climatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups in
Guatemala. consequences
• Main activities and trends
35
30
25
Mentions ( %)
20
15
10
5
0
El Salvador Honduras Nicaragua
Maize Beans Sorghum & maicillo Vegetables Cattle Poultry &eggs Rice Fruits Coffee Pork

36. Socio-economic
Results: Focal groups results Quantify socio-
Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpected economic
climatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups in
Guatemala. consequences
• Farmers‘ perceptions point to economic as well as
climatic events as main drivers of perceived trends
70
60
50
Mentions (%)
40
30
20
10
0
El Salvador Honduras Nicaragua
Climate related (1) Economics/finance (2) Lack o fResouces (3) Other (4)

37. Socio-economic– results capitals
Results: Focal groups Livelihood
Quantify socio-
Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpected economic
climatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups in
Guatemala. consequences
(a) 70
60
(b) 80
70
50 60
Mentions (%)
Mentions (%)
50
40
40
30
30
20
20
10 10
0 0
El Salvador Honduras Nicaragua El Salvador Honduras Nicaragua
Own Rent Loan Potable Irrigation No treated (wells)
(c) 80
70
60
(a) Forms of land tenure
Mentions (%)
50
(b) Water availability
40
30
(c) Main road types
20
10
0
El Salvador Honduras Nicaragua
All year Dry season only

38. Socio-economic
Results: Focal groups results Quantify socio-
(a)
50
45
economic
40 consequences
35
Menciones (%)
30
25
20
15
• Perceptions of
10
5
future threats and
0
El Salvador Honduras Nicaragua opportunities
Climate events Natural resourcs
Financial and economic resources Social events
Catastrophic events
60
(b) 50
40
Menciones (%)
(a) future threats
30
(b) future opportunities
20
10
0
El Salvador Honduras Nicaragua
Public investment Strengthening of human & social resources
Sustainable development projects Change of activities

39. Socio-economic results
Results: Socio-economic analysis
Quantify socio-
• Quantity and value of maize and beans economic
consequences
production losses in 2020
60,000
Maize/beans value of production losses (000 us$)
50,709
50,000
45,623
40,000
30,000
20,000 17,476
10,000 8,622
0
Nicaragua Honduras El Salvador Guatemala
Estimated value of maize&beans production losses at 2020 (us$)
• Summary of predicted types of changes on country level

40. Methods: Socio-economic analysis
Quantify socio-
economic
• Household vulnerability consequences
Pic by Neil Palmer (CIAT).

41. Socio-economic results - Household EXPOSURE
Methods: Socio-economic analysis
Quantify socio-
I
II III
economic
Impact on land productivity
GCC Consequences at hotspot level
Adjustment factor at the
household level
Indicator: Exposure level of the
maize/beans cropping system
consequences
(predicted by the
(estimated) (High, Medium, Low)
biophysical model)
• Exposure level of the maize/beans cropping system
The adjustment level at the farming system (Household exposure)
1. Relative change in bean yield predicted by the biophysical model
(as shown in previous slides)
2. Conservation technologies / Inclination

42. Classes of maize/beans cropping system exposure (%) Classes maize production exposure (%)
10
20
30
40
50
60
70
80
90
40
60
80
0
0
20
100
100
(c)
(a)
El Rosario El Rosario
San Felipe San Felipe
El Salvador
El Salvador
San Rafael San Rafael
Ipala
Ipala
San Manuel Chaparron
High
High
San Manuel Chaparron
Guatemala
Guatemala
Patzicia
Patzicia
Medium
Medium
Alauca
Alauca
Low
Low
Jamastran
Jamastran
Honduras
Orica
Honduras
Orica
Results: Socio-economic analysis
La Hormiga
La Hormiga
San Dionisio San Dionisio
Nicaragua
Nicaragua
Totogalpa Totogalpa
Classes of beans production exposure (%)
20
40
60
80
0
100
120
(b)
El Rosario
San Felipe
El Salvador
San Rafael
Socio-economic results - Household EXPOSURE
Ipala
High
San Manuel Chaparron
Guatemala
Patzicia
Medium
Alauca
Low
(a) Exposure level of maize
(b) Exposure level of beans
Jamastran
Honduras
Orica
(c) Exposure level of maize/beans
La Hormiga
economic
San Dionisio
consequences
Nicaragua
Quantify socio-
Totogalpa

43. Socio-economic results - Household SENSITIVITY
Results: Socio-economic analysis
Quantify socio-
economic
consequences
• Stages in the estimation of the sensitivity of livelihoods’ sources indicator
Importance of the system maize/bean farm income
Maize Beans
100 120
Classes of beans importance in farm's income (%)
Classes of maize importance in farm's income (%)
90
80 100
70 80
60
50 60
40
30 40
20 20
10
0 0
Totogalpa
Orica
Patzisia
San Manuel Chaparron
El Rosario
San Felipe
San Rafael
La Hormiga
Ipala
San Dionisio
Alauca
Jamastran
San Manuel Chaparron
San Felipe
Orica
San Rafael
Patzisia
Totogalpa
El Rosario
Ipala
La Hormiga
San Dionisio
Alauca
Jamastran
El Salvador Guatemala Honduras Nicaragua El Salvador Guatemala Honduras Nicaragua
High Medium Low High Medium Low

44. Classes of household sensitivity (%) Classes of maize sensitivity (%)
20
40
60
80
0
20
40
60
80
0
100
(c)
(a)
El Rosario El Rosario
San Felipe San Felipe
El Salvador
El Salvador
San Rafael San Rafael
Ipala Ipala
San Manuel Chaparron San Manuel Chaparron
High
High
Guatemala
Guatemala
Patzisia Patzisia
Medium
Medium
Alauca Alauca
Low
Low
Jamastran Jamastran
Orica Honduras Orica
Honduras
Results: Socio-economic analysis
La Hormiga La Hormiga
San Dionisio San Dionisio
Nicaragua
Nicaragua
Totogalpa Totogalpa
Classes of beans sensitivity (%)
(b)
20
60
80
0
40
100
El Rosario
San Felipe
El Salvador
San Rafael
Socio-economic results - Household SENSITIVITY
Ipala
High
San Manuel Chaparron
Guatemala
Patzisia
Medium
Alauca
Low
Jamastran
Orica
Honduras
(a) Households sensitivity of maize
(b) Households sensitivity of beans
La Hormiga
economic
San Dionisio
consequences
Quantify socio-
Nicaragua
(c) ) Households sensitivity of maize/beans
Totogalpa

45. Socio-economic results - Household ADAPTABILITY
Methods: Socio-economic analysis
Quantify socio-
economic
• Stages used to estimate the household adaptive capacity consequences

46. Classes of natural captal availabilty (%) Classes of physical capital availabilty (%)
60
80
0
20
40
100
20
40
60
80
0
100
120
(c)
(a)
El Rosario El Rosario
San Felipe San Felipe
San Rafael San Rafael
El Salvador
El Salvador
Ipala Ipala
San Manuel Chaparron San Manuel Chaparron
Low
Low
Patzisia Patzisia
Guatemala
Guatemala
Media
Media
High
High
Alauca Alauca
Jamastran Jamastran
Orica
Honduras
Orica
Honduras
Results: Socio-economic analysis
La Hormiga La Hormiga
San Dionisio San Dionisio
Nicaragua
Nicaragua
Totogalpa Totogalpa
Classes of credit access (%)
20
40
60
80
0
100
(b)
El Rosario
San Felipe
San Rafael
El Salvador
Socio-economic results - Household ADAPTABILITY
Ipala
(c) Natural capital
(a) Physical capital
Low
San Manuel Chaparron
• Stages used to estimate the household adaptive capacity
Guatemala
Fair
Alauca
Jamastran
Orica
Honduras
(b) Financial capital (credit access)
La Hormiga
economic
San Dionisio
consequences
Quantify socio-
Nicaragua
Totogalpa

47. Socio-economic results - Household ADAPTABILITY
Results: Socio-economic analysis
Quantify socio-
• Stages used to estimate the household adaptive capacity economic
100
consequences
Classes of human captal availabilty (%)
80
60
40
20
(a)
0
San Manuel Chaparron
Ipala
Orica
Patzisia
Totogalpa
El Rosario
San Rafael
La Hormiga
San Felipe
San Dionisio
Alauca
Jamastran
El Salvador Guatemala Honduras Nicaragua
Low Media High
100
Classes of social capital availability (%)
80
60 (b)
40
20
0
(a) Human capital
Orica
Patzisia
El Rosario
San Rafael
Alauca
La Hormiga
Totogalpa
San Felipe
Ipala
San Dionisio
Jamastran
San Manuel Chaparron
(b) Social capital
El Salvador Guatemala Honduras Nicaragua
Low Media High

48. Socio-economic results - Household ADAPTABILITY
Results: Socio-economic analysis
Quantify socio-
economic
consequences
Households adaptive capacity
100
Classes of household's capacity of adaptation
80
60
40
20
(%)
0
Totogalpa
Orica
Patzisia
San Rafael
La Hormiga
El Rosario
San Felipe
Ipala
San Dionisio
Alauca
San Manuel Chaparron
Jamastran
El Salvador Guatemala Honduras Nicaragua
Low Media High

49. Socio-economic results - Household VULNERABILITY
Results: Socio-economic analysis
Quantify socio-
economic
• Households vulnerability consequences
100
80
Classes of vulnerabilility (%)
60
40
20
0
San Manuel Chaparron
Totogalpa
Patzisia
Orica
El Rosario
San Rafael
Ipala
La Hormiga
San Felipe
San Dionisio
Alauca
Jamastran
El Salvador Guatemala Honduras Nicaragua
High Medium Low

50. Adaptation- & Mitigation strategies

51. Result: Local Adaptation- Mitigation strategies
We derived five principal strategies for adaptation at farm level
• Sustainable intensification: Aimed at increasing physical
productivity while preserving natural resources (land and
water) in productive systems (eco-efficiency).
• Diversification: Increases the amount of consumption
sources and income from agriculture
• Expansion: Expands the endowment of different types of
capitals
• Increasing off-farm income: Increase the importance of
sources of income from more secure out-of-the-household
activities.
• Out of agriculture as a livelihood strategy: The household
leaves agriculture as a source of income and consumption.
51

52. Result: Local Adaptation- Mitigation strategies
Sustainable intensification
Increase rain water use efficiency!
• Improved soil and pest management
– Socially integrated soil and pest management with coordinated actions
across the community and national actors.
• Irrigation and water-catchment
– Extent production into drought season with lower temperatures using
irrigation and water-catchment systems.
• Improve plant nutrition management
– water use efficiency can be increased by 15-25% through adequate
nutrient management
• Genetic improvement for heat stress and drought tolerance
– Breeding for common bean improvement in Central America for several
stresses associated with climate change.
52

53. Result: Local Adaptation- Mitigation strategies
Diversification
Increase consumption sources and income from agriculture!
• Agua-Agro-Silvo-Pastoral Systems
– Nutrient cycling is enhanced through the integration of crops and
animals resulting in higher crop yields.
– Improved soil and water quality and increased biodiversity
– Lower greenhouse gas emissions and increased carbon sequestration
– Trees and shrubs offer sources of bio-energy
– Fruits, nuts, horticulture nursery stock, wood fiber and livestock shelter
– Opportunities for restoration of degraded lands
– Allow for livestock integration
53

54. Result: Local Adaptation- Mitigation strategies
Expansion
Expansion of land occupation & expansion of the endowment of
natural, physical, financial, human and social capitals on farm level!
• Natural shift to “Apante” areas
– To avoid deforestation, increase effectiveness of bean production by optimal management of abiotic
stress and biotic constraints through a multidimensional farming system approach.
– Start with farmers’ awareness building to climate change mitigation and build up conservation incentives
for farmer groups.
• Converting grazing land into cropland
– Controlled agricultural land use shift (caused by changing climate patterns) inside existing agricultural
frontiers in Central America by using improved forages for livestock and convert liberated grazing land
into cropland.
• The land tenure complex
– Long-term land lease is not common, perspectives investments in sustainable soil and water
management will not to be made
– Policy interventions are urgently needed
• Expansion of human and social capital
– Learning framework for farmer groups
• Bring Climate Change research to the ground
– Generate site-specific adaptation- and mitigation strategies and share them spatially with concrete
incentives among farmer communities.
54

55. Result: Local Adaptation- Mitigation strategies
Increasing off-farm income
Central American smallholders traditionally generate off-farm income
during e.g. coffee harvest, in processing facilities or mostly for women.
These are temporal activities during the dry season associated with
migration. Remittances are also an important source of off-farm
income and largely spent on consumption.
Out of agriculture
In general, rural areas provide limited opportunities for income
generation which leads to migration to urban areas or outside Central
America.
55

56. Thank you
a.eitzinger@cgiar.org
p.laderach@cgiar.org
http://dapa.ciat.cgiar.org/
with without
Climate Adaptation Strategies