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Climate change adaptation from
start to finish: The case for
Colombia
The
challenge:
Colombia
is a victim
of climate
variability

Maíz

Arroz

T-Max
Rendimient
o

Rendimient
o
T-Max

Climate anomolies and
production anomolies go
hand in hand

Fuente: Agronet y CRU (http://badc.nerc.ac.uk/data/cru/)
And in the long
term
• Coffee suitability in the
Cauca region
• Significant changes to
2020, drastic changes
to 2050
• The Cauca case:
reduced coffeee
growing area and
changes in geographic
distribution. Some
new opportunities.

MECETA
Diagnose the problem
Vulnerability mapping
Food security

Income
Listen to farmers
• Listen to local perspectives of vulnerability
• Participatory identification of adaptation options
Girardot - Maize

Ipiales - Potato

San Gil - Beans
A systematic look at impacts and
economic costs across LAC
• Detailed crop
modelling of climate
impacts across LAC
• Analysis of social and
economic impacts
• Zoom in analysis at
national level

Crops

Food Supply
(kcal/capita/day)

Harvested Area
(Ha)

Contribution
to GDP

DSSAT
model?

41,171,982.60
10,270,087.00
28,633,254.40
8,988,559.80
5,943,971.40
7,240,355.00
342,874.20
199,494.00
1,190,503.00
5,706,389.00
2,052,958.80
998,955.20
242,739.60
1,495,006.20

Net Production
Value (constant
2004-2006 1000 I$)
27,707,066
25,072,226
6,087,072
3,505,114
7,092,796
3,602,564
4,107,232
1,902,269
7,065,053
4,986,781
127,456
2,453,723
1,167,699
5,170,592

Soybean
Sugar Cane
Maize
Wheat
Rice
Beans
Tomatoes
Pineapples
Bananas
Coffee
Sorghum
Potatoes
Onions
Oranges,
Mandarines
Palm
Lemons,
Limes
Grapes
Cottonseed
Oil
Apples
Nuts
Coconut
Groundnuts
Cassava
Plantains
Cocoa Beans
sunflower
Yams
Mangoes

170
383
435
331
296
90
11
5
34
3
6
34
7
17

5
13
5
1
8
2
1
2
16
4
0
1
0
1

y
n
y
y
y
y
n
n
n
n
y
y
n
n

64
3

1,085,119.00
319,617.20

1,078,458
2,095,406

1
0

n
n

3
8

547,380.00
1,873,788.60

4,124,806
953,103

1
0

n
n

5
6
21
11
44
33
3
18
12
NA

192,937.80
574,795.65
676,841.00
534,019.00
2,747,793.60
948,142.00
1,476,678.40
2,588,299.00
173,591.40
435,323.20

1,748,896
943,316
589,058
515,363
2,027,450
1,572,812
520,235
1,142,205
270,343
2,705,656

0
0
0
1
3
1
0
0
2
2

n
n
n
y
n
n
n
n
n
n
Prioritise
Ranked List of Practices
Practice
CBA Quality
1 Silvopastoral Systems
1.5 2.11
2 Efficient Use of Fertilizer 1.4 2.87
3 Improved Forages
1.3 2.85
4 Biogas
1.2 2.36
Grass-Legume
5 Association
1.2 2.11
6 Water harvest structure
1.2 2.08
Silage, haylage and
7 nutritional blocks
1 2.01
9 Early warning systems
1 1.89
Act
Objectives
1) Generate capacity to reduce agroclimatic risk: modelling impacts
and seasonal forecasting
2) Close the yield gap through climate specific agriculture
3) Evaluate and generate new adapted technologies
4) Evaluate resource efficient production systems (water and carbon
footprints) and establish new incentive structures for their
adoption (PES, NAMAs, low carbon development pathways etc.)
In action
9 Partners
52 Municipalities
16 Departments
>800 experimental lots in 20 localities
> 200 on farm participatory experiments
> 40 events with 660 participants and 32
institutions
97 researchers working on the program
> 70 technologies being evaluated
What defines yield?

51% of yield variation is caused by climate for rice
Matching technologies
with climate in space
and time
PROBABILISTIC PRECIPITATION FORECAST

33

Normal
Below

33

31

22
27

31

33

Above

38

39

33
51

37

33

31

28

Agroclimatic
Seasonal
forecasting
Big opportunities for
reducing water
dependency
Global learning
Leb by

Climate smart villages:
Key agricultural activities for managing risks
Climate resilience

Pulling the pieces together

Adapted
technologies

Baseline

Adapted
technologies
+
Climatespecific
management

Adapted
Adapted
technologies
technologies
+
+
Climate-specific
Climatemanagement
specific
+
management
Seasonal
+
agroclimatic
Seasonal
forecasts
agroclimatic
+
forecasts
Efficient
resource use

Climate smartness

Adapted
technologies
+
Climatespecific
management
+
Seasonal
agroclimatic
forecasts
+
Efficient
resource use
+
Enabling
environment
NAPs and
NAMAs
Gracias!

www.aclimatesectoragropecuariocolombiano.org

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Adaptation Start to Finish in Colombia

  • 1. Climate change adaptation from start to finish: The case for Colombia
  • 2. The challenge: Colombia is a victim of climate variability Maíz Arroz T-Max Rendimient o Rendimient o T-Max Climate anomolies and production anomolies go hand in hand Fuente: Agronet y CRU (http://badc.nerc.ac.uk/data/cru/)
  • 3. And in the long term • Coffee suitability in the Cauca region • Significant changes to 2020, drastic changes to 2050 • The Cauca case: reduced coffeee growing area and changes in geographic distribution. Some new opportunities. MECETA
  • 6. Listen to farmers • Listen to local perspectives of vulnerability • Participatory identification of adaptation options Girardot - Maize Ipiales - Potato San Gil - Beans
  • 7. A systematic look at impacts and economic costs across LAC • Detailed crop modelling of climate impacts across LAC • Analysis of social and economic impacts • Zoom in analysis at national level Crops Food Supply (kcal/capita/day) Harvested Area (Ha) Contribution to GDP DSSAT model? 41,171,982.60 10,270,087.00 28,633,254.40 8,988,559.80 5,943,971.40 7,240,355.00 342,874.20 199,494.00 1,190,503.00 5,706,389.00 2,052,958.80 998,955.20 242,739.60 1,495,006.20 Net Production Value (constant 2004-2006 1000 I$) 27,707,066 25,072,226 6,087,072 3,505,114 7,092,796 3,602,564 4,107,232 1,902,269 7,065,053 4,986,781 127,456 2,453,723 1,167,699 5,170,592 Soybean Sugar Cane Maize Wheat Rice Beans Tomatoes Pineapples Bananas Coffee Sorghum Potatoes Onions Oranges, Mandarines Palm Lemons, Limes Grapes Cottonseed Oil Apples Nuts Coconut Groundnuts Cassava Plantains Cocoa Beans sunflower Yams Mangoes 170 383 435 331 296 90 11 5 34 3 6 34 7 17 5 13 5 1 8 2 1 2 16 4 0 1 0 1 y n y y y y n n n n y y n n 64 3 1,085,119.00 319,617.20 1,078,458 2,095,406 1 0 n n 3 8 547,380.00 1,873,788.60 4,124,806 953,103 1 0 n n 5 6 21 11 44 33 3 18 12 NA 192,937.80 574,795.65 676,841.00 534,019.00 2,747,793.60 948,142.00 1,476,678.40 2,588,299.00 173,591.40 435,323.20 1,748,896 943,316 589,058 515,363 2,027,450 1,572,812 520,235 1,142,205 270,343 2,705,656 0 0 0 1 3 1 0 0 2 2 n n n y n n n n n n
  • 9.
  • 10. Ranked List of Practices Practice CBA Quality 1 Silvopastoral Systems 1.5 2.11 2 Efficient Use of Fertilizer 1.4 2.87 3 Improved Forages 1.3 2.85 4 Biogas 1.2 2.36 Grass-Legume 5 Association 1.2 2.11 6 Water harvest structure 1.2 2.08 Silage, haylage and 7 nutritional blocks 1 2.01 9 Early warning systems 1 1.89
  • 11. Act
  • 12. Objectives 1) Generate capacity to reduce agroclimatic risk: modelling impacts and seasonal forecasting 2) Close the yield gap through climate specific agriculture 3) Evaluate and generate new adapted technologies 4) Evaluate resource efficient production systems (water and carbon footprints) and establish new incentive structures for their adoption (PES, NAMAs, low carbon development pathways etc.)
  • 13. In action 9 Partners 52 Municipalities 16 Departments >800 experimental lots in 20 localities > 200 on farm participatory experiments > 40 events with 660 participants and 32 institutions 97 researchers working on the program > 70 technologies being evaluated
  • 14. What defines yield? 51% of yield variation is caused by climate for rice
  • 17. Big opportunities for reducing water dependency
  • 19. Leb by Climate smart villages: Key agricultural activities for managing risks
  • 20. Climate resilience Pulling the pieces together Adapted technologies Baseline Adapted technologies + Climatespecific management Adapted Adapted technologies technologies + + Climate-specific Climatemanagement specific + management Seasonal + agroclimatic Seasonal forecasts agroclimatic + forecasts Efficient resource use Climate smartness Adapted technologies + Climatespecific management + Seasonal agroclimatic forecasts + Efficient resource use + Enabling environment NAPs and NAMAs

Notas do Editor

  1. The results from the CSA study of the Silvopastoral system in colombia are displayed here. In the matrix, you can see how well the practice performed with regards to the different indicators. 0 = negative change, 1 = no change, 2 = positive change. Some of the matrix squares aggregate the results from more than one indicator. Spider diagrams show the difference between the baseline scenario and the scenarios with the CSA practice for the different CSA pillars and dimensions of CSA. A table at the bottom list the projected barriers to adoption for this practice and if they can be overcome.
  2. After assessing all of the results for all practices in the generic list, a ranked list of the best practices is compiled Here you can see the top 8 practices. All of the practices are ranked based on their final score for the indicators. These practices have now undergone a CBA assessment (only done on the practices ranked highest based on the indicators) and the quality of the data used for the indicators and CBA is included here, giving an overall quality score for the assessment of this practice.