Climate change impacts on cocoa in Ghana

Presentation of CRIG workshop results to Ghanaian cocoa community by C.Bunn (CIAT) et al. (October 2015)

Climate change impacts on cocoa in Ghana

Outline

• Mainstreaming climate-smart cocoa project

• Previous studies

• Methods and data

• Results for current climate• Results for future climate

• Conclusions

The project objectives

Mainstreaming Climate-Smart Cocoa

Project consortium members

Mainstreaming Climate-Smart Cocoa

International Center for Tropical Agriculture, CIAT

• 50 years of applied research for improved livelihoods and environmental sustainability in the global tropics.

• 900 staff active in Africa, Latin America and South East Asia.

• Annual budget of US 130m.• Lead center for the global

Climate Change, Agriculture and Food Security Program of the CGIAR.

International Center for Tropical Agriculture, CIAT

Role in this project• Mapping risk of climate

change for cocoa in Ghana• Economic analysis of cost

and benefits of adaptation strategies

• How to scale CSA practices in cocoa systems

• Overall project and consortium management, reporting and learning.

International Institute of Tropical Agriculture

One of the world's leading research institutes working with partners in Africa and beyond to reduce producer and consumer risks, enhance crop quality and productivity, improve livelihoods and generate wealth from agriculture.

International Institute of Tropical AgricultureProject role

• Coordination in Ghana together with RA• Situational analysis• Stakeholder engagement • Social learning• Identify strategic learning sites along climate gradients• Develop relevant adaptation practices for cocoa • Climate Smart Agriculture planning that fosters gradual

change/transition in the identified high impact zones• Match CSA to value chain actors’ needs according to the

agreed identified adaptation zones

Proposed impact pathway

Proposed impact pathway - roles

Project objectives

The project expects to contribute to: Clear knowledge of what types of CSA practices to promote

where, for whom and with what return on investment Knowledge of under what conditions extension and PO

investments function as incentives for CSA uptake at scale Identification of additional public, private or public-private

incentives needed to promote widespread CSA adoption in the cocoa sector

Functional multi-stakeholder platforms that combines climate science with industry knowledge to reduce risk faced by cocoa in Ghana going forward.

• We seek to add value to what all of you are already doing around climate change and look forward to hearing what you think, how we might best collaborate and what additional issues should be considered.

Climate change for cocoa in West Africa

Previous studies

Previous studies

• Suitability losses in the West

• Some gains towards Lake Volta.

Läderach et al. (2013) Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire” Climatic Change.

Previous studies

• Ghana: Losses in the North, Gains in central areas.

• West-A.: Maximum dry season temperatures seen to be problematic.

• West-A.: Areas at the margins to Savanna are most vulnerable.

Schroth et al.,“Vulnerability to climate change of cocoa in West Africa: patterns, opportunities and limits to adaptation” Agriculture, Ecosystems and Environment (Submitted).

Previous studies

• Some disagreement about distribution of impacts

• Unspecific to Ghana Which are the climate events Ghana has to prepare for?

• „Suitability“ = Probabilities from binary classification method How much „suitability loss“ is critical? What can be done to adapt?

Application of a machine learning tool to cocoa in Ghana

Random Forest classification

When to apply machine learning?

Where can we grow cocoa? „For optimal conditions maximum temperatures should

not exceed 32 °C (Lass and Wood 1985)

Not in Ghana!• Our understanding of climatic

requirements is often limited• Our climate data is often bad• Crop simulation models are

very complex• Crop simulation models often

give unsatisfactory results even for rice and maize

Random Forests for classification

• A forest is an ensemble of trees. The trees are all slightly different from one another.

• The output is the mean classification

• Very robust against overfitting

Is the soil good?

Is the dry season long?

Is the heatstrong?

One decision tree

Source: Criminisi et al 2013


• Training classes: 5 AEZ clusters as

suitable classes Random sample

from the area of Ghana

Balanced subsample

• Climate variables: 20 bioclimatic

variables • 25 repeats Different

subsamples Random repeats

• 1000 trees grown, 4-5 variables picked

Type Bioclimatic variable Description Current

Mean 2030s Mean

2050s Mean Unit

Tem

pera

ture

BIO 1 Annual Mean Temperature 26,2 27,3 27,7 °C

BIO 2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) 9,1 8,7 8,6 °C

BIO 3 Isothermality (BIO2/BIO7) (*100) 72 70 70 -

BIO 4 Temperature Seasonality (standard deviation *100) 103,7 110,1 109,8 °C

BIO 5 Max Temperature of Warmest Month 33,1 34,1 34,5 °C

BIO 6 Min Temperature of Coldest Month 20,6 21,9 22,3 °C

BIO 7 Temperature Annual Range (BIO5-BIO6) 12,5 12,3 12,2 °C

BIO 8 Mean Temperature of Wettest Quarter 26,5 27,2 27,6 °C

BIO 9 Mean Temperature of Driest Quarter 26,6 27,8 28,2 °C

BIO 10 Mean Temperature of Warmest Quarter 27,4 28,7 29,1 °C

BIO 11 Mean Temperature of Coldest Quarter 24,7 25,8 26,2 °C

Prec

ipita

tion

BIO 12 Annual Precipitation 1453 1463 1476 mm BIO 13 Precipitation of Wettest Month 234 233 235 mm BIO 14 Precipitation of Driest Month 22 21 21 mm

BIO 15 Precipitation Seasonality (Coefficient of Variation) 53 54 55 -

BIO 16 Precipitation of Wettest Quarter 570 567 575 mm BIO 17 Precipitation of Driest Quarter 117 114 113 mm BIO 18 Precipitation of Warmest Quarter 335 326 329 mm BIO 19 Precipitation of Coldest Quarter 361 379 382 mm

BIO 20 Number of Consecutive Months < 100mm precipitation 3,63 3,65 3,63 -

Input locations

Soil variables

• Soils interact with climate suitability

• Soil characteristics provide resilience against climate hazard

40 soil variables for cocoa rooting zone Soil organic matter Rootability Silt, sand, clay content Exchangeable bases, acidity, cations

Classification A: Cocoa locations vs. No-cocoa locationsClassification B: Unsupervised grouping

Soils for cocoa in Ghana

Results


Clustering result

Clustering result

1 Elevated temperatures Reliable precipitation Average soils

2 Low annual precipitation Strong dry season Below average soils

3 High temp Low seasonal variation Above average soils

4 Low temperatures Long dry season Average soils

Current distribution of suitability classes for cocoa


• MSNW Moist semi-decidious North

West• MSSE Moist semi-decidious South-

East• ME Moist evergreen




AEZ Bioclim A Bioclim B Soils

Type 1 Low annual precipitation Strong dry season Below average soils

Type 2 Low temperatures Long dry season Average soils

Type 3 Elevated temperatures Reliable precipitation Average soils

Type 4 High temp Low seasonal variation Above average soils

Future distribution of suitability classes for cocoa



Transition of suitability classes

Climate change impact gradient

Climate impact variables

Temperatures at locations that become unsuitable

are beyond today‘s limitsMore analysis required about precipitation changes

Climate impact variables

Temperatures at locations that become unsuitable

are beyond today‘s limitsMore analysis required about precipitation changes

Conclusion

• Cocoa production is shaped by climate and soils• Cocoa soil characteristics are different from other soils in the

country

• Results show four distinct production zones that align with ecological zones in Ghana: Moist semi-deciduous, subtypes NW, (central),SE Moist evergreen

• The moist evergreen climate (type 4) will be the dominant climate in the future

• The moist semi-deciduous (type 1) region in the North West will become marginal

• Soils will determine the resilience against climatic change

Acknowledgements

• Sander Muilerman (IITA)• Christian Mensah (Rainforest

Alliance)• Dr. Anim-Kwapong (CRIG)• Dr. Amos Quaye (CRIG)• Patrick Adjewodah (IITA/RA)• Workshop participants from CRIG:

E. Amamoo-Otchere Patrick Adjewodah A. Afrifa Godfrend Awudzi Robert Asugre Jerome Dogbatse Dr. Sampson Kolan Fredrick Amon-Armah Esther Gyan Williams Atakorah Mustapha Alasan Dalaa (IITA)

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Climate change impacts on cocoa in Ghana

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