Climate change and roots and tubers

Andy Jarvis, Julian Ramirez, Emmanuel Zapata

Contents

• About climate change and predictive models

• Expected changes for potato and cassava growing regions

• Implications on suitability

• Challenges ahead

How can we be sure that it is changing?

Arctic Ice is Melting

In order to prepare, we need to know what to prepare for….

….but how?

Global Climate Models (GCMs)

• 21 global climate models in the world, based on atmospheric sciences, chemistry, biology, and a touch of astrology

• Run from the past to present to calibrate, then into the future

• Run using different emissions scenarios

So, what do they say?

Changes in rainfall…

CIAT’s Data

• 18 GCM models to 2050, 9 to 2020

• Different scenarios, A1b, B1, commit

• Downscaled using empirical methods

http://gisweb.ciat.cgiar.org/GCMPage/

Región PaísCambio en

Precipitación total (mm)

Cambio en Temperatura

media anual (°C)

Belize -144.951 2.190 2.866 0 7.560 3.204Bolivia -0.209 2.818 2.193 0 8.318 4.364Brazil 7.024 2.524 0.909 0 5.563 3.844Chile 34.353 2.335 6.955 1 46.694 5.600Colombia 59.364 2.364 -2.463 -1 5.780 3.484Costa Rica -66.537 2.082 -0.481 0 7.427 2.319Ecuador 114.114 2.094 -3.993 0 6.472 2.850El Salvador -60.796 2.302 -2.452 0 7.414 3.387French Guiana -126.417 2.508 4.306 0 14.194 4.469Guatemala -93.319 2.367 0.647 0 5.361 3.650Guyana -136.198 2.368 5.945 0 12.711 3.631Honduras -116.272 2.339 2.798 0 8.296 3.481Mexico -46.867 2.372 -0.803 0 4.856 3.780Nicaragua -118.649 2.298 0.059 0 9.259 2.833Panama 4.867 1.828 -2.061 -1 6.490 2.485Paraguay -7.375 2.585 5.823 0 7.717 4.692Peru 82.387 2.547 1.548 0 5.849 4.878Suriname -115.235 2.286 6.485 0 13.964 3.743Uruguay 68.274 1.948 6.405 0 6.727 5.152Venezuela -34.579 2.666 2.271 1 10.005 4.254

Norte América United States 3.997 2.780 18.928 1 16.839 4.838

América Latina

Changes in Cassava production areas in Latin America

Country RegionTotal annual

rainfall change (mm)

Annual mean temperature change (mm)

Precipitation seasonality

change

Cons. Dry months change

Precipitation coefficient of

variation

Temperature coefficient of

variationangola Sub Saharan Africa 5.02 2.59 -1.60 1.00 6.76 3.98benin Sub Saharan Africa 87.10 2.47 -9.69 -1.00 11.21 3.58botswana Sub Saharan Africa 8.65 3.01 -5.45 0.00 8.76 5.76burkina faso Sub Saharan Africa 119.22 2.66 -12.03 -1.00 11.45 4.30burundi Sub Saharan Africa 139.93 2.37 -6.76 0.00 12.57 4.06cameroon Sub Saharan Africa 16.71 2.28 -0.40 0.00 2.71 3.45central african republic Sub Saharan Africa 39.88 2.49 -4.34 0.00 4.24 3.49chad Sub Saharan Africa 46.40 2.75 -8.46 0.00 9.84 4.52congo Sub Saharan Africa -1.09 2.31 0.77 0.00 5.47 3.20equatorial guinea Sub Saharan Africa 12.16 2.09 0.95 0.00 3.59 3.15eritrea Sub Saharan Africa 48.18 2.89 -14.50 0.00 29.63 4.14ethiopia Sub Saharan Africa 82.93 2.54 -11.11 0.00 7.49 3.82gabon Sub Saharan Africa 11.47 2.14 0.06 0.00 3.37 2.94gambia Sub Saharan Africa 13.10 2.45 -4.54 0.00 10.93 3.74ghana Sub Saharan Africa 23.02 2.25 -3.25 0.00 7.05 3.16guinea Sub Saharan Africa 71.24 2.47 -4.45 0.00 6.76 3.85guinea-bissau Sub Saharan Africa 26.02 2.38 -3.17 0.00 8.49 3.41ivory coast Sub Saharan Africa 36.21 2.29 -3.61 0.00 7.14 3.57kenya Sub Saharan Africa 142.14 2.26 -4.06 -1.00 11.61 4.77liberia Sub Saharan Africa 58.39 2.11 -1.08 -1.00 6.48 2.94madagascar Sub Saharan Africa -7.39 2.05 0.21 0.00 1.77 2.79malawi Sub Saharan Africa 33.48 2.49 -4.67 0.00 5.33 3.59mali Sub Saharan Africa 75.18 2.70 -7.99 0.00 6.64 4.47mozambique Sub Saharan Africa 1.64 2.31 -0.08 0.00 3.14 3.03myanmar Sub Saharan Africa 107.79 2.25 -6.03 0.00 4.87 4.35namibia Sub Saharan Africa 7.62 2.96 -4.22 0.00 9.36 4.97niger Sub Saharan Africa 68.27 2.85 -10.10 0.00 14.37 5.29nigeria Sub Saharan Africa 51.35 2.51 -6.43 0.00 5.84 3.98rwanda Sub Saharan Africa 112.05 2.32 -3.96 -1.00 12.01 3.99senegal Sub Saharan Africa 33.87 2.42 -7.07 0.00 10.17 3.69sierra leone Sub Saharan Africa 80.30 2.18 -1.56 0.00 6.30 2.92south africa Sub Saharan Africa -50.44 2.38 8.62 0.00 7.22 4.45sudan Sub Saharan Africa 50.49 2.61 -9.11 0.00 5.66 4.15swaziland Sub Saharan Africa -38.85 2.24 6.49 0.00 5.88 4.30tanzania Sub Saharan Africa 103.53 2.28 -4.27 0.00 8.11 3.70togo Sub Saharan Africa 85.58 2.41 -8.08 0.00 10.73 3.29uganda Sub Saharan Africa 137.61 2.28 -4.36 -1.00 11.85 4.07zaire Sub Saharan Africa 29.16 2.39 -1.06 0.00 2.34 3.61zambia Sub Saharan Africa 32.15 2.70 -5.31 1.00 4.80 4.57zimbabwe Sub Saharan Africa 12.32 2.81 -5.45 0.00 6.76 4.97

Climate characteristic

Climate Seasonality

The coefficient of variation of temperature predictions between models is 9.13%

The maximum number of cumulative dry months decreases from 8 months to 7 months

These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 18 GCM models from the 3th (2001) and the 4th (2007) IPCC assessment, run under the A2a scenario (business as usual). Further information please check the website http://www.ipcc-

data.org

The coefficient of variation of precipitation predictions between models is 16.03%

General climate

characteristics

Extreme conditions

Variability between models

Overall this climate becomes less seasonal in terms of variability through the year in temperature and less seasonal in precipitation

The driest month gets drier with 9.64 millimeters instead of 11.32 millimeters while the driest quarter gets wetter by 19.28 mm in 2050

Temperature predictions were uniform between models and thus no outliers were detected

The mean daily temperature range increases from 14.28 ºC to 14.74 ºC in 2050

Precipitation predictions were uniform between models and thus no outliers were detected

Average Climate Change Trends of Junin (Peru)

General climate change description

The maximum temperature of the year increases from 14.68 ºC to 18.2 ºC while the warmest quarter gets hotter by 2.55 ºC in 2050The minimum temperature of the year increases from -3.51 ºC to -1.06 ºC while the coldest quarter gets hotter by 2.78 ºC in 2050The wettest month gets wetter with 152.07 millimeters instead of 141.48 millimeters, while the wettest quarter gets wetter by 20.25 mm in

The rainfall increases from 853.51 millimeters to 942.96 millimeters in 2050 passing through 829.18 in 2020Temperatures increase and the average increase is 2.57 ºC passing through an increment of 0.91 ºC in 2020

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Current precipitation

Precipitation 2020

Precipitation 2050

Mean temperature 2020

Mean temperature 2050

Current mean temperature

Maximum temperature 2020

Maximum temperature 2050

Current maximum temperature

Minimum temperature 2020

Minimum temperature 2050

Current minimum temperature

The Impacts on Crop Suitability

The Model: EcoCrop

It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation…

…and calculates the climatic suitability of the resulting interaction between rainfall and temperature…

• So, how does it work?

Agricultural systems analysis• 50 target crops selected based on area

harvested in FAOSTATN FAO name Scientific name

Area harvested

(kha)26 African oil palm Elaeis guineensis Jacq. 1327727 Olive, Europaen Olea europaea L. 889428 Onion Allium cepa L. v cepa 334129 Sweet orange Citrus sinensis (L.) Osbeck 361830 Pea Pisum sativum L. 673031 Pigeon pea Cajanus cajan (L.) Mill ssp 468332 Plantain bananas Musa balbisiana Colla 543933 Potato Solanum tuberosum L. 1883034 Swede rap Brassica napus L. 2779635 Rice paddy (Japonica) Oryza sativa L. s. japonica 15432436 Rye Secale cereale L. 599437 Perennial reygrass Lolium perenne L. 551638 Sesame seed Sesamum indicum L. 753939 Sorghum (low altitude) Sorghum bicolor (L.) Moench 4150040 Perennial soybean Glycine wightii Arn. 9298941 Sugar beet Beta vulgaris L. v vulgaris 544742 Sugarcane Saccharum robustum Brandes 2039943 Sunflower Helianthus annuus L v macro 2370044 Sweet potato Ipomoea batatas (L.) Lam. 899645 Tea Camellia sinensis (L) O.K. 271746 Tobacco Nicotiana tabacum L. 389747 Tomato Lycopersicon esculentum M. 459748 Watermelon Citrullus lanatus (T) Mansf 378549 Wheat, common Triticum aestivum L. 21610050 White yam Dioscorea rotundata Poir. 4591

N FAO name Scientific nameArea

harvested (kha)

1 Alfalfa Medicago sativa L. 152142 Apple Malus sylvestris Mill. 47863 Banana Musa acuminata Colla 41804 Barley Hordeum vulgare L. 555175 Bean, Common Phaseolus vulgaris L. 265406 Common buckwheat* Fagopyrum esculentum Moench 27437 Cabbage Brassica oleracea L.v capi. 31388 Cashew Anacardium occidentale L. 33879 Cassava Manihot esculenta Crantz. 18608

10 Chick pea Cicer arietinum L. 1067211 White clover Trifolium repens L. 262912 Cacao Theobroma cacao L. 756713 Coconut Cocos nucifera L. 1061614 Coffee arabica Coffea arabica L. 1020315 Cotton, American upland Gossypium hirsutum L. 3473316 Cowpea Vigna unguiculata unguic. L 1017617 European wine grape Vitis vinifera L. 740018 Groundnut Arachis hypogaea L. 2223219 Lentil Lens culinaris Medikus 384820 Linseed Linum usitatissimum L. 301721 Maize Zea mays L. s. mays 14437622 mango Mangifera indica L. 415523 Millet, common Panicum miliaceum L. 3284624 Rubber * Hevea brasiliensis (Willd.) 825925 Oats Avena sativa L. 11284

Average change in suitability for all crops in 2050s

Winners and losers

Number of crops with more than 5% loss

Number of crops with more than 5% gain

And potato???

Current Suitability

Suitability 2020

Suitability 2050

Current Suitability

Change in suitability 2020

Change in suitability 2050

Impacts on production of cassava

Worldwide cassava production climatic constraints

Grey areas are the crop’s main niche.

Blue areas constrained by precipitation

Yellow-orange constrained by temperature

Impact of climate change on cassava suitable environments

Global cassava suitability will increase 5.1% on average by 2050… but many areas of Latin America suffer negative impacts

What are the expected global benefits?

Increase of 5-10% in potential land area for cassava when implementing either drought or flood tolerance

21.9 million hectares (16.9% of global cassava fields) under cultivation would benefit

63.3 million hectares of new land would become suitable for cassava

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Ropmin ------------------------------- Ropmax

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) Waterlogging tolerance

Drought tolerance

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Drought tolerance (Ropmin) Waterlogging tolerance (Ropmax) Not benefited

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Currently cropped lands

Not currently cropped lands

…….and for Latin America?Drought or flooding tolerance

30% of current cassava fields would benefit from enhanced drought or flooding tolerance

1.6m Ha still suffering climatic constraint

2.23m Ha of current production

2.1m Ha of new land would become suitable for cassava

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-2.5% -2% -1.5% -1% -0.5% None +0.5% +1% +1.5% +2% +2.5%

Mejora en la resiliencia de los cultivos

Cam

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dap

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[>80

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Áreas cultivadas

Áreas no-cultivadas

Total áreasadaptables

Toleracia a sequias

Toleracia a inundación

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-2.5% -2% -1.5% -1% -0.5% None +0.5% +1% +1.5% +2% +2.5%

Mejora en la resiliencia de los cultivos

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Áreas no-cultivadas

Total áreasadaptables

Toleracia a sequias

Toleracia a inundación

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Áreas cultivadas actualmente

Áreas no-cultivadasactualmente

…….and for Latin America?Heat or cold tolerance

27% of current cassava fields would benefit from enhanced cold or heat tolerance

2.23m Ha of current production

2.2m Ha of new land would become suitable for cassava

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-2.5ºC -2ºC -1.5ºC -1ºC -0.5ºC None +0.5ºC +1ºC +1.5ºC +2ºC +2.5ºC

Mejoramiento en la resiliencia del cultivo

Cam

bio

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áre

as a

dap

tab

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[>80

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Áreas cultivadas

Áreas no-cultivadas

Total áreas adaptables

Toleracia al calor

Toleracia al frío

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-2.5ºC -2ºC -1.5ºC -1ºC -0.5ºC None +0.5ºC +1ºC +1.5ºC +2ºC +2.5ºC

Mejoramiento en la resiliencia del cultivo

Cam

bio

en

áre

as a

dap

tab

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[>80

%]

(%)

Áreas cultivadas

Áreas no-cultivadas

Total áreas adaptables

Toleracia al calor

Toleracia al frío

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Topmin Topmax Not benefited

Áre

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Áreas no-cultivadasactualmente

Evaluating Technology Options: Crop Improvement for Cassava

Grey areas would get no benefit from drought or flood tolerance.

Blue areas benefit from drought tolerance improvement

Purple areas benefit from flood tolerance improvement

Pest and Disease Impacts

Impacts on green mite

to 2020

Impacts on whitefly to

2020

Challenges ahead

• Further analysis: improvement of the parameters, greater uncertainty in GCMs

• Inclusion of post-harvest impacts – drying for example

• Further work in pest and disease impacts• ….but too late to wait for 100% certainty• Crop improvement and targeted varietal

selection can support adaptation

GRACIAS!!!!

a.jarvis@cgiar.org