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Chapter 3.1

Impacts of Climate Change on CropProduction in Latin AmericaAndy Jarvis, Julian Ramirez, Osana Bonilla-Findji, and Emmanuel Zapata

Introduction: A background onagriculture in Latin America

Latin American countries (LAC), defined forthis chapter as countries in Latin America fromGuatemala southward to the southern tip ofSouth America, can be characterized as under-going a transition from rural-based economiesinto more manufacturing-based economies. Yetfarmers in these countries are still poor and vul-nerable; they are starting to acquire and imple-ment novel and better adapted technologies forproduction. However, while even the least devel-oped countries within the region have economiesdominated by the industrial and service sector,Latin America still remains a major global pro-ducer and exporter of basic staple commoditiesand high-value agricultural exports (De Gregorio1992; OECD 2010). According to Food andAgriculture Organization of the United Nations(FAO) data (2009), the gross value of agricul-tural crops in LAC amounted to $110 billion in2005, which is 11% of the global value. WithinLatin American economies, the agricultural sec-tor accounts for an average 5% of Gross Do-mestic Product (GDP) (ECLAC 2010), althoughthere is significant variability in importance ofagriculture between different countries (ranging

from some 4.1% [Chile] to some 22.2%[Guatemala]). LAC export on average nearlythree times what they import in terms of agricul-tural produce (FAO 2010). The principal cropswith the greatest area under cultivation in LACinclude soybeans, maize, wheat, sugarcane, andbeans. Soybean and maize have had particularlyhigh annual growth rates over the past 20 years(7.5% and 3.4%, respectively).

Brazil is the leading agricultural power interms of export crops in the region as wellas in terms of GDP growth (OECD 2010;Tables 3.1.1 and 3.1.2). In 2007, it ranked first inexports of coffee (1.5 million tons, Table 3.1.2),sweet potato (5000 tons, Table 3.1.2), and soy-bean (23.7 million tons, Table 3.1.1) and secondin maize and sorghum (11 million and 225 billiontons, respectively).

Another important country in terms ofexports is Argentina, with sorghum (1.1 mil-lion tons, Table 3.1.1), wheat (9.6 milliontons, Table 3.1.1), and maize (15 million tons,Table 3.1.1) being the key crops. Meanwhile,Colombia is the main exporter of sugarcane(56 thousand tons), but also the largest im-porter of maize (3.3 million tons, Table 3.1.2).Ecuador is a strong exporter of bananas (5.1 mil-lion tons, Table 3.1.1), plantain (112 thousand

Crop Adaptation to Climate Change, First Edition. Edited by Shyam S. Yadav, Robert J. Redden, Jerry L. Hatfield,Hermann Lotze-Campen and Anthony E. Hall.c© 2011 John Wiley & Sons, Ltd. Published 2011 by John Wiley & Sons, Ltd.

44



IMPACTS OF CLIMATE CHANGE ON CROP PRODUCTION IN LATIN AMERICA 45

Table 3.1.1. Import (I) and export (E) of the five principal crops by country in Latin America for 2007 based on FAOSTAT(tons).

Banana Maize Sorghum Soybeans Wheat

Country I E I E I E I E I E

Argentina 319 0 4 14,990 2 1,072 2,245 11,843 0 9,645Bolivia 0 79 4 24 1 11 244 66 110Brazil 0 66 1,096 10,933 3 225 98 23,734 6,638 104Chile 178 0 1,777 148 261 0 419 3 1,086 0Colombia 6 1,640 3,323 2 67 332 0 1,286Costa Rica 24 2,272 688 1 301 0 207Ecuador 0 5,175 562 18 0 0 5 468El Salvador 54 596 0 0 1 221Guatemala 12 1,409 701 9 0 1 3 1 494 2Guyana 0 6 0 1 45Honduras 7 567 230 1 0 0 0 0 149 0Nicaragua 3 39 132 6 0 0 0 0 107 0Panama 0 437 338 0 5 119 0Paraguay 2 12 13 2,109 1 6 15 3,521 13 274Peru 0 0 1,571 8 22 0 49 0 1,531 0Suriname 0 54 14 0 4Uruguay 42 0 74 10 2 0 27 773 47 112Venezuela 0 0 546 0 0 2 1,466Total 647 11,750 11,674 28,259 359 1,315 3,744 39,945 13,990 10,137

tons, Table 3.1.2), and tropical fruits (2.668 tons,Table 3.1.2). Within the Central American coun-tries, Honduras (178 thousand tons), Costa Rica(144 tons), and Guatemala (111 tons) are strongin exports of palm oil (Table 3.1.2).

Given the significant reliance of Latin Ameri-can economies on agricultural production, andthe exposure of agriculture to a variable cli-mate, there is considerable concern in the regionfrom climate change. The impacts of climatechange on Latin American Agriculture have,however, been only studied for a limited numberof crops and/or production systems or have beenrestricted to a small geographic domain (in re-lation to the whole region) (e.g., Travasso et al.2006; Laderach et al. 2009a, 2009b; Ramirezet al. 2009; Schroth et al. 2009). In addition, fu-ture climate forecasts and modeling approachesare limited to a few platforms and no compre-hensive assessment has been done with regard tothe validity of global climate model (GCM) fore-casts over the region. All these result in substan-tial difficulties for local and regional decision-making processes and therefore adaptation

measures’ development and implementation.Here, we provide an overview of the importanceof agriculture in Latin America, its vulnerability,and the likely expected changes in climates by2020s, 2050s, and 2080s. We intend to broadlyassess the most significant issues and depict im-portant knowledge gaps as well as suggesting theorientation of future research over the region.

Expected climate changein Latin America

Two different types of changes are expected dur-ing the twenty-first century. First, changes in cli-mate variability (i.e., frequency and intensity ofextreme events), and second, changes in base-line climates (i.e., long-term averages aroundspecific periods). Development and hence vul-nerability of Latin American agriculture isconsiderably subjected to climate variability(Altieri and Koohafkan 2008), although the con-siderable uncertainty arising from it makes ithard to both forecast the possible changes andassess the impacts of such changes (Barnett et al.



Tab

le3.

1.2.

Impo

rt(I

)an

dex

port

(E)

ofot

her

impo

rtan

tcro

psby

coun

try

inL

atin

Am

eric

afo

r20

07ba

sed

onFA

OST

AT

(ton

s).

Bea

nsC

offe

eO

ilpa

lmPl

anta

inPo

tato

Ric

eSu

garc

ane

Swee

tpo

tato

Tro

pica

lfr

uits

Cou

ntry

IE

IE

IE

IE

IE

IE

IE

IE

IE

Arg

enti

na0.

528

0.9

0.2

1.3

148.

30.

23.

47.

90.

1B

oliv

ia0

31.9

00.

417

.10.

10.

10.

10

Bra

zil

96.3

30.9

0.2

5.5

98.6

2.4

3.7

13.3

46.5

0.1

5.9

Chi

le3.

63

0.3

05.

55.

71.

50.

30.

5C

olom

bia

32.4

581.

40.

914

.631

5.6

8310

9.4

22.3

0.3

55.2

0C

osta

Ric

a39

0.3

00.

63.

314

4.3

0.2

30.6

0.9

183.

10.

30.

10.

20

Ecu

ador

115

0.1

00.

117

1.6

011

1.6

0.1

0.1

00

02.

7E

lSal

vado

r22

.73.

40.

70.

173

.72.

465

.50

7572

.30

0.1

0.1

0.3

Gua

tem

ala

14.8

70.

10.

224

.811

0.2

0.1

116.

93.

481

.686

.70.

20.

20

00.

1G

uyan

a0.

20.

20

3.2

01.

28.

32.

30.

1H

ondu

ras

10.5

10

0.2

1.7

178

8.9

0.1

1.5

2.3

71.3

05.

4N

icar

agua

2.7

53.3

0.1

0.5

32.1

1.1

0.2

23.6

8.3

137

0.3

00

Pana

ma

20

00.

24.

32.

71.

50.

673

.40

0Pa

ragu

ay0

0.2

0.1

011

.10.

451

.90

Peru

7.6

32.9

024

0.7

65.5

0.3

00.

3Su

rina

me

0.1

00

6.9

Uru

guay

1.2

00.

14.

90.

28.

90

0.3

5.2

0.4

Ven

ezue

la91

.50

0.2

253

.410

.431

.60.

10.

20

0T

otal

325.

951

8.1

3.4

10.4

345.

892

9.2

157.

847

0.8

196.

113

0.7

671.

764

.10.

355

.58.

712

.30.

52.

7

46




2006; Collins et al. 2006; Weitzman 2009); yetthe farmers generally tend to quickly respond andadapt to new extreme conditions by means ofestablishing complex mechanisms and farmingsystems (Altieri and Koohafkan 2008). Changesin baseline climates, on the other hand, pose astrong pressure on agricultural production, sincethese changes tend to overwhelm crop vari-eties adaptation thresholds, causing considerableyield decreases and thus increasing vulnerability(Fischer et al. 2001; Challinor et al. 2007; Lobellet al. 2008). Different types of adaptation mea-sures are required for the two types of changes.

A key element of climate variability in theLatin American region is the El Nino South-ern Oscillation (ENSO) phenomenon. Recentchanges in frequencies and intensities of ENSO,especially regarding a perceptible increase inthe magnitude of El Nino, suggest that anthro-pogenic activities could have influenced thesechanges (Bronnimann et al. 2004). While GCMstry to address this, their ability to fully sim-ulate ENSO has been debated (Trenberth andHoar 1997; Timmermann et al. 1999; Bronni-mann et al. 2004). Whether the frequency and/orintensity is going to increase with human-led cli-mate change is still open to debate (Trenberth andHoar 1997; Fedorov and Philander 2000; Bron-nimann et al. 2004), but it is highly likely thatEl Nino and La Nina cycles will continue beingpart of the drivers of Latin American agricul-ture and therefore economies (Adams et al. 1999;Timmermann et al. 1999).

In order to look at the likely impacts of climatechange on long-term climate baselines, we cal-culated the changes in baselines (i.e., anomalies)using the SRES-A1B emission scenario (IPCC2000) and a representative selection of sevenGCMs from the IPCC Fourth Assessment Re-port (IPCC 2007a) for the entire Latin Americanregion (Figs. 3.1.1 and 3.1.2).

Temperatures are predicted to increase mostlyin central South America and Central Americaand the Caribbean, with the least warming in thesouthern cone. The greatest uncertainty lies in theCentral Amazon. For precipitation, many coastal

areas of South America and the northern An-des (Peru, Ecuador, and Colombia) are predictedto receive increases in overall annual rainfall,while the Amazon, the northern coast of SouthAmerica (including Venezuela and the Guyanas),and Central America and the Caribbean arepredicted to receive in some cases significantlosses.

Decision-making processes are considerablyaffected by uncertainties arising from climateprediction (Stott and Kettleborough 2002; Stain-forth et al. 2005; Collins et al. 2006; Jarvis et al.2010), making uncertainty assessment and con-sideration critical in order to assess and addressadaptation issues within the twenty-first century.In addition, there’s no certainty that an agree-ment between several GCMs means that a pre-diction is more accurate, since anything beyondthe baseline period is merely a forecast. Given thedirection and magnitude of changes, uncertain-ties might vary and affect adaptation measuresdesign, implementation, and as a result decision-making processes could be considerably morecomplicated (Jarvis et al. 2010). Current vulner-ability of Latin American agriculture and its re-liance in a well-suited climate and soil makesit hard to define a unique adaptation strategyother than governmental support to aid small-holders’ adaptation. Yet, site-specific adaptationmeasures need to be developed, tested, and prop-erly transferred, taking into account the culturaland environmental differences among regionsand the vulnerability (both current and future)of the different production systems.

In Latin America, and using our subset ofseven GCMs, uncertainty (given by the stan-dard deviation among predictions) in precipita-tion changes is concentrated in the northern partof the continent and Central America (Figs 3.1.1(bottom) and 3.1.3b). A direct relationship (i.e.,linear trend) is observed between uncertaintiesand the extent of the change for both tempera-ture and precipitation (Fig. 3.1.3). Large changes(either increases or reductions) often involvegreater differences among GCMs, while smallchanges involve considerably less differences.



Fig

.3.1

.1.

Proj

ecte

dpr

ecip

itatio

nch

ange

s(i

nm

m)

(top

figur

es)

by(a

)20

20s,

(b)

2050

s,an

d(c

)20

80s,

and

unce

rtai

ntie

sre

port

edas

stan

dard

devi

atio

nsfr

omth

eav

erag

epr

ojec

ted

chan

geby

each

peri

od(b

otto

mfig

ures

),us

ing

seve

nG

CM

patte

rns

(i.e

.,C

CC

MA

-CG

CM

3.1,

CSI

RO

-MK

3.0,

IPSL

-CM

4,M

PI-E

CH

AM

5,N

CA

R-C

CSM

3.0,

UK

MO

-HA

DC

M3,

and

UK

MO

-HA

DG

EM

1)an

dth

eSR

ES-

A1B

emis

sion

scen

ario

.

48



Cha

nge

Cha

nge

Cha

nge

SD

Cha

nge

SD

Cha

nge

SD

Cha

nge

Fig

.3.1

.2.

Proj

ecte

dte

mpe

ratu

rein

crea

ses

(in

C∗ 1

0)(t

opfig

ures

)by

(a)2

020s

,(b)

2050

s,an

d(c

)208

0s,a

ndun

cert

aint

ies

repo

rted

asst

anda

rdde

viat

ions

from

the

aver

age

proj

ecte

dch

ange

byea

chpe

riod

(bot

tom

figur

es),

usin

gse

ven

GC

Mpa

ttern

s(i

.e.,

CC

CM

A-C

GC

M3.

1,C

SIR

O-M

K3.

0,IP

SL-C

M4,

MPI

-EC

HA

M5,

NC

AR

-CC

SM3.

0,U

KM

O-H

AD

CM

3,an

dU

KM

O-H

AD

GE

M1)

and

the

SRE

S-A

1Bem

issi

onsc

enar

io.

49



50 CROP ADAPTATION TO CLIMATE CHANGE

SD

am

ong

GC

Ms

(C×

10)

Temperature change (C×10)

Precipitation change (mm)

SD

am

ong

GC

Ms

(mm

)

Fig. 3.1.3. Changes in annual mean temperatures (a) and total annual precipi-tation (b) by 2020s (triangles), 2050s (circles), and 2080s (squares), using sevenGCM patterns and the SRES-A1B emission scenario. Associated uncertainties(y-axis) are reported as standard deviations of anomalies to each period amongGCMs.

Differences between countries also tend to in-crease toward the future (Fig. 3.1.3)

The predicted changes for particular countriesare shown in Table 3.1.3. Average increases inannual temperature are 1.0◦C in 2020, to 2.4◦Cin 2050, and to 3.8◦C in 2080, although the un-certainty increases considerably the further intothe future you go. The quite significant predictedchange in temperature to 2020 is notable, es-pecially for countries such as Paraguay (1.2◦C)and Venezuela (1.1◦C). Meaningful predictionsoriented and focused on policy-making pro-cesses are critical for vulnerable regions of LatinAmerica, such as the Andes. Here, we provide

average changes and uncertainties among a set ofrepresentative GCMs. Policy-making processesshould involve both uncertainties and magni-tudes of the changes in order to optimize theinvestments in adaptation.

The significant decreases in precipitation inCentral American, Venezuela, French Guiana,Guyana, and Suriname are of most concern,especially when combined with the expectedchanges in temperature. This is likely to signifi-cantly increase water stress for many crops, bothannual and perennial, and to alter food fluxesto markets as a result of the changes in photo-synthetic rates and therefore maturity. Summing



Tab

le3.

1.3.

Exp

ecte

dch

ange

sin

prec

ipita

tion

and

tem

pera

ture

sby

2020

s,20

50s,

and

2080

s,fo

rth

eem

issi

onsc

enar

ioSR

ES-

A1B

(ave

rage

san

dst

anda

rdde

viat

ions

ofse

ven

GC

Ms)

.

Cou

ntry

Tem

pera

ture

chan

ge(2

020s

)(±

SD)

Tem

pera

ture

chan

ge(2

050s

)(±

SD)

Tem

pera

ture

chan

ge(2

080s

)(±

SD)

Prec

ipit

atio

nch

ange

(202

0s)

(±SD

)Pr

ecip

itat

ion

chan

ge(2

050s

)(±

SD)

Prec

ipit

atio

nch

ange

(208

0s)

(±SD

)

Arg

enti

na0.

73(±

0.11

)1.

89(±

0.26

)2.

97(±

0.39

)−2

4(±

24.5

)−2

1(±

54.5

)−1

5(±

79.5

)B

oliv

ia1.

27(±

0.18

)2.

95(±

0.43

)4.

52(±

0.66

)−4

6(±

28)

−54

(±64

.1)

−57

(±95

.8)

Bra

zil

1.03

(±0.

19)

2.59

(±0.

46)

4.04

(±0.

71)

−31

(±10

7.7)

−40

(±23

5.2)

−37

(±33

4.5)

Chi

le0.

74(±

0.09

)1.

9(±

0.2)

2.98

(±0.

31)

−16

(±11

.2)

−47

(±24

.8)

−72

(±35

.9)

Col

ombi

a1.

02(±

0.18

)2.

53(±

0.44

)3.

93(±

0.67

)−7

(±14

0.6)

37(±

315.

8)83

(±45

7.2)

Cos

taR

ica

0.69

(±0.

17)

1.96

(±0.

4)3.

14(±

0.61

)−3

77(±

248.

9)−7

40(±

513.

7)−9

87(±

702.

8)E

cuad

or0.

82(±

0.13

)2.

13(±

0.29

)3.

36(±

0.45

)−5

1(±

112.

9)59

(±25

3.9)

155

(±36

5.2)

ElS

alva

dor

0.78

(±0.

16)

2.29

(±0.

37)

3.7

(±0.

57)

−231

(±12

5.7)

−479

(±24

5.6)

−646

(±32

0.4)

Fren

chG

uian

a1.

03(±

0.16

)2.

37(±

0.36

)3.

61(±

0.56

)−2

12(±

309.

9)−3

71(±

658.

8)−4

72(±

915.

7)G

uate

mal

a0.

87(±

0.14

)2.

41(±

0.33

)3.

84(±

0.52

)−3

14(±

185.

8)−6

41(±

366.

3)−8

63(±

480.

5)G

uyan

a1.

01(±

0.18

)2.

48(±

0.43

)3.

84(±

0.66

)−2

35(±

204.

3)−4

37(±

431.

3)−5

72(±

596.

4)H

ondu

ras

0.69

( ±0.

14)

2.08

(±0.

32)

3.38

(±0.

5)−2

70(±

129.

6)−5

57(±

251.

5)−7

53(±

326.

1)N

icar

agua

0.77

(±0.

17)

2.13

(±0.

41)

3.4

(±0.

63)

−324

(±21

7.3)

−653

(±42

6.4)

−874

(±55

8.3)

Pana

ma

0.8

(±0.

14)

2.05

(±0.

34)

3.21

(±0.

52)

−289

(±19

0.4)

−510

(±39

9.6)

−662

(±55

2.4)

Para

guay

1.24

(±0.

19)

2.76

(±0.

45)

4.18

(±0.

69)

−41

(±33

.2)

−44

(±76

.1)

−44

(±11

3.7)

Peru

1.07

(±0.

16)

2.65

(±0.

39)

4.12

(±0.

6)25

(±55

.8)

63(±

129.

5)10

0(±

193.

3)Su

rina

me

0.97

(±0.

17)

2.36

(±0.

4)3.

65(±

0.61

)−1

66(±

246.

1)−3

04( ±

519.

1)−3

92(±

716.

9)U

rugu

ay0.

68(±

0.1)

1.76

(±0.

25)

2.77

(±0.

38)

0(±

29.4

)46

(±68

.7)

91(±

104)

Ven

ezue

la1.

08(±

0.21

)2.

68(±

0.5)

4.16

(±0.

76)

−178

(±12

0)−3

28(±

253.

3)−4

32(±

353.

2)M

exic

o0.

91(±

0.12

)2.

44(±

0.29

)3.

87(±

0.44

)−6

4(±

45.2

)−1

14(±

94.5

)−1

49(±

130.

7)B

eliz

e0.

77(±

0.17

)2.

19(±

0.4)

3.51

(±0.

62)

−308

(±13

2.5)

−603

(±25

3.7)

−804

(±32

5.5)

Lat

inA

mer

ica

0.96

(±0.

16)

2.42

(±0.

38)

3.78

(±0.

59)

−47

(±81

.5)

−69

(±17

7.3)

−78

(±25

1.8)

51




this with changing sunshine hours could resultin either too early or too late physiological ma-turity for some crops. Further analysis is neededto examine within cropping cycle changes in cli-mate and equate these with likely crop responses.High altitude crops (and therefore farmers crop-ping these crops), for example, are likely to re-spond in a different manner than low-altitudecrops. However, this issue is largely dependentupon the complexity of the cropping system (i.e.,location, photoperiod, varieties and technolo-gies, planting dates, among others) and needsto be addressed independently for each crop, ge-ographic area, and, probably, farming system.

Past impacts of climateon production

The impact of ENSO-related climate variabilityon the agricultural sector has been well docu-mented (IPCC 2001). Over recent years, relativeto 1970–1999 and 2000–2005 periods, the in-crease in extreme weather events led to a 2.4-fold increase in flooding, droughts, and land-slides (IPCC 2007a in Magrin et al. 2007),many of them related to ENSO. During thelast quarter of century, two very severe ENSOepisodes (1982–1983 and 1997–1998) inflictedgreat losses and increased vulnerability of agri-culture to natural disasters (Magrin et al. 2007).In the case of El Nino 1997–1998, losses in theagricultural sector totaled about 20% in the re-gion: 17% in Peru, 19% in Colombia, 23% inBolivia, and almost 50% in Ecuador (ECLAC2009).

Irregular rains and high temperature in Peruare affecting potatoes and maize crops (Datafrom MINAM Estudio Nacional Ambiental2009). During the last 12 agricultural campaigns,80,000 ha of potatoes and 60,000 ha of whitemaize have been lost due to climate change. Pro-duction has been divided by a factor of 2. In theArgentinean Pampas region, the potential wheatyield has been declining at increasing rates since1930 mainly due to minimum temperature in-creases (Magrin et al. 2009).

However, the impacts are not always nega-tive. It has been determined that maize and soy-bean yields tend to be higher than normal dur-ing El Nino and lower during La Nina (Berlatoand Fontana 1997; Grondona et al. 1997;Magrin et al. 1998; Baethgen and Romero 2000).In Brazil and Argentina, abundant soil mois-ture typical from El Nino conditions has pro-duced a record soybean crop. In the Argentineandry pampas and in Uruguay, wheat productiv-ity has shown increases (24% and 3%, respec-tively) (Magrin et al. 2007 in ECLAC 2009).During the last decade, El Nino’s influence inEastern Paraguay region also led to a dramaticincrease on soybean production. The country isnow the fourth largest soybean exporter in theworld (USDA-FAS 2007 in Fraisse et al. 2008),producing about 3% of the world’s production.

Increased precipitation in the 1960–2000 pe-riod resulted in higher productivity for maizecrops in the Argentinean humid pampas (26%)and the Argentinean dry pampas (41%), inUruguay (49%) and southern Brazil (12%), aswell as higher yields from Uruguay pastures(7%) (Magrin et al. 2007 in ECLAC 2009). Overlast century, maize productivity in the Argen-tinean pampas increased from 1.500 kg/ha to4.000 kg/ha; trend also partially explained bytechnological improvements. There was also apositive impact on soybean and sunflower withincreases in crop yields accounting for 38% and12%, respectively (Magrin et al. 2005). On thenorthern coast of Peru, increases in temperatureduring El Nino have caused the shortening ofcotton and mango growing cycles (Torres et al.2001).

Looking toward the future

According to the observations on Latin Americacompiled in the Fourth Assessment Report of theIPCC (IPCC 2007), soybean and wheat cropsand to a less extent maize are expected to risein temperate areas such as southeastern SouthAmerica. Increased heat stress and dryer soilsare expected to reduce productivity to a third




of current levels, in tropical and subtropical re-gions where crops are currently close to the heattolerance limit (ECLAC 2009). On the basis ofestimation of the FAO, the more sensitive cropsin the Andean region are palm, soybean, Sug-arcane, cassava, potato, maize, barley, rice, andwheat. In Brazil, these are soybean, Sugarcane,cassava, maize, rice, and wheat (ECLAC 2009).Projections indicate that changes will be modestup to 2020 but will increase after 2050 and couldbe substantial even with a rise of just 1.5–2◦Cfrom the current temperature. The most substan-tial increase in temperature and precipitation isexpected to take place in the Amazon region, inboth wet and dry season (ECLAC 2009).

Because cereals, oilseed, and protein cropsdepend on temperature and, in many cases,daylength to reach maturity, increases in tem-perature may shorten the length of their growingperiod and, in the absence of compensatory man-agement responses, reduce yields (Porter andGawith 1999; Tubiello et al. 2000) and changesuitable area for their cultivation.

According to International Food Policy Re-search Institute (IFPRI; Nelson et al. 2009), whoused the IMPACT model coupled with DSSAT,climate change will have a negative effect oncrop yields in Latin America and the Caribbeanin 2050. The region will face average yield de-clines of up to 6.4% for rice, 3% for maize, 3%for soybean, and up to 6% for wheat. These re-sults show the same trend of a predominantlynegative impact on crops in Latin America thanother studies that use crop-simulation modelsand future climate scenarios:

Maize productivity among small producers in LatinAmerica and the Caribbean could drop by an averageof 10% by 2055, although in Colombia yields remainessentially unchanged while in Venezuelan Piedmontyields are expected to decline to almost zero (Jonesand Thornton 2003).

Productivity of rice will generally decline inLatin America falling by between 3% and 16%in Guyana and ca. 31% Costa Rica, 16–27%

in Guatemala and between 2% and 15% inBolivia.

According to 95% of projections, Sugarcaneproductivity will fluctuate by +5% in Brazil andthe Andean region (ECLAC 2009). Maize pro-ductivity projections forecast a decline in Braziland an oscillation of +5% in the Andean region(ECLAC 2009).

For soybean and maize in southeastern LatinAmerica, modeling which considered increasedCO2 concentration, adaptive measures includingoptimal planting dates and nitrogen rates pre-dicted maize mean yield increases of 14% and23% for 2020 and 2050 under SRES A2 (and11% and 15% under SRES B2). The correspond-ing figures for mean soybean yields were 35%and 52% for 2020 and 2050, respectively, un-der SRES A2 (under SRES B2 24% and 38%)(Gimenez 2006).

In Sao Paolo region of Brazil, by the turn ofthe century, the land area suited for growing cof-fee in the state of Sao Paulo will have diminishedby between 10% (if temperatures increase by1◦C and precipitations by 15%) and 97% (withincreases of 5.8◦C and 15%, respectively) (Pintoet al. 2002).

Conclusions

Latin America countries have a significant per-centage of their GDP in agriculture (5% aver-age), and the region is a net exporter of foodglobally, accounting for 11% of the global value.Climate change is predicted to increase temper-atures throughout the continent, but especiallyin Central America and the Caribbean (wheremuch of the poverty lies) and countries on theCaribbean coast in South America. Rainfall in-creases are expected in many parts of the con-tinent, but yet again it is Central America andCaribbean countries that are predicted to receivethe greatest reductions in precipitation. For someregions (coastal regions of Costa Rica, for exam-ple), this does not imply severe impacts, while forother areas (currently subjected to drought suchas the Caribbean coasts of Colombia, Venezuela,




and in the Caribbean) these changes do requirethe urgent definition of adaptation actions to copewith negative impacts (such as severe and pro-longed drought). Moreover, the impacts of thesechanges on crop production are largely unknownand research is needed to further understand thecomplex crop responses to climate changes bothin terms of variability and long-term average cli-mates. However, the current data indicates ex-pected increases (or very little decreases) in someof the major commodities in the continent (e.g.,soybean and cassava) as well as reductions inmost other crops (e.g., beans, bananas, pota-toes). Research must work toward better esti-mations of impacts on crop production, whilecrop specialists must examine different adapta-tion options to address the specific issues. Giventhe high heterogeneity in production landscapesin Latin America, multiple adaptation measuresmust be developed and implemented in a site-specific manner.

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