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Studies of future scenarios for tropical forages

Dra. Patricia Menezes Santos Embrapa Southeast Livestock

Tela de abertura da apresentação: opção 2

Prospec>ve studies

“The study of the future to develop strategies for

building a desirable future”

Mayerhoff (2008)

Tela de conteúdo (somente texto): opção 1

Scenarios Studies

“Method to prospect the future through systema:c building of alterna:ve ways, determining possible

scenarios”

Mayerhoff (2008)


“Special Report on Emissions Scenarios”


Fonte: IPCC, 2000


•  A1: a future world of very rapid economic growth, global popula>on that peaks in mid-‐century and declines thereaRer, and rapid introduc>on of new and more efficient technologies • A2: a very heterogeneous world with con>nuously increasing global popula>on and regionally oriented economic growth that is more fragmented and slower than in other storylines.



•  B1: a convergent world with the same global popula>on as in the A1 but with rapid changes in economic structures toward a service and informa>on economy, with reduc>ons in material intensity, and the introduc>on of clean and resource-‐efficient technologies •  B2: a world in which the emphasis is on local solu>ons to economic, social, and environmental sustainability, with con>nuously increasing popula>on (lower than A2) and intermediate economic development.


“Representa>ve Concentra>on Pathways Scenarios”

•  RCP 2.6: very low GHG concentra>on levels; radia>ve forces peaks around 3.1 W/m2 mid century and returns to 2.6 W/m2 by 2100; GHG emissions are reduced substan>ally over >me

•  RCP 4.5: stabiliza>on scenario; radia>ve forcing is stabilized before 2100 by employment of technologies and strategies for reducing GHG emissions


“Representa>ve Concentra>on Pathways Scenarios”

•  RCP 6.0: stabiliza>on scenario; radia>ve forcing is stabilized aRer 2100 without overshoot by employment of technologies and strategies for reducing GHG emissions

•  RCP 8.5: representa>ve for high GHG concentra>on levels with increasing GHG emissions over >me


Scenarios for tropical forages

•  IPCC scenarios •  ETA and PRECIS models used for regional climate projec>on •  Approaches:

-‐ Climate suitability -‐ Forage produc>on – empirical models -‐ Forage produc>on – mechanis>c models


Climate suitability

Climate suitability -‐ Cenchrus ciliaris


•  Search of climate characteris>cs of na>ve areas and places where species is cul>vated: •  Rainfall = 348 a 1.027 mm •  Mean temperature = 22 a 29oC •  Water deficit = 246 a 971 mm

•  Criteria: •  Rainfall •  Water deficit •  Mean temperature and rainfall

•  Maps: •  Geographic Informa>on Systems -‐ GIS •  20% of pixels with occurrence >= 0,6


hhp://tecnologias.cppse.embrapa.br/scafforragem/

Empirical models


Grass Variable Slope Inter-cept R² Reference

B. brizanta cv. Marandu Tmin 11.93 -134.95 0.73 Cruz et al. (2011) B. brizanta cv. Marandu Tmincorr 5.78 -17.24 0.75 Cruz et al. (2011) B. brizanta cv. Marandu GDDcorr 12.9 6.52 0.75 Cruz et al. (2011) Brachiaria Group 1§ Tmin 8.19 -94.92 0.5 Tonato et al. (2010) Brachiaria Group 2¶ Tmin 10.66 -128.07 0.6 Tonato et al. (2010) Cynodon Group 1† Tmin 9.06 -84.69 0.6 to 0.7 Tonato et al. (2010) Cynodon Group 2§§ Tmin 7.97 -67.01 0.6 to 0.7 Tonato et al. (2010) Panicum Group1¶¶ Tmin 6.36 -55.22 <0.4 Tonato et al. (2010) Panicum Group 2†† Tmin 5.93 -29.15 <0.4 Tonato et al. (2010) P. maximum cv. Mombaça ƩUF 0.226 600.01 0.86 Araujo et al. (2013) P. maximum cv. Mombaça ƩICC 368.14 -311.94 0.83 Araujo et al. (2013) P. maximum cv. Mombaça ƩGDD 11.52 -304.8 0.78 Araujo et al. (2013) P. maximum cv. Tanzânia AET 34.73 -21.58 0.87 Pezzopane et al. (2012) P. maximum cv. Tanzânia GDDcorr 18.80 -17.02 0.84 Pezzopane et al. (2012) P. maximum cv. Tanzânia GDDcorr 18.90 -6.38 0.87 Pezzopane et al. (2012) P. maximum cv. Tanzânia CGI 330.09 -12.88 0.84 Pezzopane et al. (2012)

Empirical models


Climate x forage produc>on

Panicum maximum – empirical models


y = 10,76***xR² = 0,7927

0

50

100

150

200

250

0 5 10 15 20

Observeted

DMAR

(Kg D

M ha -‐

1 dia-‐1

)

Mean degree-days

São Carlos

Piracicaba

Sobral

Juiz de Fora

y = 0.6756x + 13.255R² = 0.8137d = 0,93

MSEP= 265,6E = -‐19,58

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100 120 140 160

Estim

ated

DM

AR (K

g DM

ha

-1 d

ia-1

)

Observeted DMAR (Kg DM ha -1 dia-1)

São Carlos

Piracicaba

Juiz de Fora

Actual scenario -‐ Panicum


Future scenarios -‐ Panicum Tela de conteúdo (somente texto): opção 1

Future scenarios -‐ Panicum


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 5000 10000 15000 20000 25000 30000 35000 40000 45000

Prob

abilit

y

Annual Production (kg DM ha-1 year-1)

BaselineETA B2 2042-2070ETA A2 2042-2070PRECIS B2 2042-2070PRECIS A2 2042-2070

0

15

30

45

60

75

90

105

120

135

150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Dry

mat

ter (

kg D

M h

a-1.d

ay)

Month

Baseline

ETA B2 2042-2070

ETA A2 2042-2070

PRECIS B2 2042-2070

PRECIS A2 2042-2070

Accumulated probability of annual produc>on

Seasonal herbage accumula>on rate

Actual scenario – Brachiaria


Future scenarios -‐ Brachiaria Tela de conteúdo (somente texto): opção 1

10000

15000

20000

25000

30000

35000

1963

1967

1971

1975

1979

1983

1987

1991

1995

1999

2003

2007

2011

2015

2019

2023

2027

2031

2035

2039

2043

2047

2051

2055

2059

2063

2067

Ann

ual y

ield

(kg

DM

ha-

1ye

ar-1

)

Years

Moi 60 mmObserved climatePRECIS HIPRECIS LOWETA-CPTEC HIETA-CPTEC LOW

Mean Brachiaria brizantha produc>on in Sao Paulo State

Mean Brachiaria brizantha produc>on in Sao Paulo State

0

20

40

60

80

100

120

140


DM

AR

(kg

DM

ha-1

day-1

)

DMAR atualDMAR A.E. 2013-2040DMAR A.E. 2043-2070DMAR B.E. 2013-2040DMAR B.E. 2043-2070

0102030405060708090

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecVa

riat

ion

(%)

Moi 40Moi 60Moi 100

0

20

40

60

80

100

120

140


DMAR

(kg

DM h

a-1da

y-1)

DMAR atualDMAR A.E. 2013-2040DMAR A.E. 2043-2070DMAR B.E. 2013-2040DMAR B.E. 2043-2070

0102030405060708090

100


Varia

tion

(%)

Moi 40Moi 60Moi 100

Limita>ons of meteorological empirical models

•  Soil proper>es •  Fer>liza>on •  Pasture management •  CO2 concentra>on


Mechanis>c models

Mechanis>c models – Cropgro Perennial Forage Model


Does not simulate more than 10,000 days!



Simula>on stops under extreme water deficit!



Higher temperature (C5, C6 e C7) = Lower produc>on and higher dispersion Lower temperature (C4) = slight increase in produc>on

Mechanis>c models – Apsim


0

5000

10000

15000

20000

25000

-‐3 atual 3 6 9

Aragarças

Pelotas

Porto dos Gaúchos

São Carlos

Sobral

Votuporanga

Mechanis>c models – Apsim


Thank you!

[email protected]

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