Upland Rice Breeding in Brazil: Progress and PerspectivesProgress and Perspectives

Flavio Breseghello

Head of Research and Development

Embrapa Rice and BeansEmbrapa Rice and Beans

Santo Antônio de Goiás, Brazil

Amount of Rice Produced in Upland andIrrigated Systems in Brazilg y

14000

10000

12000

20%

6000

8000 Production Upland

,000

 t 55%

4000

6000

Production Irrigated

x 1,

0

2000

6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

Production Irrigated

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Variation of Area Planted and Yield ofUpland and Irrigated Rice in Brazilp g

7000

Yield Irrigated

5000

6000g

3000

4000

Area Upland000 ha 80%

1000

2000Yield Upland

Area Upland

x 1,0

50%

0

1000

986

987

988

989

990

991

992

993

994

995

996

997

998

999

000

001

002

003

004

005

006

007

008

009

Area Irrigated

1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2

Opportunities for Sustainable UplandRice ProductionRice Production

SprinklerSprinkler Irrigation

Crop‐livestock‐forest integration

Pasture renovation

Crop rotation

Upland Rice in No‐Tillage SystemUpland Rice in No Tillage System

N h fNorth ofMato Grosso

3.5 t/ha

45 cm45 cm

Importance of Drought Tolerance

Soil water storage Soil water storageSoil water storagecapacity: 30 mm

Soil water storagecapacity: 50 mm

Low Risk (20%)Intermediate Risk (50%)

( %)High Risk (80%)

Drought Phenotyping in the Field(Porangatu Station)( g )

Drought Phenotyping in Soil Columns

PROGRESS OF 25 YEARS OF UPLANDPaper accepted for publication in Crop Science

PROGRESS OF 25 YEARS OF UPLAND RICE BREEDING IN BRAZIL 

Year Number of trials Total number of lines tested

Number of new lines from E b

Number of field plots

Data from records of 26 years

Embrapa1984 12 54 37 7001985 38 58 26 20521986 19 77 40 13581987 10 67 9 6741988 23 54 4 14841988 23 54 4 14841989 10 34 9 7921990 8 32 16 4501991 16 34 3 10601992 15 60 23 9441993 24 79 24 14771993 24 79 24 14771994 19 44 9 15361995 39 49 12 25971996 22 28 6 20461997 26 32 13 21431998 33 37 21 25521998 33 37 21 25521999 32 42 16 24852000 34 45 16 27562001 24 34 15 22972002 18 28 9 18192003 27 28 9 27332003 27 28 9 27332004 26 25 10 24082005 27 26 13 22992006 18 28 13 18302007 25 18 5 15202008 31 17 6 18412008 31 17 6 18412009 27 23 12 1835

Arithmetic Mean 23.2 40.5 14.5 1,757Total 603 493 376 45,688

Statistical Analysis

Group j: set of lines debuting in VCU in year j

Mixed model: “Group” fixed, all other factors random

Yijkmn = μ + gj + li/gj + ak + tm/ak + bn/atkm + εijkmn

BLUE of Group meansBLUP of linesf

Generalized Linear Regression:θ = (X` V‐1 X)‐1 (X` V‐1 Y)

θ = [α, β]β is the genetic gain per yearβ g g p y

Results: Change in Grain Type

Factors Restraining Genetic Gain for GY

• Very strict grain quality parametersVery strict grain quality parameters.

• Non durable blast disease resistance• Non‐durable blast disease resistance.

b d l f• Too broad target population of environments.

Factors Accelerating Genetic Gain for GY 

• Early selection for grain yield.

• Recombination of high‐yielding families.

• Homogenization of cycle duration, plant

height and grain type, allowing stronger

selection pressure on grain yieldselection pressure on grain yield.

The Rice Breeding Scheme

Crosses 200

200

||||||on

Generation   Traits  N. of materials

Type of exp.

F2 – ERC 

F3 – VS1  

YIELD

BLAST

PLANT TYPE

200

20

1000 ||||||||| ||

::::::::::

ite recurren

t 

tion

 pop

ulatio

F4 – EOF

F3:5 – ERF

BLASTGRAIN QUALITY

YIELDGRAIN QUALITY

1000

250

|||||||||...|| Cultivar deveEli

select

F3:6 – VS2

F7 – EOL 

F6 8 EP

BLAST

PLANT TYPEBLAST

YIELD

50

2000

500

::::::::::

|||||||||...||

elopment

F6:8 – EP

F6:9 – ER

F6 10 VCU

YIELDGRAIN QUALITY

YIELDGRAIN QUALITY

YIELD

500

50

10F6:10 – VCU YIELDGRAIN QUALITY 10

The Recurrent Selection Approach

IRRN, v. 34, 4 p. 2009

The same methods are being applied toThe same methods are being applied to irrigated lowland rice, with similar results:

LINE CROSS GY (kg/ha)BRA 040081 (BRS Pampa) IRGA 417/CNA7830 12,984 aBRA 040079 IRGA 417/CNA7830 11,994 aBRA 040079 IRGA 417/CNA7830 11,994 aBRA 040311 JAVAÉ/CNAi9039 11,793 aBRA 040257 DIAMANTE/CNA8642 11,583 a

BRA 040075 IRGA 417/CNA7830 11,320 aBRA 040286 IRGA 417/CNA7830 11,235 aBRA 040308 TAIM/CNAi9050 11,208 aBRA 040307 TAIM/CNAi9050 10,918 aBRA 040272 IR22/CNA8502 10 917 aBRA 040272 IR22/CNA8502 10,917 aMean of Checks(BR‐IRGA 409, IRGA 417, BRS 7 Taim, BRS 6 Chuí) 8,589 c

BRS SertanejaBRS Sertaneja

Upland Rice Breeding: The Way Forwardy

• Improve drought tolerance through better root system.

Ad t t till d id i• Adapt to no‐till and wider row spacing.

• Explore the genetic diversity for biotic and abiotic stress tolerancetolerance.

• Implement MAS for blast resistance and grain quality.

T t d i t t t i t i t t• Test and incorporate strategic transgenics or mutants. 

• Develop aerobic rice hybrids.

The Brazilian Rice Breeding Project(Leader: Dr. Orlando Peixoto de Morais)( )

The 2009 – 2013 project includes:p j

• 10 Embrapa Centers: CNPAF, CPACT, CPAO, CPAF‐RO, CPATU, CPAA, CPAF‐RR, CPAMN, 

CENARGEN, SNT

• 6 State Institutions: IRGA EPAGRI EPAMIG EMPAER MT SEAGRO TO SEAGRO GOIRGA, EPAGRI, EPAMIG, EMPAER‐MT, SEAGRO‐TO, SEAGRO‐GO

• 7 Universities: UFLA, UFG, UFSM, UFT, UNITINS, URCAMP, UNIPAMPA.UFLA, UFG, UFSM, UFT, UNITINS, URCAMP, UNIPAMPA.