20 years sunflo in Argentina

8/7/2019 20 years sunflo in Argentina

1/12

Progress over 20 years of sunflower breeding in central Argentina

Abelardo J. de la Vega a,*, Ian H. DeLacy b, Scott C. Chapman c

a Advanta Semillas S.A.I.C., Ruta Nac. 33 Km 636, C.C. 559, 2600 Venado Tuerto, Argentinab School of Land and Food Sciences and A.C.P.F.G., The University of Queensland, Brisbane, Qld 4072, Australia

c CSIRO Plant Industry, Queensland Bioscience Precinct, 306 Carmody Rd., St. Lucia, Qld 4067, Australia

Received 11 January 2006; received in revised form 16 May 2006; accepted 17 May 2006

Abstract

This paper applies linear mixed model analysis to 122 on-farm trials of commercial and near-commercial sunflower (Helianthus annuus L.)

hybrids grown over 15 years in 32 locations of central Argentina to quantify increases in oil yield and to determine the contributions of change inboth biotic stress resistance and yielding ability in favourable environments. The best linear unbiased predictors (BLUPs) from this analysis can be

regarded as measures of relative peak performance of hybrids in environments for which they were selected, and are a better measure of their

adaptation compared to small trial sets of historical hybrids. The BLUPs of 49 commercial hybrids released between 1983 and 2005 showed a

genetic gain for oil yield of 11.9 kg ha1 yr1. Special purpose hybrids that were converted for single traits or that were developed for low-

technology markets lagged by 515 years in terms of genetic gain. Genetic gains came about due to both an increase in the number of hybrids with

resistance to the major biotic stress (Verticillium dahliae Klebahn) and a genetic gain in oil yield of 14.4 kg ha1 yr1 within these hybrids. Based

on the data and the estimated time lag between commercial release and peak use, the improvement in oil and grain yield of conventional hybrids in

central Argentina will be sustained until at least 2010, with evidence that the new germplasm pools still have substantial genetic variance to be

exploited.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Genetic gain; Helianthus annuus L.; Meta-analysis; Mixed model analysis; Relative peak performance; Repeatability; Sunflower; Variance components

1. Introduction

Commercial sunflower (Helianthus annuus L.) production in

Argentina commenced in the early 1930s with open-pollinated

varieties. From a maximum production area of about

3.5 million ha in the mid 1990s, current production has

stabilised to 1.82.2 million ha. A major portion of this area

(ca. 1.0 million ha) is planted in central Argentina in the region

between latitudes 33.58 and 36.58 S and longitudes 618 and 658

W. The average grain yield remained around 700 kg ha1 for a

long period from 1930 to the early 1970s, when the first hybridswere released (Lopez Pereira et al., 1999), and increased at a

rate of 49 kg ha1 yr1 from the early 1970s to the mid 1990s

(Fig. 1) during the period of hybrid adoption by farmers.

Although almost 250 sunflower hybrids were registered for

commercialization in Argentina during the last 10 years

(Comision Nacional de Semillas, unpublished data), average

national grain yield has not increased (Fig. 1). This apparent

slow down of the progress in yield improvement of sunflower,

particularly when compared with maize and soybean (SAG-

PyA, 2005), emphasises the need to examine the role of current

plant breeding and crop management practices in the

development of a sustainable sunflower production system.

Two observations can be made to this respect. Firstly, oil

yield (i.e., the product of grain yield and grain-oil concentra-

tion) is the main selection criterion of most sunflower breedingprograms, and best represents the real return to farmers.

National data (Fig. 1) only accounts for mean grain yield over

time, and effectively assumes that oil content of grain does not

change. However, the relative magnitude of the impact of

increases in grain yield and grain-oil concentration on the

genetic gains achieved for oil yield may differ depending on the

period of time considered. Secondly, the explosive growth of

soybean in Argentina, which increased from 6.0 million ha

planted area in 1994 to 14.5 million ha in 2004 (SAGPyA,

2005), has pushed sunflower production toward more marginal

www.elsevier.com/locate/fcrField Crops Research 100 (2007) 6172

DOI of original article: 10.1016/j.fcr.2006.05.007.

* Corresponding author. Tel.: +54 3462 435235; fax: +54 3462 435231.

E-mail addresses: [email protected] (A.J. de la Vega),

[email protected] (I.H. DeLacy), [email protected] (S.C. Chapman).

0378-4290/$ see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.fcr.2006.05.012
mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.fcr.2006.05.012http://dx.doi.org/10.1016/j.fcr.2006.05.012mailto:[email protected]:[email protected]:[email protected]


2/12

western environments and the genetic progress for oil yield may

have been masked in national figures by the decline inagronomic quality of the target growing region.

The relative contributions of plant breeding and crop

management to yield improvement over time in a given

cropping region can be separated (e.g., Slafer et al., 1993;

Duvick and Cassman, 1999). Genetic gains can be estimated

by comparing an historic set of cultivars with uniform

management or from the trial data collected by breeding

programs. This gain in relative terms is subtracted from the

total gain in farmers fields and the residual is assumed to be

due to changes in management practices (Bell et al., 1995).

Using the first approach, Lopez Pereira et al. (1999) found no

significant increase in yield potential of 11 sunflower (H.annuus L.) hybrids released in Argentina between 1973 and

1995. These authors hypothesized that the historic require-

ment for disease tolerance and grain quality, together with a

rather narrow genetic base, has imposed restrictions for

improvement in yield potential. In further studies, in which

four sets of hybrids released between 1983 and 1998 were

tested in four experiments, Sadras et al. (2000) found a

positive association between oil yield and year of commercial

release, which was related to both resistance to the fungal

disease Verticillium wilt (Verticillium dahliae Klebahn) and

response to intraspecific competition (measured as the yield

response when competition between rows was reduced by

uprooting plants in buffer rows).Two main drawbacks could bias studies of genetic gain

based on comparisons of historical sets of cultivars grown under

uniform management (Bell et al., 1995). Firstly, agronomic

practices and incidence of pests and diseases change with time,

such that performance of older hybrids under todays growing

conditions may be different to their performance when they

were at their peak of use and popularity (Duvick et al., 2004).

Sencondly, environmental conditions during the period of

experimental estimation of relative yields of historic cultivars

may not be representative of the target population of

environments, so that components of genotype-by-environment

(G E) interactions are not considered.

Theseandotherissuescanbeaddressedthroughmeta-analysis

of the multi-environment trials (METs) that are routinely

conducted as part of the breeding programs (e.g., Frensham

et al., 1998) or long series of productivity data (e.g., Ellis et al.,

2001). The data collected from METs over time canbe utilised to

developa historicaldatabase which greatly expandsthesample of

environments and genotypes considered in the analysis of

genotypic adaptation (DeLacy et al., 1996a and references

therein). Therefore, many questions that cannot be addressed in

one or a few years of experiments can be investigated using a

larger database that represents the crops target population of

environments and in which every commercial cultivar was tested

at the environment for which it was created. Thus, in this type of

analysis, the hybrid estimates become a measure of their

performance at the environment of their peak of use and will be

named as relative peak performance in this study.

Analysis of historical data has been severely complicated in

the past by the imbalance endemic to METs in the way that

commercial checks, experimental cultivars and trial locations

change over time. Different methods described by DeLacy et al.(1996a) for dealing with imbalance in METs have been devised.

The general mixed model (Henderson, 1963, 1977) using either

general least square (GLS) or residual maximum likelihood

(REML, Patterson and Thompson, 1975) provides a powerful

method for analysing unbalanced (or balanced) MET data as it

enables the analysis of any linear model with or without cov-

ariates. Kempton (1984) describedhow fixed effect models could

be designed to better accommodate genotype and G E effectsthan do standard ANOVA. Smith et al. (2005) have recently

reviewedtheroleofthesetypesofmodelsinalinearmixedmodel

context for application to cultivar breeding and evaluation trials.

In sunflower, mixed model analyses were used to comparetesting strategies (Chapman and de la Vega, 2002) and to assess

the convenience of dividingthe growing region of Argentina into

subregions (de la Vega and Chapman, 2006a). This paper reports

on a sunflower MET dataset consisting of 122 advanced trials

grown in 215 locations per year over 15 years in the central

sunflower subregion of Argentina. The analyses described here

were used to measure genetic progress of sunflower breeding

programs through the removal of environment effects from the

datasets. The objectives are: (1) to extend the mixed model

analysis described by Chapman and de la Vega (2002) to a much

larger MET dataset and to compare estimates with those from

smaller trials of fixed entries; (2) to use MET data to estimate the

contribution of more than 20 years of sunflower breeding to oilyield and grain yield progresses in Argentina; (3) to predict what

the industry should expect in the future from current hybrids in

terms of improved performance; (4) to examine how changes in

relative performance (product longevity)of hybrids relate to their

time of release and tolerance to major stresses.

2. Materials and methods

2.1. Study zone

The study zone comprises the central-west and north-west

portions of the Argentinian Pampas. Previous studies (de la

A.J. de la Vega et al. / Field Crops Research 100 (2007) 617262

Fig. 1. Bi-lineal relationship between mean sunflower grain yield for Argentina

and year. Data obtained from SAGPyA (2005).


3/12

Vega et al., 2001; de la Vega and Chapman, 2006a) reported

that this geographical zone corresponds to a single mega-

environment for sunflower, i.e., it shows a pattern of

genotypic discrimination consistently different to other

sunflower regions. Soil, climate and technology of this

region have been described by Hall et al. (1992), Mercau et al.

(2001) and Chapman and de la Vega (2002). Briefly, the

typical sunflower growing soils are deep sandy mollisols

(Entic Haplustoll, Entic Hapludoll, Typic Hapludoll) formed

over loessic sediments. Moving from the northeast to the

southwest of the region, OctoberMarch total rainfall

decreases from over 700 to 500 mm, although large seasonal

and spatial variation in total water availability is common

within a zone (Mercau et al., 2001). Fungal diseases are the

main biotic stress in this sunflower cropping system: in

particular Verticillium wilt and Sclerotinia head rot (Scler-

otinia sclerotiorum (Lib.) de Bary) (Sadras et al., 2000).

Diseases produced by the fungi Albugo tragopogonis Pers.,

Alternaria helianthi (Hansf.) Tubaki & Nishihara, and Phoma

oleracea var. helianthi tuberosi Sacc. also reduce yield in

humid years (Chapman and de la Vega, 2002). Standard

sowing dates are from the end of September through to early

December. Industry production figures show sunflower grain

yield in this region averaged 1.8 t ha1 (19952004).

2.2. Trial dataset

The dataset was 122 sunflower hybrid trials conducted at 32

locations of central Argentina during the period 1990/1991

2004/2005 (16,468 data points for oil yield) (Table 1). The

entries were the market-leading hybrids (from all major

companies) and experimental hybrids that were being evaluated

by Advanta Semillas for commercial release. These trials are

the final stage of approximately 4 years of successively

extensive testing prior to commercial consideration and

therefore do not represent the entire genetic variation available,

i.e., the germplasm has undergone selection for oil and grain

yield, grain-oil concentration, disease tolerance, maturity and

other agronomic traits and represents near-elite hybrids.

Trials affected by spontaneous disease infections were retained

A.J. de la Vega et al. / Field Crops Research 100 (2007) 6172 63

Table 1

Locations used in the Advanta Semillas hybrid testing program (32 locations with 16,468 data points for oil yield across 122 three-replicate trials conducted from

1991 to 2005)

Location Latitude (S) Longitude (W) Trial number (total) Years

A. Ledesma 33.6 62.6 1 2002

America 35.5 63.0 2 1999, 2002

Arboledas 36.9 61.5 2 2004, 2005

Bolvar 36.2 61.1 1 1998

C. Casares 35.6 61.4 1 1994

C. de Areco 34.4 59.8 6 20002005

Daireaux 36.6 61.8 12 19911993, 1996,

19982005G. Moreno 35.6 63.4 1 2005

G. Pico 35.7 63.7 8 1991, 1994, 1997,

1998, 20022005

G. Pinto 34.8 61.9 2 1995, 1996

G. Villegas 35.0 63.0 7 1991, 1993, 1995, 1996,

2000, 2003, 2005

Henderson 36.3 61.7 3 20022004

H. Lagos 35.0 64.1 1 2001

H. Renanco 34.9 64.4 2 2003, 2005

I. Alvear 35.2 63.6 2 2000, 2001

Junn 34.6 61.0 6 19941997, 2000, 2003

M. Lauquen 36.2 63.0 2 2002, 2003

9 de Julio 35.5 60.9 7 1994, 19962001

Pehuajo 35.8 61.9 3 1991, 19992000

Piedritas 34.8 63.0 4 19961999Q. Quemu 36.1 63.6 3 2001, 2002, 2005

Riestra 35.3 59.8 2 1991, 1997

Sampacho 33.4 64.7 9 1991, 19952000,

2002, 2003

S. Basilio 33.5 64.3 7 19962002

S. Rosa 36.6 64.3 1 1991

S. Spiritu 34.0 62.3 5 19982002

30 de Agosto 36.3 62.5 1 2004

T. Lauquen 36.0 62.7 4 1999, 2000, 2002, 2005

Trili 35.9 63.7 2 2002, 2003

V. Maza 36.8 63.3 2 2004, 2005

V. Tuerto 33.7 62.0 12 19921998, 20002002,

2004, 2005

V. Valeria 34.3 64.9 1 2002


4/12

in the dataset, but those that suffered significant bird, insect

pest, or hail damage were excluded.

Within years, the same hybrids were grown in all locations.

Across years, the data were unbalanced and no single genotype

was grown every year (Table 2). In total, 216 experimental and

88 commercial (Table 3) hybrids were tested. Since not all the

locations were represented in each of the 15 years of this study,

the terms trial and environment will be used here to define a

particular location in a given year. In fact, sunflower

production, and the evaluation locations, were pushed further

west during the rapid expansion of soybean production over this

time (Fig. 6, discussed later in text).

Throughout this paper, single years in tables or figures (e.g.,

1991) refer to the summer season of that year (i.e., 1990/1991).

As for most datasets of this type, the location is a loose spatial

reference, as, in different seasons, the location (identified by a

town name) may actually be different paddocks, farms and/or

soil types subject to slightly different management regimes.

There was only a single trial at a location in any 1 year. Most

trials were located on-farm, although one site per year wasalways on the Advanta Argentina research station (V. Tuerto).

Crops were sown within the normal sowing window at each

location (i.e., ca. end of September to early December). In all

trials, hybrids were laid out in alpha- or square-lattice designs,

with three replicates and 3049 entries per trial. The trials were

over-planted and thinned to 47,600 plants ha1. A plot size of

three or four rows 6 m and inter-row spacing of 0.70 m wasused. Conventional tillage practices were used in all trials until

2002. Afterwards, an increasing proportion of testing sites were

under zero-tillage (now comprising about 80% of trials). All

trials were rain-fed and nutrient deficiencies were generally

avoided through fertilization. Weeds and insect pests werecontrolled chemically, but fungal diseases were not controlled.

Plot data of grain (achene) yield were determined by either

hand harvesting of 4.0 m2 (central row, discarding the border

plants) or machine harvesting of 8.4 m2 (four-row plot trials,

two central rows). All yield data are presented at 11% grain

moisture. Grain-oil concentration (% dry matter) was

determined on a plot basis by nuclear magnetic resonance

(Granlund and Zimmerman, 1975) using 10 g oven-dried

samples. Oil yield was calculated as the product of grain yield

and grain-oil concentration. Time to anthesis (days), defined as

the time at which 50% of the plot plant population reached full

anthesis (R-5.5, Schneiter and Miller, 1981), was recorded for

46 out of the 122 trials.

2.3. Statistical analyses

2.3.1. Hybrid performance across trial seasons

To accommodate the imbalance of the dataset, the data for

grain yield (kg ha1, at 11% grain moisture) and oil yield

(kg ha1) were analysed as mixed models with separate

residual terms for the different trials (van Eeuwijk et al., 2001;

Smith et al., 2005). The phenotypic observation yijmn on hybrid i

in incomplete blockn of replicate m of environment j wasmodelled as:

yijmn m e j r=e jm b=r=e jmn gi gei j ei jmn

(1)

where m is the grand mean; ej the fixed effect of the environ-

ment j; (r/e)jm the random effect of the replicate m nested within

the environment j and is $NID0; s2r, m = 1, . . ., r; (b/r/e)jmnthe random effect of the incomplete blockn nested within the

replicate m of environment j and is$NID0; s2b, n = 1, . . ., b; githe random effect of hybrid i and is $NID0; s2g, i = 1, . . ., g;

(ge)ij the random effect of the interaction between the hybrid iand environment j and is $NID0; s2ge; eijmn is the randomresidual effect for hybrid i in the incomplete blockn of replicate

m of environment j (experimental error) and is $NID0; s2e j.


Table 2

Number of hybrids common across years in the Advanta Semillas sunflower trial dataset (diagonal entries are numbers for individual years)

Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

1991 36

1992 19 30

1993 13 19 36

1994 11 14 21 42

1995 10 13 19 29 42

1996 8 8 13 21 26 421997 8 8 10 13 16 24 49

1998 5 5 5 9 11 17 30 42

1999 2 2 2 2 4 9 16 17 42

2000 5 5 6 6 9 13 19 20 21 49

2001 2 2 2 2 5 8 12 13 16 33 49

2002 1 1 1 1 3 6 8 8 8 16 25 49

2003 0 0 0 0 2 4 6 6 7 13 16 21 49

2004 0 0 0 0 2 3 3 3 3 9 11 15 24 49

2005 0 0 0 0 1 2 2 2 3 8 10 11 13 27 49

Number of locations 7 2 3 5 5 9 8 9 9 12 9 15 10 8 11

Oil yield data points 756 178 323 600 628 1124 1176 1088 1127 1764 1315 2204 1458 1142 1585

Mean oil yield (kg ha1) 1346 1492 1095 1871 1540 1591 1111 1038 1664 1516 1571 1891 1545 1746 1615

Number of locations, data points and arithmetic means for oil yield (kg ha1) per year are given in the table. Across the entire dataset, average oil yield was

1551 kg ha1

.


5/12

Another model was also tested with the environment term

partitioned into years (Y), locations (L) and Y L interaction.Since 75% of the total G E interaction observed for oil andgrain yield was accounted for in the G Y L interaction(results not shown) and a high proportion of trial locations

varied over years (Table 1), this model produced very similar

results when compared with model (1), the latter being finally

selected on the basis of parsimony. REML (Patterson and

Thompson, 1975) using the sparse Average Information algo-

rithm (Gilmour et al., 1995) was used to estimate the variance

components and standard errors of random terms, as imple-

mented in GenStat 8.2 (2005).The use of breeding programs METs to estimate the genetic

gain of a cropping system was compared to the typical approach

of designing specific trials by comparing hybrid-mean

repeatability and reliability of the estimated genotypic variance

components for oil yield of both strategies. The components of

variance estimated for the four agronomic traits by the REML

analysis were used to estimate hybrid-mean repeatability (Fehr,

1987, p. 97) as:

Hs

2g

s2g s2ge=e s

2e j=er

(2)

where s2g is the G variance component, s2ge the G E inter-

action variance component, s2e j the residual variance compo-

nent and e and rare a given number of environments and

replicates, respectively. The estimates of residual variance from

each trial were used to compute a pooled estimate of residual

variance, which was used to calculate repeatability. Another

model was also used to calculate repeatability in which the

effect of the environment was partitioned into years and trials

within years. Hybrid-mean repeatability was estimated as:

H

s2g

s2g s2gy=y s2ge=y=ye s2e j=yer (3)

where s2gy is the G Y interaction variance component, s2ge=y

the within-year G E interaction variance component pooledacross years, and y, e and rare a given number of years, trials

per year and replicates, respectively.

The oil yield datasets of two specifically designed sunflower

experiments were re-analysed using model (1), to assess the

reliability of the estimated genotypic variance components

when compared to the breeding programs MET data. The

datasets were: (1) a reference set of 10 differentially adapted

sunflower hybrids tested across 7 years in 22 trials (de la Vega

and Chapman, 2006a) and (2) a sunflower North Carolina


Table 3

Commercial sunflower hybrids (alphabetically ordered) from the Advanta Semillas multi-environment trials 19912005

Hybrida Release Year Hybrida Release Year Hybrida Release Year

ACA 864 2003 1 DK 3920 2002 2 Paraso 27 2003 1

ACA 872 1999 2 DK 4000 CL 2003 1 Paraso 30 1999 5

ACA 884 1991 9 DK 4030 1993 7 Paraso 33 2003 2

ACA 885 1999 2 DK 4040 1997 6 Paraso 35 2003 1

Agrobel 910 1993 1 DK 4050 1999 5 Paraso 40 2000 1Agrobel 920 1995 1 DK 4200 2003 1 Paraso 50 2001 2

Aguara 1997 3 DK G100 1984 4 Paraso 101CL 2003 1

Asgrow 548 1989 3 DK G103 1987 6 Puelche 1991 3

Atuel 1999 2 MG 2 1997 6 SPS 3130 1986 7

CF 11 1997 3 MG 4 1999 1 Super 407 1988 1

CF 13 1999 3 MG 50 2002 3 Super 515 1991 1

CF 17 1997 10 Morgan 731 1984 4 T 600 1998 2

CF 17 DMR 2004 1 Morgan 733 1988 2 TC 2000 1991 9

CF 19 1997 11 Morgan 734 1989 5 TC 2001 1994 5

CF 21 1997 7 Morgan 742 1996 8 TC 3001 1991 6

CF 23 CL 2005 1 Mycosol 2 1995 1 TC 3002 1991 1

CF 25 1997 7 Olisun 2002 3 TC 3003 1992 4

CF 27 2004 3 Olisun 2 2005 1 VDH 370 2005 2

CF 29 2004 4 P 6440 1988 4 VDH 475 1993 2

CF 31 2005 2 P 64A41 1996 1 VDH 480 1993 5CF 3 Negro 1997 3 P 6510 1989 5 VDH 481 2003 3

Contiflor 15 1989 11 P 6520 1993 2 VDH 483 1998 4

Contiflor 3 1983 10 Paraso 1990 5 VDH 485 2000 4

Contiflor 7 1988 9 Paraso 2 1990 5 VDH 487 2004 3



DK 3880 CL 2003 2 Paraso 5 1993 1 VDH 96 1994 4

DK 3881 1993 4 Paraso 6 1993 4 Zenith 1993 1

DK 3900 1997 4 Paraso 20 1997 6

DK 3915 1997 6 Paraso 22 2003 2

Years of testing (yr) in the Advanta dataset are indicated.a Seed companies and hybrid codes are: Asociacion de Cooperativas Argentinas (ACA), La Tijereta (Agrobel), Nidera (Asgrow, Paraso), Syngenta (Atuel,

Puelche), Advanta (Aguara, CF, Contiflor, Olisun, VDH), Monsanto (DK, Super), Dow (MG, Morgan, Mycosol, T), SPS (SPS), Agroatar (TC), and Sursem (Zenith).


6/12

Design II (4 females 4 males) tested across five locations inone season (de la Vega and Chapman, 2006b). The two datasets

included trials conducted in other sunflower regions of

Argentina, but during re-analysis here, only those grown in

the central region were retained.

In order to conduct a genetic gain analysis for a particular

trait, it is necessary to obtain a single estimate for each cultivar,

which best predicts the true genotypic effect. By definition, this

implies the use of best linear unbiased predictors (BLUPs)

(Robinson, 1991), so that the genotypic effect should be

regarded as random (Smith et al., 2005; Piepho and Mohring,

2006), as in model (1). In this study, all experimental hybrids

were developed by the sunflower program of Advanta Semillas

but the commercial checks were developed by different seed

companies. Thus, treating the collection of hybrids as a sample

from one reference population is a first approximation to the

study of the genotypic variation of this dataset. Using GenStat

8.2 (2005), the BLUPs of the genotypic effects (i.e., predictors

that were adjusted for the unbalanced nature of the data) were

computed from REML analysis and allowed, under certainassumptions (Smith et al., 2005), comparison of genotypes that

were never tested together in the same trial via their relative

performance for a particular trait across the target population of

environments. These BLUPs are therefore estimates of the

relative peak performance of genotypes, as evaluated in their

environments of selection and release. As trials, years and trials

nested within years were defined as fixed effects in models (1)

and (4), their estimates computed from REML are best linear

unbiased estimates (BLUEs).

A key point in genetic gain studies is the inference to be

made from the set of genotypes that are included in the

analysis. In crops like maize,for which yield and yield stabilityare by far the most important selection criteria used by

breeders, the most applied criterion is to retain the hybrids that

were widely grown andpopular with farmers in their time (e.g.,

Duvick and Cassman, 1999). In the case of sunflower, the

central objective of commercial breeding programs is to

increase oil yield and oil yield stability. However, secondary,

special purpose targets may lead to the release of a cultivar that

does not outperform the existing ones (in yield or stability), at

least under uniform management conditions, and is briefly

illustrated in the results. We propose that these types of

cultivars should be excluded when estimating the contribution

of breeding to yield gains in the farmers fields. In this study, a

setof 49 commercial hybrids outof the88 testedin theAdvantaSemillas MET (Table 3) was selected. The hybrids excluded

from the estimation of genetic gain were those that were: (1)

expressly developed for sunflower regions other than central

Argentina (e.g., Aguara and CF 13, developed for the northern

and southern regions, respectively); (2) developed for a low-

price/low-technology seed market (e.g., TC 3001); (3) created

through the backcross conversion of a previously released

hybrid to incorporate disease resistance (e.g., CF 17 DMR),

differential oil quality (e.g., Olisun), or herbicide resistance

(e.g., CF 23 CL); or (4) evaluated for less than 3 years, with the

exception of the last releases in the dataset for which 2 years of

evaluation were accepted. The BLUPs for oil yield were

plotted against the year of hybrid commercial release and

genetic gain estimates were computed as the slopes of fitted

linear regressions.

2.3.2. Past and future grain and oil yields in farmers fields

To evaluate if mean increases for oil yield during the period

19952005 were mostly achieved by increasing grain-oil

concentration rather than grain yield, we compared the BLUPs

of the hybrid effects for oil yield and grain yield derived from

model (1) against year of peak of use. For these relationships we

only used the commercial hybrids that had a high level of

impact on the market (i.e., >5% of market share). Due to thelack of official data for hybrid market share, the selection of

these hybrids was made on the basis of the empirical knowledge

of the commercial team of Advanta Semillas, and thus may be

subject to some level of error. Similarly, it was also considered

that a successful hybrid reaches its peak of use and popularity 4

years after its commercial release (Jorge Moutous, Advanta

Semillas, personal communication). The BLUPs of the recently

released hybrids, which have not yet reached their peak of use,were used as prediction values for their performance 4 years

after commercial release (6 years after their first testing in pre-

commercial trials).

2.3.3. Hybrid performance within trial season

In order to study the dynamics of the performance of a

genotype over time, it is necessary to obtain a single estimate

for each genotype at every trial season in which it was tested.

The BLUPs of the within-year genotypic effects (i.e.,

genotype + genotype-by-year interaction effects) for oil yield

(kg ha1) were computed from REML (Patterson and

Thompson, 1975) analysis, using GenStat 8.2 (2005). Thedata were analysed as mixed models with separate residual

terms for the different trials (van Eeuwijk et al., 2001). The

phenotypic observation yikjmn on hybrid i in incomplete blockn

of replicate m of trial j in year kwas modelled as:

yik jmn m yk e=yk j r=e=yk jm b=r=e=yk jmn

gyik ge=yki j eik jmn (4)

where m is the grand mean; ykthe fixed effect of year k; (e/y)kjthe fixed effect of the trial j nested within year k; (r/e/y)kjm the

random effect of the replicate m nested within the trial j and

year kand is $NID0; s2r, m = 1, . . ., r; (b/r/e/y)kjmn therandom effect of the incomplete blockn nested within the

replicate m of trial j and year kand is$NID0; s2b, n = 1, . . ., b;(gy)ikthe random effect of the interaction between the hybrid i

and year kand is $NID0; s2gy; (ge/y)kij the random effect ofthe interaction between the hybrid i and trial j nested within

year kand is $NID0; s2ge=y and eijmn is the random residualeffect for hybrid i in the incomplete blockn of replicate m of

environment j and year k(experimental error) and is

$NID0; s2ek j. REML using the sparse Average Information

algorithm (Gilmour et al., 1995) was used to estimate the

variance components and standard errors of random terms,

as implemented in GenStat 8.2 (2005).



7/12

Commercial hybrids tested across 5 years or more (30 out of

88) were used to study the dynamics of hybrid performance for

oil yield over time, i.e., hybrid longevity. The BLUPs of the

within-year genotypic effects for oil yield were plotted against

trial season and the rates of performance change were computed

as the slopes of the linear regression functions fitted to thoserelationships.

3. Results and discussion

3.1. Reliability of breeding programs METs and

specifically designed trials

Significant G and G E interaction components of variancewere found for oil yield (Table 4). Although the ratio of G Einteraction to G variance components was large, this value was

smaller than other ratios reported for large MET datasets in

diverse sunflower genotypes grown over two regions (de laVega and Chapman, 2006a) and other crops (e.g., wheat,

Cooper et al., 1996; maize, Chapman et al., 1997; sorghum,

Alagarswamy and Chandra, 1998 and Chapman et al., 2000;

rice, Cooper et al., 1999; quinoa, Bertero et al., 2004). The

relatively low magnitude of the G E interaction variancecomponent relative to G observed in this study can be regarded

as the consequence of the restriction of the analysis within a

single mega-environment of Argentina (i.e., the central

subregion) (de la Vega et al., 2001; de la Vega and Chapman,

2006a) and due to the use of advanced elite hybrids. In these

conditions, the predictable G E interactions (genoty-pe subregion) begin to become part of the G effect

(DeLacy et al., 1996b). If commercial hybrids developed forother regions, secondary targets and first round of conversions

are removed from the analysis, a small reduction of the

genotypic variance component is observed (i.e., G Einteraction to G variance components ratio: 1.66). This

suggests that the inclusion of these hybrids in small proportions

in METs do not contribute to artificial inflation of the G effect.

From the MET analysis, for a hybrid evaluation based on 10

trials, the estimates of hybrid-mean repeatability for oil yield

were 0.63 and 0.77, for one and three replicates per trial,

respectively (Fig. 2a). For 25 trials, the calculated repeat-

abilities were 0.82 and 0.90, for the same number of replicates.

If the trial effect is partitioned into years and trials within years,

calculated repeatabilities for two trials are 0.25 and 0.26,

provided they are conducted across 1 or 2 years, respectively

(Fig. 2b). For three trials, repeatabilities are 0.32 and 0.35 for

trials conducted across 1 and 3 years, respectively. This

observation confirms previous studies (de la Vega and

Chapman, 2006a) and indicates that in the central sunflower


Fig.2. Predicted hybrid-mean repeatability for oil yieldin the central subregion

of Argentina as the number of environments of testing in multi-environment

trials are changed, based on the components of variance given in Table 4 (a) and

on those estimated using a mixed model in which the environment effect was

partitioned into years and trials within years (b).

Table 4

Estimated variance components (standard errors) for oil yield derived from the trials (e), hybrids (g), replicates (r), incomplete blocks (b) and experimental error (e)model applied to the Advanta Semillas sunflower hybrid trial dataset and two sunflower trials designed for specific studies

Source of variation Oil yield (kg ha1) Oil yielda (kg ha1) Oil yieldb (kg ha1)

s2r=e

2496 372 2636 1057 2945 2505

s2b=r=e

4442 352 0 2478 2829

s

2

g 9897 1066 17026 8541 12102 6205s

2ge

14540 592 14682 2489 12546 4559

s2e

c 42596 6410 31204 9756 31187 8432

G E to G ratio 1.47 0.86 1.04

a Reference set of 10 sunflower hybrids tested across 7 years in 22 trials in the central region of Argentina ( de la Vega and Chapman, 2006a).b Sunflower North Carolina Design II (4 females 4 males) tested in 5 trials in the central region of Argentina (de la Vega and Chapman, 2006b).c Variance components and standard errors were averaged across environments.


8/12

region of Argentina there is scope to redefine testing strategies

by replacing years with locations at no costs in ability to predict

genotype performance. Predicted hybrid-mean repeatabilities

for oil yield (Fig. 2) indicate that a small number of trials (e.g.,

16) would not provide the minimum environment sampling

needed to adequately accommodate the effect of the

unpredictable G E interactions on the estimation of cultivarmeans. This suggests that caution should be exercised when

using the traditional approach of a small number of specific

trials to compare historical sets of cultivars and highlights the

convenience of using existing MET databases to estimate

genetic gain.

The ratios of standard error to genotypic variance

component were around 0.5 for the two specifically designed

trials and 0.1 for the breeding programs MET dataset (Table 4).

Thus, the mixed model analysis of a much larger sampling of

genotypes and environments in breeding programs METs

produced more reliable estimates than trials designed and

conducted with specific purposes. The lower ratio of G Einteraction to G observed in the specifically designed trial

datasets is related to the inclusion of hybrids specifically

adapted to the northern region, which failed in performing

better than the central-adapted hybrids in most central-region

trials, expanding the genotypic effect. However, this should

have a relatively small effect on the relevant point here, which is

the poorer estimate of parameter error in smaller trial sets.

3.2. The effect of different breeding targets on the

estimation of genetic gain

In considering only the 49 conventional commercial hybrids,

i.e., released for the core market, the analysis showed that oil

yield has increased linearly by 11.9 kg ha1 yr1 over the

period from 1983 to 2005 (Fig. 3a). If only the highest yielding

releases of each year are considered, the estimated genetic

progress is 13.0 kg ha1 yr1 (Fig. 3a).

As noted earlier, one aspect of these analyses is that

sunflower breeding programs do not only release hybrids thatoutperform the current market cultivars for oil yield. Different

hybrid types are often needed for different markets and end

uses, and comparisons between them are not only based on

yield. For example, a strategy to participate in the low-

technology (i.e., low seed price) market segment has been to

release an easy-to-produce three-way version of an existing

single-cross hybrid and to deliver it to the market through a

license agreement. This new release does not contribute to the

genetic progress, but allows a company to supply different

market segments. Backcross conversions may also be designed

to deal with particular market issues. Disease-resistant

converted forms of an existing successful hybrid will quicklyreplace the original version. Converted hybrids carrying the

Pervenets mutation produce high-oleic acid (i.e. >80%) oil(Fernandez-Martnez et al., 1989), which is highly suitable for

the industry and reaches higher prices in the market than

conventional high-linoleic sunflower oil. Recently developed

hybrids having resistance to imidazolinone herbicides (Al-

Kathib and Miller, 2000) allow better weed control and,

although they are currently lower yielding than the conven-

tional hybrids, are preferred by farmers in weed-infested fields.

When the BLUPs of the genotypic effects for oil yield were

plotted against year of commercial release (Fig. 3a), it was clear

that the hybrids developed for special purpose secondary targets

and backcross conversions of existing hybrids do not sit on thesame trend of genetic progress over time shown by the

conventional hybrids, leading to an underestimation of genetic

gain. In the case of the conversions, this effect is likely to be a

temporary phenomenon because, once these genes are spread

throughout elite breeding materials, they will become back-

ground genes (Duvick and Cassman, 1999). As might be

expected, hybrids that were specifically developed for other

regions rather than that under study, if included in the analysis,

would also produce a similar negative effect on the estimation

of genetic gain (Fig. 3a). Consequently, it is relevant for a

genetic gain study to concentrate only on those cultivars that

were developed with the aim of increasing yield and stability


Fig. 3. Best linear unbiased predictors (BLUPs) of relative peak performance

foroil yield forcommercial sunflowerhybridsin thecentral regionof Argentina

plotted against year of commercial registration. Different symbols indicate

commercial hybrids released for primary (conventional) and secondary target

markets (a) or having contrasting behaviour for Verticillium wilt (b). The

regression functionsdescribe the associations between the variablesonly for the

conventional hybrids (solid line in (a)), only for the highest yielding conven-

tional hybrids released each year (dotted line in (a)), and only for the hybrids

resistant to Verticillium wilt (b).


9/12

within a target region. This criterion, together with the number

of years in evaluation, lead us to focus on the BLUPs of the

genotypic effects of 49 out of the 88 commercially released

hybrids.

3.3. Genetic gain for conventional sunflower in central

Argentina

The BLUPs of the genotypic effects for oil yield (Fig. 3a)

indicate a clear and continuous improvement due to plant

breeding during the last 20 years. Lopez Pereira et al. (1999)

and Sadras et al. (2000) proposed that variation in actual yield

among sunflower hybrids released in different years was limited

in environments with low incidence of diseases, in comparison

to the larger variation found in environments dominated by

major diseases, particularly Verticillium wilt. Although in this

study the proportion of hybrids having genetic resistance to

Verticillium wilt and tolerance to other major diseases did

increase with the year of commercial release, it can be shown

that both biotic stress resistance and yielding ability infavourable growing conditions have been genetically improved

(Fig. 3b). If the 122 trials are grouped on the basis of their

BLUEs for oil yield, REML analyses conducted within each

dataset show that the increase in relative peak performance for

oil yield was positive in the high (P < 0.0001), intermediate(P < 0.0001) and low (P = 0.0051) oil-yield productionenvironments (data not presented). When the 49 commercial

hybrids are divided on the basis of their resistance to

Verticillium wilt and only the oil yield BLUPs of the resistant

hybrids are regressed on year of commercial release (Fig. 3b),

the trend indicates an overall gain due to an increase in relative

peak performance within the Verticillium-resistant group (25

hybrids) of 14.4 kg ha1 yr1. These resistant hybrids now

dominate the market.

3.4. Past and future grain and oil yields

When only the hybrids with high impact on the market are

considered, the trend for oil yield indicates a continuous

increase in relative peak performance for sunflower in central

Argentina of 11.9 kg ha1 yr1 from 1986 to 2005 (Fig. 4a).

However, the trend for grain yield can be better described by abi-linear function (Fig. 4b) that shows a discontinuity in the

increase in relative peak performance for this trait from 1995 to

2005. This trend is quite consistent with the grain yield trend

shown over the same years in the national data (Fig. 1). It is

likely that at least part of the slow down observed in the grain

yield gains in the farmers fields during the last 10 years is the

result of a change in the breeding process which, for that period,

increased oil yield mostly through an increase in grain-oil

concentration. In another paper (de la Vega et al., 2007), we

discuss how these changes arose from the merging of two

distinct germplasm pools over the last 20 years. The BLUPs of

the effects of the recently released hybrids for oil yield (Fig. 4a)predict that improvements in production oil yields are likely in

the period from 2005 to 2010. For grain yield (Fig. 4b), an

improvement in average production yield can be expected from

the removal of lower grain yield hybrids, but the highest

predicted hybrid grain yield appears to be no greater than that

shown for hybrids in previous years.


Fig. 4. Best linear unbiased predictors (BLUPs) of relative peak performance

for oil yield (a) and grain yield (b) for commercial sunflower hybrids in the

central region of Argentina as a function of year of peak of use (i.e. year of

commercial release + 4). The hybrids plotted are those that had a high level of

impact on the market. Lineal (a) and bi-lineal (b) regressions describe the

associations between the variables until 2005.

Fig. 5. Best linear unbiased estimates (BLUEs) of the year effects for oil yield

as a function of trial season.


10/12

3.5. Dynamics of hybrid performance across years

The variance components and standard errors derived from

model (4) were 9494 732, 13212 580 and 42510 6391,for s2gy; s

2ge=y and s

2e, respectively. A strong inter-annual

variation for oil yield was observed in the MET dataset

(Fig. 5). Both the maximum range of the year-to-year variation

and the low relative oil yields observed in the years of the warm

phase of El Nino Southern Oscillation (1992/1993, 1997/1998

and 2002/2003) are consistent with previous reports on the

sunflower central region of Argentina (Magrin et al., 1998;

Chapman and de la Vega, 2002; de la Vega and Chapman,

2006a). In such years, the higher rainfall in late spring and early

summer in these areas of Argentina can reduce sunflower due to

direct water-logging stress; a net decrease in the seasonal

radiation received by the crop and used for photosynthesis

(Cantagallo et al., 1997; Magrin et al., 1998; Cantagallo and

Hall, 2000; Aguirrezabal et al., 2003); and a reduction in the

partitioning of biomass to grain (de la Vega and Hall, 2002).

Despite the seasonal variation observed for oil yield, theMET data is consistent with the national data for grain yield in

showing no significant trend (fitted regressions not shown) over

the period from 1991 to 2005 (Figs. 1 and 5). The yield plateau

observed in the MET data could also be partially regarded as the

consequence of the dynamics of trial location substitution over

time in the Advanta Semillas testing net (Fig. 6). With the

exception of the trials conducted in V. Tuerto (research station)

and C. de Areco (eastern location used for disease evaluation),

all trials analysed in this study were conducted on commercial

sunflower production paddocks. From 1991 to 2005, but

particularly since 2000, the explosive growth of soybean in

central Argentina pushed the sunflower industry productiontoward more marginal, lower rainfall western environments.

The location selection in the Advanta Semillas testing network

reflects the geographical dynamics of the sunflower industry

during the period covered by the MET dataset.

When the noise due to seasonal oil yield variation is

removed from the analysis, it is observed that relative

performance for oil yield of individual hybrids declined over

time (Fig. 7a). This is expected, as new hybrids are tested each

season. However, since no significant trend was observed for oil

yield across years (Fig. 5), the dynamics shown by the BLUPs

of the within-year genotypic effects for oil yield also reflects the

decline in actual oil yield. This phenomenon could be regarded

as a consequence of the evolution of the pathogen populations,

which become more infective on the current cultivars, and/or

changes in the abiotic challenges, due to the movement of the

crop to more marginal environments.

The comparison of the dynamics of oil yield BLUPs over

time for sequentially released hybrids by the same company

(Fig. 7a) highlights the role of plant breeding in making a

production system sustainable. Oil yields across years have

been maintained through the continuous release of hybrids thatyield better than their predecessors, allowing counteraction of

the decline in agronomic quality of the sunflower growing

environments.

Sadras et al. (2000) postulated that a major part of the

improved performance of more recent hybrids was related to

increased resistance to Verticillium wilt. In our study, resistant

hybrids were characterised as those that showed no symptoms

or few symptoms of wilt infection in the bottom third of the

plant for their entire production life. The conclusions ofSadras

et al. (2000) are supported in our results by the observation that,

for hybrids released in the same year (i.e., from the same stage

of germplasm pool development), those that have Verticilliumresistance have a longer product life, i.e., a slower decline in


Fig. 6. Geographical location of the 32 sites of the Advanta Semillas sunflower multi-environment trial (MET) used in this study. Crosses represent locations

representative of all period covered by the MET. Black circles and open triangles represent locations that have been discontinued and incorporated, respectively,

during the period 20002005. OctoberMarch total rainfall isohyets (Mercau et al., 2001) are also presented.


11/12

oil yield (Fig. 7b). When averaged over all of the releases

considered, this rate of decline was less than half of that

observed for Verticilium susceptible lines (inset in Fig. 7b).

4. Conclusions

Linear mixed model analysis allows estimates of the genetic

progress in a cropping system using the largely unbalanced

long-term MET datasets that are obtained as part of the routine

activities of breeding programs. Two critical issues should be

considered: (1) for a MET to be useful for this type of analysis,all commercial hybrids with high impact on the market should

be included in the trials, and most importantly, during their

period of peak of performance; (2) secondary breeding targets

lead to underestimation of the effective genetic gain achieved

by the system (Fig. 3a). Compared to alternative progress

evaluation trials, linear mixed models allow: (1) expansion of

the sample of genotypes and environments; (2) estimation of

the performance of genotypes at the environment for which

they were bred, i.e., relative peak performance; (3)

accommodation of G E interactions (Fig. 2 and Table 4).Mixed model analysis can also provide useful information for

breeding programs and seed companies, such as the genetic

progress of the experimental germplasm or the trends followed

by proprietary and competitor commercial releases.

The analyses reported in this paper show that oil yield gains

in commercial releases of sunflower for central Argentina due

to plant breeding have been continuous over the last 20 years.

Changes in both biotic stress resistance and yielding ability in

favourable environments have contributed to yield mainte-

nance, allowing counteraction of the decline in agronomic

quality of the sunflower growing region and hybrid run down.

It is likely that at least part of the slow down observed in

grain yield gains in the national data during 19952005 is the

result of a breeding process which, for that period, increased oil

yield mostly through an increase in grain-oil concentration. In a

companion paper (de la Vega et al., 2007) we explore the role of

changes in grain yield, grain-oil concentration and time to

flowering in improving sunflower oil yield as the initial

germplasm pools were merged and further exploited.

Acknowledgments

This research was supported by Advanta Semillas SAIC.

The authors would like to thank Aldo Martnez, Alejandro Dell

Else, Sergio Solian, Carlos Ghanem, Daniel Kennedy, Ney

Flores, Cesar Sanchez and Hugo Baravalle for collaborating in

the field experiments and data management; previous Advanta

sunflower program managers (Ricardo Siciliano and Jorge

Moutous); Advanta sunflower research coordinator (Alan

Scott); Drs. Vctor Sadras, Fernanda Dreccer and Craig

Hardner for review comments.

References

Aguirrezabal, L.A.N., Lavaud, Y., Dosio, G.A.A., Izquierdo, N.G., Andrade,

F.H., Gonzalez, L.M., 2003. Intercepted solar radiation effect during grain

filling determines sunflower weight per seed and oil concentration. Crop

Sci. 43, 152161.

Alagarswamy, G., Chandra, S., 1998. Pattern analysis of international sorghum

multi-environment trials for grain-yield adaptation. Theor. Appl. Genet. 96,

397405.

Al-Kathib, K., Miller, J.F., 2000. Registration of four genetic stocks of sun-

flower resistant to imidazolinone herbicides. Crop Sci. 40, 869870.

Bell, M.A., Fischer, R.A., Byerlee, D., Sayre, K., 1995. Genetic and agro-

nomic contributions to yield gains: a case study for wheat. Field Crops

Res. 44, 5565.

Bertero, H.D., de la Vega, A.J., Correa, G., Jacobsen, S.E., Mujica, A., 2004.

Genotype and genotype-by-environment interaction effects for grain yieldand grain size of quinoa (Chenopodium quinoa Willd.) as revealed by

pattern analysis of international multi-environment trials. Field Crops Res.

89, 299318.

Cantagallo, J.E., Hall, A.J., 2000. Reduction in the number of filled seed in

sunflower (Helianthus annuus L.)by light stress. In:Proceedingsof the15th

International Sunflower Conference, Tolouse, France, pp. D35D40.

Cantagallo, J.E., Chimenti,C.A.,Hall,A.J.,1997. Numberof seedsper unit area

in sunflower correlates well with a photothermal quotient. Crop Sci. 37,

17801786.

Chapman, S.C.,de la Vega, A.J.,2002. Spatial and seasonal effects confounding

interpretation of sunflower yields in Argentina. Field Crops Res. 73, 107

120.

Chapman, S.C., Crossa, J., Edmeades, G.O., 1997. Genotype by environment

effects and selection for drought tolerance in tropical maize. I. Two mode

pattern analysis of yield. Euphytica 95, 19.


Fig. 7. Within-year best linear unbiased predictors (BLUPs) of relative peak

performance foroil yield as a function of trial season. In (a) commercial hybrids

sequentially released by Advanta Semillas are represented. In (b) hybrids

differing in year of release and behaviour against Verticillium wilt are repre-

sented. Inset in (b): box plot of rates of decline in oil yield across years for

hybrids susceptible and resistant to Verticillium wilt.


12/12

Chapman, S.C., Cooper, M., Butler, D., Henzell, R., 2000. Genotype

by environment interactions affecting grain sorghum. I. Characteristics

that confound interpretation of hybrid yield. Aust. J. Agric. Res. 51,

197207.

Cooper, M., Brennan, P.S., Sheppard, J.A., 1996. A strategy for yield improve-

ment of wheat which accommodates large genotype by environment inter-

actions. In: Cooper, M., Hammer, G.L. (Eds.), Plant Adaptation and Crop

Improvement. CAB International, ICRISAT and IRRI, Wallingford, UK,

pp. 487512.Cooper, M., Rajatasereekul, S., Immark, S., Fukai, S., Basnayake, J., 1999.

Rainfed lowland rice breeding strategies for Northeast Thailand. I. Geno-

typic variation and genotype environment interactions for grain yield.Field Crops Res. 64, 131151.

DeLacy, I.H., Basford, K.E., Cooper, M., Fox, P.N., 1996a. Retrospective

analysis of historical data sets from multi-environment trialstheoretical

development. In: Cooper, M., Hammer, G.L. (Eds.), Plant Adaptation and

Crop Improvement. CAB International, ICRISAT and IRRI, Wallingford,

UK, pp. 243267.

DeLacy, I.H., Basford, K.E., Cooper, M.C., Bull, J.K., McLaren, C.G., 1996b.

Analysis of multi-environment trialsan historical perspective. In: Cooper,

M., Hammer, G.L. (Eds.), Plant Adaptation and Crop Improvement. CAB

International, ICRISAT and IRRI, Wallingford, UK, pp. 39124.

de la Vega, A.J., Chapman, S.C., 2006a. Defining sunflower selection strategies

for a highly heterogeneous target population of environments. Crop Sci. 46,136144.

de la Vega, A.J., Chapman, S.C., 2006b. Multivariate analyses to display

interactions between environment and general or specific combining ability

in hybrid crops. Crop Sci. 46, 957967.

de la Vega, A.J., Hall, A.J., 2002. Effects of planting date, genotype and their

interactions on sunflower yield. I. Determinants of oil-corrected grain yield.

Crop Sci. 42, 11911201.

de la Vega, A.J., Chapman, S.C., Hall, A.J., 2001. Genotype by environment

interaction and indirect selection for yieldin sunflower. I. Two-mode pattern

analysis of oil and biomass yield across environments in Argentina. Field

Crops Res. 72, 1738.

de la Vega, A.J., DeLacy, I.H., Chapman, S.C., 2007. Changes in agronomic

traits of sunflower hybrids over 20 years of breeding in central Argentina.

Field Crops Res. 100, 7381.

Duvick, D.N., Cassman, K.G., 1999. Post-green revolution trends in yieldpotential of temperate maize in the north-central United States. Crop Sci.

39, 16221630.

Duvick, D.N., Smith, J.S.C., Cooper, M., 2004. Long-term selection in a

commercial hybrid maize breeding program. Plant Breeding Rev. 24 (Part

2), 109151.

Ellis, R.N., Basford, K.E., Cooper, M., Leslie, J.K., Byth, D.E., 2001. A

methodology for analysis of sugarcane productivity trends. I. Analysis

across districts. Aust. J. Agric. Res. 52, 10011009.

Fehr, W.R., 1987. Principles of Cultivar Development, vol. 1. Theory and

Technique. MacMillan, New York.

Fernandez-Martnez, J., Jimenez, A., Domnguez, J., Garca, J.M., Garces, R.,

Mancha, M., 1989. Genetic analysis of the high oleic acid content in

cultivated sunflower (Helianthus annuus L.). Euphytica 41, 3951.

Frensham, A.B., Barr, A.R., Cullis, B.R., Pelham, S.D., 1998. A mixed model

analysis of 10 years of oat evaluation data: use of agronomic information toexplain genotype by environment interaction. Euphytica 99, 4356.

GenStat 8.2, 2005. GenStat1 for WindowsTM 8th edition Introduction. VSN

International, Oxford, UK, p. 343.

Gilmour, A.R., Thompson, R., Cullis, B.R., 1995. AI, an efficient algorithm for

REML estimation in linear mixed models. Biometrics 51, 14401450.

Granlund, M., Zimmerman, D.C., 1975. Effect of drying conditions on oil

content of sunflower (H. annuus L.) seeds as determined by wide-line

nuclear magnetic resonance (NMR). North Dakota Acad. Sci. Proc. 27 (Pt.

2), 128132.

Hall, A.J., Rebella, C.M., Ghersa, C.M., Culot, J.P., 1992. Field-crop systems ofthe Pampas. In: Pearson, C.J. (Ed.), Field Crop Ecosystems. Elsevier,

Amsterdam, pp. 413450.

Henderson, C.R., 1963. Selection index and expected genetic advance, in:

Hanson, W.V., Robinson, H.F. (Eds.), Statistical Genetics and Plant Breed-

ing. NAS-NRC, Publication 982, Washington, DC, pp. 141163.

Henderson, C.R., 1977. Prediction of future records. In: Pollack, E.,

Kempthorne, O., Bailey, T.B. (Eds.), Proceedings of the International

Conference on Quantitative Genetics. Iowa State University Press, Ames,

Iowa, pp. 615638.

Kempton, R.A., 1984. The use of biplots in intepreting variety by environment

interactions. J. Agr. Sci. Cam. 103, 123135.

Lopez Pereira, M.L., Sadras, V.O., Trapani, N., 1999. Genetic improvement of

sunflower in Argentina between 1930and 1995. I. Yield and its components.

Field Crops Res. 62, 157166.

Magrin, G.O., Grondona, M.O., Travasso, M.I., Boullon, D.R., Rodrguez,G.R., Messina, C.D., 1998. Impacto del fenomeno El Nino sobre la

produccion decultivos enla region pampeana. I.N.T.A., Institutode Climay

Agua, Castelar, Buenos Aires, Argentina.

Mercau, J.L., Sadras, V.O., Satorre, E.H., Messina, C., Balbi, C., Uribelarrea,

M., Hall, A.J., 2001. On-farm assessment of regional and seasonal variation

in sunflower yield in Argentina. Agr. Syst. 67, 83103.

Patterson, H.D., Thompson, R., 1975. Maximum likelihood estimation of

components of variance. In: Proceedings of the Eighth International

Biometrics Conference. pp. 197207.

Piepho, H.P., Mohring, J., 2006. Selection in cultivar trialsIs it ignorable?

Crop Sci. 46, 192201.

Robinson, G.K., 1991. That BLUP is a good thing: the estimation of random

effects. Stat. Sci. 6, 1551.

SAGPyA, 2005. Secretara de Agricultura, Ganadera, Pesca y Alimentos.

Republica Argentina. http://www.sagpya.mecon.gov.ar/new/0-0/agricul-tura/index.php.

Sadras, V.O., Trapani, N., Pereyra, V.R., Lopez Pereira, M., Quiroz, F.,

Mortarini, M., 2000. Intraspecific competition and fungal diseases as

sources of variation in sunflower yield. Field Crops Res. 67, 5158.

Schneiter, A.A.,Miller, J.F., 1981. Description of sunflower growth stages. Crop

Sci. 21, 901903.

Slafer, G.A., Satorre, E.H., Andrade, F.H., 1993. Increases in grain yield in

wheat from breeding and associated physiological changes. In: Slafer, G.A.

(Ed.), Genetic Improvement of Field Crops. Marcel Dekker, New York, pp.

168.

Smith, A.B., Cullis, B.R., Thompson, R., 2005. The analysis of crop cultivar

breeding and evaluation trials: an overview of current mixed model

approaches. J. Agr. Sci. 143, 114.

van Eeuwijk, F.A., Cooper, M., DeLacy, I.H., Ceccarelli, S., Grando, S., 2001.

Some vocabulary and grammar for the analysis of multi-environment trials,as applied to the analysis of FPB and PPB trials. Euphytica 122, 477490.

http://www.sagpya.mecon.gov.ar/new/0-0/agricultura/index.phphttp://www.sagpya.mecon.gov.ar/new/0-0/agricultura/index.phphttp://www.sagpya.mecon.gov.ar/new/0-0/agricultura/index.phphttp://www.sagpya.mecon.gov.ar/new/0-0/agricultura/index.php

Documents

20 years sunflo in Argentina