Progress over 20 years of sunflower breeding in central Argentina

Vega, Abelardo J. de la; DeLacy, I. H.; Chapman, Scott C.

doi:10.1016/j.fcr.2006.05.012

Cited by 62 publications

(50 citation statements)

References 28 publications

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“…In both situations, the magnitude of the GEI is larger than the cultivar effect, indicating the presence of a strong GEI in the NENSU. Similar results have been reported in sunflower (de la Vega et al, 2007) as in other crops (i.e. rice, Cooper et al, 1999;sorghum, Chapman et al, 2000;corn, Chapman et al, 1997;and barley, Ceretta & van Eeuwijk, 2008).…”

Section: Met Network Efficiencysupporting

confidence: 75%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

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Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.Additional keywords: genotype by environment interaction; multi-environment trials; sunflower; network efficiency; yield stability. Abbreviations used: CPD (critical percentage difference); GEI (genotype by environment interaction); GGE (genotype plus genotype by environment); LE (La Estanzuela); MET (multi-environment trial); NENSU (National evaluation network of sunflower cultivars of Uruguay); PLS (partial least squares); RCBD (randomized complete block design); YG (Young).

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Section: Met Network Efficiencysupporting

confidence: 75%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

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“…Lodging was estimated at physiological maturity based on a 1 (fully erect) to 5 (fully prostrate) scale. A quantile regression at the 90th percentile was used to estimate genetic gain for the top 10% of cultivars across years of release (de la Vega et al, 2007;Gizzi and Gambin, 2016). Since plots were hand-clipped, there was not any issue related with the difference in days to maturity across cultivars differing in maturity group.…”

Section: Experimental Details and Measurementsmentioning

confidence: 99%

Soybean Genetic Gain in Maturity Groups III to V in Argentina from 1980 to 2015

Felipe

Gerde

2016

Crop Science

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Genetic progress is assessed to estimate its contribution to on‐farm yield increases and to identify traits that have been improved over some period of time. Although Argentina is a major soybean [Glycine max (L.) Merr.] producer, there is limited information about genetic progress in this system. Argentinean soybean cultivars were developed from US commercial cultivars. Because the genetic base of US cultivars is narrow, it would be expected that genetic progress in Argentina to be slower than in the United States. We assessed the genetic gain for yield and related traits in cultivars released in Argentina from 1980 to 2015. One hundred and eighty‐one cultivars belonging to maturity groups (MGs) III, IV, and V were evaluated in three environments in the northern pampas from Argentina. Genetic gain in yield was 43 kg ha−1 yr−1 and was not different across MGs. Relative genetic gain was 1.1% yr−1, similar to reports from the United States or Brazil. Newer cultivars from MGs III and IV had increased days to maturity, while cultivars from MG V showed the opposite trend. Vegetative period was also reduced in newer cultivars from MGs IV and V. Seed protein concentration was reduced over the years. Genetic progress explained 50% of total on‐farm yield increase. Results from this experiment showed that breeding programs in Argentina were able to attain a similar genetic gain to the United States even though the starting parents were only a few US cultivars selected from an already narrow genetic base.

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“…At present, this is also the main region devoted to the sunflower crop, which has been displaced towards drier and poorer soils by soybean crop (de la Vega et al 2007). Sunflower commercial seed in Argentina is of high purity and constantly gives rise to highly uniform plant stands.…”

Section: Discussionmentioning

confidence: 98%

Gene flow between cultivated sunflower and Helianthus petiolaris (Asteraceae)

et al. 2009

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Helianthus petiolaris (Asteraceae) native to North America has naturalized in Argentina. The extensive overlapping with sunflower crop regions, the coincidence of life cycles and the common pollinators facilitate interspecific crosses with sunflower, H. annuus var. macrocarpus. To estimate the occurrence of crop-to-wild and wild-to-crop gene flow, off-type plants in progenies of pure stands of both species flowering in coincidence and the presence of crop alleles in H. petiolaris populations were investigated in 26 wild populations and nine crop fields. Morphological traits and RAPD markers were used to attempt hybrid characterization. Off-type individuals were found in frequencies of 0.5 and 0.3% among crop progenies and wild populations, respectively. Off-type plants showed intermediate values for metric morphological traits and low fertility traits. Some off-type plants proved to carry crop alleles based on molecular analysis. The average frequency of cultivar-marker introgression across the wild populations was very low (0.02). Although observed hybridization rates seem to be low, the extension of crop-wild overlapping in Argentina make hybrid formation a noticeable process. Therefore, the new sunflower varieties and eventually GM varieties would transfer their traits through pollen flow and they would persist in H. petiolaris populations.

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Progress over 20 years of sunflower breeding in central Argentina

Cited by 62 publications

References 28 publications

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

Soybean Genetic Gain in Maturity Groups III to V in Argentina from 1980 to 2015

Gene flow between cultivated sunflower and Helianthus petiolaris (Asteraceae)

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