Spatial and seasonal effects confounding interpretation of sunflower yields in Argentina

Chapman, Scott C.; Vega, Abelardo J. de la

doi:10.1016/s0378-4290(01)00185-x

Cited by 26 publications

(17 citation statements)

References 18 publications

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“…This observation strongly suggests that GCA × E and SCA × E interactions for these traits should not be ignored when selecting new testers for a breeding program aimed at releasing hybrids to this complex target population of environments. The ratio of total G × E interaction to G components of variance for oil concentration was 0.35 (Table 3), confirming the relatively high heritability previously reported for this trait (Chapman and de la Vega, 2002). This also suggests that GCA and SCA for oil concentration could be studied averaged across environments.…”

Section: Resultssupporting

confidence: 86%

Multivariate Analyses to Display Interactions between Environment and General or Specific Combining Ability in Hybrid Crops

Vega¹,

Chapman

2006

Crop Science

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Estimates of general combining ability (GCA) and specific combining ability (SCA) help plant breeders devise breeding and selection strategies. The objective of this paper was to apply two‐mode principal component analysis (PCA) to environment‐centered and normalized female (F) or male (M) × environment (E) tables and three‐mode PCA on F × M × E tables to display the variability associated with GCA, SCA, and their interactions with environments. A sunflower (Helianthus annuus L.) North Carolina Design II (4 females × 4 males) was grown in 11 environments in Argentina. Site regression (SREG2) and additive main effects and multiplicative interaction (AMMI2) models identified lines with high GCA and SCA. The two‐ and three‐mode PCAs revealed GCA × E and SCA × E interactions and were able to identify the best tester for either broad or specific adaptation. Two combinations of analyses accounted for all sources of variation. In Strategy 1 (explaining 59% of the genotype (G) + G × E interaction), two‐mode PCA displayed GCA + SCA while three‐mode PCA displayed GCA × E + SCA × E interactions. Strategy 2 (77%) used two‐mode PCA to display GCA + GCA × E interaction and three‐mode PCA to display SCA + SCA × E interaction. Both strategies were suitable approaches to understand the combining ability of the core germplasm of a hybrid crop breeding program and allowed decisions to be made about the required focus on line development within sub‐regions encompassed by the program.

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Section: Resultssupporting

confidence: 86%

Multivariate Analyses to Display Interactions between Environment and General or Specific Combining Ability in Hybrid Crops

Vega¹,

Chapman

2006

Crop Science

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“…In the study area, it has been demonstrated that water shortage during reproductive stages reduces crop yields, the critical months being December for maize (Hall et al, 1982), January to March for soybean (Andriani and Bodrero, 1995) and November-December for sunflower (Chapman and de la Vega, 2002). Wheat yields are fewer dependants on rainfall occurring during the crop cycle than summer crops.…”

Section: Wheatmentioning

confidence: 91%

Changes in Climate and Crop Production During the 20th Century in Argentina

2005

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This work was focused on the assessment of changes occurring in crop production and climate during the 20th century in Argentina. The study was carried out for nine sites located in the Pampas region that are representative of contrasting environments. We have considered the four main crops cultivated in this area (wheat, maize, sunflower and soybean). Historical climatic data and crop production related variables (yield, planted area, harvested area) were analyzed and, by means of crop simulation models, we quantified the impact of climate on crop yields. Changes occurring in climate during the three last decades of the 20th century were characterized by important increases in precipitation especially between October and March, decreases in maximum temperature and solar radiation in particular during spring and summer and increases in minimum temperature during almost all of the year. These changes contributed to increases in yields, especially in summer crops and in the semiarid zone, mostly due to increases in precipitation, although changes in temperature and radiation also affected crop yields but to a lesser extent. Comparing the period 1950Comparing the period -1970Comparing the period with 1971Comparing the period -1999 yields increases attributable to changes in climate were 38% in soybean, 18% in maize, 13% in wheat, and 12% in sunflower while mean observed yield increases were 110% for maize, 56% for wheat and 102% for sunflower.

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“…1). Soil, climate, and technology of these regions have been described by Hall et al (1992), Mercau et al (2001), Chapman and de la Vega (2002), and de la Vega et al (2007a) Standard sowing dates are from the end of July to early September for the northern region and from the end of September through early December for the central and southern regions. The mean weather conditions of the three regions during the different sunflower crop developmental phases are summarized in Table 1…”

Section: Methodsmentioning

confidence: 99%

“…However, the crop is also grown in the subtropical (northern) region where temperatures are higher and there is greater spatial variation in soil type and consequent interactions across the region for water supply during crop growth. Combined with the wide range of commercial planting dates of sunfl ower, this geographic diversity creates a highly heterogeneous target population of environments (Chapman and de la Vega, 2002;de la Vega and Chapman, 2006). Analyses of large-scale METs have demonstrated that large and regional G × E interactions can be a major impediment for genetic progress in the crop (de la Vega et al, 2001;Chapman and de la Vega, 2002;de la Vega and Chapman, 2006) and have emphasized the value of subdivision of sunfl ower production into mega-environments (subregions) and also the selection for specifi c and broad adaptations between and within subregions, respectively.…”

mentioning

confidence: 99%

“…Combined with the wide range of commercial planting dates of sunfl ower, this geographic diversity creates a highly heterogeneous target population of environments (Chapman and de la Vega, 2002;de la Vega and Chapman, 2006). Analyses of large-scale METs have demonstrated that large and regional G × E interactions can be a major impediment for genetic progress in the crop (de la Vega et al, 2001;Chapman and de la Vega, 2002;de la Vega and Chapman, 2006) and have emphasized the value of subdivision of sunfl ower production into mega-environments (subregions) and also the selection for specifi c and broad adaptations between and within subregions, respectively. Overall, the research suggested that because of the lack of genotypic association between central and northern subregions, yield gains in northern environments would have been reduced if selection had been done in central environments and vice versa.…”

mentioning

confidence: 99%

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Mega‐Environment Differences Affecting Genetic Progress for Yield and Relative Value of Component Traits

Vega¹,

Chapman

2010

Crop Science

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Large and regional genotype × environment interactions can be a major impediment to sunflower (Helianthus annuus L) genetic progress in Argentina, where yield improvements have slowed since the mid‐1990s. Previous studies in the central subregion show that oil yield gains were maintained for the last 20 yr and contributed to counteracting a decline in agronomic quality of the sunflower area. In this study, mixed models, quantile regression, and pattern analyses were applied to three long‐term (18 yr) multienvironment datasets (67 sites) to show that: (i) in high‐yielding hybrids, oil yield has increased linearly by 6.7, 10.5, and 6.2 kg ha−1 yr−1 over 25 yr in the northern, central, and southern subregions, respectively; (ii) selection for specific adaptation to the central region did not improve oil yield in northern environments (consistent with previous pattern analysis–based predictions), nor in the southern region, suggesting that the south is a third mega‐environment; (iii) the central subregion had the greatest, most consistent genetic gain; (iv) while the relative regional responses of hybrid groups were similar for oil concentration and grain yield, there was strong hybrid × region crossover for relative responses of time to flowering; and (v) the merger of the original gene pools via breeding for oil yield reduced the original range on phenological responses within subregions and produced different maturity types for each region.

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Spatial and seasonal effects confounding interpretation of sunflower yields in Argentina

Cited by 26 publications

References 18 publications

Multivariate Analyses to Display Interactions between Environment and General or Specific Combining Ability in Hybrid Crops

Multivariate Analyses to Display Interactions between Environment and General or Specific Combining Ability in Hybrid Crops

Changes in Climate and Crop Production During the 20th Century in Argentina

Mega‐Environment Differences Affecting Genetic Progress for Yield and Relative Value of Component Traits

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