Optimum environment number for the national sunflower trials network

Abstract

This work aimed to present the optimum environment number methodology and propose the optimization of the National Sunflower Trials Network, by means of the environments exclusion that do not provide loss of the environmental variability already established. Grain and oil yield data of 16 genotypes evaluated at 16 environments of the National Sunflower Trials Network, obtained from trials conducted out-of-season in 2012 and 2013 were used. An analysis was proposed to establish the optimum environment number for genotypes evaluation, based on genotype performance in the various environmental combinations. The removal or maintenance of environments in the experimental network was dynamic, since different environmental combinations impacted the representativeness of the complete network in a different way. This analysis also provides a graphical view of the impact of the environment removal from the network. Once detected points below the established correlation, the researcher could infer about the network minimum environment number and, suggest through consistent information of several testing years, the environment exclusion.

Adaptability, stability and environmental stratification of genetically and nongenetically modified corn in the Cerrado

“…In order to optimize human and financial resources in breeding programs, the number of environments can be reduced from two growing seasons to one and two locations to one, with less nitrogen fertilization (50 kg de N ha -1 ). This finding is corroborated by , who evaluated the adaptability and stability of corn genotypes, and by Matta et al (2020) in research with sunflowers.…”

“…In studies by Santos et al (2020) and Dias et al (2021), cultivars 1 and 6 and cultivar 1, respectively, were also classified as adapted to unfavorable environments and nonresponsive to increased N.…”

Identification of adaptable sunflower (Helianthus annuus L.) genotypes using yield performance and multiple-traits index

“…However, it is possible to develop genotypes with low GE interactions via sub-division of heterogeneous area into smaller, more homogeneous sub-regions; and by selecting genotypes with a better stability across a wide range of environments (Mahmodi et al, 2011;Tai, 1971). In the present study the combined ANOVA showed that, E, G and GEI explained 54.18, 16.9 and 23.50% of the total sum of squares correspondingly which is in line to the ndings of (Matta et al, 2020;Shojaeİ et al, 2023). This highest contribution of the environment con rms that sun ower genotypes might be sensitive to environmental factors and this corroborates to the ndings of (Matta et al, 2020;Van Der Merwe et al, 2013) who reported that environment is the highest contributors of the total variation in multi environment trials of sun ower.…”

“…Therefore, the present study aimed (i) to decipher the GEI of sun ower genotypes and thereby (ii) to identify high yielding and stable genotypes in the lowlands of Tigray, Northern Ethiopia. Sun ower oil yield and seed yield are highly variable across different environments and (Matta et al, 2020), (Kadhem, 2014)con rmed signi cant GEI on its grain yield.…”