Heterogeneous Winter Wheat Populations Differ in Yield Stability Depending on their Genetic Background and Management System

Abstract

Twelve winter wheat composite cross populations (CCPs), based on three genetic backgrounds and maintained at the University of Kassel, Germany, under both organic and conventional management, were assessed for yield performance and stability in comparison to two commercial varieties over eight and 10 experimental years. A number of stability parameters were chosen in order to identify populations with either adaptation to specific environments or broad adaptation across environments. The genetic effects of the CCP parental varieties were clearly present when comparing CCP yield performance in both management systems. Compared to the variety ‘Capo’, CCPs yielded similarly under organic, but poorer under conventional conditions. Under both management systems, CCPs with the broadest or with a more modern (high yielding) genetic base achieved the greatest yield stability, exceeding that of ‘Capo’, and demonstrating the buffering capacity of genetic diversity. CCPs with a genetic background of high yielding parents reacted most strongly to the different environments and apparently diverged under conventional management over time. Possibilities to improve CCPs through the addition of new genetic material while maintaining the benefits of diversity to achieve higher and more stable yields, particularly in light of increasingly unpredictable climatic conditions are discussed.

Assessment of durum wheat (Triticum durum Desf.) genotypes based on their agro-physiological characteristics and stress tolerance indices

“…Selection, based on W²i identifies stable genotypes which deviate little from the grand mean (Figure 3). These results corroborated those of Weedon and Finckh (2019) who reported that W²i identifies stable genotypes which exhibit low GEI, achieving a yield response parallel to the mean yield response of all genotypes. These results suggest that selection based on W²i and YSI parameters favors stable but below average yielding genotypes.…”

“…Grain yield stability index (YSI) was calculated using the following formulae: YSI = Ys / Yp; and stress tolerance index (STI) was determined as follow: STI = (Yp × Ys) / Ȳp², where Ys, Yp and Ȳp are grain yield means observed under stress, non stress and average of all assessed breeding lines under non stress envi-ronments, respectively (Benmahammed et al, 2010). The ecovalence (W²i) was calculated as described in Weedon and Finckh (2019) as follow: W² i = ∑ (X ij -X i. -X .j + X .. ) 2 , where X ij represents grain yield of the i th genotype in the j th environment, X i. is the genotype main effect, X .j is the environment main effect and X .. is the grand mean.…”

Combining Genetic Gain and Diversity in Plant Breeding: Heritability of Root Selection in Wheat Populations

“…Evolutionary breeding of composite cross populations (CCPs) has been developed to foster intraspecific diversity to enhance resilience against biotic and abiotic stresses that occur in organic and agroecologically managed farming systems [5][6][7]. Recent long-term studies provide robust evidence, by analyzing yield data for up to 13 years with stability indices, that CCPs are highly adaptive to environmental stress and provide higher yield stability than genetically homogeneous line cultivars in wheat [8][9][10]. Diversity remains high in wheat populations even after many years if sufficient effective population sizes are maintained [11].…”

A multi-criteria qualitative tool for the sustainability assessment of organic durum wheat-based farming systems designed through a participative process

“…Climate change indicators are actually based on a sum of mitigation and adaptation strategies cited by the involved actors to which a score was assigned. Above all, stakeholders considered very relevant the use of wheat cultivar mixtures or heterogeneous populations, able to evolve and adapt over time thanks to the natural selection of the environment (evolutionary breeding) for facing the changing climatic conditions (Weedon and Finckh, 2019;Bocci et al, 2020). These simple indicators could be changed in the future with complementary basic attributes assessing greenhouse gases emissions from modelling approach.…”