Search

Wider pea (Pisum sativum L.) cultivation has great interest for European agriculture, owing to its favorable environmental impact and provision of high-protein feedstuff. This work aimed to investigate the extent of genotype × environment interaction (GEI), genetically based trade-offs and polygenic control for crude protein content and grain yield of pea targeted to Italian environments, and to assess the efficiency of genomic selection (GS) as an alternative to phenotypic selection (PS) to increase protein yield per unit area. Some 306 genotypes belonging to three connected recombinant inbred line (RIL) populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield and protein content on a dry matter basis in three autumn-sown environments of northern or central Italy. Line variation for mean protein content ranged from 21.7 to 26.6%. Purely genetic effects, compared with GEI effects, were over two-fold larger for protein content, and over 2-fold smaller for grain and protein yield per unit area. Grain yield and protein content exhibited no inverse genetic correlation. A genome-wide association study revealed a definite polygenic control not only for grain yield but also for protein content, with small amounts of trait variation accounted for by individual loci. On average, the GS predictive ability for individual RIL populations based on the rrBLUP model (which was selected out of four tested models) using by turns two environments for selection and one for validation was moderately high for protein content (0.53) and moderate for grain yield (0.40) and protein yield (0.41). These values were about halved for inter-environment, inter-population predictions using one RIL population for model construction to predict data of the other populations. The comparison between GS and PS for protein yield based on predicted gains per unit time and similar evaluation costs indicated an advantage of GS for model construction including the target RIL population and, in case of multi-year PS, even for model training based on data of a non-target population. In conclusion, protein content is less challenging than grain yield for phenotypic or genome-enabled improvement, and GS is promising for the simultaneous improvement of both traits. [ABSTRACT FROM AUTHOR]

This study aimed to support field pea (Pisum sativum L.) breeding strategies for organic systems of southern European environments, by assessing the size of genotype × environment interaction (GEI) due to spatial and temporal factors across climatically contrasting regions and identifying plant characters associated with genotype adaptive responses. Twelve recent varieties were evaluated for grain yield and other traits in six organically managed environments (three sites × two cropping years) of northern, central and southern Italy. GEI for grain yield was large, with the variety × site × year interaction greatly exceeding the variety × site interaction. This finding, and the similar magnitude of the mean genetic correlations for variety yields across pairs of sites (rg = 0.56) and pairs of years (rg = 0.51), indicated the difficulty of exploiting variety × site interaction effects by breeding for specific climatic regions. Pattern analysis highlighted the large inconsistency across years for GEI pattern of the sites from central and southern Italy. GEI also complicated the targeting of varieties, owing to inconsistent top-performing material across years according to additive main effects and multiplicative interaction (AMMI)-modelled yields. Higher genotype mean yield was strictly associated (P < 0.01) with lower weed proportion (hence, greater competitiveness against weeds: r = –0.96), taller plants (r = 0.89) and larger seeds (r = 0.78), with looser associations with lower susceptibility to lodging and ascochyta blight. These traits, which also contributed to preferential adaptation to the moisture-favourable environments of northern Italy, could be selected in breeding widely adapted varieties. The choice of appropriate varieties is a great challenge when growing organic field pea. We assessed the effect of temporal and spatial variation of abiotic and biotic stresses on variety responses, defined the adaptation patterns of recent varieties across climatically contrasting environments of Italy, and highlighted useful plant characters under organic farming. The results suggest that selecting widely adapted varieties by multi-year evaluation in climatically contrasting sites can be an efficient strategy for field pea breeding targeting organic farming in Italy. [ABSTRACT FROM AUTHOR]

Background: A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction). Results: Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits. Conclusions: Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection. [ABSTRACT FROM AUTHOR]