Dataset Information

The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.

ABSTRACT: Wheat yields in many of the main producing European countries stagnate since about 20 years. Hence, it is of high interest, to analyze breeding progress in terms of yield and how associated traits changed. Therefore, a set of 42 cultivars (released between 1966 and 2012) was selected and yield as well as functional traits defined by the Monteith and Moss equation were evaluated under three levels of management intensity. The Monteith Moss equation thereby calculates grain yield as the product of incident photosynthetically active radiation, fraction of intercepted radiation, radiation use efficiency, and harvest index. The field trial was performed in a high yielding environment in Northern Germany in two seasons (2016-2017 and 2017-2018) with very contrasting rainfall rates. The three differing managements were: intensive (high N + pesticides), semi-intensive (high N - pesticides), and extensive (low N - pesticides). The results indicate that the stagnation of wheat yields in Central-Europe is not caused by a diminishing effect of breeding on yield potential. This equally applies to suboptimal growing conditions like extensified crop management and restricted water supply. Nearly all functional sub-traits showed a parallel progress but coefficients of determination of relationships between traits and year of variety release are decreasing along the hierarchy of yield formation. One exception is radiation interception which did not show a stable linear increase during breeding history. In recent years, biomass is getting more important in comparison to harvest index. Values of harvest index are slowly approaching theoretical maxima and correlations with grain yield are decreasing.

SUBMITTER: Rose T

PROVIDER: S-EPMC6908521 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.

Rose Till T Kage Henning H

Frontiers in plant science 20191206

Wheat yields in many of the main producing European countries stagnate since about 20 years. Hence, it is of high interest, to analyze breeding progress in terms of yield and how associated traits changed. Therefore, a set of 42 cultivars (released between 1966 and 2012) was selected and yield as well as functional traits defined by the Monteith and Moss equation were evaluated under three levels of management intensity. The Monteith Moss equation thereby calculates grain yield as the product of ...[more]

PMID: 31867026

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Project description:Key messageOver the last 32 years, a large gain in grain yield (24 %) was achieved in official German variety trials, and despite considerable loss in protein concentration (-7.9 %), winter wheat baking quality was partially improved over the last 32 years. On-farm gain in grain yield (32 %) exceeded gain in trials, but at yield level about 25 dt ha -1 lower. Breeding progress was very successfully transferred into both progress in grain yield and on-farm baking quality. Long-term gains in grain yield and baking quality of 316 winter wheat varieties from German official trials were evaluated. We dissected progress into a genetic and a non-genetic part to quantify the contribution of genetic improvement. We further investigated the influence of genotype and environment on total variation by estimating variance components. We also estimated genetic and phenotypic correlation between quality traits. For trial data, we found a large gain in grain yield (24%), but a strong decline in protein concentration (-8.0%) and loaf volume (-8.5%) relative to 1983. Improvement of baking quality could be achieved for falling number (5.8%), sedimentation value (7.9%), hardness (13.4%), water absorption (1.2%) and milling yield (2.4%). Grain yield, falling number and protein concentration were highly influenced by environment, whereas for sedimentation value, hardness, water absorption and loaf volume genotypes accounted for more than 60% of total variation. Strong to very strong relations exist among protein concentration, sedimentation value, and loaf volume. On-farm yields were obtained from national statistics, and grain quality data from samples collected by national harvest survey. These on-farm data were compared with trial results. On-farm gain in grain yield was 31.6%, but at a mean level about 25 dt ha-1 lower. Improvement of on-farm quality exceeded trial results considerably. A shift to varieties with improved baking quality can be considered as the main reason for this remarkable improvement of on-farm baking quality.

Project description:Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Dataset Information

The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.

Publications

The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.

Similar Datasets

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