Dataset Information

Chlorophyll fluorescence, physiology, and yield of winter wheat under different irrigation and shade durations during the grain-filling stage.

ABSTRACT: The uneven spatial and temporal distribution of light resources and water scarcity during the grain-filling stage pose significant challenges for sustainable crop production, particularly in the arid areas of the Loess Plateau in Northwest China. This study aims to investigate the combined effects of drought and shading stress on winter wheat growth and its physio-biochemical and antioxidative responses. Wheat plants were subjected to different drought levels- full irrigation (I100), 75% of full irrigation (I75), 50% of full irrigation (I50), and 25% of full irrigation (I25), and shading treatments - 12, 9, 6, 3 and 0 days (SD12, SD9, SD6, SD3, and CK, respectively) during the grain-filling stage. The effects of drought and shading treatments reduced yield in descending order, with the most significant reductions observed in the SD12 and I25 treatments. These treatments decreased grain yield, spikes per plant, 1000-grain weight, and spikelets per spike by 160.67%, 248.13%, 28.22%, and 179.55%, respectively, compared to the CK. Furthermore, MDA content and antioxidant enzyme activities exhibited an ascending trend with reduced irrigation and longer shading durations. The highest values were recorded in the I75 and SD12 treatments, which increased MDA, SOD, POD, and CAT activities by 65.22, 66.79, 65.07 and 58.38%, respectively, compared to the CK. The Pn, E, Gs, and iCO₂ exhibited a decreasing trend (318.14, 521.09, 908.77, and 90.85%) with increasing shading duration and decreasing irrigation amount. Drought and shading treatments damage leaf chlorophyll fluorescence, decreasing yield and related physiological and biochemical attributes.

SUBMITTER: Naseer MA

PROVIDER: S-EPMC11317437 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Chlorophyll fluorescence, physiology, and yield of winter wheat under different irrigation and shade durations during the grain-filling stage.

Naseer Muhammad Asad MA Hussain Sadam S Mukhtar Ahmed A Rui Qian Q Ru Guo G Ahmad Haseeb H Zhang Zhi Qin ZQ Shi Li Bo LB Asad Muhammad Shoaib MS Chen Xiaoli X Zhou Xun Bo XB Ren Xiaolong X

Frontiers in plant science 20240729

The uneven spatial and temporal distribution of light resources and water scarcity during the grain-filling stage pose significant challenges for sustainable crop production, particularly in the arid areas of the Loess Plateau in Northwest China. This study aims to investigate the combined effects of drought and shading stress on winter wheat growth and its physio-biochemical and antioxidative responses. Wheat plants were subjected to different drought levels- full irrigation (I100), 75% of full ...[more]

PMID: 39135649

Similar Datasets

Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Project description:Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

Project description:Increased genetic gain for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS (PS+GS). Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel and F5 and double haploid lines evaluated from 2015 to 2019 growing seasons for grain yield in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield "enhancing" alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual selection strategies (MS and GS) showed the highest mean response (R = 0.61) within the same environment. When combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS within the same environment. Validating selection strategies in different environments resulted in low to negative R values indicating the effects of genotype-by-environment interactions for grain yield. MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches. An integrated PS+GS approach could result in optimal genetic gain within the same environment, whereas a PS strategy might be a viable option for grain yield validated in different environments. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

Dataset Information

Chlorophyll fluorescence, physiology, and yield of winter wheat under different irrigation and shade durations during the grain-filling stage.

Publications

Chlorophyll fluorescence, physiology, and yield of winter wheat under different irrigation and shade durations during the grain-filling stage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets