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Genetic and Environmental Factors Affecting the Grain Quality and Yield...
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Genetic and Environmental Factors Affecting the Grain Quality and Yield of Cereals

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Crop Physiology and Crop Production".

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 7186

Special Issue Editor

Dr. Thierry Aussenac

E-Mail Website
Guest Editor
Institut Polytechnique UniLaSalle, Université d’Artois, ULR 7519, 19 rue Pierre Waguet, BP 30313, 60026 Beauvais, France
Interests: FTIR; NIR; agricultural product quality; food matrice characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cereals contribute a substantial part of the world’s plant-derived food production and comprise a majority of the crops that are harvested. In fact, FAO statistics show that in 2019, the production of primary crops was 9.4 billion tons, and four crops account for about half of global primary crop production: sugar cane, maize, wheat, and rice. In addition, according to the Foreign Agricultural Service/USDA, the preliminary world production in 2019 of maize, wheat, and rice-paddy was estimated at around 1148, 765, and 755 million tons, respectively. Given the importance of cereals as staple foods worldwide (more than 40% of the total dietary energy supply was provided by cereals at world level in 2018), one of the major challenges that plant breeders/producers are currently facing is to increase cereal grain production (grain yield) while improving grain quality, which has important implications for the nutritional quality of foods, especially in developing countries. Considering the economic and social importance of cereal crops in the context of global climate changes, this Special Issue will focus on the effects and interactions of genetic and environmental factors (drought, elevated temperatures, CO2 concentration, etc.) that can affect the grain qualities and/or productivity of cereals.

Dr. Thierry Aussenac
Guest Editor

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Keywords

  • cereals
  • yield and quality
  • grain quality traits
  • environmental factors
  • genetic factors

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Published Papers (3 papers)

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Research

17 pages, 3024 KiB  
Article
Identifying and Characterizing Candidate Genes Contributing to a Grain Yield QTL in Wheat
by Md Atik Us Saieed, Yun Zhao, Shahidul Islam and Wujun Ma
Plants 2024, 13(1), 26; https://doi.org/10.3390/plants13010026 - 20 Dec 2023
Viewed by 1674
Abstract
The current study focuses on identifying the candidate genes of a grain yield QTL from a double haploid population, Westonia × Kauz. The QTL region spans 20 Mbp on the IWGSC whole-genome sequence flank with 90K SNP markers. The IWGSC gene annotation revealed [...] Read more.
The current study focuses on identifying the candidate genes of a grain yield QTL from a double haploid population, Westonia × Kauz. The QTL region spans 20 Mbp on the IWGSC whole-genome sequence flank with 90K SNP markers. The IWGSC gene annotation revealed 16 high-confidence genes and 41 low-confidence genes. Bioinformatic approaches, including functional gene annotation, ontology investigation, pathway exploration, and gene network study using publicly available gene expression data, enabled the short-listing of four genes for further confirmation. Complete sequencing of those four genes demonstrated that only two genes are polymorphic between the parental cultivars, which are the ferredoxin-like protein gene and the tetratricopeptide-repeat (TPR) protein gene. The two genes were selected for downstream investigation. Two SNP variations were observed in the exon for both genes, with one SNP resulting in changes in amino acid sequence. qPCR-based gene expression showed that both genes were highly expressed in the high-yielding double haploid lines along with the parental cultivar Westonia. In contrast, their expression was significantly lower in the low-yielding lines in the other parent. It can be concluded that these two genes are the contributing genes to the grain yield QTL. Full article
(This article belongs to the Special Issue Genetic and Environmental Factors Affecting the Grain Quality and Yield of Cereals)
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22 pages, 4713 KiB  
Article
Characterization and Differentiation of Grain Proteomes from Wild-Type Puroindoline and Variants in Wheat
by Peixun Liu, Zehou Liu, Xiaofei Ma, Hongshen Wan, Jianmin Zheng, Jiangtao Luo, Qingyan Deng, Qiang Mao, Xiaoye Li and Zongjun Pu
Plants 2023, 12(10), 1979; https://doi.org/10.3390/plants12101979 - 15 May 2023
Viewed by 1528
Abstract
Premium wheat with a high end-use quality is generally lacking in China, especially high-quality hard and soft wheat. Pina-D1 and Pinb-D1 (puroindoline genes) influence wheat grain hardness (i.e., important wheat quality-related parameter) and are among the main targets in wheat breeding programs. [...] Read more.
Premium wheat with a high end-use quality is generally lacking in China, especially high-quality hard and soft wheat. Pina-D1 and Pinb-D1 (puroindoline genes) influence wheat grain hardness (i.e., important wheat quality-related parameter) and are among the main targets in wheat breeding programs. However, the mechanism by which puroindoline genes control grain hardness remains unclear. In this study, three hard wheat puroindoline variants (MY26, GX3, and ZM1) were compared with a soft wheat variety (CM605) containing the wild-type puroindoline genotype. Specifically, proteomic methods were used to screen for differentially abundant proteins (DAPs). In total, 6253 proteins were identified and quantified via a high-throughput tandem mass tag quantitative proteomic analysis. Of the 208 DAPs, 115, 116, and 99 proteins were differentially expressed between MY26, GX3, and ZM1 (hard wheat varieties) and CM605, respectively. The cluster analysis of protein relative abundances divided the proteins into six clusters. Of these proteins, 67 and 41 proteins were, respectively, more and less abundant in CM605 than in MY26, GX3, and ZM1. Enrichment analyses detected six GO terms, five KEGG pathways, and five IPR terms that were shared by all three comparisons. Furthermore, 12 proteins associated with these terms or pathways were found to be differentially expressed in each comparison. These proteins, which included cysteine proteinase inhibitors, invertases, low-molecular-weight glutenin subunits, and alpha amylase inhibitors, may be involved in the regulation of grain hardness. The candidate genes identified in this study may be relevant for future analyses of the regulatory mechanism underlying grain hardness. Full article
(This article belongs to the Special Issue Genetic and Environmental Factors Affecting the Grain Quality and Yield of Cereals)
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19 pages, 4539 KiB  
Article
Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning
by Yiting Ren, Qiangzi Li, Xin Du, Yuan Zhang, Hongyan Wang, Guanwei Shi and Mengfan Wei
Plants 2023, 12(3), 446; https://doi.org/10.3390/plants12030446 - 18 Jan 2023
Cited by 17 | Viewed by 3496
Abstract
Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid [...] Read more.
Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn’s vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production. Full article
(This article belongs to the Special Issue Genetic and Environmental Factors Affecting the Grain Quality and Yield of Cereals)
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