Corn Cultivation and Improvement

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1732

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Guest Editor
Department of Agronomy, Federal Technological University of Paraná, Santa Helena 85892-000, Paraná, Brazil
Interests: plant breeding; soybean breeding; corn breeding; Industry 4.0 technologies for crop management
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Special Issue Information

Dear Colleagues,

The development of germplasm is essential for creating cultivars with new traits that differentiate them for competitive advantage in the market or for specific environments, enabling food production in all regions where human life exists. Furthermore, germplasm development needs to align with breeding programs, addressing future gaps in genetic variability required for new regions, emerging biotic and abiotic stresses, or even new biotechnologies that do not allow direct use of elite lineages.

In turn, the genetic improvement of corn with well-defined heterotic groups requires germplasm aligned with the heterosis implemented in the program, ensuring that the introduction of new genetic material maintains the genetic gains achieved in previous cycles. Additionally, identifying new traits within germplasm enables breeding programs to access new genetic variability, which can be incorporated to enhance cultivars for new regions or to increase productivity.

The development of germplasm and corn improvement can yield excellent results by applying and integrating a set of new technologies from both biotechnology and Industry 4.0 (artificial intelligence, visual computing, big data, etc.). These advancements enhance genetic gains through shorter selection cycles and smaller field populations or individuals, thereby reducing long-term costs and operations. High-throughput genotyping and phenotyping have enabled the evaluation of thousands of plants before they reach maturity or even before they are sown.

In this Special Issue, we aim to demonstrate the application of different biotechnologies, omics technologies, phenotyping, and corn breeding techniques that develop corn collection, select new germplasm, and enhance genetic gains and precision in germplasm development, catering to diverse environments and traits where corn cultivation takes place.

Prof. Dr. Glauco Vieira Miranda
Guest Editor

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Keywords

  • genetics
  • phenotyping
  • artificial intelligence
  • omics technologies
  • breeding
  • genomics
  • transcriptomic
  • biotechnology
  • Corn cultivation techniques
  • Maize farming practices
  • Hybrid corn varieties
  • Genetically modified corn
  • Corn yield optimization
  • Sustainable corn production
  • Pest management
  • Corn disease resistance
  • Soil management
  • Irrigation practices
  • Climate impact on corn growth
  • Fertilization strategies
  • Crop rotation with corn
  • Corn harvesting methods
  • Post-harvest corn processing

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Published Papers (2 papers)

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Research

13 pages, 2243 KiB  
Article
Enhancing Across-Population Genomic Prediction for Maize Hybrids
by Guangning Yu, Furong Li, Xin Wang, Yuxiang Zhang, Kai Zhou, Wenyan Yang, Xiusheng Guan, Xuecai Zhang, Chenwu Xu and Yang Xu
Plants 2024, 13(21), 3105; https://doi.org/10.3390/plants13213105 - 4 Nov 2024
Viewed by 583
Abstract
In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals [...] Read more.
In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency. Full article
(This article belongs to the Special Issue Corn Cultivation and Improvement)
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13 pages, 439 KiB  
Article
Semi-Arid Environmental Conditions and Agronomic Traits Impact on the Grain Quality of Diverse Maize Genotypes
by Nicolás Francisco Bongianino, María Eugenia Steffolani, Claudio David Morales, Carlos Alberto Biasutti and Alberto Edel León
Plants 2024, 13(17), 2482; https://doi.org/10.3390/plants13172482 - 5 Sep 2024
Viewed by 573
Abstract
We assessed the impact of environmental conditions and agronomic traits on maize grain quality parameters. The study was conducted using genotypes with distinct genetic constitutions developed specifically for late sowing in semi-arid environments. We evaluated the agronomic, physical, and chemical characteristics of eight [...] Read more.
We assessed the impact of environmental conditions and agronomic traits on maize grain quality parameters. The study was conducted using genotypes with distinct genetic constitutions developed specifically for late sowing in semi-arid environments. We evaluated the agronomic, physical, and chemical characteristics of eight maize open-pollinated varieties, six inbred lines, and three commercial hybrids. The yield of the open-pollinated varieties showed a positive correlation with protein content (r = 0.33), while it exhibited a negative correlation with the carbohydrate percentage (r = −0.36 and −0.42) in conjunction with the inbred lines. The flotation index of the hybrids was influenced primarily by the environmental effect (50.15%), whereas in the inbred lines it was nearly evenly divided between the genotype effect (45.51%) and the environmental effect (43.15%). In the open-pollinated varieties, the genotype effect accounted for 35.09% and the environmental effect for 42.35%. The characteristics of plant structure were associated with grain quality attributes relevant for milling, including hardness and test weight. Inbred lines exhibited significant genotype contributions to grain hardness, protein, and carbohydrate content, distinguishing them from the other two germplasm types. These associations are crucial for specific genotypes and for advancing research and development of cultivars for the food industry. Full article
(This article belongs to the Special Issue Corn Cultivation and Improvement)
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