Assessment of germination performance and early seedling growth of Malaysian indica rice genotypes under drought conditions for strategic cropping during water scarcity

Evamoni, Fatematuz Zuhura; Nulit, Rosimah; Yap, Chee Kong; Ibrahim, Mohd Hafiz; Sidek, Noorhazira

doi:10.4067/s0718-58392023000300281

Cited by 5 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, employing tolerance indices and cluster analysis proves to be an effective strategy for identifying the genetic configurations of maize that are either tolerant or sensitive to environmental stresses, facilitating targeted improvements in maize breeding programs. This approach aligns with the methodologies adopted in other studies by Sedhom et al [18], Kapoor et al [21], Sinana et al [22], Evamoni et al [23], and Wang and Peng [24], who also leveraged drought indices and cluster analysis to pinpoint superior genotypes exhibiting a high degree of drought tolerance.…”

Section: Discussionsupporting

confidence: 55%

Genetic Potential of Newly Developed Maize Hybrids under Different Water-Availability Conditions in an Arid Environment

Sedhom,

Rabie,

Awaad

et al. 2024

Life

View full text Add to dashboard Cite

Drought is a crucial environmental stress that tremendously impacts maize production, particularly under abrupt climate changes. Consequently, breeding drought-tolerant and high-yielding maize hybrids has become decisive in sustaining its production and ensuring global food security under the global fast-growing population. The present study aimed to explore drought tolerance and agronomic performance of newly developed maize inbred lines and their hybrids. Ten newly developed maize inbred lines were crossed with two high-yielding testers using a line × tester mating design. The developed twenty hybrids alongside two high-yielding commercial hybrids were evaluated under water-deficit (5411 m3/ha) and well-watered (7990 m3/ha) conditions in dry summer climate conditions. Highly significant variations were detected among the evaluated hybrids for all studied agronomic traits under well-watered and water-deficit conditions. The inbred lines L10 and L6 were particularly notable, demonstrating the most significant negative general combining ability (GCA) effects for earliness, which is crucial for stress avoidance in both environmental settings. Inbred lines L11, L7, L6, and L1 also showed the highest positive and most significant GCA effects for key yield traits, indicating their potential as parents in breeding programs. The crosses L-10×T-1 and L-6×T-2 were outstanding for their heterotic effects on earliness in days to tasseling and silking. Similarly, the crosses L-4×T-2 and L-1×T-1 excelled in plant and ear heights under both irrigation regimes. The hybrids L-1×T-2 and L-7×T-1 demonstrated superior heterosis for chlorophyll content, number of rows per ear, and overall grain yield. Additionally, hybrids L-11×T-1 and L-11×T-2 exhibited remarkable heterotic effects for the number of grains per row, number of rows per ear, 100-kernel weight, and grain yield, highlighting their potential in breeding for productivity. Based on drought tolerance indices and cluster analysis, the cross combinations L-11×T-1, L-11×T-2, L-7×T-1, and L-1×T-2 were classified as the most drought-tolerant crosses. The principal component analysis highlighted traits such as days to tasseling, days to silking, chlorophyll content, plant height, ear height, number of grains per row, number of rows per ear, and 100-kernel weight can be taken as selection criteria for improving grain yield in maize breeding programs under limited water conditions. Based on the summarized results, the identified genetic materials could be considered promising under both conditions and hold potential for future breeding programs.

show abstract

Section: Discussionsupporting

confidence: 55%

Genetic Potential of Newly Developed Maize Hybrids under Different Water-Availability Conditions in an Arid Environment

Sedhom,

Rabie,

Awaad

et al. 2024

Life

View full text Add to dashboard Cite

show abstract

Unravelling population structure and marker trait association using SSR markers among the identified drought tolerant rice landraces (Oryza sativa L.)

Manju Devi,

Joel,

Raveendran

et al. 2024

Czech J. Genet. Plant Breed.

View full text Add to dashboard Cite

Harnessing the power of machine learning for crop improvement and sustainable production

Khatibi,

Ali

2024

Front. Plant Sci.

View full text Add to dashboard Cite

Crop improvement and production domains encounter large amounts of expanding data with multi-layer complexity that forces researchers to use machine-learning approaches to establish predictive and informative models to understand the sophisticated mechanisms underlying these processes. All machine-learning approaches aim to fit models to target data; nevertheless, it should be noted that a wide range of specialized methods might initially appear confusing. The principal objective of this study is to offer researchers an explicit introduction to some of the essential machine-learning approaches and their applications, comprising the most modern and utilized methods that have gained widespread adoption in crop improvement or similar domains. This article explicitly explains how different machine-learning methods could be applied for given agricultural data, highlights newly emerging techniques for machine-learning users, and lays out technical strategies for agri/crop research practitioners and researchers.

show abstract

Assessment of germination performance and early seedling growth of Malaysian indica rice genotypes under drought conditions for strategic cropping during water scarcity

Cited by 5 publications

References 28 publications

Genetic Potential of Newly Developed Maize Hybrids under Different Water-Availability Conditions in an Arid Environment

Genetic Potential of Newly Developed Maize Hybrids under Different Water-Availability Conditions in an Arid Environment

Unravelling population structure and marker trait association using SSR markers among the identified drought tolerant rice landraces (Oryza sativa L.)

Harnessing the power of machine learning for crop improvement and sustainable production

Contact Info

Product

Resources

About