Unveiling hybrid potential and exploring combining ability for yield and related traits in maize (Zea mays L.) through line × tester mating design

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Unveiling hybrid potential and exploring combining ability for yield and related traits in maize (Zea mays L.) through line × tester mating design

Mohammad Ashraful Alam, Salahuddin Ahmed, Mohammad Alamgir Miah, Nasrin Jahan, Mohammad Marufur Rahman, Mohammad Rafiqul Islam

Article ID: 3048
Vol 5, Issue 4, 2024

DOI: https://doi.org/10.54517/ama3048
VIEWS - 264 (Abstract)

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Abstract

Combining ability analysis serves as an invaluable tool for evaluating the compatibility of parental lines and testers, as well as for elucidating the intricate genetic mechanisms at play within their hybrid progeny. This study was designed to ascertain the combining ability of maize lines when paired with testers, specifically focusing on yield-related traits through the utilization of a line × tester mating design. A total of fifteen advanced lines were systematically crossbred with three distinct testers to produce forty-five hybrid test crosses. The performance of these progenies was rigorously assessed across three distinct locations, thereby enhancing the robustness of the findings. The field trials were conducted using an alpha lattice design. Variance analysis, combining ability effects, and genetic components were estimated following a line × tester analysis. Employing variance analysis, significant variations were discerned in both general and specific combining abilities, underscoring the contribution of both additive and non-additive gene actions to the expression of the targeted traits. Notably, the magnitudes variance component indicated the prevalence of additive gene effects across the traits studied. Amidst the comprehensive exploration of parental lines and testers, it was evident that lines L10 and Tester T2 exhibited notable compatibility as general combiners, particularly in the context of maize grain yield. Additionally, Line L12 demonstrated favorable characteristics related to earliness. The superior performance of certain hybrid combinations emerged as a noteworthy outcome of this investigation. Specifically, the hybrid cross L10 × T2 displayed remarkable performance in terms of grain yield, while L12 × T1 demonstrated strong potential for the trait days to anthesis. Furthermore, in terms of specific combining ability, the cross L13 × T1 demonstrated the most pronounced effect, particularly concerning grain yield. Following closely were the combinations L5 × T1 and L2 × T2, each exhibiting significant potential for enhancing maize productivity. To conclude, this study underscores the indispensable role of combining ability analysis in elucidating the interplay between parental lines and testers, thus unraveling the intricate genetic dynamics within their hybrid offspring. The insights gathered hold promise for advancing maize production by employing judicious selection strategies, with a specific focus on the highlighted hybrid combinations.

Keywords

maize; general combining ability; line × tester analysis; specific combining ability; hybrid

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