12 research outputs found
Development of Cytoplasmic–Nuclear Male Sterility, Its Inheritance, and Potential Use in Hybrid Pigeonpea Breeding
Pigeonpea [Cajanus cajan (L.) Millsp.] is a unique food legume because of its partial (20–30%) outcrossing nature,
which provides an opportunity to breed commercial hybrids. To achieve this, it is essential to have a stable male-sterility system. This paper reports the selection of a cytoplasmic–nuclear male-sterility (CMS) system derived from an interspecific cross between a wild relative of pigeonpea (Cajanus sericeus Benth. ex. Bak.) and a cultivar. This male-sterility source was used to breed agronomically superior CMS lines in early (ICPA 2068), medium (ICPA 2032), and late (ICPA 2030) maturity durations. Twentythree fertility restorers and 30 male-sterility maintainers were selected to develop genetically diverse hybrid combinations. Histological studies revealed that vacuolation of growing tetrads and persistence of tetrad wall were primary causes of the manifestation of male sterility. Genetic studies showed that 2 dominant genes, of which one had inhibitory gene action, controlled fertility restoration in the hybrids. The experimental hybrids such as TK 030003 and TK 030009 in early, ICPH 2307 and TK 030625 in medium, and TK 030861 and TK 030851 in late maturity groups exhibited 30–88% standard heterosis in multilocation trials
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Stacked Hybridization to Enhance the Performance of Artificial Neural Networks (ANN) for Prediction of Water Quality Index in the Bagh River Basin, India
Data availability statement:
The data pertaining to this study have not been deposited in a publicly accessible repository, given that all relevant data are thoroughly detailed in the article or appropriately cited in the manuscript.Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.No funding was received for conducting this study