TL;DR
The results indicate that incorporating advanced computational methods into forage breeding programs can speed genetic progress while safeguarding sustainability in stress-prone agroecosystems and offer a robust, transparent, and reproducible means of evaluating multiple traits in genotype selection.
The findings indicate that integrating biologically significant parameters with advanced AI optimization techniques enhances predictive reliability and provides an effective decision-support tool for precision breeding and sustainable chickpea production.
Background Rainfed cereal production in southwestern Saudi Arabia depends largely on farmer-saved landraces, creating an important opportunity to strengthen grain and fodder productivity in mixed crop-livestock systems exposed to heat and variable rainfall. This study evaluated introduced dual-purpose sorghum and pearl...
Plant breeding forms a vital part of horticulture, contributing significantly to the creation of new varieties with desirable traits. Diverse breeding methodologies have been adopted to achieve improvements in yield, biotic stress resistance, nutritional attributes, and adaptability to varying environments. In this pur...
Phenomic selection (PS) offers a cost‐effective , breeder‐friendly approach for public breeding programmes with limited access to genotyping or restricted financial resources for laboratory infrastructure. Since PS relies on high‐throughput phenotyping data, which is often derived from near‐infrared spectroscopy...
Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consumi...
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