Lint yield and fiber quality are the most important drivers of profitability for cotton producers. Selection for high lint percentage has produced consistent yield improvement in cotton but has resulted in smaller-seeded cultivars, which could negatively affect seedling vigor. Thus, selection for functional traits that maximize productivity without penalizing early-season vigor will be important for long-term sustainability. Yield can be manipulated by altering radiation capture by the canopy, radiation use efficiency (RUE) or harvest index. Harvest index can be further influenced by altering the ratio of reproductive to vegetative dry matter or by altering within-boll yield components. Thus, the first objective of this research was to quantify the importance of each process to yield in elite lines in order to identify functional traits that improve productivity without unnecessarily penalizing seedling vigor. To this end, a controlled environment study was conducted first to evaluate seedling vigor under a range of growth temperatures. Secondly, a field study was conducted to characterize crop development, and to evaluate yield and underlying yield drivers in sixteen advanced breeding lines. From results for both the studies, three breeding lines were identified that had the highest lint yield, highest RUE, and highest lint weight boll-1. However, out of these three lines, only one breeding line had the best fiber quality, and greatest seedling vigor. Thus, these lines could be used in future breeding efforts aimed at improving cotton productivity through targeted selection while also minimizing risks of stand loss in the seedling phase. The second objective was to quantify genotypic and environmental contributions to lint yield, yield components, and fiber quality in multi-site cotton variety trials conducted in the Mid-South over a nineteen year period. From the aforementioned data, yield components were identified that were primarily driven by genotype in the majority of years evaluated, and a function was developed to predict genptypic yield variability using the underlying yield components.