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Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
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Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
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Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
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Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets
Journal Article

Assessing Cotton Cultivar Competitiveness Against Commercial Checks and Yield Targets

2026
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Overview
Breeding programmes increasingly rely on head-to-head comparisons to inform stakeholders about the optimal cultivar for local conditions. This study implemented and evaluated a practical, transparent Excel® framework for assessing cotton cultivar competitiveness by evaluating seven cultivars against a commercial check at three winning margins (exceeding the check and beating it by more than 2.5% and 10% of the environment mean) over 36 environments. By utilising non-parametric statistical formulas in Microsoft Excel®, we calculated three competitiveness indices (W0, W2.5, and W10) to measure the probability of cultivars outperforming the commercial check. Additionally, scores were calculated to determine how often each cultivar ranked in the top, middle, and bottom third in each test, leading to the creation of two indices, namely Top and Mid. Two genotypes showed a higher probability of winning (≥53% of the time). On the other hand, for the minor (2.5%) yield difference, only one cultivar (cv. G3) outperformed the check in 19 of the 36 environments (53% of the time) and occurred mostly in the top third (58% of the time). Because the 10% threshold proved unrealistic for near-commercial cultivars, we propose replacing it with a user-defined economic threshold. This methodology effectively quantified the probability of the cultivar’s specific and general competitiveness in a format easily interpretable by decision-makers. The three indices W0, W2.5, and Top were associated with the dynamic concept of stability, whereas the index Mid was related to static and dynamic concepts.