McKnight, B. M. | bioRxiv Science

bioRxiv2025

Does sample size of leaf osmotic potential affect its relationship with cotton yield?

Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.

plant biology↗

bioRxiv2023

Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton

Recent progress in ecological remote sensing calls for a more rapid measurement and a closer assessment of crop drought tolerance traits under field conditions. This study addresses three main questions: (1) If leaf dry matter content (LDMC) is equally effective in indicating cotton drought tolerance as leaf osmotic potential at full turgor ({pi}o); (2) if drought tolerance is inversely related to fiber yield/quality in line with the leaf economics spectrum; and (3) if a reliable statistical model can be developed to rank cotton drought tolerance. The values of{pi} o, along with those of LDMC, of 2736 leaves obtained from cotton variety trials conducted during 2020-2022 in both dryland and irrigated regimes were measured using osmometry. The relationships between{pi} o and LDMC, as well as those between traits and lint yield and fiber quality indices, were investigated using regression analysis. A Bayesian hierarchical linear model was developed to rank cotton drought tolerance based on differences (or adjustments) in{pi} o and LDMC between dryland and irrigated sites. LDMC was not only shown to be an alternate and equally effective drought tolerance trait compared with{pi} o obtained from the widely accepted osmometry method, its use is also estimated to lead to a tenfold increase in measuring speed. A stronger drought tolerance capacity of the tested cotton varieties correlated with a lower lint yield and quality, which is generally consistent with the prediction of the leaf economics spectrum. The drought tolerance rankings using the Bayesian hierarchical model help divide the selected 17 cotton varieties into three groups: (a) more-drought tolerant, (b) less-drought tolerant, and (c) intermediate. The ranking results are interpreted using field-measured data of root distribution and diurnal leaf gas exchange from selected cotton varieties. Our work provides new opportunities for a more rapid measurement and an unambiguous ranking of drought tolerance capacity for crop genotypes under various management regimes.

ecology↗