PHYSIOLOGICAL CHARACTERISTICS IN COTTON GENOTYPES AS AFFECTED BY PLANT AGE AND PLANTING DENSITIES.

Abstract

Physiological characteristics of three cotton genotypes were evaluated for their responses to plant aging under high and low planting density (HPD and LPD) treatments. In addition, the relationship of these physiological characteristics to fruit production were determined. Two genotypes are sympodia producing, "Pima S-6" (Gossypium barbadense L.) and "Deltapine 90" (DPL-90) (G. hirustum L.). The third, a "Cluster Selection" (G. barbadense L.) does not produce sympodia. These genotypes were grown on a Gila sandy loam soil at Tucson, Arizona in 1984 and 1985. Plant physiological characteristics were measured under field conditions at 5 intervals. Leaf physiological characteristics were determined at 7 different leaf ages. LPD treatment significantly reduced total dry weight (TDW), fruit dry weight (FDW), and leaf area index (LAI) in each genotype, but reductions were more pronounced in Pima S-6 and Cluster Selection in both seasons. However, LPD treatment significantly increased plant leaf area, but had no effect on specific leaf weight (SLW), petiole nitrate-nitrogen (Petiole NO₃-N) concentration, leaf area ratio (LAR), and fruiting index (FI), for all genotypes in both seasons. The aging patterns of all physiological characteristics were similar in both planting density treatments, regardless of genotype or season. Regression analyses showed that photosynthetic rate was curvilinearly correlated with leaf age (r² = 0.65 to 0.77, P < 0.01). However, petiole NO₃-N concentration decreased linearly with increasing leaf age (r² = 0.90 to 0.91, P < 0.01). Photosynthetic rate increased curvilinearly with increasing petiole NO₃-N concentration (r² = 0.61 to 0.79, P < 0.01). SLW was not correlated with leaf age, or other leaf physiological characteristics regardless of planting density treatment in 1985. TDW, FI, and LAI were directly related, while petiole NO₃-N concentration and LAR were inversely related to fruit production for all genotypes in both seasons. Multiple regression analyses showed that excluding planting density treatment effect, TDW, FI, and LAI were the most important variables incorporated for fruit prediction in both seasons.