Date of Graduation


Document Type


Degree Name

Master of Science in Crop, Soil & Environmental Sciences (MS)

Degree Level



Crop, Soil & Environmental Sciences


Derrick M. Oosterhuis

Committee Member

Curt Rom

Second Committee Member

Fred Bourland

Third Committee Member

Leonel Espinoza


Biological sciences, Cotton, Nutrient deficiency, Partitioning, Potasssium, Reflectance, Uptake


For cotton (Gossypium hirsutum L.) to grow and develop normally, plants need to uptake the necessary amount of nutrients and use those nutrients in a beneficial fashion. It is recognized that cotton needs a certain tissue concentration of ions to achieve and maintain growth rates (Siddiqi et al., 1987). One of the most essential and abundant nutrients in cotton is potassium (K), second only by mass to nitrogen (N) (Marschner, 1995; Oosterhuis et al., 2013). Potassium exists in the soil in four separate pools and moves through soil to roots mainly through diffusion (Rengel & Damon, 2008; Samal et al., 2010; Ogaard et al., 2001). Potassium plays a vital role in plant growth and metabolism.

The objectives of this study were to determine the Michaelis-Menten parameters for the high-affinity transport system (HATS) and low-affinity transport system (LATS) uptake mechanisms of cotton, observe how K is partitioned throughout the cotton plant over a growing season with differing K fertilization rates, and to determine if cultivars differed in values from currently available indices formulated for N-status detection from active sensors. It also set out to determine if these N-sensitive indices were sensitive to leaf K concentration and available K2O in the soil, and to evaluate the role these indices play in predicting yield. It was hypothesized that a high K hydroponic environment would lead to more K uptake by cotton roots, which would lead to an increase in VMAX and KM. It was also hypothesized that with increased K fertilization, there would be greater K uptake and larger shift to reproductive components due to the plant having more than enough K in all other parts enabling it to send more to the reproductive components, and that greater K rates would lead to higher yields across all cultivars. It was believed that normalized difference vegetation index (NDVI) would more accurately predict leaf K, available K2O, and yield than normalized difference red edge (NDRE), that NDVI and NDRE would more accurately determine the K parameters chosen than canopy chlorophyll content index (CCCI), due to the strong influence of the red-edge band in the index and that yield would be most accurately predicted by the CCCI, due to yield being influenced by both chlorophyll content and biomass, and the CCCI involving the red-edge band to reflect chlorophyll content and the near infrared band to detect biomass.