Varco, Jac J.

Committee Member

Wallace, Teddy P.

Committee Member

Ambinakudige, Shrinidhi S.

Committee Member

Henry, W. Brien

Committee Member

Cox, Michael S.

Date of Degree


Document Type

Dissertation - Open Access



Degree Name

Doctor of Philosophy


College of Agriculture and Life Sciences


Department of Plant and Soil Sciences


Fertilizer N is one of the most costly inputs in corn (Zea mays L.) and cotton (Gossypium hirsutum L.) production and is a strong yield determining factor. Variable rate N fertilization has the potential to improve resource use efficiency, profitability, and help to minimize adverse environmental impacts. Vegetation indices (VIs) may be useful for in-season crop health monitoring to assist in fertilizer N management and yield prediction. This research determined the utility of aerial imagery in detecting corn and cotton response to varying N supply using five selected VIs. The VIs derived from aerial images, chlorophyll readings and tissue N for corn from V5 to V9 growth stages and cotton beginning the 1st week of flowering through to latelowering were used to relate to fertilizer N rates and plant N status and yield. The results showed that VIs derived from aerial imagery could be used to differentiate N supply and in-season grain yield of corn beginning at V5 to V6; however, models from later growth stages had greater r2 values than earlier growth stages. Single variable models that used VI, chlorophyll content, or plant N concentration as an independent variable were overall stronger than 2 variable Multiple Linear Regression models (MLRs). Three independent variables used in MLRs contained multicollinearity. For cotton, the use of VIs derived from aerial imagery to differentiate N supply may depend on environmental factors such as soil and weather. However, VIs may be useful for in-season lint yield prediction beginning the 1st week of flowering although later stages improved accuracy. The MLRs that were developed with 2 independent variables may be more suitable for in-season lint yield prediction than single independent variable models.