Analysis of the coefficient of variation of remote sensor readings in winter wheat, and development of a sensor based mid-season nitrogen recommendation for cotton

• Main Author: Arnall, Daryl Brian
• Format: Dissertation
• Language: English
• Subjects: agriculture, agronomy
• ISBN: 0549545506; 9780549545507

Scope and method of study. For chapter one, Hard red winter wheat (Triticum aestivum L.) experiments were conducted to better understand how the coefficient of variation (CV) could be used to better mid-season N rate recommendations. The CV's were calculated from the normalized difference vegetation index (NDVI) collected from each plot with a GreenSeeker Hand Held optical reflectance sensor. For chapter two, Cotton (Gossypium hirsutum L.) experiments were conducted to evaluate if spectral reflectance measurements could predict yield mid-season and be used to determine a mid-season N rate recommendation.
Findings and conclusions. For chapter one, CV was found to be a good predictor of plant population and when used as a component of mid-season response index calculation improved the relationship with the response index measured at harvest in terms of yield. A relationship between yield and CV was also observed. This work indicated that a previously proposed RINDVI-CV equation did not improve the prediction of the RI at harvest. For chapter two, over sites and years lint yield was predicted using the division of NDVI and Cumulative Growing Degree Day (CummGDD) units that accumulated from planting to sensing, the prediction was best when data was collected between 800 and 1300 CummGDD. The yield prediction model combined with the establishment of the relationship between the response index at harvest and mid-season; a nitrogen fertilization optimization algorithm was developed.