"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

"Major Subject: Molecular and Enviromental Plant Sciences" Title from author supplied metadata (automated record created 2010-03-12 12:08:51). Includes bibliographical references.

Cotton is sold by weight, but a bales lint price per pound is determined by its fiber quality profile. Cotton quality is defined by a set of standardized properties (length, strength, elongation, uniformity, color, trash, and micronaire) collected on every United States bale. Each cotton fiber is the remnant of a single cell which upon harvest exists as a dry, hollow tube of crystalline cellulose. The length, perimeter, and thickness are a fibers physical dimensions. These dimensions influence both the mechanics involved in yarn spinning and the quality of the yarn produced. Genetic and environmental factors affect the development and consequently, the final properties of cotton crops. However, information is lacking about the degree of influence they impart, especially on fiber perimeter (fineness) and cell wall thickness (maturity), both components of micronaire. The goals of this dissertation were to: 1) Summarize and review the techniques available to industry to measure fiber perimeter and maturity in order to discuss their advantages and limitations, 2) Validate the use Cottonscope to measure fiber quality variation, 3) Determine the significance of within-plant yield variation, and 4) Determine the significance of within-plant quality variation. Small differences in micronaire are often indistinguishable, making breeding efforts difficult. With new instruments, selecting for the components of micronaire may increase selection efficiency and genetic gain compared to breeding for micronaire directly. In addition, these results show that yield and quality within genotypes are highly variable, and a significant amount of the variation is attributable to a bolls fruiting site. Substantial bias can be introduced if boll sampling does not consider fruiting position. The results show that plot sampling techniques can greatly influence fiber quality testing results and as a result the effectiveness of genetic selection. The Cottonscope is a very accurate and precise tool for measuring fiber fineness and maturity ratio and improving the interpretation of micronaire. Micronaire had strong correlation with fiber fineness data. Breeding for lower micronaire would be a useful strategy to improve fiber fineness in environments where low fiber maturity is not a problem.

A glasshouse study was conducted on the Louisiana State University campus in Baton Rouge to evaluate herbicide activity on Nealley's sprangletop. Herbicides were applied to Nealleys sprangletop plants in the one-two tiller stage with height of 20-30 cm. Nealley's sprangletop control, leaf number, height, tiller number, and fresh weight biomass were evaluated. Nealley's sprangletop treated with glyphosate, quizalofop, fenoxaprop, and clethodim was controlled 89 to 99%. A field study was conducted at the LSU AgCenter Rice Research Station (RRS) and a grower location (GL) to evaluate herbicide rates and timings for control of Nealley's sprangletop in drill-seeded rice. Herbicide treatments were cyhalofop at 271, 314, and 417 g ai ha-1 and fenoxaprop at 66, 86, and 122 g ai ha-1 applied pre- or post-flood, propanil at 3360 g ai ha-1 applied pre-flood, and propanil plus thiobencarb at 5040 g ai ha-1 applied pre-flood. Cyhalofop increased control of Nealley's sprangletop compared with control observed with propanil plus thiobencarb. Nealley's sprangletop treated with fenoxaprop at 86 or 122 g ha-1 pre-flood resulted in increased control of Nealley's sprangletop over propanil or propanil plus thiobencarb. Field studies were conducted at the RRS and a GL on drill-seeded rice to evaluate removal timings of Nealley's sprangletop and the impact on rice yield. Fenoxaprop was applied at 122 g ha-1 at 7, 14, 21, 28, 35, and 42 days after emergence (DAE). Rice from the 7 DAE removal yielded 1910 kg ha-1 more than the nontreated. Delaying the initial herbicide application from 7 to 42 DAE caused a rice yield loss of 1790 kg ha-1 with a net loss of $460 ha-1, or $13 ha-1 loss per day. Field studies were conducted at the RRS and a GL in drill-seeded rice to evaluate Nealley's sprangletop infestation densities in rice and the impact on rice yield. Analysis indicated significance for Nealley's sprangletop density on rice yield where the linear effects of density were significant (P < 0.0064). Based on economic evaluations, Nealley's sprangletop at densities of 5 to 10 plants m2 are sufficient threshold levels for treatment.

Proper nitrogen (N) management is essential to optimize crop production. This study was conducted to evaluate different N fertilizer management strategies to improve N use efficiency and yield in sugarcane production in Louisiana. This research was initiated in 2013 at the Sugar Research Station in St. Gabriel, LA and was arranged in a randomized complete block design with four replications consisting of different N rates (0, 45, 90, and 135 kg N ha-1) and sources (urea-46% N, ammonium nitrate [AN]-34% N, and urea-ammonium-nitrate solution [UAN]-32% N dribbled and knifed-in) as treatments. Sensor readings were taken from different N response trials to validate the sugarcane yield potential prediction and N response index (RI) models based on normalized difference vegetation index (NDVI). Soil nitrate (NO3-) and ammonium (NH4+) at 0-15 and 15-30 cm depths were also measured at different dates after N fertilization. At the grand growth stage, plots which were knifed-in with UAN showed a more even distribution of NO3- and NH4+ compared to urea- and AN-treated plots for both depths. Among the treatments, the highest sugarcane yield was achieved from plots treated with 90 kg N ha-1 as UAN knife-in and 135 kg N ha-1 as AN. Yield potential prediction models established in 2012 and 2015 could be used to estimate sugar and cane yield using NDVI readings collected at 21 (r2=0.30 and r2=0.51) and 60 (r2=0.41 and r2=0.52) days after N fertilization (DANF), respectively. Both RI and modified RI models demonstrated a better level of precision when RI was predicted at 60 DANF (r2=0.30) for both cane and sugar yield compared to 21 DANF (r2=0.15). The outcomes of this study demonstrated the effectivity of UAN knife-in as N source and the current N recommendation, but there were indications that application of higher N rate may further maximize yield. This study also revealed some limitations of the models used for predicting the components of remote sensor-based N recommendations for Louisiana sugarcane production. Apart from strengthening the yield and sensor readings database, areas of focus for future research include the use of different vegetation indices and reflectance readings from different wavebands.