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185 results on '"Ritchie, Glen L."'

6. Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images.

Author

Gu, Haibin, Mills, Cory, Ritchie, Glen L., and Guo, Wenxuan

Subjects
NORMALIZED difference vegetation index, PLANT breeding, IRRIGATION management, SUSTAINABLE agriculture, CROP management, IRRIGATION farming
Abstract

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Effects of 2,4-D choline on fruiting in sensitive cotton

Author

Russell, Kyle R., Dotray, Peter A., Ritchie, Glen L., and Kelly, Brendan R.

Abstract

AbstractWith the increase in hectares planted to auxin-resistant cotton, the number of preplant, at-plant, and postplant applications of dicamba and 2,4-D choline to aid in the control of troublesome broadleaf weeds, including glyphosate-resistant Palmer amaranth, has increased. More dicamba and 2,4-D choline applications mean an increased risk of off-target movement. Field studies were conducted in 2019 to 2021 at the Texas Tech University New Deal Research Farm to evaluate dicamba-resistant cotton response to various rates of 2,4-D choline when applied at four growth stages (first square [FS] + 2 wk, first bloom [FB], FB + 2 wk, and FB + 4 wk). Applications of 2,4-D choline were applied at 1,060 (1X), 106 (1/10X), 21 (1/50X), 10.6 (1/100X), 2.1 (1/500X), and 1.06 (1/1000X) g ae ha−1to Deltapine 1822 XF cotton. Relative to the nontreated control, yield losses were observed in all years at FS + 2 wk and FB from rates of 2,4-D choline ≥ 1/100X. At the FB + 4 wk application, only the 1X rate of 2,4-D choline resulted in a yield reduction in all three years. Micronaire, fiber length, and uniformity were negatively influenced by the 1/10X and 1X rates of 2,4-D choline at various timings in 2019, 2020, and 2021. In addition, short fiber content, neps, and seed coat neps increased where micronaire, fiber length, and uniformity were negatively impacted.

Published
2023
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18. Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta.

Author

Pabuayon, Isaiah Catalino M., Pabuayon, Irish Lorraine B., Singh, Rakesh Kumar, Ritchie, Glen L., and de los Reyes, Benildo G.

Subjects
SPECTRAL imaging, PARTIAL least squares regression, SOIL salinity, SALINITY, IMAGE processing, ANALYTICAL chemistry, RICE
Abstract

The ratio of Na+ and K+ is an important determinant of the magnitude of Na+ toxicity and osmotic stress in plant cells. Traditional analytical approaches involve destructive tissue sampling and chemical analysis, where real-time observation of spatio-temporal experiments across genetic or breeding populations is unrealistic. Such an approach can also be very inaccurate and prone to erroneous biological interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging non-destructive alternative for tracking plant nutrient status in a time-course with higher accuracy and reduced cost for chemical analysis. In this study, the feasibility and predictive power of HSI-based approach for spatio-temporal tracking of Na+ and K+ levels in tissue samples was explored using a panel recombinant inbred line (RIL) of rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with differential activities of the Na+ exclusion mechanism conferred by the SalTol QTL. In this panel of RILs the spectrum of salinity tolerance was represented by FL499 (super-sensitive), FL454 (sensitive), FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing pipeline was optimized to generate HSI spectra during salinity stress at EC = 9 dS m-1. Spectral data was used to create models for Na+ and K+ prediction by partial least squares regression (PLSR). Three datasets, i.e., mean image pixel spectra, smoothened version of mean image pixel spectra, and wavelength bands, with wide differences in intensity between control and salinity facilitated the prediction models with high R2. The smoothened and filtered datasets showed significant improvements over the mean image pixel dataset. However, model prediction was not fully consistent with the empirical data. While the outcome of modeling-based prediction showed a great potential for improving the throughput capacity for salinity stress phenotyping, additional technical refinements including tissue-specific measurements is necessary to maximize the accuracy of prediction models. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Cotton boll distribution: A review

Author

Pabuayon, Irish Lorraine B., primary, Kelly, Brendan R., additional, Mitchell‐McCallister, Donna, additional, Coldren, Cade L., additional, and Ritchie, Glen L., additional

Published
2021
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29. Registration of six upland cotton germplasm lines with improved fiber quality through ethyl methane sulfonate treatments and selection

Author

Bechere, Efrem, primary, Auld, Dick L., additional, Smith, C. Wayne, additional, Cantrell, Roy G., additional, Hequet, Eric F., additional, Ritchie, Glen L., additional, Pabuayon, Irish L. B., additional, Mishra, Deepika, additional, Hendon, Bralie R., additional, Brown, Nino, additional, and Kelly, Branden R., additional

Published
2020
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42. Inter-relationships of cotton plant height, canopy width, ground cover and plant nitrogen status indicators

Author

Muharam, Farrah Melissa, Bronson, Kevin F., Maas, Stephen J., Ritchie, Glen L., Muharam, Farrah Melissa, Bronson, Kevin F., Maas, Stephen J., and Ritchie, Glen L.

Abstract

Petiole-NO3, leaf N and chlorophyll (SPAD) meter readings are good in-season indicators of the N status of the uppermost part of cotton (Gossypium hirsutum L.) plants. Petiole-NO3, particularly is widely used in the USA as an in-season plant N test that guides N fertilizer recommendations in cotton. However, these N status indicators do not take account of plant biomass, canopy width or percent cover. The objectives of this study were to assess the effect of N fertilizer rates on the commonly used indicators of plant N status; leaf N, petiole sap NO3 and chlorophyll meter (SPAD) readings and the plant growth measurements; plant height, canopy width, and percent ground cover, and determine to inter-correlations among the them. Irrigated field studies were conducted at Lubbock, TX USA in 2010 and 2011, New Deal, TX in 2010, and at Halfway, TX in 2011. Zero-N and a full N fertilizer rate of 134, 101, and 112 kg N ha−1 were used at Lubbock, New Deal, and Halfway, respectively. The 2010 cotton growing season in West Texas was much wetter than average, and the 2011 season was much drier than normal. As a result, plant height, canopy width, and ground cover were greater in the 2010 sites than in 2011. The effects of N fertilizer were greatest for the two cultivars in subsurface drip irrigation (SDI) at New Deal in 2010 for all three N status indicators, and for the three plant growth measures compared to the other site-years. Correlation analysis indicated that among the three plant N indicators, leaf N was the most sensitive to plant parameters. These effects were positive in 2010 and negative in the 2011 dry year. Petiole NO3 was the plant N indicator that was the most insensitive to plant growth, but the marked seasonal decline pattern reduces its usefulness for late-season N management.

Published
2014

46. Use of Ground-Based Canopy Reflectance to Determine Radiation Capture, Nitrogen and Water Status, and Final Yield in Wheat

Author

Ritchie, Glen L.

Subjects
ground cover, wheat, Plant Sciences, ground-based canopy reflectance, final yield
Abstract

Ground-based spectral imaging devices offer an important supplement to satellite imagery. Hand-held, ground-based sensors allow rapid, inexpensive measurements that are not affected by the earth���s atmosphere. They also provide a basis for high altitude spectral indices. We quantified the spectral reflectance characteristics of hard red spring wheat (Triticum aestivum cv. Westbred 936) in research plots subjected to either nitrogen or water stress in a two year study. Both types of stress reduced ground cover, which was evaluated by digital photography and compared with ten spectral reflectance indices. On plots with a similar soil background, simple indices such as the normalized difference vegetation index, ratio vegetation index, and difference vegetation index were equal to or superior to more complex vegetation indices for predicting ground cover. Yield was estimated by integrating the normalized difference vegetation index over the growing season. The coefficient of determination (r2) between integrated normalized difference vegetation index and final yield was 0.86. Unfortunately, none of these indices were able to differentiate between the intensity of green leaf color and ground cover fraction, and thus could not distinguish nitrogen from water stress. We developed a reflective index that can differentiate nitrogen and water stress over a wide range of ground cover. The index is based on the ratio of the green and red variants of the normalized difference vegetation index. The new index was able to distinguish nitrogen and water stress from satellite data using wavelengths less than 1000 nm. This index should be broadly applicable over a wide range of plant types and environments.

Published
2003

50. YIELD DYNAMICS OF TWO COTTON VARIETIES IN GEORGIA.

Author

Ritchie, Glen L., Davis, Wheelus A., Sexton, Lola, Bednarz, Craig, and Mills, Cory

Abstract

In Georgia, the dominant cotton variety is Delta & Pineland 555 BR, while in West Texas, FiberMax 960 B2R and 9063 B2RF are commonly grown, high-yielding varieties with good fiber quality parameters. Several factors may play roles in the performance and popularity of these varieties, including season length characteristics of both varieties and phenotypic response to the very different environments between Georgia and West Texas. The objective was to determine growth characteristics of these two varieties in Texas and Georgia to determine growth and source-to-sink relationships in each environment based on temperature, sunlight, and precipitation/soil moisture. However, due to hail at the Texas location, the study was conducted at two locations in Georgia in 2007. In 2008, the study was conducted at one location in Georgia and one in Texas. The parameters were used to ascertain contributing factors to the yield and quality of the plants. There was a unique variety affect on fruiting response and growth response throughout the season, and these changes in fruiting and growth response can potentially affect yield and/or quality. [ABSTRACT FROM AUTHOR]

Published
2009

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